From 1b2194cdceab686a28323b207f19fa25a17bc2df Mon Sep 17 00:00:00 2001 From: Megvii Engine Team Date: Sat, 5 Sep 2020 06:55:00 +0800 Subject: [PATCH] feat(whl/internal): put imperative rt to megbrain GitOrigin-RevId: f3b6e492d751ccef48fa4bf02be90dd1506faeb2 --- CMakeLists.txt | 42 +- .../core/tensor/multipledispatch/utils.py | 25 +- imperative/python/setup.py | 7 +- imperative/python/test/run.sh | 21 + python_module/.gitignore | 8 - python_module/CMakeLists.txt | 113 -- python_module/megengine/__init__.py | 11 - python_module/megengine/_internal/__init__.py | 729 ---------- .../_internal/_timed_func_fork_exec_entry.py | 37 - .../megengine/_internal/comp_graph_tools.py | 274 ---- python_module/megengine/_internal/config.py | 439 ------ .../megengine/_internal/craniotome.py | 432 ------ python_module/megengine/_internal/dtype.py | 286 ---- python_module/megengine/_internal/enum36.py | 947 ------------- python_module/megengine/_internal/exc.py | 58 - .../megengine/_internal/global_init.py 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100644 python_module/test/unit/quantization/test_fake_quant.py delete mode 100644 python_module/test/unit/random/test_random.py diff --git a/CMakeLists.txt b/CMakeLists.txt index 347da9f9..a4329709 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -47,10 +47,9 @@ option(MGE_DEBUG_UTIL "Enable debug utility" ON) option(MGE_ENABLE_EXCEPTIONS "Build with exceptions" ON) option(MGE_WITH_TEST "Enable test for MegEngine." OFF) option(MGE_WITH_DISTRIBUTED "Build with distributed support" ON) -option(MGE_BUILD_IMPERATIVE_RT "Build _imperative_rt.so instead of _mgb.so " OFF) +option(MGE_BUILD_IMPERATIVE_RT "Build _imperative_rt Python Module " ON) option(MGE_BUILD_SDK "Build load_and_run" ON) option(MGE_INFERENCE_ONLY "Build inference only library." OFF) -option(MGE_WITH_PYTHON_MODULE "Build MegEngine Python Module." ON) option(MGE_WITH_MKLDNN "Enable Intel MKL_DNN support," ON) option(MGE_WITH_ROCM "Enable ROCM support" OFF) @@ -256,8 +255,8 @@ endif() if(MGE_INFERENCE_ONLY) message("-- Disable distributed support for inference only build.") set(MGE_WITH_DISTRIBUTED OFF) - message("-- Disable python module for inference only build.") - set(MGE_WITH_PYTHON_MODULE OFF) + message("-- Disable imperative_rt python module for inference only build.") + set(MGE_BUILD_IMPERATIVE_RT OFF) endif() if(MGE_WITH_DISTRIBUTED) @@ -694,43 +693,18 @@ if(MGE_BUILD_SDK) add_subdirectory(sdk/load-and-run) endif() -if(MGE_WITH_PYTHON_MODULE) - if(MGE_BUILD_IMPERATIVE_RT) - add_subdirectory(imperative) - message("-- Enable imperative python wrapper runtime") - else() - add_subdirectory(python_module) - message("-- Enable legacy python wrapper runtime") - endif() + +if(MGE_BUILD_IMPERATIVE_RT) + add_subdirectory(imperative) + message("-- Enable imperative python wrapper runtime") endif() if(MGE_WITH_TEST AND MGE_ENABLE_RTTI) add_subdirectory(test) endif() -if(TARGET mgb) - add_custom_target( - develop - COMMAND ${CMAKE_COMMAND} -E create_symlink - ${CMAKE_CURRENT_BINARY_DIR}/python_module/megengine/_internal/$ - ${CMAKE_CURRENT_SOURCE_DIR}/python_module/megengine/_internal/$ - COMMAND ${CMAKE_COMMAND} -E create_symlink - ${CMAKE_CURRENT_BINARY_DIR}/python_module/megengine/_internal/mgb.py - ${CMAKE_CURRENT_SOURCE_DIR}/python_module/megengine/_internal/mgb.py - COMMAND ${CMAKE_COMMAND} -E create_symlink - ${CMAKE_CURRENT_BINARY_DIR}/python_module/megengine/_internal/opr.py - ${CMAKE_CURRENT_SOURCE_DIR}/python_module/megengine/_internal/opr.py - COMMAND ${CMAKE_COMMAND} -E create_symlink - ${CMAKE_CURRENT_BINARY_DIR}/python_module/megengine/_internal/opr_param_defs.py - ${CMAKE_CURRENT_SOURCE_DIR}/python_module/megengine/_internal/opr_param_defs.py - COMMAND ${CMAKE_COMMAND} -E create_symlink - ${CMAKE_CURRENT_BINARY_DIR}/python_module/megengine/_internal/include - ${CMAKE_CURRENT_SOURCE_DIR}/python_module/megengine/_internal/include - DEPENDS mgb - VERBATIM - ) -elseif(TARGET _imperative_rt) +if(TARGET _imperative_rt) add_custom_target( develop COMMAND ${CMAKE_COMMAND} -E create_symlink diff --git a/imperative/python/megengine/core/tensor/multipledispatch/utils.py b/imperative/python/megengine/core/tensor/multipledispatch/utils.py index 4dcd0dc4..968430f3 100644 --- a/imperative/python/megengine/core/tensor/multipledispatch/utils.py +++ b/imperative/python/megengine/core/tensor/multipledispatch/utils.py @@ -183,25 +183,16 @@ def typename(type): # parse typing.Union -if sys.version_info < (3, 6): - - def parse_union(ann): +def parse_union(ann): + if hasattr(typing, "UnionMeta"): if type(ann) is not typing.UnionMeta: return return ann.__union_params__ - - -elif sys.version_info < (3, 7): - - def parse_union(ann): + elif hasattr(typing, "_Union"): if type(ann) is not typing._Union: return return ann.__args__ - - -elif sys.version_info < (3, 8): - - def parse_union(ann): + elif hasattr(typing, "_GenericAlias"): if type(ann) is not typing._GenericAlias: if type(ann) is not typing.Union: return @@ -209,11 +200,9 @@ elif sys.version_info < (3, 8): if ann.__origin__ is not typing.Union: return return ann.__args__ - - -else: - - def parse_union(ann): + elif hasattr(typing, "Union"): if typing.get_origin(ann) is not typing.Union: return return typing.get_args(ann) + else: + raise NotImplementedError("unsupported Python version") diff --git a/imperative/python/setup.py b/imperative/python/setup.py index c788b75c..19c6123c 100644 --- a/imperative/python/setup.py +++ b/imperative/python/setup.py @@ -6,6 +6,7 @@ # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + import os import re import pathlib @@ -55,11 +56,13 @@ package_data = [ str(f.relative_to('megengine')) for f in pathlib.Path('megengine', 'core', 'include').glob('**/*') ] + package_data += [ str(f.relative_to('megengine')) for f in pathlib.Path('megengine', 'core', 'lib').glob('**/*') ] + with open('requires.txt') as f: requires = f.read().splitlines() with open('requires-style.txt') as f: @@ -67,6 +70,7 @@ with open('requires-style.txt') as f: with open('requires-test.txt') as f: requires_test = f.read().splitlines() +prebuild_modules=[PrecompiledExtesion('megengine.core._imperative_rt')] setup_kwargs = dict( name=package_name, version=__version__, @@ -78,7 +82,7 @@ setup_kwargs = dict( package_data={ 'megengine': package_data, }, - ext_modules=[PrecompiledExtesion('megengine.core._imperative_rt')], + ext_modules=prebuild_modules, install_requires=requires, extras_require={ 'dev': requires_style + requires_test, @@ -87,6 +91,7 @@ setup_kwargs = dict( cmdclass={'build_ext': build_ext}, ) + setup_kwargs.update(dict( classifiers=[ 'Development Status :: 3 - Alpha', diff --git a/imperative/python/test/run.sh b/imperative/python/test/run.sh new file mode 100755 index 00000000..1e9676fb --- /dev/null +++ b/imperative/python/test/run.sh @@ -0,0 +1,21 @@ +#!/bin/bash -e + +test_dirs="test" +TEST_PLAT=$1 + +if [[ "$TEST_PLAT" == cpu ]]; then + echo "only test cpu pytest" +elif [[ "$TEST_PLAT" == cuda ]]; then + echo "test both cpu and gpu pytest" +else + log "Argument must cpu or cuda" + exit 1 +fi + +pushd $(dirname "${BASH_SOURCE[0]}")/.. >/dev/null + PYTHONPATH="." PY_IGNORE_IMPORTMISMATCH=1 python3 -m pytest $test_dirs -m 'not isolated_distributed' + if [[ "$TEST_PLAT" == cuda ]]; then + echo "test GPU pytest now" + PYTHONPATH="." PY_IGNORE_IMPORTMISMATCH=1 python3 -m pytest $test_dirs -m 'isolated_distributed' + fi +popd >/dev/null diff --git a/python_module/.gitignore b/python_module/.gitignore deleted file mode 100644 index f31367f5..00000000 --- a/python_module/.gitignore +++ /dev/null @@ -1,8 +0,0 @@ -/megbrain/_mgb.so -/megbrain/_mgb.*.so -/MegBrain.egg-info/ -/dist -/dist_cuda -/dist_nocuda -/wheel_dist -.cache diff --git a/python_module/CMakeLists.txt b/python_module/CMakeLists.txt deleted file mode 100644 index e23c2488..00000000 --- a/python_module/CMakeLists.txt +++ /dev/null @@ -1,113 +0,0 @@ -cmake_policy(SET CMP0086 NEW) - -find_package(PythonLibs ${PYTHON_VERSION_STRING} EXACT REQUIRED) - -find_package(Git) -if(GIT_FOUND) - message("git found: ${GIT_EXECUTABLE}") -endif() - -find_package(NumPy REQUIRED) - -find_package(SWIG REQUIRED) -set(SWIG_SRC src/swig/mgb.i) -if(MSVC OR WIN32) - set(CMAKE_SWIG_FLAGS -Wall -threads -py3 -DSWIGWORDSIZE64) - message("WARN: swig have some define issue at windows(64) env") - message("Please refs scripts/whl/BUILD_PYTHON_WHL_README.md to init windows build env") -else() - set(CMAKE_SWIG_FLAGS -Wall -threads -py3 -modern -DSWIGWORDSIZE64) -endif() -set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-unused-parameter") - -file(GLOB_RECURSE OPR_DECL_SRCS "${PROJECT_SOURCE_DIR}/src/**/*.oprdecl") -file(GLOB_RECURSE PYTHON_SRCS setup.py - src/python/*.py - test/*.py - megengine/*.py) -list(REMOVE_ITEM PYTHON_SRCS - ${CMAKE_CURRENT_SOURCE_DIR}/megengine/_internal/mgb.py - ${CMAKE_CURRENT_SOURCE_DIR}/megengine/_internal/opr.py - ${CMAKE_CURRENT_SOURCE_DIR}/megengine/_internal/opr_param_defs.py -) -list(APPEND PYTHON_SRCS ${MGB_SRCS}) - -file(GLOB_RECURSE ALL_HEADERS src/cpp/megbrain_pubapi.h - ${PROJECT_SOURCE_DIR}/src/core/include/* - ${PROJECT_SOURCE_DIR}/src/opr/include/* - ${PROJECT_SOURCE_DIR}/src/serialization/include/* - ${PROJECT_SOURCE_DIR}/src/plugin/include/* - ${PROJECT_SOURCE_DIR}/dnn/include/*) - -file(COPY ${PROJECT_SOURCE_DIR}/dnn/scripts/opr_param_defs.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}) -file(READ ${PROJECT_SOURCE_DIR}/tools/param_defs/mgb_opr_param_defs.py CONTENTS) -file(APPEND ${CMAKE_CURRENT_BINARY_DIR}/opr_param_defs.py ${CONTENTS}) - -add_custom_command( - OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/megengine/_internal/opr.py ${CMAKE_CURRENT_BINARY_DIR}/megengine/_internal/opr_param_defs.py - COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/src/python ${CMAKE_CURRENT_BINARY_DIR}/src/python - COMMAND ${CMAKE_COMMAND} -E make_directory ${CMAKE_CURRENT_BINARY_DIR}/megengine/_internal - COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/src/python/genopr.py ${OPR_DECL_SRCS} - COMMAND ${PYTHON_EXECUTABLE} ${PROJECT_SOURCE_DIR}/dnn/scripts/gen_param_defs.py -t py ${CMAKE_CURRENT_BINARY_DIR}/opr_param_defs.py ${CMAKE_CURRENT_BINARY_DIR}/megengine/_internal/opr_param_defs.py - DEPENDS ${OPR_DECL_SRCS} - VERBATIM -) - -add_custom_target(mgb_opr_py DEPENDS ${CMAKE_CURRENT_BINARY_DIR}/megengine/_internal/opr.py) - -set(SRCS src/cpp/craniotome.cpp src/cpp/function_replace.cpp src/cpp/intbx.cpp src/cpp/bfloat16.cpp src/cpp/megbrain_config.cpp src/cpp/megbrain_pubapi.cpp src/cpp/megbrain_serialize.cpp src/cpp/megbrain_wrap.cpp src/cpp/opr_defs.cpp src/cpp/opr_helper.cpp src/cpp/plugin.cpp src/cpp/python_helper.cpp) - -include(UseSWIG) -set_property(SOURCE ${SWIG_SRC} PROPERTY CPLUSPLUS ON) - -# cmake < 3.12 do not honor INCLUDE_DIRECTORIES property, just add include directory into SWIG_FLAGS -# Add -I${PROJECT_BINARY_DIR}/genfiles in order to include megbrain_build_config.h so that we don't need to pass cmake flags by -D. -set_property(SOURCE ${SWIG_SRC} PROPERTY SWIG_FLAGS -I${PROJECT_SOURCE_DIR}/src/serialization/include -I${PROJECT_BINARY_DIR}/genfiles) - -set(SWIG_OUTFILE_DIR ${CMAKE_CURRENT_BINARY_DIR}) -set(CMAKE_SWIG_OUTDIR ${CMAKE_CURRENT_BINARY_DIR}/megengine/_internal) -swig_add_library(mgb LANGUAGE python SOURCES ${SWIG_SRC} ${SRCS}) - -set(VERSION_SCRIPT ${CMAKE_CURRENT_SOURCE_DIR}/src/version.ld) -add_custom_target(version_ld SOURCES ${VERSION_SCRIPT}) - -set_target_properties(mgb PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/megengine/_internal) -if (APPLE) - target_link_libraries(mgb megbrain megdnn) - set_target_properties(mgb PROPERTIES LINK_FLAGS "-undefined dynamic_lookup") -elseif (MSVC OR WIN32) - target_link_libraries(mgb megbrain megdnn) -else() - target_link_libraries(mgb megbrain megdnn -Wl,--version-script=${VERSION_SCRIPT}) -endif() -target_include_directories(mgb PRIVATE ${PYTHON_INCLUDE_DIRS} src/cpp ${CMAKE_CURRENT_BINARY_DIR} ${NUMPY_INCLUDE_DIR}) -# only windows need link PYTHON_LIBRARIES -if(MSVC OR WIN32) - target_link_libraries(mgb ${PYTHON_LIBRARIES}) -endif() - -if (MGE_WITH_DISTRIBUTED) - target_link_libraries(mgb megray) -endif() - -add_dependencies(mgb mgb_opr_py version_ld) - -add_custom_command( - TARGET mgb POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy ${PROJECT_SOURCE_DIR}/LICENSE ${PROJECT_SOURCE_DIR}/ACKNOWLEDGMENTS ${PROJECT_BINARY_DIR} - COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/megengine ${CMAKE_CURRENT_BINARY_DIR}/megengine - COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/test ${CMAKE_CURRENT_BINARY_DIR}/test - COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/setup.py ${CMAKE_CURRENT_BINARY_DIR}/setup.py - COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/requires.txt ${CMAKE_CURRENT_BINARY_DIR}/requires.txt - COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/requires-style.txt ${CMAKE_CURRENT_BINARY_DIR}/requires-style.txt - COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/requires-test.txt ${CMAKE_CURRENT_BINARY_DIR}/requires-test.txt - COMMAND ${CMAKE_COMMAND} -E remove_directory ${CMAKE_CURRENT_BINARY_DIR}/megengine/_internal/include - COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/src/cpp/megbrain_pubapi.h ${CMAKE_CURRENT_BINARY_DIR}/megengine/_internal/include/megbrain_pubapi.h - COMMAND ${CMAKE_COMMAND} -E copy_directory ${PROJECT_SOURCE_DIR}/src/core/include ${CMAKE_CURRENT_BINARY_DIR}/megengine/_internal/include - COMMAND ${CMAKE_COMMAND} -E copy_directory ${PROJECT_SOURCE_DIR}/src/opr/include ${CMAKE_CURRENT_BINARY_DIR}/megengine/_internal/include - COMMAND ${CMAKE_COMMAND} -E copy_directory ${PROJECT_SOURCE_DIR}/src/serialization/include ${CMAKE_CURRENT_BINARY_DIR}/megengine/_internal/include - COMMAND ${CMAKE_COMMAND} -E copy_directory ${PROJECT_SOURCE_DIR}/src/plugin/include ${CMAKE_CURRENT_BINARY_DIR}/megengine/_internal/include - COMMAND ${CMAKE_COMMAND} -E copy_directory ${PROJECT_SOURCE_DIR}/dnn/include ${CMAKE_CURRENT_BINARY_DIR}/megengine/_internal/include - COMMAND ${CMAKE_COMMAND} -E copy ${PROJECT_BINARY_DIR}/genfiles/megbrain_build_config.h ${CMAKE_CURRENT_BINARY_DIR}/megengine/_internal/include/megbrain_build_config.h -) - diff --git a/python_module/megengine/__init__.py b/python_module/megengine/__init__.py deleted file mode 100644 index 81c59cd0..00000000 --- a/python_module/megengine/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .core import * -from .logger import enable_debug_log, get_logger, set_log_file, set_log_level -from .version import __version__ diff --git a/python_module/megengine/_internal/__init__.py b/python_module/megengine/_internal/__init__.py deleted file mode 100644 index bac0e791..00000000 --- a/python_module/megengine/_internal/__init__.py +++ /dev/null @@ -1,729 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -"""the megbrain python package - -Note that all the submodules are automatically imported, so you usually only -need to ``import megengine._internal as mgb``. -""" - -import collections -import json -import os -import sys -import platform -import ctypes - -if sys.platform == "win32": - lib_path = os.path.join(os.path.dirname(__file__), "lib") - Lib_path = os.path.join(os.path.dirname(__file__), "Lib") - dll_paths = list(filter(os.path.exists, [lib_path, Lib_path])) - assert len(dll_paths) > 0 - - kernel32 = ctypes.WinDLL("kernel32.dll", use_last_error=True) - has_load_library_attr = hasattr(kernel32, "AddDllDirectory") - old_error_mode = kernel32.SetErrorMode(0x0001) - - kernel32.LoadLibraryW.restype = ctypes.c_void_p - if has_load_library_attr: - kernel32.AddDllDirectory.restype = ctypes.c_void_p - kernel32.LoadLibraryExW.restype = ctypes.c_void_p - - for dll_path in dll_paths: - if sys.version_info >= (3, 8): - os.add_dll_directory(dll_path) - elif has_load_library_attr: - res = kernel32.AddDllDirectory(dll_path) - if res is None: - err = ctypes.WinError(ctypes.get_last_error()) - err.strerror += ' Error adding "{}" to the DLL search PATH.'.format( - dll_path - ) - raise err - else: - print("WARN: python or OS env have some issue, may load DLL failed!!!") - - import glob - - dlls = glob.glob(os.path.join(lib_path, "*.dll")) - path_patched = False - for dll in dlls: - is_loaded = False - if has_load_library_attr: - res = kernel32.LoadLibraryExW(dll, None, 0x00001100) - last_error = ctypes.get_last_error() - if res is None and last_error != 126: - err = ctypes.WinError(last_error) - err.strerror += ' Error loading "{}" or one of its dependencies.'.format( - dll - ) - raise err - elif res is not None: - is_loaded = True - if not is_loaded: - if not path_patched: - os.environ["PATH"] = ";".join(dll_paths + [os.environ["PATH"]]) - path_patched = True - res = kernel32.LoadLibraryW(dll) - if res is None: - err = ctypes.WinError(ctypes.get_last_error()) - err.strerror += ' Error loading "{}" or one of its dependencies.'.format( - dll - ) - raise err - - kernel32.SetErrorMode(old_error_mode) - -import numpy as np - -from . import comp_graph_tools as cgtools -from . import config, craniotome, dtype -from . import global_init as _global_init -from . import helper as _helper -from . import mgb as _detail -from . import opr, opr_extra, opr_param_defs, plugin -from .exc import MegBrainError -from .logconf import get_logger -from .mgb import ( - CompGraph, - CompNode, - SharedND, - SharedScalar, - SymbolVar, - TensorValueDumperContext, - TensorValueLoaderContext, -) -from .mgb import as_comp_node as comp_node -from .mgb_helper import SharedNDLazyInitializer, callback_lazycopy, copy_output -from .plugin import CompGraphProfiler -from .plugin import GlobalInfkernFinder as _GlobalInfkernFinder -from .plugin import NumRangeChecker -from .version import __version__, version_info - -if sys.version_info.major < 3: - raise ImportError("megbrain requires python 3") - - -class ProxySharedNDAndSymbolVar(_detail.SymbolVar): - """this is a :class:`.SymbolVar` with a corresponding :class:`.SharedND`. - It can participate in graph computating and also provides :meth:`set_value` - and :meth:`get_value`. It should be constructed by :func:`make_shared`. - """ - - __shared_nd = None - __kwargs = None - - def __init__(self, snd, comp_graph, name, **kwargs): - self.__shared_nd = snd - self.__kwargs = kwargs - self.this = snd.symvar(comp_graph=comp_graph, name=name, **kwargs).this - - def set_value(self, v, **kwargs): - ret = self.__shared_nd.set_value(v, **kwargs) - self._reeval_if_eager_eval() - return ret - - def get_value(self): - return self.__shared_nd.get_value() - - def reset_zero(self): - self.__shared_nd.reset_zero() - - -def make_shared( - comp_node, - *, - dtype=None, - shape=None, - value=None, - comp_graph=None, - name=None, - volatile=None -): - """make a shared tensor which is stored on device and could be modified - later, either as a :class:`.SymbolVar` or a :class:`.SharedND` object - - :param comp_node: computing node - :type comp_node: :class:`.CompNode` - :param dtype: data type; if it is None, then dtype of value would be used - if value is not None, and float32 would be used as default dtype if - value is None - :type dtype: :class:`numpy.dtype` compatible - :param value: initializing value - :type value: None or :class:`numpy.ndarray` - :param comp_graph: the computing graph to which this shared value should - belong; if provided, the retuned object could be used as a - :class:`.SymbolVar` - :type comp_graph: None or :class:`.CompGraph` - :param name: node name to be used in computing graph; only meaningful if - *comp_graph* is provided - :param volatile: if *comp_graph* is given then *volatile* indicates whether - shape or mem ptr of this SharedND can be changed - :rtype: :class:`.SharedND` if *comp_graph* is not given; or - :class:`ProxySharedNDAndSymbolVar` otherwise - """ - if dtype is None: - if value is not None: - value = np.ascontiguousarray(value) - dtype = to_mgb_supported_dtype(value.dtype) - else: - dtype = np.float32 - comp_node = _detail.as_comp_node(comp_node) - rst = _detail.SharedND(comp_node, dtype) - if value is not None: - assert shape is None, "could not provide both value and shape" - rst.set_value(value) - elif shape is not None: - rst._set_init_shape(shape) - if comp_graph is None: - assert name is None and volatile is None - return rst - assert isinstance(comp_graph, CompGraph), "expect CompGraph but got {}".format( - comp_graph - ) - if volatile is None: - volatile = False - else: - assert isinstance(volatile, bool) - return ProxySharedNDAndSymbolVar(rst, comp_graph, name, volatile=volatile) - - -def make_immutable(comp_node, comp_graph, value, *, dtype=None, name=None): - """make a graph node containing an immutable tensor from host tensor value - - :param dtype: required data type; if not None, the data would be converted - to that type; otherwise - """ - - comp_node = _detail.as_comp_node(comp_node) - assert isinstance( - comp_graph, _detail.CompGraph - ), "expect CompGraph but got {!r}".format(comp_graph) - - config = _detail.make_opr_config(name, comp_node) - return _helper.cvt_opr_result( - _detail._make_immutable(comp_graph, value, dtype, config) - ) - - -def make_arg( - comp_node, - comp_graph, - *, - dtype=np.float32, - shape=None, - name=None, - value=None, - enable_static_infer=True -): - """make an argument to be passed to compiled function during runtime; - - :type shape: None or tuple of int - :param shape: expected tensor shape to be used for shape inferring; actual - tesor shape could be different - :type name: str - :param name: name of the generated var node - :type value: None or ndarray-compatible - :param value: initial value used for static inference; if not given, static - infer would be deferred to first graph execution - :param enable_static_infer: whether to enable static inference for this var - """ - comp_node = _detail.as_comp_node(comp_node) - host_val = mgb._HostSharedND(comp_node, dtype) - - if value is not None: - value = np.ascontiguousarray(value, dtype=dtype) - if shape is None: - shape = value.shape - else: - assert shape == value.shape - if shape is not None: - host_val._resize(shape) - - if value is not None: - host_val.set_value(value) - - return _helper.cvt_opr_result( - ProxySharedNDAndSymbolVar( - host_val, comp_graph, name, enable_static_infer=enable_static_infer - ) - ) - - -def comp_graph(*, extra_opts=None, check_env_var=True): - """allocate a new computing graph - - :param extra_opts: extra options to be set; would be updated (modified - inplace) from ``MGB_COMP_GRAPH_OPT`` environment var. See - :func:`.set_comp_graph_option` for list of supported options. - :type extra_opts: dict - :param check_env_var: whether to check environment vars - :type check_env_var: bool - - :return: the comp graph object - :rtype: :class:`.CompGraph` - """ - cg = _detail.CompGraph() - if extra_opts is None: - extra_opts = {} - if check_env_var: - setting = os.getenv("MGB_COMP_GRAPH_OPT") - if setting: - for item in setting.split(";"): - k, v = item.split("=", 1) - extra_opts.setdefault(k, v) - get_logger().warning( - "set comp graph option from env: {}".format(extra_opts) - ) - user_data = os.getenv("MGB_COMP_GRAPH_USER_DATA") - if user_data: - storage = cg.user_data - for ud in user_data.split(";"): - k, v = ud.split("=", 1) - storage[k] = eval(v) - _GlobalInfkernFinder.add_graph(cg) - for k, v in extra_opts.items(): - cg.set_option(k, v) - return cg - - -def grad( - target, wrt, warn_mid_wrt=True, use_virtual_grad=None, return_zero_for_nodep=True -): - r"""compute symbolic grad - - :param target: grad target var - :type target: :class:`.SymbolVar` - :param wrt: with respect to which to compute the grad - :type wrt: :class:`.SymbolVar` or Iterable[SymbolVar] - :param warn_mid_wrt: whether to give warning if *wrt* is not endpoint - :type warn_mid_wrt: bool - :param use_virtual_grad: whether to use virtual grad opr, so fwd graph can - be optimized before applying grad; if ``None`` is given, then virtual - grad would be used if ``graph_opt_level >= 2`` - :type use_virtual_grad: :class:`bool` or ``None`` - :param return_zero_for_nodep: if *target* does not depend on *wrt*, set to True to return - a zero-valued `.SymbolVar` rather than ``None``; can't be set to False when using - virtual grad opr. - :type return_zero_for_nodep: bool - :rtype: :class:`.SymbolVar` or None - :return: :math:`\frac{\partial\text{target}}{\partial\text{wrt}}` - """ - if use_virtual_grad is None: - use_virtual_grad = -1 - else: - use_virtual_grad = 1 if use_virtual_grad else 0 - - if isinstance(wrt, SymbolVar): - wrts = [ - wrt, - ] - else: - wrts = wrt - - assert isinstance(wrts, collections.Iterable) - # return a invalid SymbolVar (with nullptr VarNode*) when return_zero_for_nodep is False - # and target doesn't depend on wrt - grads = _detail._grad( - target, wrts, bool(warn_mid_wrt), use_virtual_grad, return_zero_for_nodep - ) - grads = list(grads) - - for i in range(len(grads)): - if not grads[i].valid: - assert ( - not return_zero_for_nodep - ), "invalid grad SymbolVar: target={}, wrt={}".format(target, wrts[i]) - grads[i] = None - - if len(grads) == 1: - grads = grads[0] - - return grads - - -def current_grad_target(comp_graph): - """get current target var to compute grad, used for implementing custom - gradient""" - return _detail._current_grad_target(comp_graph) - - -def add_device_map(map_location): - """add map location while loading models""" - _detail.CompNode.cn_thread_local.__setattr__("map_location", map_location) - - -def del_device_map(): - """delete map location""" - _detail.CompNode.cn_thread_local.__delattr__("map_location") - - -def inter_graph_trans_var(dest_graph, src): - """get the corresponding var of *src* in *dest_graph*; assuming - *dest_graph* is a copy of owner graph of *src*; usually used in callback of - set_grad to get grad of vars in loop - - :param dest_graph: target computing graph - :type dest_graph: :class:`.CompGraph` - :param src: source var node - :type src: :class:`.SymbolVar` - :return: corresponding var in *dest_graph* - :rtype: :class:`.SymbolVar` - """ - return _detail._inter_graph_trans_var(dest_graph, src) - - -def get_graph_optimizer_replaced_var(src): - """get optimized var corresponding to given var; usually used in callback - of set_grad to get grad w.r.t. some var - - :param src: source var node - :type src: :class:`.SymbolVar` - :rtype: :class:`.SymbolVar` - """ - return _detail._get_graph_optimizer_replaced_var(src) - - -CompGraphSerializationResult = collections.namedtuple( - "CompGraphSerializationResult", - [ - "nr_opr", - "tot_bytes", - "tensor_value_bytes", - "content_hash", - "inputs", - "outputs", - "params", - ], -) - - -def serialize_comp_graph_to_file( - fpath, - output_vars, - *, - keep_var_name=1, - keep_param_name=False, - keep_opr_priority=False, - tensor_value_dumper=None, - output_strip_info=False, - append=False, - format=None, - **kwargs -): - """serialize this computing graph and write result to a file. Note: - ``kwargs`` exists for backward compatibility; there is no additional - arguments. - - :parma fpath: path for the output file - :type fpath: ``str`` - :param output_vars: output variables that need to be retrieved when - deserializing - - .. note:: - - The underlying C++ API only accepts a var list. If a dict is given, - the vars would be renamed to given names. - - :type output_vars: dict(name => :class:`.SymbolVar`), or a list of vars - :param keep_var_name: level for keeping variable names: - - * 0: none of the names are kept - * 1: keep names of output vars - * 2: keep names of all (output and internal) vars - :param keep_param_name: whether to keep param names, so param values can be - easily manipulated after loading model - :param keep_opr_priority: whether to keep priority setting for operators - :param tensor_value_dumper: a callable to dump tensor values; it should - only write the tensor value without layout information. It would be - given a :class:`.TensorValueDumperContext` object as its sole argument. - :param output_strip_info: if set to True, then a json file containing - information for code strip would be written to ``fpath+'.json'`` - :param append: whether to open output file in append mode - :return: an instance of namedtuple :class:`CompGraphSerializationResult`, - whose fields are: - - * ``nr_opr`` number of operators dumped - * ``tot_bytes`` total bytes for the whole graph - * ``tensor_value_bytes`` bytes consumed for dumping tensor values - * ``inputs`` names of input tensors - * ``params`` list of names of dumped params - * ``outputs`` names of output vars - :param format: serialization format of the resulting model, should be either - "mdl" or "fbs"; none means default. - :type format: ``str`` - """ - - assert isinstance(fpath, str), "bad file path: {!r}".format(fpath) - ov = _detail._VectorSymbolVar() - SUPPORTED_FORMATS = { - # default - None: _detail.GraphDumpFormat_FLATBUFFERS, - "fbs": _detail.GraphDumpFormat_FLATBUFFERS, - } - resolved_fmt = SUPPORTED_FORMATS.get(format, None) - if resolved_fmt is None: - raise ValueError( - "unknown format {} requested, supported ones are {}".format( - format, list(filter(None, SUPPORTED_FORMATS.keys())) - ) - ) - if isinstance(output_vars, dict): - used_vars = set() - for name, var in output_vars.items(): - assert isinstance(var, _detail.SymbolVar), "bad output var: {!r}".format( - var - ) - assert var.id not in used_vars, ( - "var name is associated with a var object, so we can not have " - "two names given to the same var: {}".format(var) - ) - used_vars.add(var.id) - var.rename(name) - ov.push_back(var) - else: - for i in output_vars: - assert isinstance(i, _detail.SymbolVar), "bad output var: {!r}".format(i) - ov.push_back(i) - - if tensor_value_dumper is not None: - assert isinstance(tensor_value_dumper, collections.Callable) - - class Callback(_detail._TensorValueDumperCallback): - def call(self, ctx, *, _f=tensor_value_dumper): - _f(ctx) - - tensor_value_dumper = Callback() - - # for backward compatibility - mangle_opr_name = kwargs.pop("mangle_opr_name", ov) - if mangle_opr_name is not ov: - get_logger().warning("mangle_opr_name is deprecated; use keep_var_name instead") - keep_var_name = 1 if mangle_opr_name else 2 - mangle_param_name = kwargs.pop("mangle_param_name", ov) - assert ( - not kwargs - ), "extra kwargs provided to serialize_comp_graph_to_file: {}".format(kwargs) - - if mangle_param_name is not ov: - get_logger().warning( - "mangle_param_name is deprecated; use keep_param_name instead" - ) - keep_param_name = not mangle_param_name - - inputs = _detail._VectorString() - outputs = _detail._VectorString() - params = _detail._VectorString() - stat = _detail._VectorSizeT() - - _detail._serialize_comp_graph_to_file( - fpath, - append, - resolved_fmt, - ov, - keep_var_name, - keep_param_name, - keep_opr_priority, - tensor_value_dumper, - stat, - inputs, - outputs, - params, - ) - - dump_ret = CompGraphSerializationResult( - *stat, list(inputs), list(outputs), list(params) - ) - - if output_strip_info: - with open(fpath + ".json", "w") as fout: - strip_info = _detail._get_info_for_strip(ov) - strip_info_dict = json.loads(strip_info) - strip_info_dict["hash"] = dump_ret.content_hash - json.dump(strip_info_dict, fout) - - return dump_ret - - -CompGraphLoadResult = collections.namedtuple( - "CompGraphLoadResult", ["graph", "output_vars_dict", "output_vars_list"] -) - - -def load_comp_graph_from_file( - fpath, *, comp_node_mapper=None, tensor_value_loader=None -): - """Load a serialized computing graph from file. - - :parma fpath: Path for the output file - :type fpath: ``str`` - :param comp_node_mapper: A callable to modify comp node locator, takes old - locator as argument and returns new locator. - :type comp_node_mapper: Callable[[str], str] - :param tensor_value_loader: A callable to load tensor values. It should - read the tensor value with the given shape and dtype and return it as - NumPy ndarray. It would be given a :class:`.TensorValueLoaderContext` - object as its sole argument. - :type tensor_value_loader: Callable[[TensorValueLoaderContext], numpy.ndarray] - :return: An instance of namedtuple :class:`CompGraphLoadResult`, - whose fields are: - - * ``graph`` loaded CompGraph - * ``output_vars_dict`` A Python dict, mapping name to output SymbolVar - * ``output_vars_list`` A Python list, containing output vars in the - order passed to serialize_comp_graph_to_file - """ - assert isinstance(fpath, str), "bad file path: {!r}".format(fpath) - - if comp_node_mapper is not None: - assert isinstance(comp_node_mapper, collections.Callable) - - class Callback(_detail._CompNodeMapperCallback): - def call(self, desc, *, _f=comp_node_mapper): - return _f(desc) - - comp_node_mapper = Callback() - if tensor_value_loader is not None: - assert isinstance(tensor_value_loader, collections.Callable) - - class Callback(_detail._TensorValueLoaderCallback): - def call(self, ctx, *, _f=tensor_value_loader): - return _f(ctx) - - tensor_value_loader = Callback() - output_vars_map = _detail._VectorPairStringSymbolVar() - output_vars_list = _detail._VectorSymbolVar() - cg = _detail._load_comp_graph_from_file( - fpath, comp_node_mapper, tensor_value_loader, output_vars_map, output_vars_list - ) - return CompGraphLoadResult(cg, dict(list(output_vars_map)), list(output_vars_list)) - - -def optimize_for_inference( - output_vars, - *, - f16_io_f32_comp=False, - f16_io_comp=False, - use_nhwcd4=False, - fuse_conv_bias_nonlinearity=False, - use_nchw32=False, - fuse_conv_bias_with_z=False, - use_nchw4=False, - use_nchw88=False, - use_nchw44=False, - use_nchw44_dot=False, - use_chwn4=False -): - """optimize computing graph for inference - - This applies a predefined set of optimization passes. Refer to the mnist - sdk example and C++ code for fine-grained control. - - :param output_vars: output symvars - :type output_vars: list of :class:`.SymbolVar` - :param f16_io_f32_comp: whether to use float16 for I/O between oprs and use - float32 as internal computation precision. Note the output var would be - changed to float16 - :param f16_io_comp: whether to use float16 for both I/O and computation - precision - :param use_nhwcd4: whether to use NHWCD4 data format. This is faster on some - OpenCL devices - :param fuse_conv_bias_nonlinearity: whether to fuse conv+bias+nonlinearty - into one opr. This is supported only in NHWCD4 format. - :param use_nchw4: whether to use NCHW4 tensor format. - :param use_nchw88: whether to use NCHW88 tensor format. This maybe faster some - times. - :param use_nchw44: whether to use NCHW44 tensor format. This maybe faster some - times. - :param use_nchw44_dot: whether to use NCHW44_DOT tensor format. This format is - optimized for inference in armv8.2 - :param use_nchw32: whether to use NCHW32 tensor format. Mainly used for - nvidia tensorcore. - :param use_chwn4: whether to use CHWN4 tensor format. Mainly used for - nvidia tensorcore. - - - :return: list of transformed vars corresponding to given output vars - """ - - assert isinstance(output_vars, (list, tuple)) - opt = _detail._OptimizeForInferenceOptions() - settings = locals() - for i in [ - "f16_io_f32_comp", - "f16_io_comp", - "fuse_conv_bias_nonlinearity", - "fuse_conv_bias_with_z", - ]: - if settings[i]: - getattr(opt, "enable_{}".format(i))() - - layout_tranform = None - for k, v in { - "use_nchw4": "nchw4", - "use_nhwcd4": "nhwcd4", - "use_nchw32": "nchw32", - "use_nchw88": "nchw88", - "use_nchw44": "nchw44", - "use_nchw44_dot": "nchw44_dot", - "use_chwn4": "chwn4", - }.items(): - if settings[k]: - assert ( - not layout_tranform - ), "Only one layout transform supported, both {} and {}".format( - layout_tranform, k - ) - getattr(opt, "enable_{}".format(v))() - layout_tranform = k - - vec = _detail._VectorSymbolVar() - for i in output_vars: - assert isinstance(i, _detail.SymbolVar), "bad var: {}".format(i) - vec.push_back(i) - return list(_detail._optimize_for_inference(vec, opt)) - - -def get_opr_fp_graph_exec(comp_graph, output_vars): - """get opr footprint and graph exec info - - This function will recompile the compute graph, the AsyncExecutable compiled - before will be invalid. - - :param comp_graph: ComputingGraph - :param output_vars: list of :class:'.SymbolVar' - """ - assert isinstance(output_vars, (list, tuple)) - vec = _detail._VectorSymbolVar() - for i in output_vars: - assert isinstance(i, _detail.SymbolVar), "bad var: {}".format(i) - vec.push_back(i) - return json.loads(_detail._get_opr_fp_graph_exec(comp_graph, output_vars)) - - -def to_mgb_supported_dtype(dtype_): - """get the dtype supported by megbrain nearest to given dtype""" - if ( - dtype.is_lowbit(dtype_) - or dtype.is_quantize(dtype_) - or dtype.is_bfloat16(dtype_) - ): - return dtype_ - return _detail._to_mgb_supported_dtype(dtype_) - - -def return_free_memory(): - """return free memory chunks on all devices. - - This function will try it best to free all consecutive free chunks back to - operating system, small pieces may not be returned. - - Please notice that this function will not move any memory in-use. - """ - _detail.CompNode._try_coalesce_all_free_memory() diff --git a/python_module/megengine/_internal/_timed_func_fork_exec_entry.py b/python_module/megengine/_internal/_timed_func_fork_exec_entry.py deleted file mode 100644 index 50492ec1..00000000 --- a/python_module/megengine/_internal/_timed_func_fork_exec_entry.py +++ /dev/null @@ -1,37 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -import argparse -import os -import sys - -import megengine._internal.mgb as _mgb - -try: - from setproctitle import setproctitle -except ImportError: - setproctitle = None - - -def main(): - parser = argparse.ArgumentParser( - description="entry point for fork-exec callback in TimedFuncInvoker;" - " this file should not be used directly by normal user." - ) - parser.add_argument("user_data") - args = parser.parse_args() - - if setproctitle: - setproctitle("megbrain:timed_func_exec:ppid={}".format(os.getppid())) - _mgb._timed_func_exec_cb(args.user_data) - raise SystemError("_timed_func_exec_cb returned") - - -if __name__ == "__main__": - main() diff --git a/python_module/megengine/_internal/comp_graph_tools.py b/python_module/megengine/_internal/comp_graph_tools.py deleted file mode 100644 index 5777d7d0..00000000 --- a/python_module/megengine/_internal/comp_graph_tools.py +++ /dev/null @@ -1,274 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -"""tools for graph manipulation""" - -import collections - -from . import mgb as _mgb - - -def get_dep_vars(var, var_type=None): - """return :class:`.SymbolVar` of type ``var_type`` that input ``var`` - depands on. If ``var_type`` is None, return all types. - - :type var: an instance or iterable of :class:`.SymbolVar` - :type var_type: ``str`` or an iterable of ``str`` - "rtype: list of :class:`.SymbolVar` - """ - outputs = [] - memo = set() - - if isinstance(var, _mgb.SymbolVar): - var = [var] - - if isinstance(var_type, str): - var_type = [var_type] - - q = list(var) - while q: - v = q.pop() - if v in memo: - continue - memo.add(v) - q.extend(get_inputs(v)) - if var_type is not None: - if get_type(v) in var_type: - outputs.append(v) - else: - outputs.append(v) - - return outputs - - -def get_inputs(var): - """get the inputs of owner opr of a variable - - :type var: :class:`.SymbolVar` - :rtype: list of :class:`.SymbolVar` - """ - assert isinstance(var, _mgb.SymbolVar) - return _mgb._get_owner_opr_inputs(var) - - -def get_type(var): - """get the type of owner opr of a variable - - :type var: :class:`.SymbolVar` - :rtype: ``str`` - """ - assert isinstance(var, _mgb.SymbolVar) - return _mgb._get_owner_opr_type(var) - - -def get_opr_type(opr): - """get the type of a opr - - :type var: :class:`.Operator` - :rtype: ``str`` - """ - assert isinstance(opr, _mgb.Operator) - return _mgb._get_opr_type(opr) - - -def graph_traversal(outputs): - """helper function to traverse the computing graph and reeturn enough useful information - - :param outputs: model outputs - :type outputs: :class:`.Symbolvar` - :return: tuple (map_oprs, map_vars, var2oprs, opr2receivers, indegree2opr, opr2indegree) - WHERE - map_oprs is dict from opr_id to actual opr - map_vars is dict from var_id to actual var - var2oprs is dict from var to dest oprs along with index - opr2receivers is dict from current opr to next opr - indegree2opr is dict from in_degree to opr in computing graph - opr2indegree is dict from opr in computing graph to in_degree - - (indegree2opr, opr2indegree) are only used in topological sort in get_oprs_seq function - """ - # meta information for comp graph - map_oprs = collections.defaultdict(set) - map_vars = collections.defaultdict(set) - - var2oprs = collections.defaultdict(list) - opr2receivers = collections.defaultdict(list) - - queue = list(map(lambda x: x.owner_opr, outputs)) - visited = set(map(lambda x: x.id, queue)) - - # iterate through whole comp_graph, fill in meta information - indegree2opr = collections.defaultdict(set) - opr2indegree = {} - - idx = 0 - while idx < len(queue): - cur_opr = queue[idx] - map_oprs[cur_opr.id] = cur_opr - - idx += 1 - - indegree = 0 - for var_idx, var in enumerate(cur_opr.inputs): - map_vars[var.id] = var - var2oprs[var.id].append((cur_opr.id, var_idx)) - - pre_opr = var.owner_opr - - if pre_opr.id not in visited: - visited.add(pre_opr.id) - queue.append(pre_opr) - - indegree += 1 - opr2receivers[pre_opr.id].append(cur_opr.id) - - indegree2opr[indegree].add(cur_opr.id) - opr2indegree[cur_opr.id] = indegree - - return map_oprs, map_vars, var2oprs, opr2receivers, indegree2opr, opr2indegree - - -def get_oprs_seq(outputs, prune_reshape=False): - """get oprs in some topological order for a dumped model - - :param outputs: model outputs - :param prune_reshape: whether to prune the operators useless during inference - :return: opr list with some correct execution order - """ - - def topological_sort(map_oprs, opr2receivers, indegree2opr, opr2indegree): - # generate an execution order with topological sort algorithm - oprs_seq = [] - nr_remain = len(map_oprs) - while indegree2opr[0]: - opr_id = indegree2opr[0].pop() - opr = map_oprs[opr_id] - nr_remain -= 1 - - # skip const value generation operator - if get_opr_type(opr) != "ImmutableTensor": - oprs_seq.append(opr) - - for post_id in opr2receivers[opr_id]: - indegree = opr2indegree[post_id] - indegree2opr[indegree].remove(post_id) - - indegree -= 1 - indegree2opr[indegree].add(post_id) - opr2indegree[post_id] = indegree - - assert nr_remain == 0, "there are {} remaining nodes; cyclic graph?".format( - nr_remain - ) - return oprs_seq - - # reshape op definition: reshape(input_tensor, dest_shape) -> output_tensor - # when inferencing, shape of output_tensor is already known, so one can prune some operators related to dest_shape in the loaded graph - def prune_reshape_oprs(outputs, oprs_seq, var2oprs): - def iterative_pruning(cur_opr, post_opr, marked_opr_ids): - useless = True - for oup in cur_opr.outputs: - if "workspace" not in oup.name: - var_idx = post_opr.inputs.index(oup) - var2oprs[oup.id].remove((post_opr.id, var_idx)) - useless = useless and (len(var2oprs[oup.id]) == 0) - - if useless: - marked_opr_ids.append(cur_opr.id) - - for inp in cur_opr.inputs: - iterative_pruning(inp.owner_opr, cur_opr, marked_opr_ids) - - reshape_vars = get_dep_vars(outputs, "Reshape") - reshape_oprs = [var.owner_opr for var in reshape_vars] - - marked_opr_ids = [] - for reshape_opr in reshape_oprs: - iterative_pruning( - reshape_opr.inputs[1].owner_opr, reshape_opr, marked_opr_ids - ) - - # filter out all marked oprs - return list(filter(lambda x: x.id not in marked_opr_ids, oprs_seq)) - - map_oprs, _, var2oprs, opr2receivers, indegree2opr, opr2indegree = graph_traversal( - outputs - ) - oprs_seq = topological_sort(map_oprs, opr2receivers, indegree2opr, opr2indegree) - if prune_reshape is True: - oprs_seq = prune_reshape_oprs(outputs, oprs_seq, var2oprs.copy()) - return oprs_seq - - -def replace_vars(dst, varmap): - """replace vars in the graph - - :param dst: target vars representing the graph - :type dst: list of :class:`.SymbolVar` - :param varmap: the map that specifies how to replace the vars - :type varmap: dict that maps from src var to dst var - - :return: new vars that correspond to ``dst`` with all the dependencies - replaced - :rtype: list of :class:`.SymbolVar` - """ - dst_vec = _mgb._VectorSymbolVar() - repl_src_vec = _mgb._VectorSymbolVar() - repl_dst_vec = _mgb._VectorSymbolVar() - for i in dst: - assert isinstance(i, _mgb.SymbolVar) - dst_vec.push_back(i) - - for i, j in getattr(varmap, "items", lambda: varmap)(): - assert isinstance(i, _mgb.SymbolVar) - assert isinstance(j, _mgb.SymbolVar) - repl_src_vec.push_back(i) - repl_dst_vec.push_back(j) - - return _mgb._replace_vars(repl_src_vec, repl_dst_vec, dst_vec) - - -def replace_oprs(dst, oprmap): - """Replace operators in the graph. Roughly equivalent to - - :param dst: target vars representing the graph - :type dst: list of :class:`.SymbolVar` - :param oprmap: the map that specifies how to replace the operators - :type oprmap: dict that maps from src operator to dst operator - - :return: new vars that correspond to ``dst`` with all the dependencies - replaced - :rtype: list of :class:`.SymbolVar` - """ - dst_vec = _mgb._VectorSymbolVar() - repl_src_vec = _mgb._VectorOperator() - repl_dst_vec = _mgb._VectorOperator() - for i in dst: - assert isinstance(i, _mgb.SymbolVar) - dst_vec.push_back(i) - - for i, j in getattr(oprmap, "items", lambda: oprmap)(): - assert isinstance(i, _mgb.Operator) - assert isinstance(j, _mgb.Operator) - repl_src_vec.push_back(i) - repl_dst_vec.push_back(j) - - return _mgb._replace_oprs(repl_src_vec, repl_dst_vec, dst_vec) - - -def set_priority_to_id(dest_vars): - """For all oprs in the subgraph constructed by dest_vars - set its priority to id if its original priority is zero - :param dest_vars: target vars representing the graph - """ - dest_vec = _mgb._VectorSymbolVar() - for i in dest_vars: - assert isinstance(i, _mgb.SymbolVar) - dest_vec.push_back(i) - _mgb._set_priority_to_id(dest_vec) diff --git a/python_module/megengine/_internal/config.py b/python_module/megengine/_internal/config.py deleted file mode 100644 index 3bac8ab5..00000000 --- a/python_module/megengine/_internal/config.py +++ /dev/null @@ -1,439 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -import collections -import os - -from . import mgb as _mgb - -_default_device_type = "CUDA" - - -def set_device_map(logical_dev, physical_dev, device_type=None): - """map from *logical_dev* to *physical_dev* for furture comp node - loading - - example:: - - set_device_map(0, 2, 'CPU') # cpu0 -> cpu2 - set_device_map('gpu3', 'gpu0') # gpu0 -> gpu0 - - :param device_type: specify the device type if devices are given by - integers; if devices are given by integers and ``device_type`` is not - given, the default value ``'CUDA'`` would be used. Possible values are - ``'CUDA'`` and ``'CPU'``. - """ - - if device_type is None: - device_type = _default_device_type - - if device_type == "CUDA": - xpu = "gpu" - else: - assert device_type == "CPU" - xpu = "cpu" - - def rmxpu(v): - if isinstance(v, str): - assert v.startswith(xpu) or v.startswith("xpu"), ( - "bad comp node in set_device_map: " - "device_type={} comp_node={}".format(device_type, v) - ) - return v[3:] - return v - - logical_dev, physical_dev = map(rmxpu, [logical_dev, physical_dev]) - _mgb.CompNode._set_device_map(device_type, int(logical_dev), int(physical_dev)) - - -def set_default_device(physical_dev, device_type=None): - """set physcal device for xpux - - when *device_type* is None and *physical_dev* starts with *gpu* or *cpu*, - the default device type would be modified accordingly for future calls to - :func:`set_device_map` when remapping device number. - """ - global _default_device_type - if ( - device_type is None - and isinstance(physical_dev, str) - and not physical_dev.isdigit() - and not physical_dev.startswith("xpu") - ): - t = physical_dev[:3] - if t == "gpu": - _default_device_type = "CUDA" - else: - assert t == "cpu", "bad physical_dev: {}".format(physical_dev) - _default_device_type = "CPU" - set_default_device_type(_default_device_type) - device_type = _default_device_type - set_device_map(-1, physical_dev, device_type) - - -def set_default_device_type(device_type): - """set device type for xpu""" - global _default_device_type - device_type = device_type.upper() - _mgb.CompNode._set_unspec_device_type(device_type) - _default_device_type = device_type - - -def set_fork_cuda_warning_flag(flag): - """set warning to be printed at fork if cuda has been initialized - - :type flag: int - :param flag: controls how the warning should be printed: - - * 0: disable warning - * 1: print warning to log - * 2: print warning to log and raise exception - """ - _mgb._config.set_fork_cuda_warning_flag(int(flag)) - - -def get_device_count(device_type="xpu", warn=True): - """get number of devices installed on this system - - :param device_type: device type, one of 'xpu', 'gpu' or 'cpu' - :type device_type: str - """ - return _mgb.CompNode._get_device_count(device_type.upper(), warn) - - -def parse_locator(device_name: str) -> tuple: - """get the tensor locator expression by device name. - - :param device_name: device name, like 'cpu0', 'gpu1' and 'xpux' - :type device_name: str - - :return: (device_type, dev_num, stream_num) - """ - return _mgb.CompNode._parse_locator(device_name) - - -def set_mem_reserve_size(size): - """set memory reserve size: - - * If *size* is greater than 1, it is the absolute amount of memory to - be reserved in MB; - * If *size* is in the range (0, 1), it is the ratio of total memory; - * If *size* is 0, memory reservation and pre-allocation would be - disabled; - * If *size* is -1, disable custom memory allocator and use cuda APIs - directly. - """ - _mgb._config.set_mem_reserve_size(float(size)) - - -def set_comp_graph_option(comp_graph, name, val): - """set computing graph option and return its old value - :type comp_graph: :class:`.CompGraph` - :param comp_graph: the computing graph whose option should be modified - :type name: str - :param name: option name - Currently supported options are: - - * "no_profiling_on_shape_change": bool; - When execution strategy is set to profiling, always use the - initial profile result and do not re-run profiling even if input - shape changes. - * "seq_opt.enable_mem_plan_opt": bool - * "seq_opt.enable_mem_reuse_alloc": bool - * "seq_opt.enable_seq_comp_node_opt": bool - * "force_dynamic_alloc": bool - * "var_sanity_check_first_run": bool - * "enable_sublinear_memory_opt": bool - * "enable_memory_swap": bool; whether to enable memory swap; it - usually performs worse than sublinear memory - * "enable_var_mem_defragment": bool - * "allocate_static_mem_after_graph_compile": bool - * "enable_grad_var_static_reshape": bool: - If set to ``True``, dynamically-shaped gradients whose original - shape is statically inferrable would be reshaped, so static - shape inference can continue - * "async_exec_level": int - - * ``0``: do not dispatch asynchronously - * ``1``: async dispatch if there are more than 1 cuda comp - nodes - * mask ``0b10``: async for comp nodes with unlimited queue - (e.g. CPU comp nodes) - * mask ``0b100``: async for even one comp node - * "log_level": int - - * ``0``: no log info for graph construction/compiling - * ``1``: static memory allocation status, - WorkspaceLimitGetter summary, and optimizer summary - * ``2``: optimizer details and duplicated operators tha are - removed - * "graph_opt.jit": whether to enable JIT - * "graph_opt.tensorrt": whether to enable fine-grained automatic - replacement for TensorRT operators - * "graph_opt.android_nn": whether to enable fine-grained automatic - replacement for Android NN operators - * "graph_opt_level": int - - * ``0``: disable - * ``1``: level-1: inplace arith transformations during graph - construction - * ``2``: (default) level-2: level-1, plus global optimization - before graph compiling - * ``3``: also enable JIT - :param val: new option value - :return: old option value - """ - if name == "log_static_mem_alloc": - name = "log_level" - if name == "enable_async_exec": - name = "async_exec_level" - return _mgb._config.set_comp_graph_option(comp_graph, name, int(val)) - - -def comp_graph_is_eager(comp_graph): - return _mgb._config.comp_graph_is_eager(comp_graph) - - -def add_extra_vardep(var, dep): - """add *dep* as an extra dependency of *var*, so if *var* is required to - compute the final output when compiling a comp graph, *dep* would also be - included in the computing sequence. Note that the order computing of these - two vars is not guaranteed. - """ - assert isinstance(var, _mgb.SymbolVar) and isinstance(dep, _mgb.SymbolVar) - assert var.owner_graph == dep.owner_graph - return _mgb._config.add_extra_vardep(var, dep) - - -class _GraphPropertyBase: - """helper class for implementing operator property setter context managers""" - - _cur_graph = None - - _graph2stack = None - """class attribute that maintains mapping from graph to property stack; - should be defined by child classes""" - - __prop_setup__ = None - """overwritten by subclass to setup property""" - - __prop_clear__ = None - """overwritten by subclass to clear property""" - - def __init__(self, comp_graph, prop): - """:param comp_graph: computing graph, or None to not set this - property""" - if comp_graph is not None: - assert isinstance( - comp_graph, _mgb.CompGraph - ), "invalid comp graph: {!r}".format(comp_graph) - self._cur_graph = comp_graph - self._graph2stack.setdefault(comp_graph, []).append(prop) - - def __setup(self, prop): - self.__prop_setup__(self._cur_graph, prop) - - def __clear(self): - self.__prop_clear__(self._cur_graph) - - def __enter__(self): - if self._cur_graph is None: - return - - stack = self._graph2stack[self._cur_graph] - if len(stack) > 1: - # clear nested property - self.__clear() - self.__setup(stack[-1]) - - def __exit__(self, exc_type, exc_value, exc_traceback): - if self._cur_graph is None: - return - - stack = self._graph2stack[self._cur_graph] - self.__clear() - stack.pop() - if stack: - # restore nested property - self.__setup(stack[-1]) - else: - del self._graph2stack[self._cur_graph] - - -class exc_opr_tracker_scope(_GraphPropertyBase): - """context manager for associating an object with all operators created - within this context; so when an exception is raised, information about the - corresponding operator could be retrieved from - :attr:`.MegBrainError.tracker` - - :param comp_graph: the computing graph where the operators should be tracked - :type comp_graph: :class:`.CompGraph` - :param tracker: an arbitrary python object to track the operators - """ - - _graph2stack = {} - - def __init__(self, comp_graph, tracker): - assert ( - tracker is not None - ), "bad args for exc_opr_tracker_scope: {!r} {!r}".format(comp_graph, tracker) - super().__init__(comp_graph, tracker) - - __prop_setup__ = staticmethod(_mgb._config.begin_set_exc_opr_tracker) - __prop_clear__ = staticmethod(_mgb._config.end_set_exc_opr_tracker) - - -class opr_priority_scope(_GraphPropertyBase): - """context manager for setting priority for all operators created in this - context - - :param comp_graph: the computing graph for which operator priority should - be set - :type comp_graph: :class:`.CompGraph` - :param priority: operator priority. Smaller number means higher priority. - Default value is 0. Grad operator would use negative priority by - default. - """ - - _graph2stack = {} - - LOWEST_PRIORITY = 2 ** 31 - 1 - """lowest prority (i.e. max possible value)""" - - HIGHEST_PRIORITY = -LOWEST_PRIORITY - """highest prority (i.e. min possible value)""" - - def __init__(self, comp_graph, priority): - super().__init__(comp_graph, int(priority)) - - __prop_setup__ = staticmethod(_mgb._config.begin_set_opr_priority) - __prop_clear__ = staticmethod(_mgb._config.end_set_opr_priority) - - -OprTrackerResult = collections.namedtuple( - "OprTrackerResult", ["msg", "tracker", "grad_tracker"] -) - - -def get_opr_tracker(cg, var_id): - """get the tracking object associated with the owner operator of a var - - :param cg: the computing graph - :param var_id: id of the var whose owner opr tracker should be found - - :return: if no var is found, ``None`` is returned; otherwise return an - :class:`OprTrackerResult` object - """ - assert isinstance(cg, _mgb.CompGraph) - ret = _mgb._config.get_opr_tracker(cg, int(var_id)) - if ret is None: - return - return OprTrackerResult(*ret) - - -def set_opr_sublinear_memory_endpoint(var): - """set the owner operator of a symvar to be endpoint of sublinear memory - optimizer - - - :type var: :class:`.SymbolVar` - """ - _mgb._config.set_opr_sublinear_memory_endpoint(var) - - -def max_size_t(): - """get max value of size_t type on local architecture""" - return _mgb.max_size_t() - - -def is_cuda_ctx_set(): - """return whether current thread has an active cuda driver context""" - return _mgb._config.is_cuda_ctx_set() - - -def get_include_path(): - """get include path for building megbrain extensions""" - return os.path.join(os.path.realpath(os.path.dirname(__file__)), "include") - - -def get_cuda_gencode(only_cap=False): - """get -gencode options to be passed to nvcc for compiling on local - machine - - :param only_cap: if True, return only a list of cuda compute capability - strings (like ``['35', '52']`` ) - """ - ret = _mgb._config.get_cuda_gencode().split() - if not only_cap: - ret = " ".join(map("-gencode arch=compute_{0},code=sm_{0}".format, ret)) - return ret - - -def get_cuda_lib_path(): - """get the cuda lib64 path by locating nvcc - """ - return _mgb._config.get_cuda_lib_path() - - -def get_cuda_include_path(): - """get the cuda include path by locating nvcc, including - parent path and `parent path`/include - """ - return _mgb._config.get_cuda_include_path() - - -def get_cuda_version(): - """get runtime cuda version - """ - return _mgb._config.get_cuda_version() - - -def is_local_cuda_env_ok(): - """check whether local cuda environment ok by locating nvcc - """ - return _mgb._config.is_local_cuda_env_ok() - - -def is_compiled_with_cuda(): - """whether cuda is enabled at compile time""" - return _mgb._config.is_compiled_with_cuda() - - -def load_opr_library(path): - """Load an external operator library. This essentially sets megbrain - symbols as public and load the library. - - :param path: path to the shared object; if it is None, then only megbrain - symbols are made public. - """ - _mgb._config.load_opr_library( - os.path.realpath(os.path.join(os.path.dirname(__file__), "_mgb.so")), path - ) - - -def dump_registered_oprs(): - """ - get all registered oprs, return dict(id, name) - """ - return dict(_mgb._config.dump_registered_oprs()) - - -def create_mm_server(server_addr, port): - """ - create mm server with server address - throw exception if server_addr is already used - """ - return _mgb._config.create_mm_server(server_addr, port) - - -def group_barrier(server_addr, port, size, rank): - """ - block until all ranks reach this barrier - """ - return _mgb._config.group_barrier(server_addr, port, size, rank) diff --git a/python_module/megengine/_internal/craniotome.py b/python_module/megengine/_internal/craniotome.py deleted file mode 100644 index 3a1e6d50..00000000 --- a/python_module/megengine/_internal/craniotome.py +++ /dev/null @@ -1,432 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -"""used for creating a megbrain operator from python""" - -import copy -import itertools -from abc import ABCMeta, abstractmethod, abstractproperty - -from . import helper as _helper -from . import mgb as _mgb - - -class _CraniotomeBaseMeta(ABCMeta): - _base_created = False - - def __init__(cls, name, bases, member_dict): - if _CraniotomeBaseMeta._base_created: - assert "__init__" not in member_dict, ( - "Craniotome operators should not overwrite __init__ method; " - "use setup() instead." - ) - forbidden = set( - k for k in dir(CraniotomeBase) if k[0] == "_" and k[1] != "_" - ) - forbidden.add("get_io_vars") - check_key = member_dict.get("__check_key__", True) - whitelist = ["__classcell__"] - for k in member_dict.keys(): - assert k not in forbidden, "{} could not be overwritten".format(k) - if ( - check_key - and k.startswith("__") - and k.endswith("__") - and k not in whitelist - and not hasattr(CraniotomeBase, k) - ): - raise KeyError( - "name {} in class {} does not exist in the baseclass".format( - k, name - ) - ) - else: - _CraniotomeBaseMeta._base_created = True - super().__init__(name, bases, member_dict) - - -class CraniotomeBase(_mgb.CraniotomeDesc, metaclass=_CraniotomeBaseMeta): - """base class used for extending megbrain core operators in python - - Note: all names starting and ending with two underscores in the subclasses - would be checked and KeyError would be raised if the name does not exist in - the base class. This behavor can be disabled by setting ``__check_key__`` - to ``False`` (see the testcase for more details) - """ - - # methods and attributes to be overwritten by subclasses - - __expand_single_outputs__ = True - """if :attr:`__nr_outputs__` is 1, whether to return a single - :class:`.SymbolVar` instead of a tuple in :meth:`make`""" - - __is_dynamic_output_shape__ = False - """whether output shape could not be inferred from input shape. If value of - this attribute is ``False``, :meth:`infer_shape` must be implemented. If - this attribute is ``True`` but the operator has no inputs, then - :meth:`infer_shape` would also be called to infer output shape before - operator execution. - """ - - __disable_sys_mem_alloc__ = False - """whether to disable system memory allocator. This is used when - :attr:`__is_dynamic_output_shape__` is ``False`` but the output memory - should not be managed by megbrain system (so it can be forwarded from - external buffer)""" - - __allow_duplicate__ = True - """whether this operator can be duplicated (e.g. used in sublinear - memory)""" - - __allow_empty_out__ = False - """whether empty output shape is allowed; if it is set as ``False``, then - an exception would be raised if output var is empty to prevent erroneously - forgetting initializing output vars""" - - @abstractproperty - def __nr_inputs__(self): - """number of input vars""" - - @abstractproperty - def __nr_outputs__(self): - """number of output vars""" - - @abstractmethod - def execute(self, inputs, outputs): - """execute the operator, read values from *inputs* by calling - :meth:`.CompGraphCallbackValueProxy.get_value` and write results into - *outputs* by calling :meth:`.SharedND.set_value` - - :param inputs: values for each input var - :type inputs: tuple of :class:`.CompGraphCallbackValueProxy` - :param outputs: values for each output var - :type outputs: tuple of :class:`.SharedND` - """ - - def setup(self): - """overwritten by subclass to accept kwargs passed to :meth:`make` to - setup the operator""" - - def infer_shape(self, inp_shapes): - """infer output shape from input shapes - - :type inp_shapes: tuple of tuple of ints - :param inp_shapes: input shapes for each input var - :rtype: tuple of tuple of ints - :return: output shapes for each output var - """ - raise NotImplementedError( - "{}: infer_shape() not implemented; for operators with dynamic " - "output shape, __is_dynamic_output_shape__ should be set to True".format( - self - ) - ) - - def grad(self, wrt_idx, inputs, outputs, out_grad): - """compute symbolic gradient; should be overwritten by differentiable - subclasses - - :type wrt_idx: int - :param wrt_idx: the input var with respect to which the gradient should - be computed; please also see the notes below - :type inputs: tuple of :class:`.SymbolVar` - :param inputs: input symbol vars - :type outputs: tuple of :class:`.SymbolVar` - :param outputs: output symbol vars - :type out_grad: tuple of (:class:`.SymbolVar` or None) - :param out_grad: gradients of loss with respect to each output var - - .. note:: - - In case when loss does not depend on some var (i.e. zero grad), - the corresponding value in *out_grad* would be ``None``. It is - guaranteed that at least one element in *out_grad* is not - ``None``. - - .. note:: - - This function can return either of the following: - - 1. Gradient of the input specified by ``wrt_idx`` - 2. A list containing gradients of all inputs. In this case, - ``wrt_idx`` can be ignored. - - And the so called gradient can be either one of: - - 1. A :class:`.SymbolVar` representing the symbolic gradient - value - 2. ``0`` representing zero gradient - """ - raise NotImplementedError("grad for {} not implemented".format(self)) - - def init_output_dtype(self, input_dtypes): - """infer output dtypes from input dtypes; return None to use default - infer function in megbrain. - - .. note:: - This method must be implemented if there is no input var - - :param input_dtypes: input dtypes - :type input_dtypes: list of :class:`numpy.dtype` - :rtype: None or list of :class:`numpy.dtype`-compatible - """ - - def get_serialize_params(self): - """get params for megbrain graph serialization. This function should - return a list or tuple, containing one or two elements: the first - element must be a string, representing the name passed to - ``opr_loader_maker`` during deserializing; the second element, if - exists, must be convertible to ``bytes`` and is used for dumping any - extra opr params, which can be retrieved by ``load_buf_with_len`` - during deserializing. - """ - raise NotImplementedError( - "get_serialize_params() for {} not implemented".format(self) - ) - - def copy(self): - """copy this craniotome descriptor; the default implementation creates - a new object, and copies object ``__dict__``""" - ret = type(self)() - d0 = self.__dict__.copy() - d0.pop("this") - ret.__dict__.update(copy.deepcopy(d0)) - return ret - - def on_graph_compiled(self, used_outputs): - """a callback that would be invoked when the graph is compiled; it - would always have a matching :meth:`on_compiled_func_deleted` call - - :param used_outputs: indices of outputs that are needed for the - computation - :type used_outputs: ``tuple of int`` - """ - - def on_compiled_func_deleted(self): - """a callback that would be invoked when the compiled function is - destructed; it would always have a matching :meth:`on_graph_compiled` - call""" - - def get_io_vars(self): - """get input vars, comp order dep vars and output vars - - :return: a dict with keys ``'input'``, ``'output'`` and - ``'comp_order'`` that maps to corresponding list of vars - """ - all_vars = list(self._get_all_io_vars()) - nr_inp = self.__nr_inputs__ - nr_out = self.__nr_outputs__ - nr_comp_order = self._get_nr_dev_comp_order_deps() - s0 = nr_inp + nr_comp_order - return dict( - input=all_vars[:nr_inp], - comp_order=all_vars[nr_inp:s0], - output=all_vars[s0:], - ) - - @property - def owner_opr_id(self): - """ID of the operator that owns this descriptor""" - return self._get_opr_id() - - @property - def comp_node(self): - """comp node on which this operator runs""" - return self._get_comp_node() - - # below are methods that should not be changed - - def _hash(self): - return int(hash(self)) % (1 << 64) - - def _setup_self(self, dst): - dst.append(self) - - def _is_same(self, rhs): - return bool(self == rhs) - - def _node_flag(self): - return ( - (int(bool(self.__is_dynamic_output_shape__)) << 0) - | (int(not self.__allow_duplicate__) << 1) - | (int(bool(self.__allow_empty_out__)) << 2) - | (int(bool(self.__disable_sys_mem_alloc__)) << 3) - ) - - def _get_opr_type_name(self): - return str(self.__class__.__name__) - - def _get_nr_outputs(self): - return int(self.__nr_outputs__) - - def _execute(self, inputs, outputs): - inputs = tuple(inputs) - outputs = tuple(outputs) - if not self.__is_dynamic_output_shape__: - out_shapes = [i.shape for i in outputs] - self.execute(inputs, outputs) - if not self.__is_dynamic_output_shape__: - new_shapes = [i.shape for i in outputs] - assert ( - out_shapes == new_shapes - ), "output shape changed after executing {}: before={} after={}".format( - self, out_shapes, new_shapes - ) - - def _infer_shape(self, inp_shapes): - inp_shapes = tuple(tuple(map(int, i)) for i in inp_shapes) - oshp_get = self.infer_shape(inp_shapes) - assert ( - len(oshp_get) == self.__nr_outputs__ - ), "{}: expect {} outputs; got {}(val: {}) from infer_shape".format( - self, self.__nr_outputs__, len(oshp_get), oshp_get - ) - return _helper.cvt_to_vector_of_shape(oshp_get) - - def _grad(self, wrt_idx, inputs, outputs, out_grad): - og = [] - for i in out_grad: - if i.valid: - og.append(i) - else: - og.append(None) - rst = self.grad(int(wrt_idx), tuple(inputs), tuple(outputs), tuple(og)) - if not isinstance(rst, (list, tuple)): - rst = [rst] - else: - assert len(rst) == len( - inputs - ), "{}: opr has {} inputs but {} grads are returned".format( - self, len(inputs), len(rst) - ) - - for i in range(len(rst)): - cur = rst[i] - if cur is 0: - rst[i] = _mgb.SymbolVar() - else: - assert isinstance(cur, _mgb.SymbolVar), ( - "{}: invalid grad result; it should be either " - "0 or a SymbolVar, got {!r} instead".format(self, cur) - ) - return rst - - def _get_nr_dev_comp_order_deps(self): - return 0 - - def _init_output_dtype(self, input_dtypes, ret): - get = self.init_output_dtype(input_dtypes) - if get is not None: - assert isinstance(ret, (list, tuple)) and len(get) == len(ret) - ret[:] = get - return True - assert self.__nr_inputs__, ( - "{}: init_output_dtype must be implemented " - "if there is no input var".format(self) - ) - return False - - def _setup_serialize_params(self, output): - val = list(self.get_serialize_params()) - assert len(val) in [1, 2] - name = val[0] - assert isinstance(name, str) - output.append(name) - if len(val) == 2: - output.append(bytes(val[1])) - - def _copy(self): - ret = self.copy() - assert type(ret) is type( - self - ), "copy() returned different type: src={} copied={}".format( - type(self), type(ret) - ) - assert ret is not self - ret.__disown__() - self._set_copy_result(ret) - - def _on_graph_compile_or_func_del(self, used_outputs): - if used_outputs: - self.on_graph_compiled(used_outputs) - else: - self.on_compiled_func_deleted() - - def __repr__(self): - return "cranoiotome:{}".format(self.__class__.__name__) - - @classmethod - def make( - cls, - *inputs, - comp_graph=None, - name=None, - comp_node=None, - config=None, - dev_comp_order_deps=[], - **kwargs - ): - """apply this operator on some input vars and return corresponding - output vars - - :type inputs: tuple of :class:`.SymbolVar` - :param inputs: input symvars; immediate values could also be accepted, - as long as there is symvar to infer comp node and comp graph - :param comp_graph: if there is no input vars, *comp_graph* must be - provided to specify which computing graph to insert this operator - :param dev_comp_order_deps: vars that must have been computed - before executing this operator - :param kwargs: extra keyword arguments to be passed to :meth:`setup` of - this class - :param name: name of the resulting operator - :rtype: tuple of :class:`.SymbolVar` - :return: output symvars - """ - - if not inputs and not dev_comp_order_deps: - assert isinstance( - comp_graph, _mgb.CompGraph - ), "{}: comp_graph must be given if no inputs provided".format(self) - - desc = cls() - desc.setup(**kwargs) - assert ( - len(inputs) == desc.__nr_inputs__ - ), "{}: expected {} inputs, got {}".format( - desc, desc.__nr_inputs__, len(inputs) - ) - - config = _helper.gen_config(name, comp_node, config) - - # get inp_vec - inp_vec = _mgb._VectorSymbolVar() - for i in _helper.canonize_input_vars( - itertools.chain(inputs, dev_comp_order_deps), - comp_graph=comp_graph, - config=config, - ): - inp_vec.push_back(i) - desc._get_nr_dev_comp_order_deps = lambda *, val=len(dev_comp_order_deps): val - - if comp_graph is not None: - desc._get_comp_graph = lambda: comp_graph - expand_single_outputs = desc.__expand_single_outputs__ - desc.__disown__() - rst = _mgb.make_opr_from_craniotome_desc(desc, inp_vec, config) - if expand_single_outputs and len(rst) == 1: - return rst[0] - return tuple(rst) - - -def make_opr(cls): - """decorator used to wrap a :class:`.CraniotomeBase` subclass and return - its :meth:`~.CraniotomeBase.make` method - """ - assert issubclass(cls, CraniotomeBase) - return cls.make diff --git a/python_module/megengine/_internal/dtype.py b/python_module/megengine/_internal/dtype.py deleted file mode 100644 index 6bb32f86..00000000 --- a/python_module/megengine/_internal/dtype.py +++ /dev/null @@ -1,286 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -import collections -from typing import Union - -import numpy as np - -from .mgb import bfloat16, intb1, intb2, intb4 - -_QuantDtypeMetadata = collections.namedtuple( - "QuantDtypeMetadata", ["name", "np_dtype_str", "is_unsigned", "qmin", "qmax",] -) - -_metadata_dict = { - "quint8": _QuantDtypeMetadata("Quantized8Asymm", "uint8", True, 0, 255), - "qint8": _QuantDtypeMetadata("QuantizedS8", "int8", False, -128, 127), - "quint4": _QuantDtypeMetadata("Quantized4Asymm", "uint8", True, 0, 15), - "qint4": _QuantDtypeMetadata("QuantizedS4", "int8", False, -8, 7), - "qint32": _QuantDtypeMetadata( - "QuantizedS32", "int32", False, -(2 ** 31), 2 ** 31 - 1, - ), - # NOTE: int2 is not supported for model dump yet - "quint2": _QuantDtypeMetadata(None, "uint8", True, 0, 3), - "qint2": _QuantDtypeMetadata(None, "int8", False, -2, 1), -} - - -def is_quantize(dtype): - return ( - hasattr(dtype, "metadata") - and dtype.metadata is not None - and "mgb_dtype" in dtype.metadata - ) - - -def is_lowbit(dtype): - return (dtype is intb1) or (dtype is intb2) or (dtype is intb4) - - -def is_bfloat16(dtype): - return dtype is bfloat16 - - -def get_scale(dtype): - assert is_quantize(dtype) - return dtype.metadata["mgb_dtype"]["scale"] - - -def get_zero_point(dtype): - assert is_quantize(dtype) - metadata = dtype.metadata["mgb_dtype"] - assert metadata["name"] in ("Quantized8Asymm", "Quantized4Asymm") - return metadata["zero_point"] - - -def _check_zero_point(zp: int, dtype_str: str): - qmin = _metadata_dict[dtype_str].qmin - qmax = _metadata_dict[dtype_str].qmax - if zp < qmin or zp > qmax: - raise ValueError( - "zero_point should be within [{}, {}] for {}".format(qmin, qmax, dtype_str) - ) - - -def get_quantized_dtype(dtype_str: str, scale: float, zp: Union[int, None]): - r""" - Get quantized dtype with metadata attribute according to _metadata_dict. - - Note that unsigned dtype must have ``zero_point`` and signed dtype must - not have ``zero_point``, to be consitent with tensor generated by calling - compiled function from `CompGraph.compile(inputs, outspec)`. - - :param dtype: a string indicating which dtype to return - :param scale: a number for scale to store in dtype's metadata - :param zp: a number for zero_point to store in dtype's metadata - """ - metadata = _metadata_dict[dtype_str] - np_dtype_str = metadata.np_dtype_str - is_unsigned = metadata.is_unsigned - if is_unsigned: - if zp is None or int(zp) != zp: - raise ValueError("zero_point should be an integer") - zp = int(zp) - _check_zero_point(zp, dtype_str) - return np.dtype( - np_dtype_str, - metadata={ - "mgb_dtype": { - "name": metadata.name, - "scale": float(scale), - "zero_point": zp, - } - }, - ) - else: - return np.dtype( - np_dtype_str, - metadata={"mgb_dtype": {"name": metadata.name, "scale": float(scale)}}, - ) - - -def quint8(scale, zero_point): - """ - Consturct a quantized unsigned int8 data type with ``scale`` (float) and - ``zero_point`` (uint8). The real value represented by a quint8 data type is - float_val = scale * (uint8_val - zero_point) - """ - return get_quantized_dtype("quint8", scale, zero_point) - - -def qint8(scale): - """ - Construct a quantized int8 data type with ``scale`` (float). The real value - represented by a qint8 data type is float_val = scale * int8_val - """ - return get_quantized_dtype("qint8", scale, None) - - -def qint32(scale): - """ - Construct a quantized int32 data type with ``scale`` (float). The real value - represented by a qint32 data type is float_val = scale * int32_val - """ - return get_quantized_dtype("qint32", scale, None) - - -def quint4(scale, zero_point): - """ - Consturct a quantized unsigned int4 data type with ``scale`` (float) and - ``zero_point`` (uint8). The real value represented by a quint4 data type is - float_val = scale * (uint4_val - zero_point) - """ - return get_quantized_dtype("quint4", scale, zero_point) - - -def qint4(scale): - """ - Construct a quantized int4 data type with ``scale`` (float). The real value - represented by a qint4 data type is float_val = scale * int4_val - """ - return get_quantized_dtype("qint4", scale, None) - - -def _convert_to_quantized_dtype(arr: np.ndarray, dtype: np.dtype, dtype_str: str): - metadata = _metadata_dict[dtype_str] - arr_metadata = dtype.metadata["mgb_dtype"] - if not isinstance(arr, np.ndarray): - raise ValueError("arr parameter should be instance of np.ndarray") - if not is_quantize(dtype) or arr_metadata["name"] != metadata.name: - raise ValueError("dtype parameter should be a {} dtype".format(dtype_str)) - is_unsigned = metadata.is_unsigned - if is_unsigned: - scale, zp = ( - arr_metadata["scale"], - arr_metadata["zero_point"], - ) - return ( - (np.round(arr / scale) + zp) - .clip(metadata.qmin, metadata.qmax) - .astype(dtype) - ) - else: - # don't trick to combine with is_unsigned, seeing ``get_quantized_dtype`` - scale = arr_metadata["scale"] - return np.round(arr / scale).clip(metadata.qmin, metadata.qmax).astype(dtype) - - -def _convert_from_quantized_dtype(arr: np.ndarray, dtype_str: str): - metadata = _metadata_dict[dtype_str] - arr_metadata = arr.dtype.metadata["mgb_dtype"] - if not isinstance(arr, np.ndarray): - raise ValueError("arr parameter should be instance of np.ndarray") - if not is_quantize(arr.dtype) or arr_metadata["name"] != metadata.name: - raise ValueError("arr's dtype should be a {} dtype".format(dtype_str)) - is_unsigned = metadata.is_unsigned - if is_unsigned: - scale, zp = ( - arr_metadata["scale"], - arr_metadata["zero_point"], - ) - return (arr.astype(np.float32) - zp) * scale - else: - # don't trick to combine with is_unsigned, seeing ``get_quantized_dtype`` - scale = arr_metadata["scale"] - return (arr.astype(np.float32)) * scale - - -def convert_to_quint8(arr: np.ndarray, q: np.dtype): - """ - Quantize a float NumPy ndarray into a quint8 one with specified params. - - :param arr: Input ndarray. - :param q: Target data type, should be a quint8. - """ - return _convert_to_quantized_dtype(arr, q, "quint8") - - -def convert_from_quint8(arr: np.ndarray): - """ - Dequantize a quint8 NumPy ndarray into a float one. - - :param arr: Input ndarray. - """ - return _convert_from_quantized_dtype(arr, "quint8") - - -def convert_to_qint8(arr: np.ndarray, q: np.dtype): - """ - Quantize a float NumPy ndarray into a qint8 one with specified params. - - :param arr: Input ndarray. - :param q: Target data type, should be a qint8. - """ - return _convert_to_quantized_dtype(arr, q, "qint8") - - -def convert_from_qint8(arr: np.ndarray): - """ - Dequantize a qint8 NumPy ndarray into a float one. - - :param arr: Input ndarray. - """ - return _convert_from_quantized_dtype(arr, "qint8") - - -def convert_to_qint32(arr: np.ndarray, q: np.dtype): - """ - Quantize a float NumPy ndarray into a qint32 one with specified params. - - :param arr: Input ndarray. - :param q: Target data type, should be a qint8. - """ - return _convert_to_quantized_dtype(arr, q, "qint32") - - -def convert_from_qint32(arr): - """ - Dequantize a qint32 NumPy ndarray into a float one. - - :param arr: Input ndarray. - """ - return _convert_from_quantized_dtype(arr, "qint32") - - -def convert_to_quint4(arr: np.ndarray, q: np.dtype): - """ - Quantize a float NumPy ndarray into a quint4 one with specified params. - - :param arr: Input ndarray. - :param q: Target data type, should be a quint4. - """ - return _convert_to_quantized_dtype(arr, q, "quint4") - - -def convert_from_quint4(arr: np.ndarray): - """ - Dequantize a quint4 NumPy ndarray into a float one. - - :param arr: Input ndarray. - """ - return _convert_from_quantized_dtype(arr, "quint4") - - -def convert_to_qint4(arr: np.ndarray, q: np.dtype): - """ - Quantize a float NumPy ndarray into a qint4 one with specified params. - - :param arr: Input ndarray. - :param q: Target data type, should be a qint4. - """ - return _convert_to_quantized_dtype(arr, q, "qint4") - - -def convert_from_qint4(arr: np.ndarray): - """ - Dequantize a qint4 NumPy ndarray into a float one. - - :param arr: Input ndarray. - """ - return _convert_from_quantized_dtype(arr, "qint4") diff --git a/python_module/megengine/_internal/enum36.py b/python_module/megengine/_internal/enum36.py deleted file mode 100644 index 929eecf7..00000000 --- a/python_module/megengine/_internal/enum36.py +++ /dev/null @@ -1,947 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright [2001] [Cython] -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# --------------------------------------------------------------------- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# -# This file has been modified by Megvii ("Megvii Modifications"). -# All Megvii Modifications are Copyright (C) 2014-2020 Megvii Inc. All rights reserved. -# ---------------------------------------------------------------------- - -import sys -from functools import reduce -from operator import or_ as _or_ -from types import DynamicClassAttribute, MappingProxyType - -# try _collections first to reduce startup cost -try: - from _collections import OrderedDict -except ImportError: - from collections import OrderedDict - - -__all__ = [ - "EnumMeta", - "Enum", - "IntEnum", - "Flag", - "IntFlag", - "auto", - "unique", -] - - -def _is_descriptor(obj): - """Returns True if obj is a descriptor, False otherwise.""" - return ( - hasattr(obj, "__get__") or hasattr(obj, "__set__") or hasattr(obj, "__delete__") - ) - - -def _is_dunder(name): - """Returns True if a __dunder__ name, False otherwise.""" - return ( - name[:2] == name[-2:] == "__" - and name[2:3] != "_" - and name[-3:-2] != "_" - and len(name) > 4 - ) - - -def _is_sunder(name): - """Returns True if a _sunder_ name, False otherwise.""" - return ( - name[0] == name[-1] == "_" - and name[1:2] != "_" - and name[-2:-1] != "_" - and len(name) > 2 - ) - - -def _make_class_unpicklable(cls): - """Make the given class un-picklable.""" - - def _break_on_call_reduce(self, proto): - raise TypeError("%r cannot be pickled" % self) - - cls.__reduce_ex__ = _break_on_call_reduce - cls.__module__ = "" - - -_auto_null = object() - - -class auto: - """ - Instances are replaced with an appropriate value in Enum class suites. - """ - - value = _auto_null - - -class _EnumDict(dict): - """Track enum member order and ensure member names are not reused. - - EnumMeta will use the names found in self._member_names as the - enumeration member names. - - """ - - def __init__(self): - super().__init__() - self._member_names = [] - self._last_values = [] - - def __setitem__(self, key, value): - """Changes anything not dundered or not a descriptor. - - If an enum member name is used twice, an error is raised; duplicate - values are not checked for. - - Single underscore (sunder) names are reserved. - - """ - if _is_sunder(key): - if key not in ( - "_order_", - "_create_pseudo_member_", - "_generate_next_value_", - "_missing_", - ): - raise ValueError("_names_ are reserved for future Enum use") - if key == "_generate_next_value_": - setattr(self, "_generate_next_value", value) - elif _is_dunder(key): - if key == "__order__": - key = "_order_" - elif key in self._member_names: - # descriptor overwriting an enum? - raise TypeError("Attempted to reuse key: %r" % key) - elif not _is_descriptor(value): - if key in self: - # enum overwriting a descriptor? - raise TypeError("%r already defined as: %r" % (key, self[key])) - if isinstance(value, auto): - if value.value == _auto_null: - value.value = self._generate_next_value( - key, 1, len(self._member_names), self._last_values[:] - ) - value = value.value - self._member_names.append(key) - self._last_values.append(value) - super().__setitem__(key, value) - - -# Dummy value for Enum as EnumMeta explicitly checks for it, but of course -# until EnumMeta finishes running the first time the Enum class doesn't exist. -# This is also why there are checks in EnumMeta like `if Enum is not None` -Enum = None - - -class EnumMeta(type): - """Metaclass for Enum""" - - @classmethod - def __prepare__(metacls, cls, bases): - # create the namespace dict - enum_dict = _EnumDict() - # inherit previous flags and _generate_next_value_ function - member_type, first_enum = metacls._get_mixins_(bases) - if first_enum is not None: - enum_dict["_generate_next_value_"] = getattr( - first_enum, "_generate_next_value_", None - ) - return enum_dict - - def __new__(metacls, cls, bases, classdict): - # an Enum class is final once enumeration items have been defined; it - # cannot be mixed with other types (int, float, etc.) if it has an - # inherited __new__ unless a new __new__ is defined (or the resulting - # class will fail). - member_type, first_enum = metacls._get_mixins_(bases) - __new__, save_new, use_args = metacls._find_new_( - classdict, member_type, first_enum - ) - - # save enum items into separate mapping so they don't get baked into - # the new class - enum_members = {k: classdict[k] for k in classdict._member_names} - for name in classdict._member_names: - del classdict[name] - - # adjust the sunders - _order_ = classdict.pop("_order_", None) - - # check for illegal enum names (any others?) - invalid_names = set(enum_members) & { - "mro", - } - if invalid_names: - raise ValueError( - "Invalid enum member name: {0}".format(",".join(invalid_names)) - ) - - # create a default docstring if one has not been provided - if "__doc__" not in classdict: - classdict["__doc__"] = "An enumeration." - - # create our new Enum type - enum_class = super().__new__(metacls, cls, bases, classdict) - enum_class._member_names_ = [] # names in definition order - enum_class._member_map_ = OrderedDict() # name->value map - enum_class._member_type_ = member_type - - # save attributes from super classes so we know if we can take - # the shortcut of storing members in the class dict - base_attributes = {a for b in enum_class.mro() for a in b.__dict__} - - # Reverse value->name map for hashable values. - enum_class._value2member_map_ = {} - - # If a custom type is mixed into the Enum, and it does not know how - # to pickle itself, pickle.dumps will succeed but pickle.loads will - # fail. Rather than have the error show up later and possibly far - # from the source, sabotage the pickle protocol for this class so - # that pickle.dumps also fails. - # - # However, if the new class implements its own __reduce_ex__, do not - # sabotage -- it's on them to make sure it works correctly. We use - # __reduce_ex__ instead of any of the others as it is preferred by - # pickle over __reduce__, and it handles all pickle protocols. - if "__reduce_ex__" not in classdict: - if member_type is not object: - methods = ( - "__getnewargs_ex__", - "__getnewargs__", - "__reduce_ex__", - "__reduce__", - ) - if not any(m in member_type.__dict__ for m in methods): - _make_class_unpicklable(enum_class) - - # instantiate them, checking for duplicates as we go - # we instantiate first instead of checking for duplicates first in case - # a custom __new__ is doing something funky with the values -- such as - # auto-numbering ;) - for member_name in classdict._member_names: - value = enum_members[member_name] - if not isinstance(value, tuple): - args = (value,) - else: - args = value - if member_type is tuple: # special case for tuple enums - args = (args,) # wrap it one more time - if not use_args: - enum_member = __new__(enum_class) - if not hasattr(enum_member, "_value_"): - enum_member._value_ = value - else: - enum_member = __new__(enum_class, *args) - if not hasattr(enum_member, "_value_"): - if member_type is object: - enum_member._value_ = value - else: - enum_member._value_ = member_type(*args) - value = enum_member._value_ - enum_member._name_ = member_name - enum_member.__objclass__ = enum_class - enum_member.__init__(*args) - # If another member with the same value was already defined, the - # new member becomes an alias to the existing one. - for name, canonical_member in enum_class._member_map_.items(): - if canonical_member._value_ == enum_member._value_: - enum_member = canonical_member - break - else: - # Aliases don't appear in member names (only in __members__). - enum_class._member_names_.append(member_name) - # performance boost for any member that would not shadow - # a DynamicClassAttribute - if member_name not in base_attributes: - setattr(enum_class, member_name, enum_member) - # now add to _member_map_ - enum_class._member_map_[member_name] = enum_member - try: - # This may fail if value is not hashable. We can't add the value - # to the map, and by-value lookups for this value will be - # linear. - enum_class._value2member_map_[value] = enum_member - except TypeError: - pass - - # double check that repr and friends are not the mixin's or various - # things break (such as pickle) - for name in ("__repr__", "__str__", "__format__", "__reduce_ex__"): - class_method = getattr(enum_class, name) - obj_method = getattr(member_type, name, None) - enum_method = getattr(first_enum, name, None) - if obj_method is not None and obj_method is class_method: - setattr(enum_class, name, enum_method) - - # replace any other __new__ with our own (as long as Enum is not None, - # anyway) -- again, this is to support pickle - if Enum is not None: - # if the user defined their own __new__, save it before it gets - # clobbered in case they subclass later - if save_new: - enum_class.__new_member__ = __new__ - enum_class.__new__ = Enum.__new__ - - # py3 support for definition order (helps keep py2/py3 code in sync) - if _order_ is not None: - if isinstance(_order_, str): - _order_ = _order_.replace(",", " ").split() - if _order_ != enum_class._member_names_: - raise TypeError("member order does not match _order_") - - return enum_class - - def __bool__(self): - """ - classes/types should always be True. - """ - return True - - def __call__( - cls, value, names=None, *, module=None, qualname=None, type=None, start=1 - ): - """Either returns an existing member, or creates a new enum class. - - This method is used both when an enum class is given a value to match - to an enumeration member (i.e. Color(3)) and for the functional API - (i.e. Color = Enum('Color', names='RED GREEN BLUE')). - - When used for the functional API: - - `value` will be the name of the new class. - - `names` should be either a string of white-space/comma delimited names - (values will start at `start`), or an iterator/mapping of name, value pairs. - - `module` should be set to the module this class is being created in; - if it is not set, an attempt to find that module will be made, but if - it fails the class will not be picklable. - - `qualname` should be set to the actual location this class can be found - at in its module; by default it is set to the global scope. If this is - not correct, unpickling will fail in some circumstances. - - `type`, if set, will be mixed in as the first base class. - - """ - if names is None: # simple value lookup - return cls.__new__(cls, value) - # otherwise, functional API: we're creating a new Enum type - return cls._create_( - value, names, module=module, qualname=qualname, type=type, start=start - ) - - def __contains__(cls, member): - return isinstance(member, cls) and member._name_ in cls._member_map_ - - def __delattr__(cls, attr): - # nicer error message when someone tries to delete an attribute - # (see issue19025). - if attr in cls._member_map_: - raise AttributeError("%s: cannot delete Enum member." % cls.__name__) - super().__delattr__(attr) - - def __dir__(self): - return [ - "__class__", - "__doc__", - "__members__", - "__module__", - ] + self._member_names_ - - def __getattr__(cls, name): - """Return the enum member matching `name` - - We use __getattr__ instead of descriptors or inserting into the enum - class' __dict__ in order to support `name` and `value` being both - properties for enum members (which live in the class' __dict__) and - enum members themselves. - - """ - if _is_dunder(name): - raise AttributeError(name) - try: - return cls._member_map_[name] - except KeyError: - raise AttributeError(name) from None - - def __getitem__(cls, name): - return cls._member_map_[name] - - def __iter__(cls): - return (cls._member_map_[name] for name in cls._member_names_) - - def __len__(cls): - return len(cls._member_names_) - - @property - def __members__(cls): - """Returns a mapping of member name->value. - - This mapping lists all enum members, including aliases. Note that this - is a read-only view of the internal mapping. - - """ - return MappingProxyType(cls._member_map_) - - def __repr__(cls): - return "" % cls.__name__ - - def __reversed__(cls): - return (cls._member_map_[name] for name in reversed(cls._member_names_)) - - def __setattr__(cls, name, value): - """Block attempts to reassign Enum members. - - A simple assignment to the class namespace only changes one of the - several possible ways to get an Enum member from the Enum class, - resulting in an inconsistent Enumeration. - - """ - member_map = cls.__dict__.get("_member_map_", {}) - if name in member_map: - raise AttributeError("Cannot reassign members.") - super().__setattr__(name, value) - - def _create_( - cls, class_name, names=None, *, module=None, qualname=None, type=None, start=1 - ): - """Convenience method to create a new Enum class. - - `names` can be: - - * A string containing member names, separated either with spaces or - commas. Values are incremented by 1 from `start`. - * An iterable of member names. Values are incremented by 1 from `start`. - * An iterable of (member name, value) pairs. - * A mapping of member name -> value pairs. - - """ - metacls = cls.__class__ - bases = (cls,) if type is None else (type, cls) - _, first_enum = cls._get_mixins_(bases) - classdict = metacls.__prepare__(class_name, bases) - - # special processing needed for names? - if isinstance(names, str): - names = names.replace(",", " ").split() - if isinstance(names, (tuple, list)) and names and isinstance(names[0], str): - original_names, names = names, [] - last_values = [] - for count, name in enumerate(original_names): - value = first_enum._generate_next_value_( - name, start, count, last_values[:] - ) - last_values.append(value) - names.append((name, value)) - - # Here, names is either an iterable of (name, value) or a mapping. - for item in names: - if isinstance(item, str): - member_name, member_value = item, names[item] - else: - member_name, member_value = item - classdict[member_name] = member_value - enum_class = metacls.__new__(metacls, class_name, bases, classdict) - - # TODO: replace the frame hack if a blessed way to know the calling - # module is ever developed - if module is None: - try: - module = sys._getframe(2).f_globals["__name__"] - except (AttributeError, ValueError) as exc: - pass - if module is None: - _make_class_unpicklable(enum_class) - else: - enum_class.__module__ = module - if qualname is not None: - enum_class.__qualname__ = qualname - - return enum_class - - @staticmethod - def _get_mixins_(bases): - """Returns the type for creating enum members, and the first inherited - enum class. - - bases: the tuple of bases that was given to __new__ - - """ - if not bases: - return object, Enum - - # double check that we are not subclassing a class with existing - # enumeration members; while we're at it, see if any other data - # type has been mixed in so we can use the correct __new__ - member_type = first_enum = None - for base in bases: - if base is not Enum and issubclass(base, Enum) and base._member_names_: - raise TypeError("Cannot extend enumerations") - # base is now the last base in bases - if not issubclass(base, Enum): - raise TypeError( - "new enumerations must be created as " - "`ClassName([mixin_type,] enum_type)`" - ) - - # get correct mix-in type (either mix-in type of Enum subclass, or - # first base if last base is Enum) - if not issubclass(bases[0], Enum): - member_type = bases[0] # first data type - first_enum = bases[-1] # enum type - else: - for base in bases[0].__mro__: - # most common: (IntEnum, int, Enum, object) - # possible: (, , - # , , - # ) - if issubclass(base, Enum): - if first_enum is None: - first_enum = base - else: - if member_type is None: - member_type = base - - return member_type, first_enum - - @staticmethod - def _find_new_(classdict, member_type, first_enum): - """Returns the __new__ to be used for creating the enum members. - - classdict: the class dictionary given to __new__ - member_type: the data type whose __new__ will be used by default - first_enum: enumeration to check for an overriding __new__ - - """ - # now find the correct __new__, checking to see of one was defined - # by the user; also check earlier enum classes in case a __new__ was - # saved as __new_member__ - __new__ = classdict.get("__new__", None) - - # should __new__ be saved as __new_member__ later? - save_new = __new__ is not None - - if __new__ is None: - # check all possibles for __new_member__ before falling back to - # __new__ - for method in ("__new_member__", "__new__"): - for possible in (member_type, first_enum): - target = getattr(possible, method, None) - if target not in { - None, - None.__new__, - object.__new__, - Enum.__new__, - }: - __new__ = target - break - if __new__ is not None: - break - else: - __new__ = object.__new__ - - # if a non-object.__new__ is used then whatever value/tuple was - # assigned to the enum member name will be passed to __new__ and to the - # new enum member's __init__ - if __new__ is object.__new__: - use_args = False - else: - use_args = True - - return __new__, save_new, use_args - - -class Enum(metaclass=EnumMeta): - """Generic enumeration. - - Derive from this class to define new enumerations. - - """ - - def __new__(cls, value): - # all enum instances are actually created during class construction - # without calling this method; this method is called by the metaclass' - # __call__ (i.e. Color(3) ), and by pickle - if type(value) is cls: - # For lookups like Color(Color.RED) - return value - # by-value search for a matching enum member - # see if it's in the reverse mapping (for hashable values) - try: - if value in cls._value2member_map_: - return cls._value2member_map_[value] - except TypeError: - # not there, now do long search -- O(n) behavior - for member in cls._member_map_.values(): - if member._value_ == value: - return member - # still not found -- try _missing_ hook - return cls._missing_(value) - - def _generate_next_value_(name, start, count, last_values): - for last_value in reversed(last_values): - try: - return last_value + 1 - except TypeError: - pass - else: - return start - - @classmethod - def _missing_(cls, value): - raise ValueError("%r is not a valid %s" % (value, cls.__name__)) - - def __repr__(self): - return "<%s.%s: %r>" % (self.__class__.__name__, self._name_, self._value_) - - def __str__(self): - return "%s.%s" % (self.__class__.__name__, self._name_) - - def __dir__(self): - added_behavior = [ - m - for cls in self.__class__.mro() - for m in cls.__dict__ - if m[0] != "_" and m not in self._member_map_ - ] - return ["__class__", "__doc__", "__module__"] + added_behavior - - def __format__(self, format_spec): - # mixed-in Enums should use the mixed-in type's __format__, otherwise - # we can get strange results with the Enum name showing up instead of - # the value - - # pure Enum branch - if self._member_type_ is object: - cls = str - val = str(self) - # mix-in branch - else: - cls = self._member_type_ - val = self._value_ - return cls.__format__(val, format_spec) - - def __hash__(self): - return hash(self._name_) - - def __reduce_ex__(self, proto): - return self.__class__, (self._value_,) - - # DynamicClassAttribute is used to provide access to the `name` and - # `value` properties of enum members while keeping some measure of - # protection from modification, while still allowing for an enumeration - # to have members named `name` and `value`. This works because enumeration - # members are not set directly on the enum class -- __getattr__ is - # used to look them up. - - @DynamicClassAttribute - def name(self): - """The name of the Enum member.""" - return self._name_ - - @DynamicClassAttribute - def value(self): - """The value of the Enum member.""" - return self._value_ - - @classmethod - def _convert(cls, name, module, filter, source=None): - """ - Create a new Enum subclass that replaces a collection of global constants - """ - # convert all constants from source (or module) that pass filter() to - # a new Enum called name, and export the enum and its members back to - # module; - # also, replace the __reduce_ex__ method so unpickling works in - # previous Python versions - module_globals = vars(sys.modules[module]) - if source: - source = vars(source) - else: - source = module_globals - # We use an OrderedDict of sorted source keys so that the - # _value2member_map is populated in the same order every time - # for a consistent reverse mapping of number to name when there - # are multiple names for the same number rather than varying - # between runs due to hash randomization of the module dictionary. - members = [(name, source[name]) for name in source.keys() if filter(name)] - try: - # sort by value - members.sort(key=lambda t: (t[1], t[0])) - except TypeError: - # unless some values aren't comparable, in which case sort by name - members.sort(key=lambda t: t[0]) - cls = cls(name, members, module=module) - cls.__reduce_ex__ = _reduce_ex_by_name - module_globals.update(cls.__members__) - module_globals[name] = cls - return cls - - -class IntEnum(int, Enum): - """Enum where members are also (and must be) ints""" - - -def _reduce_ex_by_name(self, proto): - return self.name - - -class Flag(Enum): - """Support for flags""" - - def _generate_next_value_(name, start, count, last_values): - """ - Generate the next value when not given. - - name: the name of the member - start: the initital start value or None - count: the number of existing members - last_value: the last value assigned or None - """ - if not count: - return start if start is not None else 1 - for last_value in reversed(last_values): - try: - high_bit = _high_bit(last_value) - break - except Exception: - raise TypeError("Invalid Flag value: %r" % last_value) from None - return 2 ** (high_bit + 1) - - @classmethod - def _missing_(cls, value): - original_value = value - if value < 0: - value = ~value - possible_member = cls._create_pseudo_member_(value) - if original_value < 0: - possible_member = ~possible_member - return possible_member - - @classmethod - def _create_pseudo_member_(cls, value): - """ - Create a composite member iff value contains only members. - """ - pseudo_member = cls._value2member_map_.get(value, None) - if pseudo_member is None: - # verify all bits are accounted for - _, extra_flags = _decompose(cls, value) - if extra_flags: - raise ValueError("%r is not a valid %s" % (value, cls.__name__)) - # construct a singleton enum pseudo-member - pseudo_member = object.__new__(cls) - pseudo_member._name_ = None - pseudo_member._value_ = value - # use setdefault in case another thread already created a composite - # with this value - pseudo_member = cls._value2member_map_.setdefault(value, pseudo_member) - return pseudo_member - - def __contains__(self, other): - if not isinstance(other, self.__class__): - return NotImplemented - return other._value_ & self._value_ == other._value_ - - def __repr__(self): - cls = self.__class__ - if self._name_ is not None: - return "<%s.%s: %r>" % (cls.__name__, self._name_, self._value_) - members, uncovered = _decompose(cls, self._value_) - return "<%s.%s: %r>" % ( - cls.__name__, - "|".join([str(m._name_ or m._value_) for m in members]), - self._value_, - ) - - def __str__(self): - cls = self.__class__ - if self._name_ is not None: - return "%s.%s" % (cls.__name__, self._name_) - members, uncovered = _decompose(cls, self._value_) - if len(members) == 1 and members[0]._name_ is None: - return "%s.%r" % (cls.__name__, members[0]._value_) - else: - return "%s.%s" % ( - cls.__name__, - "|".join([str(m._name_ or m._value_) for m in members]), - ) - - def __bool__(self): - return bool(self._value_) - - def __or__(self, other): - if not isinstance(other, self.__class__): - return NotImplemented - return self.__class__(self._value_ | other._value_) - - def __and__(self, other): - if not isinstance(other, self.__class__): - return NotImplemented - return self.__class__(self._value_ & other._value_) - - def __xor__(self, other): - if not isinstance(other, self.__class__): - return NotImplemented - return self.__class__(self._value_ ^ other._value_) - - def __invert__(self): - members, uncovered = _decompose(self.__class__, self._value_) - inverted_members = [ - m - for m in self.__class__ - if m not in members and not m._value_ & self._value_ - ] - inverted = reduce(_or_, inverted_members, self.__class__(0)) - return self.__class__(inverted) - - -class IntFlag(int, Flag): - """Support for integer-based Flags""" - - @classmethod - def _missing_(cls, value): - if not isinstance(value, int): - raise ValueError("%r is not a valid %s" % (value, cls.__name__)) - new_member = cls._create_pseudo_member_(value) - return new_member - - @classmethod - def _create_pseudo_member_(cls, value): - pseudo_member = cls._value2member_map_.get(value, None) - if pseudo_member is None: - need_to_create = [value] - # get unaccounted for bits - _, extra_flags = _decompose(cls, value) - # timer = 10 - while extra_flags: - # timer -= 1 - bit = _high_bit(extra_flags) - flag_value = 2 ** bit - if ( - flag_value not in cls._value2member_map_ - and flag_value not in need_to_create - ): - need_to_create.append(flag_value) - if extra_flags == -flag_value: - extra_flags = 0 - else: - extra_flags ^= flag_value - for value in reversed(need_to_create): - # construct singleton pseudo-members - pseudo_member = int.__new__(cls, value) - pseudo_member._name_ = None - pseudo_member._value_ = value - # use setdefault in case another thread already created a composite - # with this value - pseudo_member = cls._value2member_map_.setdefault(value, pseudo_member) - return pseudo_member - - def __or__(self, other): - if not isinstance(other, (self.__class__, int)): - return NotImplemented - result = self.__class__(self._value_ | self.__class__(other)._value_) - return result - - def __and__(self, other): - if not isinstance(other, (self.__class__, int)): - return NotImplemented - return self.__class__(self._value_ & self.__class__(other)._value_) - - def __xor__(self, other): - if not isinstance(other, (self.__class__, int)): - return NotImplemented - return self.__class__(self._value_ ^ self.__class__(other)._value_) - - __ror__ = __or__ - __rand__ = __and__ - __rxor__ = __xor__ - - def __invert__(self): - result = self.__class__(~self._value_) - return result - - -def _high_bit(value): - """returns index of highest bit, or -1 if value is zero or negative""" - return value.bit_length() - 1 - - -def unique(enumeration): - """Class decorator for enumerations ensuring unique member values.""" - duplicates = [] - for name, member in enumeration.__members__.items(): - if name != member.name: - duplicates.append((name, member.name)) - if duplicates: - alias_details = ", ".join( - ["%s -> %s" % (alias, name) for (alias, name) in duplicates] - ) - raise ValueError( - "duplicate values found in %r: %s" % (enumeration, alias_details) - ) - return enumeration - - -def _decompose(flag, value): - """Extract all members from the value.""" - # _decompose is only called if the value is not named - not_covered = value - negative = value < 0 - # issue29167: wrap accesses to _value2member_map_ in a list to avoid race - # conditions between iterating over it and having more psuedo- - # members added to it - if negative: - # only check for named flags - flags_to_check = [ - (m, v) - for v, m in list(flag._value2member_map_.items()) - if m.name is not None - ] - else: - # check for named flags and powers-of-two flags - flags_to_check = [ - (m, v) - for v, m in list(flag._value2member_map_.items()) - if m.name is not None or _power_of_two(v) - ] - members = [] - for member, member_value in flags_to_check: - if member_value and member_value & value == member_value: - members.append(member) - not_covered &= ~member_value - if not members and value in flag._value2member_map_: - members.append(flag._value2member_map_[value]) - members.sort(key=lambda m: m._value_, reverse=True) - if len(members) > 1 and members[0].value == value: - # we have the breakdown, don't need the value member itself - members.pop(0) - return members, not_covered - - -def _power_of_two(value): - if value < 1: - return False - return value == 2 ** _high_bit(value) diff --git a/python_module/megengine/_internal/exc.py b/python_module/megengine/_internal/exc.py deleted file mode 100644 index 954756b1..00000000 --- a/python_module/megengine/_internal/exc.py +++ /dev/null @@ -1,58 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -"""exception handling""" - -from . import mgb as _mgb - - -class MegBrainError(Exception): - """exception class used by megbrain library""" - - tracker = None - """the tracker setup by :func:`.set_exc_opr_tracker` when the related - operator is created""" - - tracker_grad_orig = None - """if this operator is created by taking gradient, this var would be the - tracker of the operator that causes the grad.""" - - def __init__(self, msg, tracker, tracker_grad_orig): - assert isinstance(msg, str) - super().__init__(msg, tracker, tracker_grad_orig) - self.tracker = tracker - self.tracker_grad_orig = tracker_grad_orig - - @classmethod - def _format_tracker(cls, tracker): - return ("| " + i for i in str(tracker).split("\n")) - - def __str__(self): - lines = [] - lines.extend(self.args[0].split("\n")) - if self.tracker is not None: - lines.append("Exception tracker:") - lines.extend(self._format_tracker(self.tracker)) - if self.tracker_grad_orig is not None: - lines.append( - "Exception caused by taking grad of another operator with tracker:" - ) - lines.extend(self._format_tracker(self.tracker_grad_orig)) - while not lines[-1].strip(): - lines.pop() - for idx, ct in enumerate(lines): - if ct.startswith("bt:"): - lines[idx] = "+ " + lines[idx] - for t in range(idx + 1, len(lines)): - lines[t] = "| " + lines[t] - break - return "\n".join(lines) - - -_mgb._reg_exception_class(MegBrainError) diff --git a/python_module/megengine/_internal/global_init.py b/python_module/megengine/_internal/global_init.py deleted file mode 100644 index 1b4fff87..00000000 --- a/python_module/megengine/_internal/global_init.py +++ /dev/null @@ -1,41 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -"""global initialization work; classes/functions defined in this module should -not be used by user code""" - -import atexit -import os -import sys -import traceback - -from . import mgb -from .logconf import get_logger -from .persistent_cache import PersistentCacheOnServer - - -class PyStackExtracterImpl(mgb._PyStackExtracter): - def extract(self): - return "".join(traceback.format_stack()[:-1]) - - -mgb._register_logger(get_logger()) -assert sys.executable -mgb._timed_func_set_fork_exec_path( - sys.executable, - os.path.join(os.path.dirname(__file__), "_timed_func_fork_exec_entry.py"), -) - -persistent_cache_impl_ins = PersistentCacheOnServer() -mgb._PersistentCache.reg(persistent_cache_impl_ins) - -PyStackExtracterImplIns = PyStackExtracterImpl() -PyStackExtracterImpl.reg(PyStackExtracterImplIns) - -atexit.register(mgb._mgb_global_finalize) diff --git a/python_module/megengine/_internal/helper.py b/python_module/megengine/_internal/helper.py deleted file mode 100644 index 8fbb974f..00000000 --- a/python_module/megengine/_internal/helper.py +++ /dev/null @@ -1,316 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -import collections - -import numpy as np - -from . import mgb -from .exc import MegBrainError -from .mgb import SharedND, SymbolVar -from .opr_param_defs import OptionalAxisV1 - - -def canonize_reshape(inputs, *, comp_graph, config): - src, tshape = inputs - tshape = cvt_to_shape_desc(tshape, src, comp_graph, config) - return src, tshape - - -def canonize_shape_input(inputs, *, comp_graph, config): - assert isinstance(inputs, (list, tuple)) and len(inputs) == 1 - return [cvt_to_shape_desc(inputs[0], None, comp_graph, config)] - - -def cvt_to_shape_desc(val, inpvar, graph, config): - """convert some python object to a :class:`SymbolVar` that describes tensor - shape - - :param val: the python object to be converted from - :param inpvar, graph, config: provide graph and comp node information; can - be None if not known. Either input or (graph, config) must be provided. - :return: a new var corresponding to *val* - :rtype: :class:`.SymbolVar` - """ - if hasattr(val, "__mgb_symvar__"): - val = val.__mgb_symvar__() - elif hasattr(val, "symvar"): - val = val.symvar - if isinstance(val, SymbolVar): - return val - if not isinstance(val, collections.Iterable): - val = [val] - components = [] - has_sym = False - for i in val: - if hasattr(i, "__mgb_symvar__"): - i = i.__mgb_symvar__() - elif hasattr(i, "symvar"): - i = i.symvar - if isinstance(i, SymbolVar): - has_sym = True - components.append(i) - else: - assert isinstance(i, int), ( - "shape desc could contain either int or SymbolVar, got {}" - " actually".format(repr(i)) - ) - components.append(i) - assert components, "shape desc could not be empty" - - if inpvar is not None: - assert isinstance(inpvar, SymbolVar) - if graph is None: - graph = inpvar.owner_graph - else: - assert graph == inpvar.owner_graph - config = mgb.make_opr_config(comp_node=inpvar.comp_node) - else: - assert isinstance(graph, mgb.CompGraph), "graph must be provided" - assert isinstance(config, mgb.OperatorNodeConfig) - - if not has_sym: - shape = np.ascontiguousarray(components, dtype=np.int32) - assert np.all(shape == components), "failed to convert to shape: {}".format( - components - ) - return mgb._make_immutable(graph, shape, None, config) - - for idx, v in enumerate(components): - if not isinstance(v, SymbolVar): - vi = int(v) - assert vi == v, "could not convert {} to int".format(v) - components[idx] = mgb._make_immutable(graph, vi, None, config) - from . import opr as O - - return O.concat(components, axis=0, config=config) - - -def canonize_input_vars(inputs, *, comp_graph, config): - """convert immediate numbers and SharedND to SymbolVar in inputs; at least - one of the inputs must be SymbolVar, so comp node and comp graph can - beinferred - - :return: list of converted vars - """ - from . import make_immutable - - if ( - isinstance(inputs, (list, tuple)) - and len(inputs) == 1 - and isinstance(inputs[0], (list, tuple)) - ): - # handle the case when a list is passed to a function with - # variable-length argument (e.g. concat has signature concat(*inputs) - # and is called with concat([a, b])) - inputs = inputs[0] - - if isinstance(inputs, SymbolVar): - return [inputs] - - old_inputs = inputs - inputs = [] - get_comp_node = None - need_cvt = False - for i in old_inputs: - if isinstance(i, SymbolVar): - get_comp_node = lambda cn=i.comp_node: cn - if comp_graph is not None: - assert comp_graph == i.owner_graph - else: - comp_graph = i.owner_graph - else: - need_cvt = True - inputs.append(i) - if not need_cvt: - return inputs - - if get_comp_node is None: - - def get_comp_node(): - nonlocal get_comp_node - cn = config.require_comp_node() - get_comp_node = lambda: cn - return cn - - for idx, var in enumerate(inputs): - if not isinstance(var, SymbolVar): - if isinstance(var, SharedND): - var = var.symvar(comp_graph) - elif isinstance(var, mgb.SharedScalar): - var = var._as_sym_var(comp_graph, get_comp_node()) - elif hasattr(var, "__mgb_symvar__"): - try: - cn = get_comp_node() - except MegBrainError: - cn = None - var = var.__mgb_symvar__(comp_graph=comp_graph, comp_node=cn) - elif hasattr(var, "symvar"): - var = var.symvar - else: - var = make_immutable(get_comp_node(), comp_graph, var) - inputs[idx] = var - return inputs - - -def cvt_to_vector_of_shape(shapes): - """convert ``[[int]]`` to nested ``std::vector`` of ``size_t``""" - ret = mgb._VectorTensorShape() - for i in shapes: - val = tuple(i) - assert val and all( - j > 0 and isinstance(j, int) for j in val - ), "something returns bad shape in infer_shape(): {}".format(val) - ret.push_back(val) - return ret - - -def cvt_to_opr_param_def(param, ptype, kwargs): - if param is not None: - if isinstance(param, ptype): - return param - - param = [param] - assert len(param) == len( - ptype.__slots__ - ), "{} needs {} params, but {} are provided".format( - ptype, len(ptype.__slots__), len(param) - ) - return ptype(*param) - - ckw = {} - for i in ptype.__slots__: - val = kwargs.pop(i, ckw) - if val is not ckw: - ckw[i] = val - return ptype(**ckw) - - -def cvt_getitem_to_idx_desc(inpvar, tuple_val, *, allow_newaxis=True): - """convert ``__getitem__`` args to index desc - - :return: ``(new_var, index_desc)`` where new_var is inpvar with - ``np.newaxis`` applied; note that ``index_desc`` can be ``None``. - """ - assert isinstance(inpvar, SymbolVar), "bad input: {!r}".format(inpvar) - if not isinstance(tuple_val, tuple): - tuple_val = (tuple_val,) - - axis_indexer = mgb._VectorAxisIndexer() - - config = mgb.make_opr_config(comp_node=inpvar.comp_node) - graph = inpvar.owner_graph - - def as_symvar(v, *, allow_list=True): - if isinstance(v, SymbolVar): - return v - vi = np.ascontiguousarray(v, dtype=np.int32) - assert np.abs(vi - v).max() == 0, "bad index: {!r}".format(v) - return mgb._make_immutable(graph, vi, None, config) - - def _s(v): # convert slice item - if v is None: - return SymbolVar() - return as_symvar(v, allow_list=False) - - new_axes = [] - cur_axis = -1 - for i_idx, i in enumerate(tuple_val): - cur_axis += 1 - if i is np.newaxis: - if cur_axis >= 0: - new_axes.append(cur_axis) - continue - - if i is Ellipsis: - cur_axis = -1 - for j in tuple_val[:i_idx:-1]: - if j is Ellipsis: - raise IndexError("only one ellipsis is allowed") - if j is np.newaxis: - new_axes.append(cur_axis) - cur_axis -= 1 - continue - - if isinstance(i, slice): - if i.start is None and i.stop is None and i.step is None: - continue - cur = mgb._AxisIndexer.make_interval( - cur_axis, _s(i.start), _s(i.stop), _s(i.step) - ) - else: - cur = mgb._AxisIndexer.make_index(cur_axis, as_symvar(i)) - axis_indexer.push_back(cur) - if new_axes: - if not allow_newaxis: - raise IndexError("newaxis is not allowed here") - inpvar = mgb._Opr.add_axis(inpvar, new_axes, mgb.make_opr_config()) - if axis_indexer.empty(): - axis_indexer = None - return inpvar, axis_indexer - - -def cvt_to_reshape_unspec_axis(unspec_axis, tshape): - assert isinstance(unspec_axis, OptionalAxisV1), repr(unspec_axis) - unspec_axis = unspec_axis.axis - assert abs(unspec_axis) <= OptionalAxisV1.MAX_NDIM - if not isinstance(tshape, SymbolVar): - for idx, val in enumerate(tshape): - if val == -1: - assert ( - unspec_axis == OptionalAxisV1.INVALID_AXIS - ), "multiple unknown dimensions for reshape" - unspec_axis = idx - return OptionalAxisV1(unspec_axis) - - -def gen_config(name, comp_node, config, output_dtype=None): - if config is None: - config = mgb.make_opr_config(name, comp_node, output_dtype) - else: - assert isinstance(config, mgb.OperatorNodeConfig) - assert name is None and comp_node is None - return config - - -def cvt_opr_result(rst, *, explode_single=True): - """:param explode_single: whether to return the content of a single-item - list rather thatn the list itself""" - if not isinstance(rst, mgb.SymbolVar): - assert isinstance(rst, (list, tuple)) - if len(rst) == 1 and explode_single: - return cvt_opr_result(rst[0]) - return tuple(map(cvt_opr_result, rst)) - if not rst.valid: - return None - # TODO Because the __init__ of SwigObject can not be modified to keep the - # reference of graph, we get owner graph explicitly here. The correct - # handling is moving the reference to SwigWrapper, but it is unsupported to - # add a member variable to SwigWrapper, so we should wrap the SymbolVar - # manually in megbrain_wrap.h - rst.owner_graph - - f32 = np.float32 - if not hasattr(cvt_opr_result, "_cvt_to_float32"): - import os - from .logconf import get_logger - - cvt_opr_result._cvt_to_float32 = os.getenv("MGB_ALL_FLOAT32") - if cvt_opr_result._cvt_to_float32: - get_logger().warn( - "\n" - "+=====================================================+\n" - "| MGB_ALL_FLOAT32 is set, so all megbrain opr result |\n" - "| would to converted to float32; this should only be |\n" - "| used for loading old models. |\n" - "+=====================================================+" - ) - if cvt_opr_result._cvt_to_float32 and rst.dtype != f32: - rst = rst.astype(f32) - return rst diff --git a/python_module/megengine/_internal/logconf.py b/python_module/megengine/_internal/logconf.py deleted file mode 100644 index f88c8c08..00000000 --- a/python_module/megengine/_internal/logconf.py +++ /dev/null @@ -1,54 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -import logging -import os - -_replaced_logger = None - - -def get_logger(): - global _replaced_logger - if _replaced_logger is not None: - return _replaced_logger - logger = logging.getLogger("megbrain") - logger.propagate = False - logger.setLevel(logging.INFO) - handler = logging.StreamHandler() - handler.setFormatter(MgbLogFormatter(datefmt="%d %H:%M:%S")) - handler.setLevel(0) - del logger.handlers[:] - logger.addHandler(handler) - _replaced_logger = logger - return logger - - -class MgbLogFormatter(logging.Formatter): - def format(self, record): - date = "\x1b[32m[%(asctime)s %(lineno)d@%(filename)s:%(name)s]\x1b[0m" - msg = "%(message)s" - if record.levelno == logging.DEBUG: - fmt = "{} \x1b[32mDBG\x1b[0m {}".format(date, msg) - elif record.levelno == logging.WARNING: - fmt = "{} \x1b[1;31mWRN\x1b[0m {}".format(date, msg) - elif record.levelno == logging.ERROR: - fmt = "{} \x1b[1;4;31mERR\x1b[0m {}".format(date, msg) - else: - fmt = date + " " + msg - self._style._fmt = fmt - return super().format(record) - - -def set_logger(logger): - """replace the logger""" - global _replaced_logger - _replaced_logger = logger - from .mgb import _register_logger - - _register_logger(logger) diff --git a/python_module/megengine/_internal/mgb_helper.py b/python_module/megengine/_internal/mgb_helper.py deleted file mode 100644 index 955d5c88..00000000 --- a/python_module/megengine/_internal/mgb_helper.py +++ /dev/null @@ -1,87 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -"""helper utils for the core mgb module""" - -import collections -import inspect -import json -import threading -from abc import ABCMeta, abstractmethod - - -class callback_lazycopy: - """wraps around a callable to be passed to :meth:`.CompGraph.compile`. - - This is used to disable eager copy, so we could get rid of an h2d copy and - a d2h if values are to be passed from one callback to another - :class:`.SharedND`. - """ - - def __init__(self, func): - assert isinstance(func, collections.Callable) - self.__func = func - - @property - def func(self): - return self.__func - - -class SharedNDLazyInitializer(metaclass=ABCMeta): - """lazy initialization policy for :class:`.SharedND`""" - - @abstractmethod - def get_shape(self): - """get shape, without loading value""" - - @abstractmethod - def get_value(self): - """get value as numpy ndarray""" - - -class copy_output: - """wraps a :class:`.SymbolVar` in outspec for :meth:`.CompGraph.compile`, - to copy the output to function return value""" - - symvar = None - borrow_mem = None - - def __init__(self, symvar, *, borrow_mem=False): - """ - - :param borrow_mem: see :meth:`.CompGraphCallbackValueProxy.get_value` - """ - from .mgb import SymbolVar - - assert isinstance( - symvar, SymbolVar - ), "copy_output expects an SymbolVar, got {} instead".format(symvar) - self.symvar = symvar - self.borrow_mem = borrow_mem - - -class FuncOutputSaver: - """instance could be used as callbacks for :meth:`.CompGraph.compile` to - copy output to host buffer - """ - - _value = None - _borrow_mem = None - - def __init__(self, borrow_mem=False): - self._borrow_mem = borrow_mem - - def __call__(self, v): - self._value = v.get_value(borrow_mem=self._borrow_mem) - - def get(self): - assert ( - self._value is not None - ), "{} not called; maybe due to unwaited async func".format(self) - return self._value diff --git a/python_module/megengine/_internal/opr_extra.py b/python_module/megengine/_internal/opr_extra.py deleted file mode 100644 index 7a59a2cf..00000000 --- a/python_module/megengine/_internal/opr_extra.py +++ /dev/null @@ -1,3 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright (c) 2015-2019 Megvii Inc. All rights reserved. - diff --git a/python_module/megengine/_internal/persistent_cache.py b/python_module/megengine/_internal/persistent_cache.py deleted file mode 100644 index 47da6637..00000000 --- a/python_module/megengine/_internal/persistent_cache.py +++ /dev/null @@ -1,90 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -import argparse -import getpass -import json -import os -import shelve - -from .logconf import get_logger -from .mgb import _PersistentCache -from .version import __version__ - - -class _FakeRedisConn: - def __init__(self): - try: - from ..hub.hub import _get_megengine_home - - cache_dir = os.path.expanduser( - os.path.join(_get_megengine_home(), "persistent_cache") - ) - os.makedirs(cache_dir, exist_ok=True) - cache_file = os.path.join(cache_dir, "cache") - self._dict = shelve.open(cache_file) - self._is_shelve = True - except: - self._dict = {} - self._is_shelve = False - - def get(self, key): - if self._is_shelve and isinstance(key, bytes): - key = key.decode("utf-8") - - return self._dict.get(key) - - def set(self, key, val): - if self._is_shelve and isinstance(key, bytes): - key = key.decode("utf-8") - - self._dict[key] = val - - def __del__(self): - if self._is_shelve: - self._dict.close() - - -class PersistentCacheOnServer(_PersistentCache): - _cached_conn = None - _prefix = None - _prev_get_refkeep = None - - @property - def _conn(self): - """get redis connection""" - if self._cached_conn is None: - self._cached_conn = _FakeRedisConn() - self._prefix = self.make_user_prefix() - - return self._cached_conn - - @classmethod - def make_user_prefix(cls): - return "mgbcache:{}".format(getpass.getuser()) - - - def _make_key(self, category, key): - prefix_with_version = "{}:MGB{}".format(self._prefix, __version__) - return b"@".join( - (prefix_with_version.encode("ascii"), category.encode("ascii"), key) - ) - - def put(self, category, key, value): - conn = self._conn - key = self._make_key(category, key) - conn.set(key, value) - - def get(self, category, key): - conn = self._conn - key = self._make_key(category, key) - self._prev_get_refkeep = conn.get(key) - return self._prev_get_refkeep - - diff --git a/python_module/megengine/_internal/plugin.py b/python_module/megengine/_internal/plugin.py deleted file mode 100644 index 4290bc1b..00000000 --- a/python_module/megengine/_internal/plugin.py +++ /dev/null @@ -1,261 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -"""plugins associated with computing graph""" - -import atexit -import collections -import json -import os -import platform -import signal -import struct - -import numpy as np - -from . import mgb as _mgb -from .logconf import get_logger - -InfkernFinderInputValueRec = collections.namedtuple( - "InfkernFinderInputValueRec", ["var_name", "var_id", "run_id", "value"] -) - - -class CompGraphProfiler(_mgb._CompGraphProfilerImpl): - """a plugin to profile computing graphs""" - - def __init__(self, comp_graph): - super().__init__(comp_graph) - - def get(self): - """get visualizable profiling result on a function""" - return json.loads(self._get_result()) - - def write_json(self, fobj): - """write the result to a json file - - :param fobj: a file-like object, or a string - """ - if isinstance(fobj, str): - with open(fobj, "w") as fout: - return self.write_json(fout) - fobj.write(self._get_result()) - - -class NumRangeChecker(_mgb._NumRangeCheckerImpl): - """check that all numberical float values of variables in a computing graph - are within given range""" - - def __init__(self, comp_graph, max_abs_val): - """:param max_abs_val: max absolute value""" - super().__init__(comp_graph, float(max_abs_val)) - - -class TextOprIODump(_mgb._TextOprIODumpImpl): - """dump all internal results as text to a file""" - - def __init__(self, comp_graph, fpath, *, print_addr=None, max_size=None): - super().__init__(comp_graph, fpath) - if print_addr is not None: - self.print_addr(print_addr) - if max_size is not None: - self.max_size(max_size) - - def print_addr(self, flag): - """set whether to print var address - - :return: self - """ - self._print_addr(flag) - return self - - def max_size(self, size): - """set the number of elements to be printed for each var - - :return: self - """ - self._max_size(size) - return self - - -class BinaryOprIODump(_mgb._BinaryOprIODumpImpl): - """dump all internal results binary files to a directory; the values can be - loaded by :func:`load_tensor_binary` - """ - - def __init__(self, comp_graph, dir_path): - super().__init__(comp_graph, dir_path) - - -class InfkernFinder(_mgb._InfkernFinderImpl): - """a plugin to find kernels that cause infinite loops""" - - def __init__(self, comp_graph, record_input_value): - """ - :param record_input_value: whether need to record input var values of - all operators - :type record_input_value: bool - """ - super().__init__(comp_graph, record_input_value) - - def write_to_file(self, fpath): - """write current execution status to a text file - - :return: ID of the first operator that is still not finished, - or None if all oprs are finished - :rtype: int or None - """ - v = self._write_to_file(fpath) - if v == 0: - return - return v - 1 - - def get_input_values(self, opr_id): - """get recorded input values of a given operator. Return a list - of :class:`InfkernFinderInputValueRec`. Note that the value in - each item is either None (if it is not recorded) or a numpy - array - """ - ret = [] - for idx in range(self._get_input_values_prepare(opr_id)): - vn = self._get_input_values_var_name(idx) - vi = self._get_input_values_var_idx(idx) - ri = self._get_input_values_run_id(idx) - val = self._get_input_values_val(idx) - if not val.shape: - val = None - else: - val = val.get_value() - ret.append(InfkernFinderInputValueRec(vn, vi, ri, val)) - return ret - - -def fast_signal_hander(signum, callback): - """bypass python's signal handling system and registera handler that is - called ASAP in a dedicated thread (in contrary, python calls handlers in - the main thread) - - :param callback: signal callback, taking the signal number as its sole - argument - """ - - def cb_wrapped(): - try: - callback(signum) - except: - get_logger().exception("error calling signal handler for {}".format(signum)) - - _mgb._FastSignal.register_handler(signum, cb_wrapped) - - -atexit.register(_mgb._FastSignal.shutdown) - - -class GlobalInfkernFinder: - """ - manage a list of :class:`InfkernFinder` objects; when this process is - signaled with SIGUSR1, an interactive IPython shell would be presented for - further investigation - """ - - _signal = None - if platform.system() != "Windows": - _signal = signal.SIGUSR1 - else: - _signal = signal.CTRL_C_EVENT - _registry = [] - _shell_maker = None - - @classmethod - def add_graph(cls, comp_graph): - """register a graph so it can be tracked by :class:`InfkernFinder`""" - enabled = os.getenv("MGB_DBG_INFKERN_FINDER") - if not enabled: - return - - if enabled == "1": - record_input_value = False - else: - assert enabled == "2", ( - "MGB_DBG_INFKERN_FINDER must be either 1 or 2, indicating " - "whether to record input values" - ) - record_input_value = True - - finder = InfkernFinder(comp_graph, record_input_value) - get_logger().warning( - "interactive InfkernFinder {} registered to graph {}; all input " - "var values would be recorded and the graph would never be " - "reclaimed. You can enter the interactive debug session by " - 'executing "kill -{} {}". record_input_value={}'.format( - finder, comp_graph, cls._signal, os.getpid(), record_input_value - ) - ) - - if not cls._registry: - from IPython.terminal.embed import InteractiveShellEmbed - - cls._shell_maker = InteractiveShellEmbed - fast_signal_hander(cls._signal, cls._on_signal) - - cls._registry.append(finder) - - @classmethod - def _on_signal(cls, signum): - shell = cls._shell_maker() - shell( - header="Enter interactive InfkernFinder session; the registered " - "finder objects can be found in variable f", - local_ns={"f": cls._registry}, - ) - - -def load_tensor_binary(fobj): - """load a tensor dumped by the :class:`BinaryOprIODump` plugin; the actual - tensor value dump is implemented by ``mgb::debug::dump_tensor``. - - Multiple values can be compared by ``tools/compare_binary_iodump.py``. - - :param fobj: file object, or a string that contains the file name - :return: tuple ``(tensor_value, tensor_name)`` - """ - if isinstance(fobj, str): - with open(fobj, "rb") as fin: - return load_tensor_binary(fin) - - DTYPE_LIST = { - 0: np.float32, - 1: np.uint8, - 2: np.int8, - 3: np.int16, - 4: np.int32, - 5: _mgb.intb1, - 6: _mgb.intb2, - 7: _mgb.intb4, - 8: None, - 9: np.float16, - # quantized dtype start from 100000 - # see MEGDNN_PARAMETERIZED_DTYPE_ENUM_BASE in - # dnn/include/megdnn/dtype.h - 100000: np.uint8, - 100001: np.int32, - 100002: np.int8, - } - - header_fmt = struct.Struct("III") - name_len, dtype, max_ndim = header_fmt.unpack(fobj.read(header_fmt.size)) - assert ( - DTYPE_LIST[dtype] is not None - ), "Cannot load this tensor: dtype Byte is unsupported." - - shape = list(struct.unpack("I" * max_ndim, fobj.read(max_ndim * 4))) - while shape[-1] == 0: - shape.pop(-1) - name = fobj.read(name_len).decode("ascii") - return np.fromfile(fobj, dtype=DTYPE_LIST[dtype]).reshape(shape), name diff --git a/python_module/megengine/_internal/version.py b/python_module/megengine/_internal/version.py deleted file mode 100644 index 57803f31..00000000 --- a/python_module/megengine/_internal/version.py +++ /dev/null @@ -1,57 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -"""version information for MegBrain package""" - -import collections - -from . import mgb as _mgb - - -class Version( - collections.namedtuple("VersionBase", ["major", "minor", "patch", "dev"]) -): - """simple sematic version object""" - - @classmethod - def __normalize(cls, v): - if isinstance(v, str): - v = v.split(".") - a, b, c = map(int, v) - return cls(a, b, c) - - def __eq__(self, rhs): - return super().__eq__(self.__normalize(rhs)) - - def __ne__(self, rhs): - return super().__ne__(self.__normalize(rhs)) - - def __lt__(self, rhs): - return super().__lt__(self.__normalize(rhs)) - - def __le__(self, rhs): - return super().__le__(self.__normalize(rhs)) - - def __gt__(self, rhs): - return super().__gt__(self.__normalize(rhs)) - - def __ge__(self, rhs): - return super().__ge__(self.__normalize(rhs)) - - def __str__(self): - rst = "{}.{}.{}".format(self.major, self.minor, self.patch) - if self.dev: - rst += "-dev{}".format(self.dev) - return rst - - -Version.__new__.__defaults__ = (0,) # dev defaults to 0 - -version_info = Version(*_mgb._get_mgb_version()) -__version__ = str(version_info) diff --git a/python_module/megengine/core/__init__.py b/python_module/megengine/core/__init__.py deleted file mode 100644 index ab452954..00000000 --- a/python_module/megengine/core/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .device import ( - get_default_device, - get_device_count, - is_cuda_available, - set_default_device, -) -from .function import Function -from .graph import Graph, dump -from .serialization import load, save -from .tensor import Tensor, TensorDict, tensor, wrap_io_tensor -from .tensor_factory import ones, zeros -from .tensor_nn import Buffer, Parameter diff --git a/python_module/megengine/core/device.py b/python_module/megengine/core/device.py deleted file mode 100644 index cb3999db..00000000 --- a/python_module/megengine/core/device.py +++ /dev/null @@ -1,60 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import os - -import megengine._internal as mgb - -_default_device = os.getenv("MGE_DEFAULT_DEVICE", "xpux") - - -def get_device_count(device_type: str) -> int: - """Gets number of devices installed on this system. - - :param device_type: device type, one of 'gpu' or 'cpu' - """ - - device_type_set = ("cpu", "gpu") - assert device_type in device_type_set, "device must be one of {}".format( - device_type_set - ) - return mgb.config.get_device_count(device_type) - - -def is_cuda_available() -> bool: - """Returns whether cuda device is available on this system. - - """ - return mgb.config.get_device_count("gpu", warn=False) > 0 - - -def set_default_device(device: str = "xpux"): - r"""Sets default computing node. - - :param device: default device type. The type can be 'cpu0', 'cpu1', etc., - or 'gpu0', 'gpu1', etc., to specify the particular cpu or gpu to use. - 'cpux' and 'gupx' can also be used to specify any number of cpu or gpu devices. - - 'multithread' device type is avaliable when inference, which implements - multi-threading parallelism at the operator level. For example, - 'multithread4' will compute with 4 threads. which implements - - The default value is 'xpux' to specify any device available. - - It can also be set by environmental variable `MGE_DEFAULT_DEVICE`. - """ - global _default_device # pylint: disable=global-statement - _default_device = device - - -def get_default_device() -> str: - r"""Gets default computing node. - - It returns the value set by :func:`~.set_default_device`. - """ - return _default_device diff --git a/python_module/megengine/core/function.py b/python_module/megengine/core/function.py deleted file mode 100644 index c6fb7d43..00000000 --- a/python_module/megengine/core/function.py +++ /dev/null @@ -1,176 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import copy -from abc import ABCMeta, abstractmethod -from typing import Iterable, Tuple, Union - -import megengine._internal as mgb - -from .tensor import Tensor - - -class _OverrideGradientCraniotome(mgb.craniotome.CraniotomeBase): - __nr_inputs__ = None - __nr_outputs__ = None - __expand_single_outputs__ = False - __allow_duplicate__ = False - - grad_func = None - - def setup(self, nr_inputs, nr_outputs, grad_func): - self.__nr_inputs__ = nr_inputs + nr_outputs - self.__nr_outputs__ = nr_outputs - self.grad_func = grad_func - - def infer_shape(self, inp_shapes): - return inp_shapes[-self.__nr_outputs__ :] - - def init_output_dtype(self, input_dtypes): - return input_dtypes[-self.__nr_outputs__ :] - - def execute(self, inputs, outputs): - for ivar, ovar in zip(inputs[-self.__nr_outputs__ :], outputs): - ovar.set_value(ivar) - - def grad(self, wrt_idx, inputs, outputs, out_grad): - # TODO: Make sure grad_values really have values in eager mode. - # Porting to the new imperative engine would solve this, but if it - # don't happen, EagerEvalManager should be changed. - grads = self.grad_func( - *(Tensor(x) if x is not None else None for x in out_grad) - ) - # pylint: disable=literal-comparison - if isinstance(grads, Tensor) or grads is None or grads is 0: - grads = (grads,) - assert ( - len(grads) == self.__nr_inputs__ - self.__nr_outputs__ - ), "Function.backward should return a tuple with len = {}, got {}".format( - self.__nr_inputs__ - self.__nr_outputs__, len(grads) - ) - # pylint: disable=literal-comparison - return ( - list(x._symvar if x is not None and x is not 0 else 0 for x in grads) - + [0] * self.__nr_outputs__ - ) - - def get_serialize_params(self): - raise NotImplementedError("Serialization of Function is not implemented") - - -class Function(metaclass=ABCMeta): - """ - Defines a block of operations with customizable differentiation. - - The computation should be defined in ``forward`` method, with gradient - computation defined in ``backward`` method. - - Each instance of ``Function`` should be used only once during forwardding. - - Examples: - - .. testcode:: - - class Sigmoid(Function): - def forward(self, x): - y = 1 / (1 + F.exp(-x)) - self.save_for_backward(y) - return y - - def backward(self, output_grads): - (y, ) = self.saved_tensors - return output_grads * y * (1-y) - - """ - - _has_saved_state = False - saved_tensors = None - - def __init__(self): - self.saved_tensors = () - - @abstractmethod - def forward(self, *inputs: Iterable[Tensor]) -> Union[Tuple[Tensor], Tensor]: - """ - Applies operations to ``inputs`` and returns results. It must be overriden by all subclasses. - Users can call :meth:`~.function.Function.save_for_backward` in this method to save tensors. - - :param input: Input tensors. - :return: A tuple of Tensor or a single Tensor. - - .. note:: - - This method should return a tuple of Tensor or a single Tensor representing the output - of the function. - """ - raise NotImplementedError - - @abstractmethod - def backward( - self, *output_grads: Iterable[Union[Tensor, None]] - ) -> Union[Tuple[Tensor], Tensor]: - """ - Compute the gradient of the forward function. It must be overriden by all subclasses. - - :param output_grads: gradients of outputs that are returned by :meth:`~.function.Function.forward` - - .. note:: - - In case when some tensors of outputs are not related to loss function, the corresponding - values in ``output_grads`` would be ``None``. - - .. note:: - - This method should return a tuple which containing the gradients of all inputs, in the same order - as the ``inputs`` argument of :meth:`~.function.Function.forward` . A ``Tensor`` could be returned - instead if there is only one input. If users want to stop the propagation of some gradients, - the corresponding returned values should be set ``None`` . - - """ - raise NotImplementedError - - def save_for_backward(self, *tensors: Iterable[Tensor]): - """ - Saves tensors needed for gradient computation. This method should be called only - once in :meth:`~.function.Function.forward`, additional calls will replace values saved previously. - - The saved tensors can be accessed through the ``saved_tensors`` attribute. - """ - self.saved_tensors = tensors - - def __deepcopy__(self, memo): - """ - Defines how the operator is deeply copied - """ - cls = self.__class__ - result = cls.__new__(cls) - tmp = self.saved_tensors - self.saved_tensors = None - memo[id(self)] = result - for k, v in self.__dict__.items(): - setattr(result, k, copy.deepcopy(v, memo)) - setattr(result, "saved_tensors", tmp) - self.saved_tensors = tmp - return result - - def __call__(self, *inputs): - assert ( - not self._has_saved_state - ), "A Function instance should not be called multiple times" - outputs = self.forward(*inputs) - if isinstance(outputs, Tensor): - outputs = (outputs,) - self._has_saved_state = True - sv = (x._symvar for x in inputs + outputs) - outputs = _OverrideGradientCraniotome.make( - *sv, nr_inputs=len(inputs), nr_outputs=len(outputs), grad_func=self.backward - ) - outputs = tuple(map(Tensor, outputs)) - if len(outputs) == 1: - outputs = outputs[0] - return outputs diff --git a/python_module/megengine/core/graph.py b/python_module/megengine/core/graph.py deleted file mode 100644 index 332f198d..00000000 --- a/python_module/megengine/core/graph.py +++ /dev/null @@ -1,158 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import collections -import threading - -import megengine._internal as mgb - -from .device import get_default_device - - -class _DefaultGraph(threading.local): - r""" - An implicit thread-local graph - """ - - def __init__(self): - super(_DefaultGraph, self).__init__() - self._default_graph = None - - def get_default(self): - r"""Returns a default Graph object for eager evaluation. - """ - if self._default_graph is None: - self._default_graph = Graph() - return self._default_graph - - -_default_graph = _DefaultGraph() - - -class Graph(mgb.CompGraph): - r""" - A computing graph that supporting context management. - - :param check_env_var: whether to check environment vars including ``MGB_COMP_GRAPH_OPT``. - :param eager_evaluation: use dynamic graph(``True``) or static graph(``False``). - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - from megengine.core import Graph - - with Graph(eager_evaluation=True): - x = tensor([1, 2]) - print(x) - - Outputs: - - .. testoutput:: - - Tensor([1 2], dtype=int32) - - """ - - __saved_graph = None - - def __new__( - cls, *, check_env_var: bool = True, eager_evaluation: bool = True, **kwargs - ): - kwargs.update(eager_evaluation=eager_evaluation) - self = mgb.comp_graph(extra_opts=kwargs, check_env_var=check_env_var) - self.__class__ = cls - return self - - def __init__( - self, *, check_env_var: bool = True, eager_evaluation: bool = True, **kwargs - ): - # pylint: disable=super-init-not-called - pass - - def __enter__(self): - self.__saved_graph = _default_graph._default_graph - _default_graph._default_graph = self - return self - - def __exit__(self, type, value, traceback): - _default_graph._default_graph = self.__saved_graph - del self.__saved_graph - - -def _use_default_if_none(device, comp_graph): - if device is None: - device = get_default_device() - if comp_graph is None: - comp_graph = get_default_graph() - return device, comp_graph - - -def dump(outputs, fpath, optimize_options=None, **kwargs): - r""" - Serializes this computing graph and writes it to a file. - - :type outputs: ``Tensor`` or a collection of ``Tensor`` - :param outputs: output variables that need to be retrieved when - deserializing - :type fpath: ``str`` - :param fpath: path for the output file - :type optimize_options: ``list`` - :param optimize_options: ``['f16_io_f32_comp', 'f16_io_comp', 'use_nhwcd4', 'fuse_conv_bias_nonlinearity']`` , four elements are optional, it can be an empty list, None or a list containing any of them. - - .. note:: - - ``f16_io_f32_comp`` – whether to use float16 for I/O between oprs and use float32 as internal computation precision. Note the output var would be changed to float16; - - ``f16_io_comp`` – whether to use float16 for both I/O and computation precision; - - ``use_nhwcd4`` – whether to use NHWCD4 data format. This is faster on some OpenCL devices; - - ``fuse_conv_bias_nonlinearity`` – whether to fuse conv+bias+nonlinearty into one opr. This is supported only when ``use_nhwcd4`` is set. - - """ - from .tensor import Tensor - - assert optimize_options is None or isinstance( - optimize_options, list - ), "optimize_options must be a list" - - if isinstance(outputs, Tensor): - outputs = [outputs] - else: - assert isinstance(outputs, collections.Iterable), "{} not iterable".format( - outputs - ) - outputs = list(outputs) - - for output in outputs: - assert isinstance(output, Tensor), "All outputs must be Tensors." - - outputs = [o._symvar for o in outputs] - - if optimize_options: - opt_dict = dict.fromkeys(optimize_options, True) - mgb.optimize_for_inference(outputs, **opt_dict) - mgb.serialize_comp_graph_to_file(fpath, outputs, **kwargs) - - -def set_default_graph(default_graph): - r""" - Sets a global default Graph object. - """ - global _default_graph # pylint: disable=global-statement - _default_graph._default_graph = default_graph - - -def get_default_graph(): - r""" - Returns a default Graph object, most probably for eager evaluation. - """ - return _default_graph.get_default() diff --git a/python_module/megengine/core/serialization.py b/python_module/megengine/core/serialization.py deleted file mode 100644 index 8c18a534..00000000 --- a/python_module/megengine/core/serialization.py +++ /dev/null @@ -1,128 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import pickle - -import megengine._internal as mgb - -from ..utils.max_recursion_limit import max_recursion_limit -from .device import get_default_device - - -def save(obj, f, pickle_module=pickle, pickle_protocol=pickle.HIGHEST_PROTOCOL): - r"""Save an object to disk file. - - :type obj: object - :param obj: object to save. Only ``module`` or ``state_dict`` are allowed. - :type f: text file object - :param f: a string of file name or a text file object to which ``obj`` is saved to. - :type pickle_module: - :param pickle_module: Default: ``pickle``. - :type pickle_protocol: - :param pickle_protocol: Default: ``pickle.HIGHEST_PROTOCOL``. - - """ - if isinstance(f, str): - with open(f, "wb") as fout: - save( - obj, fout, pickle_module=pickle_module, pickle_protocol=pickle_protocol - ) - return - - with max_recursion_limit(): - assert hasattr(f, "write"), "{} does not support write".format(f) - pickle_module.dump(obj, f, pickle_protocol) - - -class dmap: - def __init__(self, map_location): - self.map_location = map_location - - def __enter__(self): - mgb.add_device_map(self.map_location) - return self - - def __exit__(self, type, value, traceback): - mgb.del_device_map() - - -def _get_callable_map_location(map_location): - if map_location is None: - - def callable_map_location(state): - return str(get_default_device()) - - elif isinstance(map_location, str): - - def callable_map_location(state): - return map_location - - elif isinstance(map_location, dict): - locator_map = {} - for key, value in map_location.items(): - locator_key = mgb.config.parse_locator(key)[:2] - locator_map[locator_key] = value - - def callable_map_location(state): - orig = mgb.config.parse_locator(state)[:2] - if orig in locator_map.keys(): - state = locator_map[orig] - return state - - else: - assert callable(map_location), "map_location should be str, dict or function" - callable_map_location = map_location - return callable_map_location - - -def load(f, map_location=None, pickle_module=pickle): - r"""Load an object saved with save() from a file. - - :type f: text file object - :param f: a string of file name or a text file object from which to load. - :type map_location: str, dict or a function specifying the map rules - :param map_location: Default: ``None``. - - .. note:: - - map_location will change the logical locator when loading models, - avoiding tensors be loading on non-existent device. If you want to - add the mapping relationship between logical locator and physical - locator in runtime, please call :func:`mge.set_device_map()` - - :type pickle_module: - :param pickle_module: Default: ``pickle``. - - .. note:: - - If you will call :func:`mge.set_default_device()`, please do it - before :func:`mge.load()`. - - Examples: - - .. testcode: - - import megengine as mge - mge.load('model.mge') - # Load all tensors based on logical location. - mge.load('model.mge', map_location='gpu0') - # Load all tensors onto the device: GPU0 - mge.load('model.mge', map_location={'gpu0':'cpu0'}) - # Load all tensors based on logical location, but 'GPU0' will be renamed to 'CPU0' - mge.load('model.mge', map_location=lambda dev: 'cpu0') - # Load all tensors onto the device" CPU0 - - """ - if isinstance(f, str): - with open(f, "rb") as fin: - return load(fin, map_location=map_location, pickle_module=pickle_module) - - map_location = _get_callable_map_location(map_location) # callable map_location - - with dmap(map_location): - return pickle_module.load(f) diff --git a/python_module/megengine/core/tensor.py b/python_module/megengine/core/tensor.py deleted file mode 100644 index f96f90f2..00000000 --- a/python_module/megengine/core/tensor.py +++ /dev/null @@ -1,771 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import collections -import copy -import functools -import itertools -import weakref -from typing import Callable, Tuple, Union - -import numpy as np - -import megengine._internal as mgb - -from .graph import _use_default_if_none, get_default_graph - - -def wrap_io_tensor(func): - r"""A wrapper to make ``func`` compatible with functions in ``_internal.opr``. - """ - - @functools.wraps(func) - def wrapper(*args, **kwargs): - comp_graph = None - for i in itertools.chain(args, kwargs.values()): - if isinstance(i, Tensor) and i._comp_graph: - comp_graph = i._comp_graph - break - else: - - comp_graph = get_default_graph() - new_args = ( - arg._attach(comp_graph) if isinstance(arg, Tensor) else arg for arg in args - ) - new_kwargs = { - k: v._attach(comp_graph) if isinstance(v, Tensor) else v - for k, v in kwargs.items() - } - ret = func(*new_args, **new_kwargs) - if isinstance(ret, mgb.SymbolVar): - ret = Tensor(ret) - elif isinstance(ret, list): - ret = [Tensor(t) if isinstance(t, mgb.SymbolVar) else t for t in ret] - elif isinstance(ret, tuple): - ret = tuple(Tensor(t) if isinstance(t, mgb.SymbolVar) else t for t in ret) - return ret - - return wrapper - - -def _wrap_symbolvar_binary_op(f): - @functools.wraps(f) - def wrapped(self, other): - comp_graph = ( - isinstance(other, Tensor) - and other._comp_graph - or self._comp_graph - or get_default_graph() - ) - if isinstance(other, Tensor): - other = other._attach(comp_graph) - return Tensor(f(self._attach(comp_graph), other)) - - return wrapped - - -def _wrap_slice(inp: slice): - r""" - A wrapper to handle Tensor values in ``inp`` slice. - """ - start = inp.start._symvar if isinstance(inp.start, Tensor) else inp.start - stop = inp.stop._symvar if isinstance(inp.stop, Tensor) else inp.stop - step = inp.step._symvar if isinstance(inp.step, Tensor) else inp.step - return slice(start, stop, step) - - -def _wrap_idx(idx: Tuple[Union[int, "Tensor"]]): - r""" - A wrapper to handle Tensor values in ``idx``. - """ - if not isinstance(idx, tuple): - idx = (idx,) - - idx = tuple(i._symvar if isinstance(i, Tensor) else i for i in idx) - idx = tuple(_wrap_slice(i) if isinstance(i, slice) else i for i in idx) - return idx - - -class _MGBIndexWrapper: - r""" - A wrapper class to handle ``__getitem__`` for index containing Tensor values. - - :param dest: a destination Tensor to do indexing on. - :param mgb_index: an ``_internal`` helper function indicating how to index. - :param val: a optional Tensor parameter used for ``mgb_index``. - """ - - def __init__(self, dest: "Tensor", mgb_index: Callable, val=None): - self.dest = dest - self.val = val - self.mgb_index = mgb_index - - def __getitem__(self, idx): - if self.val is None: - return wrap_io_tensor(self.mgb_index(self.dest._symvar).__getitem__)( - _wrap_idx(idx) - ) - else: - return wrap_io_tensor( - self.mgb_index(self.dest._symvar, self.val._symvar).__getitem__ - )(_wrap_idx(idx)) - - -class _Guard: - r""" - A wrapper class with custom ``__del__`` method calling ``deleter``. - - :param deleter: a function to be called in ``__del__``. - """ - - def __init__(self, deleter: Callable): - self.deleter = deleter - - def __del__(self): - self.deleter() - - -class Tensor: - r"""The main data container in MegEngine. - Use :func:`~.tensor` to create a Tensor with existed data. - """ - requires_grad = False - grad = None - - def __init__(self, val=None, *, requires_grad=None): - self._reset(val, requires_grad=requires_grad) - self.q_dict = {"mode": None, "scale": None, "zero_point": None} - - def _reset(self, val=None, *, requires_grad=None): - self.__sym_override = None - if val is None: - self.__val = None - self.__sym = None - elif isinstance(val, mgb.SharedND): - self.__val = val - self.__sym = None - elif isinstance(val, mgb.SymbolVar): - self.__val = None - self.__sym = val - else: - raise TypeError("must be initialized with SymbolVar or SharedND") - self.requires_grad = requires_grad - - def _as_tensor(self, obj): - r"""Convert the data into a ``Tensor``. If the data is already a Tensor - with the same dtype and device, no copy will be performed. Otherwise a - new Tensor will be returned with computational graph retained. - - """ - if isinstance(obj, Tensor): - return obj - if isinstance(obj, mgb.SymbolVar): - return Tensor(obj) - if isinstance(obj, mgb.SharedScalar): - return Tensor(obj._as_sym_var(self._comp_graph, self._comp_node)) - return tensor(data=obj, device=self.device) - - def numpy(self): - r"""Return the tensor value in numpy.ndarray format. - """ - if self.__val is not None: - assert self.__sym is None - return self.__val.get_value() - if self.__sym is None: - raise ValueError("uninitialized") - if self.__sym.eager_val is not None: - return self.__sym.eager_val.get_value() - return self.__sym.inferred_value - - def item(self): - r"""If tensor only has only one value, return it.""" - return self.numpy().item() - - def _attach(self, comp_graph, *, volatile=True): - sym = self.__sym_override or self.__sym - if sym: - if sym.owner_graph != comp_graph: - raise RuntimeError("internal error") - return sym - if self.__val: - return self.__val.symvar(comp_graph, volatile=volatile) - else: - raise ValueError("uninitialized") - - @property - def _symvar(self): - if self.__sym_override: - return self.__sym_override - if self.__sym: - assert not self.__val - return self.__sym - if not self.__val: - raise ValueError("uninitialized") - - return self._attach(get_default_graph()) - - def __mgb_symvar__(self, comp_graph=None, **_): - if self.__sym_override: - return self.__sym_override - if self.__val and comp_graph: - return self._attach(comp_graph) - return self._symvar # read by mgb.opr - - def _override_symvar_during_trace(self, trace, symvar): - assert self.__val and not self.__sym - assert trace is type(trace)._active_instance - deleters = trace._user_cache.setdefault(Tensor, set()) - self_ref = weakref.ref(self) - - def restore(): - self = self_ref() - if self is not None: - self.__sym_override = None - - deleters.add(_Guard(restore)) - self.__sym_override = symvar - - @property - def dtype(self): - r"""Return the data type of the tensor. - """ - if self.__val is not None: - return self.__val.dtype - return self._symvar.dtype - - @dtype.setter - def dtype(self, dtype: str = None): - r"""Set the data type of the tensor. - """ - if self.__val is not None: - self.__val = mgb.make_shared(self.device, value=self.astype(dtype).numpy()) - elif self.__sym_override is not None: - self.__sym_override = self.__sym_override.astype(dtype) - elif self.__sym is not None: - self.__sym = self.__sym.astype(dtype) - - @property - def name(self): - r"""Get the tensor name, does not support Parameter and Buffer. - """ - return self._symvar.name - - @name.setter - def name(self, name: str = None): - r"""Set the tensor name, does not support Parameter and Buffer. - """ - if self.__val is not None: - raise ValueError("name setting is not available for Parameter or Buffer.") - if self.__sym_override is not None: - self.__sym_override = self.__sym_override.rename(name) - if self.__sym is not None: - assert not self.__val - self.__sym = self.__sym.rename(name) - - @property - def _comp_node(self): - if self.__val is not None: - return self.__val.comp_node - return self._symvar.comp_node - - device = _comp_node - - @property - def _comp_graph(self): - if self.__sym is not None: - return self.__sym.owner_graph - return None - - @property - def shape(self): - r"""Return an int tuple that is the shape/layout of the tensor. - Could be invalid in static graph mode. - """ - from ..jit import trace - - if trace._active_instance: # pylint: disable=protected-access - # NOTE: this is an hack - shape = mgb.opr.get_var_shape(self._symvar) - return tuple(Tensor(shape[i]) for i in range(self.ndim)) - return self._symvar.imm_shape - - def set_value(self, value, *, sync=True, inplace=False, share=False): - r"""Set value to the tensor. - """ - if not self.__val: - raise ValueError("not detached") - if isinstance(value, Tensor): - value = value.__val or value.__sym.eager_val - self.__val.set_value(value, sync=sync, inplace=inplace, share=share) - - def fill(self, value): - r"""Fills the tensor with the specified value. - """ - self.set_value(np.full(self.shape, value, dtype=self.dtype)) - - def reset_zero(self): - r"""Reset the tensor and fills with zeros. - """ - if not self.__val: - raise ValueError("not detached") - self.__val.reset_zero() - - def to(self, device): - r"""Performs Tensor device conversion, returns Tensor with the specified device. - """ - return wrap_io_tensor(mgb.opr.copy)(self, comp_node=device) - - # https://docs.python.org/3/reference/datamodel.html#object.__hash__ - # > If a class does not define an __eq__() method it should not define a - # > __hash__() operation either - __hash__ = None # type: ignore[assignment] - - def __eq__(self, rhs): - rhs = self._as_tensor(rhs) - return Tensor(self._symvar._binary_opr("EQ", rhs._symvar)) - - def __ne__(self, rhs): - return 1 - self.__eq__(rhs) - - def __len__(self): - if self._symvar.eager_val is not None: - return self._symvar.eager_val.shape[0] - raise TypeError( - "__len__ and __iter__ is not available for tensors on non eager graph." - ) - - __add__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__add__) - __radd__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__radd__) - __sub__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__sub__) - __rsub__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__rsub__) - __mul__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__mul__) - __rmul__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__rmul__) - __matmul__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__matmul__) - __rmatmul__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__rmatmul__) - __lshift__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__lshift__) - __rshift__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__rshift__) - __truediv__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__truediv__) - __rtruediv__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__rtruediv__) - __floordiv__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__floordiv__) - __rfloordiv__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__rfloordiv__) - __mod__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__mod__) - __rmod__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__rmod__) - __pow__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__pow__) - __rpow__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__rpow__) - __lt__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__lt__) - __gt__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__gt__) - __le__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__le__) - __ge__ = _wrap_symbolvar_binary_op(mgb.SymbolVar.__ge__) - __neg__ = wrap_io_tensor(mgb.SymbolVar.__neg__) - sum = wrap_io_tensor(mgb.SymbolVar.sum) - """ - Sum up the given tensors. - """ - max = wrap_io_tensor(mgb.SymbolVar.max) - """ - Return the maximum value of given tensor. - """ - min = wrap_io_tensor(mgb.SymbolVar.min) - """ - Return the minimum value of given tensor. - """ - prod = wrap_io_tensor(mgb.SymbolVar.prod) - """ - Return the product value of the given tensor. - """ - mean = wrap_io_tensor(mgb.SymbolVar.mean) - """ - Return the mean value of the given tensor. - """ - dimshuffle = wrap_io_tensor(mgb.SymbolVar.dimshuffle) - """ - See more details in :func:`~.functional.tensor.dimshuffle`. - """ - astype = wrap_io_tensor(mgb.SymbolVar.astype) - """ - Cast the tensor to a specified type. - """ - - def reshape(self, *target_shape): - r"""Return a tensor which has given target shape - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - - inp = tensor(np.arange(1, 17, dtype=np.int32).reshape(4,4)) - out = tensor(np.arange(100, 116, dtype=np.int32).reshape(1,16)) - out = out.reshape(inp.shape) - print(out.numpy()) - - .. testoutput:: - - [[100 101 102 103] - [104 105 106 107] - [108 109 110 111] - [112 113 114 115]] - """ - - if isinstance(target_shape[0], tuple): - if len(target_shape) > 1: - raise ValueError("Only single tuple is accepted in reshape") - target_shape = target_shape[0] - target_shape = (t._symvar if isinstance(t, Tensor) else t for t in target_shape) - return Tensor(mgb.SymbolVar.reshape(self._symvar, *target_shape)) - - def broadcast(self, *target_shape): - r"""Return a tesnor broadcasted by current tensor to given target shape - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - - data = tensor(np.arange(100, 104, dtype=np.int32).reshape(1,4)) - data = data.broadcast((4,4)) - print(data.numpy()) - - .. testoutput:: - - [[100 101 102 103] - [100 101 102 103] - [100 101 102 103] - [100 101 102 103]] - """ - - if isinstance(target_shape[0], tuple): - if len(target_shape) > 1: - raise ValueError("Only single tuple is accepted in broadcast") - target_shape = target_shape[0] - target_shape = (t._symvar if isinstance(t, Tensor) else t for t in target_shape) - return Tensor(mgb.SymbolVar.broadcast(self._symvar, *target_shape)) - - # Prefer operators on Tensor instead of convert to numpy - __array_priority__ = 1000 - - # mgb indexing family - def __getitem__(self, idx): - return wrap_io_tensor(self._symvar.__getitem__)(_wrap_idx(idx)) - - def set_subtensor(self, val: "Tensor") -> _MGBIndexWrapper: - r""" - Return a object which supports using ``__getitem__`` to set subtensor. - - ``c = a.set_subtensor(b)[idx]`` is equivalent to ``c = a.copy()`` and ``c[idx] = b``. - """ - return _MGBIndexWrapper(self, mgb.opr.set_subtensor, val) - - def incr_subtensor(self, val: "Tensor") -> _MGBIndexWrapper: - r""" - Return a object which supports using ``__getitem__`` to increase subtensor. - - ``c = a.incr_subtensor(b)[idx]`` is equivalent to ``c = a.copy()`` and ``c[idx] += b``. - """ - return _MGBIndexWrapper(self, mgb.opr.incr_subtensor, val) - - @property - def ai(self) -> _MGBIndexWrapper: - r""" - Return a object which supports complex index method to get subtensor. - - Examples: - - .. testcode:: - - from megengine import tensor - a = tensor(np.arange(16, dtype=np.float32).reshape((4, 4))) - print(a.ai[:, [2, 3]]) - - Outputs: - - .. testoutput:: - - Tensor([[ 2. 3.] - [ 6. 7.] - [10. 11.] - [14. 15.]]) - """ - return _MGBIndexWrapper(self, mgb.opr.advanced_indexing) - - def set_ai(self, val: "Tensor") -> _MGBIndexWrapper: - r""" - Equal to :meth:`~.Tensor.set_subtensor` which supports advanced indexing. - """ - return _MGBIndexWrapper(self, mgb.opr.set_advanced_indexing, val) - - def incr_ai(self, val: "Tensor") -> _MGBIndexWrapper: - r""" - Equal to :meth:`~.Tensor.incr_subtensor` which supports advanced indexing. - """ - return _MGBIndexWrapper(self, mgb.opr.incr_advanced_indexing, val) - - @property - def mi(self) -> _MGBIndexWrapper: - r""" - Return a object which supports getting subtensor by - the coordinates which is Cartesian product of given index. - - Examples: - - .. testcode:: - - from megengine import tensor - a = tensor(np.arange(16, dtype=np.float32).reshape((4, 4))) - print(a.mi[[1, 2], [2, 3]]) - # is equal to elements on [1, 2] * [2, 3] = [[(1,2), (1, 3)], [(2, 2), (2, 3)]] - # a[1,2] = 6, a[1,3] = 7, a[2,2] = 10, a[2,3] = 11 - - Outputs: - - .. testoutput:: - - Tensor([[ 6. 7.] - [10. 11.]]) - """ - return _MGBIndexWrapper(self, mgb.opr.mesh_indexing) - - def set_mi(self, val: "Tensor") -> _MGBIndexWrapper: - r""" - Equal to :meth:`~.Tensor.set_subtensor` which using mesh indexing. - """ - return _MGBIndexWrapper(self, mgb.opr.set_mesh_indexing, val) - - def incr_mi(self, val: "Tensor") -> _MGBIndexWrapper: - r""" - Equal to :meth:`~.Tensor.incr_subtensor` which using mesh indexing. - """ - return _MGBIndexWrapper(self, mgb.opr.incr_mesh_indexing, val) - - @property - def batched_mi(self) -> _MGBIndexWrapper: - r""" - Return a object which supports getting subtensor by - batched mesh indexing. - - For Tensor ``a`` and index ``idx``, each value of the ``idx`` need to be a 2-dim matrix or slice. - Cartesian product ``... * idx[k-1][i] * idx[k][i] * idx[k+1][i] * ...`` will be a subtensor from ``a[i]``. - Each matrix ``idx[k]`` should have the size of ``batched_dim`` rows as ``idx[0]`` indicated. - And for slice value, it will apply same slice for each ``batched_dim``. For more details see the example below. - - Examples: - - .. testcode:: - - from megengine import tensor - a = tensor(np.arange(144, dtype=np.float32).reshape((3, 3, 4, 4))) - - print(a.batched_mi[:2, [[0],[1]],[[0,1],[2,3]],[[0],[1]]]) - # is equal to elements from a[0] with ``[0] * [0,1] * [0] = [[[(0,0,0)], [(0,1,0)]]]``(shape is [1,2,1]) - # and from a[1] with ``[1] * [2,3] * [1] = [[[(1,2,1)], [(1,3,1)]]]``(shape is also [1,2,1]) - # a[0,0,0,0] = 0, a[0,0,1,0] = 4, a[1,1,2,1] = 73, a[1,1,3,1] = 77 - - print(a.batched_mi[:2, [[0],[1]], :2, :1]) - # is equal to ``a.batched_mi[:2, [[0],[1]], [[0,1],[0,1]],[[0],[0]]]`` - - Outputs: - - .. testoutput:: - - Tensor([[[[ 0.] - [ 4.]]] - [[[73.] - [77.]]]]) - Tensor([[[[ 0.] - [ 4.]]] - [[[64.] - [68.]]]]) - """ - return _MGBIndexWrapper(self, mgb.opr.batched_mesh_indexing) - - def batched_set_mi(self, val: "Tensor") -> _MGBIndexWrapper: - r""" - Equal to :meth:`~.Tensor.incr_subtensor` which using batched mesh indexing. - """ - return _MGBIndexWrapper(self, mgb.opr.batched_set_mesh_indexing, val) - - def batched_incr_mi(self, val: "Tensor") -> _MGBIndexWrapper: - r""" - Equal to :meth:`~.Tensor.incr_subtensor` which using batched mesh indexing. - """ - return _MGBIndexWrapper(self, mgb.opr.batched_incr_mesh_indexing, val) - - def __array__(self, dtype=None): - if dtype is None: - return self.numpy() - else: - return self.numpy().astype(dtype, copy=False) - - def __int__(self): - return int(self.item()) - - def __index__(self): - return int(self.item()) - - def __round__(self, ndigits=0): - if ndigits != 0: - raise ValueError("ndigits must be 0 for Tensor.round") - return Tensor(mgb.opr.elemwise([self._symvar], mode="ROUND")) - - round = __round__ - - def sqrt(self): - r"""Return a tensor that each element is the square root of its - original value. - - """ - return Tensor(mgb.opr.sqrt(self._symvar)) - - def shapeof(self, axis=None): - r"""Return a Tensor that represent the shape of the tensor. - """ - return Tensor(mgb.opr.get_var_shape(self._symvar, axis=axis)) - - @property - def ndim(self): - r"""Return the number of dimensions of the tensor. - """ - return len(self._symvar.imm_shape) - - def __repr__(self): - piece = "Tensor(" - with np.printoptions(precision=4, suppress=True): - piece += "{}".format(str(self.numpy())) - if self.dtype != np.float32: - piece += ", dtype={}".format(np.dtype(self.dtype).name) - if self._comp_node.locator_logical != ("XPU", -1, 0): - piece += ", device={}".format(self.device) - piece += ")" - return piece - - def __bool__(self): - raise RuntimeError( - "Tensor object should not be converted to bool or used in a if statement. Use .numpy(), int() or float() if you want to use its value in if statement, be aware that this may lead to incorrect result in non-eager mode." - ) - - def __getstate__(self): - r""" __getstate__ will be called for pickle serialization or deep copy - """ - - assert (self.__val is not None) and ( - self.__sym is None - ), "Only SharedND initialized Tensor can be serialized or deep copied" - metadata = {"requires_grad": self.requires_grad} - state = { - "data": self.numpy(), - "device": self.device, - "dtype": self.dtype, - "metadata": metadata, - } - return state - - def __setstate__(self, state): - data = state.pop("data") - device = state.pop("device") - dtype = state.pop("dtype") - metadata = state.pop("metadata", {}) - requires_grad = metadata.pop("requires_grad", None) - snd = mgb.make_shared(device, value=data, dtype=dtype) - self._reset(snd, requires_grad=requires_grad) - - def __deepcopy__(self, memo): - """ - The default deepcopy will ignore other attributes except those defined at - __getstate__ and __setstate__ method. - So we need to add __deepcopy__ method to deepcopy correct attributes. - """ - assert (self.__val is not None) and ( - self.__sym is None - ), "Only SharedND initialized Tensor can be serialized or deep copied" - cls = self.__class__ - result = cls.__new__(cls) - memo[id(self)] = result - for k, v in self.__dict__.items(): - setattr(result, k, copy.deepcopy(v, memo)) - return result - - -def tensor( - data: Union[list, np.ndarray] = None, - *, - dtype: str = None, - device: mgb.CompNode = None, - requires_grad: bool = None -): - r"""A helper function to create a :class:`~.Tensor` using existing data. - - :param data: an existing data array, must be Python list, NumPy array or None. - :param dtype: target Tensor data type, one of ``("uint8", "int8", "int16", "int32", "float32", "float16")``. - :param device: target device for Tensor storing. - :param requires_grad: whether its gradiant will be calculated during :meth:`~.Optimizer.backward` - """ - supported_dtypes = ("uint8", "int8", "int16", "int32", "float32", "float16") - if isinstance(data, Tensor): - raise NotImplementedError - if dtype is not None and np.dtype(dtype).name not in supported_dtypes: - raise TypeError("unsupported dtype {}".format(dtype)) - if data is not None: - if not isinstance(data, np.ndarray): - data = np.array(data, dtype=dtype) - # In order to accept tensor([1]), - # Automaticlly convert to 32-bit number instead of numpy's default 64-bit when input data is not nparray. - dtype = mgb.to_mgb_supported_dtype(data.dtype) - if dtype is None: - if data.dtype.name not in supported_dtypes: - raise TypeError("unsupported dtype {}".format(data.dtype)) - - device, _ = _use_default_if_none(device, None) - shared_nd = mgb.make_shared(device, value=data, dtype=dtype) - return Tensor(shared_nd, requires_grad=requires_grad) - - -class TensorDict(collections.MutableMapping): - r""" - A helper class to maintain dict with Tensor key. - """ - - def __init__(self, *args, **kwargs): - self.data = {} - for i in args: - self.update(i) - self.update(**kwargs) - - class keyfn: - def __new__(cls, x: Tensor): - if not isinstance(x, Tensor): - return x - return super().__new__(cls) - - def __init__(self, x: Tensor): - self._data = x # do not save id directly to make pickle work - - def __hash__(self): - return id(self._data) - - def __eq__(self, other): - return isinstance(other, type(self)) and id(self._data) == id(other._data) - - def __getitem__(self, key): - _, v = self.data[self.keyfn(key)] - return v - - def __setitem__(self, key, value): - self.data[self.keyfn(key)] = key, value - - def __delitem__(self, key): - del self.data[self.keyfn(key)] - - def __iter__(self): - for _, (k, _) in self.data.items(): - yield k - - def __len__(self): - return len(self.data) diff --git a/python_module/megengine/core/tensor_factory.py b/python_module/megengine/core/tensor_factory.py deleted file mode 100644 index 4e83bdba..00000000 --- a/python_module/megengine/core/tensor_factory.py +++ /dev/null @@ -1,109 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from typing import Iterable, Optional, Union - -import megengine._internal as mgb - -from .graph import _use_default_if_none -from .tensor import Tensor - -__all__ = ["zeros", "ones"] - - -def scalar( - value, - dtype: type = None, - device: Optional[mgb.CompNode] = None, - comp_graph: Optional[mgb.CompGraph] = None, -) -> Tensor: - """ - convert ``value`` to the type of :class:`~.Tensor`. - """ - device, comp_graph = _use_default_if_none(device, comp_graph) - return Tensor(mgb.make_immutable(device, comp_graph, value, dtype=dtype, name=None)) - - -def zeros( - shape: Union[int, Iterable[int], Tensor], - dtype: type = None, - device: Optional[mgb.CompNode] = None, - comp_graph: Optional[mgb.CompGraph] = None, -) -> Tensor: - """ - Create a tensor filled with 0. - - :param shape: tensor shape - :param dtype: data type, Default: "int32" - :param device: Compute node of the matrix, Default: None - :param comp_graph: Compute graph of the matrix, Default: None - :return: tensor of zeros - - Examples: - - .. testcode:: - - import megengine as mge - - t = mge.zeros((2, 2), dtype="int32") - print(t.numpy()) - - Outputs: - - .. testoutput:: - - [[0 0] - [0 0]] - - """ - device, comp_graph = _use_default_if_none(device, comp_graph) - if isinstance(shape, (int, Tensor)): - shape = (shape,) - tensor = scalar(0, dtype=dtype, device=device, comp_graph=comp_graph) - tensor = tensor.broadcast(*shape) - return tensor - - -def ones( - shape: Union[int, Iterable[int], Tensor], - dtype: type = None, - device: Optional[mgb.CompNode] = None, - comp_graph: Optional[mgb.CompGraph] = None, -) -> Tensor: - """ - Create a tensor filled with 1. - - :param shape: tensor shape - :param dtype: data type, Default: "int32" - :param device: Compute node of the matrix, Default: None - :param comp_graph: Compute graph of the matrix, Default: None - :return: tensor of ones - - Examples: - - .. testcode:: - - import megengine as mge - - t = mge.ones((2, 2), dtype="float32") - print(t.numpy()) - - Outputs: - - .. testoutput:: - - [[1. 1.] - [1. 1.]] - - """ - device, comp_graph = _use_default_if_none(device, comp_graph) - if isinstance(shape, (int, Tensor)): - shape = (shape,) - tensor = scalar(1, dtype=dtype, device=device, comp_graph=comp_graph) - tensor = tensor.broadcast(*shape) - return tensor diff --git a/python_module/megengine/core/tensor_nn.py b/python_module/megengine/core/tensor_nn.py deleted file mode 100644 index e2bbc927..00000000 --- a/python_module/megengine/core/tensor_nn.py +++ /dev/null @@ -1,45 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .tensor import Tensor, tensor - - -class Buffer(Tensor): - r"""A kind of Tensor with ``requires_grad=False``. - """ - - def __init__(self, value, *, dtype=None, device=None, requires_grad=False): - # pylint: disable=super-init-not-called - t = tensor(value, dtype=dtype, device=device, requires_grad=requires_grad) - self.__dict__.update(t.__dict__) - - -class Parameter(Tensor): - r"""A kind of Tensor that is to be considered a module parameter. - """ - - def __init__(self, value, *, dtype=None, device=None, requires_grad=True): - # pylint: disable=super-init-not-called - if isinstance(value, Tensor): - t = value - else: - t = tensor(value, dtype=dtype, device=device, requires_grad=requires_grad) - self.__dict__.update(t.__dict__) - - # broadcast and allreduce will not be performed in optimizer if replica_mode is False - self.replica_mode = True - - @property - def shape(self): - r"""Return shape of parameter. - """ - if self._Tensor__val is not None: - return self._Tensor__val.shape - elif self._Tensor__sym is not None: - return self._Tensor__sym.imm_shape - return None diff --git a/python_module/megengine/data/__init__.py b/python_module/megengine/data/__init__.py deleted file mode 100644 index 3b1e0d55..00000000 --- a/python_module/megengine/data/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .collator import Collator -from .dataloader import DataLoader -from .sampler import ( - Infinite, - RandomSampler, - ReplacementSampler, - Sampler, - SequentialSampler, -) diff --git a/python_module/megengine/data/_queue.py b/python_module/megengine/data/_queue.py deleted file mode 100644 index 8e359ae0..00000000 --- a/python_module/megengine/data/_queue.py +++ /dev/null @@ -1,144 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import binascii -import os -import queue -import subprocess -from multiprocessing import Queue - -import pyarrow -import pyarrow.plasma as plasma - -MGE_PLASMA_MEMORY = int(os.environ.get("MGE_PLASMA_MEMORY", 4000000000)) # 4GB - -# Each process only need to start one plasma store, so we set it as a global variable. -# TODO: how to share between different processes? -MGE_PLASMA_STORE_MANAGER = None - - -def _clear_plasma_store(): - # `_PlasmaStoreManager.__del__` will not be called automaticly in subprocess, - # so this function should be called explicitly - global MGE_PLASMA_STORE_MANAGER - if MGE_PLASMA_STORE_MANAGER is not None and MGE_PLASMA_STORE_MANAGER.refcount == 0: - del MGE_PLASMA_STORE_MANAGER - MGE_PLASMA_STORE_MANAGER = None - - -class _PlasmaStoreManager: - __initialized = False - - def __init__(self): - self.socket_name = "/tmp/mge_plasma_{}".format( - binascii.hexlify(os.urandom(8)).decode() - ) - debug_flag = bool(os.environ.get("MGE_DATALOADER_PLASMA_DEBUG", 0)) - # NOTE: this is a hack. Directly use `plasma_store` may make subprocess - # difficult to handle the exception happened in `plasma-store-server`. - # For `plasma_store` is just a wrapper of `plasma-store-server`, which use - # `os.execv` to call the executable `plasma-store-server`. - cmd_path = os.path.join(pyarrow.__path__[0], "plasma-store-server") - self.plasma_store = subprocess.Popen( - [cmd_path, "-s", self.socket_name, "-m", str(MGE_PLASMA_MEMORY),], - stdout=None if debug_flag else subprocess.DEVNULL, - stderr=None if debug_flag else subprocess.DEVNULL, - ) - self.__initialized = True - self.refcount = 1 - - def __del__(self): - if self.__initialized and self.plasma_store.returncode is None: - self.plasma_store.kill() - - -class PlasmaShmQueue: - def __init__(self, maxsize: int = 0): - r"""Use pyarrow in-memory plasma store to implement shared memory queue. - - Compared to native `multiprocess.Queue`, `PlasmaShmQueue` avoid pickle/unpickle - and communication overhead, leading to better performance in multi-process - application. - - :type maxsize: int - :param maxsize: maximum size of the queue, `None` means no limit. (default: ``None``) - """ - - # Lazy start the plasma store manager - global MGE_PLASMA_STORE_MANAGER - if MGE_PLASMA_STORE_MANAGER is None: - try: - MGE_PLASMA_STORE_MANAGER = _PlasmaStoreManager() - except Exception as e: - err_info = ( - "Please make sure pyarrow installed correctly!\n" - "You can try reinstall pyarrow and see if you can run " - "`plasma_store -s /tmp/mge_plasma_xxx -m 1000` normally." - ) - raise RuntimeError( - "Exception happened in starting plasma_store: {}\n" - "Tips: {}".format(str(e), err_info) - ) - else: - MGE_PLASMA_STORE_MANAGER.refcount += 1 - - self.socket_name = MGE_PLASMA_STORE_MANAGER.socket_name - - # TODO: how to catch the exception happened in `plasma.connect`? - self.client = None - - # Used to store the header for the data.(ObjectIDs) - self.queue = Queue(maxsize) # type: Queue - - def put(self, data, block=True, timeout=None): - if self.client is None: - self.client = plasma.connect(self.socket_name) - try: - object_id = self.client.put(data) - except plasma.PlasmaStoreFull: - raise RuntimeError("plasma store out of memory!") - try: - self.queue.put(object_id, block, timeout) - except queue.Full: - self.client.delete([object_id]) - raise queue.Full - - def get(self, block=True, timeout=None): - if self.client is None: - self.client = plasma.connect(self.socket_name) - object_id = self.queue.get(block, timeout) - if not self.client.contains(object_id): - raise RuntimeError( - "ObjectID: {} not found in plasma store".format(object_id) - ) - data = self.client.get(object_id) - self.client.delete([object_id]) - return data - - def qsize(self): - return self.queue.qsize() - - def empty(self): - return self.queue.empty() - - def join(self): - self.queue.join() - - def disconnect_client(self): - if self.client is not None: - self.client.disconnect() - - def close(self): - self.queue.close() - self.disconnect_client() - global MGE_PLASMA_STORE_MANAGER - MGE_PLASMA_STORE_MANAGER.refcount -= 1 - _clear_plasma_store() - - def cancel_join_thread(self): - self.queue.cancel_join_thread() diff --git a/python_module/megengine/data/collator.py b/python_module/megengine/data/collator.py deleted file mode 100644 index 952fc398..00000000 --- a/python_module/megengine/data/collator.py +++ /dev/null @@ -1,76 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright (c) 2016- Facebook, Inc (Adam Paszke) -# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) -# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) -# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) -# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) -# Copyright (c) 2011-2013 NYU (Clement Farabet) -# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) -# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) -# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) -# --------------------------------------------------------------------- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# -# This file has been modified by Megvii ("Megvii Modifications"). -# All Megvii Modifications are Copyright (C) 2014-2020 Megvii Inc. All rights reserved. -# ---------------------------------------------------------------------- -import collections.abc -import re - -import numpy as np - -np_str_obj_array_pattern = re.compile(r"[aO]") -default_collate_err_msg_format = ( - "default_collator: inputs must contain numpy arrays, numbers, " - "Unicode strings, bytes, dicts or lists; found {}" -) - - -class Collator: - r""" - Used for merge a list of samples to form a mini-batch of Tenor(s). Used when using batched loading from a dataset. - modified from https://github.com/pytorch/pytorch/blob/master/torch/utils/data/_utils/collate.py - """ - - def apply(self, inputs): - """ - input : sequence_N(tuple(CHW, C, CK)) - output : tuple(NCHW, NC, NCK) - """ - elem = inputs[0] - elem_type = type(elem) - if ( - elem_type.__module__ == "numpy" - and elem_type.__name__ != "str_" - and elem_type.__name__ != "string_" - ): - elem = inputs[0] - if elem_type.__name__ == "ndarray": - # array of string classes and object - if np_str_obj_array_pattern.search(elem.dtype.str) is not None: - raise TypeError(default_collate_err_msg_format.format(elem.dtype)) - - return np.ascontiguousarray(np.stack(inputs)) - elif elem.shape == (): # scalars - return np.array(inputs) - elif isinstance(elem, float): - return np.array(inputs, dtype=np.float64) - elif isinstance(elem, int): - return np.array(inputs) - elif isinstance(elem, (str, bytes)): - return inputs - elif isinstance(elem, collections.abc.Mapping): - return {key: self.apply([d[key] for d in inputs]) for key in elem} - elif isinstance(elem, tuple) and hasattr(elem, "_fields"): # namedtuple - return elem_type(*(self.apply(samples) for samples in zip(*inputs))) - elif isinstance(elem, collections.abc.Sequence): - transposed = zip(*inputs) - return [self.apply(samples) for samples in transposed] - - raise TypeError(default_collate_err_msg_format.format(elem_type)) diff --git a/python_module/megengine/data/dataloader.py b/python_module/megengine/data/dataloader.py deleted file mode 100644 index 1fd3482d..00000000 --- a/python_module/megengine/data/dataloader.py +++ /dev/null @@ -1,500 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import collections -import math -import multiprocessing -import queue -import random -import time - -import numpy as np - -from ..logger import get_logger -from ..random.rng import _random_seed_generator -from .collator import Collator -from .dataset import Dataset -from .sampler import Sampler, SequentialSampler -from .transform import PseudoTransform, Transform - -logger = get_logger(__name__) - - -MP_QUEUE_GET_TIMEOUT = 5 - - -class DataLoader: - __initialized = False - - def __init__( - self, - dataset: Dataset, - sampler: Sampler = None, - transform: Transform = None, - collator: Collator = None, - num_workers: int = 0, - timeout: int = 0, - divide: bool = False, - ): - r"""Provides a convenient way to iterate on a given dataset. - - `DataLoader` combines a dataset with sampler, transform and collator, - make it flexible to get minibatch continually from a dataset. - - :type dataset: Dataset - :param dataset: dataset from which to load the minibatch. - :type sampler: Sampler - :param sampler: defines the strategy to sample data from the dataset. - If specified, :attr:`shuffle` must be ``False``. - :type transform: Transform - :param transform: defined the transforming strategy for a sampled batch. - (default: ``None``) - :type collator: Collator - :param collator: defined the merging strategy for a transformed batch. - (default: ``None``) - :type num_workers: int - :param num_workers: the number of sub-process to load, transform and collate - the batch. ``0`` means using single-process. (default: ``0``) - :type timeout: int - :param timeout: if positive, means the timeout value(second) for collecting a - batch from workers. (default: 0) - :type divide: bool - :param divide: define the paralleling strategy in multi-processing mode. - ``True`` means one batch is divided into :attr:`num_workers` pieces, and - the workers will process these pieces parallelly. ``False`` means - different sub-process will process different batch. (default: ``False``) - - """ - - if num_workers < 0: - raise ValueError("num_workers should not be negative") - - if timeout < 0: - raise ValueError("timeout should not be negative") - - if divide and num_workers <= 1: - raise ValueError("divide should not be set to True when num_workers <= 1") - - self.dataset = dataset - self.num_workers = num_workers - self.timeout = timeout - - self.divide = divide - - if sampler is None: - self.sampler = SequentialSampler(dataset, batch_size=1, drop_last=False) - else: - self.sampler = sampler - - if divide: - if self.sampler.batch_size <= self.num_workers: - raise ValueError( - "batch size must not smaller than num_workers in divide mode." - ) - elif self.sampler.batch_size % self.num_workers: - logger.warning( - "batch size is not divisible by num_workers, may lose performance in divide mode." - ) - - if transform is None: - self.transform = PseudoTransform() - else: - self.transform = transform - - if collator is None: - self.collator = Collator() - else: - self.collator = collator - - self.__initialized = True - - def __iter__(self): - if self.num_workers == 0: - return _SerialDataLoaderIter(self) - else: - return _ParallelDataLoaderIter(self) - - def __len__(self): - return len(self.sampler) - - -class _BaseDataLoaderIter: - def __init__(self, loader): - self.dataset = loader.dataset - self.sampler = loader.sampler - self.seed = _random_seed_generator().__next__() - self.transform = loader.transform - self.collator = loader.collator - self.num_workers = loader.num_workers - self.timeout = loader.timeout - self.divide = loader.divide - self.num_processed = 0 - - def _get_next_batch(self): - raise NotImplementedError - - def __len__(self): - return len(self.sampler) - - def __iter__(self): - return self - - def __next__(self): - if self.num_processed >= len(self): - raise StopIteration - minibatch = self._get_next_batch() - self.num_processed += 1 - return minibatch - - -class _SerialDataLoaderIter(_BaseDataLoaderIter): - def __init__(self, loader): - super(_SerialDataLoaderIter, self).__init__(loader) - self.indices_iter = iter(self.sampler) - - def _get_next_batch(self): - indices = next(self.indices_iter) - items = [self.dataset[idx] for idx in indices] - trans_items = self.transform.apply_batch(items) - return self.collator.apply(trans_items) - - -class _ParallelDataLoaderIter(_BaseDataLoaderIter): - __initialized = False - - def __init__(self, loader): - super(_ParallelDataLoaderIter, self).__init__(loader) - - self.task_queues = [ - multiprocessing.Queue(maxsize=2) for _ in range(self.num_workers) - ] - - self.feed_batch_idx = multiprocessing.Value("i", 0) - self.target_batch_idx = multiprocessing.Value("i", 0) - self.shutdown_flag = multiprocessing.Value("i", 0) - - self.trans_data_queues = [ - multiprocessing.Queue(maxsize=1) for _ in range(self.num_workers) - ] - - # use shared-memory queue implemented by pyarrow plasma store. - from ._queue import PlasmaShmQueue - - self.batch_queue = PlasmaShmQueue(maxsize=2) - - self.task_feeding_worker = multiprocessing.Process( - target=_task_feeding_loop, - args=( - iter(self.sampler), - self.task_queues, - self.num_workers, - self.divide, - self.shutdown_flag, - self.feed_batch_idx, - ), - daemon=True, - ) - self.task_feeding_worker.start() - - self.workers = [] - for worker_id in range(self.num_workers): - worker = multiprocessing.Process( - target=_worker_loop, - args=( - self.dataset, - self.task_queues[worker_id], - self.trans_data_queues[worker_id], - self.transform, - self.seed + worker_id + 1, - self.shutdown_flag, - ), - daemon=True, - ) - worker.start() - self.workers.append(worker) - - if self.divide: - self.data_collecting_worker = multiprocessing.Process( - target=_data_gathering_loop, - args=( - self.trans_data_queues, - self.batch_queue, - self.collator, - len(self), - self.num_workers, - self.shutdown_flag, - self.target_batch_idx, - ), - daemon=True, - ) - else: - self.data_collecting_worker = multiprocessing.Process( - target=_data_selecting_loop, - args=( - self.trans_data_queues, - self.batch_queue, - self.collator, - len(self), - self.num_workers, - self.shutdown_flag, - self.target_batch_idx, - ), - daemon=True, - ) - self.data_collecting_worker.start() - - self.__initialized = True - - def _check_workers(self): - # Check the status of each worker. - if not self.data_collecting_worker.is_alive(): - exitcode = self.task_feeding_worker.exitcode - if exitcode != 0: - raise RuntimeError("data collecting worker died. {}".format(exitcode)) - - if not self.task_feeding_worker.is_alive(): - exitcode = self.task_feeding_worker.exitcode - if exitcode != 0: - raise RuntimeError("task feeding worker died. {}".format(exitcode)) - - for worker_id, worker in enumerate(self.workers): - if not worker.is_alive(): - exitcode = worker.exitcode - if exitcode != 0: - raise RuntimeError("worker:{} died. {}".format(worker_id, exitcode)) - - logger.debug("all workers are alive.") - - def _try_get_next_batch(self): - start_time = time.time() - while True: - self._check_workers() - try: - return self.batch_queue.get(timeout=1) - except queue.Empty: - logger.debug("batch queue empty!") - waited_time = time.time() - start_time - if self.timeout > 0: - if waited_time > self.timeout: - raise RuntimeError("get_next_batch timeout!") - - def _get_next_batch(self): - batch_data = self._try_get_next_batch() - return batch_data - - def _shutdown(self): - with self.shutdown_flag.get_lock(): - self.shutdown_flag.value = 1 - - if self.task_feeding_worker.is_alive(): - self.task_feeding_worker.terminate() - self.task_feeding_worker.join() - - if self.data_collecting_worker.is_alive(): - self.data_collecting_worker.terminate() - self.data_collecting_worker.join() - - for worker in self.workers: - if worker.is_alive(): - worker.terminate() - worker.join() - - for q in self.trans_data_queues: - q.cancel_join_thread() - q.close() - - for q in self.task_queues: - q.cancel_join_thread() - q.close() - - self.batch_queue.cancel_join_thread() - self.batch_queue.close() - - def __del__(self): - if self.__initialized: - self._shutdown() - - -def _task_feeding_loop( - indices_iter, task_queues, num_workers, divide, shutdown_flag, feed_batch_idx -): - # Feed the indices into the task queues - while True: - if shutdown_flag.value == 1: - break - batch_idx = feed_batch_idx.value - try: - indices = next(indices_iter) - except StopIteration: - break - if divide: - # make sure all task_queues is ready for put - while any([q.full() for q in task_queues]): - if shutdown_flag.value == 1: - return - # divide into small pieces, feed to different workers. - sub_num = math.ceil(len(indices) / num_workers) - for worker_id in range(num_workers): - sub_indices = indices[worker_id * sub_num : (worker_id + 1) * sub_num] - task_queues[worker_id].put((batch_idx, sub_indices)) - else: - # distribute tasks to different workers uniformly. - target_id = batch_idx % num_workers - while task_queues[target_id].full(): - if shutdown_flag.value == 1: - return - task_queues[target_id].put((batch_idx, indices)) - with feed_batch_idx.get_lock(): - feed_batch_idx.value += 1 - - -def _worker_loop(dataset, task_queue, trans_data_queue, transform, seed, shutdown_flag): - # Get dataset items and do the transform - random.seed(seed) - np.random.seed(seed) - while True: - if shutdown_flag.value == 1: - break - try: - batch_idx, indices = task_queue.get(timeout=MP_QUEUE_GET_TIMEOUT) - except queue.Empty: - continue - if len(indices) > 0: - items = [dataset[idx] for idx in indices] - trans_items = transform.apply_batch(items) - else: - # in case of incomplete last batch - trans_items = () - while True: - try: - trans_data_queue.put((batch_idx, trans_items), timeout=1) - break - except queue.Full: - if shutdown_flag.value == 1: - break - logger.debug("batch part queue is full!") - - -def _data_gathering_loop( - trans_data_queues, - batch_queue, - collator, - length, - num_workers, - shutdown_flag, - target_idx, -): - # Gathering the small pieces of batch data into full batch data - while True: - if shutdown_flag.value == 1: - break - - target_batch_idx = target_idx.value - - if target_batch_idx >= length: - break - - full_trans_items = [] - for worker_id in range(num_workers): - while True: - try: - batch_idx, trans_items = trans_data_queues[worker_id].get( - timeout=MP_QUEUE_GET_TIMEOUT - ) - break - except queue.Empty: - if shutdown_flag.value == 1: - break - logger.debug( - "worker:{} data queue get timeout! target batch idx:{}".format( - worker_id, target_batch_idx - ) - ) - if batch_idx != target_batch_idx: - raise RuntimeError( - "Unexperted batch_idx in data gathering loop. worker_id:{}.".format( - worker_id - ) - ) - else: - full_trans_items.extend(trans_items) - - # Merge different parts into a batch. - full_batch = collator.apply(full_trans_items) - - while True: - try: - batch_queue.put(full_batch, timeout=1) - break - except queue.Full: - if shutdown_flag.value == 1: - break - logger.debug("batch queue is full!") - - with target_idx.get_lock(): - target_idx.value += 1 - - batch_queue.disconnect_client() - - -def _data_selecting_loop( - trans_data_queues, - batch_queue, - collator, - length, - num_workers, - shutdown_flag, - target_idx, -): - # Make sure that batch is generated exactly with the same order as generated indices - while True: - if shutdown_flag.value == 1: - break - - target_batch_idx = target_idx.value - - if target_batch_idx >= length: - break - - target_worker_id = target_batch_idx % num_workers - while True: - try: - batch_idx, trans_items = trans_data_queues[target_worker_id].get( - timeout=MP_QUEUE_GET_TIMEOUT - ) - batch_data = collator.apply(trans_items) - break - except queue.Empty: - if shutdown_flag.value == 1: - break - logger.debug( - "worker:{} data queue get timeout! target batch idx:{}".format( - target_worker_id, target_batch_idx - ) - ) - - if batch_idx != target_batch_idx: - raise RuntimeError( - "batch_idx {} mismatch the target_batch_idx {}".format( - batch_idx, target_batch_idx - ) - ) - - while True: - try: - batch_queue.put(batch_data, timeout=1) - break - except queue.Full: - if shutdown_flag.value == 1: - break - logger.debug("batch queue is full!") - - with target_idx.get_lock(): - target_idx.value += 1 - - batch_queue.disconnect_client() diff --git a/python_module/megengine/data/dataset/__init__.py b/python_module/megengine/data/dataset/__init__.py deleted file mode 100644 index 8b70d221..00000000 --- a/python_module/megengine/data/dataset/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .meta_dataset import ArrayDataset, Dataset, MapDataset, StreamDataset -from .vision import * diff --git a/python_module/megengine/data/dataset/meta_dataset.py b/python_module/megengine/data/dataset/meta_dataset.py deleted file mode 100644 index 4415a427..00000000 --- a/python_module/megengine/data/dataset/meta_dataset.py +++ /dev/null @@ -1,73 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from abc import ABC, abstractmethod -from typing import Tuple - - -class Dataset(ABC): - r""" - An abstract class for all Datasets - """ - - @abstractmethod - def __init__(self): - pass - - -class MapDataset(Dataset): - r""" - An abstract class for map data - __getitem__ and __len__ method are aditionally needed - """ - - @abstractmethod - def __init__(self): - pass - - @abstractmethod - def __getitem__(self, index): - pass - - @abstractmethod - def __len__(self): - pass - - -class StreamDataset(Dataset): - r""" - An abstract class for stream data - __iter__ method is aditionally needed - """ - - @abstractmethod - def __init__(self): - pass - - @abstractmethod - def __iter__(self): - pass - - -class ArrayDataset(MapDataset): - def __init__(self, *arrays): - r""" - ArrayDataset is a dataset for numpy array data, one or more numpy arrays - are needed to initiate the dataset. And the dimensions represented sample number - are expected to be the same. - """ - super().__init__() - if not all(len(arrays[0]) == len(array) for array in arrays): - raise ValueError("lengths of input arrays are inconsistent") - self.arrays = arrays - - def __getitem__(self, index: int) -> Tuple: - return tuple(array[index] for array in self.arrays) - - def __len__(self) -> int: - return len(self.arrays[0]) diff --git a/python_module/megengine/data/dataset/vision/__init__.py b/python_module/megengine/data/dataset/vision/__init__.py deleted file mode 100644 index dd2b0fc3..00000000 --- a/python_module/megengine/data/dataset/vision/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .cifar import CIFAR10, CIFAR100 -from .cityscapes import Cityscapes -from .coco import COCO -from .folder import ImageFolder -from .imagenet import ImageNet -from .meta_vision import VisionDataset -from .mnist import MNIST -from .objects365 import Objects365 -from .voc import PascalVOC diff --git a/python_module/megengine/data/dataset/vision/cifar.py b/python_module/megengine/data/dataset/vision/cifar.py deleted file mode 100644 index 9ce73688..00000000 --- a/python_module/megengine/data/dataset/vision/cifar.py +++ /dev/null @@ -1,171 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import os -import pickle -import tarfile -from typing import Tuple - -import numpy as np - -from ....logger import get_logger -from .meta_vision import VisionDataset -from .utils import _default_dataset_root, load_raw_data_from_url - -logger = get_logger(__name__) - - -class CIFAR10(VisionDataset): - r""" ``Dataset`` for CIFAR10 meta data - """ - - url_path = "http://www.cs.utoronto.ca/~kriz/" - raw_file_name = "cifar-10-python.tar.gz" - raw_file_md5 = "c58f30108f718f92721af3b95e74349a" - raw_file_dir = "cifar-10-batches-py" - train_batch = [ - "data_batch_1", - "data_batch_2", - "data_batch_3", - "data_batch_4", - "data_batch_5", - ] - test_batch = ["test_batch"] - meta_info = {"name": "batches.meta"} - - def __init__( - self, - root: str = None, - train: bool = True, - download: bool = True, - timeout: int = 500, - ): - super().__init__(root, order=("image", "image_category")) - - self.timeout = timeout - - # process the root path - if root is None: - self.root = self._default_root - if not os.path.exists(self.root): - os.makedirs(self.root) - else: - self.root = root - if not os.path.exists(self.root): - if download: - logger.debug( - "dir %s does not exist, will be automatically created", - self.root, - ) - os.makedirs(self.root) - else: - raise ValueError("dir %s does not exist" % self.root) - - self.target_file = os.path.join(self.root, self.raw_file_dir) - - # check existence of target pickle dir, if exists load the - # pickle file no matter what download is set - if os.path.exists(self.target_file): - if train: - self.arrays = self.bytes2array(self.train_batch) - else: - self.arrays = self.bytes2array(self.test_batch) - else: - if download: - self.download() - if train: - self.arrays = self.bytes2array(self.train_batch) - else: - self.arrays = self.bytes2array(self.test_batch) - else: - raise ValueError( - "dir does not contain target file %s, please set download=True" - % (self.target_file) - ) - - def __getitem__(self, index: int) -> Tuple: - return tuple(array[index] for array in self.arrays) - - def __len__(self) -> int: - return len(self.arrays[0]) - - @property - def _default_root(self): - return os.path.join(_default_dataset_root(), self.__class__.__name__) - - @property - def meta(self): - meta_path = os.path.join(self.root, self.raw_file_dir, self.meta_info["name"]) - with open(meta_path, "rb") as f: - meta = pickle.load(f, encoding="bytes") - return meta - - def download(self): - url = self.url_path + self.raw_file_name - load_raw_data_from_url( - url, self.raw_file_name, self.raw_file_md5, self.root, self.timeout - ) - self.process() - - def untar(self, file_path, dirs): - assert file_path.endswith(".tar.gz") - logger.debug("untar file %s to %s", file_path, dirs) - t = tarfile.open(file_path) - t.extractall(path=dirs) - - def bytes2array(self, filenames): - data = [] - label = [] - for filename in filenames: - path = os.path.join(self.root, self.raw_file_dir, filename) - logger.debug("unpickle file %s", path) - with open(path, "rb") as fo: - dic = pickle.load(fo, encoding="bytes") - batch_data = dic[b"data"].reshape(-1, 3, 32, 32).transpose((0, 2, 3, 1)) - data.extend(list(batch_data[..., [2, 1, 0]])) - label.extend(dic[b"labels"]) - label = np.array(label, dtype=np.int32) - return (data, label) - - def process(self): - logger.info("process raw data ...") - self.untar(os.path.join(self.root, self.raw_file_name), self.root) - - -class CIFAR100(CIFAR10): - url_path = "http://www.cs.utoronto.ca/~kriz/" - raw_file_name = "cifar-100-python.tar.gz" - raw_file_md5 = "eb9058c3a382ffc7106e4002c42a8d85" - raw_file_dir = "cifar-100-python" - train_batch = ["train"] - test_batch = ["test"] - meta_info = {"name": "meta"} - - @property - def meta(self): - meta_path = os.path.join(self.root, self.raw_file_dir, self.meta_info["name"]) - with open(meta_path, "rb") as f: - meta = pickle.load(f, encoding="bytes") - return meta - - def bytes2array(self, filenames): - data = [] - fine_label = [] - coarse_label = [] - for filename in filenames: - path = os.path.join(self.root, self.raw_file_dir, filename) - logger.debug("unpickle file %s", path) - with open(path, "rb") as fo: - dic = pickle.load(fo, encoding="bytes") - batch_data = dic[b"data"].reshape(-1, 3, 32, 32).transpose((0, 2, 3, 1)) - data.extend(list(batch_data[..., [2, 1, 0]])) - fine_label.extend(dic[b"fine_labels"]) - coarse_label.extend(dic[b"coarse_labels"]) - fine_label = np.array(fine_label, dtype=np.int32) - coarse_label = np.array(coarse_label, dtype=np.int32) - return data, fine_label, coarse_label diff --git a/python_module/megengine/data/dataset/vision/cityscapes.py b/python_module/megengine/data/dataset/vision/cityscapes.py deleted file mode 100644 index aa05ac92..00000000 --- a/python_module/megengine/data/dataset/vision/cityscapes.py +++ /dev/null @@ -1,151 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# --------------------------------------------------------------------- -# Part of the following code in this file refs to torchvision -# BSD 3-Clause License -# -# Copyright (c) Soumith Chintala 2016, -# All rights reserved. -# --------------------------------------------------------------------- -import json -import os - -import cv2 -import numpy as np - -from .meta_vision import VisionDataset - - -class Cityscapes(VisionDataset): - r"""`Cityscapes `_ Dataset. - """ - - supported_order = ( - "image", - "mask", - "info", - ) - - def __init__(self, root, image_set, mode, *, order=None): - super().__init__(root, order=order, supported_order=self.supported_order) - - city_root = self.root - if not os.path.isdir(city_root): - raise RuntimeError("Dataset not found or corrupted.") - - self.mode = mode - self.images_dir = os.path.join(city_root, "leftImg8bit", image_set) - self.masks_dir = os.path.join(city_root, self.mode, image_set) - self.images, self.masks = [], [] - # self.target_type = ["instance", "semantic", "polygon", "color"] - - # for semantic segmentation - if mode == "gtFine": - valid_modes = ("train", "test", "val") - else: - valid_modes = ("train", "train_extra", "val") - - for city in os.listdir(self.images_dir): - img_dir = os.path.join(self.images_dir, city) - mask_dir = os.path.join(self.masks_dir, city) - for file_name in os.listdir(img_dir): - mask_name = "{}_{}".format( - file_name.split("_leftImg8bit")[0], - self._get_target_suffix(self.mode, "semantic"), - ) - self.images.append(os.path.join(img_dir, file_name)) - self.masks.append(os.path.join(mask_dir, mask_name)) - - def __getitem__(self, index): - target = [] - for k in self.order: - if k == "image": - image = cv2.imread(self.images[index], cv2.IMREAD_COLOR) - target.append(image) - elif k == "mask": - mask = cv2.imread(self.masks[index], cv2.IMREAD_GRAYSCALE) - mask = self._trans_mask(mask) - mask = mask[:, :, np.newaxis] - target.append(mask) - elif k == "info": - if image is None: - image = cv2.imread(self.images[index], cv2.IMREAD_COLOR) - info = [image.shape[0], image.shape[1], self.images[index]] - target.append(info) - else: - raise NotImplementedError - - return tuple(target) - - def __len__(self): - return len(self.images) - - def _trans_mask(self, mask): - trans_labels = [ - 7, - 8, - 11, - 12, - 13, - 17, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 31, - 32, - 33, - ] - label = np.ones(mask.shape) * 255 - for i, tl in enumerate(trans_labels): - label[mask == tl] = i - return label.astype(np.uint8) - - def _get_target_suffix(self, mode, target_type): - if target_type == "instance": - return "{}_instanceIds.png".format(mode) - elif target_type == "semantic": - return "{}_labelIds.png".format(mode) - elif target_type == "color": - return "{}_color.png".format(mode) - else: - return "{}_polygons.json".format(mode) - - def _load_json(self, path): - with open(path, "r") as file: - data = json.load(file) - return data - - class_names = ( - "road", - "sidewalk", - "building", - "wall", - "fence", - "pole", - "traffic light", - "traffic sign", - "vegetation", - "terrain", - "sky", - "person", - "rider", - "car", - "truck", - "bus", - "train", - "motorcycle", - "bicycle", - ) diff --git a/python_module/megengine/data/dataset/vision/coco.py b/python_module/megengine/data/dataset/vision/coco.py deleted file mode 100644 index 11366de0..00000000 --- a/python_module/megengine/data/dataset/vision/coco.py +++ /dev/null @@ -1,366 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# --------------------------------------------------------------------- -# Part of the following code in this file refs to maskrcnn-benchmark -# MIT License -# -# Copyright (c) 2018 Facebook -# --------------------------------------------------------------------- -import json -import os -from collections import defaultdict - -import cv2 -import numpy as np - -from .meta_vision import VisionDataset - -min_keypoints_per_image = 10 - - -def _count_visible_keypoints(anno): - return sum(sum(1 for v in ann["keypoints"][2::3] if v > 0) for ann in anno) - - -def has_valid_annotation(anno, order): - # if it"s empty, there is no annotation - if len(anno) == 0: - return False - if "boxes" in order or "boxes_category" in order: - if "bbox" not in anno[0]: - return False - if "keypoints" in order: - if "keypoints" not in anno[0]: - return False - # for keypoint detection tasks, only consider valid images those - # containing at least min_keypoints_per_image - if _count_visible_keypoints(anno) < min_keypoints_per_image: - return False - return True - - -class COCO(VisionDataset): - r"""`MS COCO `_ Dataset. - """ - - supported_order = ( - "image", - "boxes", - "boxes_category", - "keypoints", - # TODO: need to check - # "polygons", - "info", - ) - - def __init__( - self, root, ann_file, remove_images_without_annotations=False, *, order=None - ): - super().__init__(root, order=order, supported_order=self.supported_order) - - with open(ann_file, "r") as f: - dataset = json.load(f) - - self.imgs = dict() - for img in dataset["images"]: - # for saving memory - if "license" in img: - del img["license"] - if "coco_url" in img: - del img["coco_url"] - if "date_captured" in img: - del img["date_captured"] - if "flickr_url" in img: - del img["flickr_url"] - self.imgs[img["id"]] = img - - self.img_to_anns = defaultdict(list) - for ann in dataset["annotations"]: - # for saving memory - if ( - "boxes" not in self.order - and "boxes_category" not in self.order - and "bbox" in ann - ): - del ann["bbox"] - if "polygons" not in self.order and "segmentation" in ann: - del ann["segmentation"] - self.img_to_anns[ann["image_id"]].append(ann) - - self.cats = dict() - for cat in dataset["categories"]: - self.cats[cat["id"]] = cat - - self.ids = list(sorted(self.imgs.keys())) - - # filter images without detection annotations - if remove_images_without_annotations: - ids = [] - for img_id in self.ids: - anno = self.img_to_anns[img_id] - # filter crowd annotations - anno = [obj for obj in anno if obj["iscrowd"] == 0] - anno = [ - obj for obj in anno if obj["bbox"][2] > 0 and obj["bbox"][3] > 0 - ] - if has_valid_annotation(anno, order): - ids.append(img_id) - self.img_to_anns[img_id] = anno - else: - del self.imgs[img_id] - del self.img_to_anns[img_id] - self.ids = ids - - self.json_category_id_to_contiguous_id = { - v: i + 1 for i, v in enumerate(sorted(self.cats.keys())) - } - - self.contiguous_category_id_to_json_id = { - v: k for k, v in self.json_category_id_to_contiguous_id.items() - } - - def __getitem__(self, index): - img_id = self.ids[index] - anno = self.img_to_anns[img_id] - - target = [] - for k in self.order: - if k == "image": - file_name = self.imgs[img_id]["file_name"] - path = os.path.join(self.root, file_name) - image = cv2.imread(path, cv2.IMREAD_COLOR) - target.append(image) - elif k == "boxes": - boxes = [obj["bbox"] for obj in anno] - boxes = np.array(boxes, dtype=np.float32).reshape(-1, 4) - # transfer boxes from xywh to xyxy - boxes[:, 2:] += boxes[:, :2] - target.append(boxes) - elif k == "boxes_category": - boxes_category = [obj["category_id"] for obj in anno] - boxes_category = [ - self.json_category_id_to_contiguous_id[c] for c in boxes_category - ] - boxes_category = np.array(boxes_category, dtype=np.int32) - target.append(boxes_category) - elif k == "keypoints": - keypoints = [obj["keypoints"] for obj in anno] - keypoints = np.array(keypoints, dtype=np.float32).reshape( - -1, len(self.keypoint_names), 3 - ) - target.append(keypoints) - elif k == "polygons": - polygons = [obj["segmentation"] for obj in anno] - polygons = [ - [np.array(p, dtype=np.float32).reshape(-1, 2) for p in ps] - for ps in polygons - ] - target.append(polygons) - elif k == "info": - info = self.imgs[img_id] - info = [info["height"], info["width"], info["file_name"]] - target.append(info) - else: - raise NotImplementedError - - return tuple(target) - - def __len__(self): - return len(self.ids) - - def get_img_info(self, index): - img_id = self.ids[index] - img_info = self.imgs[img_id] - return img_info - - class_names = ( - "person", - "bicycle", - "car", - "motorcycle", - "airplane", - "bus", - "train", - "truck", - "boat", - "traffic light", - "fire hydrant", - "stop sign", - "parking meter", - "bench", - "bird", - "cat", - "dog", - "horse", - "sheep", - "cow", - "elephant", - "bear", - "zebra", - "giraffe", - "backpack", - "umbrella", - "handbag", - "tie", - "suitcase", - "frisbee", - "skis", - "snowboard", - "sports ball", - "kite", - "baseball bat", - "baseball glove", - "skateboard", - "surfboard", - "tennis racket", - "bottle", - "wine glass", - "cup", - "fork", - "knife", - "spoon", - "bowl", - "banana", - "apple", - "sandwich", - "orange", - "broccoli", - "carrot", - "hot dog", - "pizza", - "donut", - "cake", - "chair", - "couch", - "potted plant", - "bed", - "dining table", - "toilet", - "tv", - "laptop", - "mouse", - "remote", - "keyboard", - "cell phone", - "microwave", - "oven", - "toaster", - "sink", - "refrigerator", - "book", - "clock", - "vase", - "scissors", - "teddy bear", - "hair drier", - "toothbrush", - ) - - classes_originID = { - "person": 1, - "bicycle": 2, - "car": 3, - "motorcycle": 4, - "airplane": 5, - "bus": 6, - "train": 7, - "truck": 8, - "boat": 9, - "traffic light": 10, - "fire hydrant": 11, - "stop sign": 13, - "parking meter": 14, - "bench": 15, - "bird": 16, - "cat": 17, - "dog": 18, - "horse": 19, - "sheep": 20, - "cow": 21, - "elephant": 22, - "bear": 23, - "zebra": 24, - "giraffe": 25, - "backpack": 27, - "umbrella": 28, - "handbag": 31, - "tie": 32, - "suitcase": 33, - "frisbee": 34, - "skis": 35, - "snowboard": 36, - "sports ball": 37, - "kite": 38, - "baseball bat": 39, - "baseball glove": 40, - "skateboard": 41, - "surfboard": 42, - "tennis racket": 43, - "bottle": 44, - "wine glass": 46, - "cup": 47, - "fork": 48, - "knife": 49, - "spoon": 50, - "bowl": 51, - "banana": 52, - "apple": 53, - "sandwich": 54, - "orange": 55, - "broccoli": 56, - "carrot": 57, - "hot dog": 58, - "pizza": 59, - "donut": 60, - "cake": 61, - "chair": 62, - "couch": 63, - "potted plant": 64, - "bed": 65, - "dining table": 67, - "toilet": 70, - "tv": 72, - "laptop": 73, - "mouse": 74, - "remote": 75, - "keyboard": 76, - "cell phone": 77, - "microwave": 78, - "oven": 79, - "toaster": 80, - "sink": 81, - "refrigerator": 82, - "book": 84, - "clock": 85, - "vase": 86, - "scissors": 87, - "teddy bear": 88, - "hair drier": 89, - "toothbrush": 90, - } - - keypoint_names = ( - "nose", - "left_eye", - "right_eye", - "left_ear", - "right_ear", - "left_shoulder", - "right_shoulder", - "left_elbow", - "right_elbow", - "left_wrist", - "right_wrist", - "left_hip", - "right_hip", - "left_knee", - "right_knee", - "left_ankle", - "right_ankle", - ) diff --git a/python_module/megengine/data/dataset/vision/folder.py b/python_module/megengine/data/dataset/vision/folder.py deleted file mode 100644 index 7124ef56..00000000 --- a/python_module/megengine/data/dataset/vision/folder.py +++ /dev/null @@ -1,90 +0,0 @@ -# -*- coding: utf-8 -*- -# BSD 3-Clause License - -# Copyright (c) Soumith Chintala 2016, -# All rights reserved. -# --------------------------------------------------------------------- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# -# This file has been modified by Megvii ("Megvii Modifications"). -# All Megvii Modifications are Copyright (C) 2014-2020 Megvii Inc. All rights reserved. -# --------------------------------------------------------------------- -import os -from typing import Dict, List, Tuple - -import cv2 -import numpy as np - -from .meta_vision import VisionDataset -from .utils import is_img - - -class ImageFolder(VisionDataset): - def __init__(self, root: str, check_valid_func=None, class_name: bool = False): - r""" - ImageFolder is a class for loading image data and labels from a organized folder. - - the folder is expected to be organized as followed - root/cls/xxx.img_ext - - labels are indices of sorted classes in the root directory - - :param root: root directory of an image folder - :param loader: a function used to load image from path, - if ``None``, default function that loads - images with PILwill be called - :param check_valid_func: a function used to check if files in folder are - expected image files, if ``None``, default function - that checks file extensions will be called - :param class_name: if ``True``, return class name instead of class index - - """ - super().__init__(root, order=("image", "image_category")) - - self.root = root - - if check_valid_func is not None: - self.check_valid = check_valid_func - else: - self.check_valid = is_img - - self.class_name = class_name - - self.class_dict = self.collect_class() - self.samples = self.collect_samples() - - def collect_samples(self) -> List: - samples = [] - directory = os.path.expanduser(self.root) - for key in sorted(self.class_dict.keys()): - d = os.path.join(directory, key) - if not os.path.isdir(d): - continue - for r, _, filename in sorted(os.walk(d, followlinks=True)): - for name in sorted(filename): - path = os.path.join(r, name) - if self.check_valid(path): - if self.class_name: - samples.append((path, key)) - else: - samples.append((path, self.class_dict[key])) - return samples - - def collect_class(self) -> Dict: - classes = [d.name for d in os.scandir(self.root) if d.is_dir()] - classes.sort() - return {classes[i]: np.int32(i) for i in range(len(classes))} - - def __getitem__(self, index: int) -> Tuple: - path, label = self.samples[index] - img = cv2.imread(path, cv2.IMREAD_COLOR) - return img, label - - def __len__(self): - return len(self.samples) diff --git a/python_module/megengine/data/dataset/vision/imagenet.py b/python_module/megengine/data/dataset/vision/imagenet.py deleted file mode 100644 index 449401bf..00000000 --- a/python_module/megengine/data/dataset/vision/imagenet.py +++ /dev/null @@ -1,248 +0,0 @@ -# -*- coding: utf-8 -*- -# BSD 3-Clause License -# -# Copyright (c) Soumith Chintala 2016, -# All rights reserved. -# --------------------------------------------------------------------- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# -# This file has been modified by Megvii ("Megvii Modifications"). -# All Megvii Modifications are Copyright (C) 2014-2020 Megvii Inc. All rights reserved. -# --------------------------------------------------------------------- -import os -import shutil - -from tqdm import tqdm - -from ....core.serialization import load, save -from ....distributed.util import is_distributed -from ....logger import get_logger -from .folder import ImageFolder -from .utils import _default_dataset_root, calculate_md5, untar, untargz - -logger = get_logger(__name__) - - -class ImageNet(ImageFolder): - r""" - Load ImageNet from raw files or folder, expected folder looks like - - .. code-block:: bash - - ${root}/ - | [REQUIRED TAR FILES] - |- ILSVRC2012_img_train.tar - |- ILSVRC2012_img_val.tar - |- ILSVRC2012_devkit_t12.tar.gz - | [OPTIONAL IMAGE FOLDERS] - |- train/cls/xxx.${img_ext} - |- val/cls/xxx.${img_ext} - |- ILSVRC2012_devkit_t12/data/meta.mat - |- ILSVRC2012_devkit_t12/data/ILSVRC2012_validation_ground_truth.txt - - If the image folders don't exist, raw tar files are required to get extracted and processed. - """ - - raw_file_meta = { - "train": ("ILSVRC2012_img_train.tar", "1d675b47d978889d74fa0da5fadfb00e"), - "val": ("ILSVRC2012_img_val.tar", "29b22e2961454d5413ddabcf34fc5622"), - "devkit": ("ILSVRC2012_devkit_t12.tar.gz", "fa75699e90414af021442c21a62c3abf"), - } # ImageNet raw files - default_train_dir = "train" - default_val_dir = "val" - default_devkit_dir = "ILSVRC2012_devkit_t12" - - def __init__(self, root: str = None, train: bool = True, **kwargs): - r""" - initialization: - - * if ``root`` contains ``self.target_folder`` depent on ``train``: - - * initialize ImageFolder with target_folder - - * else: - - * if all raw files are in ``root``: - - * parse ``self.target_folder`` from raw files - * initialize ImageFolder with ``self.target_folder`` - - * else: - - * raise error - - :param root: root directory of imagenet data, if root is ``None``, used default_dataset_root - :param train: if ``True``, load the train split, otherwise load the validation split - """ - - # process the root path - if root is None: - self.root = self._default_root - else: - self.root = root - - if not os.path.exists(self.root): - raise FileNotFoundError("dir %s does not exist" % self.root) - - self.devkit_dir = os.path.join(self.root, self.default_devkit_dir) - - if not os.path.exists(self.devkit_dir): - logger.warning("devkit directory %s does not exists", self.devkit_dir) - self._prepare_devkit() - - self.train = train - - if train: - self.target_folder = os.path.join(self.root, self.default_train_dir) - else: - self.target_folder = os.path.join(self.root, self.default_val_dir) - - if not os.path.exists(self.target_folder): - logger.warning( - "expected image folder %s does not exist, try to load from raw file", - self.target_folder, - ) - if not self.check_raw_file(): - raise FileNotFoundError( - "expected image folder %s does not exist, and raw files do not exist in %s" - % (self.target_folder, self.root) - ) - elif is_distributed(): - raise RuntimeError( - "extracting raw file shouldn't be done in distributed mode, use single process instead" - ) - elif train: - self._prepare_train() - else: - self._prepare_val() - - super().__init__(self.target_folder, **kwargs) - - @property - def _default_root(self): - return os.path.join(_default_dataset_root(), self.__class__.__name__) - - @property - def valid_ground_truth(self): - groud_truth_path = os.path.join( - self.devkit_dir, "data", "ILSVRC2012_validation_ground_truth.txt" - ) - if os.path.exists(groud_truth_path): - with open(groud_truth_path, "r") as f: - val_labels = f.readlines() - return [int(val_label) for val_label in val_labels] - else: - raise FileNotFoundError( - "valid ground truth file %s does not exist" % groud_truth_path - ) - - @property - def meta(self): - try: - return load(os.path.join(self.devkit_dir, "meta.pkl")) - except FileNotFoundError: - import scipy.io - - meta_path = os.path.join(self.devkit_dir, "data", "meta.mat") - if not os.path.exists(meta_path): - raise FileNotFoundError("meta file %s does not exist" % meta_path) - meta = scipy.io.loadmat(meta_path, squeeze_me=True)["synsets"] - nums_children = list(zip(*meta))[4] - meta = [ - meta[idx] - for idx, num_children in enumerate(nums_children) - if num_children == 0 - ] - idcs, wnids, classes = list(zip(*meta))[:3] - classes = [tuple(clss.split(", ")) for clss in classes] - idx_to_wnid = dict(zip(idcs, wnids)) - wnid_to_classes = dict(zip(wnids, classes)) - logger.info( - "saving cached meta file to %s", - os.path.join(self.devkit_dir, "meta.pkl"), - ) - save( - (idx_to_wnid, wnid_to_classes), - os.path.join(self.devkit_dir, "meta.pkl"), - ) - return idx_to_wnid, wnid_to_classes - - def check_raw_file(self) -> bool: - return all( - [ - os.path.exists(os.path.join(self.root, value[0])) - for _, value in self.raw_file_meta.items() - ] - ) - - def _organize_val_data(self): - id2wnid = self.meta[0] - val_idcs = self.valid_ground_truth - val_wnids = [id2wnid[idx] for idx in val_idcs] - - val_images = sorted( - [ - os.path.join(self.target_folder, image) - for image in os.listdir(self.target_folder) - ] - ) - - logger.debug("mkdir for val set wnids") - for wnid in set(val_wnids): - os.makedirs(os.path.join(self.root, self.default_val_dir, wnid)) - - logger.debug("mv val images into wnids dir") - for wnid, img_file in tqdm(zip(val_wnids, val_images)): - shutil.move( - img_file, - os.path.join( - self.root, self.default_val_dir, wnid, os.path.basename(img_file) - ), - ) - - def _prepare_val(self): - assert not self.train - raw_filename, checksum = self.raw_file_meta["val"] - raw_file = os.path.join(self.root, raw_filename) - logger.info("checksum valid tar file %s ...", raw_file) - assert ( - calculate_md5(raw_file) == checksum - ), "checksum mismatch, {} may be damaged".format(raw_file) - logger.info("extract valid tar file... this may take 10-20 minutes") - untar(os.path.join(self.root, raw_file), self.target_folder) - self._organize_val_data() - - def _prepare_train(self): - assert self.train - raw_filename, checksum = self.raw_file_meta["train"] - raw_file = os.path.join(self.root, raw_filename) - logger.info("checksum train tar file %s ...", raw_file) - assert ( - calculate_md5(raw_file) == checksum - ), "checksum mismatch, {} may be damaged".format(raw_file) - logger.info("extract train tar file.. this may take several hours") - untar( - os.path.join(self.root, raw_file), self.target_folder, - ) - paths = [ - os.path.join(self.target_folder, child_dir) - for child_dir in os.listdir(self.target_folder) - ] - for path in tqdm(paths): - untar(path, os.path.splitext(path)[0], remove=True) - - def _prepare_devkit(self): - raw_filename, checksum = self.raw_file_meta["devkit"] - raw_file = os.path.join(self.root, raw_filename) - logger.info("checksum devkit tar file %s ...", raw_file) - assert ( - calculate_md5(raw_file) == checksum - ), "checksum mismatch, {} may be damaged".format(raw_file) - logger.info("extract devkit file..") - untargz(os.path.join(self.root, self.raw_file_meta["devkit"][0])) diff --git a/python_module/megengine/data/dataset/vision/meta_vision.py b/python_module/megengine/data/dataset/vision/meta_vision.py deleted file mode 100644 index 6d03d3ed..00000000 --- a/python_module/megengine/data/dataset/vision/meta_vision.py +++ /dev/null @@ -1,41 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import collections.abc -import os - -from ..meta_dataset import MapDataset - - -class VisionDataset(MapDataset): - _repr_indent = 4 - - def __init__(self, root, *, order=None, supported_order=None): - if isinstance(root, (str, bytes)): - root = os.path.expanduser(root) - self.root = root - - if order is None: - order = ("image",) - if not isinstance(order, collections.abc.Sequence): - raise ValueError( - "order should be a sequence, but got order={}".format(order) - ) - - if supported_order is not None: - assert isinstance(supported_order, collections.abc.Sequence) - for k in order: - if k not in supported_order: - raise NotImplementedError("{} is unsupported data type".format(k)) - self.order = order - - def __getitem__(self, index): - raise NotImplementedError - - def __len__(self): - raise NotImplementedError diff --git a/python_module/megengine/data/dataset/vision/mnist.py b/python_module/megengine/data/dataset/vision/mnist.py deleted file mode 100644 index 5e89a314..00000000 --- a/python_module/megengine/data/dataset/vision/mnist.py +++ /dev/null @@ -1,197 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import gzip -import os -import struct -from typing import Tuple - -import numpy as np -from tqdm import tqdm - -from ....logger import get_logger -from .meta_vision import VisionDataset -from .utils import _default_dataset_root, load_raw_data_from_url - -logger = get_logger(__name__) - - -class MNIST(VisionDataset): - r""" ``Dataset`` for MNIST meta data - """ - - url_path = "http://yann.lecun.com/exdb/mnist/" - """ - url prefix for downloading raw file - """ - raw_file_name = [ - "train-images-idx3-ubyte.gz", - "train-labels-idx1-ubyte.gz", - "t10k-images-idx3-ubyte.gz", - "t10k-labels-idx1-ubyte.gz", - ] - """ - raw file names of both training set and test set (10k) - """ - raw_file_md5 = [ - "f68b3c2dcbeaaa9fbdd348bbdeb94873", - "d53e105ee54ea40749a09fcbcd1e9432", - "9fb629c4189551a2d022fa330f9573f3", - "ec29112dd5afa0611ce80d1b7f02629c", - ] - """ - md5 for checking raw files - """ - - def __init__( - self, - root: str = None, - train: bool = True, - download: bool = True, - timeout: int = 500, - ): - r""" - :param root: path for mnist dataset downloading or loading, if ``None``, - set ``root`` to the ``_default_root`` - :param train: if ``True``, loading trainingset, else loading test set - :param download: if raw files do not exists and download sets to ``True``, - download raw files and process, otherwise raise ValueError, default is True - - """ - super().__init__(root, order=("image", "image_category")) - - self.timeout = timeout - - # process the root path - if root is None: - self.root = self._default_root - if not os.path.exists(self.root): - os.makedirs(self.root) - else: - self.root = root - if not os.path.exists(self.root): - if download: - logger.debug( - "dir %s does not exist, will be automatically created", - self.root, - ) - os.makedirs(self.root) - else: - raise ValueError("dir %s does not exist" % self.root) - - if self._check_raw_files(): - self.process(train) - elif download: - self.download() - self.process(train) - else: - raise ValueError( - "root does not contain valid raw files, please set download=True" - ) - - def __getitem__(self, index: int) -> Tuple: - return tuple(array[index] for array in self.arrays) - - def __len__(self) -> int: - return len(self.arrays[0]) - - @property - def _default_root(self): - return os.path.join(_default_dataset_root(), self.__class__.__name__) - - @property - def meta(self): - return self._meta_data - - def _check_raw_files(self): - return all( - [ - os.path.exists(os.path.join(self.root, path)) - for path in self.raw_file_name - ] - ) - - def download(self): - for file_name, md5 in zip(self.raw_file_name, self.raw_file_md5): - url = self.url_path + file_name - load_raw_data_from_url(url, file_name, md5, self.root, self.timeout) - - def process(self, train): - # load raw files and transform them into meta data and datasets Tuple(np.array) - logger.info("process the raw files of %s set...", "train" if train else "test") - if train: - meta_data_images, images = parse_idx3( - os.path.join(self.root, self.raw_file_name[0]) - ) - meta_data_labels, labels = parse_idx1( - os.path.join(self.root, self.raw_file_name[1]) - ) - else: - meta_data_images, images = parse_idx3( - os.path.join(self.root, self.raw_file_name[2]) - ) - meta_data_labels, labels = parse_idx1( - os.path.join(self.root, self.raw_file_name[3]) - ) - - self._meta_data = { - "images": meta_data_images, - "labels": meta_data_labels, - } - self.arrays = (images, labels.astype(np.int32)) - - -def parse_idx3(idx3_file): - # parse idx3 file to meta data and data in numpy array (images) - logger.debug("parse idx3 file %s ...", idx3_file) - assert idx3_file.endswith(".gz") - with gzip.open(idx3_file, "rb") as f: - bin_data = f.read() - - # parse meta data - offset = 0 - fmt_header = ">iiii" - magic, imgs, height, width = struct.unpack_from(fmt_header, bin_data, offset) - meta_data = {"magic": magic, "imgs": imgs, "height": height, "width": width} - - # parse images - image_size = height * width - offset += struct.calcsize(fmt_header) - fmt_image = ">" + str(image_size) + "B" - images = [] - bar = tqdm(total=meta_data["imgs"], ncols=80) - for image in struct.iter_unpack(fmt_image, bin_data[offset:]): - images.append(np.array(image, dtype=np.uint8).reshape((height, width, 1))) - bar.update() - bar.close() - return meta_data, images - - -def parse_idx1(idx1_file): - # parse idx1 file to meta data and data in numpy array (labels) - logger.debug("parse idx1 file %s ...", idx1_file) - assert idx1_file.endswith(".gz") - with gzip.open(idx1_file, "rb") as f: - bin_data = f.read() - - # parse meta data - offset = 0 - fmt_header = ">ii" - magic, imgs = struct.unpack_from(fmt_header, bin_data, offset) - meta_data = {"magic": magic, "imgs": imgs} - - # parse labels - offset += struct.calcsize(fmt_header) - fmt_image = ">B" - labels = np.empty(imgs, dtype=int) - bar = tqdm(total=meta_data["imgs"], ncols=80) - for i, label in enumerate(struct.iter_unpack(fmt_image, bin_data[offset:])): - labels[i] = label[0] - bar.update() - bar.close() - return meta_data, labels diff --git a/python_module/megengine/data/dataset/vision/objects365.py b/python_module/megengine/data/dataset/vision/objects365.py deleted file mode 100644 index e56e6462..00000000 --- a/python_module/megengine/data/dataset/vision/objects365.py +++ /dev/null @@ -1,498 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# --------------------------------------------------------------------- -# Part of the following code in this file refs to maskrcnn-benchmark -# MIT License -# -# Copyright (c) 2018 Facebook -# --------------------------------------------------------------------- -import json -import os -from collections import defaultdict - -import cv2 -import numpy as np - -from .meta_vision import VisionDataset - - -class Objects365(VisionDataset): - r"""`Objects365 `_ Dataset. - """ - - supported_order = ( - "image", - "boxes", - "boxes_category", - "info", - ) - - def __init__( - self, root, ann_file, remove_images_without_annotations=False, *, order=None - ): - super().__init__(root, order=order, supported_order=self.supported_order) - - with open(ann_file, "r") as f: - dataset = json.load(f) - - self.imgs = dict() - for img in dataset["images"]: - self.imgs[img["id"]] = img - - self.img_to_anns = defaultdict(list) - for ann in dataset["annotations"]: - # for saving memory - if ( - "boxes" not in self.order - and "boxes_category" not in self.order - and "bbox" in ann - ): - del ann["bbox"] - self.img_to_anns[ann["image_id"]].append(ann) - - self.cats = dict() - for cat in dataset["categories"]: - self.cats[cat["id"]] = cat - - self.ids = list(sorted(self.imgs.keys())) - - # filter images without detection annotations - if remove_images_without_annotations: - ids = [] - for img_id in self.ids: - anno = self.img_to_anns[img_id] - # filter crowd annotations - anno = [obj for obj in anno if obj["iscrowd"] == 0] - anno = [ - obj for obj in anno if obj["bbox"][2] > 0 and obj["bbox"][3] > 0 - ] - if len(anno) > 0: - ids.append(img_id) - self.img_to_anns[img_id] = anno - else: - del self.imgs[img_id] - del self.img_to_anns[img_id] - self.ids = ids - - self.json_category_id_to_contiguous_id = { - v: i + 1 for i, v in enumerate(sorted(self.cats.keys())) - } - - self.contiguous_category_id_to_json_id = { - v: k for k, v in self.json_category_id_to_contiguous_id.items() - } - - def __getitem__(self, index): - img_id = self.ids[index] - anno = self.img_to_anns[img_id] - - target = [] - for k in self.order: - if k == "image": - file_name = self.imgs[img_id]["file_name"] - path = os.path.join(self.root, file_name) - image = cv2.imread(path, cv2.IMREAD_COLOR) - target.append(image) - elif k == "boxes": - boxes = [obj["bbox"] for obj in anno] - boxes = np.array(boxes, dtype=np.float32).reshape(-1, 4) - # transfer boxes from xywh to xyxy - boxes[:, 2:] += boxes[:, :2] - target.append(boxes) - elif k == "boxes_category": - boxes_category = [obj["category_id"] for obj in anno] - boxes_category = [ - self.json_category_id_to_contiguous_id[c] for c in boxes_category - ] - boxes_category = np.array(boxes_category, dtype=np.int32) - target.append(boxes_category) - elif k == "info": - info = self.imgs[img_id] - info = [info["height"], info["width"], info["file_name"]] - target.append(info) - else: - raise NotImplementedError - - return tuple(target) - - def __len__(self): - return len(self.ids) - - def get_img_info(self, index): - img_id = self.ids[index] - img_info = self.imgs[img_id] - return img_info - - class_names = ( - "person", - "sneakers", - "chair", - "hat", - "lamp", - "bottle", - "cabinet/shelf", - "cup", - "car", - "glasses", - "picture/frame", - "desk", - "handbag", - "street lights", - "book", - "plate", - "helmet", - "leather shoes", - "pillow", - "glove", - "potted plant", - "bracelet", - "flower", - "tv", - "storage box", - "vase", - "bench", - "wine glass", - "boots", - "bowl", - "dining table", - "umbrella", - "boat", - "flag", - "speaker", - "trash bin/can", - "stool", - "backpack", - "couch", - "belt", - "carpet", - "basket", - "towel/napkin", - "slippers", - "barrel/bucket", - "coffee table", - "suv", - "toy", - "tie", - "bed", - "traffic light", - "pen/pencil", - "microphone", - "sandals", - "canned", - "necklace", - "mirror", - "faucet", - "bicycle", - "bread", - "high heels", - "ring", - "van", - "watch", - "sink", - "horse", - "fish", - "apple", - "camera", - "candle", - "teddy bear", - "cake", - "motorcycle", - "wild bird", - "laptop", - "knife", - "traffic sign", - "cell phone", - "paddle", - "truck", - "cow", - "power outlet", - "clock", - "drum", - "fork", - "bus", - "hanger", - "nightstand", - "pot/pan", - "sheep", - "guitar", - "traffic cone", - "tea pot", - "keyboard", - "tripod", - "hockey", - "fan", - "dog", - "spoon", - "blackboard/whiteboard", - "balloon", - "air conditioner", - "cymbal", - "mouse", - "telephone", - "pickup truck", - "orange", - "banana", - "airplane", - "luggage", - "skis", - "soccer", - "trolley", - "oven", - "remote", - "baseball glove", - "paper towel", - "refrigerator", - "train", - "tomato", - "machinery vehicle", - "tent", - "shampoo/shower gel", - "head phone", - "lantern", - "donut", - "cleaning products", - "sailboat", - "tangerine", - "pizza", - "kite", - "computer box", - "elephant", - "toiletries", - "gas stove", - "broccoli", - "toilet", - "stroller", - "shovel", - "baseball bat", - "microwave", - "skateboard", - "surfboard", - "surveillance camera", - "gun", - "life saver", - "cat", - "lemon", - "liquid soap", - "zebra", - "duck", - "sports car", - "giraffe", - "pumpkin", - "piano", - "stop sign", - "radiator", - "converter", - "tissue ", - "carrot", - "washing machine", - "vent", - "cookies", - "cutting/chopping board", - "tennis racket", - "candy", - "skating and skiing shoes", - "scissors", - "folder", - "baseball", - "strawberry", - "bow tie", - "pigeon", - "pepper", - "coffee machine", - "bathtub", - "snowboard", - "suitcase", - "grapes", - "ladder", - "pear", - "american football", - "basketball", - "potato", - "paint brush", - "printer", - "billiards", - "fire hydrant", - "goose", - "projector", - "sausage", - "fire extinguisher", - "extension cord", - "facial mask", - "tennis ball", - "chopsticks", - "electronic stove and gas stove", - "pie", - "frisbee", - "kettle", - "hamburger", - "golf club", - "cucumber", - "clutch", - "blender", - "tong", - "slide", - "hot dog", - "toothbrush", - "facial cleanser", - "mango", - "deer", - "egg", - "violin", - "marker", - "ship", - "chicken", - "onion", - "ice cream", - "tape", - "wheelchair", - "plum", - "bar soap", - "scale", - "watermelon", - "cabbage", - "router/modem", - "golf ball", - "pine apple", - "crane", - "fire truck", - "peach", - "cello", - "notepaper", - "tricycle", - "toaster", - "helicopter", - "green beans", - "brush", - "carriage", - "cigar", - "earphone", - "penguin", - "hurdle", - "swing", - "radio", - "CD", - "parking meter", - "swan", - "garlic", - "french fries", - "horn", - "avocado", - "saxophone", - "trumpet", - "sandwich", - "cue", - "kiwi fruit", - "bear", - "fishing rod", - "cherry", - "tablet", - "green vegetables", - "nuts", - "corn", - "key", - "screwdriver", - "globe", - "broom", - "pliers", - "volleyball", - "hammer", - "eggplant", - "trophy", - "dates", - "board eraser", - "rice", - "tape measure/ruler", - "dumbbell", - "hamimelon", - "stapler", - "camel", - "lettuce", - "goldfish", - "meat balls", - "medal", - "toothpaste", - "antelope", - "shrimp", - "rickshaw", - "trombone", - "pomegranate", - "coconut", - "jellyfish", - "mushroom", - "calculator", - "treadmill", - "butterfly", - "egg tart", - "cheese", - "pig", - "pomelo", - "race car", - "rice cooker", - "tuba", - "crosswalk sign", - "papaya", - "hair drier", - "green onion", - "chips", - "dolphin", - "sushi", - "urinal", - "donkey", - "electric drill", - "spring rolls", - "tortoise/turtle", - "parrot", - "flute", - "measuring cup", - "shark", - "steak", - "poker card", - "binoculars", - "llama", - "radish", - "noodles", - "yak", - "mop", - "crab", - "microscope", - "barbell", - "bread/bun", - "baozi", - "lion", - "red cabbage", - "polar bear", - "lighter", - "seal", - "mangosteen", - "comb", - "eraser", - "pitaya", - "scallop", - "pencil case", - "saw", - "table tennis paddle", - "okra", - "starfish", - "eagle", - "monkey", - "durian", - "game board", - "rabbit", - "french horn", - "ambulance", - "asparagus", - "hoverboard", - "pasta", - "target", - "hotair balloon", - "chainsaw", - "lobster", - "iron", - "flashlight", - ) diff --git a/python_module/megengine/data/dataset/vision/utils.py b/python_module/megengine/data/dataset/vision/utils.py deleted file mode 100644 index de6657d7..00000000 --- a/python_module/megengine/data/dataset/vision/utils.py +++ /dev/null @@ -1,89 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import hashlib -import os -import tarfile - -from ....distributed.util import is_distributed -from ....logger import get_logger -from ....utils.http_download import download_from_url - -IMG_EXT = (".jpg", ".png", ".jpeg", ".ppm", ".bmp", ".pgm", ".tif", ".tiff", ".webp") - -logger = get_logger(__name__) - - -def _default_dataset_root(): - default_dataset_root = os.path.expanduser( - os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "megengine") - ) - - return default_dataset_root - - -def load_raw_data_from_url( - url: str, filename: str, target_md5: str, raw_data_dir: str, timeout: int -): - cached_file = os.path.join(raw_data_dir, filename) - logger.debug( - "load_raw_data_from_url: downloading to or using cached %s ...", cached_file - ) - if not os.path.exists(cached_file): - if is_distributed(): - logger.warning( - "Downloading raw data in DISTRIBUTED mode\n" - " File may be downloaded multiple times. We recommend\n" - " users to download in single process first." - ) - md5 = download_from_url(url, cached_file, http_read_timeout=timeout) - else: - md5 = calculate_md5(cached_file) - if target_md5 == md5: - logger.debug("%s exists with correct md5: %s", filename, target_md5) - else: - os.remove(cached_file) - raise RuntimeError("{} exists but fail to match md5".format(filename)) - - -def calculate_md5(filename): - m = hashlib.md5() - with open(filename, "rb") as f: - while True: - data = f.read(4096) - if not data: - break - m.update(data) - return m.hexdigest() - - -def is_img(filename): - return filename.lower().endswith(IMG_EXT) - - -def untar(path, to=None, remove=False): - if to is None: - to = os.path.dirname(path) - with tarfile.open(path, "r") as tar: - tar.extractall(path=to) - - if remove: - os.remove(path) - - -def untargz(path, to=None, remove=False): - if path.endswith(".tar.gz"): - if to is None: - to = os.path.dirname(path) - with tarfile.open(path, "r:gz") as tar: - tar.extractall(path=to) - else: - raise ValueError("path %s does not end with .tar" % path) - - if remove: - os.remove(path) diff --git a/python_module/megengine/data/dataset/vision/voc.py b/python_module/megengine/data/dataset/vision/voc.py deleted file mode 100644 index b22fd2fa..00000000 --- a/python_module/megengine/data/dataset/vision/voc.py +++ /dev/null @@ -1,185 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# --------------------------------------------------------------------- -# Part of the following code in this file refs to torchvision -# BSD 3-Clause License -# -# Copyright (c) Soumith Chintala 2016, -# All rights reserved. -# --------------------------------------------------------------------- -import collections.abc -import os -import xml.etree.ElementTree as ET - -import cv2 -import numpy as np - -from .meta_vision import VisionDataset - - -class PascalVOC(VisionDataset): - r"""`Pascal VOC `_ Dataset. - """ - - supported_order = ( - "image", - "boxes", - "boxes_category", - "mask", - "info", - ) - - def __init__(self, root, image_set, *, order=None): - if ("boxes" in order or "boxes_category" in order) and "mask" in order: - raise ValueError( - "PascalVOC only supports boxes & boxes_category or mask, not both." - ) - - super().__init__(root, order=order, supported_order=self.supported_order) - - if not os.path.isdir(self.root): - raise RuntimeError("Dataset not found or corrupted.") - - self.image_set = image_set - image_dir = os.path.join(self.root, "JPEGImages") - - if "boxes" in order or "boxes_category" in order: - annotation_dir = os.path.join(self.root, "Annotations") - splitdet_dir = os.path.join(self.root, "ImageSets/Main") - split_f = os.path.join(splitdet_dir, image_set.rstrip("\n") + ".txt") - with open(os.path.join(split_f), "r") as f: - self.file_names = [x.strip() for x in f.readlines()] - self.images = [os.path.join(image_dir, x + ".jpg") for x in self.file_names] - self.annotations = [ - os.path.join(annotation_dir, x + ".xml") for x in self.file_names - ] - assert len(self.images) == len(self.annotations) - elif "mask" in order: - if "aug" in image_set: - mask_dir = os.path.join(self.root, "SegmentationClass_aug") - else: - mask_dir = os.path.join(self.root, "SegmentationClass") - splitmask_dir = os.path.join(self.root, "ImageSets/Segmentation") - split_f = os.path.join(splitmask_dir, image_set.rstrip("\n") + ".txt") - with open(os.path.join(split_f), "r") as f: - self.file_names = [x.strip() for x in f.readlines()] - self.images = [os.path.join(image_dir, x + ".jpg") for x in self.file_names] - self.masks = [os.path.join(mask_dir, x + ".png") for x in self.file_names] - assert len(self.images) == len(self.masks) - else: - raise NotImplementedError - - self.img_infos = dict() - - def __getitem__(self, index): - target = [] - for k in self.order: - if k == "image": - image = cv2.imread(self.images[index], cv2.IMREAD_COLOR) - target.append(image) - elif k == "boxes": - anno = self.parse_voc_xml(ET.parse(self.annotations[index]).getroot()) - boxes = [obj["bndbox"] for obj in anno["annotation"]["object"]] - # boxes type xyxy - boxes = [ - (bb["xmin"], bb["ymin"], bb["xmax"], bb["ymax"]) for bb in boxes - ] - boxes = np.array(boxes, dtype=np.float32).reshape(-1, 4) - target.append(boxes) - elif k == "boxes_category": - anno = self.parse_voc_xml(ET.parse(self.annotations[index]).getroot()) - boxes_category = [obj["name"] for obj in anno["annotation"]["object"]] - boxes_category = [ - self.class_names.index(bc) + 1 for bc in boxes_category - ] - boxes_category = np.array(boxes_category, dtype=np.int32) - target.append(boxes_category) - elif k == "mask": - if "aug" in self.image_set: - mask = cv2.imread(self.masks[index], cv2.IMREAD_GRAYSCALE) - else: - mask = cv2.imread(self.masks[index], cv2.IMREAD_COLOR) - mask = self._trans_mask(mask) - mask = mask[:, :, np.newaxis] - target.append(mask) - elif k == "info": - info = self.get_img_info(index, image) - info = [info["height"], info["width"], info["file_name"]] - target.append(info) - else: - raise NotImplementedError - - return tuple(target) - - def __len__(self): - return len(self.images) - - def get_img_info(self, index, image=None): - if index not in self.img_infos: - if image is None: - image = cv2.imread(self.images[index], cv2.IMREAD_COLOR) - self.img_infos[index] = dict( - height=image.shape[0], - width=image.shape[1], - file_name=self.file_names[index], - ) - return self.img_infos[index] - - def _trans_mask(self, mask): - label = np.ones(mask.shape[:2]) * 255 - for i in range(len(self.class_colors)): - b, g, r = self.class_colors[i] - label[ - (mask[:, :, 0] == b) & (mask[:, :, 1] == g) & (mask[:, :, 2] == r) - ] = i - return label.astype(np.uint8) - - def parse_voc_xml(self, node): - voc_dict = {} - children = list(node) - if children: - def_dic = collections.defaultdict(list) - for dc in map(self.parse_voc_xml, children): - for ind, v in dc.items(): - def_dic[ind].append(v) - if node.tag == "annotation": - def_dic["object"] = [def_dic["object"]] - voc_dict = { - node.tag: { - ind: v[0] if len(v) == 1 else v for ind, v in def_dic.items() - } - } - if node.text: - text = node.text.strip() - if not children: - voc_dict[node.tag] = text - return voc_dict - - class_names = ( - "aeroplane", - "bicycle", - "bird", - "boat", - "bottle", - "bus", - "car", - "cat", - "chair", - "cow", - "diningtable", - "dog", - "horse", - "motorbike", - "person", - "pottedplant", - "sheep", - "sofa", - "train", - "tvmonitor", - ) diff --git a/python_module/megengine/data/sampler.py b/python_module/megengine/data/sampler.py deleted file mode 100644 index dbd5d3a3..00000000 --- a/python_module/megengine/data/sampler.py +++ /dev/null @@ -1,274 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import collections.abc -import math -from abc import ABC -from typing import Any, Generator, Iterator, List, Union - -import numpy as np - -import megengine.distributed as dist - - -class Sampler(ABC): - def __init__( - self, - dataset, - batch_size=1, - drop_last=False, - num_samples=None, - world_size=None, - rank=None, - seed=None, - ): - r""" - An abstract class for all sampler - - :type dataset: `dataset` - :param dataset: dataset to sample from - :type batch_size: positive integer - :param batch_size: batch size for batch method - :type drop_last: bool - :param drop_last: set ``True`` to drop the last incomplete batch, - if the dataset size is not divisible by the batch size. If ``False`` and - the size of dataset is not divisible by the batch_size, then the last batch will - be smaller. (default: ``False``) - :type num_samples: positive integer - :param num_samples: number of samples assigned to one rank - :type world_size: positive integer - :param world_size: number of ranks - :type rank: non-negative integer within 0 and world_size - :param rank: rank id, non-negative interger within 0 and ``world_size`` - :type seed: non-negative integer - :param seed: seed for random operators - """ - if ( - not isinstance(batch_size, int) - or isinstance(batch_size, bool) - or batch_size <= 0 - ): - raise ValueError( - "batch_size should be a positive integer value, " - "but got batch_size={}".format(batch_size) - ) - if not isinstance(drop_last, bool): - raise ValueError( - "drop_last should be a boolean value, but got " - "drop_last={}".format(drop_last) - ) - if num_samples is not None and ( - not isinstance(num_samples, int) - or isinstance(num_samples, bool) - or num_samples <= 0 - ): - raise ValueError( - "num_samples should be a positive integer " - "value, but got num_samples={}".format(num_samples) - ) - - self.batch_size = batch_size - self.dataset = dataset - self.drop_last = drop_last - - if world_size is None: - world_size = dist.get_world_size() if dist.is_distributed() else 1 - self.world_size = world_size - if rank is None: - rank = dist.get_rank() if dist.is_distributed() else 0 - self.rank = rank - - if num_samples is None: - num_samples = len(self.dataset) - self.num_samples = int(math.ceil(num_samples / self.world_size)) - - # Make sure seeds are the same at each rank - if seed is None and self.world_size > 1: - seed = 0 - self.rng = np.random.RandomState(seed) - - def __iter__(self) -> Union[Generator, Iterator]: - return self.batch() - - def __len__(self) -> int: - if self.drop_last: - return self.num_samples // self.batch_size - else: - return int(math.ceil(self.num_samples / self.batch_size)) - - def sample(self): - """ - return a list contains all sample indices - """ - raise NotImplementedError - - def scatter(self, indices) -> List: - r""" - scatter method is used for splitting indices into subset, each subset - will be assigned to a rank. Indices are evenly splitted by default. - If customized indices assignment method is needed, please rewrite this method - """ - total_size = self.num_samples * self.world_size - - # add extra indices to make it evenly divisible - indices += indices[: (total_size - len(indices))] - assert len(indices) == total_size - - # subsample - indices = indices[self.rank : total_size : self.world_size] - assert len(indices) == self.num_samples - - return indices - - def batch(self) -> Iterator[List[Any]]: - r""" - batch method provides a batch indices generator - """ - indices = list(self.sample()) - - # user might pass the world_size parameter without dist, - # so dist.is_distributed() should not be used - if self.world_size > 1: - indices = self.scatter(indices) - - step, length = self.batch_size, len(indices) - batch_index = [indices[i : i + step] for i in range(0, length, step)] - - if self.drop_last and len(batch_index[-1]) < self.batch_size: - batch_index.pop() - - return iter(batch_index) - - -class SequentialSampler(Sampler): - def __init__( - self, - dataset, - batch_size=1, - drop_last=False, - indices=None, - world_size=None, - rank=None, - ): - r""" - Sample elements sequentially - """ - super().__init__(dataset, batch_size, drop_last, None, world_size, rank) - if indices is not None and not isinstance(indices, collections.abc.Sequence): - raise ValueError( - "indices should be None or a sequence, " - "but got indices={}".format(indices) - ) - self.indices = indices - - def sample(self) -> Iterator[Any]: - r""" - return a generator - """ - if self.indices is None: - return iter(range(len(self.dataset))) - else: - return self.indices - - -class RandomSampler(Sampler): - def __init__( - self, - dataset, - batch_size=1, - drop_last=False, - indices=None, - world_size=None, - rank=None, - seed=None, - ): - r""" - Sample elements randomly without replacement - """ - super().__init__(dataset, batch_size, drop_last, None, world_size, rank, seed) - if indices is not None and not isinstance(indices, collections.abc.Sequence): - raise ValueError( - "indices should be None or a sequence, " - "but got indices={}".format(indices) - ) - self.indices = indices - - def sample(self) -> List: - if self.indices is None: - return self.rng.permutation(len(self.dataset)).tolist() - else: - return self.rng.permutation(self.indices).tolist() - - -class ReplacementSampler(Sampler): - def __init__( - self, - dataset, - batch_size=1, - drop_last=False, - num_samples=None, - weights=None, - world_size=None, - rank=None, - seed=None, - ): - r""" - Sample elements randomly with replacement - - :type weights: List - :param weights: weights for sampling indices, it could be unnormalized weights - """ - super().__init__( - dataset, batch_size, drop_last, num_samples, world_size, rank, seed - ) - if weights is not None: - if not isinstance(weights, collections.abc.Sequence): - raise ValueError( - "weights should be None or a sequence, " - "but got weights={}".format(weights) - ) - if len(weights) != len(dataset): - raise ValueError( - "len(dataset)={} should be equal to" - "len(weights)={}".format(len(dataset), len(weights)) - ) - self.weights = weights - if self.weights is not None: - self.weights = np.array(weights) / sum(weights) - - def sample(self) -> List: - n = len(self.dataset) - if self.weights is None: - return self.rng.randint(n, size=self.num_samples).tolist() - else: - return self.rng.multinomial(n, self.weights, self.num_samples).tolist() - - -class Infinite(Sampler): - r"""Infinite Sampler warper for basic sampler""" - - def sample(self): - raise NotImplementedError("sample method not supported in Infinite") - - def __init__(self, sampler): - self.sampler = sampler - self.sampler_iter = iter(self.sampler) - - def __iter__(self): - return self - - def __next__(self): - try: - index = next(self.sampler_iter) - except StopIteration: - self.sampler_iter = iter(self.sampler) - index = next(self.sampler_iter) - return index - - def __len__(self): - return np.iinfo(np.int64).max diff --git a/python_module/megengine/data/transform/__init__.py b/python_module/megengine/data/transform/__init__.py deleted file mode 100644 index 30424cbc..00000000 --- a/python_module/megengine/data/transform/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .meta_transform import PseudoTransform, Transform -from .vision import * diff --git a/python_module/megengine/data/transform/meta_transform.py b/python_module/megengine/data/transform/meta_transform.py deleted file mode 100644 index d7fd4f47..00000000 --- a/python_module/megengine/data/transform/meta_transform.py +++ /dev/null @@ -1,31 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from abc import ABC, abstractmethod -from typing import Sequence, Tuple - - -class Transform(ABC): - """ - rewrite apply method in subclass - """ - - def apply_batch(self, inputs: Sequence[Tuple]): - return tuple(self.apply(input) for input in inputs) - - @abstractmethod - def apply(self, input: Tuple): - pass - - def __repr__(self): - return self.__class__.__name__ - - -class PseudoTransform(Transform): - def apply(self, input: Tuple): - return input diff --git a/python_module/megengine/data/transform/vision/__init__.py b/python_module/megengine/data/transform/vision/__init__.py deleted file mode 100644 index d90c9e98..00000000 --- a/python_module/megengine/data/transform/vision/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .transform import * diff --git a/python_module/megengine/data/transform/vision/functional.py b/python_module/megengine/data/transform/vision/functional.py deleted file mode 100644 index e2f4e512..00000000 --- a/python_module/megengine/data/transform/vision/functional.py +++ /dev/null @@ -1,111 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import collections.abc -import functools -import random - -import cv2 -import numpy as np - - -def wrap_keepdims(func): - """Wraper to keep the dimension of input images unchanged""" - - @functools.wraps(func) - def wrapper(image, *args, **kwargs): - if len(image.shape) != 3: - raise ValueError( - "image must have 3 dims, but got {} dims".format(len(image.shape)) - ) - ret = func(image, *args, **kwargs) - if len(ret.shape) == 2: - ret = ret[:, :, np.newaxis] - return ret - - return wrapper - - -@wrap_keepdims -def to_gray(image): - r""" - Change BGR format image's color space to gray - - :param image: Input BGR format image, with (H, W, C) shape - :return: Gray format image, with (H, W, C) shape - """ - return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) - - -@wrap_keepdims -def to_bgr(image): - r""" - Change gray format image's color space to BGR - - :param image: input Gray format image, with (H, W, C) shape - :return: BGR format image, with (H, W, C) shape - """ - return cv2.cvtColor(image, cv2.COLOR_GRAY2BGR) - - -@wrap_keepdims -def pad(input, size, value): - r""" - Pad input data with *value* and given *size* - - :param input: Input data, with (H, W, C) shape - :param size: Padding size of input data, it could be integer or sequence. - If it's an integer, the input data will be padded in four directions. - If it's a sequence contains two integer, the bottom and right side - of input data will be padded. - If it's a sequence contains four integer, the top, bottom, left, right - side of input data will be padded with given size. - :param value: Padding value of data, could be a sequence of int or float. - if it's float value, the dtype of image will be casted to float32 also. - :return: Padded image - """ - if isinstance(size, int): - size = (size, size, size, size) - elif isinstance(size, collections.abc.Sequence) and len(size) == 2: - size = (0, size[0], 0, size[1]) - if np.array(value).dtype == float: - input = input.astype(np.float32) - return cv2.copyMakeBorder(input, *size, cv2.BORDER_CONSTANT, value=value) - - -@wrap_keepdims -def flip(image, flipCode): - r""" - Accordding to the flipCode (the type of flip), flip the input image - - :param image: Input image, with (H, W, C) shape - :param flipCode: code that indicates the type of flip. - 1 : Flip horizontally - 0 : Flip vertically - -1 : Flip horizontally and vertically - :return: BGR format image, with (H, W, C) shape - """ - return cv2.flip(image, flipCode=flipCode) - - -@wrap_keepdims -def resize(input, size, interpolation=cv2.INTER_LINEAR): - r""" - resize the input data to given size - - :param input: Input data, could be image or masks, with (H, W, C) shape - :param size: Target size of input data, with (height, width) shape. - :param interpolation: Interpolation method. - :return: Resized data, with (H, W, C) shape - """ - if len(size) != 2: - raise ValueError("resize needs (h, w), but got {}".format(size)) - - if isinstance(interpolation, collections.abc.Sequence): - interpolation = random.choice(interpolation) - return cv2.resize(input, size[::-1], interpolation=interpolation) diff --git a/python_module/megengine/data/transform/vision/transform.py b/python_module/megengine/data/transform/vision/transform.py deleted file mode 100644 index bf3834a9..00000000 --- a/python_module/megengine/data/transform/vision/transform.py +++ /dev/null @@ -1,1025 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import collections.abc -import math -from typing import Sequence, Tuple - -import cv2 -import numpy as np - -from megengine.data.transform import Transform -from megengine.data.transform.vision import functional as F - -__all__ = [ - "VisionTransform", - "ToMode", - "Compose", - "TorchTransformCompose", - "Pad", - "Resize", - "ShortestEdgeResize", - "RandomResize", - "RandomCrop", - "RandomResizedCrop", - "CenterCrop", - "RandomHorizontalFlip", - "RandomVerticalFlip", - "Normalize", - "GaussianNoise", - "BrightnessTransform", - "SaturationTransform", - "ContrastTransform", - "HueTransform", - "ColorJitter", - "Lighting", -] - - -class VisionTransform(Transform): - r""" - Base class of all transforms used in computer vision. - calling logic: apply_batch() -> apply() -> _apply_image() and other _apply_*() - method. If you want to implement a self-defined transform method for image, - rewrite _apply_image method in subclass. - - :param order: Input type order. Input is a tuple contains different structures, - order is used to specify the order of structures. For example, if your input - is (image, boxes) type, then the order should be ("image", "boxes"). - Current available strings & data type are describe below: - - * "image": input image, with shape of (H, W, C) - * "coords": coordinates, with shape of (N, 2) - * "boxes": bounding boxes, with shape of (N, 4), "xyxy" format, - the 1st "xy" represents top left point of a box, - the 2nd "xy" represents right bottom point. - * "mask": map used for segmentation, with shape of (H, W, 1) - * "keypoints": keypoints with shape of (N, K, 3), N for number of instances, - and K for number of keypoints in one instance. The first two dimensions - of last axis is coordinate of keypoints and the the 3rd dimension is - the label of keypoints. - * "polygons": A sequence contains numpy array, its length is number of instances. - Each numpy array represents polygon coordinate of one instance. - * "category": categories for some data type. For example, "image_category" - means category of the input image and "boxes_category" means categories of - bounding boxes. - * "info": information for images such as image shapes and image path. - - You can also customize your data types only if you implement the corresponding - _apply_*() methods, otherwise ``NotImplementedError`` will be raised. - """ - - def __init__(self, order=None): - super().__init__() - if order is None: - order = ("image",) - elif not isinstance(order, collections.abc.Sequence): - raise ValueError( - "order should be a sequence, but got order={}".format(order) - ) - for k in order: - if k in ("batch",): - raise ValueError("{} is invalid data type".format(k)) - elif k.endswith("category") or k.endswith("info"): - # when the key is *category or info, we should do nothing - # if the corresponding apply methods are not implemented. - continue - elif self._get_apply(k) is None: - raise NotImplementedError("{} is unsupported data type".format(k)) - self.order = order - - def apply_batch(self, inputs: Sequence[Tuple]): - r"""Apply transform on batch input data""" - return tuple(self.apply(input) for input in inputs) - - def apply(self, input: Tuple): - r"""Apply transform on single input data""" - if not isinstance(input, tuple): - input = (input,) - - output = [] - for i in range(min(len(input), len(self.order))): - apply_func = self._get_apply(self.order[i]) - if apply_func is None: - output.append(input[i]) - else: - output.append(apply_func(input[i])) - if len(input) > len(self.order): - output.extend(input[len(self.order) :]) - - if len(output) == 1: - output = output[0] - else: - output = tuple(output) - return output - - def _get_apply(self, key): - return getattr(self, "_apply_{}".format(key), None) - - def _get_image(self, input: Tuple): - if not isinstance(input, tuple): - input = (input,) - return input[self.order.index("image")] - - def _apply_image(self, image): - raise NotImplementedError - - def _apply_coords(self, coords): - raise NotImplementedError - - def _apply_boxes(self, boxes): - idxs = np.array([(0, 1), (2, 1), (0, 3), (2, 3)]).flatten() - coords = np.asarray(boxes).reshape(-1, 4)[:, idxs].reshape(-1, 2) - coords = self._apply_coords(coords).reshape((-1, 4, 2)) - minxy = coords.min(axis=1) - maxxy = coords.max(axis=1) - trans_boxes = np.concatenate((minxy, maxxy), axis=1) - return trans_boxes - - def _apply_mask(self, mask): - raise NotImplementedError - - def _apply_keypoints(self, keypoints): - coords, visibility = keypoints[..., :2], keypoints[..., 2:] - trans_coords = [self._apply_coords(p) for p in coords] - return np.concatenate((trans_coords, visibility), axis=-1) - - def _apply_polygons(self, polygons): - return [[self._apply_coords(p) for p in instance] for instance in polygons] - - -class ToMode(VisionTransform): - r"""Change input data to a target mode. - For example, most transforms use HWC mode image, - while the Neural Network might use CHW mode input tensor - - :param mode: Output mode of input. Use "CHW" mode by default. - :param order: The same with :class:`VisionTransform` - """ - - def __init__(self, mode="CHW", *, order=None): - super().__init__(order) - assert mode in ["CHW"], "unsupported mode: {}".format(mode) - self.mode = mode - - def _apply_image(self, image): - if self.mode == "CHW": - return np.ascontiguousarray(np.rollaxis(image, 2)) - return image - - def _apply_coords(self, coords): - return coords - - def _apply_mask(self, mask): - if self.mode == "CHW": - return np.ascontiguousarray(np.rollaxis(mask, 2)) - return mask - - -class Compose(VisionTransform): - r""" - Composes several transforms together. - - :param transforms: List of :class:`VisionTransform` to compose. - :param batch_compose: Whether use shuffle_indices for batch data or not. - If True, use original input sequence. - Otherwise, the shuffle_indices will be used for transforms. - :param shuffle_indices: Indices used for random shuffle, start at 1. - For example, if shuffle_indices is [(1, 3), (2, 4)], then the 1st and 3rd transform - will be random shuffled, the 2nd and 4th transform will also be shuffled. - :param order: The same with :class:`VisionTransform` - - Example: - - ..testcode:: - - from megengine.data.transform import RandomHorizontalFlip, RandomVerticalFlip, CenterCrop, ToMode, Compose - - transform_func = Compose([ - RandomHorizontalFlip(), - RandomVerticalFlip(), - CenterCrop(100), - ToMode("CHW"), - ], - shuffle_indices=[(1, 2, 3)] - ) - """ - - def __init__( - self, transforms=[], batch_compose=False, shuffle_indices=None, *, order=None - ): - super().__init__(order) - self.transforms = transforms - self._set_order() - - if batch_compose and shuffle_indices is not None: - raise ValueError( - "Do not support shuffle when apply transforms along the whole batch" - ) - self.batch_compose = batch_compose - - if shuffle_indices is not None: - shuffle_indices = [tuple(x - 1 for x in idx) for idx in shuffle_indices] - self.shuffle_indices = shuffle_indices - - def _set_order(self): - for t in self.transforms: - t.order = self.order - if isinstance(t, Compose): - t._set_order() - - def apply_batch(self, inputs: Sequence[Tuple]): - if self.batch_compose: - for t in self.transforms: - inputs = t.apply_batch(inputs) - return inputs - else: - return super().apply_batch(inputs) - - def apply(self, input: Tuple): - for t in self._shuffle(): - input = t.apply(input) - return input - - def _shuffle(self): - if self.shuffle_indices is not None: - source_idx = list(range(len(self.transforms))) - for idx in self.shuffle_indices: - shuffled = np.random.permutation(idx).tolist() - for src, dst in zip(idx, shuffled): - source_idx[src] = dst - return [self.transforms[i] for i in source_idx] - else: - return self.transforms - - -class TorchTransformCompose(VisionTransform): - r""" - Compose class used for transforms in torchvision, only support PIL image, - some transforms with tensor in torchvision are not supported, - such as Normalize and ToTensor in torchvision. - - :param transforms: The same with ``Compose`` - :param order: The same with :class:`VisionTransform` - """ - - def __init__(self, transforms, *, order=None): - super().__init__(order) - self.transforms = transforms - - def _apply_image(self, image): - from PIL import Image - - try: - import accimage - except ImportError: - accimage = None - - if image.shape[0] == 3: # CHW - image = np.ascontiguousarray(image[[2, 1, 0]]) - elif image.shape[2] == 3: # HWC - image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - image = Image.fromarray(image.astype(np.uint8)) - - for t in self.transforms: - image = t(image) - - if isinstance(image, Image.Image) or ( - accimage is not None and isinstance(image, accimage.Image) - ): - image = np.array(image, dtype=np.uint8) - if image.shape[0] == 3: # CHW - image = np.ascontiguousarray(image[[2, 1, 0]]) - elif image.shape[2] == 3: # HWC - image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) - return image - - -class Pad(VisionTransform): - r"""Pad the input data. - - :param size: Padding size of input image, it could be integer or sequence. - If it's an integer, the input image will be padded in four directions. - If it's a sequence contains two integer, the bottom and right side - of image will be padded. - If it's a sequence contains four integer, the top, bottom, left, right - side of image will be padded with given size. - :param value: Padding value of image, could be a sequence of int or float. - if it's float value, the dtype of image will be casted to float32 also. - :param mask_value: Padding value of segmentation map. - :param order: The same with :class:`VisionTransform` - """ - - def __init__(self, size=0, value=0, mask_value=0, *, order=None): - super().__init__(order) - if isinstance(size, int): - size = (size, size, size, size) - elif isinstance(size, collections.abc.Sequence) and len(size) == 2: - size = (0, size[0], 0, size[1]) - elif not (isinstance(size, collections.abc.Sequence) and len(size) == 4): - raise ValueError( - "size should be a list/tuple which contains " - "(top, down, left, right) four pad sizes." - ) - self.size = size - self.value = value - if not isinstance(mask_value, int): - raise ValueError( - "mask_value should be a positive integer, " - "but got mask_value={}".format(mask_value) - ) - self.mask_value = mask_value - - def _apply_image(self, image): - return F.pad(image, self.size, self.value) - - def _apply_coords(self, coords): - coords[:, 0] += self.size[2] - coords[:, 1] += self.size[0] - return coords - - def _apply_mask(self, mask): - return F.pad(mask, self.size, self.mask_value) - - -class Resize(VisionTransform): - r"""Resize the input data. - - :param output_size: Target size of image, with (height, width) shape. - :param interpolation: Interpolation method. All methods are listed below: - - * cv2.INTER_NEAREST – a nearest-neighbor interpolation. - * cv2.INTER_LINEAR – a bilinear interpolation (used by default). - * cv2.INTER_AREA – resampling using pixel area relation. - * cv2.INTER_CUBIC – a bicubic interpolation over 4×4 pixel neighborhood. - * cv2.INTER_LANCZOS4 – a Lanczos interpolation over 8×8 pixel neighborhood. - :param order: The same with :class:`VisionTransform` - """ - - def __init__(self, output_size, interpolation=cv2.INTER_LINEAR, *, order=None): - super().__init__(order) - self.output_size = output_size - self.interpolation = interpolation - - def apply(self, input: Tuple): - self._shape_info = self._get_shape(self._get_image(input)) - return super().apply(input) - - def _apply_image(self, image): - h, w, th, tw = self._shape_info - if h == th and w == tw: - return image - return F.resize(image, (th, tw), self.interpolation) - - def _apply_coords(self, coords): - h, w, th, tw = self._shape_info - if h == th and w == tw: - return coords - coords[:, 0] = coords[:, 0] * (tw / w) - coords[:, 1] = coords[:, 1] * (th / h) - return coords - - def _apply_mask(self, mask): - h, w, th, tw = self._shape_info - if h == th and w == tw: - return mask - return F.resize(mask, (th, tw), cv2.INTER_NEAREST) - - def _get_shape(self, image): - h, w, _ = image.shape - if isinstance(self.output_size, int): - if min(h, w) == self.output_size: - return h, w, h, w - if h < w: - th = self.output_size - tw = int(self.output_size * w / h) - else: - tw = self.output_size - th = int(self.output_size * h / w) - return h, w, th, tw - else: - return (h, w, *self.output_size) - - -class ShortestEdgeResize(VisionTransform): - def __init__( - self, - min_size, - max_size, - sample_style="range", - interpolation=cv2.INTER_LINEAR, - *, - order=None - ): - super().__init__(order) - if sample_style not in ("range", "choice"): - raise NotImplementedError( - "{} is unsupported sample style".format(sample_style) - ) - self.sample_style = sample_style - if isinstance(min_size, int): - min_size = (min_size, min_size) - self.min_size = min_size - self.max_size = max_size - self.interpolation = interpolation - - def apply(self, input: Tuple): - self._shape_info = self._get_shape(self._get_image(input)) - return super().apply(input) - - def _apply_image(self, image): - h, w, th, tw = self._shape_info - if h == th and w == tw: - return image - return F.resize(image, (th, tw), self.interpolation) - - def _apply_coords(self, coords): - h, w, th, tw = self._shape_info - if h == th and w == tw: - return coords - coords[:, 0] = coords[:, 0] * (tw / w) - coords[:, 1] = coords[:, 1] * (th / h) - return coords - - def _apply_mask(self, mask): - h, w, th, tw = self._shape_info - if h == th and w == tw: - return mask - return F.resize(mask, (th, tw), cv2.INTER_NEAREST) - - def _get_shape(self, image): - h, w, _ = image.shape - if self.sample_style == "range": - size = np.random.randint(self.min_size[0], self.min_size[1] + 1) - else: - size = np.random.choice(self.min_size) - - scale = size / min(h, w) - if h < w: - th, tw = size, scale * w - else: - th, tw = scale * h, size - if max(th, tw) > self.max_size: - scale = self.max_size / max(th, tw) - th = th * scale - tw = tw * scale - th = int(round(th)) - tw = int(round(tw)) - return h, w, th, tw - - -class RandomResize(VisionTransform): - r"""Resize the input data randomly. - - :param scale_range: . - :param order: The same with :class:`VisionTransform` - """ - - def __init__(self, scale_range, interpolation=cv2.INTER_LINEAR, *, order=None): - super().__init__(order) - self.scale_range = scale_range - self.interpolation = interpolation - - def apply(self, input: Tuple): - self._shape_info = self._get_shape(self._get_image(input)) - return super().apply(input) - - def _apply_image(self, image): - h, w, th, tw = self._shape_info - if h == th and w == tw: - return image - return F.resize(image, (th, tw), self.interpolation) - - def _apply_coords(self, coords): - h, w, th, tw = self._shape_info - if h == th and w == tw: - return coords - coords[:, 0] = coords[:, 0] * (tw / w) - coords[:, 1] = coords[:, 1] * (th / h) - return coords - - def _apply_mask(self, mask): - h, w, th, tw = self._shape_info - if h == th and w == tw: - return mask - return F.resize(mask, (th, tw), cv2.INTER_NEAREST) - - def _get_shape(self, image): - h, w, _ = image.shape - scale = np.random.uniform(*self.scale_range) - th = int(round(h * scale)) - tw = int(round(w * scale)) - return h, w, th, tw - - -class RandomCrop(VisionTransform): - r"""Crop the input data randomly. Before applying the crop transform, - pad the image first. And if target size is still bigger than the size of - padded image, pad the image size to target size. - - :param output_size: Target size of output image, with (height, width) shape. - :param padding_size: The same with `size` in ``Pad`` - :param padding_value: The same with `value` in ``Pad`` - :param order: The same with :class:`VisionTransform` - """ - - def __init__( - self, - output_size, - padding_size=0, - padding_value=[0, 0, 0], - padding_maskvalue=0, - *, - order=None - ): - super().__init__(order) - if isinstance(output_size, int): - self.output_size = (output_size, output_size) - else: - self.output_size = output_size - self.pad = Pad(padding_size, padding_value, order=self.order) - self.padding_value = padding_value - self.padding_maskvalue = padding_maskvalue - - def apply(self, input): - input = self.pad.apply(input) - self._h, self._w, _ = self._get_image(input).shape - self._th, self._tw = self.output_size - self._x = np.random.randint(0, max(0, self._w - self._tw) + 1) - self._y = np.random.randint(0, max(0, self._h - self._th) + 1) - return super().apply(input) - - def _apply_image(self, image): - if self._th > self._h: - image = F.pad(image, (self._th - self._h, 0), self.padding_value) - if self._tw > self._w: - image = F.pad(image, (0, self._tw - self._w), self.padding_value) - return image[self._y : self._y + self._th, self._x : self._x + self._tw] - - def _apply_coords(self, coords): - coords[:, 0] -= self._x - coords[:, 1] -= self._y - return coords - - def _apply_mask(self, mask): - if self._th > self._h: - mask = F.pad(mask, (self._th - self._h, 0), self.padding_maskvalue) - if self._tw > self._w: - mask = F.pad(mask, (0, self._tw - self._w), self.padding_maskvalue) - return mask[self._y : self._y + self._th, self._x : self._x + self._tw] - - -class RandomResizedCrop(VisionTransform): - r"""Crop the input data to random size and aspect ratio. - A crop of random size (default: of 0.08 to 1.0) of the original size and a random - aspect ratio (default: of 3/4 to 1.33) of the original aspect ratio is made. - After applying crop transfrom, the input data will be resized to given size. - - :param output_size: Target size of output image, with (height, width) shape. - :param scale_range: Range of size of the origin size cropped. Default: (0.08, 1.0) - :param ratio_range: Range of aspect ratio of the origin aspect ratio cropped. Default: (0.75, 1.33) - :param order: The same with :class:`VisionTransform` - """ - - def __init__( - self, - output_size, - scale_range=(0.08, 1.0), - ratio_range=(3.0 / 4, 4.0 / 3), - interpolation=cv2.INTER_LINEAR, - *, - order=None - ): - super().__init__(order) - if isinstance(output_size, int): - self.output_size = (output_size, output_size) - else: - self.output_size = output_size - assert ( - scale_range[0] <= scale_range[1] - ), "scale_range should be of kind (min, max)" - assert ( - ratio_range[0] <= ratio_range[1] - ), "ratio_range should be of kind (min, max)" - self.scale_range = scale_range - self.ratio_range = ratio_range - self.interpolation = interpolation - - def apply(self, input: Tuple): - self._coord_info = self._get_coord(self._get_image(input)) - return super().apply(input) - - def _apply_image(self, image): - x, y, w, h = self._coord_info - cropped_img = image[y : y + h, x : x + w] - return F.resize(cropped_img, self.output_size, self.interpolation) - - def _apply_coords(self, coords): - x, y, w, h = self._coord_info - coords[:, 0] = (coords[:, 0] - x) * self.output_size[1] / w - coords[:, 1] = (coords[:, 1] - y) * self.output_size[0] / h - return coords - - def _apply_mask(self, mask): - x, y, w, h = self._coord_info - cropped_mask = mask[y : y + h, x : x + w] - return F.resize(cropped_mask, self.output_size, cv2.INTER_NEAREST) - - def _get_coord(self, image, attempts=10): - height, width, _ = image.shape - area = height * width - - for _ in range(attempts): - target_area = np.random.uniform(*self.scale_range) * area - log_ratio = tuple(math.log(x) for x in self.ratio_range) - aspect_ratio = math.exp(np.random.uniform(*log_ratio)) - - w = int(round(math.sqrt(target_area * aspect_ratio))) - h = int(round(math.sqrt(target_area / aspect_ratio))) - - if 0 < w <= width and 0 < h <= height: - x = np.random.randint(0, width - w + 1) - y = np.random.randint(0, height - h + 1) - return x, y, w, h - - # Fallback to central crop - in_ratio = float(width) / float(height) - if in_ratio < min(self.ratio_range): - w = width - h = int(round(w / min(self.ratio_range))) - elif in_ratio > max(self.ratio_range): - h = height - w = int(round(h * max(self.ratio_range))) - else: # whole image - w = width - h = height - x = (width - w) // 2 - y = (height - h) // 2 - return x, y, w, h - - -class CenterCrop(VisionTransform): - r"""Crops the given the input data at the center. - - :param output_size: Target size of output image, with (height, width) shape. - :param order: The same with :class:`VisionTransform` - """ - - def __init__(self, output_size, *, order=None): - super().__init__(order) - if isinstance(output_size, int): - self.output_size = (output_size, output_size) - else: - self.output_size = output_size - - def apply(self, input: Tuple): - self._coord_info = self._get_coord(self._get_image(input)) - return super().apply(input) - - def _apply_image(self, image): - x, y = self._coord_info - th, tw = self.output_size - return image[y : y + th, x : x + tw] - - def _apply_coords(self, coords): - x, y = self._coord_info - coords[:, 0] -= x - coords[:, 1] -= y - return coords - - def _apply_mask(self, mask): - x, y = self._coord_info - th, tw = self.output_size - return mask[y : y + th, x : x + tw] - - def _get_coord(self, image): - th, tw = self.output_size - h, w, _ = image.shape - assert th <= h and tw <= w, "output size is bigger than image size" - x = int(round((w - tw) / 2.0)) - y = int(round((h - th) / 2.0)) - return x, y - - -class RandomHorizontalFlip(VisionTransform): - r"""Horizontally flip the input data randomly with a given probability. - - :param p: probability of the input data being flipped. Default: 0.5 - :param order: The same with :class:`VisionTransform` - """ - - def __init__(self, prob: float = 0.5, *, order=None): - super().__init__(order) - self.prob = prob - - def apply(self, input: Tuple): - self._flipped = np.random.random() < self.prob - self._w = self._get_image(input).shape[1] - return super().apply(input) - - def _apply_image(self, image): - if self._flipped: - return F.flip(image, flipCode=1) - return image - - def _apply_coords(self, coords): - if self._flipped: - coords[:, 0] = self._w - coords[:, 0] - return coords - - def _apply_mask(self, mask): - if self._flipped: - return F.flip(mask, flipCode=1) - return mask - - -class RandomVerticalFlip(VisionTransform): - r"""Vertically flip the input data randomly with a given probability. - - :param p: probability of the input data being flipped. Default: 0.5 - :param order: The same with :class:`VisionTransform` - """ - - def __init__(self, prob: float = 0.5, *, order=None): - super().__init__(order) - self.prob = prob - - def apply(self, input: Tuple): - self._flipped = np.random.random() < self.prob - self._h = self._get_image(input).shape[0] - return super().apply(input) - - def _apply_image(self, image): - if self._flipped: - return F.flip(image, flipCode=0) - return image - - def _apply_coords(self, coords): - if self._flipped: - coords[:, 1] = self._h - coords[:, 1] - return coords - - def _apply_mask(self, mask): - if self._flipped: - return F.flip(mask, flipCode=0) - return mask - - -class Normalize(VisionTransform): - r"""Normalize the input data with mean and standard deviation. - Given mean: ``(M1,...,Mn)`` and std: ``(S1,..,Sn)`` for ``n`` channels, - this transform will normalize each channel of the input data. - ``output[channel] = (input[channel] - mean[channel]) / std[channel]`` - - :param mean: Sequence of means for each channel. - :param std: Sequence of standard deviations for each channel. - :param order: The same with :class:`VisionTransform` - """ - - def __init__(self, mean=0.0, std=1.0, *, order=None): - super().__init__(order) - self.mean = np.array(mean, dtype=np.float32) - self.std = np.array(std, dtype=np.float32) - - def _apply_image(self, image): - return (image - self.mean) / self.std - - def _apply_coords(self, coords): - return coords - - def _apply_mask(self, mask): - return mask - - -class GaussianNoise(VisionTransform): - r"""Add random gaussian noise to the input data. - Gaussian noise is generated with given mean and std. - - :param mean: Gaussian mean used to generate noise. - :param std: Gaussian standard deviation used to generate noise. - :param order: The same with :class:`VisionTransform` - """ - - def __init__(self, mean=0.0, std=1.0, *, order=None): - super().__init__(order) - self.mean = np.array(mean, dtype=np.float32) - self.std = np.array(std, dtype=np.float32) - - def _apply_image(self, image): - dtype = image.dtype - noise = np.random.normal(self.mean, self.std, image.shape) * 255 - image = image + noise.astype(np.float32) - return np.clip(image, 0, 255).astype(dtype) - - def _apply_coords(self, coords): - return coords - - def _apply_mask(self, mask): - return mask - - -class BrightnessTransform(VisionTransform): - r"""Adjust brightness of the input data. - - :param value: How much to adjust the brightness. Can be any - non negative number. 0 gives the original image - :param order: The same with :class:`VisionTransform` - """ - - def __init__(self, value, *, order=None): - super().__init__(order) - if value < 0: - raise ValueError("brightness value should be non-negative") - self.value = value - - def _apply_image(self, image): - if self.value == 0: - return image - - dtype = image.dtype - image = image.astype(np.float32) - alpha = np.random.uniform(max(0, 1 - self.value), 1 + self.value) - image = image * alpha - return image.clip(0, 255).astype(dtype) - - def _apply_coords(self, coords): - return coords - - def _apply_mask(self, mask): - return mask - - -class ContrastTransform(VisionTransform): - r"""Adjust contrast of the input data. - - :param value: How much to adjust the contrast. Can be any - non negative number. 0 gives the original image - :param order: The same with :class:`VisionTransform` - """ - - def __init__(self, value, *, order=None): - super().__init__(order) - if value < 0: - raise ValueError("contrast value should be non-negative") - self.value = value - - def _apply_image(self, image): - if self.value == 0: - return image - - dtype = image.dtype - image = image.astype(np.float32) - alpha = np.random.uniform(max(0, 1 - self.value), 1 + self.value) - image = image * alpha + F.to_gray(image).mean() * (1 - alpha) - return image.clip(0, 255).astype(dtype) - - def _apply_coords(self, coords): - return coords - - def _apply_mask(self, mask): - return mask - - -class SaturationTransform(VisionTransform): - r"""Adjust saturation of the input data. - - :param value: How much to adjust the saturation. Can be any - non negative number. 0 gives the original image - :param order: The same with :class:`VisionTransform` - """ - - def __init__(self, value, *, order=None): - super().__init__(order) - if value < 0: - raise ValueError("saturation value should be non-negative") - self.value = value - - def _apply_image(self, image): - if self.value == 0: - return image - - dtype = image.dtype - image = image.astype(np.float32) - alpha = np.random.uniform(max(0, 1 - self.value), 1 + self.value) - image = image * alpha + F.to_gray(image) * (1 - alpha) - return image.clip(0, 255).astype(dtype) - - def _apply_coords(self, coords): - return coords - - def _apply_mask(self, mask): - return mask - - -class HueTransform(VisionTransform): - r"""Adjust hue of the input data. - - :param value: How much to adjust the hue. Can be any number - between 0 and 0.5, 0 gives the original image - :param order: The same with :class:`VisionTransform` - """ - - def __init__(self, value, *, order=None): - super().__init__(order) - if value < 0 or value > 0.5: - raise ValueError("hue value should be in [0.0, 0.5]") - self.value = value - - def _apply_image(self, image): - if self.value == 0: - return image - - dtype = image.dtype - image = image.astype(np.uint8) - hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV_FULL) - h, s, v = cv2.split(hsv_image) - - alpha = np.random.uniform(-self.value, self.value) - h = h.astype(np.uint8) - # uint8 addition take cares of rotation across boundaries - with np.errstate(over="ignore"): - h += np.uint8(alpha * 255) - hsv_image = cv2.merge([h, s, v]) - return cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR_FULL).astype(dtype) - - def _apply_coords(self, coords): - return coords - - def _apply_mask(self, mask): - return mask - - -class ColorJitter(VisionTransform): - r"""Randomly change the brightness, contrast, saturation and hue of an image. - - :param brightness: How much to jitter brightness. - Chosen uniformly from [max(0, 1 - brightness), 1 + brightness] - or the given [min, max]. Should be non negative numbers. - :param contrast: How much to jitter contrast. - Chosen uniformly from [max(0, 1 - contrast), 1 + contrast] - or the given [min, max]. Should be non negative numbers. - :param saturation: How much to jitter saturation. - Chosen uniformly from [max(0, 1 - saturation), 1 + saturation] - or the given [min, max]. Should be non negative numbers. - :param hue: How much to jitter hue. - Chosen uniformly from [-hue, hue] or the given [min, max]. - Should have 0<= hue <= 0.5 or -0.5 <= min <= max <= 0.5. - :param order: The same with :class:`VisionTransform` - """ - - def __init__(self, brightness=0, contrast=0, saturation=0, hue=0, *, order=None): - super().__init__(order) - transforms = [] - if brightness != 0: - transforms.append(BrightnessTransform(brightness)) - if contrast != 0: - transforms.append(ContrastTransform(contrast)) - if saturation != 0: - transforms.append(SaturationTransform(saturation)) - if hue != 0: - transforms.append(HueTransform(hue)) - self.transforms = Compose( - transforms, - shuffle_indices=[tuple(range(1, len(transforms) + 1))], - order=order, - ) - - def apply(self, input): - return self.transforms.apply(input) - - -class Lighting(VisionTransform): - def __init__(self, scale, *, order=None): - super().__init__(order) - if scale < 0: - raise ValueError("lighting scale should be non-negative") - self.scale = scale - self.eigvec = np.array( - [ - [-0.5836, -0.6948, 0.4203], - [-0.5808, -0.0045, -0.8140], - [-0.5675, 0.7192, 0.4009], - ] - ) # reverse the first dimension for BGR - self.eigval = np.array([0.2175, 0.0188, 0.0045]) - - def _apply_image(self, image): - if self.scale == 0: - return image - - dtype = image.dtype - image = image.astype(np.float32) - alpha = np.random.normal(scale=self.scale, size=3) - image = image + self.eigvec.dot(alpha * self.eigval) - return image.clip(0, 255).astype(dtype) - - def _apply_coords(self, coords): - return coords - - def _apply_mask(self, mask): - return mask diff --git a/python_module/megengine/distributed/__init__.py b/python_module/megengine/distributed/__init__.py deleted file mode 100644 index 1416e82c..00000000 --- a/python_module/megengine/distributed/__init__.py +++ /dev/null @@ -1,33 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .functional import ( - all_gather, - all_reduce_max, - all_reduce_min, - all_reduce_sum, - all_to_all, - bcast_param, - broadcast, - gather, - reduce_scatter_sum, - reduce_sum, - scatter, -) -from .util import ( - get_backend, - get_free_ports, - get_master_ip, - get_master_port, - get_rank, - get_world_size, - group_barrier, - init_process_group, - is_distributed, - synchronized, -) diff --git a/python_module/megengine/distributed/functional.py b/python_module/megengine/distributed/functional.py deleted file mode 100644 index 56ad089f..00000000 --- a/python_module/megengine/distributed/functional.py +++ /dev/null @@ -1,302 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from typing import Optional, Union - -import megengine._internal as mgb -from megengine._internal.opr_param_defs import CollectiveComm as Param - -from ..core import Buffer, Parameter, Tensor, wrap_io_tensor -from ..functional import add_update -from .helper import collective_comm_symvar -from .util import get_rank, is_distributed - - -@wrap_io_tensor -def _collective_comm(*args, **kargs): - return collective_comm_symvar(*args, **kargs) - - -def _group_check(*args): - """Return True when arguments are all None or all not None - """ - l = [val is None for val in args] - return len(set(l)) <= 1 - - -def reduce_sum( - tensor: Tensor, - key: Optional[str] = None, - nr_ranks: Optional[int] = None, - is_root: Optional[bool] = None, -) -> Tensor: - """Create reduce_sum operator for collective communication - - :param tensor: input tensor - :param key: unique identifier for collective communication - :param nr_ranks: number of ranks, use util.get_world_size() as default - :param is_root: whether this is a root node - """ - assert _group_check( - key, nr_ranks, is_root - ), "key, nr_ranks, is_root should be set at the same time" - return _collective_comm( - tensor, key, Param.Mode.REDUCE_SUM, nr_ranks, is_root, device=tensor.device, - ) - - -def gather( - tensor: Tensor, - key: Optional[str] = None, - nr_ranks: Optional[int] = None, - is_root: Optional[bool] = None, - rank: Optional[int] = None, -) -> Tensor: - """Create gather operator for collective communication - - :param tensor: input tensor - :param key: unique identifier for collective communication - :param nr_ranks: number of ranks, use util.get_world_size() as default - :param is_root: whether this is a root node - :param rank: rank of this node - """ - assert _group_check( - key, nr_ranks, is_root, rank - ), "key, nr_ranks, is_root, rank should be set at the same time" - return _collective_comm( - tensor, key, Param.Mode.GATHER, nr_ranks, is_root, rank, device=tensor.device, - ) - - -def broadcast( - tensor: Tensor, - key: Optional[str] = None, - nr_ranks: Optional[int] = None, - is_root: Optional[bool] = None, -) -> Tensor: - """Create broadcast operator for collective communication - - :param tensor: input tensor - :param key: unique identifier for collective communication - :param nr_ranks: number of ranks, use util.get_world_size() as default - :param is_root: whether this is a root node - """ - assert _group_check( - key, nr_ranks, is_root - ), "key, nr_ranks, is_root should be set at the same time" - - if is_root is None: - is_root = get_rank() == 0 - if is_root: - inp = tensor - else: - inp = tensor._symvar.owner_graph - - return _collective_comm( - inp, - key, - Param.Mode.BROADCAST, - nr_ranks, - is_root, - dtype=tensor.dtype, - device=tensor.device, - ) - - -def scatter( - tensor: Tensor, - key: Optional[str] = None, - nr_ranks: Optional[int] = None, - is_root: Optional[bool] = None, - rank: Optional[int] = None, -) -> Tensor: - """Create scatter operator for collective communication - - :param tensor: input tensor - :param key: unique identifier for collective communication - :param nr_ranks: number of ranks, use util.get_world_size() as default - :param is_root: whether this is a root node - :param rank: rank of this node - """ - assert _group_check( - key, nr_ranks, is_root, rank - ), "key, nr_ranks, is_root, rank should be set at the same time" - if key is None: - key = tensor._symvar.name - if is_root is None: - is_root = get_rank() == 0 - - if is_root: - inp = tensor - else: - inp = tensor._symvar.owner_graph - - return _collective_comm( - inp, - key, - Param.Mode.SCATTER, - nr_ranks, - is_root, - rank, - dtype=tensor.dtype, - device=tensor.device, - ) - - -def all_to_all( - tensor: Tensor, - key: Optional[str] = None, - nr_ranks: Optional[int] = None, - rank: Optional[int] = None, - local_grad: Optional[bool] = False, -) -> Tensor: - """Create all_to_all operator for collective communication - - :param tensor: input tensor - :param key: unique identifier for collective communication - :param nr_ranks: number of ranks, use util.get_world_size() as default - :param rank: rank of this node - :param local_grad: whether use local grad - """ - assert _group_check( - key, nr_ranks, rank - ), "key, nr_ranks, rank should be set at the same time" - return _collective_comm( - tensor, key, Param.Mode.ALL_TO_ALL, nr_ranks, rank=rank, local_grad=local_grad, - ) - - -def all_gather( - tensor: Tensor, - key: Optional[str] = None, - nr_ranks: Optional[int] = None, - rank: Optional[int] = None, - local_grad: Optional[bool] = False, -) -> Tensor: - """Create all_gather operator for collective communication - - :param tensor: input tensor - :param key: unique identifier for collective communication - :param nr_ranks: number of ranks, use util.get_world_size() as default - :param rank: rank of this node - :param local_grad: whether use local grad - """ - assert _group_check( - key, nr_ranks, rank - ), "key, nr_ranks, rank should be set at the same time" - return _collective_comm( - tensor, key, Param.Mode.ALL_GATHER, nr_ranks, rank=rank, local_grad=local_grad - ) - - -def reduce_scatter_sum( - tensor: Tensor, - key: Optional[str] = None, - nr_ranks: Optional[int] = None, - rank: Optional[int] = None, - local_grad: Optional[bool] = False, -) -> Tensor: - """Create reduce_scatter_sum operator for collective communication - - :param tensor: input tensor - :param key: unique identifier for collective communication - :param nr_ranks: number of ranks, use util.get_world_size() as default - :param rank: rank of this node - :param local_grad: whether use local grad - """ - assert _group_check( - key, nr_ranks, rank - ), "key, nr_ranks, rank should be set at the same time" - return _collective_comm( - tensor, - key, - Param.Mode.REDUCE_SCATTER_SUM, - nr_ranks, - rank=rank, - local_grad=local_grad, - ) - - -def all_reduce_sum( - tensor: Tensor, - key: Optional[str] = None, - nr_ranks: Optional[int] = None, - local_grad: Optional[bool] = False, -) -> Tensor: - """Create all_reduce_sum operator for collective communication - - :param tensor: input tensor - :param key: unique identifier for collective communication - :param nr_ranks: number of ranks, use util.get_world_size() as default - :param local_grad: whether use local grad - """ - assert _group_check(key, nr_ranks), "key, nr_ranks should be set at the same time" - return _collective_comm( - tensor, key, Param.Mode.ALL_REDUCE_SUM, nr_ranks, local_grad=local_grad - ) - - -def all_reduce_max( - tensor: Tensor, - key: Optional[str] = None, - nr_ranks: Optional[int] = None, - local_grad: Optional[bool] = False, -) -> Tensor: - """Create all_reduce_max operator for collective communication - - :param tensor: input tensor - :param key: unique identifier for collective communication - :param nr_ranks: number of ranks, use util.get_world_size() as default - :param local_grad: whether use local grad - """ - assert _group_check(key, nr_ranks), "key, nr_ranks should be set at the same time" - return _collective_comm( - tensor, key, Param.Mode.ALL_REDUCE_MAX, nr_ranks, local_grad=local_grad - ) - - -def all_reduce_min( - tensor: Tensor, - key: Optional[str] = None, - nr_ranks: Optional[int] = None, - local_grad: Optional[bool] = False, -) -> Tensor: - """Create all_reduce_min operator for collective communication - - :param tensor: input tensor - :param key: unique identifier for collective communication - :param nr_ranks: number of ranks, use util.get_world_size() as default - :param local_grad: whether use local grad - """ - assert _group_check(key, nr_ranks), "key, nr_ranks should be set at the same time" - return _collective_comm( - tensor, key, Param.Mode.ALL_REDUCE_MIN, nr_ranks, local_grad=local_grad - ) - - -def bcast_param( - inp: Union[Buffer, Parameter], - key: Optional[str] = None, - nr_ranks: Optional[int] = None, - is_root: Optional[bool] = None, -) -> None: - """Broadcast parameters among devices - - :param inp: input Buffer or Parameter to be synchronized - :param key: unique identifier for collective communication - :param nr_ranks: number of ranks, use util.get_world_size() as default - :param is_root: whether this is a root node - """ - if not is_distributed(): - return - assert _group_check( - key, nr_ranks, is_root - ), "key, nr_ranks, is_root should be set at the same time" - assert isinstance(inp, (Buffer, Parameter)) - bcast_res = broadcast(inp, key, nr_ranks, is_root) - add_update(inp, bcast_res, alpha=0) diff --git a/python_module/megengine/distributed/helper.py b/python_module/megengine/distributed/helper.py deleted file mode 100644 index 7d2d84bd..00000000 --- a/python_module/megengine/distributed/helper.py +++ /dev/null @@ -1,63 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from typing import Optional, Union - -import megengine._internal as mgb -from megengine._internal.opr_param_defs import CollectiveComm as CollParam - -from .util import ( - get_backend, - get_group_id, - get_master_ip, - get_master_port, - get_rank, - get_world_size, -) - - -def collective_comm_symvar( - inp: Union[mgb.SymbolVar, mgb.CompGraph], - key: Optional[str] = None, - op: CollParam.Mode = None, - nr_ranks: Optional[int] = None, - is_root: Optional[bool] = None, - rank: Optional[int] = None, - local_grad: Optional[bool] = False, - dtype: Optional[type] = None, - device: Optional[mgb.CompNode] = None, - comp_graph: Optional[mgb.CompGraph] = None, -) -> mgb.SymbolVar: - """Helper function for creating collective_comm operators - - :param inp: tensor or comp_graph - :param key: unique identifier for collective communication - :param op: mode of collective communication - :param nr_ranks: number of ranks, use util.get_world_size() as default - :param is_root: whether this node is root node - :param rank: rank of this node - :param local_grad: whether use local grad - :param dtype: output data type, use dtype of inp as default - :param device: output comp node, use comp node of inp as default - :param comp_graph: output comp graph, use comp graph of inp as default - """ - return mgb.opr.collective_comm( - inp, - key=key if key is not None else ("collective_comm_" + str(get_group_id())), - nr_devices=nr_ranks if nr_ranks is not None else get_world_size(), - is_root=is_root if is_root is not None else (get_rank() == 0), - rank=rank if rank is not None else get_rank(), - local_grad=local_grad, - server_addr=get_master_ip(), - port=get_master_port(), - param=CollParam(mode=op), - dtype=dtype, - backend=get_backend(), - comp_node=device, - comp_graph=comp_graph, - ) diff --git a/python_module/megengine/distributed/util.py b/python_module/megengine/distributed/util.py deleted file mode 100644 index 5166a9fc..00000000 --- a/python_module/megengine/distributed/util.py +++ /dev/null @@ -1,146 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import functools -import socket -from typing import Callable, List, Optional - -import megengine._internal as mgb - -from ..core import set_default_device - -_master_ip = None -_master_port = 0 -_world_size = 0 -_rank = 0 -_backend = None -_group_id = 0 - - -def init_process_group( - master_ip: str, - master_port: int, - world_size: int, - rank: int, - dev: int, - backend: Optional[str] = "nccl", -) -> None: - """Initialize the distributed process group, and also specify the device used in the current process. - - :param master_ip: IP address of the master node. - :param master_port: Port available for all processes to communicate. - :param world_size: Total number of processes participating in the job. - :param rank: Rank of the current process. - :param dev: The GPU device id to bind this process to. - :param backend: Communicator backend, currently support 'nccl' and 'ucx' - """ - global _master_ip # pylint: disable=global-statement - global _master_port # pylint: disable=global-statement - global _world_size # pylint: disable=global-statement - global _rank # pylint: disable=global-statement - global _backend # pylint: disable=global-statement - global _group_id # pylint: disable=global-statement - - if not isinstance(master_ip, str): - raise TypeError("Expect type str but got {}".format(type(master_ip))) - if not isinstance(master_port, int): - raise TypeError("Expect type int but got {}".format(type(master_port))) - if not isinstance(world_size, int): - raise TypeError("Expect type int but got {}".format(type(world_size))) - if not isinstance(rank, int): - raise TypeError("Expect type int but got {}".format(type(rank))) - if not isinstance(backend, str): - raise TypeError("Expect type str but got {}".format(type(backend))) - - _master_ip = master_ip - _master_port = master_port - _world_size = world_size - _rank = rank - _backend = backend - _group_id = 0 - - set_default_device(mgb.comp_node("gpu" + str(dev))) - - if rank == 0: - _master_port = mgb.config.create_mm_server("0.0.0.0", master_port) - if _master_port == -1: - raise Exception("Failed to start server on port {}".format(master_port)) - else: - assert master_port > 0, "master_port must be specified for non-zero rank" - - -def is_distributed() -> bool: - """Return True if the distributed process group has been initialized""" - return _world_size is not None and _world_size > 1 - - -def get_master_ip() -> str: - """Get the IP address of the master node""" - return str(_master_ip) - - -def get_master_port() -> int: - """Get the port of the rpc server on the master node""" - return _master_port - - -def get_world_size() -> int: - """Get the total number of processes participating in the job""" - return _world_size - - -def get_rank() -> int: - """Get the rank of the current process""" - return _rank - - -def get_backend() -> str: - """Get the backend str""" - return str(_backend) - - -def get_group_id() -> int: - """Get group id for collective communication""" - global _group_id - _group_id += 1 - return _group_id - - -def group_barrier() -> None: - """Block until all ranks in the group reach this barrier""" - mgb.config.group_barrier(_master_ip, _master_port, _world_size, _rank) - - -def synchronized(func: Callable): - """Decorator. Decorated function will synchronize when finished. - Specifically, we use this to prevent data race during hub.load""" - - @functools.wraps(func) - def wrapper(*args, **kwargs): - if not is_distributed(): - return func(*args, **kwargs) - - ret = func(*args, **kwargs) - group_barrier() - return ret - - return wrapper - - -def get_free_ports(num: int) -> List[int]: - """Get one or more free ports. - """ - socks, ports = [], [] - for i in range(num): - sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - sock.bind(("", 0)) - socks.append(sock) - ports.append(sock.getsockname()[1]) - for sock in socks: - sock.close() - return ports diff --git a/python_module/megengine/functional/__init__.py b/python_module/megengine/functional/__init__.py deleted file mode 100644 index 6220c599..00000000 --- a/python_module/megengine/functional/__init__.py +++ /dev/null @@ -1,118 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# pylint: disable=redefined-builtin -from .elemwise import ( - abs, - add, - arccos, - arcsin, - ceil, - clamp, - cos, - divide, - equal, - exp, - floor, - greater, - greater_equal, - isinf, - isnan, - less, - less_equal, - log, - maximum, - minimum, - mod, - multiply, - power, - relu, - round, - sigmoid, - sin, - subtract, - tanh, -) -from .graph import add_extra_vardep, add_update, grad -from .loss import ( - binary_cross_entropy, - cross_entropy, - cross_entropy_with_softmax, - hinge_loss, - l1_loss, - nll_loss, - smooth_l1_loss, - square_loss, - triplet_margin_loss, -) -from .math import ( - argmax, - argmin, - logsumexp, - max, - mean, - min, - norm, - normalize, - prod, - sqrt, - sum, -) -from .nn import ( - assert_equal, - avg_pool2d, - batch_norm2d, - batched_matrix_mul, - conv2d, - conv_transpose2d, - dropout, - embedding, - eye, - flatten, - identity, - indexing_one_hot, - interpolate, - leaky_relu, - linear, - local_conv2d, - matrix_mul, - max_pool2d, - one_hot, - prelu, - remap, - roi_align, - roi_pooling, - softmax, - softplus, - sync_batch_norm, - warp_perspective, -) -from .quantized import conv_bias_activation -from .sort import argsort, sort, top_k -from .tensor import ( - add_axis, - arange, - broadcast_to, - concat, - cond_take, - dimshuffle, - gather, - linspace, - remove_axis, - reshape, - scatter, - shapeof, - transpose, - where, - zeros_like, -) -from .utils import accuracy, zero_grad - -# delete namespace -# pylint: disable=undefined-variable -del elemwise, graph, loss, math, nn, tensor # type: ignore[name-defined] diff --git a/python_module/megengine/functional/debug_param.py b/python_module/megengine/functional/debug_param.py deleted file mode 100644 index b27f4b4b..00000000 --- a/python_module/megengine/functional/debug_param.py +++ /dev/null @@ -1,49 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import os - -_conv_execution_strategy = os.getenv("MEGENGINE_CONV_EXECUTION_STRATEGY", "HEURISTIC") - - -def get_conv_execution_strategy() -> str: - """Returns the execuation strategy of :class:`~.Conv2d`. - - See :func:`~.set_conv_execution_strategy` for possible return values - """ - return _conv_execution_strategy - - -def set_conv_execution_strategy(option: str): - """Sets the execuation strategy of :class:`~.Conv2d`. - - :param option: Decides how :class:`~.Conv2d` algorithm is chosen. - Available values: - - * 'HEURISTIC' uses heuristic to choose the fastest algorithm. - * 'PROFILE' runs possible algorithms on real device to find the best. - * 'PROFILE_HEURISTIC' uses profile result and heuristic to choose the fastest algorithm. - * 'PROFILE_REPRODUCIBLE' uses the fastest of profile result that is also reproducible. - * 'HEURISTIC_REPRODUCIBLE' uses heuristic to choose the fastest algorithm that is also reproducible. - - The default strategy is 'HEURISTIC'. - - It can also be set through the environmental variable 'MEGENGINE_CONV_EXECUTION_STRATEGY'. - """ - valid_option = ( - "HEURISTIC", - "PROFILE", - "PROFILE_HEURISTIC", - "PROFILE_REPRODUCIBLE", - "HEURISTIC_REPRODUCIBLE", - ) - if not option in valid_option: - raise ValueError("Valid option can only be one of {}".format(valid_option)) - - global _conv_execution_strategy # pylint: disable=global-statement - _conv_execution_strategy = option diff --git a/python_module/megengine/functional/elemwise.py b/python_module/megengine/functional/elemwise.py deleted file mode 100644 index 16889652..00000000 --- a/python_module/megengine/functional/elemwise.py +++ /dev/null @@ -1,299 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# pylint: disable=unused-argument,invalid-name,redefined-builtin,arguments-out-of-order -import functools - -import megengine._internal as mgb - -from ..core.graph import _use_default_if_none -from ..core.tensor import Tensor, wrap_io_tensor - -__all__ = [ - "abs", - "arccos", - "add", - "arcsin", - "clamp", - "ceil", - "cos", - "divide", - "equal", - "exp", - "greater", - "greater_equal", - "floor", - "isinf", - "isnan", - "less", - "less_equal", - "log", - "maximum", - "minimum", - "mod", - "multiply", - "power", - "relu", - "round", - "sigmoid", - "sin", - "subtract", - "tanh", -] - - -def _elemwise(mode): # DONT export - """Decorator helps to wrap megbrain element-wise oprs""" - - def elemwise_decorator(func): - @functools.wraps(func) - @wrap_io_tensor - def elemwise_func(*inputs) -> Tensor: - if all(isinstance(i, (int, float)) for i in inputs): - device, comp_graph = _use_default_if_none(None, None) - ret = mgb.opr.elemwise( - *inputs, mode=mode, comp_node=device, comp_graph=comp_graph - ) - return ret.inferred_value[0] - return mgb.opr.elemwise(*inputs, mode=mode) - - return elemwise_func - - return elemwise_decorator - - -@_elemwise("ABS") -def abs(x): - """Calculate the absolute value element-wise.""" - - -@_elemwise("ACOS") -def arccos(x): - """Inverse cosine, element-wise.""" - - -@_elemwise("ADD") -def add(x, y): - """Element-wise addition.""" - - -@_elemwise("ASIN") -def arcsin(x): - """Inverse sine, element-wise.""" - - -@_elemwise("CEIL") -def ceil(x): - """Return the ceil of the input, element-wise.""" - - -@_elemwise("COS") -def cos(x): - """Cosine, element-wise.""" - - -@_elemwise("TRUE_DIV") -def divide(x, y): - """Return (x / y) element-wise.""" - - -@_elemwise("EQ") -def equal(x, y): - """Return (x == y) element-wise.""" - - -@_elemwise("EXP") -def exp(x): - """Calculate the exponential element-wise""" - - -@_elemwise("FLOOR") -def floor(x): - """Return the floor of the input, element-wise""" - - -def greater(x, y): - """Return (x > y) element-wise.""" - return less(y, x) - - -def greater_equal(x, y): - """Return (x >= y) element-wise""" - return less_equal(y, x) - - -@_elemwise("LT") -def less(x, y): - """Return (x < y) element-wise.""" - - -@_elemwise("LEQ") -def less_equal(x, y): - """Return (x =< y) element-wise.""" - - -@_elemwise("LOG") -def log(x): - """Natural logarithm (base `e`), element-wise.""" - - -@_elemwise("MAX") -def maximum(x, y): - """Element-wise maximum of array elements.""" - - -@_elemwise("MIN") -def minimum(x, y): - """Element-wise minimum of array elements.""" - - -@_elemwise("MOD") -def mod(x, y): - """Return element-wise remainder of division.""" - - -@_elemwise("MUL") -def multiply(x, y): - """Element-wise multiplication.""" - - -@_elemwise("POW") -def power(x, y): - """First tensor elements raised to powers from second tensor (x ** y), element-wise.""" - - -@_elemwise("RELU") -def relu(x): - """Return `max(x, 0)` element-wise.""" - - -@_elemwise("ROUND") -def round(x): - """Round tensor to int element-wise.""" - - -@_elemwise("SIGMOID") -def sigmoid(x): - """Return 1 / ( 1 + exp( -x ) ) element-wise.""" - - -@_elemwise("SIN") -def sin(x): - """Sine, element-wise.""" - - -@_elemwise("SUB") -def subtract(x, y): - """Subtract arguments element-wise""" - - -@_elemwise("TANH") -def tanh(x): - """Compute hyperbolic tangent element-wise.""" - - -@wrap_io_tensor -def clamp(inp: Tensor, lower=None, upper=None) -> Tensor: - r""" - Clamp all elements in :attr:`inp` into the range `[` :attr:`lower`, :attr:`upper` `]` and return - a resulting tensor: - - .. math:: - y_i = \begin{cases} - \text{lower} & \text{if } x_i < \text{lower} \\ - x_i & \text{if } \text{lower} \leq x_i \leq \text{upper} \\ - \text{upper} & \text{if } x_i > \text{upper} - \end{cases} - - :param inp: the input tensor. - :param lower: lower-bound of the range to be clamped to - :param upper: upper-bound of the range to be clamped to - - Example: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - a = tensor(np.arange(5).astype(np.int32)) - - print(F.clamp(a, 2, 4).numpy()) - - print(F.clamp(a, lower=3).numpy()) - - print(F.clamp(a, upper=3).numpy()) - - .. testoutput:: - - [2 2 2 3 4] - [3 3 3 3 4] - [0 1 2 3 3] - - """ - assert ( - lower is not None or upper is not None - ), "At least one of 'lower' or 'upper' must not be None" - if lower is not None: - if upper is not None: - assert lower <= upper, "clamp lower bound is bigger that upper bound" - return minimum(maximum(inp, lower), upper) - else: - return maximum(inp, lower) - else: - return minimum(inp, upper) - - -def isnan(inp: Tensor) -> Tensor: - r"""Returns a new tensor representing if each element is NaN or not. - - :param: inp - :return: a new tensor representing if each element in :attr:`inp` is NaN or not. - - Examples: - - .. testcode:: - - from megengine import tensor - import megengine.functional as F - - x = tensor([1, float("nan"), 0]) - - print(F.isnan(x)) - - .. testoutput:: - - Tensor([0 1 0], dtype=uint8) - - """ - return (inp != inp).astype("uint8") - - -def isinf(inp: Tensor) -> Tensor: - r"""Returns a new tensor representing if each element is Inf or not. - - :param: inp - :return: a new tensor representing if each element in :attr:`inp` is Inf or not. - - Examples: - - .. testcode:: - - from megengine import tensor - import megengine.functional as F - - x = tensor([1, float("inf"), 0]) - - print(F.isinf(x)) - - .. testoutput:: - - Tensor([0 1 0], dtype=uint8) - - """ - return (abs(inp) == float("inf")).astype("uint8") diff --git a/python_module/megengine/functional/external.py b/python_module/megengine/functional/external.py deleted file mode 100644 index 6c93d217..00000000 --- a/python_module/megengine/functional/external.py +++ /dev/null @@ -1,65 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# pylint: disable=too-many-lines -from typing import List - -import megengine._internal as mgb - -from ..core import Tensor, wrap_io_tensor - - -@wrap_io_tensor -def cambricon_subgraph( - inputs: List[Tensor], data: bytes, symbol: str, tensor_dim_mutable: bool, -) -> List[Tensor]: - """Load a serialized Cambricon subgraph (i.e. cnrtModel_t) and - execute the operations defined in the subgraph. - - :param inputs: List of input tensors of the subgraph. - :param data: The serialized subgraph. - :param symbol: The name of the function in the subgraph. - The function is corresponding to a cnmlFusionOp - which is added to the cnmlModel_t/cnrtModel_t. - :param tensor_dim_mutable: Whether the input tensors' shapes are mutalbe - in cnrtModel_t - """ - return mgb.opr.cambricon_runtime( - data, symbol, tuple(map(lambda x: x._symvar, inputs)), tensor_dim_mutable - ) - - -@wrap_io_tensor -def atlas_subgraph(inputs: List[Tensor], data: bytes) -> List[Tensor]: - """Load a serialized Atlas subgraph (i.e. om model) and - execute the operations defined in the subgraph. - - :param inputs: List of input tensors of the subgraph. - :param data: The serialized subgraph. - """ - return mgb.opr.atlas_runtime(tuple(map(lambda x: x._symvar, inputs)), data) - - -@wrap_io_tensor -def extern_opr_subgraph( - inputs, output_shapes: List[tuple], dump_name: str, dump_data: bytes, -) -> List[Tensor]: - """Load a serialized extern opr subgraph and fake execute the operator - - :param inputs: Tensor or list of input tensors. - :param output_shapes: The output shapes. - :param dump_name: The serialized subgraph name. - :param dump_data: The serialized subgraph. - - :return: List of tensors - """ - if not isinstance(inputs, list): - inputs = [inputs] - return mgb.opr.extern_c_opr_placeholder( - inputs, output_shapes, dump_name=dump_name, dump_data=dump_data, - ) diff --git a/python_module/megengine/functional/graph.py b/python_module/megengine/functional/graph.py deleted file mode 100644 index 5dbdadb6..00000000 --- a/python_module/megengine/functional/graph.py +++ /dev/null @@ -1,125 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import collections -from typing import Iterable, Optional, Union - -import megengine._internal as mgb - -from ..core.graph import get_default_graph -from ..core.tensor import Tensor, wrap_io_tensor -from ..jit import barrier, mark_impure, trace - - -@wrap_io_tensor -def grad( - target: Tensor, - wrt: Union[Tensor, Iterable[Tensor]], - warn_mid_wrt: bool = True, - use_virtual_grad: bool = None, - return_zero_for_nodep: bool = True, -) -> Union[Tensor, Iterable[Optional[Tensor]], None]: - r"""Compute the symbolic gradient of ``target`` with repect to ``wrt``. - - ``wrt`` can either be a single tensor or a sequence of tensors. - - :param target: ``grad`` target tensor - :param wrt: with respect to which to compute the gradient - :param warn_mid_wrt: whether to give warning if ``wrt`` is not endpoint - :param use_virtual_grad: whether to use virtual ``grad`` opr, so fwd graph can - be optimized before applying ``grad``; if ``None`` is given, then virtual - ``grad`` would be used if ``graph_opt_level >= 2`` - :param return_zero_for_nodep: if ``target`` does not depend on ``wrt``, set to True to return - a zero-valued :class:`~.Tensor` rather than ``None``; can't be set to False when using - virtual ``grad`` opr. - :return: :math:`\partial\text{target} / \partial\text{wrt}` - """ - if not isinstance(wrt, mgb.SymbolVar): - assert isinstance(wrt, collections.Iterable) - wrt = [w._symvar for w in wrt] - - return mgb.grad(target, wrt, warn_mid_wrt, use_virtual_grad, return_zero_for_nodep) - - -_add_update_cache = {} # type: dict - -_dummy = mgb.SharedScalar(0) - - -def add_update( - dest: Tensor, - delta: Tensor, - *, - alpha: Union[Tensor, float, int] = 1.0, - beta: Union[Tensor, float, int] = 1.0, - bias: Union[Tensor, float, int] = 0.0 -): - r"""Inplace modify ``dest`` as follows: - - .. math:: - dest = alpha * dest + beta * delta + bias - - :param dest: input data that will be inplace modified. - :param delta: update value that will be added to ``dest``. - :param alpha: weight ratio of ``dest``. Default: 1.0 - :param beta: weight ratio of ``delta``. Default: 1.0 - :param bias: bias value appended to the result. Default: 0.0 - """ - - if isinstance(beta, Tensor) or isinstance(alpha, Tensor): - delta *= beta - beta = 1.0 - if isinstance(alpha, Tensor): - delta += (alpha - 1.0) * dest - alpha = 1.0 - if isinstance(bias, Tensor): - delta += bias - bias = 0.0 - - comp_graph = dest._comp_graph or get_default_graph() - comp_node = dest._comp_node - - if not isinstance(delta, Tensor): - _delta = mgb.make_immutable( - value=delta, comp_node=comp_node, comp_graph=comp_graph - ) - else: - _delta = delta._attach(comp_graph) - - _dest = dest._attach(comp_graph) - - # use (dest, delta) as the key, so we could not add the same delta to dest in static graph - key = (comp_graph._id(), _dest.id, _delta.id) - if key in _add_update_cache: - _alpha, _beta, _bias, config = _add_update_cache[key] - mgb.mgb._mgb.SharedScalar__set(_alpha, alpha) - mgb.mgb._mgb.SharedScalar__set(_beta, beta) - mgb.mgb._mgb.SharedScalar__set(_bias, bias) - else: - _alpha = mgb.SharedScalar(alpha) - _beta = mgb.SharedScalar(beta) - _bias = mgb.SharedScalar(bias) - config = mgb.helper.gen_config(None, comp_node, None) - _add_update_cache[key] = (_alpha, _beta, _bias, config) - - u = mgb.mgb._Opr.add_update( - _dest, barrier(_delta), _alpha, _beta, _bias, _dummy, config - ) - mark_impure(u) - - if trace._active_instance: - dest._override_symvar_during_trace(trace._active_instance, u) - - return Tensor(u) - - -@wrap_io_tensor -def add_extra_vardep(oup: Tensor, dep: Tensor): - r"""Explicitly set the dependency that tensor ``oup`` depends on tensor ``dep``. - """ - return mgb.config.add_extra_vardep(oup, dep) diff --git a/python_module/megengine/functional/loss.py b/python_module/megengine/functional/loss.py deleted file mode 100644 index 0755825d..00000000 --- a/python_module/megengine/functional/loss.py +++ /dev/null @@ -1,391 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import megengine._internal as mgb - -from ..core.tensor import Tensor -from .elemwise import abs, equal, log, maximum, power, relu -from .nn import assert_equal, indexing_one_hot -from .tensor import where -from .utils import zero_grad - - -def l1_loss(pred: Tensor, label: Tensor) -> Tensor: - r""" - Calculates the mean absolute error (MAE) between - each element in the pred :math:`x` and label :math:`y`. - - The mean absolute error can be described as: - - .. math:: \ell(x,y) = mean\left(L \right) - - where - - .. math:: - - L = \{l_1,\dots,l_N\}, \quad - l_n = \left| x_n - y_n \right|, - - :math:`x` and :math:`y` are tensors of arbitrary shapes with a total - of :math:`N` elements each. :math:`N` is the batch size. - - :param pred: The predicted result from model. - :param label: The ground truth to compare. - - Examples: - - .. testcode:: - - import numpy as np - import megengine as mge - import megengine.functional as F - ipt = mge.tensor(np.array([3, 3, 3, 3]).astype(np.float32)) - tgt = mge.tensor(np.array([2, 8, 6, 1]).astype(np.float32)) - loss = F.l1_loss(ipt,tgt) - print(loss.numpy()) - - Outputs: - - .. testoutput:: - - [2.75] - - """ - - diff = pred - label - return abs(diff).mean() - - -def square_loss(pred: Tensor, label: Tensor) -> Tensor: - r""" - Calculates the mean squared error (squared L2 norm) between - each element in the pred :math:`x` and label :math:`y`. - - The mean squared error can be described as: - - .. math:: \ell(x, y) = mean\left( L \right) - - where - - .. math:: - - L = \{l_1,\dots,l_N\}, \quad - l_n = \left( x_n - y_n \right)^2, - - :math:`x` and :math:`y` are tensors of arbitrary shapes with a total - of :math:`N` elements each. :math:`N` is the batch size. - - :param pred: The predicted result from model. - :param label: The ground truth to compare. - - Shape: - - pred: :math:`(N, *)` where :math:`*` means any number of additional - dimensions - - label: :math:`(N, *)`. Same shape as ``pred`` - - """ - diff = pred - label - return (diff ** 2).mean() - - -def cross_entropy( - inp: Tensor, target: Tensor, axis: int = 1, ignore_index: int = -1 -) -> Tensor: - r""" - Returns the cross entropy loss in a classification problem. - - .. math:: \textrm{CrossEntropy}(x, y) = - \sum_{i} y_i\log(x_i) - - :param inp: The input tensor representing the predicted probability. - :param label: The input tensor representing the classification label. - :param axis: An axis along which cross_entropy will be applied. Default: 1 - :param ignore_index: Specifies a target value that is ignored and does not contribute to the input gradient. Default: -1 - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - - data_shape = (1, 2) - label_shape = (1, ) - - pred = tensor(np.array([0.5, 0.5], dtype=np.float32).reshape(data_shape)) - label = tensor(np.ones(label_shape, dtype=np.int32)) - loss = F.cross_entropy(pred, label) - print(loss.numpy()) - - Outputs: - - .. testoutput:: - - [0.69] - - """ - n0 = inp.ndim - n1 = target.ndim - assert n0 == n1 + 1, ( - "target ndim must be one less than input ndim; input_ndim={} " - "target_ndim={}".format(n0, n1) - ) - - if ignore_index != -1: - mask = 1 - equal(target, ignore_index) - target = target * mask - loss = -log(indexing_one_hot(inp, target, axis)) * mask - return loss.sum() / maximum(mask.sum(), 1.0) - else: - return -log(indexing_one_hot(inp, target, axis)).mean() - - -def cross_entropy_with_softmax( - pred: Tensor, label: Tensor, axis: int = 1, label_smooth: float = 0 -) -> Tensor: - r""" - Returns loss after applying :func:`~.softmax` + :func:`~.cross_entropy`. - - It has better numerical stability compared with sequential calls to :func:`~.softmax` and :func:`~.cross_entropy`. - - When using label smoothing, the label distribution is as follows: - - .. math:: y^{LS}_{k}=y_{k}\left(1-\alpha\right)+\alpha/K - - where :math:`y^{LS}` and :math:`y` are new label distribution and origin label distribution respectively. - k is the index of label distribution. :math:`\alpha` is label_smooth and :math:`K` is the number of classes. - - :param pred: The input tensor representing the predicted probability. - :param label: The input tensor representing the classification label. - :param axis: An axis along which softmax will be applied. Default: 1. - :param label_smooth: A label smoothing of parameter that can re-distribute target distribution. Default: 0. - """ - - n0 = pred.ndim - n1 = label.ndim - assert n0 == n1 + 1, ( - "target ndim must be one less than input ndim; input_ndim={} " - "target_ndim={}".format(n0, n1) - ) - - num_classes = pred.shapeof(axis) - - # Denominator of the softmax - offset = zero_grad(pred.max(axis=axis, keepdims=True)) - pred = pred - offset - down = mgb.opr.elem.exp(pred).sum(axis=axis, keepdims=True) - - up = indexing_one_hot(pred, label, axis) - - if label_smooth != 0: - factor = label_smooth / num_classes - up = up * (1 - label_smooth) + pred.sum(axis=axis, keepdims=True) * factor - - return (log(down) - up).mean() - - -def triplet_margin_loss( - anchor: Tensor, positive: Tensor, negative: Tensor, margin: float = 1.0, p: int = 2 -) -> Tensor: - r""" - Creates a criterion that measures the triplet loss given an input tensors. - - .. math:: - - L(a, p, n) = max\left\{d\left(a_{i},p_{i}\right)-d\left(a_{i}, n_{i}\right)+margin, 0\right\},\ - d\left(x_{i},y_{i}\right)=\left\|x_{i}-y_{i}\right\|_{p} - - :param anchor: The input tensor representing the anchor samples. - :param positive: The input tensor representing the positive samples. - :param negative: The input tensor representing the negative samples. - :param margin: Default: 1.0 - :param p: The norm degree for pairwise distance. Default: 2.0 - """ - - s0 = anchor.shapeof() - s1 = positive.shapeof() - s2 = negative.shapeof() - assert_equal(s0, s1) - assert_equal(s1, s2) - - n0 = anchor.ndim - n1 = positive.ndim - n2 = negative.ndim - assert n0 == 2 and n1 == 2 and n2 == 2, ( - "anchor ndim, positive ndim, and negative ndim must be 2; " - "anchor_ndim={} positive_ndim={} negative_ndim={}".format(n0, n1, n2) - ) - assert p > 0, "a margin with a value greater than 0; p={}".format(p) - - diff0 = abs(anchor - positive) - diff1 = abs(anchor - negative) - - d1 = power(power(diff0, p).sum(axis=1, keepdims=True), 1 / p) - d2 = power(power(diff1, p).sum(axis=1, keepdims=True), 1 / p) - - loss = maximum(d1 - d2 + margin, 0) - - return loss.mean() - - -def binary_cross_entropy(pred: Tensor, label: Tensor) -> Tensor: - r"""Function that measures the Binary Cross Entropy between the target and the prediction. - - :param pred: (N,*) where * means, any number of additional dimensions. - :param label: (N,*), same shape as the input. - - """ - s0 = pred.shapeof() - s1 = label.shapeof() - - assert_equal(s0, s1) - - return -1.0 * (label * log(pred) + (1.0 - label) * log(1 - pred)).mean() - - -def nll_loss( - pred: Tensor, label: Tensor, axis: int = 1, ignore_index: int = -1 -) -> Tensor: - r""" - The negative log likelihood loss. - - :param pred: The predicted result from model. - :param label: The ground truth to compare. - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - data_shape = (2, 2) - label_shape = (2, ) - - data = tensor( - np.array([[1, 0.5], [0.3, 1.2]], dtype=np.float32).reshape(data_shape), - ) - label = tensor( - np.ones(label_shape, dtype=np.int32) - ) - pred = F.log(F.softmax(data)) - loss1 = F.nll_loss(pred, label) - loss2 = F.cross_entropy_with_softmax(data, label) - print(loss1.numpy(), loss2.numpy()) - - Outputs: - - .. testoutput:: - - [0.6576154] [0.6576154] - - """ - n0 = pred.ndim - n1 = label.ndim - assert n0 == n1 + 1, ( - "target ndim must be one less than input ndim; input_ndim={} " - "target_ndim={}".format(n0, n1) - ) - - mask = 1.0 - equal(label, ignore_index) - label = label * mask - - loss = indexing_one_hot(pred, label, axis) * mask - - return -1.0 * loss.sum() / maximum(mask.sum(), 1.0) - - -def hinge_loss(pred: Tensor, label: Tensor, norm: str = "L1") -> Tensor: - r""" - Caculate the hinge loss which is often used in SVMs. - - The hinge loss can be described as: - - .. math:: loss(x, y) = \frac{1}{N}\sum_i\sum_j(max(0, 1 - x_{ij}*y_{ij})) - - :param pred: The input tensor representing the predicted probability, shape is (N, C). - :param label: The input tensor representing the binary classification label, shape is (N, C). - :param norm: Specify the norm to caculate the loss, should be "L1" or "L2". - - Examples: - - .. testcode:: - - from megengine import tensor - import megengine.functional as F - - pred = tensor([[0.5, -0.5, 0.1], [-0.6, 0.7, 0.8]]) - label = tensor([[1, -1, -1], [-1, 1, 1]]) - - loss = F.hinge_loss(pred, label) - - print(loss.numpy()) - - Outputs: - - .. testoutput:: - - [1.5] - - """ - assert norm in ["L1", "L2"], "norm must be L1 or L2" - # Converts binary labels to -1/1 labels. - loss = relu(1.0 - pred * label) - if norm == "L1": - return loss.sum(axis=1).mean() - else: - return (loss ** 2).sum(axis=1).mean() - - -def smooth_l1_loss(pred: Tensor, label: Tensor) -> Tensor: - r""" - Caculate the smooth l1 loss proposed in `Fast R-CNN paper by Ross Girshick`. - - The smooth l1 loss can be described as: - - .. math:: - \text{loss}(x, y) = \frac{1}{n} \sum_{i} l_{i} - - where :math:`l_{i}` is given by: - - .. math:: - l_{i} = - \begin{cases} - 0.5 (x_i - y_i)^2, & \text{if } |x_i - y_i| < 1 \\ - |x_i - y_i| - 0.5, & \text{otherwise } - \end{cases} - - :param pred: The predicted result from model. - :param label: The ground truth to compare. - - Examples: - - .. testcode:: - - from megengine import tensor - import megengine.functional as F - - pred = tensor([[0.5, -0.5, 0.1], [-0.6, 0.7, 0.8]]) - label = tensor([[0.4, 1.5, 1.2], [0., 0.1, 2.2]]) - - loss = F.smooth_l1_loss(pred, label) - - print(loss.numpy()) - - Outputs: - - .. testoutput:: - - [0.5608334] - """ - diff = abs(pred - label) - l2_loss = 0.5 * (diff ** 2) - l1_loss = diff - 0.5 - mask = diff < 1 - loss = where(mask, l2_loss, l1_loss) - return loss.mean() diff --git a/python_module/megengine/functional/math.py b/python_module/megengine/functional/math.py deleted file mode 100644 index 06f9cebe..00000000 --- a/python_module/megengine/functional/math.py +++ /dev/null @@ -1,333 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import math -import numbers -from typing import Optional, Sequence, Union - -import megengine._internal as mgb - -from ..core import Tensor, wrap_io_tensor -from .elemwise import clamp, exp, isinf, log -from .tensor import remove_axis, where, zeros_like - - -@wrap_io_tensor -def sum(inp: Tensor, axis: Optional[int] = None, keepdims: bool = False) -> Tensor: - r"""Returns the sum of each row of the ``inp`` tensor in the given ``axis``. - - :param inp: The input tensor. - :param axis: The dimension to reduce. If None, all the dimensions will be reduced. - Default: None - :param keepdims: Whether the output tensor has ``axis`` retained or not. - Default: False - :return: The output tensor - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - - data = tensor(np.arange(1, 7, dtype=np.int32).reshape(2, 3)) - out = F.sum(data) - print(out.numpy()) - - .. testoutput:: - - [21] - - """ - return mgb.opr.reduce_(inp, "SUM", axis, keepdims) - - -@wrap_io_tensor -def prod(inp: Tensor, axis: Optional[int] = None, keepdims=False) -> Tensor: - r""" - Returns the element product of input tensor along given *axis*. - - :param inp: The input tensor - :param axis: The dimension to reduce. If None, all the dimensions will be reduced. Default: ``None`` - :param keepdims: Whether the output tensor has *axis* retained or not. Default: ``False`` - :return: The output tensor - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - - data = tensor(np.arange(1, 7, dtype=np.int32).reshape(2, 3)) - out = F.prod(data) - print(out.numpy()) - - Outputs: - - .. testoutput:: - - [720] - - """ - return mgb.opr.reduce_(inp, "PRODUCT", axis, keepdims) - - -@wrap_io_tensor -def mean(inp: Tensor, axis: Optional[int] = None, keepdims: bool = False) -> Tensor: - """Returns the mean value of each row of the ``inp`` tensor in - the given ``axis``. If axis is a list of dimensions, - reduce over all of them. - - :param inp: The input tensor - :param axis: The dimension to reduce. If None, all the dimensions will be reduced. Default: None - :param keepdims: Whether the output tensor has ``axis`` retained or not. Default: False - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - - data = tensor(np.arange(1, 7, dtype=np.int32).reshape(2, 3)) - out = F.mean(data) - print(out.numpy()) - - .. testoutput:: - - [3.5] - - """ - return mgb.opr.mean(inp, axis, keepdims) - - -@wrap_io_tensor -def min(inp: Tensor, axis: Optional[int] = None, keepdims: bool = False) -> Tensor: - r""" - Returns the min value of input tensor along given *axis*. - - :param inp: The input tensor - :param axis: The dimension to reduce. If None, all the dimensions will be reduced. Default: None - :param keepdims: Whether the output tensor has *axis* retained or not. Default: False - :return: The output tensor - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - - x = tensor(np.arange(1, 7, dtype=np.int32).reshape(2,3)) - y = F.min(x) - print(y.numpy()) - - Outputs: - - .. testoutput:: - - [1] - - """ - return mgb.opr.reduce_(inp, "MIN", axis, keepdims) - - -@wrap_io_tensor -def max(inp: Tensor, axis: Optional[int] = None, keepdims: bool = False) -> Tensor: - r"""Returns the max value of the input tensor along given *axis*. - - :param inp: The input tensor - :param axis: The dimension to reduce. If None, all the dimensions will be reduced. Default: None - :param keepdims: Whether the output tensor has *axis* retained or not. Default: False - :return: The output tensor - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - - x = tensor(np.arange(1, 7, dtype=np.int32).reshape(2,3)) - y = F.max(x) - print(y.numpy()) - - .. testoutput:: - - [6] - - """ - return mgb.opr.reduce_(inp, "MAX", axis, keepdims) - - -@wrap_io_tensor -def sqrt(inp: Tensor) -> Tensor: - """ - Return a new tensor with the square-root of the elements of ``inp`` - - :param inp: The input tensor - :return: The computed tensor - - Examples: - - .. testcode:: - - import numpy as np - import megengine as mge - import megengine.functional as F - - data = mge.tensor(np.arange(0, 6, dtype=np.float32).reshape(2, 3)) - out = F.sqrt(data) - print(out.numpy()) - - Outputs: - - .. testoutput:: - - [[0. 1. 1.4142] - [1.7321 2. 2.2361 ]] - - """ - - return mgb.opr.sqrt(inp) - - -def norm(inp: Tensor, p: int = 2, axis: Optional[int] = None, keepdims=False): - """Calculate ``p``-norm of input tensor along certain axis. - - :param inp: The input tensor - :param p: power of value ``p`` applied to ``inp``. Default: 2 - :param axis: The dimension to reduce. If None, all the dimensions will be reduced. Default: None - :param keepdims: Whether the output tensor has ``axis`` retained or not. Default: False - :return: The output tensor - - """ - if axis is None: - inp = inp.reshape(-1) - return (inp ** p).sum(axis=axis, keepdims=keepdims) ** (1.0 / p) - - -@wrap_io_tensor -def argmin(inp: Tensor, axis: Optional[int] = None, keepdims: bool = False) -> Tensor: - r"""Returns the indices of the minimum values along an axis - - :param inp: The input tensor - :param axis: The dimension to reduce. If None, all the dimensions will be reduced. Default: None - :param keepdims: Whether the output tensor has *axis* retained or not. Default: False - :return: The output tensor - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - - x = tensor(np.arange(1, 7, dtype=np.int32).reshape(2,3)) - y = F.argmin(x) - print(y.numpy()) - - .. testoutput:: - - [0] - - """ - return mgb.opr.argmin(inp, axis, keepdims) - - -@wrap_io_tensor -def argmax(inp: Tensor, axis: Optional[int] = None, keepdims: bool = False) -> Tensor: - r"""Returns the indices of the maximum values along an axis - - :param inp: The input tensor - :param axis: The dimension to reduce. If None, all the dimensions will be reduced. Default: None - :param keepdims: Whether the output tensor has *axis* retained or not. Default: False - :return: The output tensor - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - - x = tensor(np.arange(1, 7, dtype=np.int32).reshape(2,3)) - y = F.argmax(x) - print(y.numpy()) - - .. testoutput:: - - [5] - - """ - return mgb.opr.argmax(inp, axis, keepdims) - - -def normalize( - inp: Tensor, p: int = 2, axis: Optional[int] = None, eps: float = 1e-12 -) -> Tensor: - r"""Perform :math:`L_p` normalization of input tensor along certain axis. - - For a tensor :attr:`inp` of shape :math:`(n_0, ..., n_{dim}, ..., n_k)`, each - :math:`n_{dim}` -element vector :math:`v` along dimension :attr:`axis` is transformed as: - - .. math:: - v = \frac{v}{\max(\lVert v \rVert_p, \epsilon)}. - - :param inp: the input tensor - :param p: power of value ``p`` applied to ``inp``. Default: 2 - :param axis: The dimension to reduce. If None, all the dimensions will be reduced - to calculate the norm. Default: None - :param eps: a small value to avoid division by zero. Default: 1e-12 - :return: the normalized output tensor - - """ - if axis is None: - return inp / clamp(norm(inp, p), lower=eps) - else: - return inp / clamp(norm(inp, p, axis, keepdims=True), lower=eps) - - -def logsumexp(inp: Tensor, axis: Union[int, Sequence[int]], keepdims: bool = False): - r""" - Compute the log of the sum of exponentials of inputs along the given :attr:`axis`. The computation is numerically stabilized. - - .. math:: - - \mathsf{logsumexp}(x_1, \dots, x_n) = \log(\exp(x_1) + \cdots + \exp(x_n)) - - :param inp: The input tensor. - :param axis: Axis over which the sum is taken. It can be a single axis or a list of axes. - :param keepdims: whether to retain :attr:`axis` or not for the output tensor. - - """ - if isinstance(axis, numbers.Integral): - axis = (axis,) - max_value = inp - for dim in axis: - max_value = max_value.max(axis=dim, keepdims=True) - max_value = where( - isinf(max_value).astype("int32"), zeros_like(max_value), max_value - ) - x = exp(inp - max_value) - for dim in axis: - x = x.sum(axis=dim, keepdims=True) - x = max_value + log(x) - if not keepdims: - axis = sorted(axis, reverse=True) - for i in axis: - x = remove_axis(x, axis=i) - return x diff --git a/python_module/megengine/functional/nn.py b/python_module/megengine/functional/nn.py deleted file mode 100644 index 44438d83..00000000 --- a/python_module/megengine/functional/nn.py +++ /dev/null @@ -1,1234 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# pylint: disable=too-many-lines -from typing import Optional, Tuple, Union - -import megengine._internal as mgb -from megengine._internal import CompGraph, CompNode -from megengine._internal.config import add_extra_vardep -from megengine._internal.opr import add_update -from megengine._internal.opr_param_defs import CollectiveComm as CollParam - -from .. import distributed as dist -from ..core import Tensor, wrap_io_tensor -from ..core.graph import _use_default_if_none -from ..distributed.util import get_group_id -from ..jit import barrier, mark_impure -from ..random import uniform -from ..utils.types import _pair, _pair_nonzero -from .debug_param import get_conv_execution_strategy -from .elemwise import exp, log -from .tensor import where -from .utils import _decide_comp_node_and_comp_graph - - -@wrap_io_tensor -def linear(inp: Tensor, weight: Tensor, bias: Optional[Tensor] = None) -> Tensor: - """Applies a linear transformation to the input. - - Refer to :class:`~.module.linear.Linear` for more information. - - :param inp: the input tensor with shape `(N, in_features)`. - :param weight: the weight with shape `(out_features, in_features)`. - :param bias: the bias with shape `(out_features,)`. - Default: ``None`` - """ - orig_shape = inp.shape - inp = inp.reshape(-1, orig_shape[-1]) - ret = mgb.opr.matrix_mul(inp, weight, transposeB=True) - ret = ret.reshape(orig_shape[:-1], weight.shape[0]) - if bias is not None: - ret += bias.reshape(1, bias.shape[0]) - return ret - - -@wrap_io_tensor -def conv2d( - inp: Tensor, - weight: Tensor, - bias: Optional[Tensor] = None, - stride: Union[int, Tuple[int, int]] = 1, - padding: Union[int, Tuple[int, int]] = 0, - dilation: Union[int, Tuple[int, int]] = 1, - groups: int = 1, - conv_mode="CROSS_CORRELATION", - compute_mode="DEFAULT", -) -> Tensor: - """2D convolution operation. - - Refer to :class:`~.Conv2d` for more information. - - :param inp: The feature map of the convolution operation - :param weight: The convolution kernel - :param bias: The bias added to the result of convolution (if given) - :param stride: Stride of the 2D convolution operation. Default: 1 - :param padding: Size of the paddings added to the input on both sides of its - spatial dimensions. Only zero-padding is supported. Default: 0 - :param dilation: Dilation of the 2D convolution operation. Default: 1 - :param groups: number of groups to divide input and output channels into, - so as to perform a "grouped convolution". When ``groups`` is not 1, - ``in_channels`` and ``out_channels`` must be divisible by ``groups``, - and the shape of weight should be ``(groups, out_channel // groups, - in_channels // groups, height, width)``. - :type conv_mode: string or :class:`mgb.opr_param_defs.Convolution.Mode` - :param conv_mode: Supports 'CROSS_CORRELATION' or 'CONVOLUTION'. Default: - 'CROSS_CORRELATION'. - :type compute_mode: string or - :class:`mgb.opr_param_defs.Convolution.ComputeMode` - :param compute_mode: When set to 'DEFAULT', no special requirements will be - placed on the precision of intermediate results. When set to 'FLOAT32', - Float32 would be used for accumulator and intermediate result, but only - effective when input and output are of Float16 dtype. - - """ - ph, pw = _pair(padding) - sh, sw = _pair_nonzero(stride) - dh, dw = _pair_nonzero(dilation) - Sparse = mgb.opr_param_defs.Convolution.Sparse - sparse_type = Sparse.DENSE if groups == 1 else Sparse.GROUP - res = mgb.opr.convolution( - inp, - weight, - pad_h=ph, - pad_w=pw, - stride_h=sh, - stride_w=sw, - dilate_h=dh, - dilate_w=dw, - format="NCHW", - strategy=get_conv_execution_strategy(), - mode=conv_mode, - compute_mode=compute_mode, - sparse=sparse_type, - ) - if bias is not None: - res += bias - return res - - -@wrap_io_tensor -def conv_transpose2d( - inp: Tensor, - weight: Tensor, - bias: Optional[Tensor] = None, - stride: Union[int, Tuple[int, int]] = 1, - padding: Union[int, Tuple[int, int]] = 0, - dilation: Union[int, Tuple[int, int]] = 1, - groups: int = 1, - conv_mode="CROSS_CORRELATION", - compute_mode="DEFAULT", -) -> Tensor: - """2D transposed convolution operation. - - Refer to :class:`~.ConvTranspose2d` for more information. - - :param inp: The feature map of the convolution operation - :param weight: The convolution kernel - :param bias: The bias added to the result of convolution (if given) - :param stride: Stride of the 2D convolution operation. Default: 1 - :param padding: Size of the paddings added to the input on both sides of its - spatial dimensions. Only zero-padding is supported. Default: 0 - :param dilation: Dilation of the 2D convolution operation. Default: 1 - :param groups: number of groups to divide input and output channels into, - so as to perform a "grouped convolution". When ``groups`` is not 1, - ``in_channels`` and ``out_channels`` must be divisible by ``groups``, - and the shape of weight should be ``(groups, out_channel // groups, - in_channels // groups, height, width)``. Default: 1 - :type conv_mode: string or :class:`mgb.opr_param_defs.Convolution.Mode` - :param conv_mode: Supports 'CROSS_CORRELATION' or 'CONVOLUTION'. Default: - 'CROSS_CORRELATION'. - :type compute_mode: string or - :class:`mgb.opr_param_defs.Convolution.ComputeMode` - :param compute_mode: When set to 'DEFAULT', no special requirements will be - placed on the precision of intermediate results. When set to 'FLOAT32', - Float32 would be used for accumulator and intermediate result, but only - effective when input and output are of Float16 dtype. - - """ - ph, pw = _pair(padding) - sh, sw = _pair_nonzero(stride) - dh, dw = _pair_nonzero(dilation) - Sparse = mgb.opr_param_defs.Convolution.Sparse - sparse_type = Sparse.DENSE if groups == 1 else Sparse.GROUP - res = mgb.opr.deconvolution( - inp, - weight, - pad_h=ph, - pad_w=pw, - stride_h=sh, - stride_w=sw, - dilate_h=dh, - dilate_w=dw, - format="NCHW", - strategy=get_conv_execution_strategy(), - mode=conv_mode, - compute_mode=compute_mode, - sparse=sparse_type, - ) - if bias is not None: - res += bias - return res - - -@wrap_io_tensor -def local_conv2d( - inp: Tensor, - weight: Tensor, - stride: Union[int, Tuple[int, int]] = 1, - padding: Union[int, Tuple[int, int]] = 0, - dilation: Union[int, Tuple[int, int]] = 1, - conv_mode="CROSS_CORRELATION", -) -> Tensor: - """Applies spatial 2D convolution over an image with untied kernels. - - Refer to :class:`~.LocalConv2d` for more information. - """ - ret = mgb.opr.group_local( - inp, - weight, - pad_h=padding[0], - pad_w=padding[1], - stride_h=stride[0], - stride_w=stride[1], - dilate_h=dilation[0], - dilate_w=dilation[1], - format="NCHW", - mode=conv_mode, - ) - return ret - - -@wrap_io_tensor -def max_pool2d( - inp: Tensor, - kernel_size: Union[int, Tuple[int, int]], - stride: Optional[Union[int, Tuple[int, int]]] = None, - padding: Union[int, Tuple[int, int]] = 0, -) -> Tensor: - """Applies a 2D max pooling over an input. - - Refer to :class:`~.MaxPool2d` for more information. - - :param inp: The input tensor. - :param kernel_size: The size of the window. - :param stride: The stride of the window. If not provided, its value is set to ``kernel_size``. - Default: None - :param padding: Implicit zero padding to be added on both sides. Default: 0 - - """ - - kh, kw = _pair_nonzero(kernel_size) - sh, sw = _pair_nonzero(stride or kernel_size) - ph, pw = _pair(padding) - mode = mgb.opr_param_defs.Pooling.Mode.MAX - return mgb.opr.pooling( - inp, - mode=mode, - format="NCHW", - stride_h=sh, - stride_w=sw, - pad_h=ph, - pad_w=pw, - window_h=kh, - window_w=kw, - ) - - -@wrap_io_tensor -def avg_pool2d( - inp: Tensor, - kernel_size: Union[int, Tuple[int, int]], - stride: Optional[Union[int, Tuple[int, int]]] = None, - padding: Union[int, Tuple[int, int]] = 0, -) -> Tensor: - """ Applies a 2D average pooling over an input. - - Refer to :class:`~.AvgPool2d` for more information. - - :param inp: The input tensor. - :param kernel_size: The size of the window. - :param stride: The stride of the window. If not provided, its value is set to ``kernel_size``. - Default: None - :param padding: Implicit zero padding to be added on both sides. Default: 0 - - """ - kh, kw = _pair_nonzero(kernel_size) - sh, sw = _pair_nonzero(stride or kernel_size) - ph, pw = _pair(padding) - mode = mgb.opr_param_defs.Pooling.Mode.AVERAGE - return mgb.opr.pooling( - inp, - mode=mode, - format="NCHW", - stride_h=sh, - stride_w=sw, - pad_h=ph, - pad_w=pw, - window_h=kh, - window_w=kw, - ) - - -@wrap_io_tensor -def prelu(inp: Tensor, weight: Tensor) -> Tensor: - r""" - Applies the element-wise PReLU function. - - Refer to :class:`~.PReLU` for more information. - """ - - return mgb.opr.elemwise(inp, 0, mode="MAX") + weight * mgb.opr.elemwise( - inp, 0, mode="MIN" - ) - - -@wrap_io_tensor -def leaky_relu(inp: Tensor, negative_slope: float = 0.01) -> Tensor: - r""" - Applies the element-wise leaky_relu function - - Refer to :class:`~.LeakyReLU` for more information. - """ - - return mgb.opr.elemwise(inp, 0, mode="MAX") + negative_slope * mgb.opr.elemwise( - inp, 0, mode="MIN" - ) - - -@wrap_io_tensor -def softplus(inp: Tensor, beta: float = 1, threshold: float = 20) -> Tensor: - r""" - Performs the elementwise function: - - .. math:: - - \mathsf{softplus}(x) = \log(1+\exp(\beta x)) / \beta. - - For numerical stability the identity function is used when :math:`\beta x > \textrm{threshold}`. - - """ - mask = beta * inp <= threshold - out = log(1 + exp(beta * inp)) / beta - out = where(mask, out, inp) - return out - - -@wrap_io_tensor -def flatten(inp: Tensor, start_axis: int = 0, end_axis: int = -1) -> Tensor: - r""" - Reshapes the tensor by flattening the sub-tensor from dimension ``start_axis`` to dimension ``end_axis``. - - :param inp: The input tensor. - :param start_axis: The start dimension that the sub-tensor to be flattened. Default: 0 - :param end_axis: The end dimension that the sub-tensor to be flattened. Default: -1 - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - - inp_shape = (2, 2, 3, 3) - inp = tensor( - np.arange(36, dtype=np.int32).reshape(inp_shape), - ) - oup = F.flatten(inp, 2) - print(inp.numpy().shape) - print(oup.numpy().shape) - - Outputs: - - .. testoutput:: - - (2, 2, 3, 3) - (2, 2, 9) - - """ - - target_shape = tuple(inp.shape[i] for i in range(start_axis)) + (-1,) - if end_axis != -1: - target_shape += (inp.shape[end_axis + 1 :],) - return inp.reshape(*target_shape) - - -def _get_softmax_axis(ndim: int) -> int: - if ndim in (0, 1, 3): - return 0 - return 1 - - -@wrap_io_tensor -def softmax(inp: Tensor, axis: Optional[int] = None) -> Tensor: - r""" - Applies a softmax function. Softmax is defined as: - - .. math:: - \text{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)} - - It is applied to all elements along axis, and will re-scale them so that - the elements lie in the range `[0, 1]` and sum to 1. - - See :class:`~megengine.module.activation.Softmax` for more details. - - :param inp: The input tensor. - :param axis: An axis along which softmax will be applied. By default, - softmax will apply along the highest ranked axis. - - """ - if axis is None: - axis = _get_softmax_axis(len(inp.imm_shape)) - offset = mgb.opr.zero_grad(inp.max(axis=axis, keepdims=True)) - inp = inp - offset - down = mgb.opr.elem.exp(inp).sum(axis=axis, keepdims=True) - return mgb.opr.elem.exp(inp) / down - - -@wrap_io_tensor -def batch_norm2d( - inp: Tensor, - running_mean: Tensor, - running_var: Tensor, - weight: Optional[Tensor] = None, - bias: Optional[Tensor] = None, - training: bool = False, - momentum: float = 0.9, - eps: float = 1e-5, -) -> Tensor: - """Applies batch normalization to the input. - - Refer to :class:`~.BatchNorm2d` and :class:`~.BatchNorm1d` for more information. - - :param inp: input tensor. - :param running_mean: tensor to store running mean. - :param running_var: tensor to store running variance. - :param weight: scaling tensor in the learnable affine parameters. - See :math:`\gamma` in :class:`~.BatchNorm2d` - :param bias: bias tensor in the learnable affine parameters. - See :math:`\beta` in :class:`~.BatchNorm2d` - :param training: a boolean value to indicate whether batch norm is performed - in traning mode. Default: ``False`` - :param momentum: the value used for the ``running_mean`` and ``running_var`` - computation. - Default: 0.9 - :param eps: a value added to the denominator for numerical stability. - Default: 1e-5. - - """ - - inp = mgb.opr.mark_no_broadcast_elemwise(inp) - _channels = inp.imm_shape[1] - _ndim = len(inp.imm_shape) - _param_shape = (1, _channels) + (1,) * (_ndim - 2) - - assert _ndim == 4, "only 4D tensor supported" - - if weight is not None: - weight = weight.reshape(*_param_shape) - else: - weight = mgb.make_immutable(*_use_default_if_none(None, None), 1.0).broadcast( - *_param_shape - ) - - if bias is not None: - bias = bias.reshape(*_param_shape) - else: - bias = mgb.make_immutable(*_use_default_if_none(None, None), 0.0).broadcast( - *_param_shape - ) - - FwdMode = mgb.opr_param_defs.BN.FwdMode - fwdmode = FwdMode.TRAINING if training else FwdMode.INFERENCE - avg_factor = 1 - momentum - - if running_mean is not None and running_var is not None: - if training: - inp = barrier(inp) - - output = mgb.opr.batch_norm( - inp, - weight, - bias, - running_mean, - running_var, - param_dim="DIM_1C11", - fwd_mode=fwdmode, - epsilon=eps, - avg_factor=avg_factor, - )[-1] - if training: - mark_impure(output) - else: - output = mgb.opr.batch_norm_no_statistic( - inp, - weight, - bias, - param_dim="DIM_1C11", - fwd_mode=fwdmode, - epsilon=eps, - avg_factor=avg_factor, - )[-1] - - return output - - -@wrap_io_tensor -def sync_batch_norm( - input: Tensor, - running_mean: Tensor, - running_var: Tensor, - weight: Optional[Tensor] = None, - bias: Optional[Tensor] = None, - training: bool = False, - momentum: Union[float, Tensor] = 0.9, - eps: float = 1e-5, - eps_mode="ADDITIVE", -) -> Tensor: - """ Applies synchronized batch normalization to the input. - - Refer to :class:`~.BatchNorm2d` and :class:`~.BatchNorm1d` for more information. - - :param inp: input tensor. - :param running_mean: tensor to store running mean. - :param running_var: tensor to store running variance. - :param weight: scaling tensor in the learnable affine parameters. - See :math:`\gamma` in :class:`~.BatchNorm2d` - :param bias: bias tensor in the learnable affine parameters. - See :math:`\beta` in :class:`~.BatchNorm2d` - :param training: a boolean value to indicate whether batch norm is performed - in traning mode. Default: ``False`` - :param momentum: the value used for the ``running_mean`` and ``running_var`` - computation. - Default: 0.9 - :param eps: a value added to the denominator for numerical stability. - Default: 1e-5. - """ - - assert eps_mode in {"MAX", "ADDITIVE"}, "unknown eps_mode: {}".format(eps_mode) - input = mgb.opr.mark_no_broadcast_elemwise(input) - _channels = input.imm_shape[1] - _ndim = len(input.imm_shape) - _param_shape = (1, _channels) + (1,) * (_ndim - 2) - - if training: - - def _sum_on_channel(input): - return mgb.opr.reduce_general([input, _param_shape], mode="sum") - - def _allreduce(stat, key): - return dist.helper.collective_comm_symvar( - stat, key, CollParam.Mode.ALL_REDUCE_SUM - ) - - reduce_size = input.shape[0] - for i in range(2, _ndim): - reduce_size = reduce_size * input.shape[i] - channel_x1s = _sum_on_channel(input) - channel_x2s = _sum_on_channel(input ** 2) - - if dist.is_distributed(): - # reduce all nodes' data to calculate mean and variance - reduce_size = reduce_size.reshape(*(1,) * _ndim) - stat = mgb.opr.concat([reduce_size, channel_x1s, channel_x2s], axis=1) - stat = _allreduce(stat, key="sync_bn_" + str(get_group_id())) - reduce_size = stat[:, :1].reshape(1) - channel_x1s = stat[:, 1 : 1 + _channels] - channel_x2s = stat[:, 1 + _channels :] - - channel_mean = channel_x1s / reduce_size - channel_variance = ( - channel_x1s ** 2 / (-reduce_size * reduce_size) + channel_x2s / reduce_size - ) - else: - assert running_var is not None and running_mean is not None - channel_variance = running_var.reshape(*_param_shape) - channel_mean = running_mean.reshape(*_param_shape) - - invsqrt_channel_variance = ( - mgb.opr.elem.max(channel_variance, eps) - if eps_mode == "MAX" - else mgb.opr.elem.add(channel_variance, eps) - ) ** -0.5 - - if weight is not None: - weight = weight.reshape(*_param_shape) - if bias is not None: - bias = bias.reshape(*_param_shape) - - # outvar = output * weight + bias - # where output = input * invsqrt_channel_variance + ( - # -channel_mean * invsqrt_channel_variance - # ) - # Manually expand output for gopt - - if weight is not None: - inv_var_wt = invsqrt_channel_variance * weight - neg_channel_mean = -channel_mean - if bias is not None: - outvar = input * inv_var_wt + (neg_channel_mean * inv_var_wt + bias) - else: - outvar = input * inv_var_wt + neg_channel_mean * inv_var_wt - else: - outvar = input * invsqrt_channel_variance + ( - -channel_mean * invsqrt_channel_variance - ) - if bias is not None: - outvar = outvar + bias - - if training and running_var is not None and running_mean is not None: - _mean_update = add_update( - running_mean, channel_mean, alpha=momentum, beta=1 - momentum, - ) - channel_variance_unbiased = channel_x1s ** 2 / ( - -reduce_size * (reduce_size - 1) - ) + channel_x2s / (reduce_size - 1) - _variance_update = add_update( - running_var, channel_variance_unbiased, alpha=momentum, beta=1 - momentum - ) - for dep in (_mean_update, _variance_update): - add_extra_vardep(outvar, dep) - - return outvar - - -def one_hot(inp: Tensor, num_classes: int) -> Tensor: - r""" - Perform one-hot encoding for the input tensor. - - :param inp: input tensor - :param num_classes: number of classes denotes the last dimension of the output tensor - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - - inp = tensor(np.arange(1, 4, dtype=np.int32)) - out = F.one_hot(inp, num_classes=4) - print(out.numpy()) - - Outputs: - - .. testoutput:: - - [[0 1 0 0] - [0 0 1 0] - [0 0 0 1]] - - """ - comp_node, comp_graph = _decide_comp_node_and_comp_graph(inp) - - zeros = mgb.make_immutable(value=0, comp_node=comp_node, comp_graph=comp_graph) - zeros_symvar = zeros.broadcast(inp.shapeof(), num_classes) - - ones = mgb.make_immutable(value=1, comp_node=comp_node, comp_graph=comp_graph) - ones_symvar = ones.broadcast(inp.shapeof(), 1) - - return Tensor( - mgb.opr.indexing_set_one_hot( - zeros_symvar, axis=len(inp.shapeof()), index=inp, value=ones_symvar - ) - ) - - -@wrap_io_tensor -def warp_perspective( - inp: Tensor, - M: Tensor, - dsize: Union[Tuple[int, int], int, Tensor], - border_mode: str = "REPLICATE", - border_val: float = 0.0, - interp_mode: str = "LINEAR", -): - r""" - Applies perspective transformation to batched 2D images. - - The input images are transformed to the output images by the transformation matrix: - - .. math:: - \text{output}(n, c, h, w) = \text{input} \left( n, c, - \frac{M_{00}h + M_{01}w + M_{02}}{M_{20}h + M_{21}w + M_{22}}, - \frac{M_{10}h + M_{11}w + M_{12}}{M_{20}h + M_{21}w + M_{22}} - \right) - - :param inp: input image - :param M: (batch, 3, 3) transformation matrix - :param dsize: (h, w) size of the output image - :param border_mode: pixel extrapolation method. Default: ``"REPLICATE"`` - :param border_val: value used in case of a constant border. Default: ``0`` - :param interp_mode: interpolation methods. Default: ``"LINEAR"`` - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - inp_shape = (1, 1, 4, 4) - inp = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape)) - M_shape = (1, 3, 3) - # M defines a translation: dst(1, 1, h, w) = rst(1, 1, h+1, w+1) - M = tensor(np.array([[1., 0., 1.], - [0., 1., 1.], - [0., 0., 1.]], dtype=np.float32).reshape(M_shape)) - out = F.warp_perspective(inp, M, (2, 2)) - print(out.numpy()) - - Outputs: - - .. testoutput:: - - [[[[ 5. 6.] - [ 9. 10.]]]] - - """ - - return mgb.opr.warp_perspective( - inp, - M, - dsize, - bmode=border_mode, - border_val=border_val, - imode=interp_mode, - format="NCHW", - ) - - -@wrap_io_tensor -def remap( - inp: Tensor, - map_xy: Tensor, - border_mode: str = "REPLICATE", - scalar: float = 0.0, - interp_mode: str = "LINEAR", -) -> Tensor: - r""" - Applies remap transformation to batched 2D images. - - The input images are transformed to the output images by the tensor map_xy. - The output's H and W are same as map_xy's H and W. - - :param inp: input image - :param map_xy: (batch, oh, ow, 2) transformation matrix - :param border_mode: pixel extrapolation method. Default: ``"REPLICATE"`` - :param scalar: value used in case of a constant border. Default: ``0`` - :param interp_mode: interpolation methods. Default: ``"LINEAR"`` - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - inp_shape = (1, 1, 4, 4) - inp = tensor(np.arange(16, dtype=np.float32).reshape(inp_shape)) - map_xy_shape = (1, 2, 2, 2) - map_xy = tensor(np.array([[[1., 0.],[0., 1.]], - [[0., 1.],[0., 1.]]], - dtype=np.float32).reshape(map_xy_shape)) - out = F.remap(inp, map_xy) - print(out.numpy()) - - Outputs: - - .. testoutput:: - - [[[[1. 4.] - [4. 4.]]]] - - """ - - return mgb.opr.remap( - inp, - map_xy, - border_type=border_mode, - scalar=scalar, - imode=interp_mode, - format="NCHW", - ) - - -@wrap_io_tensor -def eye( - n: int, - m: Optional[int] = None, - *, - dtype=None, - device: Optional[CompNode] = None, - comp_graph: Optional[CompGraph] = None -) -> Tensor: - """ - Returns a 2D tensor with ones on the diagonal and zeros elsewhere. - - :param n: The number of rows - :param m: The number of columns. Default: None - :param dtype: The data type. Default: None - :param device: Compute node of the matrix. Default: None - :param comp_graph: Compute graph of the matrix. Default: None - :return: The eye matrix - - Examples: - - .. testcode:: - - import numpy as np - import megengine.functional as F - - data_shape = (4, 6) - n, m = data_shape - out = F.eye(n, m, dtype=np.float32) - print(out.numpy()) - - Outputs: - - .. testoutput:: - - [[1. 0. 0. 0. 0. 0.] - [0. 1. 0. 0. 0. 0.] - [0. 0. 1. 0. 0. 0.] - [0. 0. 0. 1. 0. 0.]] - - """ - - device, comp_graph = _use_default_if_none(device, comp_graph) - if m is None: - m = n - return mgb.opr.eye((n, m), dtype=dtype, comp_node=device, comp_graph=comp_graph) - - -@wrap_io_tensor -def matrix_mul(inp1: Tensor, inp2: Tensor) -> Tensor: - """ - Performs a matrix multiplication of the matrices ``inp1`` and ``inp2`` - - :param inp1: The first matrix to be multiplied (a, b) - :param inp2: The second matrix to be multiplied (b, c) - :return: The output tensor (a, c) - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - - shape_1 = (2, 3) - shape_2 = (3, 4) - data1 = tensor(np.arange(0, 6, dtype=np.float32).reshape(2, 3)) - data2 = tensor(np.arange(0, 6, dtype=np.float32).reshape(3, 2)) - out = F.matrix_mul(data1, data2) - print(out.numpy()) - - Outputs: - - .. testoutput:: - - [[10. 13.] - [28. 40.]] - - """ - return mgb.opr.matrix_mul(inp1, inp2) - - -@wrap_io_tensor -def batched_matrix_mul(inp1: Tensor, inp2: Tensor) -> Tensor: - """ - Performs a batched multiplication of th batched matrices ``inp1`` and ``inp2`` - - :param inp1: The first batch matrix to be multiplied (n, a, b) - :param inp2: The second batch matrix to be multiplied (n, b, c) - :return: The output batch (n, a, c) - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - - batch_size = 3 - shape_1 = (batch_size, 2, 3) - shape_2 = (batch_size, 3, 4) - data1 = tensor( - np.arange(0, batch_size * 6, dtype=np.float32).reshape(batch_size, 2, 3)) - data2 = tensor( - np.arange(0, batch_size * 12, dtype=np.float32).reshape(batch_size, 3, 4)) - out = F.batched_matrix_mul(data1, data2) - print(out.numpy()) - - Outputs: - - .. testoutput:: - - [[[ 20. 23. 26. 29.] - [ 56. 68. 80. 92.]] - - [[ 344. 365. 386. 407.] - [ 488. 518. 548. 578.]] - - [[1100. 1139. 1178. 1217.] - [1352. 1400. 1448. 1496.]]] - - """ - return mgb.opr.batched_matrix_mul(inp1, inp2) - - -@wrap_io_tensor -def interpolate( - inp: Tensor, - size: Optional[Union[int, Tuple[int, int]]] = None, - scale_factor: Optional[Union[float, Tuple[float, float]]] = None, - mode: str = "BILINEAR", - align_corners: bool = None, -) -> Tensor: - r""" - Down/up samples the input tensor to either the given :attr:`size` or the given - :attr:`scale_factor` - - :param inp: input tensor - :param size: size of the output tensor. Default: ``None`` - :param scale_factor: scaling factor of the output tensor. Default: ``None`` - :param mode: interpolation methods, acceptable values are: - 'BILINEAR', 'LINEAR'. Default: ``BILINEAR`` - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - from megengine.test import assertTensorClose - - inp = tensor(np.arange(1, 5, dtype=np.float32).reshape(1, 1, 2, 2)) - out = F.interpolate(inp, [4, 4], align_corners=False) - print(out.numpy()) - - out2 = F.interpolate(inp, scale_factor=2.) - assertTensorClose(out.numpy(), out2.numpy()) - - Outputs: - - .. testoutput:: - - [[[[1. 1.25 1.75 2. ] - [1.5 1.75 2.25 2.5 ] - [2.5 2.75 3.25 3.5 ] - [3. 3.25 3.75 4. ]]]] - - """ - mode = mode.upper() - if mode not in ["BILINEAR", "LINEAR"]: - raise ValueError("interpolate only support linear or bilinear mode") - if mode not in ["BILINEAR", "LINEAR"]: - if align_corners is not None: - raise ValueError( - "align_corners option can only be set in the bilinear/linear interpolating mode" - ) - else: - if align_corners is None: - align_corners = False - - if mode == "LINEAR": - inp = mgb.opr.add_axis(inp, 3) - - if len(inp.imm_shape) != 4: - raise ValueError("shape of input tensor must correspond to the operartion mode") - - if size is None: - if scale_factor is None: - raise ValueError("scale_factor must not be None when size is None") - - if isinstance(scale_factor, (float, int)): - scale_factor = float(scale_factor) - if mode == "LINEAR": - scale_factor = (scale_factor, float(1)) - else: - scale_factor = (scale_factor, scale_factor) - else: - if mode == "LINEAR": - raise ValueError( - "under LINEAR mode, scale_factor can only be single value" - ) - - assert len(scale_factor) == 2, "shape of scale_factor must be equal to (2, )" - assert isinstance(scale_factor[0], float) and isinstance( - scale_factor[1], float - ), "scale_factor must be float type" - dsize = tuple( - mgb.opr.elemwise(inp.shape[i + 2] * scale_factor[i], mode="FLOOR") - for i in range(2) - ) - dsize = mgb.opr.concat([dsize[0], dsize[1]], axis=0) - else: - if scale_factor is not None: - raise ValueError("scale_factor must be None when size is provided") - - if isinstance(size, int): - size = (size, 1) - else: - if mode == "LINEAR": - raise ValueError("under LINEAR mode, size can only be single value") - dsize = size - - oh, ow = dsize[0], dsize[1] - ih, iw = inp.shape[2], inp.shape[3] - - if align_corners: - hscale = (ih - 1.0) / (oh - 1.0) - wscale = 1.0 * iw / ow - if mode != "LINEAR": - wscale = (iw - 1.0) / (ow - 1.0) - row0 = mgb.opr.concat([wscale, [0, 0]], axis=0).reshape(1, 3) - row1 = mgb.opr.concat([[0], hscale, [0]], axis=0).reshape(1, 3) - weight = mgb.opr.concat([row0, row1, [[0, 0, 1]]], axis=0).reshape(1, 3, 3) - weight = mgb.opr.broadcast(weight, (inp.shape[0], 3, 3)) - else: - hscale = 1.0 * ih / oh - wscale = 1.0 * iw / ow - row0 = mgb.opr.concat([wscale, [0], 0.5 * wscale - 0.5], axis=0).reshape(1, 3) - row1 = mgb.opr.concat([[0], hscale, 0.5 * hscale - 0.5], axis=0).reshape(1, 3) - weight = mgb.opr.concat([row0, row1, [[0, 0, 1]]], axis=0).reshape(1, 3, 3) - weight = mgb.opr.broadcast(weight, (inp.shape[0], 3, 3)) - - ret = mgb.opr.warp_perspective(inp, weight, dsize, imode="LINEAR", format="NCHW") - if mode == "LINEAR": - ret = mgb.opr.reshape(ret, ret.shape[0:3]) - return ret - - -@wrap_io_tensor -def dropout(inp: Tensor, drop_prob: float, rescale: bool = True) -> Tensor: - """ - Returns a new tensor where each of the elements are randomly set to zero - with probability P = ``drop_prob``. Optionally rescale the output tensor. - - :param inp: The input tensor - :param drop_prob: The probability to drop (set to zero) a single element - :param rescale: The default behavior of ``dropout`` during training is to rescale the output, - then it can be replaced by an :class:`~.Identity` during inference, default to True. - :return: The output tensor - - Examples: - - .. testcode:: - - import numpy as np - import megengine as mge - - import megengine.functional as F - from megengine import tensor - - data = tensor(np.ones(10, dtype=np.float32)) - out = F.dropout(data, 1./3.) - print(out.numpy()) - - Outputs: - - .. testoutput:: - :options: +SKIP - - [1.5 1.5 0. 1.5 1.5 1.5 1.5 1.5 1.5 1.5] - - """ - assert 0 <= drop_prob < 1 - rv = uniform(inp.shape) - mask = rv > drop_prob - inp *= mask.astype(inp.dtype) - if rescale: - inp *= 1 / (1 - drop_prob) - return inp - - -@wrap_io_tensor -def identity(inp: Tensor) -> Tensor: - """applies an identity transform to the input tensor. - - :param inp: The input tensor - """ - return mgb.opr.identity(inp) - - -@wrap_io_tensor -def embedding( - input: Tensor, - weight: Tensor, - padding_idx: Optional[int] = None, - max_norm: Optional[float] = None, - norm_type: Optional[float] = None, -): - """ - Applies lookup table for embedding. - - :param input: the tensor with indices. - :param weight: the learnable weights which embedding from. - :param padding_idx: should be set to None, not support now. - :param max_norm: should be set to None, not support now. - :param norm_type: should be set to None, not support now. - - - Refer to :class:`~.Embedding` for more information. - """ - if padding_idx is not None: - raise ValueError("Not support padding_idx Now!") - if max_norm is not None or norm_type is not None: - raise ValueError("Not support weight normlization Now!") - - return mgb.opr.advanced_indexing(weight)[input.reshape(-1), :].reshape( - input.shape, weight.shape[-1] - ) - - -@wrap_io_tensor -def roi_pooling( - input: Tensor, - rois: Tensor, - output_shape: Union[int, tuple, list], - mode: str = "max", - scale: float = 1.0, -) -> Tensor: - """ - Apply roi pooling on input feature - - :param input: tensor that represents the input feature, (N, C, H, W) images - :param rois: (K, 5) boxes. First column is the index into N. The other 4 columns are xyxy - :param output_shape: (height, width) of output rois feature - :param mode: "max" or "average", use max/average align just like max/average pooling. Default: ``"max"`` - :param scale: scale the input boxes by this number. Default: 1.0 - :return: (K, C, output_shape[0], output_shape[1]) feature of rois - """ - assert mode in ["max", "average"], "only max/average mode is supported" - if isinstance(output_shape, int): - output_shape = (output_shape, output_shape) - - return mgb.opr.roi_pooling( - input, rois, output_shape, mode=mode.upper(), scale=scale - ) - - -@wrap_io_tensor -def roi_align( - input: Tensor, - rois: Tensor, - output_shape: Union[int, tuple, list], - mode: str = "average", - spatial_scale: float = 1.0, - sample_points: Union[int, tuple, list] = 2, - aligned: bool = True, -) -> Tensor: - """ - Apply roi align on input feature - - :param input: tensor that represents the input feature, (N, C, H, W) images - :param rois: (N, 5) boxes. First column is the index into N. The other 4 columns are xyxy - :param output_shape: (height, width) shape of output rois feature. - :param mode: "max" or "average", use max/average align just like max/average pooling. Default: ``"average"`` - :param spatial_scale: scale the input boxes by this number. Default: 1.0 - :param sample_points: number of inputs samples to take for each output sample. - 0 to take samples densely. Default: 2 - :param aligned: wheather align the input feature, with `aligned=True`, - we first appropriately scale the ROI and then shift it by -0.5. Default: True - """ - assert mode in ["max", "average"], "only max/average mode is supported" - if isinstance(output_shape, int): - output_shape = (output_shape, output_shape) - pooled_height, pooled_width = output_shape - if isinstance(sample_points, int): - sample_points = (sample_points, sample_points) - sample_height, sample_width = sample_points - offset = 0.5 if aligned else 0.0 - - return mgb.opr.roi_align( - input, - rois, - mode=mode.upper(), - spatial_scale=spatial_scale, - offset=offset, - pooled_height=pooled_height, - pooled_width=pooled_width, - sample_height=sample_height, - sample_width=sample_width, - ) - - -@wrap_io_tensor -def assert_equal( - get: Tensor, expect: Tensor, max_err: float = 1e-4, verbose: bool = False -) -> Tensor: - r""" - Asserts that ``get`` equals to ``expect``, and returns value of ``expect``. - - :param get: tensor to be checked. - :param expect: tensor with expected values. - :param max_err: tolerance that two float values are asserted equal. Default: 1e-4 - :param verbose: whether to print details if two tensors are not equal. Default: False - - Examples: - - .. testcode:: - - import megengine.functional as F - from megengine import tensor - - get = tensor([1.0, 2.0]) - max_err = 0.1 - expect = get + max_err / 2.0 - val = F.assert_equal(expect, get, max_err=max_err) - print(val.numpy()) - - Outputs: - - .. testoutput:: - - [1.05 2.05] - - """ - - return mgb.opr.assert_equal(get, expect, maxerr=max_err, verbose=verbose) - - -@wrap_io_tensor -def indexing_one_hot( - src: Tensor, index: Tensor, axis: int = 1, keepdims=False -) -> Tensor: - r""" - One-hot indexing for some axis. - - :param src: input data tensor. - :param index: index tensor. - :param axis: the axis on src for which values in index index. Default: 1 - :param keepdims: whether not to remove the axis in result. Default: ``False`` - - Examples: - - .. testcode:: - - import megengine.functional as F - from megengine import tensor - - src = tensor([[1.0, 2.0]]) - index = tensor([0]) - val = F.indexing_one_hot(src, index) - print(val.numpy()) - - .. testoutput:: - - [1.] - - """ - - return mgb.opr.indexing_one_hot(src, axis, index, keepdims=keepdims) diff --git a/python_module/megengine/functional/quantized.py b/python_module/megengine/functional/quantized.py deleted file mode 100644 index 8cfb4a5b..00000000 --- a/python_module/megengine/functional/quantized.py +++ /dev/null @@ -1,80 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# pylint: disable=too-many-lines -from typing import Tuple, Union - -from .. import _internal as mgb -from ..core import Tensor, wrap_io_tensor -from ..utils.types import _pair, _pair_nonzero -from .debug_param import get_conv_execution_strategy - - -@wrap_io_tensor -def conv_bias_activation( - inp: Tensor, - weight: Tensor, - bias: Tensor, - dtype=None, - stride: Union[int, Tuple[int, int]] = 1, - padding: Union[int, Tuple[int, int]] = 0, - dilation: Union[int, Tuple[int, int]] = 1, - groups: int = 1, - nonlinear_mode="IDENTITY", - conv_mode="CROSS_CORRELATION", - compute_mode="DEFAULT", -) -> Tensor: - """ convolution bias with activation operation, only for inference. - - :param inp: The feature map of the convolution operation - :param weight: The convolution kernel - :param bias: The bias added to the result of convolution - :param stride: Stride of the 2D convolution operation. Default: 1 - :param padding: Size of the paddings added to the input on both sides of its - spatial dimensions. Only zero-padding is supported. Default: 0 - :param dilation: Dilation of the 2D convolution operation. Default: 1 - :param groups: number of groups to divide input and output channels into, - so as to perform a "grouped convolution". When ``groups`` is not 1, - ``in_channels`` and ``out_channels`` must be divisible by ``groups``, - and the shape of weight should be ``(groups, out_channel // groups, - in_channels // groups, height, width)``. - :type conv_mode: string or :class:`mgb.opr_param_defs.Convolution.Mode` - :param conv_mode: Supports 'CROSS_CORRELATION' or 'CONVOLUTION'. Default: - 'CROSS_CORRELATION'. - :param dtype: Support for np.dtype, Default: - np.int8. - :type compute_mode: string or - :class:`mgb.opr_param_defs.Convolution.ComputeMode` - :param compute_mode: When set to 'DEFAULT', no special requirements will be - placed on the precision of intermediate results. When set to 'FLOAT32', - Float32 would be used for accumulator and intermediate result, but only - effective when input and output are of Float16 dtype. - - """ - ph, pw = _pair(padding) - sh, sw = _pair_nonzero(stride) - dh, dw = _pair_nonzero(dilation) - sparse_type = "DENSE" if groups == 1 else "GROUP" - res = mgb.opr.conv_bias_activation( - inp, - weight, - bias, - compute_mode=compute_mode, - dtype=dtype, - strategy=get_conv_execution_strategy(), - nonlineMode=nonlinear_mode, - sparse=sparse_type, - format="NCHW", - pad_h=ph, - pad_w=pw, - stride_h=sh, - stride_w=sw, - dilate_h=dh, - dilate_w=dw, - mode=conv_mode, - ) - return res diff --git a/python_module/megengine/functional/sort.py b/python_module/megengine/functional/sort.py deleted file mode 100644 index 70597d52..00000000 --- a/python_module/megengine/functional/sort.py +++ /dev/null @@ -1,123 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import functools -from typing import Optional, Tuple, Union - -import megengine._internal as mgb - -from ..core.tensor import Tensor, wrap_io_tensor - -__all__ = ["argsort", "sort", "top_k"] - - -@wrap_io_tensor -def argsort(inp: Tensor, descending: bool = False) -> Tuple[Tensor, Tensor]: - r""" - Sort the target 2d matrix by row, return both the sorted tensor and indices. - - :param inp: The input tensor, if 2d, each row will be sorted - :param descending: Sort in descending order, where the largest comes first. Default: ``False`` - :return: Tuple of two tensors (sorted_tensor, indices_of_int32) - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - data = tensor(np.array([1,2], dtype=np.float32)) - sorted, indices = F.argsort(data) - print(sorted.numpy(), indices.numpy()) - - Outputs: - - .. testoutput:: - - [1. 2.] [0 1] - - """ - assert len(inp.imm_shape) <= 2, "Input should be 1d or 2d" - if descending: - order = mgb.opr_param_defs.Argsort.Order.DESCENDING - else: - order = mgb.opr_param_defs.Argsort.Order.ASCENDING - if len(inp.imm_shape) == 1: - inp = inp.reshape(1, -1) - tns, ind = mgb.opr.argsort(inp, order=order) - return tns[0], ind[0] - return mgb.opr.argsort(inp, order=order) - - -@functools.wraps(argsort) -def sort(*args, **kwargs): - return argsort(*args, **kwargs) - - -@wrap_io_tensor -def top_k( - inp: Tensor, - k: int, - descending: bool = False, - kth_only: bool = False, - no_sort: bool = False, -) -> Tuple[Tensor, Tensor]: - r""" - Selected the Top-K (by default) smallest elements of 2d matrix by row. - - :param inp: The input tensor, if 2d, each row will be sorted - :param k: The number of elements needed - :param descending: If true, return the largest elements instead. Default: ``False`` - :param kth_only: If true, only the k-th element will be returned. Default: ``False`` - :param no_sort: If true, the returned elements can be unordered. Default: ``False`` - :return: Tuple of two tensors (topk_tensor, indices_of_int32) - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - data = tensor(np.array([2, 4, 6, 8, 7, 5, 3, 1], dtype=np.float32)) - top, indices = F.top_k(data, 5) - print(top.numpy(), indices.numpy()) - - Outputs: - - .. testoutput:: - - [1. 2. 3. 4. 5.] [7 0 6 1 5] - - """ - assert len(inp.imm_shape) <= 2, "Input should be 1d or 2d" - if kth_only: - raise NotImplementedError( - "TODO: would enconter:" - "NotImplementedError: SymbolVar var could not be itered" - ) - if descending: - inp = -inp - Mode = mgb.opr_param_defs.TopK.Mode - if kth_only: - mode = Mode.KTH_ONLY - elif no_sort: - mode = Mode.VALUE_IDX_NOSORT - else: - mode = Mode.VALUE_IDX_SORTED - if len(inp.imm_shape) == 1: - inp = inp.reshape(1, -1) - tns, ind = mgb.opr.top_k(inp, k, mode=mode) - tns = tns[0] - ind = ind[0] - else: - tns, ind = mgb.opr.top_k(inp, k, mode=mode) - if descending: - tns = -tns - return tns, ind diff --git a/python_module/megengine/functional/tensor.py b/python_module/megengine/functional/tensor.py deleted file mode 100644 index b9a14ee2..00000000 --- a/python_module/megengine/functional/tensor.py +++ /dev/null @@ -1,667 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import functools -from typing import Iterable, List, Optional, Union - -import numpy as np - -import megengine._internal as mgb -from megengine._internal import CompGraph, CompNode - -from ..core import zeros -from ..core.graph import _use_default_if_none -from ..core.tensor import Tensor, wrap_io_tensor -from .elemwise import ceil -from .utils import _decide_comp_node_and_comp_graph - - -@wrap_io_tensor -def broadcast_to(inp: Tensor, shape: Union[int, Iterable[int]]) -> Tensor: - """ - Broadcast a tensor to ``shape`` - - :param inp: The input tensor - :param shape: The target shape - :return: The output tensor - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - - data = tensor(np.arange(0, 6, dtype=np.float32).reshape(2, 3)) - out = F.broadcast_to(data, (4, 2, 3)) - print(out.numpy()) - - Outputs: - - .. testoutput:: - - [[[0. 1. 2.] - [3. 4. 5.]] - - [[0. 1. 2.] - [3. 4. 5.]] - - [[0. 1. 2.] - [3. 4. 5.]] - - [[0. 1. 2.] - [3. 4. 5.]]] - - """ - - if isinstance(shape, int): - shape = (shape,) - return mgb.opr.broadcast(inp, shape) - - -def _get_idx(index, axis): - index_dims = len(index.imm_shape) - idx = [] - comp_node, comp_graph = _decide_comp_node_and_comp_graph(index) - for i in range(index_dims): - if i != axis: - shape = [1] * index_dims - shape[i] = index.axis_shape(i) - arange = mgb.opr.linspace( - 0, - index.axis_shape(i) - 1, - index.axis_shape(i), - comp_node=comp_node, - comp_graph=comp_graph, - ) - arange = ( - arange.reshape(*shape) - .broadcast(index.shape) - .reshape(-1) - .astype(np.int32) - ) - idx.append(arange) - else: - idx.append(index.reshape(-1)) - return tuple(idx) - - -@wrap_io_tensor -def gather(inp: Tensor, axis: int, index: Tensor) -> Tensor: - r""" - Gather data from :attr:`inp` on :attr:`axis` using :attr:`index`. - - For a 3-D tensor, the output is specified by:: - - out[i][j][k] = inp[index[i][j][k]][j][k] # if axis == 0 - out[i][j][k] = inp[i][index[i][j][k]][k] # if axis == 1 - out[i][j][k] = inp[i][j][index[i][j][k]] # if axis == 2 - - if :attr:`inp` is an n-dimensional tensor with size - :math:`(x_0,x_1,...,x_{i-1},x_i,x_{i+1},...,x_{n-1})` and axis=i, - then :attr:`index` must be an n-dimensional tensor with size - :math:`(x_0,x_1,...,x_{i-1},y,x_{i+1},...,x_{n-1})` where :math:`y\ge 1` and - output will have the same size as :attr:`index`. - - - :param inp: the source tensor - :param axis: the axis along which to index - :param index: the indices of elements to gather - - Examples: - - .. testcode:: - - import megengine.functional as F - from megengine.core import tensor - - inp = tensor([ - [1,2], [3,4], [5,6], - ]) - index = tensor([[0,2], [1,0]]) - oup = F.gather(inp, 0, index) - print(oup.numpy()) - - Outputs: - - .. testoutput:: - - [[1 6] - [3 2]] - - """ - - input_shape = inp.imm_shape - index_shape = index.imm_shape - input_dims = len(input_shape) - index_dims = len(index_shape) - if input_dims != index_dims: - raise ValueError( - "The index tensor must have same dimensions as input tensor, " - "But the input dims:{}, the index dims:{}".format(input_dims, index_dims) - ) - - if axis < 0 or axis >= input_dims: - raise ValueError( - "Index axis {} is output of bounds, should in range [0 {})".format( - axis, input_dims - ) - ) - - for i in range(input_dims): - if i != axis and input_shape[i] != index_shape[i]: - raise ValueError( - "The input {} and index {} must have the same size apart from axis {}".format( - input_shape, index_shape, axis - ) - ) - - idx = _get_idx(index, axis) - return mgb.opr.advanced_indexing(inp)[idx].reshape( - index.shape - ) # pylint: disable=no-member - - -@wrap_io_tensor -def concat( - inps: Iterable[Tensor], - axis: int = 0, - device: Optional[CompNode] = None, - comp_graph: Optional[CompGraph] = None, -) -> Tensor: - r""" - Concat some tensors - - :param inps: Input tensors to concat - :param axis: the dimension over which the tensors are concatenated. Default: 0 - :param device: The comp node output on. Default: None - :param comp_graph: The graph in which output is. Default: None - :return: The output tensor - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - - data1 = tensor(np.arange(0, 6, dtype=np.float32).reshape((2, 3))) - data2 = tensor(np.arange(6, 12, dtype=np.float32).reshape((2, 3))) - out = F.concat([data1, data2]) - print(out.numpy()) - - Outputs: - - .. testoutput:: - - [[ 0. 1. 2.] - [ 3. 4. 5.] - [ 6. 7. 8.] - [ 9. 10. 11.]] - - """ - - # Output buffer not supported - return mgb.opr.concat( - *list(inps), axis=axis, comp_node=device, comp_graph=comp_graph - ) - - -@wrap_io_tensor -def scatter(inp: Tensor, axis: int, index: Tensor, source: Tensor) -> Tensor: - r""" - Writes all values from the tensor :attr:`source` into :attr:`inp` at the indices specified in the :attr:`index` tensor. - - For each value in :attr:`source`, its output index is specified by its index - in :attr:`source` for ``axis != dimension`` and by the corresponding value in - :attr:`index` for ``axis = dimension``. - - For a 3-D tensor, :attr:`inp` is updated as:: - - inp[index[i][j][k]][j][k] = source[i][j][k] # if axis == 0 - inp[i][index[i][j][k]][k] = source[i][j][k] # if axis == 1 - inp[i][j][index[i][j][k]] = source[i][j][k] # if axis == 2 - - :attr:`inp`, :attr:`index` and :attr:`source` should have same number of dimensions. - - It is also required that ``source.shape(d) <= inp.shape(d)`` and ``index.shape(d) == source.shape(d)`` - for all dimensions ``d``. - - Moreover, the values of :attr:`index` must be between ``0`` and ``inp.shape(axis) - 1`` inclusive. - - .. note:: - Please notice that, due to performance issues, the result is uncertain on the GPU device - if scatter difference positions from source to the same destination position - regard to index tensor. - - Show the case using the following examples, the oup[0][2] is maybe - from source[0][2] which value is 0.2256 or source[1][2] which value is 0.5339 - if set the index[1][2] from 1 to 0. - - :param inp: the inp tensor which to be scattered - :param axis: the axis along which to index - :param index: the indices of elements to scatter - :param source: the source element(s) to scatter - - Examples: - - .. testcode:: - - import numpy as np - import megengine.functional as F - from megengine.core import tensor - - inp = tensor(np.zeros(shape=(3,5),dtype=np.float32)) - source = tensor([[0.9935,0.9465,0.2256,0.8926,0.4396],[0.7723,0.0718,0.5939,0.357,0.4576]]) - index = tensor([[0,2,0,2,1],[2,0,1,1,2]]) - oup = F.scatter(inp, 0, index,source) - print(oup.numpy()) - - Outputs: - - .. testoutput:: - - [[0.9935 0.0718 0.2256 0. 0. ] - [0. 0. 0.5939 0.357 0.4396] - [0.7723 0.9465 0. 0.8926 0.4576]] - - """ - - input_shape = inp.imm_shape - index_shape = index.imm_shape - source_shape = source.imm_shape - input_dims = len(input_shape) - index_dims = len(index_shape) - source_dims = len(source_shape) - - if input_dims != index_dims or input_dims != source_dims: - raise ValueError("The input, source and index tensor must have same dimensions") - - if axis < 0 or axis >= input_dims: - raise ValueError( - "Index axis {} is output of bounds, should in range [0 {})".format( - axis, input_dims - ) - ) - - for i in range(source_dims): - if source_shape[i] > input_shape[i]: - raise ValueError( - "The each shape size for source {} must be less than or equal to input {} ".format( - source_shape, input_shape - ) - ) - - for i in range(index_dims): - if index_shape[i] != source_shape[i]: - raise ValueError( - "The each shape size for index {} must be equal to source {} ".format( - index_shape, source_shape - ) - ) - - for i in range(index_dims): - if i != axis and index_shape[i] > input_shape[i]: - raise ValueError( - "The index {} must be less than or equal to input {} size apart from axis {}".format( - index_shape, input_shape, axis - ) - ) - - idx = _get_idx(index, axis) - return mgb.opr.set_advanced_indexing(inp, source.flatten())[idx] - - -@wrap_io_tensor -def where(mask: Tensor, x: Tensor, y: Tensor) -> Tensor: - r""" - Select elements either from Tensor x or Tensor y, according to mask. - - .. math:: - - \textrm{out}_i = x_i \textrm{ if } \textrm{mask}_i \textrm{ is True else } y_i - - :param mask: a mask used for choosing x or y - :param x: the first choice - :param y: the second choice - - Examples: - - .. testcode:: - - from megengine import tensor - import megengine.functional as F - mask = tensor(np.array([[1, 0], [0, 1]], dtype=np.int32)) - x = tensor(np.array([[1, np.inf], [np.nan, 4]], - dtype=np.float32)) - y = tensor(np.array([[5, 6], [7, 8]], dtype=np.float32)) - out = F.where(mask, x, y) - print(out.numpy()) - - Outputs: - - .. testoutput:: - - [[1. 6.] - [7. 4.]] - """ - v0, index0 = mgb.opr.cond_take( - x, mask, mode=mgb.opr_param_defs.CondTake.Mode.EQ, val=1 - ) - v1, index1 = mgb.opr.cond_take( - y, mask, mode=mgb.opr_param_defs.CondTake.Mode.EQ, val=0 - ) - out = x.flatten() - index = mgb.opr.concat(index0, index1, axis=0) - v = mgb.opr.concat(v0, v1, axis=0) - out = mgb.opr.set_advanced_indexing(out, v)[index] - out = out.reshape(x.shape) - return out - - -@wrap_io_tensor -def cond_take(mask: Tensor, x: Tensor, val=1) -> Tensor: - r""" - Take elements from data if specific condition is satisfied on mask. This operator has two outputs: the first is the elements taken, and the second is the indices corresponding to those elements; they are both 1-dimensional. High-dimension input would first be flattened. - - :param mask: condition param; must be the same shape with data - :param x: input tensor from which to take elements - :param val: value to be compared to by mode - - Examples: - - .. testcode:: - - from megengine import tensor - import megengine.functional as F - mask = tensor(np.array([[1, 0], [0, 1]], dtype=np.int32)) - x = tensor(np.array([[1, np.inf], [np.nan, 4]], - dtype=np.float32)) - v, index = F.cond_take(mask, x, 1) - print(v, index) - - Outputs: - - .. testoutput:: - - Tensor([1. 4.]) Tensor([0 3], dtype=int32) - - """ - - v, index = mgb.opr.cond_take( - x, mask, mode=mgb.opr_param_defs.CondTake.Mode.EQ, val=val - ) - return v, index - - -def shapeof(x: Tensor, axis=None): - r""" - The shape of input tensor. - """ - return x.shapeof(axis=axis) - - -@wrap_io_tensor -def dimshuffle(inp: Tensor, pattern: Iterable[int]) -> Tensor: - r""" - Swap shapes and strides according to given pattern - - :param inp: Input tensor - :param pattern: a list of integers including 0, 1, ... , ``ndim``-1, and any number of ``'x'`` char in dimensions where this tensor should be broadcasted. For examples: - - * (``'x'``) -> make a 0d (scalar) into a 1d vector - * (0, 1) -> identity for 2d vectors - * (1, 0) -> inverts the first and second dimensions - * (``'x'``, 0) -> make a row out of a 1d vector (N to 1xN) - * (0, ``'x'``) -> make a column out of a 1d vector (N to Nx1) - * (2, 0, 1) -> AxBxC to CxAxB - * (0, ``'x'``, 1) -> AxB to Ax1xB - * (1, ``'x'``, 0) -> AxB to Bx1xA - * (1,) -> This remove dimensions 0. It must be a broadcastable dimension (1xA to A) - - :return: The output tensor - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - x = tensor(np.array([[1, 1], [0, 0]], dtype=np.int32)) - out = F.dimshuffle(x, (1, 0)) - print(out.numpy()) - - Outputs: - - .. testoutput:: - - [[1 0] - [1 0]] - - """ - return mgb.opr.dimshuffle(inp, pattern) - - -@wrap_io_tensor -def reshape(inp: Tensor, target_shape: Iterable[int]) -> Tensor: - r""" - Reshape a tensor to given target shape; total number of logical elements must - remain unchanged - - :param inp: Input tensor - :param target_shape: target shape, the components would be concatenated to form the - target shape, and it can contain an element of -1 representing unspec_axis. - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - x = tensor(np.arange(12, dtype=np.int32)) - out = F.reshape(x, (3, 2, 2)) - print(out.numpy()) - - Outputs: - - .. testoutput:: - - [[[ 0 1] - [ 2 3]] - - [[ 4 5] - [ 6 7]] - - [[ 8 9] - [10 11]]] - - """ - return mgb.opr.reshape(inp, target_shape) - - -def transpose(inp: Tensor, pattern: Iterable[int]) -> Tensor: - r"""Equivalent to :func:`dimshuffle` - """ - return dimshuffle(inp, pattern) - - -@wrap_io_tensor -def add_axis(inp: Tensor, axis: int) -> Tensor: - r""" - Add dimension before given axis. - - :param inp: Input tensor - :param axis: Place of new axes - :return: The output tensor - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - x = tensor([1, 2]) - out = F.add_axis(x, 0) - print(out.shape) - - Outputs: - - .. testoutput:: - - (1, 2) - - """ - if not isinstance(axis, int): - raise ValueError("axis must be int, but got type:{}".format(type(axis))) - return mgb.opr.add_axis(inp, axis) - - -@wrap_io_tensor -def remove_axis(inp: Tensor, axis: int) -> Tensor: - r""" - Remove dimension of shape 1. - - :param inp: Input tensor - :param axis: Place of axis to be removed - :return: The output tensor - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - x = tensor(np.array([1, 2], dtype=np.int32).reshape(1, 1, 2, 1)) - out = F.remove_axis(x, 3) - print(out.shape) - - Outputs: - - .. testoutput:: - - (1, 1, 2) - - """ - if not isinstance(axis, int): - raise ValueError("axis must be int, but got type:{}".format(type(axis))) - return mgb.opr.remove_axis(inp, axis) - - -def linspace( - start: Union[int, float, Tensor], - stop: Union[int, float, Tensor], - num: Union[int, Tensor], - dtype=np.float32, - device: Optional[CompNode] = None, - comp_graph: Optional[CompGraph] = None, -) -> Tensor: - r""" - Return equally spaced numbers over a specified interval - - :param start: Starting value of the squence, shoule be scalar - :param stop: The last value of the squence, shoule be scalar - :param num: number of values to generate - :param dtype: result data type - :return: The generated tensor - - Examples: - - .. testcode:: - - import numpy as np - import megengine.functional as F - - a = F.linspace(3,10,5) - print(a.numpy()) - - .. testoutput:: - - [ 3. 4.75 6.5 8.25 10. ] - - """ - if dtype is not np.float32: - raise ValueError("linspace is only implemented for float32") - - device, comp_graph = _use_default_if_none(device, comp_graph) - ret = Tensor( - mgb.opr.linspace(start, stop, num, comp_node=device, comp_graph=comp_graph) - ) - return ret.astype(dtype) - - -def arange( - start: Union[int, float, Tensor], - end: Union[int, float, Tensor], - step: Union[int, float, Tensor] = 1, - dtype=np.float32, - device: Optional[CompNode] = None, - comp_graph: Optional[CompGraph] = None, -) -> Tensor: - r""" - Returns a Tensor with values from `start` to `end` with adjacent interval `step` - - :param start: starting value of the squence, shoule be scalar - :param end: ending value of the squence, shoule be scalar - :param step: the gap between each pair of adjacent values. Default 1 - :param dtype: result data type - :return: The generated tensor - - Examples: - - .. testcode:: - - import numpy as np - import megengine.functional as F - - a = F.arange(1, 5, 1) - print(a.numpy()) - - .. testoutput:: - - [1. 2. 3. 4.] - - """ - if dtype is not np.float32: - raise ValueError("arange is only implemented for float32") - num = ceil((end - start) / step) - stop = start + step * (num - 1) - ret = linspace(start, stop, num, device=device, comp_graph=comp_graph) - return ret - - -def zeros_like(inp: Tensor) -> Tensor: - r""" - Returns a zero tensor with the same shape as input tensor - - :param inp: input tensor - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - - inp = tensor(np.arange(1, 7, dtype=np.int32).reshape(2,3)) - out = F.zeros_like(inp) - print(out.numpy()) - - .. testoutput:: - - [[0 0 0] - [0 0 0]] - - """ - return zeros(inp.shapeof()).astype(inp.dtype) diff --git a/python_module/megengine/functional/utils.py b/python_module/megengine/functional/utils.py deleted file mode 100644 index 0ad969ca..00000000 --- a/python_module/megengine/functional/utils.py +++ /dev/null @@ -1,81 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from typing import Iterable, Union - -import megengine._internal as mgb - -from ..core.graph import _use_default_if_none -from ..core.tensor import Tensor, wrap_io_tensor -from .elemwise import equal -from .sort import top_k - - -def _decide_comp_node_and_comp_graph(*args: mgb.SymbolVar): - for i in args: - if isinstance(i, mgb.SymbolVar): - return i.comp_node, i.owner_graph - return _use_default_if_none(None, None) - - -def accuracy( - logits: Tensor, target: Tensor, topk: Union[int, Iterable[int]] = 1 -) -> Union[Tensor, Iterable[Tensor]]: - r""" - Calculate the classification accuracy given predicted logits and ground-truth labels. - - :param logits: Model predictions of shape [batch_size, num_classes], - representing the probability (likelyhood) of each class. - :param target: Ground-truth labels, 1d tensor of int32 - :param topk: Specifies the topk values, could be an int or tuple of ints. Default: 1 - :return: Tensor(s) of classification accuracy between 0.0 and 1.0 - - Examples: - - .. testcode:: - - import numpy as np - from megengine import tensor - import megengine.functional as F - - logits = tensor(np.arange(80, dtype=np.int32).reshape(8,10)) - target = tensor(np.arange(8, dtype=np.int32)) - top1, top5 = F.accuracy(logits, target, (1, 5)) - print(top1.numpy(), top5.numpy()) - - Outputs: - - .. testoutput:: - - [0.] [0.375] - """ - if isinstance(topk, int): - topk = (topk,) - _, pred = top_k(logits, k=max(topk), descending=True) - accs = [] - for k in topk: - correct = equal( - pred[:, :k], target.dimshuffle(0, "x").broadcast(target.shapeof(0), k) - ) - accs.append(correct.sum() / target.shapeof(0)) - if len(topk) == 1: # type: ignore[arg-type] - accs = accs[0] - return accs - - -@wrap_io_tensor -def zero_grad(inp: Tensor) -> Tensor: - r""" - Returns a tensor which is treated as constant during backward gradient calcuation, - i.e. its gradient is zero. - - :param inp: Input tensor. - - See implementation of :func:`~.softmax` for example. - """ - return mgb.opr.zero_grad(inp) diff --git a/python_module/megengine/hub/__init__.py b/python_module/megengine/hub/__init__.py deleted file mode 100644 index f07c3979..00000000 --- a/python_module/megengine/hub/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .hub import ( - help, - import_module, - list, - load, - load_serialized_obj_from_url, - pretrained, -) diff --git a/python_module/megengine/hub/const.py b/python_module/megengine/hub/const.py deleted file mode 100644 index 5f53420b..00000000 --- a/python_module/megengine/hub/const.py +++ /dev/null @@ -1,17 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -DEFAULT_BRANCH_NAME = "master" -HUBCONF = "hubconf.py" -HUBDEPENDENCY = "dependencies" -DEFAULT_GIT_HOST = "github.com" -ENV_MGE_HOME = "MGE_HOME" -ENV_XDG_CACHE_HOME = "XDG_CACHE_HOME" -DEFAULT_CACHE_DIR = "~/.cache" -DEFAULT_PROTOCOL = "HTTPS" -HTTP_READ_TIMEOUT = 120 diff --git a/python_module/megengine/hub/exceptions.py b/python_module/megengine/hub/exceptions.py deleted file mode 100644 index aab0a134..00000000 --- a/python_module/megengine/hub/exceptions.py +++ /dev/null @@ -1,30 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -class FetcherError(Exception): - """Base class for fetch related error.""" - - -class InvalidRepo(FetcherError): - """The repo provided was somehow invalid.""" - - -class InvalidGitHost(FetcherError): - """The git host provided was somehow invalid.""" - - -class GitPullError(FetcherError): - """A git pull error occurred""" - - -class GitCheckoutError(FetcherError): - """A git checkout error occurred""" - - -class InvalidProtocol(FetcherError): - """The protocol provided was somehow invalid""" diff --git a/python_module/megengine/hub/fetcher.py b/python_module/megengine/hub/fetcher.py deleted file mode 100644 index dfbfb0e7..00000000 --- a/python_module/megengine/hub/fetcher.py +++ /dev/null @@ -1,300 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import hashlib -import os -import re -import shutil -import subprocess -from tempfile import NamedTemporaryFile -from typing import Tuple -from zipfile import ZipFile - -import requests -from tqdm import tqdm - -from megengine.utils.http_download import ( - CHUNK_SIZE, - HTTP_CONNECTION_TIMEOUT, - HTTPDownloadError, -) - -from ..distributed.util import is_distributed, synchronized -from ..logger import get_logger -from .const import DEFAULT_BRANCH_NAME, HTTP_READ_TIMEOUT -from .exceptions import GitCheckoutError, GitPullError, InvalidGitHost, InvalidRepo -from .tools import cd - -logger = get_logger(__name__) - -HTTP_TIMEOUT = (HTTP_CONNECTION_TIMEOUT, HTTP_READ_TIMEOUT) - -pattern = re.compile( - r"^(?:[a-z0-9]" # First character of the domain - r"(?:[a-z0-9-_]{0,61}[a-z0-9])?\.)" # Sub domain + hostname - r"+[a-z0-9][a-z0-9-_]{0,61}" # First 61 characters of the gTLD - r"[a-z]$" # Last character of the gTLD -) - - -class RepoFetcherBase: - @classmethod - def fetch( - cls, - git_host: str, - repo_info: str, - use_cache: bool = False, - commit: str = None, - silent: bool = True, - ) -> str: - raise NotImplementedError() - - @classmethod - def _parse_repo_info(cls, repo_info: str) -> Tuple[str, str, str]: - try: - branch_info = DEFAULT_BRANCH_NAME - if ":" in repo_info: - prefix_info, branch_info = repo_info.split(":") - else: - prefix_info = repo_info - repo_owner, repo_name = prefix_info.split("/") - return repo_owner, repo_name, branch_info - except ValueError: - raise InvalidRepo("repo_info: '{}' is invalid.".format(repo_info)) - - @classmethod - def _check_git_host(cls, git_host): - return cls._is_valid_domain(git_host) or cls._is_valid_host(git_host) - - @classmethod - def _is_valid_domain(cls, s): - try: - return pattern.match(s.encode("idna").decode("ascii")) - except UnicodeError: - return False - - @classmethod - def _is_valid_host(cls, s): - nums = s.split(".") - if len(nums) != 4 or any(not _.isdigit() for _ in nums): - return False - return all(0 <= int(_) < 256 for _ in nums) - - @classmethod - def _gen_repo_dir(cls, repo_dir: str) -> str: - return hashlib.sha1(repo_dir.encode()).hexdigest()[:16] - - -class GitSSHFetcher(RepoFetcherBase): - @classmethod - @synchronized - def fetch( - cls, - git_host: str, - repo_info: str, - use_cache: bool = False, - commit: str = None, - silent: bool = True, - ) -> str: - """ - Fetches git repo by SSH protocol - - :param git_host: - host address of git repo. - example: github.com - :param repo_info: - a string with format ``"repo_owner/repo_name[:tag_name/:branch_name]"`` with an optional - tag/branch. The default branch is ``master`` if not specified. - example: ``"brain_sdk/MegBrain[:hub]"`` - :param use_cache: - whether to use locally fetched code or completely re-fetch - :param commit: - commit id on github or gitlab - :param silent: - whether to accept the stdout and stderr of the subprocess with PIPE, instead of - displaying on the screen - :return: - directory where the repo code is stored - """ - if not cls._check_git_host(git_host): - raise InvalidGitHost("git_host: '{}' is malformed.".format(git_host)) - - repo_owner, repo_name, branch_info = cls._parse_repo_info(repo_info) - normalized_branch_info = branch_info.replace("/", "_") - repo_dir_raw = "{}_{}_{}".format( - repo_owner, repo_name, normalized_branch_info - ) + ("_{}".format(commit) if commit else "") - repo_dir = cls._gen_repo_dir(repo_dir_raw) - git_url = "git@{}:{}/{}.git".format(git_host, repo_owner, repo_name) - - if use_cache and os.path.exists(repo_dir): # use cache - logger.debug("Cache Found in %s", repo_dir) - return repo_dir - - if is_distributed(): - logger.warning( - "When using `hub.load` or `hub.list` to fetch git repositories\n" - " in DISTRIBUTED mode for the first time, processes are synchronized to\n" - " ensure that target repository is ready to use for each process.\n" - " Users are expected to see this warning no more than ONCE, otherwise\n" - " (very little chance) you may need to remove corrupt cache\n" - " `%s` and fetch again.", - repo_dir, - ) - - shutil.rmtree(repo_dir, ignore_errors=True) # ignore and clear cache - - logger.debug( - "Git Clone from Repo:%s Branch: %s to %s", - git_url, - normalized_branch_info, - repo_dir, - ) - - kwargs = ( - {"stderr": subprocess.PIPE, "stdout": subprocess.PIPE} if silent else {} - ) - if commit is None: - # shallow clone repo by branch/tag - p = subprocess.Popen( - [ - "git", - "clone", - "-b", - normalized_branch_info, - git_url, - repo_dir, - "--depth=1", - ], - **kwargs, - ) - cls._check_clone_pipe(p) - else: - # clone repo and checkout to commit_id - p = subprocess.Popen(["git", "clone", git_url, repo_dir], **kwargs) - cls._check_clone_pipe(p) - - with cd(repo_dir): - logger.debug("git checkout to %s", commit) - p = subprocess.Popen(["git", "checkout", commit], **kwargs) - _, err = p.communicate() - if p.returncode: - shutil.rmtree(repo_dir, ignore_errors=True) - raise GitCheckoutError( - "Git checkout error, please check the commit id.\n" - + err.decode() - ) - with cd(repo_dir): - shutil.rmtree(".git") - - return repo_dir - - @classmethod - def _check_clone_pipe(cls, p): - _, err = p.communicate() - if p.returncode: - raise GitPullError( - "Repo pull error, please check repo info.\n" + err.decode() - ) - - -class GitHTTPSFetcher(RepoFetcherBase): - @classmethod - @synchronized - def fetch( - cls, - git_host: str, - repo_info: str, - use_cache: bool = False, - commit: str = None, - silent: bool = True, - ) -> str: - """ - Fetches git repo by HTTPS protocol - - :param git_host: - host address of git repo - example: github.com - :param repo_info: - a string with format ``"repo_owner/repo_name[:tag_name/:branch_name]"`` with an optional - tag/branch. The default branch is ``master`` if not specified. - example: ``"brain_sdk/MegBrain[:hub]"`` - :param use_cache: - whether to use locally cached code or completely re-fetch - :param commit: - commit id on github or gitlab - :param silent: - whether to accept the stdout and stderr of the subprocess with PIPE, instead of - displaying on the screen - :return: - directory where the repo code is stored - """ - if not cls._check_git_host(git_host): - raise InvalidGitHost("git_host: '{}' is malformed.".format(git_host)) - - repo_owner, repo_name, branch_info = cls._parse_repo_info(repo_info) - normalized_branch_info = branch_info.replace("/", "_") - repo_dir_raw = "{}_{}_{}".format( - repo_owner, repo_name, normalized_branch_info - ) + ("_{}".format(commit) if commit else "") - repo_dir = cls._gen_repo_dir(repo_dir_raw) - archive_url = cls._git_archive_link( - git_host, repo_owner, repo_name, branch_info, commit - ) - - if use_cache and os.path.exists(repo_dir): # use cache - logger.debug("Cache Found in %s", repo_dir) - return repo_dir - - if is_distributed(): - logger.warning( - "When using `hub.load` or `hub.list` to fetch git repositories " - "in DISTRIBUTED mode for the first time, processes are synchronized to " - "ensure that target repository is ready to use for each process.\n" - "Users are expected to see this warning no more than ONCE, otherwise" - "(very little chance) you may need to remove corrupt hub cache %s and fetch again." - ) - - shutil.rmtree(repo_dir, ignore_errors=True) # ignore and clear cache - - logger.debug("Downloading from %s to %s", archive_url, repo_dir) - cls._download_zip_and_extract(archive_url, repo_dir) - - return repo_dir - - @classmethod - def _download_zip_and_extract(cls, url, target_dir): - resp = requests.get(url, timeout=HTTP_TIMEOUT, stream=True) - if resp.status_code != 200: - raise HTTPDownloadError( - "An error occured when downloading from {}".format(url) - ) - - total_size = int(resp.headers.get("Content-Length", 0)) - _bar = tqdm(total=total_size, unit="iB", unit_scale=True) - - with NamedTemporaryFile("w+b") as f: - for chunk in resp.iter_content(CHUNK_SIZE): - if not chunk: - break - _bar.update(len(chunk)) - f.write(chunk) - _bar.close() - f.seek(0) - with ZipFile(f) as temp_zip_f: - zip_dir_name = temp_zip_f.namelist()[0].split("/")[0] - temp_zip_f.extractall(".") - shutil.move(zip_dir_name, target_dir) - - @classmethod - def _git_archive_link(cls, git_host, repo_owner, repo_name, branch_info, commit): - archive_link = "https://{}/{}/{}/archive/{}.zip".format( - git_host, repo_owner, repo_name, commit or branch_info - ) - - return archive_link diff --git a/python_module/megengine/hub/hub.py b/python_module/megengine/hub/hub.py deleted file mode 100644 index 5342cd8e..00000000 --- a/python_module/megengine/hub/hub.py +++ /dev/null @@ -1,333 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import functools -import hashlib -import os -import sys -import types -from typing import Any, List -from urllib.parse import urlparse - -from megengine.utils.http_download import download_from_url - -from ..core.serialization import load as _mge_load_serialized -from ..distributed import is_distributed -from ..logger import get_logger -from .const import ( - DEFAULT_CACHE_DIR, - DEFAULT_GIT_HOST, - DEFAULT_PROTOCOL, - ENV_MGE_HOME, - ENV_XDG_CACHE_HOME, - HTTP_READ_TIMEOUT, - HUBCONF, - HUBDEPENDENCY, -) -from .exceptions import InvalidProtocol -from .fetcher import GitHTTPSFetcher, GitSSHFetcher -from .tools import cd, check_module_exists, load_module - -logger = get_logger(__name__) - - -PROTOCOLS = { - "HTTPS": GitHTTPSFetcher, - "SSH": GitSSHFetcher, -} - - -def _get_megengine_home() -> str: - """MGE_HOME setting complies with the XDG Base Directory Specification - """ - megengine_home = os.path.expanduser( - os.getenv( - ENV_MGE_HOME, - os.path.join(os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), "megengine"), - ) - ) - return megengine_home - - -def _get_repo( - git_host: str, - repo_info: str, - use_cache: bool = False, - commit: str = None, - protocol: str = DEFAULT_PROTOCOL, -) -> str: - if protocol not in PROTOCOLS: - raise InvalidProtocol( - "Invalid protocol, the value should be one of {}.".format( - ", ".join(PROTOCOLS.keys()) - ) - ) - cache_dir = os.path.expanduser(os.path.join(_get_megengine_home(), "hub")) - with cd(cache_dir): - fetcher = PROTOCOLS[protocol] - repo_dir = fetcher.fetch(git_host, repo_info, use_cache, commit) - return os.path.join(cache_dir, repo_dir) - - -def _check_dependencies(module: types.ModuleType) -> None: - if not hasattr(module, HUBDEPENDENCY): - return - - dependencies = getattr(module, HUBDEPENDENCY) - if not dependencies: - return - - missing_deps = [m for m in dependencies if not check_module_exists(m)] - if len(missing_deps): - raise RuntimeError("Missing dependencies: {}".format(", ".join(missing_deps))) - - -def _init_hub( - repo_info: str, - git_host: str, - use_cache: bool = True, - commit: str = None, - protocol: str = DEFAULT_PROTOCOL, -): - """Imports hubmodule like python import - - :param repo_info: - a string with format ``"repo_owner/repo_name[:tag_name/:branch_name]"`` with an optional - tag/branch. The default branch is ``master`` if not specified. - Example: ``"brain_sdk/MegBrain[:hub]"`` - :param git_host: - host address of git repo - Example: github.com - :param use_cache: - whether to use locally cached code or completely re-fetch - :param commit: - commit id on github or gitlab - :param protocol: - which protocol to use to get the repo, and HTTPS protocol only supports public repo on github. - The value should be one of HTTPS, SSH. - :return: - hubconf.py as a python module - """ - cache_dir = os.path.expanduser(os.path.join(_get_megengine_home(), "hub")) - os.makedirs(cache_dir, exist_ok=True) - absolute_repo_dir = _get_repo( - git_host, repo_info, use_cache=use_cache, commit=commit, protocol=protocol - ) - sys.path.insert(0, absolute_repo_dir) - hubmodule = load_module(HUBCONF, os.path.join(absolute_repo_dir, HUBCONF)) - sys.path.remove(absolute_repo_dir) - - return hubmodule - - -@functools.wraps(_init_hub) -def import_module(*args, **kwargs): - return _init_hub(*args, **kwargs) - - -def list( - repo_info: str, - git_host: str = DEFAULT_GIT_HOST, - use_cache: bool = True, - commit: str = None, - protocol: str = DEFAULT_PROTOCOL, -) -> List[str]: - """Lists all entrypoints available in repo hubconf - - :param repo_info: - a string with format ``"repo_owner/repo_name[:tag_name/:branch_name]"`` with an optional - tag/branch. The default branch is ``master`` if not specified. - Example: ``"brain_sdk/MegBrain[:hub]"`` - :param git_host: - host address of git repo - Example: github.com - :param use_cache: - whether to use locally cached code or completely re-fetch - :param commit: - commit id on github or gitlab - :param protocol: - which protocol to use to get the repo, and HTTPS protocol only supports public repo on github. - The value should be one of HTTPS, SSH. - :return: - all entrypoint names of the model - """ - hubmodule = _init_hub(repo_info, git_host, use_cache, commit, protocol) - - return [ - _ - for _ in dir(hubmodule) - if not _.startswith("__") and callable(getattr(hubmodule, _)) - ] - - -def load( - repo_info: str, - entry: str, - *args, - git_host: str = DEFAULT_GIT_HOST, - use_cache: bool = True, - commit: str = None, - protocol: str = DEFAULT_PROTOCOL, - **kwargs -) -> Any: - """Loads model from github or gitlab repo, with pretrained weights. - - :param repo_info: - a string with format ``"repo_owner/repo_name[:tag_name/:branch_name]"`` with an optional - tag/branch. The default branch is ``master`` if not specified. - Example: ``"brain_sdk/MegBrain[:hub]"`` - :param entry: - an entrypoint defined in hubconf - :param git_host: - host address of git repo - Example: github.com - :param use_cache: - whether to use locally cached code or completely re-fetch - :param commit: - commit id on github or gitlab - :param protocol: - which protocol to use to get the repo, and HTTPS protocol only supports public repo on github. - The value should be one of HTTPS, SSH. - :return: - a single model with corresponding pretrained weights. - """ - hubmodule = _init_hub(repo_info, git_host, use_cache, commit, protocol) - - if not hasattr(hubmodule, entry) or not callable(getattr(hubmodule, entry)): - raise RuntimeError("Cannot find callable {} in hubconf.py".format(entry)) - - _check_dependencies(hubmodule) - - module = getattr(hubmodule, entry)(*args, **kwargs) - return module - - -def help( - repo_info: str, - entry: str, - git_host: str = DEFAULT_GIT_HOST, - use_cache: bool = True, - commit: str = None, - protocol: str = DEFAULT_PROTOCOL, -) -> str: - """This function returns docstring of entrypoint ``entry`` by following steps: - - 1. Pull the repo code specified by git and repo_info - 2. Load the entry defined in repo's hubconf.py - 3. Return docstring of function entry - - :param repo_info: - a string with format ``"repo_owner/repo_name[:tag_name/:branch_name]"`` with an optional - tag/branch. The default branch is ``master`` if not specified. - Example: ``"brain_sdk/MegBrain[:hub]"`` - :param entry: - an entrypoint defined in hubconf.py - :param git_host: - host address of git repo - Example: github.com - :param use_cache: - whether to use locally cached code or completely re-fetch - :param commit: - commit id on github or gitlab - :param protocol: - which protocol to use to get the repo, and HTTPS protocol only supports public repo on github. - The value should be one of HTTPS, SSH. - :return: - docstring of entrypoint ``entry`` - """ - hubmodule = _init_hub(repo_info, git_host, use_cache, commit, protocol) - - if not hasattr(hubmodule, entry) or not callable(getattr(hubmodule, entry)): - raise RuntimeError("Cannot find callable {} in hubconf.py".format(entry)) - - doc = getattr(hubmodule, entry).__doc__ - return doc - - -def load_serialized_obj_from_url(url: str, model_dir=None) -> Any: - """Loads MegEngine serialized object from the given URL. - - If the object is already present in ``model_dir``, it's deserialized and - returned. If no ``model_dir`` is specified, it will be ``MGE_HOME/serialized``. - - :param url: url to serialized object - :param model_dir: dir to cache target serialized file - - :return: loaded object - """ - if model_dir is None: - model_dir = os.path.join(_get_megengine_home(), "serialized") - os.makedirs(model_dir, exist_ok=True) - - parts = urlparse(url) - filename = os.path.basename(parts.path) - - # use hash as prefix to avoid filename conflict from different urls - sha256 = hashlib.sha256() - sha256.update(url.encode()) - digest = sha256.hexdigest()[:6] - filename = digest + "_" + filename - - cached_file = os.path.join(model_dir, filename) - logger.info( - "load_serialized_obj_from_url: download to or using cached %s", cached_file - ) - if not os.path.exists(cached_file): - if is_distributed(): - logger.warning( - "Downloading serialized object in DISTRIBUTED mode\n" - " File may be downloaded multiple times. We recommend\n" - " users to download in single process first." - ) - download_from_url(url, cached_file, HTTP_READ_TIMEOUT) - - state_dict = _mge_load_serialized(cached_file) - return state_dict - - -class pretrained: - r""" - Decorator which helps to download pretrained weights from the given url. - - For example, we can decorate a resnet18 function as follows - - .. code-block:: - - @hub.pretrained("https://url/to/pretrained_resnet18.pkl") - def resnet18(**kwargs): - return ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) - - When decorated function is called with ``pretrained=True``, MegEngine will automatically - download and fill the returned model with pretrained weights. - """ - - def __init__(self, url): - self.url = url - - def __call__(self, func): - @functools.wraps(func) - def pretrained_model_func( - pretrained=False, **kwargs - ): # pylint: disable=redefined-outer-name - model = func(**kwargs) - if pretrained: - weights = load_serialized_obj_from_url(self.url) - model.load_state_dict(weights) - return model - - return pretrained_model_func - - -__all__ = [ - "list", - "load", - "help", - "load_serialized_obj_from_url", - "pretrained", - "import_module", -] diff --git a/python_module/megengine/hub/tools.py b/python_module/megengine/hub/tools.py deleted file mode 100644 index 0bf9c98c..00000000 --- a/python_module/megengine/hub/tools.py +++ /dev/null @@ -1,48 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import importlib.util -import os -import types -from contextlib import contextmanager -from typing import Iterator - - -def load_module(name: str, path: str) -> types.ModuleType: - """ - Loads module specified by name and path - - :param name: module name - :param path: module path - """ - spec = importlib.util.spec_from_file_location(name, path) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - return module - - -def check_module_exists(module: str) -> bool: - """Checks whether python module exists or not - - :param module: name of module - """ - return importlib.util.find_spec(module) is not None - - -@contextmanager -def cd(target: str) -> Iterator[None]: - """Changes current directory to target - - :param target: target directory - """ - prev = os.getcwd() - os.chdir(os.path.expanduser(target)) - try: - yield - finally: - os.chdir(prev) diff --git a/python_module/megengine/jit/__init__.py b/python_module/megengine/jit/__init__.py deleted file mode 100644 index d610388b..00000000 --- a/python_module/megengine/jit/__init__.py +++ /dev/null @@ -1,570 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import contextlib -import functools -import itertools -import os -from typing import Callable, Tuple, Union - -import numpy as np - -import megengine._internal as mgb -from megengine._internal.plugin import CompGraphProfiler - -from ..core import Tensor, graph, tensor -from .sublinear_memory_config import SublinearMemoryConfig - - -def sideeffect(f): - # during eager tracing, wrapped function is called with proxy inputs - # during static tracing, wrapped function will not be called at all - @functools.wraps(f) - def wrapper(*args, **kwargs): # pylint: disable=inconsistent-return-statements - if not trace._active_instance: - return f(*args, **kwargs) - - tensors = {} - for i, x in itertools.chain(enumerate(args), kwargs.items()): - if isinstance(x, Tensor): - tensors[i] = x - if tensors: - _keys, tensors = zip(*tensors.items()) - else: - _keys, tensors = (), () - - def callback(*tensors, f=f, keys=_keys, args=args, kwargs=kwargs): - replace = dict(zip(keys, tensors)) - args = tuple(replace.get(i, x) for i, x in enumerate(args)) - kwargs = {i: replace.get(i, x) for i, x in kwargs.items()} - if f(*args, **kwargs) is not None: - raise TypeError("a sideeffect function should return None") - # TODO: clear memory - - trace._active_instance._register_callback(callback, tensors) - - return wrapper - - -def mark_impure(x): - if not trace._active_instance: - return x - return trace._active_instance._mark_impure(x) - - -def barrier(x): - if not trace._active_instance: - return x - return trace._active_instance._insert_barrier(x) - - -def _dummy(): - return mgb.make_immutable(*graph._use_default_if_none(None, None), 0) - - -class unset: - pass - - -class trace: - """ - Wrap a callable and provide: - - * tracing via :meth:`.trace` and :meth:`.dump` - * accelerated evalutaion via :meth:`.__call__` - - :param func: Positional only argument. - :param symbolic: Whether to use symbolic tensor. Default: False - :param opt_level: Optimization level for compiling trace. - :param log_level: Log level. - :param sublinear_memory_config: Configuration for sublinear memory optimization. - If not None, it enables sublinear memory optimization with given setting. - :param allreduce_pack_max_size: Maximum size of an allreduce pack in MB. - If not None, multiple gradients will be packed and synchronized together - :param profiling: Whether to profile compiled trace. Default: False - """ - - _active_instance = None - enabled = not os.getenv("MGE_DISABLE_TRACE") - - _UNSTARTED = "unstarted" - _STARTED = "started" - _FINISHED = "finished" - - def __new__(cls, *args, **kwargs): - if not args: - return functools.partial(cls, **kwargs) - return super().__new__(cls) - - def __init__( - self, - func: Callable[..., Union[None, Tensor, Tuple[Tensor]]], - *, - symbolic: bool = False, - opt_level: int = None, - log_level: int = None, - sublinear_memory_config: SublinearMemoryConfig = None, - allreduce_pack_max_size: int = None, - profiling: bool = False - ): - self.__wrapped__ = func - self._symbolic = symbolic - self._graph_opt_level = opt_level - self._log_level = log_level - self._sublinear_memory_config = sublinear_memory_config - self._allreduce_pack_max_size = allreduce_pack_max_size - self._status = self._UNSTARTED - self._args = None - self._kwargs = None - self._outputs = unset - self._sym_outputs = unset - self._outspec = None - self._checkpoint = None - self._compiled_func = None - self._profiling = profiling - self._profiler = None - - @property - def _active(self): - c1 = self._status == self._STARTED - c2 = type(self)._active_instance is self - assert c1 == c2 - return c1 - - def _register_callback(self, f, args=()): - assert self._active - assert isinstance(args, (tuple, list)) - proxies = self._make_proxies(args) - self._forward(args, proxies, checkpoint=True) - # NOTE: under eager graph callback will fire immediately - job = mgb.opr.callback_injector( - self._insert_barrier(_dummy()), lambda _: f(*proxies) - ) - self._insert_checkpoint(job) - self._outspec.append(job) - - def _insert_barrier(self, x): - assert self._active - if self._checkpoint is None: - return x - if isinstance(x, Tensor): - x = x._symvar - wrap = True - else: - wrap = False - if not isinstance(x, mgb.SymbolVar): - raise TypeError - x = mgb.opr.virtual_dep([x, self._checkpoint]) - if wrap: - x = Tensor(x) - return x - - def _insert_checkpoint(self, *args, no_barrier=False): - assert self._active - if not args: - return - args = tuple(x._symvar if isinstance(x, Tensor) else x for x in args) - for x in args: - if not isinstance(x, mgb.SymbolVar): - raise TypeError - if not no_barrier and self._checkpoint is not None: - # normally no need to _insert_barrier here, but if - # someone forget to call _insert_barrier beforehand, - # this can make things less broken - args += (self._checkpoint,) - if len(args) == 1: - self._checkpoint = args[0] - else: - self._checkpoint = mgb.opr.virtual_dep(args) - - def _mark_impure(self, x): - assert self._active - ret = x - if isinstance(x, Tensor): - x = x._symvar - if not isinstance(x, mgb.SymbolVar): - raise TypeError - self._outspec.append(x) - self._insert_checkpoint(x) - return ret - - def _make_proxies(self, args): - assert isinstance(args, (tuple, list)) - for x in args: - assert isinstance(x, Tensor) - return tuple(tensor(dtype=x.dtype, device=x.device) for x in args) - - def _forward(self, srcs, dests, checkpoint=True): - # pseudo-op: does not run under static graph; traced - # TODO: use shared memory - assert len(srcs) == len(dests) - if not self._active: - for s, d in zip(srcs, dests): - d.set_value(s, share=False) - return - jobs = [] - for s, d in zip(srcs, dests): - - def callback(value, dest=d): - dest.set_value(value, share=False) - - s = self._insert_barrier(s._symvar) - # NOTE: callback immediately fire in eager graph - jobs.append(mgb.opr.callback_injector(s, callback)) - self._outspec.extend(jobs) - if checkpoint: - self._insert_checkpoint(*jobs, no_barrier=True) - - def _forward_inputs(self, *args, **kwargs): - if self._kwargs is None: - self._kwargs = kwargs - elif self._kwargs != kwargs: - raise ValueError("kwargs must not change between invocations") - - if self._args is None: - self._args = [] - for i in args: - if isinstance(i, Tensor): - self._args.append(tensor(dtype=i.dtype, device=i.device)) - self._args[-1].set_value(i, share=False) - else: - self._args.append(tensor(i)) - else: - if not len(args) == len(self._args): - raise TypeError - for i, proxy in zip(args, self._args): - proxy.set_value(i, share=False) - # XXX: sync? - - def _make_outputs(self, outputs): - if outputs is None: - self._outputs = None - return - if isinstance(outputs, Tensor): - # no one is able to call barrier after this, so no need to checkpoint - # but checkpoint do little harm anyway - (self._outputs,) = self._make_proxies([outputs]) - return - if not isinstance(outputs, (tuple, list)): - raise TypeError("should return (tuple of) tensor") - for i in outputs: - if not isinstance(i, Tensor): - raise TypeError("should return (tuple of) tensor") - self._outputs = self._make_proxies(outputs) - - def _foward_outputs(self, outputs): - # pseudo-op: does not run under static graph; traced - if self._outputs is unset: - self._make_outputs(outputs) - if self._outputs is None: - if outputs is not None: - raise TypeError("should return None") - elif isinstance(self._outputs, Tensor): - if not isinstance(outputs, Tensor): - raise TypeError("should return a tensor") - self._forward([outputs], [self._outputs]) - else: - assert isinstance(self._outputs, tuple) - - def check(): - if not isinstance(outputs, (tuple, list)): - return False - if len(self._outputs) != len(outputs): - return False - for x in outputs: - if not isinstance(x, Tensor): - return False - return True - - if not check(): - raise TypeError( - "should return tuple of %d tensors" % len(self._outputs) - ) - self._forward(outputs, self._outputs) - - def _apply_graph_options(self, cg): - # graph opt level - if self._graph_opt_level is not None: - cg.set_option("graph_opt_level", self._graph_opt_level) - # log level - if self._log_level is not None: - cg.set_option("log_level", self._log_level) - # sublinear - if self._sublinear_memory_config is not None: - cg.set_option("enable_sublinear_memory_opt", True) - cg.set_option( - "sublinear_mem_config.lb_memory", - self._sublinear_memory_config.lb_memory, - ) - cg.set_option( - "sublinear_mem_config.genetic_nr_iter", - self._sublinear_memory_config.genetic_nr_iter, - ) - cg.set_option( - "sublinear_mem_config.genetic_pool_size", - self._sublinear_memory_config.genetic_pool_size, - ) - cg.set_option( - "sublinear_mem_config.thresh_nr_try", - self._sublinear_memory_config.thresh_nr_try, - ) - cg.set_option( - "sublinear_mem_config.num_worker", - self._sublinear_memory_config.num_worker, - ) - # pack allreduce - if self._allreduce_pack_max_size is not None: - cg.set_option("allreduce_pack_max_size", self._allreduce_pack_max_size) - # profile - if self._profiling: - self._profiler = CompGraphProfiler(cg) - - def _get_graph(self, eager): - - if eager: - if not hasattr(self, "_eager_graph"): - # pylint: disable=attribute-defined-outside-init - self._eager_graph = graph.Graph(eager_evaluation=True) - self._apply_graph_options(self._eager_graph) - return self._eager_graph - else: - if not hasattr(self, "_static_graph"): - # pylint: disable=attribute-defined-outside-init - self._static_graph = graph.Graph(eager_evaluation=False) - self._apply_graph_options(self._static_graph) - return self._static_graph - - @contextlib.contextmanager - def _prepare(self, args, kwargs, enable): - # prepare for execution - self._forward_inputs(*args, **kwargs) - if not enable: - # XXX: use our own graph here? - cg = None - elif self._status == self._FINISHED: - cg = None - elif self._symbolic: - cg = self._get_graph(eager=False) - else: - cg = self._get_graph(eager=True) - try: - # NOTE: always trace in a new graph, so capturing an undetached tensor - # will never work (would work if tracing in default graph) - if cg is None: - yield - else: - with cg: - yield - finally: - # XXX: properly release memory - if cg: - cg.clear_device_memory() - - @contextlib.contextmanager - def _activate(self): - # prepare for tracing - if self._status != self._UNSTARTED: - raise RuntimeError("cannot trace a second time") - if type(self)._active_instance is not None: - raise RuntimeError("nested trace is unsupported") - self._status = self._STARTED - type(self)._active_instance = self - self._user_cache = {} - try: - yield - finally: - self._status = self._FINISHED - self._user_cache = None - type(self)._active_instance = None - - def _run_wrapped(self): - outputs = self.__wrapped__(*self._args, **self._kwargs) - self._foward_outputs(outputs) - return outputs - - def _do_trace(self): - with self._activate(): - self._outspec = [] - outputs = self._run_wrapped() - if outputs is None: - self._sym_outputs = None - else: - if isinstance(outputs, Tensor): - outputs = [outputs] - # _run_wrapped has checked validity of outputs - self._sym_outputs = tuple(i._symvar for i in outputs) - mgb.comp_graph_tools.set_priority_to_id(self._outspec) - self._compiled_func = graph.get_default_graph().compile(None, self._outspec) - - def trace(self, *args: Tensor, **kwargs): - """ - Trace wrapped callable with provided arguments. - """ - with self._prepare(args, kwargs, enable=True): - self._do_trace() - return self - - def __call__(self, *args: Tensor, **kwargs): - """ - Evaluate on provided arguments, using compiled trace - instead of the original callable if applicable. - - :return: ``None`` or :class:`~.Tensor` or tuple of :class:`~.Tensor`, depending on the - return value of wrapped callable. - """ - with self._prepare(args, kwargs, enable=self.enabled): - if not self.enabled: - self._run_wrapped() - elif self._status == self._FINISHED: - self._compiled_func() - else: - if self._status == self._UNSTARTED: - self._do_trace() - if self._symbolic: - self._compiled_func() - return self._outputs - - def dump( - self, - fpath, - *, - arg_names=None, - append=False, - optimize_for_inference=False, - output_names=None, - **kwargs - ): - """ - Serialize trace to file system. - - :param fpath: positional only argument. Path of output file. - :param arg_names: names of the input tensors in the traced function. - :param append: whether output is appended to ``fpath``. - :param optimize_for_inference: whether to enable optimize_for_inference - pass before dump. - :param output_names: names of the output tensors in the traced function, - will use the default name if does not specify. - - :param enable_io16xc32: whether to use float16 for I/O between oprs and use - float32 as internal computation precision. Note the output var would be - changed to float16. - :param enable_ioc16: whether to use float16 for both I/O and computation - precision. - - :param enable_hwcd4: whether to use NHWCD4 data layout. This is faster on some - OpenCL backend. - :param enable_nchw88: whether to use NCHW88 data layout. it currently - used in X86 AVX backend. - :param enable_nchw44: whether to use NCHW44 data layout. it currently - used in arm backend. - :param enable_nchw44_dot: whether to use NCHW44_dot data layout. it currently - used in armv8.2+dotprod backend. - :param enable_nchw4: whether to use NCHW4 data layout. it currently - used in nvidia backend(based on cudnn). - :param enable_nchw32: whether to use NCHW32 data layout. it currently - used in nvidia backend with tensorcore(based on cudnn). - :param enable_chwn4: whether to use CHWN4 data layout. it currently - used in nvidia backend with tensorcore. - - :param enable_fuse_conv_bias_nonlinearity: whether to fuse conv+bias+nonlinearty - into one opr. - :param enable_fuse_conv_bias_with_z: whether to fuse conv_bias with z - input for inference on nvidia backend(this optimization pass will - result in mismatch of the precision of output of training and - inference) - """ - if self._status != self._FINISHED: - raise ValueError("not traced") - assert isinstance(self._sym_outputs, (tuple, type(None))) - if not self._sym_outputs: - raise ValueError("not outputs") - if arg_names is None: - arg_names = ["arg_%d" % i for i in range(len(self._args))] - elif len(arg_names) != len(self._args): - raise ValueError( - "len(arg_names) should be {}, got {}".format( - len(self._args), len(arg_names) - ) - ) - if isinstance(output_names, str): - output_names = [output_names] - if output_names is None: - output_names = [var.name for var in self._sym_outputs] - elif len(output_names) != len(self._sym_outputs): - raise ValueError( - "len(output_names) should be {}, got {}".format( - len(self._sym_outputs), len(output_names) - ) - ) - - optimize_for_inference_args_map = { - "enable_io16xc32": "f16_io_f32_comp", - "enable_ioc16": "f16_io_comp", - "enable_hwcd4": "use_nhwcd4", - "enable_nchw4": "use_nchw4", - "enable_nchw88": "use_nchw88", - "enable_nchw32": "use_nchw32", - "enable_nchw44": "use_nchw44", - "enable_nchw44_dot": "use_nchw44_dot", - "enable_chwn4": "use_chwn4", - "enable_fuse_conv_bias_nonlinearity": "fuse_conv_bias_nonlinearity", - "enable_fuse_conv_bias_with_z": "fuse_conv_bias_with_z", - } - if optimize_for_inference: - optimize_for_inference_kwargs = {} - for k, v in optimize_for_inference_args_map.items(): - if kwargs.pop(k, False): - optimize_for_inference_kwargs[v] = True - else: - for k in optimize_for_inference_args_map: - if kwargs.get(k, False): - raise ValueError( - "cannot set %s when optimize_for_inference is not set" % k - ) - if kwargs: - raise ValueError("unknown options: %s" % list(kwargs)) - - cg = self._sym_outputs[0].owner_graph - replace = {} - for t, name in zip(self._args, arg_names): - # relies on symvar dedup - s = t.__mgb_symvar__(comp_graph=cg) - replace[s] = mgb.make_arg( - t.device, cg, dtype=t.dtype, shape=t.shape, name=name - ) - # Convert VolatileSharedDeviceTensor to SharedDeviceTensor, - # otherwise some optimizations would not work. The conversion is - # safe because there simply is no way (using builtin ops) to make - # a VolatileSharedDeviceTensor actually volatile. - for s in mgb.cgtools.get_dep_vars( - self._sym_outputs, "VolatileSharedDeviceTensor" - ): - if s in replace: - continue # is an input - replace[s] = mgb.SharedND._from_symvar(s).symvar( - cg, name=s.name, volatile=False - ) - sym_outputs = mgb.cgtools.replace_vars(self._sym_outputs, replace) - sym_outputs = list(sym_outputs) - if optimize_for_inference: - sym_outputs = mgb.optimize_for_inference( - sym_outputs, **optimize_for_inference_kwargs - ) - for var, name in zip(sym_outputs, output_names): - var.rename(name) - mgb.serialize_comp_graph_to_file(fpath, sym_outputs, append=append) - - def get_profile(self): - """ - Get profiling result for compiled trace. - - :return: a json compatible object. - """ - if not self._profiler: - raise RuntimeError("trace is not set with profiling=True") - return self._profiler.get() diff --git a/python_module/megengine/jit/sublinear_memory_config.py b/python_module/megengine/jit/sublinear_memory_config.py deleted file mode 100644 index fc3e46dd..00000000 --- a/python_module/megengine/jit/sublinear_memory_config.py +++ /dev/null @@ -1,56 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -from ..core.device import get_device_count - - -class SublinearMemoryConfig: - r""" - Configuration for sublinear memory optimization. - - :param thresh_nr_try: number of samples both for searching in linear space - and around current thresh in sublinear memory optimization. Default: 10. - It can also be set through the environmental variable 'MGB_SUBLINEAR_MEMORY_THRESH_NR_TRY'. - :param genetic_nr_iter: number of iterations to find the best checkpoints in genetic algorithm. - Default: 0. - It can also be set through the environmental variable 'MGB_SUBLINEAR_MEMORY_GENETIC_NR_ITER'. - :param genetic_pool_size: number of samples for the crossover random selection - during genetic optimization. Default: 20. - It can also be set through the environmental variable 'MGB_SUBLINEAR_MEMORY_GENETIC_POOL_SIZE'. - :param lb_memory: memory lower bound of bottleneck size in MB for sublinear memory optimization. - It can be used to perform manual tradeoff between memory and speed. Default: 0. - It can also be set through the environmental variable 'MGB_SUBLINEAR_MEMORY_LOWER_BOUND_MB'. - :param num_worker: number of thread workers to search the optimum checkpoints - in sublinear memory optimization. Default: half of cpu number in the system. - Note: the value must be greater or equal to one. - It can also be set through the environmental variable 'MGB_SUBLINEAR_MEMORY_WORKERS'. - - Note that the environmental variable MGB_COMP_GRAPH_OPT must be set to 'enable_sublinear_memory_opt=1' - in order for the above environmental variable to be effective. - """ - - def __init__( - self, - thresh_nr_try: int = 10, - genetic_nr_iter: int = 0, - genetic_pool_size: int = 20, - lb_memory: int = 0, - num_worker: int = max(1, get_device_count("cpu") // 2), - ): - assert thresh_nr_try >= 0, "thresh_nr_try must be greater or equal to zero" - self.thresh_nr_try = thresh_nr_try - assert genetic_nr_iter >= 0, "genetic_nr_iter must be greater or equal to zero" - self.genetic_nr_iter = genetic_nr_iter - assert ( - genetic_pool_size >= 0 - ), "genetic_pool_size must be greater or equal to zero" - self.genetic_pool_size = genetic_pool_size - self.lb_memory = lb_memory - assert num_worker > 0, "num_worker must be greater or equal to one" - self.num_worker = num_worker diff --git a/python_module/megengine/logger.py b/python_module/megengine/logger.py deleted file mode 100644 index 5cca95bd..00000000 --- a/python_module/megengine/logger.py +++ /dev/null @@ -1,231 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import contextlib -import logging -import os -import sys - -_all_loggers = [] -_default_level_name = os.getenv("MEGENGINE_LOGGING_LEVEL", "INFO") -_default_level = logging.getLevelName(_default_level_name.upper()) - - -def set_log_file(fout, mode="a"): - r"""Sets log output file. - - :type fout: str or file-like - :param fout: file-like object that supports write and flush, or string for - the filename - :type mode: str - :param mode: specify the mode to open log file if *fout* is a string - """ - if isinstance(fout, str): - fout = open(fout, mode) - MegEngineLogFormatter.log_fout = fout - - -class MegEngineLogFormatter(logging.Formatter): - log_fout = None - date_full = "[%(asctime)s %(lineno)d@%(filename)s:%(name)s] " - date = "%(asctime)s " - msg = "%(message)s" - max_lines = 256 - - def _color_exc(self, msg): - r"""Sets the color of message as the execution type. - """ - return "\x1b[34m{}\x1b[0m".format(msg) - - def _color_dbg(self, msg): - r"""Sets the color of message as the debugging type. - """ - return "\x1b[36m{}\x1b[0m".format(msg) - - def _color_warn(self, msg): - r"""Sets the color of message as the warning type. - """ - return "\x1b[1;31m{}\x1b[0m".format(msg) - - def _color_err(self, msg): - r"""Sets the color of message as the error type. - """ - return "\x1b[1;4;31m{}\x1b[0m".format(msg) - - def _color_omitted(self, msg): - r"""Sets the color of message as the omitted type. - """ - return "\x1b[35m{}\x1b[0m".format(msg) - - def _color_normal(self, msg): - r"""Sets the color of message as the normal type. - """ - return msg - - def _color_date(self, msg): - r"""Sets the color of message the same as date. - """ - return "\x1b[32m{}\x1b[0m".format(msg) - - def format(self, record): - if record.levelno == logging.DEBUG: - mcl, mtxt = self._color_dbg, "DBG" - elif record.levelno == logging.WARNING: - mcl, mtxt = self._color_warn, "WRN" - elif record.levelno == logging.ERROR: - mcl, mtxt = self._color_err, "ERR" - else: - mcl, mtxt = self._color_normal, "" - - if mtxt: - mtxt += " " - - if self.log_fout: - self.__set_fmt(self.date_full + mtxt + self.msg) - formatted = super(MegEngineLogFormatter, self).format(record) - nr_line = formatted.count("\n") + 1 - if nr_line >= self.max_lines: - head, body = formatted.split("\n", 1) - formatted = "\n".join( - [ - head, - "BEGIN_LONG_LOG_{}_LINES{{".format(nr_line - 1), - body, - "}}END_LONG_LOG_{}_LINES".format(nr_line - 1), - ] - ) - self.log_fout.write(formatted) - self.log_fout.write("\n") - self.log_fout.flush() - - self.__set_fmt(self._color_date(self.date) + mcl(mtxt + self.msg)) - formatted = super(MegEngineLogFormatter, self).format(record) - - if record.exc_text or record.exc_info: - # handle exception format - b = formatted.find("Traceback ") - if b != -1: - s = formatted[b:] - s = self._color_exc(" " + s.replace("\n", "\n ")) - formatted = formatted[:b] + s - - nr_line = formatted.count("\n") + 1 - if nr_line >= self.max_lines: - lines = formatted.split("\n") - remain = self.max_lines // 2 - removed = len(lines) - remain * 2 - if removed > 0: - mid_msg = self._color_omitted( - "[{} log lines omitted (would be written to output file " - "if set_log_file() has been called;\n" - " the threshold can be set at " - "MegEngineLogFormatter.max_lines)]".format(removed) - ) - formatted = "\n".join(lines[:remain] + [mid_msg] + lines[-remain:]) - - return formatted - - if sys.version_info.major < 3: - - def __set_fmt(self, fmt): - self._fmt = fmt - - else: - - def __set_fmt(self, fmt): - self._style._fmt = fmt - - -def get_logger(name=None, formatter=MegEngineLogFormatter): - r"""Gets megengine logger with given name. - """ - - logger = logging.getLogger(name) - if getattr(logger, "_init_done__", None): - return logger - logger._init_done__ = True - logger.propagate = False - logger.setLevel(_default_level) - handler = logging.StreamHandler() - handler.setFormatter(formatter(datefmt="%d %H:%M:%S")) - handler.setLevel(0) - del logger.handlers[:] - logger.addHandler(handler) - _all_loggers.append(logger) - return logger - - -def set_log_level(level, update_existing=True): - """Sets default logging level. - - :type level: int e.g. logging.INFO - :param level: loggin level given by python :mod:`logging` module - :param update_existing: whether to update existing loggers - """ - global _default_level # pylint: disable=global-statement - _default_level = level - if update_existing: - for i in _all_loggers: - i.setLevel(level) - - -_logger = get_logger(__name__) - -try: - if sys.version_info.major < 3: - raise ImportError() - - from megengine._internal.logconf import set_logger as _set_mgb_logger - - class MegBrainLogFormatter(MegEngineLogFormatter): - date = "%(asctime)s[mgb] " - - def _color_date(self, msg): - return "\x1b[33m{}\x1b[0m".format(msg) - - _megbrain_logger = get_logger("megbrain", MegBrainLogFormatter) - _set_mgb_logger(_megbrain_logger) - - def set_mgb_log_level(level): - r"""Sets megbrain log level - - :type level: int e.g. logging.INFO - :param level: new log level - :return: original log level - """ - logger = _megbrain_logger - rst = logger.getEffectiveLevel() - logger.setLevel(level) - return rst - - -except ImportError as exc: - - def set_mgb_log_level(level): - raise NotImplementedError("megbrain has not been imported") - - -@contextlib.contextmanager -def replace_mgb_log_level(level): - r"""Replaces megbrain log level in a block and restore after exiting. - - :type level: int e.g. logging.INFO - :param level: new log level - """ - old = set_mgb_log_level(level) - try: - yield - finally: - set_mgb_log_level(old) - - -def enable_debug_log(): - r"""Sets logging level to debug for all components. - """ - set_log_level(logging.DEBUG) - set_mgb_log_level(logging.DEBUG) diff --git a/python_module/megengine/module/__init__.py b/python_module/megengine/module/__init__.py deleted file mode 100644 index c2b3db8a..00000000 --- a/python_module/megengine/module/__init__.py +++ /dev/null @@ -1,23 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .activation import LeakyReLU, PReLU, ReLU, Sigmoid, Softmax -from .batchnorm import BatchNorm1d, BatchNorm2d, SyncBatchNorm -from .concat import Concat -from .conv import Conv2d, ConvRelu2d, ConvTranspose2d, LocalConv2d -from .conv_bn import ConvBn2d, ConvBnRelu2d -from .dropout import Dropout -from .elemwise import Elemwise -from .embedding import Embedding -from .identity import Identity -from .linear import Linear -from .module import Module -from .parampack import ParamPack -from .pooling import AvgPool2d, MaxPool2d -from .quant_dequant import DequantStub, QuantStub -from .sequential import Sequential diff --git a/python_module/megengine/module/activation.py b/python_module/megengine/module/activation.py deleted file mode 100644 index b80c10a7..00000000 --- a/python_module/megengine/module/activation.py +++ /dev/null @@ -1,231 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np - -from ..core import Parameter -from ..functional import leaky_relu, prelu, relu, sigmoid, softmax -from .module import Module - - -class Softmax(Module): - r""" - Applies a softmax function. Softmax is defined as: - - .. math:: - \text{Softmax}(x_{i}) = \frac{exp(x_i)}{\sum_j exp(x_j)} - - It is applied to an n-dimensional input Tensor and rescaling them so that the elements of the - n-dimensional output Tensor lie in the range of `[0, 1]` and sum to 1. - - :param axis: An axis along which softmax will be applied. By default, - softmax will apply along the highest ranked axis. - - Examples: - - .. testcode:: - - import numpy as np - import megengine as mge - import megengine.module as M - - data = mge.tensor(np.array([-2,-1,0,1,2]).astype(np.float32)) - softmax = M.Softmax() - output = softmax(data) - with np.printoptions(precision=6): - print(output.numpy()) - - Outputs: - - .. testoutput:: - - [0.011656 0.031685 0.086129 0.234122 0.636409] - - """ - - def __init__(self, axis=None): - super().__init__() - self.axis = axis - - def forward(self, inputs): - return softmax(inputs, self.axis) - - -class Sigmoid(Module): - r""" - Applies the element-wise function: - - .. math:: - \text{Sigmoid}(x) = \frac{1}{1 + \exp(-x)} - - Examples: - - .. testcode:: - - import numpy as np - import megengine as mge - import megengine.module as M - - data = mge.tensor(np.array([-2,-1,0,1,2,]).astype(np.float32)) - sigmoid = M.Sigmoid() - output = sigmoid(data) - with np.printoptions(precision=6): - print(output.numpy()) - - Outputs: - - .. testoutput:: - - [0.119203 0.268941 0.5 0.731059 0.880797] - - """ - - def forward(self, inputs): - return sigmoid(inputs) - - -class ReLU(Module): - r""" - Applies the element-wise function: - - .. math:: - \text{ReLU}(x) = \max(x, 0) - - Examples: - - .. testcode:: - - import numpy as np - import megengine as mge - import megengine.module as M - data = mge.tensor(np.array([-2,-1,0,1,2,]).astype(np.float32)) - relu = M.ReLU() - output = relu(data) - with np.printoptions(precision=6): - print(output.numpy()) - - Outputs: - - .. testoutput:: - - [0. 0. 0. 1. 2.] - - """ - - def forward(self, x): - return relu(x) - - -class PReLU(Module): - r""" - Applies the element-wise function: - - .. math:: - \text{PReLU}(x) = \max(0,x) + a * \min(0,x) - - or - - .. math:: - \text{PReLU}(x) = - \begin{cases} - x, & \text{ if } x \geq 0 \\ - ax, & \text{ otherwise } - \end{cases} - - Here :math:`a` is a learnable parameter. When called without arguments, `PReLU()` uses - a single paramter :math:`a` across all input channel. If called with `PReLU(num_of_channels)`, - a seperate :math:`a` is used for each input channle. - - :param num_parameters: number of :math:`a` to learn, there is only two - values are legitimate: 1, or the number of channels at input. Default: 1 - :param init: the initial value of :math:`a`. Default: 0.25 - - Examples: - - .. testcode:: - - import numpy as np - import megengine as mge - import megengine.module as M - data = mge.tensor(np.array([-1.2, -3.7, 2.7]).astype(np.float32)) - prelu = M.PReLU() - output = prelu(data) - print(output.numpy()) - - Outputs: - - .. testoutput:: - - [-0.3 -0.925 2.7 ] - - """ - - def __init__(self, num_parameters: int = 1, init: float = 0.25): - super().__init__() - self.num_parameters = num_parameters - if num_parameters > 1: - # Assume format is NCHW - self.weight = Parameter( - value=np.full((1, num_parameters, 1, 1), init, dtype=np.float32) - ) - else: - self.weight = Parameter(value=[init]) - - def forward(self, inputs): - assert self.weight.shape == (1,) or self.weight.shape == ( - 1, - int(inputs.shape[1]), - 1, - 1, - ), "invalid weight's shape" - return prelu(inputs, self.weight) - - -class LeakyReLU(Module): - r""" - Applies the element-wise function: - - .. math:: - \text{LeakyReLU}(x) = \max(0,x) + negative\_slope \times \min(0,x) - - or - - .. math:: - \text{LeakyReLU}(x) = - \begin{cases} - x, & \text{ if } x \geq 0 \\ - negative\_slope \times x, & \text{ otherwise } - \end{cases} - - Examples: - - .. testcode:: - - import numpy as np - import megengine as mge - import megengine.module as M - data = mge.tensor(np.array([-8, -12, 6, 10]).astype(np.float32)) - - leakyrelu = M.LeakyReLU(0.01) - output = leakyrelu(data) - print(output.numpy()) - - Outputs: - - .. testoutput:: - - [-0.08 -0.12 6. 10. ] - - """ - - def __init__(self, negative_slope: float = 0.01): - super().__init__() - self.negative_slope = negative_slope - - def forward(self, inputs): - return leaky_relu(inputs, self.negative_slope) diff --git a/python_module/megengine/module/batchnorm.py b/python_module/megengine/module/batchnorm.py deleted file mode 100644 index ba755616..00000000 --- a/python_module/megengine/module/batchnorm.py +++ /dev/null @@ -1,257 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np - -from ..core import Buffer, Parameter -from ..core.device import get_default_device -from ..functional import batch_norm2d, sync_batch_norm -from . import init -from .module import Module - - -class _BatchNorm(Module): - def __init__( - self, - num_features, - eps=1e-5, - momentum=0.9, - affine=True, - track_running_stats=True, - ): - super(_BatchNorm, self).__init__() - self.num_features = num_features - self.eps = eps - self.momentum = momentum - self.affine = affine - self.track_running_stats = track_running_stats - if self.affine: - self.weight = Parameter(np.ones(num_features, dtype=np.float32)) - self.bias = Parameter(np.zeros(num_features, dtype=np.float32)) - else: - self.weight = None - self.bias = None - - tshape = (1, self.num_features, 1, 1) - - if self.track_running_stats: - self.running_mean = Buffer(np.zeros(tshape, dtype=np.float32)) - self.running_var = Buffer(np.ones(tshape, dtype=np.float32)) - else: - self.running_mean = None - self.running_var = None - - def reset_running_stats(self) -> None: - if self.track_running_stats: - init.zeros_(self.running_mean) - init.ones_(self.running_var) - - def reset_parameters(self) -> None: - self.reset_running_stats() - if self.affine: - init.ones_(self.weight) - init.zeros_(self.bias) - - def _check_input_ndim(self, inp): - raise NotImplementedError - - def forward(self, inp): - self._check_input_ndim(inp) - - _ndims = len(inp.shape) - if _ndims != 4: - origin_shape = inp.shapeof() - if _ndims == 2: - n, c = inp.shapeof(0), inp.shapeof(1) - new_shape = (n, c, 1, 1) - elif _ndims == 3: - n, c, h = inp.shapeof(0), inp.shapeof(1), inp.shapeof(2) - new_shape = (n, c, h, 1) - - inp = inp.reshape(new_shape) - - if self.training and self.track_running_stats: - exponential_average_factor = self.momentum - else: - exponential_average_factor = 0.0 # useless - - # FIXME currently rocm does not support real bn opr so we just use - # sync_batch_norm(as implemented by elemwise) here, - # we will fix it in the next version - if get_default_device() == "rocmx": - output = sync_batch_norm( - inp, - self.running_mean, - self.running_var, - self.weight, - self.bias, - self.training or not self.track_running_stats, - exponential_average_factor, - self.eps, - ) - else: - output = batch_norm2d( - inp, - self.running_mean, - self.running_var, - self.weight, - self.bias, - self.training or not self.track_running_stats, - exponential_average_factor, - self.eps, - ) - - if _ndims != 4: - output = output.reshape(origin_shape) - - return output - - -class SyncBatchNorm(_BatchNorm): - r""" - Applies Synchronization Batch Normalization. - """ - - def _check_input_ndim(self, inp): - if len(inp.shape) not in {2, 3, 4}: - raise ValueError( - "expected 2D, 3D or 4D input (got {}D input)".format(len(inp.shape)) - ) - - def forward(self, inp): - self._check_input_ndim(inp) - - _ndims = len(inp.shape) - if _ndims != 4: - origin_shape = inp.shapeof() - if _ndims == 2: - n, c = inp.shapeof(0), inp.shapeof(1) - new_shape = (n, c, 1, 1) - elif _ndims == 3: - n, c, h = inp.shapeof(0), inp.shapeof(1), inp.shapeof(2) - new_shape = (n, c, h, 1) - - inp = inp.reshape(new_shape) - - if self.training and self.track_running_stats: - exponential_average_factor = self.momentum - else: - exponential_average_factor = 0.0 # useless - - output = sync_batch_norm( - inp, - self.running_mean, - self.running_var, - self.weight, - self.bias, - self.training or not self.track_running_stats, - exponential_average_factor, - self.eps, - ) - - if _ndims != 4: - output = output.reshape(origin_shape) - - return output - - -class BatchNorm1d(_BatchNorm): - r""" - Applies Batch Normalization over a 2D/3D tensor. - - Refer to :class:`~.BatchNorm2d` for more information. - """ - - def _check_input_ndim(self, inp): - if len(inp.shape) not in {2, 3}: - raise ValueError( - "expected 2D or 3D input (got {}D input)".format(len(inp.shape)) - ) - - -class BatchNorm2d(_BatchNorm): - r""" - Applies Batch Normalization over a 4D tensor. - - .. math:: - - y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta - - The mean and standard-deviation are calculated per-dimension over - the mini-batches and :math:`\gamma` and :math:`\beta` are learnable - parameter vectors. - - By default, during training this layer keeps running estimates of its - computed mean and variance, which are then used for normalization during - evaluation. The running estimates are kept with a default :attr:`momentum` - of 0.9. - - If :attr:`track_running_stats` is set to ``False``, this layer will not - keep running estimates, and batch statistics are instead used during - evaluation time. - - .. note:: - This :attr:`momentum` argument is different from one used in optimizer - classes and the conventional notion of momentum. Mathematically, the - update rule for running statistics here is - :math:`\hat{x}_\text{new} = \text{momentum} \times \hat{x} + (1 - \text{momentum}) \times x_t`, - where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the - new observed value. - - Because the Batch Normalization is done over the `C` dimension, computing - statistics on `(N, H, W)` slices, it's common terminology to call this - Spatial Batch Normalization. - - :type num_features: int - :param num_features: usually the :math:`C` from an input of size - :math:`(N, C, H, W)` or the highest ranked dimension of an input with - less than 4D. - :type eps: float - :param eps: a value added to the denominator for numerical stability. - Default: 1e-5. - :type momentum: float - :param momentum: the value used for the `running_mean` and `running_var` - computation. - Default: 0.9 - :type affine: bool - :param affine: a boolean value that when set to ``True``, this module has - learnable affine parameters. Default: ``True`` - :type track_running_stats: bool - :param track_running_stats: when set to ``True``, this module tracks the - running mean and variance. When set to ``False``, this module does not - track such statistics and always uses batch statistics in both training - and eval modes. Default: ``True``. - - - Examples: - - .. testcode:: - - import numpy as np - import megengine as mge - import megengine.module as M - - # With Learnable Parameters - m = M.BatchNorm2d(4) - inp = mge.tensor(np.random.rand(1, 4, 3, 3).astype("float32")) - oup = m(inp) - print(m.weight, m.bias) - # Without Learnable Parameters - m = M.BatchNorm2d(4, affine=False) - oup = m(inp) - print(m.weight, m.bias) - - .. testoutput:: - - Tensor([1. 1. 1. 1.]) Tensor([0. 0. 0. 0.]) - None None - """ - - def _check_input_ndim(self, inp): - if len(inp.shape) != 4: - raise ValueError("expected 4D input (got {}D input)".format(len(inp.shape))) diff --git a/python_module/megengine/module/concat.py b/python_module/megengine/module/concat.py deleted file mode 100644 index 453f951b..00000000 --- a/python_module/megengine/module/concat.py +++ /dev/null @@ -1,22 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from typing import Iterable - -from .. import functional as F -from ..core.tensor import Tensor -from .module import Module - - -class Concat(Module): - r""" - A :class:`~.Module` to do functional concat. Could be replaced with :class:`~.QATModule` - version :class:`~.qat.concat.Concat` using :func:`~.quantize.quantize_qat`. - """ - - def forward(self, inps: Iterable[Tensor], axis: int = 0): - return F.concat(inps, axis) diff --git a/python_module/megengine/module/conv.py b/python_module/megengine/module/conv.py deleted file mode 100644 index 02165b89..00000000 --- a/python_module/megengine/module/conv.py +++ /dev/null @@ -1,392 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from abc import abstractmethod -from typing import Tuple, Union - -import numpy as np - -import megengine._internal as mgb - -from .. import functional as F -from ..core import Parameter -from ..utils.types import _pair, _pair_nonzero -from . import init -from .module import Module - - -class _ConvNd(Module): - """base class for convolution modules, including transposed conv""" - - def __init__( - self, - in_channels: int, - out_channels: int, - kernel_size: Union[int, Tuple[int, int]], - stride: Union[int, Tuple[int, int]], - padding: Union[int, Tuple[int, int]], - dilation: Union[int, Tuple[int, int]], - groups: int, - bias: bool = True, - ): - super().__init__() - if in_channels % groups != 0: - raise ValueError("in_channels must be divisible by groups") - if out_channels % groups != 0: - raise ValueError("out_channels must be divisible by groups") - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.stride = stride - self.padding = padding - self.dilation = dilation - self.groups = groups - - self.weight = Parameter(np.zeros(self._infer_weight_shape(), dtype=np.float32)) - self.bias = None - if bias: - self.bias = Parameter(np.zeros(self._infer_bias_shape(), dtype=np.float32)) - self.reset_parameters() - - @abstractmethod - def _get_fanin(self): - pass - - def reset_parameters(self) -> None: - fanin = self._get_fanin() - std = np.sqrt(1 / fanin) - init.normal_(self.weight, 0.0, std) - if self.bias is not None: - init.zeros_(self.bias) - - @abstractmethod - def _infer_weight_shape(self): - pass - - @abstractmethod - def _infer_bias_shape(self): - pass - - -class Conv2d(_ConvNd): - r"""Applies a 2D convolution over an input tensor. - - For instance, given an input of the size :math:`(N, C_{\text{in}}, H, W)`, - this layer generates an output of the size - :math:`(N, C_{\text{out}}, H_{\text{out}}, W_{\text{out}})` through the - process described as below: - - .. math:: - \text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) + - \sum_{k = 0}^{C_{\text{in}} - 1} \text{weight}(C_{\text{out}_j}, k) \star \text{input}(N_i, k) - - where :math:`\star` is the valid 2D cross-correlation operator, - :math:`N` is a batch size, :math:`C` denotes a number of channels, - :math:`H` is a height of input planes in pixels, and :math:`W` is - width in pixels. - - When ``groups == in_channels`` and ``out_channels == K * in_channels``, - where `K` is a positive integer, this operation is also known as depthwise - convolution. - - In other words, for an input of size :math:`(N, C_{in}, H_{in}, W_{in})`, - a depthwise convolution with a depthwise multiplier `K`, can be constructed - by arguments :math:`(in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})`. - - :param in_channels: number of input channels. - :param out_channels: number of output channels. - :param kernel_size: size of weight on spatial dimensions. If ``kernel_size`` is - an :class:`int`, the actual kernel size would be - ``(kernel_size, kernel_size)``. Default: 1 - :param stride: stride of the 2D convolution operation. Default: 1 - :param padding: size of the paddings added to the input on both sides of its - spatial dimensions. Only zero-padding is supported. Default: 0 - :param dilation: dilation of the 2D convolution operation. Default: 1 - :param groups: number of groups to divide input and output channels into, - so as to perform a "grouped convolution". When ``groups`` is not 1, - ``in_channels`` and ``out_channels`` must be divisible by ``groups``, - and there would be an extra dimension at the beginning of the weight's - shape. Specifically, the shape of weight would be ``(groups, - out_channel // groups, in_channels // groups, *kernel_size)``. - :param bias: whether to add a bias onto the result of convolution. Default: - True - :param conv_mode: Supports `CROSS_CORRELATION` or `CONVOLUTION`. Default: - `CROSS_CORRELATION`. - :param compute_mode: When set to `DEFAULT`, no special requirements will be - placed on the precision of intermediate results. When set to `FLOAT32`, - float32 would be used for accumulator and intermediate result, but only - effective when input and output are of float16 dtype. - """ - - _conv_mode_type = mgb.opr_param_defs.Convolution.Mode - _compute_mode_type = mgb.opr_param_defs.Convolution.ComputeMode - - def __init__( - self, - in_channels: int, - out_channels: int, - kernel_size: Union[int, Tuple[int, int]], - stride: Union[int, Tuple[int, int]] = 1, - padding: Union[int, Tuple[int, int]] = 0, - dilation: Union[int, Tuple[int, int]] = 1, - groups: int = 1, - bias: bool = True, - conv_mode: str = "CROSS_CORRELATION", - compute_mode: str = "DEFAULT", - ): - kernel_size = _pair_nonzero(kernel_size) - stride = _pair_nonzero(stride) - padding = _pair(padding) - dilation = _pair_nonzero(dilation) - self.conv_mode = self._conv_mode_type.convert(conv_mode) - self.compute_mode = self._compute_mode_type.convert(compute_mode) - super().__init__( - in_channels, - out_channels, - kernel_size, - stride, - padding, - dilation, - groups, - bias, - ) - - def _get_fanin(self): - kh, kw = self.kernel_size - ic = self.in_channels - return kh * kw * ic - - def _infer_weight_shape(self): - group = self.groups - ichl = self.in_channels - ochl = self.out_channels - kh, kw = self.kernel_size - if group == 1: - # Assume format is NCHW - return (ochl, ichl, kh, kw) - - assert ( - ichl % group == 0 and ochl % group == 0 - ), "invalid config: input_channels={} output_channels={} group={}".format( - ichl, ochl, group - ) - # Assume format is NCHW - return (group, ochl // group, ichl // group, kh, kw) - - def _infer_bias_shape(self): - # Assume format is NCHW - return (1, self.out_channels, 1, 1) - - def calc_conv(self, inp, weight, bias): - return F.conv2d( - inp, - weight, - bias, - self.stride, - self.padding, - self.dilation, - self.groups, - self.conv_mode, - self.compute_mode, - ) - - def forward(self, inp): - return self.calc_conv(inp, self.weight, self.bias) - - -class ConvTranspose2d(_ConvNd): - r"""Applies a 2D transposed convolution over an input tensor. - - This module is also known as a deconvolution or a fractionally-strided convolution. - :class:`ConvTranspose2d` can ben seen as the gradient of :class:`Conv2d` operation - with respect to its input. - - Convolution usually reduces the size of input, while transposed convolution works - the opposite way, transforming a smaller input to a larger output while preserving the - connectivity pattern. - - :param in_channels: number of input channels. - :param out_channels: number of output channels. - :param kernel_size: size of weight on spatial dimensions. If ``kernel_size`` is - an :class:`int`, the actual kernel size would be - ``(kernel_size, kernel_size)``. Default: 1 - :param stride: stride of the 2D convolution operation. Default: 1 - :param padding: size of the paddings added to the input on both sides of its - spatial dimensions. Only zero-padding is supported. Default: 0 - :param dilation: dilation of the 2D convolution operation. Default: 1 - :param groups: number of groups to divide input and output channels into, - so as to perform a "grouped convolution". When ``groups`` is not 1, - ``in_channels`` and ``out_channels`` must be divisible by ``groups``, - and there would be an extra dimension at the beginning of the weight's - shape. Specifically, the shape of weight would be ``(groups, - out_channels // groups, in_channels // groups, *kernel_size)``. Default: 1 - :param bias: wether to add a bias onto the result of convolution. Default: - True - :param conv_mode: Supports `CROSS_CORRELATION` or `CONVOLUTION`. Default: - `CROSS_CORRELATION`. - :param compute_mode: When set to `DEFAULT`, no special requirements will be - placed on the precision of intermediate results. When set to `FLOAT32`, - float32 would be used for accumulator and intermediate result, but only - effective when input and output are of float16 dtype. - """ - - _conv_mode_type = mgb.opr_param_defs.Convolution.Mode - _compute_mode_type = mgb.opr_param_defs.Convolution.ComputeMode - - def __init__( - self, - in_channels: int, - out_channels: int, - kernel_size: Union[int, Tuple[int, int]], - stride: Union[int, Tuple[int, int]] = 1, - padding: Union[int, Tuple[int, int]] = 0, - dilation: Union[int, Tuple[int, int]] = 1, - groups: int = 1, - bias: bool = True, - conv_mode: str = "CROSS_CORRELATION", - compute_mode: str = "DEFAULT", - ): - kernel_size = _pair_nonzero(kernel_size) - stride = _pair_nonzero(stride) - padding = _pair(padding) - dilation = _pair_nonzero(dilation) - self.conv_mode = self._conv_mode_type.convert(conv_mode) - self.compute_mode = self._compute_mode_type.convert(compute_mode) - super().__init__( - in_channels, - out_channels, - kernel_size, - stride, - padding, - dilation, - groups, - bias, - ) - - def _get_fanin(self): - kh, kw = self.kernel_size - oc = self.out_channels - return kh * kw * oc - - def _infer_weight_shape(self): - group = self.groups - ichl = self.in_channels - ochl = self.out_channels - kh, kw = self.kernel_size - if group == 1: - # Assume format is NCHW - return (ichl, ochl, kh, kw) - - assert ( - ichl % group == 0 and ochl % group == 0 - ), "invalid config: input_channels={} output_channels={} group={}".format( - ichl, ochl, group - ) - # Assume format is NCHW - return (group, ichl // group, ochl // group, kh, kw) - - def _infer_bias_shape(self): - # Assume format is NCHW - return (1, self.out_channels, 1, 1) - - def forward(self, inp): - return F.conv_transpose2d( - inp, - self.weight, - self.bias, - self.stride, - self.padding, - self.dilation, - self.groups, - self.conv_mode, - self.compute_mode, - ) - - -class LocalConv2d(Conv2d): - r"""Applies a spatial convolution with untied kernels over an input 4D tensor. - It is also known as the locally connected layer. - - :param in_channels: number of input channels. - :param out_channels: number of output channels. - :param input_height: the height of the input images. - :param input_width: the width of the input images. - :param kernel_size: size of weight on spatial dimensions. If ``kernel_size`` is - an :class:`int`, the actual kernel size would be - ``(kernel_size, kernel_size)``. Default: 1 - :param stride: stride of the 2D convolution operation. Default: 1 - :param padding: size of the paddings added to the input on both sides of its - spatial dimensions. Only zero-padding is supported. Default: 0 - :param groups: number of groups to divide input and output channels into, - so as to perform a "grouped convolution". When ``groups`` is not 1, - ``in_channels`` and ``out_channels`` must be divisible by ``groups``. - The shape of weight is ``(groups, output_height, output_width, - in_channels // groups, *kernel_size, out_channels // groups)``. - """ - - _conv_mode_type = mgb.opr_param_defs.Convolution.Mode - - def __init__( - self, - in_channels: int, - out_channels: int, - input_height: int, - input_width: int, - kernel_size: Union[int, Tuple[int, int]], - stride: Union[int, Tuple[int, int]] = 1, - padding: Union[int, Tuple[int, int]] = 0, - dilation: Union[int, Tuple[int, int]] = 1, - groups: int = 1, - conv_mode: str = "CROSS_CORRELATION", - ): - self.input_height = input_height - self.input_width = input_width - super().__init__( - in_channels, - out_channels, - kernel_size, - stride, - padding, - dilation, - groups, - bias=False, - ) - - def _infer_weight_shape(self): - group = self.groups - output_height = ( - self.input_height + self.padding[0] * 2 - self.kernel_size[0] - ) // self.stride[0] + 1 - output_width = ( - self.input_width + self.padding[1] * 2 - self.kernel_size[1] - ) // self.stride[1] + 1 - # Assume format is NCHW - return ( - group, - output_height, - output_width, - self.in_channels // group, - self.kernel_size[0], - self.kernel_size[1], - self.out_channels // group, - ) - - def forward(self, inp): - return F.local_conv2d( - inp, self.weight, self.stride, self.padding, self.dilation, self.conv_mode - ) - - -class ConvRelu2d(Conv2d): - r""" - A fused :class:`~.Module` including Conv2d and relu. Could be replaced - with :class:`~.QATModule` version :class:`~.qat.conv.ConvRelu2d` using - :func:`~.quantize.quantize_qat`. - """ - - def forward(self, inp): - return F.relu(self.calc_conv(inp, self.weight, self.bias)) diff --git a/python_module/megengine/module/conv_bn.py b/python_module/megengine/module/conv_bn.py deleted file mode 100644 index 76713b0f..00000000 --- a/python_module/megengine/module/conv_bn.py +++ /dev/null @@ -1,69 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from typing import Tuple, Union - -from ..functional import relu -from .batchnorm import BatchNorm2d -from .conv import Conv2d -from .module import Module - - -class _ConvBnActivation2d(Module): - def __init__( - self, - in_channels: int, - out_channels: int, - kernel_size: Union[int, Tuple[int, int]], - stride: Union[int, Tuple[int, int]] = 1, - padding: Union[int, Tuple[int, int]] = 0, - dilation: Union[int, Tuple[int, int]] = 1, - groups: int = 1, - bias: bool = True, - conv_mode: str = "CROSS_CORRELATION", - compute_mode: str = "DEFAULT", - eps=1e-5, - momentum=0.9, - affine=True, - track_running_stats=True, - ): - super().__init__() - self.conv = Conv2d( - in_channels, - out_channels, - kernel_size, - stride, - padding, - dilation, - groups, - bias, - conv_mode, - compute_mode, - ) - self.bn = BatchNorm2d(out_channels, eps, momentum, affine, track_running_stats) - - -class ConvBn2d(_ConvBnActivation2d): - r""" - A fused :class:`~.Module` including Conv2d, BatchNorm2d. Could be replaced - with :class:`~.QATModule` version :class:`~.qat.conv_bn.ConvBn2d` using - :func:`~.quantize.quantize_qat`. - """ - - def forward(self, inp): - return self.bn(self.conv(inp)) - - -class ConvBnRelu2d(_ConvBnActivation2d): - r""" - A fused :class:`~.Module` including Conv2d, BatchNorm2d and relu. Could be replaced - with :class:`~.QATModule` version :class:`~.qat.conv_bn.ConvBnRelu2d` using - :func:`~.quantize.quantize_qat`. - """ - - def forward(self, inp): - return relu(self.bn(self.conv(inp))) diff --git a/python_module/megengine/module/dropout.py b/python_module/megengine/module/dropout.py deleted file mode 100644 index 146eba24..00000000 --- a/python_module/megengine/module/dropout.py +++ /dev/null @@ -1,29 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from ..functional import dropout -from .module import Module - - -class Dropout(Module): - r"""Randomly set input elements to zeros with the probability :math:`drop\_prob` during training. Commonly used in large networks to prevent overfitting. - Note that we perform dropout only during training, we also rescale(multiply) the output tensor - by :math:`\frac{1}{1 - drop\_prob}`. During inference :class:`~.Dropout` is equal to :class:`~.Identity`. - - :param drop_prob: The probability to drop (set to zero) each single element - """ - - def __init__(self, drop_prob=0.0): - super().__init__() - self.drop_prob = drop_prob - - def forward(self, inputs): - if self.training: - return dropout(inputs, self.drop_prob, rescale=True) - else: - return inputs diff --git a/python_module/megengine/module/elemwise.py b/python_module/megengine/module/elemwise.py deleted file mode 100644 index d1947b5e..00000000 --- a/python_module/megengine/module/elemwise.py +++ /dev/null @@ -1,90 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .. import _internal as mgb -from ..core import Tensor, wrap_io_tensor -from ..core.graph import _use_default_if_none -from .module import Module - - -@wrap_io_tensor -def _elemwise_func(mode, *inputs, **kwargs) -> Tensor: - if all(isinstance(i, (int, float)) for i in inputs): - device, comp_graph = _use_default_if_none(None, None) - ret = mgb.opr.elemwise( - *inputs, mode=mode, comp_node=device, comp_graph=comp_graph, **kwargs - ) - return ret.inferred_value[0] - return mgb.opr.elemwise(*inputs, mode=mode, **kwargs) - - -class Elemwise(Module): - r""" - A :class:`~.Module` to do elemwise operator. Could be replaced with :class:`~.QATModule` - version :class:`~.qat.elemwise.Elemwise` using :func:`~.quantize.quantize_qat`. - - :param method: the elemwise method, support the following string. - It will do the normal elemwise operator for float. - - * "ADD": a + b - * "FUSE_ADD_RELU": max(x+y, 0) - * "MUL": x * y - * "MIN": min(x, y) - * "MAX": max(x, y) - * "SUB": x - y - * "TRUE_DIV": x / y - * "FUSE_ADD_SIGMOID": sigmoid(x + y) - * "FUSE_ADD_TANH": tanh(x + y) - * "RELU": x > 0 ? x : 0 - * "ABS": x > 0 ? x : -x - * "SIGMOID": sigmoid(x) - * "EXP": exp(x) - * "TANH": tanh(x) - * "FUSE_MUL_ADD3": x * y + z - * "FAST_TANH": fast_tanh(x) - * "NEGATE": -x - * "ACOS": acos(x) - * "ASIN": asin(x) - * "CEIL": ceil(x) - * "COS": cos(x) - * "EXPM1": expm1(x) - * "FLOOR": floor(x) - * "LOG": log(x) - * "LOG1P": log1p(x) - * "SIN": sin(x) - * "ROUND": round(x) - * "ERF": erf(x) - * "ERFINV": erfinv(x) - * "ERFC": erfc(x) - * "ERFCINV": erfcinv(x) - * "ABS_GRAD": abs_grad - * "FLOOR_DIV": floor_div - * "MOD": mod - * "SIGMOID_GRAD": sigmoid_grad - * "SWITCH_GT0": switch_gt0 - * "TANH_GRAD": tanh_grad - * "LT": lt - * "LEQ": leq - * "EQ": eq - * "POW": pow - * "LOG_SUM_EXP": log_sum_exp - * "FAST_TANH_GRAD": fast_tanh_grad - * "ATAN2": atan2 - * "COND_LEQ_MOV": cond_leq_mov - * "H_SWISH": h_swish - * "FUSE_ADD_H_SWISH": h_swish(x+y) - * "H_SWISH_GRAD": h_swish_grad - """ - - _elemwise_mode_type = mgb.opr_param_defs.Elemwise.Mode - - def __init__(self, method): - super().__init__() - self.method = self._elemwise_mode_type.convert(method) - - def forward(self, *inps): - return _elemwise_func(self.method, *inps) diff --git a/python_module/megengine/module/embedding.py b/python_module/megengine/module/embedding.py deleted file mode 100644 index 976ac125..00000000 --- a/python_module/megengine/module/embedding.py +++ /dev/null @@ -1,171 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from typing import Optional - -import numpy as np - -from ..core import Parameter -from ..functional import embedding as embedding_func -from . import init -from .module import Module - - -class Embedding(Module): - r""" - A simple lookup table that stores embeddings of a fixed dictionary and size. - - This module is often used to store word embeddings and retrieve them using indices. - The input to the module is a list of indices, and the output is the corresponding word embeddings. - The indices should less than num_embeddings. - - :param num_embeddings: size of embedding dictionary. - :param embedding_dim: size of each embedding vector. - :param padding_idx: should be set to None, not support now. - :param max_norm: should be set to None, not support now. - :param norm_type: should be set to None, not support now. - :param initial_weight: the learnable weights of the module of shape (num_embeddings, embedding_dim). - - Examples: - - .. testcode:: - - import numpy as np - import megengine as mge - import megengine.module as M - weight = mge.tensor(np.array([(1.2,2.3,3.4,4.5,5.6),(0.1,1.1,2.1,3.1,4.1)], dtype=np.float32)) - data = mge.tensor(np.array([(0,1,1),(1,0,1),(0,0,1)], dtype=np.int32)) - - embedding = M.Embedding(2, 5, initial_weight=weight) - output = embedding(data) - with np.printoptions(precision=6): - print(output.numpy()) - - Outputs: - - .. testoutput:: - - [[[1.2 2.3 3.4 4.5 5.6] - [0.1 1.1 2.1 3.1 4.1] - [0.1 1.1 2.1 3.1 4.1]] - - [[0.1 1.1 2.1 3.1 4.1] - [1.2 2.3 3.4 4.5 5.6] - [0.1 1.1 2.1 3.1 4.1]] - - [[1.2 2.3 3.4 4.5 5.6] - [1.2 2.3 3.4 4.5 5.6] - [0.1 1.1 2.1 3.1 4.1]]] - - """ - - def __init__( - self, - num_embeddings: int, - embedding_dim: int, - padding_idx: Optional[int] = None, - max_norm: Optional[float] = None, - norm_type: Optional[float] = None, - initial_weight: Parameter = None, - ): - super().__init__() - if padding_idx is not None: - raise ValueError("Not support padding index now.") - if max_norm is not None or norm_type is not None: - raise ValueError("Not support weight normalize now.") - self.padding_idx = padding_idx - self.max_norm = max_norm - self.norm_type = norm_type - self.num_embeddings = num_embeddings - self.embedding_dim = embedding_dim - if initial_weight is None: - self.weight = Parameter( - np.random.uniform( - size=(self.num_embeddings, self.embedding_dim) - ).astype(np.float32) - ) - self.reset_parameters() - else: - if initial_weight.shape != (num_embeddings, embedding_dim): - raise ValueError( - "The weight shape should match num_embeddings and embedding_dim" - ) - self.weight = Parameter(initial_weight.numpy()) - - def reset_parameters(self) -> None: - init.normal_(self.weight) - - def forward(self, inputs): - return embedding_func(inputs, self.weight) - - @classmethod - def from_pretrained( - cls, - embeddings: Parameter, - freeze: Optional[bool] = True, - padding_idx: Optional[int] = None, - max_norm: Optional[float] = None, - norm_type: Optional[float] = None, - ): - r""" - Creates Embedding instance from given 2-dimensional FloatTensor. - - :param embeddings: Tensor contained weight for the embedding. - :param freeze: If ``True``, the weight does not get updated during the learning process. Default: ``True``. - :param padding_idx: should be set to None, not support Now. - :param max_norm: should be set to None, not support Now. - :param norm_type: should be set to None, not support Now. - - Examples: - - .. testcode:: - - import numpy as np - import megengine as mge - import megengine.module as M - weight = mge.tensor(np.array([(1.2,2.3,3.4,4.5,5.6),(0.1,1.1,2.1,3.1,4.1)], dtype=np.float32)) - data = mge.tensor(np.array([(0,1,1),(1,0,1),(0,0,1)], dtype=np.int32)) - - embedding = M.Embedding.from_pretrained(weight, freeze=False) - output = embedding(data) - print(output.numpy()) - - Outputs: - - .. testoutput:: - - [[[1.2 2.3 3.4 4.5 5.6] - [0.1 1.1 2.1 3.1 4.1] - [0.1 1.1 2.1 3.1 4.1]] - - [[0.1 1.1 2.1 3.1 4.1] - [1.2 2.3 3.4 4.5 5.6] - [0.1 1.1 2.1 3.1 4.1]] - - [[1.2 2.3 3.4 4.5 5.6] - [1.2 2.3 3.4 4.5 5.6] - [0.1 1.1 2.1 3.1 4.1]]] - - - """ - embeddings_shape = embeddings.shape - embeddings_dim = len(embeddings_shape) - if embeddings_dim != 2: - raise ValueError("Embeddings parameter is expected to be 2-dimensional") - rows = embeddings_shape[0] - cols = embeddings_shape[1] - embedding = cls( - num_embeddings=rows, - embedding_dim=cols, - initial_weight=embeddings, - padding_idx=padding_idx, - max_norm=max_norm, - norm_type=norm_type, - ) - embedding.weight.requires_grad = not freeze - return embedding diff --git a/python_module/megengine/module/external.py b/python_module/megengine/module/external.py deleted file mode 100644 index 962754e8..00000000 --- a/python_module/megengine/module/external.py +++ /dev/null @@ -1,83 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np - -from ..functional.external import ( - atlas_subgraph, - cambricon_subgraph, - extern_opr_subgraph, -) -from .module import Module - - -class CambriconSubgraph(Module): - r"""Load a serialized Cambricon subgraph. - - See :func:`~.cambricon_subgraph` for more details. - """ - - def __init__( - self, data, symbol, tensor_dim_mutable, - ): - super(CambriconSubgraph, self).__init__() - self._data = data - self.symbol = symbol - self.tensor_dim_mutable = tensor_dim_mutable - - @property - def data(self): - return self._data.tobytes() - - @data.setter - def data(self, val): - self._data = np.frombuffer(val, dtype=np.uint8) - - def forward(self, inputs): - outputs = cambricon_subgraph( - inputs, self._data, self.symbol, self.tensor_dim_mutable, - ) - return outputs - - -class AtlasSubgraph(Module): - r"""Load a serialized Atlas subgraph. - - See :func:`~.atlas_subgraph` for more details. - """ - - def __init__(self, data): - super(AtlasSubgraph, self).__init__() - self._data = data - - @property - def data(self): - return self._data.tobytes() - - @data.setter - def data(self, val): - self._data = np.frombuffer(val, dtype=np.uint8) - - def forward(self, inputs): - outputs = atlas_subgraph(inputs, self._data) - return outputs - - -class ExternOprSubgraph(Module): - r"""Load a serialized extern opr subgraph. - """ - - def __init__(self, data, name, output_shapes): - super(ExternOprSubgraph, self).__init__() - self.data = data - self.name = name - self.output_shapes = output_shapes - - def forward(self, inputs): - outputs = extern_opr_subgraph(inputs, self.output_shapes, self.name, self.data,) - return outputs diff --git a/python_module/megengine/module/identity.py b/python_module/megengine/module/identity.py deleted file mode 100644 index 51b31e50..00000000 --- a/python_module/megengine/module/identity.py +++ /dev/null @@ -1,17 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from ..functional import identity -from .module import Module - - -class Identity(Module): - r"""A placeholder identity operator that will ignore any argument.""" - - def forward(self, x): - return identity(x) diff --git a/python_module/megengine/module/init.py b/python_module/megengine/module/init.py deleted file mode 100644 index 8c39443e..00000000 --- a/python_module/megengine/module/init.py +++ /dev/null @@ -1,264 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import math -from functools import reduce -from typing import Optional, Tuple, Union - -import numpy as np - -from ..core import Graph, Tensor -from ..random import gaussian, uniform - - -def fill_(tensor: Tensor, val: Union[float, int]) -> None: - """Fill the given ``tensor`` with value ``val``. - - :param tensor: An n-dimentional tensor to be initialized - :param val: The value to be filled throughout the tensor - """ - tensor.set_value(np.full(tensor.shape, val, tensor.dtype)) - - -def zeros_(tensor: Tensor) -> None: - """Fill the given ``tensor`` with scalar value `0`. - - :param tensor: An n-dimentional tensor to be initialized - """ - fill_(tensor, 0) - - -def ones_(tensor: Tensor) -> None: - """Fill the given ``tensor`` with the scalar value `1`. - - :param tensor: An n-dimentional tensor to be initialized - """ - fill_(tensor, 1) - - -def uniform_(tensor: Tensor, a: float = 0.0, b: float = 1.0) -> None: - r"""Fill the given ``tensor`` with random value sampled from uniform distribution - :math:`\mathcal{U}(\text{a}, \text{b})`. - - :param tensor: An n-dimentional tensor to be initialized - :param a: Lower bound of the sampling interval - :param b: Upper bound of the sampling interval - """ - with Graph(eager_evaluation=True): - tensor.set_value((b - a) * uniform(tensor.shape) + a) - - -def normal_(tensor: Tensor, mean: float = 0.0, std: float = 1.0) -> None: - r"""Fill the given ``tensor`` with random value sampled from normal distribution - :math:`\mathcal{N}(\text{mean}, \text{std}^2)`. - - :param tensor: An n-dimentional tensor to be initialized - :param mean: The mean of the normal distribution - :param std: The standard deviation of the normal distribution - """ - with Graph(eager_evaluation=True): - tensor.set_value(gaussian(tensor.shape, mean=mean, std=std)) - - -def calculate_gain( - nonlinearity: str, param: Optional[Union[int, float]] = None -) -> float: - r"""Return a recommended gain value (see the table below) for the given nonlinearity - function. - - ================= ==================================================== - nonlinearity gain - ================= ==================================================== - Linear / Identity :math:`1` - Conv{1,2,3}D :math:`1` - Sigmoid :math:`1` - Tanh :math:`\frac{5}{3}` - ReLU :math:`\sqrt{2}` - Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative_{slope}}^2}}` - ================= ==================================================== - - :param nonlinearity: Name of the non-linear function - :param param: Optional parameter for leaky_relu. Only effective when - ``nonlinearity`` is "leaky_relu". - - """ - linear_fns = [ - "linear", - "conv1d", - "conv2d", - "conv3d", - "conv_transpose1d", - "conv_transpose2d", - "conv_transpose3d", - ] - if nonlinearity in linear_fns or nonlinearity == "sigmoid": - return 1 - if nonlinearity == "tanh": - return 5.0 / 3 - if nonlinearity == "relu": - return math.sqrt(2.0) - if nonlinearity == "leaky_relu": - if param is None: - negative_slope = 0.01 - elif ( - not isinstance(param, bool) - and isinstance(param, int) - or isinstance(param, float) - ): - # True/False are instances of int, hence check above - negative_slope = param - else: - raise ValueError("negative_slope {} not a valid number".format(param)) - return math.sqrt(2.0 / (1 + negative_slope ** 2)) - raise ValueError("Unsupported nonlinearity {}".format(nonlinearity)) - - -def calculate_fan_in_and_fan_out(tensor: Tensor) -> Tuple[float, float]: - """ - Calculate fan_in / fan_out value for given weight tensor. This function assumes - input tensor is stored in NCHW format. - - :param tensor: Weight tensor in NCHW format - """ - shape = tensor.shape - ndim = len(shape) - if ndim < 2: - raise ValueError( - "fan_in and fan_out can not be computed for tensor with fewer than 2 " - "dimensions" - ) - - if ndim == 2: # Linear - fan_in = shape[1] - fan_out = shape[0] - else: - num_input_fmaps = shape[1] - num_output_fmaps = shape[0] - receptive_field_size = 1 - if ndim > 2: - receptive_field_size = reduce(lambda x, y: x * y, shape[2:], 1) - fan_in = num_input_fmaps * receptive_field_size - fan_out = num_output_fmaps * receptive_field_size - return fan_in, fan_out - - -def calculate_correct_fan(tensor: Tensor, mode: str) -> float: - """ - Calculate fan_in or fan_out value for given weight tensor, depending on given - ``mode``. - - See :func:`calculate_fan_in_and_fan_out` for details. - - :param tensor: Weight tensor in NCHW format - :param mode: ``'fan_in'`` or ``'fan_out'`` - """ - mode = mode.lower() - valid_modes = ["fan_in", "fan_out"] - if mode not in valid_modes: - raise ValueError( - "Mode {} not supported, please use one of {}".format(mode, valid_modes) - ) - - fan_in, fan_out = calculate_fan_in_and_fan_out(tensor) - return fan_in if mode == "fan_in" else fan_out - - -def xavier_uniform_(tensor: Tensor, gain: float = 1.0) -> None: - r"""Fill ``tensor`` with random values sampled from :math:`\mathcal{U}(-a, a)` - where - - .. math:: - a = \text{gain} \times \sqrt{\frac{6}{\text{fan_in} + \text{fan_out}}} - - Also known as Glorot initialization. Detailed information can be retrieved from - `"Understanding the difficulty of training deep feedforward neural networks" `_. - - - :param tensor: An n-dimentional tensor to be initialized - :param gain: Scaling factor for :math:`a`. - """ - fan_in, fan_out = calculate_fan_in_and_fan_out(tensor) - std = gain * math.sqrt(2.0 / float(fan_in + fan_out)) - a = math.sqrt(3.0) * std - uniform_(tensor, -a, a) - - -def xavier_normal_(tensor: Tensor, gain: float = 1.0) -> None: - r"""Fill ``tensor`` with random values sampled from - :math:`\mathcal{N}(0, \text{std}^2)` where - - .. math:: - \text{std} = \text{gain} \times \sqrt{\frac{2}{\text{fan_in} + \text{fan_out}}} - - Also known as Glorot initialization. Detailed information can be retrieved from - `"Understanding the difficulty of training deep feedforward neural networks" `_. - - :param tensor: An n-dimentional tensor to be initialized - :param gain: Scaling factor for :math:`std`. - """ - fan_in, fan_out = calculate_fan_in_and_fan_out(tensor) - std = gain * math.sqrt(2.0 / float(fan_in + fan_out)) - normal_(tensor, 0.0, std) - - -def msra_uniform_( - tensor: Tensor, a: float = 0, mode: str = "fan_in", nonlinearity: str = "leaky_relu" -) -> None: - r"""Fill ``tensor`` wilth random values sampled from - :math:`\mathcal{U}(-\text{bound}, \text{bound})` where - - .. math:: - \text{bound} = \sqrt{\frac{6}{(1 + a^2) \times \text{fan_in}}} - - Detailed information can be retrieved from - `"Delving deep into rectifiers: Surpassing human-level performance on ImageNet - classification" `_. - - - :param tensor: An n-dimentional tensor to be initialized - :param a: Optional parameter for calculating gain for leaky_relu. See - :func:`calculate_gain` for details. - :param mode: ``'fan_in'`` or ``'fan_out'``, used to calculate :math:`gain`, the - scaling factor for :math:`bound`. See :func:`calculate_fan_in_and_fan_out` for - details. - :param nonlinearity: Name of the non-linear function used to calculate :math:`gain`. - See :func:`calculate_gain` for details. - """ - fan = calculate_correct_fan(tensor, mode) - gain = calculate_gain(nonlinearity, a) - std = gain / math.sqrt(fan) - bound = math.sqrt(3.0) * std - uniform_(tensor, -bound, bound) - - -def msra_normal_( - tensor: Tensor, a: float = 0, mode: str = "fan_in", nonlinearity: str = "leaky_relu" -) -> None: - r"""Fill ``tensor`` wilth random values sampled from - :math:`\mathcal{N}(0, \text{std}^2)` where - - .. math:: - \text{std} = \sqrt{\frac{2}{(1 + a^2) \times \text{fan_in}}} - - Detailed information can be retrieved from - `"Delving deep into rectifiers: Surpassing human-level performance on ImageNet - classification" `_. - - :param tensor: An n-dimentional tensor to be initialized - :param a: Optional parameter for calculating gain for leaky_relu. See - :func:`calculate_gain` for details. - :param mode: ``'fan_in'`` or ``'fan_out'``, used to calculate :math:`gain`, the - scaling factor for :math:`gain`. See :func:`calculate_fan_in_and_fan_out` for - details. - :param nonlinearity: Name of the non-linear function used to calculate :math:`gain`. - See :func:`calculate_gain` for details. - """ - fan = calculate_correct_fan(tensor, mode) - gain = calculate_gain(nonlinearity, a) - std = gain / math.sqrt(fan) - normal_(tensor, 0, std) diff --git a/python_module/megengine/module/linear.py b/python_module/megengine/module/linear.py deleted file mode 100644 index 30f8ea82..00000000 --- a/python_module/megengine/module/linear.py +++ /dev/null @@ -1,61 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np - -from .. import functional as F -from ..core import Parameter -from . import init -from .module import Module - - -class Linear(Module): - r"""Applies a linear transformation to the input. For instance, if input - is x, then output y is: - - .. math:: - - y = xW^T + b - - where :math:`y_i= \sum_j W_{ij} x_j + b_i` - - :param in_features: size of each input sample. - :param out_features: size of each output sample. - :param bias: If set to ``False``, the layer will not learn an additive bias. - Default: ``True`` - - """ - - def __init__( - self, in_features: int, out_features: int, bias: bool = True, **kwargs - ): - super().__init__(**kwargs) - self.out_features = out_features - self.in_features = in_features - w_shape = (out_features, in_features) - self.weight = Parameter(np.zeros(w_shape, dtype=np.float32)) - self.bias = None - if bias: - b_shape = (out_features,) - self.bias = Parameter(np.zeros(b_shape, dtype=np.float32)) - self.reset_parameters() - - def _get_fanin(self): - return self.in_features - - def reset_parameters(self) -> None: - fanin = self._get_fanin() - std = np.sqrt(1 / fanin) - init.normal_(self.weight, 0.0, std) - if self.bias is not None: - init.zeros_(self.bias) - - def _calc_linear(self, x, weight, bias): - return F.linear(x, weight, bias) - - def forward(self, x): - return self._calc_linear(x, self.weight, self.bias) diff --git a/python_module/megengine/module/module.py b/python_module/megengine/module/module.py deleted file mode 100644 index b999fb12..00000000 --- a/python_module/megengine/module/module.py +++ /dev/null @@ -1,507 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from abc import ABCMeta, abstractmethod -from collections import OrderedDict -from typing import Any, Callable, Iterable, Optional, Set, Tuple, Union - -import numpy as np - -from .._internal.dtype import is_quantize -from ..core import Buffer, Parameter, Tensor -from ..logger import get_logger -from ..utils.hook import HookHandler - -logger = get_logger(__name__) - - -def _expand_structure(key, obj): - if isinstance(obj, (Tensor, Module)): - return [(key, obj)] - elif isinstance(obj, (list, tuple, dict)): - ret = [] - if isinstance(obj, dict): - targets = ((k, obj[k]) for k in sorted(obj)) - else: - targets = ((str(k), v) for k, v in enumerate(obj)) - for k, o in targets: - sub_ret = _expand_structure(k, o) - if sub_ret and not isinstance(k, str): - raise AssertionError( - "keys for Tensor and Module must be str, error key: {}".format(k) - ) - for kt, vt in sub_ret: - ret.extend([(key + "." + kt, vt)]) - return ret - else: - return [] - - -def _is_parameter(obj): - return isinstance(obj, Parameter) - - -def _is_buffer(obj): - return isinstance(obj, Buffer) - - -def _is_module(obj): - return isinstance(obj, Module) - - -class Module(metaclass=ABCMeta): - """Base Module class. - """ - - def __init__(self): - # runtime attributes - self.training = True - self.quantize_disabled = False - - # hooks - self._forward_pre_hooks = OrderedDict() - self._forward_hooks = OrderedDict() - - @abstractmethod - def forward(self, inputs): - pass - - def register_forward_pre_hook(self, hook: Callable) -> HookHandler: - """Register a hook to handle forward inputs. `hook` should be a function - - Note that `inputs` keyword inputs - - :param hook: a function that receive `module` and `inputs`, then return - a modified `inputs` or `None`. - :return: a handler with :meth:`~.HookHandler.remove` interface to delete the hook. - """ - return HookHandler(self._forward_pre_hooks, hook) - - def register_forward_hook(self, hook: Callable) -> HookHandler: - """Register a hook to handle forward results. `hook` should be a function that - receive `module`, `inputs` and `outputs`, then return a modified `outputs` or `None`. - - This method return a handler with :meth:`~.HookHandler.remove` interface to delete the hook. - """ - return HookHandler(self._forward_hooks, hook) - - def __call__(self, *inputs, **kwargs): - for hook in self._forward_pre_hooks.values(): - modified_inputs = hook(self, inputs) - if modified_inputs is not None: - if not isinstance(modified_inputs, tuple): - modified_inputs = (modified_inputs,) - inputs = modified_inputs - - outputs = self.forward(*inputs, **kwargs) - - for hook in self._forward_hooks.values(): - modified_outputs = hook(self, inputs, outputs) - if modified_outputs is not None: - outputs = modified_outputs - return outputs - - def _flatten( - self, - *, - recursive: bool = True, - with_key: bool = False, - with_parent: bool = False, - prefix: Optional[str] = None, - predicate: Callable[[Any], bool] = lambda _: True, - seen: Optional[Set[int]] = None - ) -> Union[Iterable[Any], Iterable[Tuple[str, Any]]]: - """Scans the module object and returns an iterable for the :class:`~.Tensor` - and :class:`~.Module` attributes that agree with the ``predicate``. For multiple - calls of this function with same arguments, the order of objects within the - returned iterable is guaranteed to be identical, as long as all the involved - module objects' ``__dict__`` does not change thoughout those calls. - - :param recursive: Whether to recursively scan all the submodules. - :param with_key: Whether to yield keys along with yielded objects. - :param with_parent: Whether to yield ``self`` along with yielded objects. - :param prefix: The prefix appended to the yielded keys. - :param predicate: The predicate function applied to scanned objects. - :param seen: A dict that records whether a module has been traversed yet. - """ - if seen is None: - seen = set([id(self)]) - - module_dict = vars(self) - _prefix = "" if prefix is None else prefix + "." - - for key in sorted(module_dict): - for expanded_key, leaf in _expand_structure(key, module_dict[key]): - leaf_id = id(leaf) - if leaf_id in seen: - continue - seen.add(leaf_id) - - if predicate(leaf): - if with_key and with_parent: - yield _prefix + expanded_key, leaf, self - elif with_key: - yield _prefix + expanded_key, leaf - elif with_parent: - yield leaf, self - else: - yield leaf - - if recursive and isinstance(leaf, Module): - yield from leaf._flatten( - recursive=recursive, - with_key=with_key, - with_parent=with_parent, - prefix=_prefix + expanded_key if with_key else None, - predicate=predicate, - seen=seen, - ) - - def parameters( - self, requires_grad: Optional[bool] = None, recursive: bool = True, **kwargs - ) -> Iterable[Parameter]: - r"""Returns an iterable for the :class:`~.Parameter` of the module. - - :param requires_grad: Limitation over the :attr:`~.Parameter.requires_grad` - attribute of returned :class:`.Parameter`. ``None`` for no limitation. - :param recursive: If ``True``, returns all :class:`~.Parameter` within this - module, else only returns :class:`~.Parameter` that are direct attributes - of this module. - """ - - def predicate(obj) -> bool: - return _is_parameter(obj) and ( - requires_grad is None or obj.requires_grad == requires_grad - ) - - yield from self._flatten( - with_key=False, predicate=predicate, recursive=recursive, **kwargs - ) - - def named_parameters( - self, - requires_grad: Optional[bool] = None, - prefix: Optional[str] = None, - recursive: bool = True, - **kwargs - ) -> Iterable[Tuple[str, Parameter]]: - """Returns an iterable for key :class:`~.Parameter` pairs of the module, where - ``key`` is the dotted path from this module to the :class:`~.Parameter` . - - :param requires_grad: Limitation over the :attr:`~.Parameter.requires_grad` - attribute of returned :class:`~.Parameter` . ``None`` for no limitation. - :param prefix: The prefix prepended to the keys. - :param recursive: If ``True``, returns all :class:`~.Parameter` within this - module, else only returns :class:`~.Parameter` that are direct attributes - of this module. - """ - - def predicate(obj) -> bool: - return _is_parameter(obj) and ( - requires_grad is None or obj.requires_grad == requires_grad - ) - - yield from self._flatten( - with_key=True, - prefix=prefix, - predicate=predicate, - recursive=recursive, - **kwargs, - ) - - def buffers(self, recursive: bool = True, **kwargs) -> Iterable[Buffer]: - """Returns an iterable for the :class:`~.Buffer` of the module. - - :param recursive: If ``True``, returns all :class:`~.Buffer` within this - module, else only returns :class:`~.Buffer` that are direct attributes - of this module. - """ - yield from self._flatten( - with_key=False, predicate=_is_buffer, recursive=recursive, **kwargs - ) - - def named_buffers( - self, prefix: Optional[str] = None, recursive: bool = True, **kwargs - ) -> Iterable[Tuple[str, Buffer]]: - """Returns an iterable for key :class:`~.Buffer` pairs of the module, where - ``key`` is the dotted path from this module to the :class:`~.Buffer` . - - :param prefix: The prefix prepended to the keys. - :param recursive: If ``True``, returns all :class:`~.Buffer` within this - module, else only returns :class:`~.Buffer` that are direct attributes - of this module. - """ - yield from self._flatten( - with_key=True, - prefix=prefix, - predicate=_is_buffer, - recursive=recursive, - **kwargs, - ) - - def children(self, **kwargs) -> "Iterable[Module]": - """Returns an iterable for all the submodules that are direct attributes of this - module. - """ - yield from self._flatten( - with_key=False, predicate=_is_module, recursive=False, **kwargs - ) - - def named_children(self, **kwargs) -> "Iterable[Tuple[str, Module]]": - """Returns an iterable of key-submodule pairs for all the submodules that are - direct attributes of this module, where 'key' is the attribute name of - submodules. - """ - yield from self._flatten( - with_key=True, predicate=_is_module, recursive=False, **kwargs - ) - - def modules(self, **kwargs) -> "Iterable[Module]": - """Returns an iterable for all the modules within this module, including itself. - """ - if "with_parent" in kwargs and kwargs["with_parent"]: - yield self, None - else: - yield self - yield from self._flatten(with_key=False, predicate=_is_module, **kwargs) - - def named_modules( - self, prefix: Optional[str] = None, **kwargs - ) -> "Iterable[Tuple[str, Module]]": - """Returns an iterable of key-module pairs for all the modules within this - module, including itself, where 'key' is the dotted path from this module to the - submodules. - - :param prefix: The prefix prepended to the path. - """ - if "with_parent" in kwargs and kwargs["with_parent"]: - yield ("" if prefix is None else prefix), self, None - else: - yield ("" if prefix is None else prefix), self - yield from self._flatten( - with_key=True, prefix=prefix, predicate=_is_module, **kwargs - ) - - def apply(self, fn: "Callable[[Module], Any]") -> None: - """Apply function ``fn`` to all the modules within this module, including - itself. - - :param fn: The function to be applied on modules. - """ - for it in self.modules(): - fn(it) - - def zero_grad(self) -> None: - """Set all parameters' grads to zero - """ - for param in self.parameters(): - if param.grad is not None: - param.grad.reset_zero() - - def train(self, mode: bool = True, recursive: bool = True) -> None: - """Set training mode of all the modules within this module (including itself) to - ``mode``. This effectively sets the ``training`` attributes of those modules - to ``mode``, but only has effect on certain modules (e.g. - :class:`~.BatchNorm2d`, :class:`~.Dropout`, :class:`~.Observer`) - - :param mode: the training mode to be set on modules. - :param recursive: whether to recursively call submodules' ``train()``. - """ - if not recursive: - self.training = mode - return - - def fn(module: Module) -> None: - module.train(mode, recursive=False) - - self.apply(fn) - - def eval(self) -> None: - """Set training mode of all the modules within this module (including itself) to - ``False``. See :meth:`~.Module.train` for details. - """ - self.train(False) - - def disable_quantize(self, value=True): - r""" - Set ``module``'s ``quantize_disabled`` attribute and return ``module``. - Could be used as a decorator. - """ - - def fn(module: Module) -> None: - module.quantize_disabled = value - - self.apply(fn) - - def replace_param( - self, params: dict, start_pos: int, seen: Optional[Set[int]] = None - ): - """Replace module's parameters with `params`, used by :class:`~.ParamPack` to - speedup multimachine training. - """ - offset = 0 - if seen is None: - seen = set([id(self)]) - module_dict = vars(self) - for key in sorted(module_dict): - hash_id = id(module_dict[key]) - if hash_id in seen: - continue - seen.add(hash_id) - if isinstance(module_dict[key], Parameter): - if start_pos + offset in params: - assert module_dict[key].shape == params[start_pos + offset].shape - module_dict[key] = params[start_pos + offset] - offset += 1 - if isinstance(module_dict[key], Module): - offset += module_dict[key].replace_param( - params, start_pos + offset, seen - ) - return offset - - def state_dict(self, rst=None, prefix="", keep_var=False): - r"""Returns a dictionary containing whole states of the module. - """ - - def is_state(obj): - return _is_parameter(obj) or _is_buffer(obj) - - if rst is None: - rst = OrderedDict() - - for k, v in self._flatten(recursive=False, with_key=True, predicate=is_state): - assert prefix + k not in rst, "duplicated state: {}".format(k) - if keep_var: - rst[prefix + k] = v - else: - rst[prefix + k] = v.numpy() - - for k, submodule in self._flatten( - recursive=False, - with_key=True, - predicate=lambda obj: isinstance(obj, Module), - ): - submodule.state_dict(rst, prefix + k + ".", keep_var) - - return rst - - def load_state_dict( - self, - state_dict: Union[dict, Callable[[str, Tensor], Optional[np.ndarray]]], - strict=True, - ): - r"""Load a given dictionary created by :func:`state_dict` into this module. - If ``strict`` is ``True``, the keys of :func:`state_dict` must exactly match the keys - returned by :func:`state_dict`. - - Users can also pass a closure: `Function[key: str, var: Tensor] -> Optional[np.ndarray]` - as a `state_dict`, in order to handle complex situations. For example, load everything - except for the final linear classifier: - - .. code-block:: - - state_dict = {...} # Dict[str, np.ndarray] - model.load_state_dict({ - k: None if k.startswith('fc') else v - for k, v in state_dict.items() - }, strict=False) - - Here returning `None` means skipping parameter `k`. - - To prevent shape mismatch (e.g. load PyTorch weights), we can reshape before loading: - - .. code-block:: - - state_dict = {...} - def reshape_accordingly(k, v): - return state_dict[k].reshape(v.shape) - model.load_state_dict(reshape_accordingly) - - We can also perform inplace re-initialization or pruning: - - .. code-block:: - - def reinit_and_pruning(k, v): - if 'bias' in k: - M.init.zero_(v) - if 'conv' in k: - return v.numpy() * (np.abs(v.numpy()) > 1e-3).astype("float32) - model.load_state_dict(reinit_and_pruning, strict=False) - """ - unused = [] - if isinstance(state_dict, dict): - unused = state_dict.keys() - - def closure(k, _): # var unused - return state_dict[k] if k in state_dict else None - - elif callable(state_dict): - closure = state_dict - else: - raise ValueError( - "`state_dict` must load a dict or callable, got {}".format( - type(state_dict) - ) - ) - - loaded, skipped = self._load_state_dict_with_closure(closure) - unused = set(unused) - loaded - - if len(unused) != 0: - if strict: - raise KeyError( - "Unused params violate `strict=True`, unused={}".format(unused) - ) - else: - logger.warning( - "Unused params in `strict=False` mode, unused={}".format(unused) - ) - - if len(skipped) != 0: - if strict: - raise KeyError( - "Missing params violate `strict=True`, missing={}".format(skipped) - ) - else: - logger.warning( - "Missing params in `strict=False` mode, missing={}".format(skipped) - ) - - def _load_state_dict_with_closure(self, closure): - """Advance state_dict load through callable `closure` whose signature is - - `closure(key: str, var: Tensor) -> Union[np.ndarry, None]` - """ - assert callable(closure), "closure must be a function" - - loaded = [] - skipped = [] - - local_state_dict = self.state_dict(keep_var=True) - for k, var in local_state_dict.items(): - to_be_load = closure(k, var) - if to_be_load is None: - skipped.append(k) - continue - assert isinstance( - to_be_load, np.ndarray - ), "closure should return a `np.ndarray`, now `{}` get {}".format( - k, to_be_load - ) - assert ( - var.shape == to_be_load.shape - ), "param `{}` shape mismatch, should be {}, get {}".format( - k, var.shape, to_be_load.shape - ) - # For quantized dtype, the initialized dtype - # scale/zero_points maybe invalid, use pretrained dtype instead. - if is_quantize(to_be_load.dtype) and is_quantize(var.dtype): - var.dtype = to_be_load.dtype - var.set_value(to_be_load) - loaded.append(k) - - return set(loaded), set(skipped) diff --git a/python_module/megengine/module/parampack.py b/python_module/megengine/module/parampack.py deleted file mode 100644 index c020a41d..00000000 --- a/python_module/megengine/module/parampack.py +++ /dev/null @@ -1,157 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import collections -from typing import Callable, Iterable, Optional, Tuple - -import numpy as np - -from .._internal.opr import param_pack_split -from ..core import Parameter, Tensor -from .module import Module - - -class ParamPack(Module): - r"""Pack module's parameters by gathering their memory to continuous address. - Using (device, dtype, requires_grad) as key, for example ('gpu0', float32, True), - parameters with same key will be packed togather. - It helps a lot for multimachine training by speeding up allreduce gradients. - - :param model: the module you want to pack parameters. - :param nr_ignore_first: how many parameters will be unpacked at first. - :param max_size_per_group: upper bound of packed parameters' size in MB. - :param max_nr_params_per_group: upper bound of the number of parameters of each group. - - """ - - def __init__( - self, - model: Module, - nr_ignore_first: int = 8, - max_size_per_group: int = 10, - max_nr_params_per_group: int = 100, - group_func: Callable = lambda name, param: 0, - ): - super().__init__() - self._model = model - self._nr_ignore_first = nr_ignore_first - self._max_size_per_group = max_size_per_group - self._max_nr_params_per_group = max_nr_params_per_group - self._group_func = group_func - self._grouped_params = [] - self._packed_params = [] - - params = model.named_parameters() - self._pack_params(params) - - def parameters(self, requires_grad: Optional[bool] = None) -> Iterable[Parameter]: - for param in self._packed_params: - if requires_grad is None or param.requires_grad == requires_grad: - yield param - - def named_parameters( - self, requires_grad: Optional[bool] = None - ) -> Iterable[Tuple[str, Parameter]]: - for idx, param in enumerate(self._packed_params): - if requires_grad is None or param.requires_grad == requires_grad: - yield "packed_param_" + str(idx), param - - def _pack_params(self, params: Iterable[Tuple[str, Parameter]]): - groups = collections.defaultdict(list) - ignored = 0 - param_id = 0 - for name, param in params: - if self._nr_ignore_first > ignored: - ignored += 1 - self._grouped_params.append([{"shape": param.shape, "id": param_id}]) - param.pack_group_key = self._group_func(name, param) - self._packed_params.append(param) - else: - key = ( - param.dtype, - param.device, - param.requires_grad, - self._group_func(name, param), - ) - groups[key].append({"tensor": param, "id": param_id}) - param_id += 1 - for (dtype, device, requires_grad, group_key) in groups.keys(): - dtype_sz = np.dtype(dtype).itemsize - align = device.mem_align - if align < dtype_sz: - align = 1 - else: - assert align % dtype_sz == 0 - align //= dtype_sz - - group = groups[(dtype, device, requires_grad, group_key)] - while group: - aligned_pos = [] - offset = 0 - params = [] - idx = 0 - while idx < len(group): - param = group[idx] - assert param["tensor"].device == device - padding = (align - (offset & (align - 1))) & (align - 1) - offset += padding - aligned_pos.append(offset) - params.append(param) - offset += int(np.prod(param["tensor"].shape)) - idx += 1 - - if ( - offset * dtype_sz >= self._max_size_per_group * 1024 * 1024 - or idx >= self._max_nr_params_per_group - ): - break - group = group[idx:] - if idx == 1: - # ignore param packs with only one item - params[0]["tensor"].pack_group_key = group_key - self._packed_params.append(params[0]["tensor"]) - self._grouped_params.append( - [{"shape": params[0]["tensor"].shape, "id": params[0]["id"]}] - ) - continue - - packed_value = np.zeros((offset,), dtype=dtype) - for param, pos in zip(params, aligned_pos): - val = param["tensor"].numpy() - packed_value[pos : pos + val.size] = val.flatten() - new_param = Parameter( - value=packed_value, - device=device, - dtype=dtype, - requires_grad=requires_grad, - ) - new_param.pack_group_key = group_key - self._packed_params.append(new_param) - self._grouped_params.append( - [{"shape": i["tensor"].shape, "id": i["id"]} for i in params] - ) - - def forward(self, *args, **kwargs): - replace_param = dict() - for i in range(len(self._packed_params)): - packed_param = self._packed_params[i] - grouped_params = self._grouped_params[i] - if len(grouped_params) == 1: - continue - split = param_pack_split( - packed_param._symvar, [i["shape"] for i in grouped_params] - ) - split = [ - Parameter(Tensor(i, requires_grad=packed_param.requires_grad)) - for i in split - ] - for j in range(len(split)): - replace_param[grouped_params[j]["id"]] = split[j] - self._model.replace_param(replace_param, 0) - - return self._model.forward(*args, **kwargs) diff --git a/python_module/megengine/module/pooling.py b/python_module/megengine/module/pooling.py deleted file mode 100644 index 8126ddc1..00000000 --- a/python_module/megengine/module/pooling.py +++ /dev/null @@ -1,80 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from abc import abstractmethod -from typing import Tuple, Union - -from ..functional import avg_pool2d, max_pool2d -from .module import Module - - -class _PoolNd(Module): - def __init__( - self, - kernel_size: Union[int, Tuple[int, int]], - stride: Union[int, Tuple[int, int]] = None, - padding: Union[int, Tuple[int, int]] = 0, - ): - super(_PoolNd, self).__init__() - self.kernel_size = kernel_size - self.stride = stride or kernel_size - self.padding = padding - - @abstractmethod - def forward(self, inp): - pass - - -class MaxPool2d(_PoolNd): - r"""Applies a 2D max pooling over an input. - - For instance, given an input of the size :math:`(N, C, H, W)` and - :attr:`kernel_size` :math:`(kH, kW)`, this layer generates the output of - the size :math:`(N, C, H_{out}, W_{out})` through a process described as: - - .. math:: - \begin{aligned} - out(N_i, C_j, h, w) ={} & \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} - \text{input}(N_i, C_j, \text{stride[0]} \times h + m, - \text{stride[1]} \times w + n) - \end{aligned} - - If :attr:`padding` is non-zero, then the input is implicitly zero-padded on - both sides for :attr:`padding` number of points. - - :param kernel_size: the size of the window to take a max over. - :param stride: the stride of the window. Default value is ``kernel_size``. - :param padding: implicit zero padding to be added on both sides. - """ - - def forward(self, inp): - return max_pool2d(inp, self.kernel_size, self.stride, self.padding) - - -class AvgPool2d(_PoolNd): - r"""Applies a 2D average pooling over an input. - - For instance, given an input of the size :math:`(N, C, H, W)` and - :attr:`kernel_size` :math:`(kH, kW)`, this layer generates the output of - the size :math:`(N, C, H_{out}, W_{out})` through a process described as: - - .. math:: - - out(N_i, C_j, h, w) = \frac{1}{kH * kW} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} - input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n) - - If :attr:`padding` is non-zero, then the input is implicitly zero-padded on - both sides for :attr:`padding` number of points. - - :param kernel_size: the size of the window. - :param stride: the stride of the window. Default value is ``kernel_size``. - :param padding: implicit zero padding to be added on both sides. - """ - - def forward(self, inp): - return avg_pool2d(inp, self.kernel_size, self.stride, self.padding) diff --git a/python_module/megengine/module/pytorch/__init__.py b/python_module/megengine/module/pytorch/__init__.py deleted file mode 100644 index 5902d9c3..00000000 --- a/python_module/megengine/module/pytorch/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .pytorch import PyTorchModule diff --git a/python_module/megengine/module/pytorch/pytorch.py b/python_module/megengine/module/pytorch/pytorch.py deleted file mode 100644 index 81548a50..00000000 --- a/python_module/megengine/module/pytorch/pytorch.py +++ /dev/null @@ -1,451 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import collections -import copy -import functools -import os -from typing import Any, Callable, List, Optional, Tuple - -import torch -from torch.utils.cpp_extension import load as load_torch_extension - -import megengine._internal as mgb -from megengine._internal import CompGraph -from megengine._internal.mgb import CompGraphCallbackValueProxy - -from ...core import Parameter, Tensor, get_default_device -from ..module import Module -from .utils import device_to_torch_device, torch_dtype_to_numpy_dtype - -# A global dict to map opr during graph copy -_copy_dict = {} - - -@functools.lru_cache(None) -def _get_torch_mem_fwd_lib(): - source_file = os.path.join(os.path.dirname(__file__), "torch_mem_fwd.cpp") - return load_torch_extension( - "torch_mem_fwd", - [source_file], - extra_include_paths=[mgb.config.get_include_path()], - ) - - -def inp_mem_fwd(pubapi_dev_tensor_ptr: int) -> torch.Tensor: - """Forward a MegBrain tensor to torch tensor - - :param pubapi_dev_tensor_ptr: pointer to MegBrain tensor - """ - return _get_torch_mem_fwd_lib().inp_mem_fwd(pubapi_dev_tensor_ptr) - - -def oup_mem_fwd( - pubapi_dev_tensor_ptr: int, tensor: torch.Tensor, keep_data_ptr: bool = True -) -> None: - """Forward a torch tensor to a contiguous MegBrain tensor - - :param pubapi_dev_tensor_ptr: Pointer to the MegBrain tensor - :param tensor: The input torch tensor - :param keep_data_ptr: if True, memory copy is not allowed here, - thus the input torch tensor must be contiguous also. - defaults to True - """ - _get_torch_mem_fwd_lib().oup_mem_fwd(pubapi_dev_tensor_ptr, tensor, keep_data_ptr) - - -def torch_param_to_mge( - name: str, param: torch.nn.Parameter, device, comp_graph: CompGraph -) -> Parameter: - """Convert a torch parameter to a megengine parameter - - :param name: parametr name - :param param: torch parameter - :param device: the device on which the megengine parameter is, - should be physically the same as the one on torch parameter - :param comp_graph: the owner graph of megengine parameter - :return: megengine parameter - """ - assert isinstance(param, torch.nn.Parameter) - dtype = torch_dtype_to_numpy_dtype(param.dtype) - mge_param = Parameter(None, dtype=dtype) - shared_nd = mge_param._Tensor__val - oup_mem_fwd(shared_nd.pubapi_dev_tensor_ptr, param.data, True) - return mge_param - - -class _PyTorchSubgraphGradOpr(mgb.craniotome.CraniotomeBase): - __nr_inputs__ = None - __nr_outputs__ = None - __allow_duplicate__ = False - __disable_sys_mem_alloc__ = True - __is_dynamic_output_shape__ = True - _forward_opr = None # type: PyTorchSubgraphImplOpr - _shape_infer_func = None - _condensed_out_grad_idx = None # type: List[Optional[int]] - - _forward_input_cnt = None - _forward_output_cnt = None - _output_grad_cnt = None - _param_cnt = None - - def setup( - self, forward_opr, condensed_out_grad_idx: List[Optional[int]], infer_shape=None - ): - self._forward_opr = forward_opr - self._forward_input_cnt = forward_opr.input_cnt - self._forward_output_cnt = forward_opr.output_cnt - self._param_cnt = forward_opr.param_cnt - self._output_grad_cnt = sum([idx is not None for idx in condensed_out_grad_idx]) - self.__nr_inputs__ = ( - self._forward_input_cnt - + self._param_cnt - + self._forward_output_cnt - + self._output_grad_cnt - ) - self.__nr_outputs__ = self._forward_input_cnt + self._param_cnt - self._forward_opr = forward_opr - self._condensed_out_grad_idx = condensed_out_grad_idx - self._shape_infer_func = infer_shape - if infer_shape is not None: - type(self).__is_dynamic_output_shape__ = False - - def execute( - self, - inputs: Tuple[CompGraphCallbackValueProxy, ...], - outputs: Tuple[mgb.SharedND, ...], - ): - assert self._forward_opr._last_forward_inputs is not None - assert self._forward_opr._last_forward_outputs is not None - if self._forward_opr._last_forward_outputs is None: - self._forward_opr.execute(inputs[: self.__nr_outputs__], None) - - out_grads = [ - inp_mem_fwd(inputs[idx].pubapi_dev_tensor_ptr) if idx else None - for idx in self._condensed_out_grad_idx - ] - - grads = torch.autograd.grad( - self._forward_opr._last_forward_outputs, - self._forward_opr._last_forward_inputs - + self._forward_opr._last_forward_params, - out_grads, # type: ignore - only_inputs=True, - allow_unused=True, - ) - for ovar, oten in zip(outputs, grads): - oup_mem_fwd(ovar.pubapi_dev_tensor_ptr, oten) - - def grad(self, wrt_idx, inputs, outputs, out_grad): - raise NotImplementedError("Apply grad to a grad opr is not supported") - - def infer_shape(self, inp_shapes): - if callable(self._shape_infer_func): - return self._shape_infer_func(inp_shapes) - raise NotImplementedError( - "No shape inference function specified on PyTorchSubgraphImplOpr" - ) - - def copy(self): - - ret = type(self)() - d0 = self.__dict__.copy() - d0.pop("this") - d0.pop("_forward_opr") - - later_copy = self._forward_opr in _copy_dict - if later_copy: - assert len(_copy_dict) == 1 - forward_opr_copy = _copy_dict[self._forward_opr] - else: - forward_opr_copy = self._forward_opr - ret.__dict__["_forward_opr"] = forward_opr_copy - - ret.__dict__.update(copy.deepcopy(d0)) - _copy_dict[self] = ret - if later_copy: - forward_opr_copy._grad_opr = ret - _copy_dict.clear() - - return ret - - -class PyTorchSubgraphImplOpr(mgb.craniotome.CraniotomeBase): - # pylint: disable=abstract-method - """This is a pytorch module wrapper to operator""" - - __nr_inputs__ = None # type: int - __nr_outputs__ = None # type: int - __allow_duplicate__ = False - __disable_sys_mem_alloc__ = True - __is_dynamic_output_shape__ = True - - _grad_opr = None - _func = None # type: Callable[[Any], Any] - input_cnt = None # type: int - output_cnt = None # type: int - param_cnt = None # type: int - _shape_infer_func = None - - _last_forward_inputs = None - _last_forward_outputs = None # type: List[torch.Tensor] - _last_forward_params = None # type: List[torch.Tensor] - - def setup(self, *, input_cnt, output_cnt, func, params, infer_shape=None): - """Setup the operator by accepted kwargs - - :param input_cnt: input count of torch module - :param output_cnt: output count of torch module - :param func: a callable object accept inputs and returns outputs - usually a torch module itself - :param params: parameters of the torch module - :param infer_shape: a callable infers output shapes from input shapes, - defaults to None - """ - param_cnt = len(params) - self.input_cnt = input_cnt - self.output_cnt = output_cnt - self.param_cnt = param_cnt - self.__nr_inputs__ = input_cnt + param_cnt - self.__nr_outputs__ = output_cnt - self._func = func - self._shape_infer_func = infer_shape - if infer_shape is not None: - type(self).__is_dynamic_output_shape__ = False - self._last_forward_params = params - - def execute( - self, - inputs: Tuple[CompGraphCallbackValueProxy, ...], - outputs: Optional[Tuple[mgb.SharedND, ...]], - ): - """execute the operator, read values from *inputs*, - forward them to torch tensor and do execution by self.func - and forward results to outputs - - :param inputs: values for each input var - :param outputs: values for each output var - """ - input_value_proxys = inputs[: self.input_cnt] - - input_torch_tensors = [ - inp_mem_fwd(ivar.pubapi_dev_tensor_ptr).requires_grad_() - for ivar in input_value_proxys - ] - - output_torch_tensors = self._func(*input_torch_tensors) - - if isinstance(output_torch_tensors, torch.Tensor): - output_torch_tensors = [output_torch_tensors] - - # `execute` may be called in _PyTorchSubgraphGradOp with None as outputs - if outputs: - for ovar, oten in zip(outputs, output_torch_tensors): - oup_mem_fwd(ovar.pubapi_dev_tensor_ptr, oten) - - # Retain input / output tensors for backward - self._last_forward_inputs = input_torch_tensors - self._last_forward_outputs = output_torch_tensors - - def grad( - self, - wrt_idx, - inputs: Tuple[mgb.SymbolVar, ...], - outputs: Tuple[mgb.SymbolVar, ...], - out_grads: Tuple[mgb.SymbolVar, ...], - ): - """generate a grad opr which calculates grad by torch.autograd.grad and cache it - - :param wrt_idx: the input var with respect to which the gradient should - be computed - :param inputs: operator inputs - :param outputs: operator outputs - :param out_grads: gradients of each output var - :return: an initialized grad opr - """ - if self._grad_opr is None: - condensed_out_grad = [] - condensed_out_grad_idx = [] # type: List[Optional[int]] - idx = self.__nr_inputs__ + len(outputs) - for out_grad in out_grads: - if out_grad is None: - condensed_out_grad_idx.append(None) - else: - condensed_out_grad.append(out_grad) - condensed_out_grad_idx.append(idx) - idx += 1 - self._grad_opr = _PyTorchSubgraphGradOpr.make( - *(inputs + outputs + tuple(condensed_out_grad)), - forward_opr=self, - condensed_out_grad_idx=condensed_out_grad_idx, - ) - return self._grad_opr - - def infer_shape(self, inp_shapes): - """infer output shape from input shapes - - :param inp_shapes: input shapes as tuple - :return: output shapes - """ - if callable(self._shape_infer_func): - return self._shape_infer_func(inp_shapes) - raise NotImplementedError( - "No shape inference function specified on PyTorchSubgraphImplOpr" - ) - - def copy(self): - ret = type(self)() - d0 = self.__dict__.copy() - d0.pop("this") - - ret.__dict__["_last_forward_inputs"] = d0.pop("_last_forward_inputs") - ret.__dict__["_last_forward_outputs"] = d0.pop("_last_forward_outputs") - ret.__dict__["_last_forward_params"] = d0.pop("_last_forward_params") - ret.__dict__["_func"] = d0.pop("_func") - - d0.pop("_grad_opr") - later_copy = self._grad_opr in _copy_dict - if later_copy: - assert len(_copy_dict) == 1 - grad_opr_copy = _copy_dict[self._grad_opr] - else: - grad_opr_copy = self._grad_opr - ret.__dict__["_grad_opr"] = grad_opr_copy - - ret.__dict__.update(copy.deepcopy(d0)) - _copy_dict[self] = ret - if later_copy: - grad_opr_copy._forward_opr = ret - _copy_dict.clear() - - return ret - - -class PyTorchModule(Module): - """Wrap a pytorch module as megengine module - - :param torch_module: torch module to be wrapped - :param device: target device this module would be in - :param output_cnt: output count of this module - :param input_shape: input shape inferrer - :param comp_graph: target comp_graph on which this module would be in - """ - - __torch_module = None # type: torch.nn.Module - __output_cnt = None - __infer_shape = None - __comp_graph = None - __device = None - _torch_params = None - _param_inputs = None - _name_param_list = None # type: List[Tuple[str, Parameter]] - - def __init__( - self, - torch_module, - device=None, - output_cnt=1, - *, - infer_shape=None, - comp_graph=None - ): - super().__init__() - if not isinstance(torch_module, torch.nn.Module): - raise TypeError( - "torch_module should either be an instance of torch.nn.Module " - "or its subclass" - ) - self.__torch_module = torch_module - - if not isinstance(output_cnt, int): - raise TypeError("output_cnt must be int") - if output_cnt <= 0: - raise ValueError("output_cnt must be greater than zero") - self.__output_cnt = output_cnt - - if infer_shape and not callable(infer_shape): - raise TypeError("infer_shape should either be None or a callable object") - self.__infer_shape = infer_shape - - if comp_graph and not isinstance(comp_graph, mgb.CompGraph): - raise TypeError("comp_graph shoud eighter be None or a mgb.CompGraph") - self.__comp_graph = comp_graph - - self._torch_params = [] - self._param_inputs = [] - self._name_param_list = [] - - if device is None: - device = get_default_device() - - if isinstance(device, str): - device = mgb.comp_node(device) - self.device = device - - def init_params(self): - """forward torch parameters to megengine parameters and store, - would be called in constructor and setter of device - """ - self._torch_params = [] - self._param_inputs = [] - self._name_param_list = [] - - for name, torch_param in self.__torch_module.named_parameters(recurse=True): - formated_name = "_torch_{}_{}".format(id(self.__torch_module), name) - mge_param = torch_param_to_mge( - formated_name, torch_param, self.device, self.__comp_graph - ) - self._param_inputs.append(mge_param) - self._torch_params.append(torch_param) - self._name_param_list.append((name, mge_param)) - - def get_param_by_name(self, param_name: str) -> Parameter: - """find parameter by its name - - :param param_name: name of parameter - :return: the parameter - """ - for name, param in self._name_param_list: - if param_name == name: - return param - raise KeyError("Cannot find param: {}".format(param_name)) - - def forward(self, *inputs): - """apply the module on given inputs - - :return: output vars - """ - param_inputs = [param._symvar for param in self._param_inputs] - - inputs = [tensor._symvar for tensor in list(inputs)] + param_inputs - - out = PyTorchSubgraphImplOpr.make( - *inputs, - input_cnt=len(inputs) - len(param_inputs), - output_cnt=self.__output_cnt, - func=self.__torch_module.forward, - params=self._torch_params, - infer_shape=self.__infer_shape, - ) - if isinstance(out, mgb.SymbolVar): - return Tensor(out) - assert isinstance(out, collections.Iterable) - return [Tensor(sym) for sym in out] - - def get_device(self): - """get the device this module belongs to""" - return self.__device - - def set_device(self, device: mgb.CompNode): - """set the device and move torch module to corresponding device""" - touch_device = device_to_torch_device(device) - self.__torch_module.to(device=touch_device) - self.__device = device - self.init_params() - - device = property(get_device, set_device) diff --git a/python_module/megengine/module/pytorch/torch_mem_fwd.cpp b/python_module/megengine/module/pytorch/torch_mem_fwd.cpp deleted file mode 100644 index dfcbe8f3..00000000 --- a/python_module/megengine/module/pytorch/torch_mem_fwd.cpp +++ /dev/null @@ -1,148 +0,0 @@ -/** - * \file python_module/megengine/module/pytorch/torch_mem_fwd.cpp - * MegEngine is Licensed under the Apache License, Version 2.0 (the "License") - * - * Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - * Unless required by applicable law or agreed to in writing, - * software distributed under the License is distributed on an - * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - */ -#include "torch/extension.h" -#include "megbrain_pubapi.h" - -using MGBTensor = mgb::pubapi::DeviceTensor; - -torch::Tensor mgb_to_torch(const MGBTensor *src) { - - mgb::pubapi::CallbackOnce deleter; - void* tensor_raw_ptr; - src->forward_to(&tensor_raw_ptr, &deleter); - auto deleter_wrap = [deleter](void*) mutable { - deleter.consume(); - }; - - // TODO: support non-contiguous layout - std::vector sizes; - for (size_t i = 0; i < src->desc.ndim; ++ i) { - sizes.push_back(src->desc.shape[i]); - } - - torch::TensorOptions options; - switch (src->desc.dtype) { -#define map_dtype(mgb_dtype, torch_dtype) \ - case MGBTensor::DataType::mgb_dtype: \ - options = options.dtype(caffe2::TypeMeta::Make()); \ - break; - map_dtype(FLOAT32, float); - map_dtype(FLOAT16, torch::Half); - map_dtype(INT32, int); - map_dtype(INT16, int16_t); - map_dtype(INT8, int8_t); - map_dtype(UINT8, uint8_t); -#undef map_dtype - default: - throw std::runtime_error("bad case for data type."); - } - - // TODO: Maybe we should impl copy on different devices? - switch (src->desc.type) { - case MGBTensor::Type::CUDA: { - int device_id = src->desc.cuda_ctx.device; - if (device_id >= 0) { - options = options.device(torch::DeviceType::CUDA, device_id); - } else { - throw std::runtime_error("bad case for device(cuda) id."); - } - // TODO: consider cuda synchronization here - // Maybe all tasks issued on cuda_ctx(device, stream) should be done? - break; - } - case MGBTensor::Type::CPU: - options = options.device(torch::DeviceType::CPU); - // Torch's API are all synchronous. - src->sync(); - break; - default: - throw std::runtime_error("bad case for device type."); - } - - auto tensor = torch::from_blob(tensor_raw_ptr, sizes, deleter_wrap, options); - return tensor; -} - -void torch_to_mgb(MGBTensor* dst, torch::Tensor src) { - MGBTensor::Desc desc; - - desc.dev_ptr = src.data_ptr(); - - // src is contiguous torch tensor here, so no strides needed - std::vector shape; - // desc.shape is the pointer to a size array used to construct - // an inner-mgb tensor, which should be valid until calling of - // forward_other_memory return - for (auto &&i : src.sizes()) { - shape.push_back(i); - } - desc.shape = shape.data(); - desc.ndim = shape.size(); - - switch (src.scalar_type()) { -#define map_dtype(mgb_dtype, torch_dtype) \ - case torch::ScalarType::torch_dtype: \ - desc.dtype = MGBTensor::DataType::mgb_dtype; \ - break; - map_dtype(FLOAT32, Float); - map_dtype(FLOAT16, Half); - map_dtype(INT32, Int); - map_dtype(INT16, Short); - map_dtype(INT8, Char); - map_dtype(UINT8, Byte); -#undef map_dtype - default: - throw std::runtime_error("bad case for data type."); - } - - // TODO: cuda setting and synchronization like mgb_to_torch - if (src.device().type() == torch::DeviceType::CUDA) { - desc.type = MGBTensor::Type::CUDA; - desc.cuda_ctx.device = src.get_device(); - desc.cuda_ctx.stream = nullptr; - } else { - assert(src.device().type() == torch::DeviceType::CPU); - desc.type = MGBTensor::Type::CUDA; - } - - mgb::pubapi::CallbackOnce deleter; - deleter.user_data = new torch::Tensor(src); - deleter.fptr = [](void* ptr) { - delete static_cast(ptr); - }; - dst->forward_other_memory(desc, deleter); -} - -torch::Tensor inp_mem_fwd(uintptr_t dv_ptr) { - // construct torch Tensor from mgb DeviceTensor stored in dv_ptr. - return mgb_to_torch(reinterpret_cast(dv_ptr)); -} - -void oup_mem_fwd(uintptr_t dv_ptr, torch::Tensor src, - bool keep_data_ptr=false) { - // forward storage in torch Tensor to mgb DeviceTensor - // keep_data_ptr: set to True to ensure forwarding data_ptr under \p src - // to megbrain, or it maybe copy src to a new contiguous tensor storage. - - // which would return src itself if tensor is contiguous - auto src_contig = src.contiguous(); - - if (keep_data_ptr && src_contig.data_ptr() != src.data_ptr()) { - throw std::runtime_error("should keep tensor data ptr, but it changed"); - } - torch_to_mgb(reinterpret_cast(dv_ptr), src_contig); -} - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("inp_mem_fwd", &inp_mem_fwd, "Forward mgb DeviceTensor ptr into torch Tensor as network input."); - m.def("oup_mem_fwd", &oup_mem_fwd, "Forward torch network Tensor to corresponding mgb VarNode.", - py::arg("dv_ptr"), py::arg("src"), py::arg("keep_data_ptr") = false); -} diff --git a/python_module/megengine/module/pytorch/utils.py b/python_module/megengine/module/pytorch/utils.py deleted file mode 100644 index fea87bfb..00000000 --- a/python_module/megengine/module/pytorch/utils.py +++ /dev/null @@ -1,67 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np -import torch - -import megengine._internal as mgb - -_TORCH_NUMPY_MAPPING = { - torch.float16: np.float16, - torch.float32: np.float32, - torch.float64: np.float64, - torch.int8: np.int8, - torch.int16: np.int16, - torch.int32: np.int32, -} - - -def torch_dtype_to_numpy_dtype(torch_dtype: torch.dtype): - """map torch dtype to numpy dtype - - :param torch_dtype: torch dtype - :return: numpy dtype - """ - if not isinstance(torch_dtype, torch.dtype): - raise TypeError("Argument `torch_dtype` should be an instance of torch.dtype") - if torch_dtype not in _TORCH_NUMPY_MAPPING: - raise ValueError("Unknown PyTorch dtype: {}".format(torch_dtype)) - return _TORCH_NUMPY_MAPPING[torch_dtype] - - -def torch_device_to_device(device: torch.device): - """map torch device to device - - :param device: torch device - :return: device - """ - if not isinstance(device, torch.device): - raise TypeError("Argument `device` should be an instance of torch.device") - index = device.index - if index is None: - index = "x" - if device.type == "cpu": - return "cpu{}".format(index) - elif device.type == "cuda": - return "gpu{}".format(index) - raise ValueError("Unknown PyTorch device: {}".format(device)) - - -def device_to_torch_device(device: mgb.CompNode): - """map device to torch device - - :param device: megbrain compute node - :return: corresponding torch device - """ - t, d, _ = device.locator_physical - if t == "CUDA": - return torch.device("cuda", d) - elif t == "CPU": - return torch.device("cpu", d) - else: - raise Exception("Unsupported device type: {}".format(t)) diff --git a/python_module/megengine/module/qat/__init__.py b/python_module/megengine/module/qat/__init__.py deleted file mode 100644 index b6adab4d..00000000 --- a/python_module/megengine/module/qat/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .concat import Concat -from .conv import Conv2d, ConvRelu2d -from .conv_bn import ConvBn2d, ConvBnRelu2d -from .elemwise import Elemwise -from .linear import Linear -from .module import QATModule -from .quant_dequant import DequantStub, QuantStub diff --git a/python_module/megengine/module/qat/concat.py b/python_module/megengine/module/qat/concat.py deleted file mode 100644 index 893b1ad0..00000000 --- a/python_module/megengine/module/qat/concat.py +++ /dev/null @@ -1,30 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from typing import Iterable - -from ...core.tensor import Tensor -from .. import concat as Float -from .module import QATModule - - -class Concat(Float.Concat, QATModule): - r""" - A :class:`~.QATModule` to do functional concat with QAT support. - Could be applied with :class:`~.Observer` and :class:`~.FakeQuantize`. - """ - - def forward(self, inps: Iterable[Tensor], axis: int = 0): - return self.apply_quant_activation(super().forward(inps, axis)) - - @classmethod - def from_float_module(cls, float_module): - r""" - Return a :class:`~.QATModule` instance converted from - a float :class:`~.Module` instance. - """ - return cls() diff --git a/python_module/megengine/module/qat/conv.py b/python_module/megengine/module/qat/conv.py deleted file mode 100644 index 315da839..00000000 --- a/python_module/megengine/module/qat/conv.py +++ /dev/null @@ -1,59 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from ... import functional as F -from ...quantization.utils import fake_quant_bias -from .. import conv as Float -from .module import QATModule - - -class Conv2d(Float.Conv2d, QATModule): - r""" - A :class:`~.QATModule` Conv2d with QAT support. - Could be applied with :class:`~.Observer` and :class:`~.FakeQuantize`. - """ - - def calc_conv_qat(self, inp): - w_qat = self.apply_quant_weight(self.weight) - b_qat = fake_quant_bias(self.bias, inp, w_qat) - conv = self.calc_conv(inp, w_qat, b_qat) - return conv - - @classmethod - def from_float_module(cls, float_module: Float.Conv2d): - r""" - Return a :class:`~.QATModule` instance converted from - a float :class:`~.Module` instance. - """ - qat_module = cls( - float_module.in_channels, - float_module.out_channels, - float_module.kernel_size, - float_module.stride, - float_module.padding, - float_module.dilation, - float_module.groups, - float_module.bias is not None, - float_module.conv_mode.name, - float_module.compute_mode.name, - ) - qat_module.weight = float_module.weight - qat_module.bias = float_module.bias - return qat_module - - def forward(self, inp): - return self.apply_quant_activation(self.calc_conv_qat(inp)) - - -class ConvRelu2d(Conv2d): - r""" - A :class:`~.QATModule` include Conv2d and Relu with QAT support. - Could be applied with :class:`~.Observer` and :class:`~.FakeQuantize`. - """ - - def forward(self, inp): - return self.apply_quant_activation(F.relu(self.calc_conv_qat(inp))) diff --git a/python_module/megengine/module/qat/conv_bn.py b/python_module/megengine/module/qat/conv_bn.py deleted file mode 100644 index 9ed6ebab..00000000 --- a/python_module/megengine/module/qat/conv_bn.py +++ /dev/null @@ -1,196 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from ...core import ones, zeros -from ...functional import add_update, relu, sqrt, sum, zero_grad -from ...quantization.utils import fake_quant_bias -from .. import conv_bn as Float -from .module import QATModule - - -class _ConvBnActivation2d(Float._ConvBnActivation2d, QATModule): - def get_batch_mean_var(self, inp): - def _sum_channel(inp, axis=0, keepdims=True): - if isinstance(axis, int): - out = sum(inp, axis=axis, keepdims=keepdims) - elif isinstance(axis, tuple): - for idx, elem in enumerate(axis): - out = sum(inp if idx == 0 else out, axis=elem, keepdims=keepdims) - return out - - sum1 = _sum_channel(inp, (0, 2, 3)) - sum2 = _sum_channel(inp ** 2, (0, 2, 3)) - reduce_size = inp.shapeof().prod() / inp.shapeof(1) - batch_mean = sum1 / reduce_size - batch_var = (sum2 - sum1 ** 2 / reduce_size) / reduce_size - return batch_mean, batch_var - - def fold_weight_bias(self, bn_mean, bn_var): - # get fold bn conv param - # bn_istd = 1 / bn_std - # w_fold = gamma / bn_std * W - # b_fold = gamma * (b - bn_mean) / bn_std + beta - gamma = self.bn.weight - if gamma is None: - gamma = ones((self.bn.num_features), dtype="float32") - gamma = gamma.reshape(1, -1, 1, 1) - beta = self.bn.bias - if beta is None: - beta = zeros((self.bn.num_features), dtype="float32") - beta = beta.reshape(1, -1, 1, 1) - - if bn_mean is None: - bn_mean = zeros((1, self.bn.num_features, 1, 1), dtype="float32") - if bn_var is None: - bn_var = ones((1, self.bn.num_features, 1, 1), dtype="float32") - - conv_bias = self.conv.bias - if conv_bias is None: - conv_bias = zeros(self.conv._infer_bias_shape(), dtype="float32") - - bn_istd = 1.0 / sqrt(bn_var + self.bn.eps) - # bn_istd = 1 / bn_std - # w_fold = gamma / bn_std * W - scale_factor = gamma * bn_istd - if self.conv.groups == 1: - w_fold = self.conv.weight * scale_factor.reshape(-1, 1, 1, 1) - else: - w_fold = self.conv.weight * scale_factor.reshape( - self.conv.groups, -1, 1, 1, 1 - ) - - w_fold = self.apply_quant_weight(w_fold) - # b_fold = gamma * (b - bn_mean) / bn_std + beta - b_fold = beta + gamma * (conv_bias - bn_mean) * bn_istd - return w_fold, b_fold - - def update_running_mean_and_running_var( - self, bn_mean, bn_var, num_elements_per_channel - ): - # update running mean and running var. no grad, use unbiased bn var - bn_mean = zero_grad(bn_mean) - bn_var = ( - zero_grad(bn_var) - * num_elements_per_channel - / (num_elements_per_channel - 1) - ) - exponential_average_factor = 1 - self.bn.momentum - add_update( - self.bn.running_mean, - delta=bn_mean, - alpha=1 - exponential_average_factor, - beta=exponential_average_factor, - ) - add_update( - self.bn.running_var, - delta=bn_var, - alpha=1 - exponential_average_factor, - beta=exponential_average_factor, - ) - - def calc_conv_bn_qat(self, inp, approx=True): - if self.training and not approx: - conv = self.conv(inp) - bn_mean, bn_var = self.get_batch_mean_var(conv) - num_elements_per_channel = conv.shapeof().prod() / conv.shapeof(1) - self.update_running_mean_and_running_var( - bn_mean, bn_var, num_elements_per_channel - ) - else: - bn_mean, bn_var = self.bn.running_mean, self.bn.running_var - - # get gamma and beta in BatchNorm - gamma = self.bn.weight - if gamma is None: - gamma = ones((self.bn.num_features), dtype="float32") - gamma = gamma.reshape(1, -1, 1, 1) - beta = self.bn.bias - if beta is None: - beta = zeros((self.bn.num_features), dtype="float32") - beta = beta.reshape(1, -1, 1, 1) - # conv_bias - conv_bias = self.conv.bias - if conv_bias is None: - conv_bias = zeros(self.conv._infer_bias_shape(), dtype="float32") - - bn_istd = 1.0 / sqrt(bn_var + self.bn.eps) - # bn_istd = 1 / bn_std - # w_fold = gamma / bn_std * W - scale_factor = gamma * bn_istd - if self.conv.groups == 1: - w_fold = self.conv.weight * scale_factor.reshape(-1, 1, 1, 1) - else: - w_fold = self.conv.weight * scale_factor.reshape( - self.conv.groups, -1, 1, 1, 1 - ) - b_fold = None - if not (self.training and approx): - # b_fold = gamma * (conv_bias - bn_mean) / bn_std + beta - b_fold = beta + gamma * (conv_bias - bn_mean) * bn_istd - - w_qat = self.apply_quant_weight(w_fold) - b_qat = fake_quant_bias(b_fold, inp, w_qat) - conv = self.conv.calc_conv(inp, w_qat, b_qat) - if not (self.training and approx): - return conv - - # rescale conv to get original conv output - orig_conv = conv / scale_factor.reshape(1, -1, 1, 1) - if self.conv.bias is not None: - orig_conv = orig_conv + self.conv.bias - # calculate batch norm - bn_mean, bn_var = self.get_batch_mean_var(orig_conv) - bn_istd = 1.0 / sqrt(bn_var + self.bn.eps) - conv = gamma * bn_istd * (orig_conv - bn_mean) + beta - num_elements_per_channel = conv.shapeof().prod() / conv.shapeof(1) - self.update_running_mean_and_running_var( - bn_mean, bn_var, num_elements_per_channel - ) - return conv - - @classmethod - def from_float_module(cls, float_module: Float._ConvBnActivation2d): - r""" - Return a :class:`~.QATModule` instance converted from - a float :class:`~.Module` instance. - """ - qat_module = cls( - float_module.conv.in_channels, - float_module.conv.out_channels, - float_module.conv.kernel_size, - float_module.conv.stride, - float_module.conv.padding, - float_module.conv.dilation, - float_module.conv.groups, - float_module.conv.bias is not None, - float_module.conv.conv_mode.name, - float_module.conv.compute_mode.name, - ) - qat_module.conv.weight = float_module.conv.weight - qat_module.conv.bias = float_module.conv.bias - qat_module.bn = float_module.bn - return qat_module - - -class ConvBn2d(_ConvBnActivation2d): - r""" - A fused :class:`~.QATModule` including Conv2d, BatchNorm2d with QAT support. - Could be applied with :class:`~.Observer` and :class:`~.FakeQuantize`. - """ - - def forward(self, inp): - return self.apply_quant_activation(self.calc_conv_bn_qat(inp)) - - -class ConvBnRelu2d(_ConvBnActivation2d): - r""" - A fused :class:`~.QATModule` including Conv2d, BatchNorm2d and relu with QAT support. - Could be applied with :class:`~.Observer` and :class:`~.FakeQuantize`. - """ - - def forward(self, inp): - return self.apply_quant_activation(relu(self.calc_conv_bn_qat(inp))) diff --git a/python_module/megengine/module/qat/elemwise.py b/python_module/megengine/module/qat/elemwise.py deleted file mode 100644 index f99583bd..00000000 --- a/python_module/megengine/module/qat/elemwise.py +++ /dev/null @@ -1,31 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .. import elemwise as Float -from .module import QATModule - - -class Elemwise(Float.Elemwise, QATModule): - r""" - A :class:`~.QATModule` to do elemwise operator with QAT support. - Could be applied with :class:`~.Observer` and :class:`~.FakeQuantize`. - - :param method: the elemwise method, see :class:`~.module.elemwise.Elemwise` for detail. - """ - - with_weight = False - - def forward(self, *inps): - return self.apply_quant_activation(super().forward(*inps)) - - @classmethod - def from_float_module(cls, float_module: Float.Elemwise): - r""" - Return a :class:`~.QATModule` instance converted from - a float :class:`~.Module` instance. - """ - return cls(float_module.method.name) diff --git a/python_module/megengine/module/qat/linear.py b/python_module/megengine/module/qat/linear.py deleted file mode 100644 index 4067d51c..00000000 --- a/python_module/megengine/module/qat/linear.py +++ /dev/null @@ -1,39 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from ...quantization.utils import fake_quant_bias -from .. import linear as Float -from .module import QATModule - - -class Linear(Float.Linear, QATModule): - r""" - A :class:`~.QATModule` version of :class:`~.module.linear.Linear`. - Could be applied with :class:`~.Observer` and :class:`~.FakeQuantize`. - - :param in_features: size of each input sample. - :param out_features: size of each output sample. - :param bias: If set to ``False``, the layer will not learn an additive bias. - Default: ``True`` - - """ - - def forward(self, x): - w_qat = self.apply_quant_weight(self.weight) - b_qat = fake_quant_bias(self.bias, x, w_qat) - return self.apply_quant_activation(self._calc_linear(x, w_qat, b_qat)) - - @classmethod - def from_float_module(cls, float_module: Float.Linear): - r""" - Return a :class:`~.QATModule` instance converted from - a float :class:`~.Module` instance. - """ - qmod = cls(float_module.in_features, float_module.out_features) - qmod.weight = float_module.weight - qmod.bias = float_module.bias - return qmod diff --git a/python_module/megengine/module/qat/module.py b/python_module/megengine/module/qat/module.py deleted file mode 100644 index c7cb80cb..00000000 --- a/python_module/megengine/module/qat/module.py +++ /dev/null @@ -1,154 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from abc import abstractmethod - -from ...core import Tensor -from ...quantization import FakeQuantize, Observer, QConfig -from ..module import Module - - -class QATModule(Module): - r""" - Base class of quantized-float related Module, basically for QAT and Calibration. - - Use :meth:`~.QATModule.from_float_module` to generate a instance from float :class:`~.Module`. - Or use :func:`~.quantize.quantize_qat` to do it recursively and automatically. - - Can also be converted to :class:`~.QuantizedModule` for deployment using - :func:`~.quantize.quantize` further. - """ - - with_weight = True - with_act = True - - def __init__(self): - super().__init__() - - self.weight_observer = None # type: Observer - self.act_observer = None # type: Observer - - self.weight_fake_quant = None # type: FakeQuantize - self.act_fake_quant = None # type: FakeQuantize - - def set_qconfig(self, qconfig: QConfig): - r""" - Set quantization related configs with ``qconfig``, including - observer and fake_quant for weight and activation. - """ - - def safe_call(func): - return func() if func is not None else None - - if self.with_act: - self.act_observer = safe_call(qconfig.act_observer) - self.act_fake_quant = safe_call(qconfig.act_fake_quant) - if self.with_weight: - self.weight_observer = safe_call(qconfig.weight_observer) - self.weight_fake_quant = safe_call(qconfig.weight_fake_quant) - - def _enable_exec(self, with_module, func, enable): - if not with_module or not func: - return - if enable: - func.enable() - else: - func.disable() - - def set_fake_quant(self, enable): - self._enable_exec(self.with_act, self.act_fake_quant, enable) - self._enable_exec(self.with_weight, self.weight_fake_quant, enable) - - def set_observer(self, enable): - self._enable_exec(self.with_act, self.act_observer, enable) - self._enable_exec(self.with_weight, self.weight_observer, enable) - - def _apply_fakequant_with_observer( - self, target: Tensor, fake_quant: FakeQuantize, observer: Observer - ): - # do observer - if observer is None: - oup = target - q_dict = None - else: - oup = observer(target) - q_dict = observer.get_qparams() - # do fake quant - if fake_quant is not None: - oup = fake_quant(oup, q_dict) - # use qparams of fake_quant if have. - if hasattr(fake_quant, "get_qparams"): - q_dict = fake_quant.get_qparams() - # set to tensor qparams. - if q_dict is not None: - oup.q_dict.update(q_dict) - return oup - - def apply_quant_weight(self, target: Tensor): - r""" - Apply weight's observer and fake_quant from ``qconfig`` on ``target``. - """ - return self._apply_fakequant_with_observer( - target, self.weight_fake_quant, self.weight_observer - ) - - def apply_quant_activation(self, target: Tensor): - r""" - Apply weight's observer and fake_quant from ``qconfig`` on ``target``. - """ - return self._apply_fakequant_with_observer( - target, self.act_fake_quant, self.act_observer - ) - - def _get_method_result( - self, method: str, fake_quant: FakeQuantize, observer: Observer - ): - if hasattr(fake_quant, method): - return getattr(fake_quant, method)() - elif hasattr(observer, method): - return getattr(observer, method)() - return None - - def get_weight_dtype(self): - r""" - Get weight's quantization dtype as the method from ``qconfig``. - """ - return self._get_method_result( - "get_dtype", self.weight_fake_quant, self.weight_observer - ) - - def get_activation_dtype(self): - r""" - Get activation's quantization dtype as the method from ``qconfig``. - """ - return self._get_method_result( - "get_dtype", self.act_fake_quant, self.act_observer - ) - - def get_weight_qparams(self): - r""" - Get weight's quantization parameters. - """ - return self._get_method_result( - "get_qparams", self.weight_fake_quant, self.weight_observer - ) - - def get_activation_qparams(self): - r""" - Get activation's quantization parameters. - """ - return self._get_method_result( - "get_qparams", self.act_fake_quant, self.act_observer - ) - - @classmethod - @abstractmethod - def from_float_module(cls, float_module: Module): - r""" - Return a :class:`~.QATModule` instance converted from - a float :class:`~.Module` instance. - """ diff --git a/python_module/megengine/module/qat/quant_dequant.py b/python_module/megengine/module/qat/quant_dequant.py deleted file mode 100644 index 0baa3e1c..00000000 --- a/python_module/megengine/module/qat/quant_dequant.py +++ /dev/null @@ -1,50 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .. import quant_dequant as Float -from .module import QATModule - - -class QuantStub(Float.QuantStub, QATModule): - r""" - A helper QATModule simply return input, but will quantize - input after converted to :class:`~.QuantizedModule`. - """ - - with_weight = False - - def forward(self, inp): - return self.apply_quant_activation(inp) - - @classmethod - def from_float_module(cls, float_module: Float.QuantStub): - r""" - Return a :class:`~.QATModule` instance converted from - a float :class:`~.Module` instance. - """ - return cls() - - -class DequantStub(Float.DequantStub, QATModule): - r""" - A helper QATModule simply return input, but will de-quantize - input after converted to :class:`~.QuantizedModule`. - """ - - with_weight = False - with_act = False - - def forward(self, inp): - return inp - - @classmethod - def from_float_module(cls, float_module: Float.DequantStub): - r""" - Return a :class:`~.QATModule` instance converted from - a float :class:`~.Module` instance. - """ - return cls() diff --git a/python_module/megengine/module/quant_dequant.py b/python_module/megengine/module/quant_dequant.py deleted file mode 100644 index aaf2b0cc..00000000 --- a/python_module/megengine/module/quant_dequant.py +++ /dev/null @@ -1,28 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .module import Module - - -class QuantStub(Module): - r""" - A helper :class:`~.Module` simply returning input. Could be replaced with :class:`~.QATModule` - version :class:`~.qat.QuantStub` using :func:`~.quantize.quantize_qat`. - """ - - def forward(self, inp): - return inp - - -class DequantStub(Module): - r""" - A helper :class:`~.Module` simply returning input. Could be replaced with :class:`~.QATModule` - version :class:`~.qat.DequantStub` using :func:`~.quantize.quantize_qat`. - """ - - def forward(self, inp): - return inp diff --git a/python_module/megengine/module/quantized/__init__.py b/python_module/megengine/module/quantized/__init__.py deleted file mode 100644 index e641476d..00000000 --- a/python_module/megengine/module/quantized/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .concat import Concat -from .conv import Conv2d, ConvRelu2d -from .conv_bn import ConvBn2d, ConvBnRelu2d -from .elemwise import Elemwise -from .linear import Linear -from .module import QuantizedModule -from .quant_dequant import DequantStub, QuantStub diff --git a/python_module/megengine/module/quantized/concat.py b/python_module/megengine/module/quantized/concat.py deleted file mode 100644 index f9ef05d9..00000000 --- a/python_module/megengine/module/quantized/concat.py +++ /dev/null @@ -1,35 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from typing import Iterable - -from ... import functional as F -from ...core.tensor import Tensor -from ..qat import concat as QAT -from .module import QuantizedModule - - -class Concat(QuantizedModule): - r""" - A :class:`~.QuantizedModule` to do quantized concat, inference only. - """ - - def __init__(self, dtype=None): - super().__init__() - self.output_dtype = dtype - - def forward(self, inps: Iterable[Tensor], axis: int = 0): - new_inps = (x.astype(self.output_dtype) for x in inps) - return F.concat(new_inps, axis) - - @classmethod - def from_qat_module(cls, qat_module: QAT.Concat): - r""" - return a :class:`~.QuantizedModule` instance converted from a - :class:`~.QATModule` instance. - """ - return cls(qat_module.get_activation_dtype()) diff --git a/python_module/megengine/module/quantized/conv.py b/python_module/megengine/module/quantized/conv.py deleted file mode 100644 index 3118451d..00000000 --- a/python_module/megengine/module/quantized/conv.py +++ /dev/null @@ -1,108 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from typing import Tuple, Union - -import numpy as np - -import megengine._internal as mgb - -from ... import module as Float -from ...core import Parameter -from ...functional import conv_bias_activation -from ..qat import conv as QAT -from .module import QuantizedModule - - -class Conv2d(Float.Conv2d, QuantizedModule): - r"""quantized version of :class:`~.qat.conv.Conv2d`.""" - r"""Applies a 2D convolution over an quantized input tensor, inference only. - - The parameter is same with :class: `~.Conv2d` - """ - - def __init__( - self, - in_channels: int, - out_channels: int, - kernel_size: Union[int, Tuple[int, int]], - stride: Union[int, Tuple[int, int]] = 1, - padding: Union[int, Tuple[int, int]] = 0, - dilation: Union[int, Tuple[int, int]] = 1, - groups: int = 1, - conv_mode: str = "CROSS_CORRELATION", - compute_mode: str = "DEFAULT", - dtype=None, - ): - super().__init__( - in_channels, - out_channels, - kernel_size, - stride, - padding, - dilation, - groups, - True, - conv_mode, - compute_mode, - ) - self.output_dtype = dtype - - def calc_conv_quantized(self, inp, nonlinear_mode="IDENTITY"): - inp_scale = mgb.dtype.get_scale(inp.dtype) - w_scale = mgb.dtype.get_scale(self.weight.dtype) - bias_scale = inp_scale * w_scale - return conv_bias_activation( - inp, - self.weight, - self.bias.astype(mgb.dtype.qint32(bias_scale)), - self.output_dtype, - self.stride, - self.padding, - self.dilation, - self.groups, - conv_mode=self.conv_mode, - compute_mode=self.compute_mode, - nonlinear_mode=nonlinear_mode, - ) - - @classmethod - def from_qat_module(cls, qat_module: QAT.Conv2d): - r""" - return a :class:`~.QuantizedModule` instance converted from a - :class:`~.QATModule` instance. - """ - output_dtype = qat_module.get_activation_dtype() - qconv = cls( - qat_module.in_channels, - qat_module.out_channels, - qat_module.kernel_size, - qat_module.stride, - qat_module.padding, - qat_module.dilation, - qat_module.groups, - dtype=output_dtype, - ) - weight = qat_module.weight.astype(qat_module.get_weight_dtype()) - qconv.weight = Parameter(weight.numpy()) - if qat_module.bias is not None: - qconv.bias = Parameter(qat_module.bias.numpy()) - else: - qconv.bias = Parameter( - np.zeros(qat_module._infer_bias_shape(), dtype=np.float32) - ) - return qconv - - def forward(self, inp): - return self.calc_conv_quantized(inp, nonlinear_mode="IDENTITY") - - -class ConvRelu2d(Conv2d): - r"""quantized version of :class:`~.qat.conv.ConvRelu2d`.""" - - def forward(self, inp): - return self.calc_conv_quantized(inp, nonlinear_mode="RELU") diff --git a/python_module/megengine/module/quantized/conv_bn.py b/python_module/megengine/module/quantized/conv_bn.py deleted file mode 100644 index ceb36d13..00000000 --- a/python_module/megengine/module/quantized/conv_bn.py +++ /dev/null @@ -1,56 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from ...core import Parameter -from ..qat import conv_bn as QAT -from .conv import Conv2d - - -class _ConvBnActivation2d(Conv2d): - r"""Applies a 2D convolution over an quantized input tensor, inference only. - - The parameter is same with :class: `~.Conv2d` - """ - - @classmethod - def from_qat_module(cls, qat_module: QAT._ConvBnActivation2d): - r""" - return a :class:`~.QuantizedModule` instance converted from a - :class:`~.QATModule` instance. - """ - output_dtype = qat_module.get_activation_dtype() - qconv = cls( - qat_module.conv.in_channels, - qat_module.conv.out_channels, - qat_module.conv.kernel_size, - qat_module.conv.stride, - qat_module.conv.padding, - qat_module.conv.dilation, - qat_module.conv.groups, - dtype=output_dtype, - ) - w_fold, b_fold = qat_module.fold_weight_bias( - qat_module.bn.running_mean, qat_module.bn.running_var - ) - weight = w_fold.astype(qat_module.get_weight_dtype()) - qconv.weight = Parameter(weight.numpy()) - qconv.bias = Parameter(b_fold.numpy()) - return qconv - - -class ConvBn2d(_ConvBnActivation2d): - r"""quantized version of :class:`~.qat.conv_bn.ConvBn2d`.""" - - def forward(self, inp): - return self.calc_conv_quantized(inp, nonlinear_mode="IDENTITY") - - -class ConvBnRelu2d(_ConvBnActivation2d): - r"""quantized version of :class:`~.qat.conv_bn.ConvBnRelu2d`.""" - - def forward(self, inp): - return self.calc_conv_quantized(inp, nonlinear_mode="RELU") diff --git a/python_module/megengine/module/quantized/elemwise.py b/python_module/megengine/module/quantized/elemwise.py deleted file mode 100644 index db04ed65..00000000 --- a/python_module/megengine/module/quantized/elemwise.py +++ /dev/null @@ -1,47 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from ... import _internal as mgb -from ...core import Tensor, wrap_io_tensor -from ...core.graph import _use_default_if_none -from ..qat import elemwise as QAT -from .module import QuantizedModule - - -@wrap_io_tensor -def _elemwise_multi_type(mode, *inputs, **kwargs) -> Tensor: - if all(isinstance(i, (int, float)) for i in inputs): - device, comp_graph = _use_default_if_none(None, None) - ret = mgb.opr.elemwise_multi_type( - *inputs, mode=mode, comp_node=device, comp_graph=comp_graph, **kwargs, - ) - return ret.inferred_value[0] - return mgb.opr.elemwise_multi_type(*inputs, mode=mode, **kwargs) - - -class Elemwise(QuantizedModule): - r"""quantized version of :class:`~.qat.elemwise.Elemwise`.""" - - _elemwise_multi_type_mode = mgb.opr_param_defs.ElemwiseMultiType.Mode - - def __init__(self, method, dtype=None): - super().__init__() - self.method = self._elemwise_multi_type_mode.convert("Q" + method) - self.output_dtype = dtype - - def forward(self, *inps): - if self.training: - raise ValueError("quantized module only support inference.") - return _elemwise_multi_type(self.method, *inps, dtype=self.output_dtype) - - @classmethod - def from_qat_module(cls, qat_module: QAT.Elemwise): - r""" - return a :class:`~.QuantizedModule` instance converted from a - :class:`~.QATModule` instance. - """ - return cls(qat_module.method.name, qat_module.get_activation_dtype()) diff --git a/python_module/megengine/module/quantized/linear.py b/python_module/megengine/module/quantized/linear.py deleted file mode 100644 index a6e61a6e..00000000 --- a/python_module/megengine/module/quantized/linear.py +++ /dev/null @@ -1,55 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np - -import megengine._internal as mgb - -from ... import functional as F -from ...core import Parameter -from ..qat import linear as QAT -from .module import QuantizedModule - - -class Linear(QuantizedModule): - r"""quantized version of :class:`~.qat.linear.Linear`.""" - - def __init__( - self, dtype: np.dtype = None, - ): - super().__init__() - self.weight = None - self.bias = None - self.output_dtype = dtype - - def forward(self, inp): - if self.training: - raise ValueError("quantized module only support inference.") - inp_scale = mgb.dtype.get_scale(inp.dtype) - w_scale = mgb.dtype.get_scale(self.weight.dtype) - bias_dtype = mgb.dtype.qint32(inp_scale * w_scale) - ret = F.linear( - inp, - self.weight, - None if self.bias is None else self.bias.astype(bias_dtype), - ) - ret = ret if self.output_dtype is None else ret.astype(self.output_dtype) - return ret - - @classmethod - def from_qat_module(cls, qat_module: QAT.Linear): - r""" - return a :class:`~.QuantizedModule` instance converted from a - :class:`~.QATModule` instance. - """ - output_dtype = qat_module.get_activation_dtype() - qmod = cls(dtype=output_dtype) - weight = qat_module.weight.astype(qat_module.get_weight_dtype()) - qmod.weight = Parameter(weight.numpy()) - if qat_module.bias is not None: - qmod.bias = Parameter(qat_module.bias.numpy()) - return qmod diff --git a/python_module/megengine/module/quantized/module.py b/python_module/megengine/module/quantized/module.py deleted file mode 100644 index 4fccdbfa..00000000 --- a/python_module/megengine/module/quantized/module.py +++ /dev/null @@ -1,31 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from abc import abstractmethod - -from ..module import Module -from ..qat import QATModule - - -class QuantizedModule(Module): - r""" - Base class of quantized Module, which should be converted from QATModule - and not support traning. - """ - - def __call__(self, *inputs, **kwargs): - if self.training: - raise ValueError("quantized module only support inference.") - return super().__call__(*inputs, **kwargs) - - @classmethod - @abstractmethod - def from_qat_module(cls, qat_module: QATModule): - r""" - return a :class:`~.QuantizedModule` instance converted from a - :class:`~.QATModule` instance. - """ diff --git a/python_module/megengine/module/quantized/quant_dequant.py b/python_module/megengine/module/quantized/quant_dequant.py deleted file mode 100644 index 0c245011..00000000 --- a/python_module/megengine/module/quantized/quant_dequant.py +++ /dev/null @@ -1,49 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from ..qat import quant_dequant as QAT -from .module import QuantizedModule - - -class QuantStub(QuantizedModule): - r""" - quantized version of :class:`~.qat.quant_dequant.QuantStub`, - will convert input to quantized dtype. - """ - - def __init__(self, dtype=None): - super().__init__() - self.output_dtype = dtype - - def forward(self, inp): - return inp.astype(self.output_dtype) - - @classmethod - def from_qat_module(cls, qat_module: QAT.QuantStub): - r""" - return a :class:`~.QuantizedModule` instance converted from a - :class:`~.QATModule` instance. - """ - return cls(qat_module.get_activation_dtype()) - - -class DequantStub(QuantizedModule): - r""" - quantized version of :class:`~.qat.quant_dequant.DequantStub`, - will restore quantized input to float32 dtype. - """ - - def forward(self, inp): - return inp.astype("float32") - - @classmethod - def from_qat_module(cls, qat_module: QAT.DequantStub): - r""" - return a :class:`~.QuantizedModule` instance converted from a - :class:`~.QATModule` instance. - """ - return cls() diff --git a/python_module/megengine/module/sequential.py b/python_module/megengine/module/sequential.py deleted file mode 100644 index 01291e98..00000000 --- a/python_module/megengine/module/sequential.py +++ /dev/null @@ -1,97 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from collections import OrderedDict - -from .module import Module - - -class Sequential(Module): - r"""A sequential container. - Modules will be added to it in the order they are passed in the constructor. - Alternatively, an ordered dict of modules can also be passed in. - - To make it easier to understand, here is a small example: - - .. testcode:: - from collections import OrderedDict - import numpy as np - import megengine.functional as F - from megengine.module import Sequential, Linear - from megengine import tensor - - batch_size = 64 - data = tensor(np.zeros((batch_size, 1, 28, 28)), dtype=np.float32) - label = tensor(np.zeros(batch_size,), dtype=np.int32) - - data = data.reshape(batch_size, -1) - - net0 = Sequential( - Linear(28 * 28, 320), - Linear(320, 10) - ) - - pred0 = net0(data) - - modules = OrderedDict() - modules["fc0"] = Linear(28 * 28, 320) - modules["fc1"] = Linear(320, 10) - net1 = Sequential(modules) - - pred1 = net1(data) - """ - - def __init__(self, *args): - super().__init__() - self.layer_keys = [] - if len(args) == 1 and isinstance(args[0], OrderedDict): - for key, module in args[0].items(): - # self.add_module(key, module) - setattr(self, key, module) - self.layer_keys.append(key) - else: - for idx, module in enumerate(args): - # self.add_module(str(idx), module) - setattr(self, str(idx), module) - self.layer_keys.append(str(idx)) - - def __getitem__(self, idx): - if isinstance(idx, slice): - return self.__class__( - OrderedDict(zip(self.layer_keys[idx], self.layer_values[idx])) - ) - else: - return getattr(self, self.layer_keys[idx]) - - def __setitem__(self, idx, module): - key = self.layer_keys[idx] - return setattr(self, key, module) - - def __delitem__(self, idx): - if isinstance(idx, slice): - for key in self.layer_keys[idx]: - delattr(self, key) - del self.layer_keys[idx] - else: - delattr(self, self.layer_keys[idx]) - del self.layer_keys[idx] - - def __len__(self): - return len(self.layer_keys) - - def __iter__(self): - return iter(self.layer_values) - - @property - def layer_values(self): - return [getattr(self, key) for key in self.layer_keys] - - def forward(self, inp): - for layer in self.layer_values: - inp = layer(inp) - return inp diff --git a/python_module/megengine/optimizer/__init__.py b/python_module/megengine/optimizer/__init__.py deleted file mode 100644 index ad783e06..00000000 --- a/python_module/megengine/optimizer/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .adadelta import Adadelta -from .adagrad import Adagrad -from .adam import Adam -from .lr_scheduler import LRScheduler -from .multi_step_lr import MultiStepLR -from .optimizer import Optimizer -from .sgd import SGD diff --git a/python_module/megengine/optimizer/adadelta.py b/python_module/megengine/optimizer/adadelta.py deleted file mode 100644 index 7d793608..00000000 --- a/python_module/megengine/optimizer/adadelta.py +++ /dev/null @@ -1,78 +0,0 @@ -from typing import Iterable, Union - -import numpy as np - -from ..core import Buffer, Parameter -from ..functional import sqrt -from .internal import add_update_fastpath as add_update -from .optimizer import Optimizer - - -class Adadelta(Optimizer): - r"""Implements Adadelta algorithm. - - It has been proposed in `"ADADELTA: An Adaptive Learning Rate Method" `_. - - :param params: iterable of parameters to optimize or dicts defining - parameter groups. - :param lr: coefficient that scale delta before it is applied - to the parameters (default: 1.0). - :param rho: coefficient used for computing a running average - of squared gradients (default: 0.9). - :param eps: term added to the denominator to improve - numerical stability (default: 1e-6). - :param weight_decay: weight decay (L2 penalty) (default: 0). - """ - - def __init__( - self, - params: Union[Iterable[Parameter], dict], - lr: float = 1.0, - rho: float = 0.9, - eps: float = 1e-6, - weight_decay: float = 0.0, - ): - assert lr >= 0.0, "Invalid learning rate: {}".format(lr) - assert rho >= 0.0 and rho <= 1.0, "Invalid rho value: {}".format(rho) - assert eps >= 0.0, "Invalid epsilon value: {}".format(eps) - assert weight_decay >= 0.0, "Invalid weight_decay value: {}".format( - weight_decay - ) - - defaults = dict(lr=lr, rho=rho, eps=eps, weight_decay=weight_decay) - super().__init__(params, defaults) - - def _create_state(self, param_group): - for param in param_group["params"]: - self._add_state(param, "square_avg") - self._add_state(param, "acc_delta") - self._add_state(param, "step", initializer=0.0) - - def _updates(self, param_group): - lr = param_group["lr"] - weight_decay = param_group["weight_decay"] - rho = param_group["rho"] - eps = param_group["eps"] - - for param in param_group["params"]: - if not isinstance(param.grad, Buffer): - raise TypeError( - "grad must be a Buffer, maybe you forget to call backward()?" - ) - - if not param.requires_grad: - continue - - step = self._state[param]["step"] - step = add_update(step, 1) - grad = param.grad - if weight_decay != 0.0: - grad = add_update(grad, param, beta=weight_decay) - - square_avg = self._state[param]["square_avg"] - acc_delta = self._state[param]["acc_delta"] - square_avg = add_update(square_avg, grad ** 2, alpha=rho, beta=1 - rho) - std = sqrt(square_avg + eps) - delta = sqrt(acc_delta + eps) / std * grad - add_update(param, delta, beta=-lr) - acc_delta = add_update(acc_delta, delta ** 2, alpha=rho, beta=1 - rho) diff --git a/python_module/megengine/optimizer/adagrad.py b/python_module/megengine/optimizer/adagrad.py deleted file mode 100644 index 4683fa10..00000000 --- a/python_module/megengine/optimizer/adagrad.py +++ /dev/null @@ -1,75 +0,0 @@ -from typing import Iterable, Union - -import numpy as np - -from ..core import Buffer, Parameter -from ..functional import sqrt -from .internal import add_update_fastpath as add_update -from .optimizer import Optimizer - - -class Adagrad(Optimizer): - r"""Implements Adagrad algorithm. - - It has been proposed in `"Adaptive Subgradient Methods for Online Learning - and Stochastic Optimization" `_. - - :param params: iterable of parameters to optimize or dicts defining - parameter groups. - :param lr: coefficient that scale delta before it is applied - to the parameters (default: 1e-2). - :param lr_decay: learning rate decay (default: 0) - :param eps: term added to the denominator to improve - numerical stability (default: 1e-10). - :param weight_decay: weight decay (L2 penalty) (default: 0). - """ - - def __init__( - self, - params: Union[Iterable[Parameter], dict], - lr: float = 1e-2, - lr_decay: float = 0.0, - eps: float = 1e-10, - weight_decay: float = 0.0, - ): - assert lr >= 0.0, "Invalid learning rate: {}".format(lr) - assert lr_decay >= 0, "Invalid learning rate decay: {}".format(lr_decay) - assert eps >= 0.0, "Invalid epsilon value: {}".format(eps) - assert weight_decay >= 0.0, "Invalid weight_decay value: {}".format( - weight_decay - ) - - defaults = dict(lr=lr, lr_decay=lr_decay, eps=eps, weight_decay=weight_decay) - super().__init__(params, defaults) - - def _create_state(self, param_group): - for param in param_group["params"]: - self._add_state(param, "square_avg") - self._add_state(param, "step", initializer=0.0) - - def _updates(self, param_group): - lr = param_group["lr"] - lr_decay = param_group["lr_decay"] - weight_decay = param_group["weight_decay"] - eps = param_group["eps"] - - for param in param_group["params"]: - if not isinstance(param.grad, Buffer): - raise TypeError( - "grad must be a Buffer, maybe you forget to call backward()?" - ) - - if not param.requires_grad: - continue - - step = self._state[param]["step"] - step = add_update(step, 1) - grad = param.grad - if weight_decay != 0.0: - grad = add_update(grad, param, beta=weight_decay) - - square_avg = self._state[param]["square_avg"] - square_avg = add_update(square_avg, grad ** 2) - delta = grad / sqrt(square_avg + eps) - clr = lr / (1 + (step - 1) * lr_decay) - add_update(param, delta, beta=-clr) diff --git a/python_module/megengine/optimizer/adam.py b/python_module/megengine/optimizer/adam.py deleted file mode 100644 index 6f264d3b..00000000 --- a/python_module/megengine/optimizer/adam.py +++ /dev/null @@ -1,86 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from typing import Iterable, Tuple, Union - -from ..core import Buffer, Parameter -from .internal import add_update_fastpath as add_update -from .optimizer import Optimizer - - -class Adam(Optimizer): - r"""Implements Adam algorithm proposed in `"Adam: A Method for Stochastic Optimization" `_. - - :param params: iterable of parameters to optimize or dicts defining - parameter groups. - :param lr: learning rate. - :param betas: coefficients used for computing running averages of gradient - and its square. Default: (0.9, 0.999) - :param eps: term added to the denominator to improve numerical stability - Default: 1e-8 - :param weight_decay: weight decay (L2 penalty). Default: 0 - """ - - def __init__( - self, - params: Union[Iterable[Parameter], dict], - lr: float, - betas: Tuple[float, float] = (0.9, 0.999), - eps: float = 1e-8, - weight_decay: float = 0.0, - ): - if lr < 0.0: - raise ValueError("Invalid learning rate: {}".format(lr)) - if weight_decay < 0.0: - raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) - if not 0.0 <= betas[0] < 1.0: - raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) - if not 0.0 <= betas[1] < 1.0: - raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) - - defaults = dict(lr=lr, weight_decay=weight_decay, betas=betas, eps=eps) - super().__init__(params, defaults) - - def _create_state(self, param_group): - for param in param_group["params"]: - self._add_state(param, "exp_avg") - self._add_state(param, "exp_avg_sq") - self._add_state(param, "step", initializer=0.0) - - def _updates(self, param_group): - lr = param_group["lr"] - weight_decay = param_group["weight_decay"] - eps = param_group["eps"] - beta0, beta1 = param_group["betas"] - - for param in param_group["params"]: - if not param.requires_grad: - continue - - step = self._state[param]["step"] - step = add_update(step, 1) - if not isinstance(param.grad, Buffer): - raise TypeError( - "grad must be a Buffer, maybe you forget to call backward()?" - ) - grad = param.grad - if weight_decay != 0.0: - grad = add_update(grad, param, beta=weight_decay) - exp_avg = self._state[param]["exp_avg"] - exp_avg_sq = self._state[param]["exp_avg_sq"] - exp_avg = add_update(exp_avg, grad, alpha=beta0, beta=1 - beta0) - exp_avg_sq = add_update( - exp_avg_sq, grad * grad, alpha=beta1, beta=1 - beta1 - ) - add_update( - param, - exp_avg - / (1 - beta0 ** step) - / (exp_avg_sq.sqrt() / (1 - beta1 ** step).sqrt() + eps), - beta=-lr, - ) diff --git a/python_module/megengine/optimizer/internal.py b/python_module/megengine/optimizer/internal.py deleted file mode 100644 index 0483af9a..00000000 --- a/python_module/megengine/optimizer/internal.py +++ /dev/null @@ -1,51 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from typing import Union - -import megengine._internal as mgb - -from ..core.tensor import Tensor, tensor - - -def add_update_fastpath( - dest: Tensor, - delta: Tensor, - *, - alpha: Union[Tensor, float, int] = 1.0, - beta: Union[Tensor, float, int] = 1.0, - bias: Union[Tensor, float, int] = 0.0 -): - """a fast-path ONLY used to update parameters in optimizer, since it - would bypass computing graph and launch dnn/add_update kernel directly, - it is more efficient than functional/add_update. - """ - - if isinstance(beta, Tensor) or isinstance(alpha, Tensor): - delta *= beta - beta = 1.0 - if isinstance(alpha, Tensor): - delta += (alpha - 1.0) * dest - alpha = 1.0 - if isinstance(bias, Tensor): - delta += bias - bias = 0.0 - - if not isinstance(delta, Tensor): - delta = tensor(delta, device=dest.device, dtype=dest.dtype) - - def get_v(x): - if x._Tensor__val is None: - assert isinstance(x._Tensor__sym, mgb.SymbolVar) - return x._Tensor__sym.eager_val - else: - assert isinstance(x._Tensor__val, mgb.SharedND) - return x._Tensor__val - - mgb.mgb._add_update_fastpath(get_v(dest), get_v(delta), alpha, beta, bias) - return dest diff --git a/python_module/megengine/optimizer/lr_scheduler.py b/python_module/megengine/optimizer/lr_scheduler.py deleted file mode 100644 index 7cdb6d9b..00000000 --- a/python_module/megengine/optimizer/lr_scheduler.py +++ /dev/null @@ -1,65 +0,0 @@ -from abc import ABCMeta - -from .optimizer import Optimizer - - -class LRScheduler(metaclass=ABCMeta): - r"""Base class for all learning rate based schedulers. - - :param optimizer: Wrapped optimizer. - :param current_epoch: The index of current epoch. Default: -1 - """ - - def __init__( # pylint: disable=too-many-branches - self, optimizer: Optimizer, current_epoch: int = -1 - ): - if not isinstance(optimizer, Optimizer): - raise TypeError( - "optimizer argument given to the lr_scheduler should be Optimizer" - ) - self.optimizer = optimizer - self.current_epoch = current_epoch - if current_epoch == -1: - for group in self.optimizer.param_groups: - group.setdefault("initial_lr", group["lr"]) - else: - for i, group in enumerate(optimizer.param_groups): - if "initial_lr" not in group: - raise KeyError( - "param 'initial_lr' is not specified in " - "param_groups[{}] when resuming an optimizer".format(i) - ) - self.base_lrs = list( - map(lambda group: group["initial_lr"], self.optimizer.param_groups) - ) - - self.step() - - def state_dict(self): - r"""Returns the state of the scheduler as a :class:`dict`. - It contains an entry for every variable in self.__dict__ which - is not the optimizer. - """ - raise NotImplementedError - - def load_state_dict(self, state_dict): - r"""Loads the schedulers state. - - :param state_dict (dict): scheduler state. - """ - raise NotImplementedError - - def get_lr(self): - r""" Compute current learning rate for the scheduler. - """ - raise NotImplementedError - - def step(self, epoch=None): - if epoch is None: - self.current_epoch += 1 - else: - self.current_epoch = epoch - - values = self.get_lr() - for param_group, lr in zip(self.optimizer.param_groups, values): - param_group["lr"] = lr diff --git a/python_module/megengine/optimizer/multi_step_lr.py b/python_module/megengine/optimizer/multi_step_lr.py deleted file mode 100644 index e6ac5ca0..00000000 --- a/python_module/megengine/optimizer/multi_step_lr.py +++ /dev/null @@ -1,67 +0,0 @@ -from bisect import bisect_right -from typing import Iterable as Iter - -from .lr_scheduler import LRScheduler -from .optimizer import Optimizer - - -class MultiStepLR(LRScheduler): - r"""Decays the learning rate of each parameter group by gamma once the - number of epoch reaches one of the milestones. - - :param optimizer: Wrapped optimizer. - :param milestones (list): List of epoch indices. Must be increasing. - :param gamma (float): Multiplicative factor of learning rate decay. Default: 0.1. - :param current_epoch: The index of current epoch. Default: -1. - """ - - def __init__( - self, - optimizer: Optimizer, - milestones: Iter[int], - gamma: float = 0.1, - current_epoch: int = -1, - ): - if not list(milestones) == sorted(milestones): - raise ValueError( - "Milestones should be a list of increasing integers. Got {}".format( - milestones - ) - ) - - self.milestones = milestones - self.gamma = gamma - super().__init__(optimizer, current_epoch) - - def state_dict(self): - r"""Returns the state of the scheduler as a :class:`dict`. - It contains an entry for every variable in self.__dict__ which - is not the optimizer. - """ - return { - key: value - for key, value in self.__dict__.items() - if key in ["milestones", "gamma", "current_epoch"] - } - - def load_state_dict(self, state_dict): - r"""Loads the schedulers state. - - :param state_dict (dict): scheduler state. - """ - tmp_dict = {} - for key in ["milestones", "gamma", "current_epoch"]: - if not key in state_dict.keys(): - raise KeyError( - "key '{}'' is not specified in " - "state_dict when loading state dict".format(key) - ) - tmp_dict[key] = state_dict[key] - - self.__dict__.update(tmp_dict) - - def get_lr(self): - return [ - base_lr * self.gamma ** bisect_right(self.milestones, self.current_epoch) - for base_lr in self.base_lrs - ] diff --git a/python_module/megengine/optimizer/optimizer.py b/python_module/megengine/optimizer/optimizer.py deleted file mode 100644 index 2596d26a..00000000 --- a/python_module/megengine/optimizer/optimizer.py +++ /dev/null @@ -1,307 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from abc import ABCMeta, abstractmethod -from collections import Iterable -from typing import Dict -from typing import Iterable as Iter -from typing import Union - -import numpy as np - -from .._internal.config import opr_priority_scope -from ..core import Buffer, Parameter, Tensor, TensorDict -from ..core.graph import get_default_graph -from ..distributed import ( - all_reduce_sum, - bcast_param, - get_rank, - get_world_size, - is_distributed, -) -from ..distributed.util import get_group_id -from ..functional import add_update -from ..functional import grad as grad_func -from ..jit import sideeffect - - -class _RequiredParameter: - def __repr__(self): - return "" - - -required = _RequiredParameter() - - -class Optimizer(metaclass=ABCMeta): - r"""Base class for all optimizers. - - :param params: specifies what Tensors should be optimized. - :param defaults: a dict of default parameters of Optimizer, like learning rate or momentum. - :param bcast_period: interval time between two broadcast of distributed training. Default: 500 - """ - - def __init__( # pylint: disable=too-many-branches - self, - params: Union[Iter[Parameter], dict], - defaults: dict, - bcast_period: int = 500, - ): - self._state = TensorDict() - self._defaults = defaults - self._bcast_iter = 0 - self._bcast_period = bcast_period - - if isinstance(params, (Parameter, dict)): - params = [params] - else: - if not isinstance(params, Iterable): - raise TypeError( - "params argument given to the optimizer should be " - "Parameter or dict, or Iterable of them" - ) - - self.param_groups = [] # type: list - - param_groups = list(params) - if len(param_groups) == 0: - raise ValueError("optimizer got an empty parameter list") - - param_type = type(param_groups[0]) - for param in param_groups: - if not isinstance(param, param_type): - raise TypeError( - "types of params argument given to the optimizer shoud be same" - ) - - if not isinstance(param_groups[0], dict): - param_groups = [{"params": param_groups}] - - for group in param_groups: - self.add_param_group(group) - - for group in self.param_groups: - self._create_state(group) - - if is_distributed() and bcast_period != -1: - self.bcast_param() - - def add_param_group(self, param_group: dict): - r"""Add a param group to ``param_groups`` of the :class:`~megengine.optim.optimizer.Optimizer`. - - This can be useful when fine tuning a pre-trained network as frozen layers can be made - trainable and added to the :class:`~megengine.optim.optimizer.Optimizer` as training progresses. - - :param param_group: specifies what tensors should be optimized along with group. - - """ - assert isinstance(param_group, dict), "param group must be a dict" - - if isinstance(param_group["params"], Parameter): - param_group["params"] = [param_group["params"]] - else: - param_group["params"] = list(param_group["params"]) - - for param in param_group["params"]: - if not isinstance(param, Parameter): - raise TypeError( - "optimizer can only optimize Parameters, but one of the params is " - + type(param) - ) - if not param.requires_grad: - raise ValueError( - "optimizer can only optimize Parameters with requires_grad=True" - ) - - for name, default in self._defaults.items(): - if default is required and name not in param_group: - raise ValueError( - "parameter group didn't specify a value of " - "required optimization parameter " + name - ) - param_group.setdefault(name, default) - - param_set = set() - - for group in self.param_groups: - param_set.update(set(map(id, group["params"]))) - - assert param_set.isdisjoint( - set(map(id, param_group["params"])) - ), "some parameters appear in more than one parameter group" - - self.param_groups.append(param_group) - - def _add_state(self, param, state_name, initializer=None): - if initializer is None: - initializer = np.zeros(param.shape, dtype=np.float32) - state_dict = self._state.setdefault(param, {}) - assert state_name not in state_dict - state = Buffer(value=initializer) - state_dict[state_name] = state - - @abstractmethod - def _create_state(self, param_group): - pass - - @abstractmethod - def _updates(self, param_group): - pass - - def backward(self, loss: Tensor): - """Computes the back-propagation of the network given loss. - - :param loss: The obtained loss tensor - """ - rst = [] - params = [] - for group in self.param_groups: - for param in group["params"]: - if param.grad is None: - param.grad = Buffer( - value=np.zeros(shape=param.shape, dtype=np.float32) - ) - - params.append(param) - assert hasattr(param, "grad"), "param has no grad" - assert isinstance(param.grad, Buffer), "grad must be a buffer" - - cg = get_default_graph() - grads = grad_func(loss, params, use_virtual_grad=not cg.is_eager()) - if not isinstance(grads, list): - grads = [grads] - assert len(grads) == len(params) - - for param, grad in zip(params, grads): - if is_distributed() and param.replica_mode: - with opr_priority_scope(cg, -(2 ** 30)): - # always run all_reduce_mean first except add_update - grad = ( - all_reduce_sum( - grad, "grad_" + str(get_group_id()), get_world_size() - ) - / get_world_size() - ) - with opr_priority_scope(cg, -(2 ** 31)): - # always run add_update first - grad_update = add_update(param.grad, grad) - else: - grad_update = add_update(param.grad, grad) - rst.append(grad_update) - - return rst - - @sideeffect - def step(self): - r"""Performs a single optimization step. - - """ - for group in self.param_groups: - if isinstance(group["params"], set): - raise TypeError( - "optimized parameters need to be organized in ordered collections, " - "but the ordering of parameters in sets will change between runs. " - "Please use a list instead." - ) - self._updates(group) - - if is_distributed() and self._bcast_period != -1: - self._bcast_iter += 1 - if self._bcast_iter == self._bcast_period: - self.bcast_param() - self._bcast_iter = 0 - - @sideeffect - def zero_grad(self): - r"""Reset the grad to zeros. - - """ - for param_group in self.param_groups: - for param in param_group["params"]: - if param.grad is not None: - param.grad.reset_zero() - - def bcast_param(self): - key = 0 - for group in self.param_groups: - for param in group["params"]: - if param.replica_mode: - bcast_param( - param, - "bcast_param_" + str(key), - get_world_size(), - get_rank() == 0, - ) - key += 1 - - def state_dict(self) -> Dict: - r"""Export the optimizer state. - - :return: optimizer state. Can be loaded by :meth:`load_state_dict`. - """ - param_groups = [] - state = dict() - param2id = TensorDict() - - cur_id = 0 - for group in self.param_groups: - for param in group["params"]: - if param not in param2id: - param2id[param] = cur_id - cur_id += 1 - - for param, st in self._state.items(): - state[param2id[param]] = st - - for group in self.param_groups: - param_group = {k: v for k, v in group.items() if k != "params"} - param_group["params"] = [param2id[param] for param in group["params"]] - param_groups.append(param_group) - - return {"param_groups": param_groups, "state": state} - - def load_state_dict(self, state: dict): - r"""Loads the optimizer state. - - :param state: optimizer state. Should be an object returned - from a call to :meth:`state_dict`. - """ - if len(self.param_groups) != len(state["param_groups"]): - raise ValueError( - "loaded state dict has a different number of parameter groups" - ) - parameter_map = dict() # type: Dict - for group_new, group_saved in zip(self.param_groups, state["param_groups"]): - if len(group_new["params"]) != len(group_saved["params"]): - raise ValueError( - "loaded state dict contains a parameter group that " - "doesn't match the size of optimizer's group" - ) - for param_new, param_saved in zip( - group_new["params"], group_saved["params"] - ): - p = param_new - self._state[p] = state["state"][param_saved].copy() - for k, v in self._state[p].items(): - if isinstance(v, Buffer) and v._comp_graph != p._comp_graph: - self._state[p][k] = Buffer(v.numpy()) - - if set(group_new.keys()) != set(group_saved.keys()): - raise ValueError( - "loaded state dict contains a parameter group that " - "doesn't match the keys of optimizer's group" - ) - for key in group_new.keys(): - if key != "params": - group_new[key] = group_saved[key] - - if len(self._state.keys()) != len(state["state"].keys()): - raise ValueError( - "loaded state dict contains a state that doesn't match " - "the size of optimizer's state" - ) diff --git a/python_module/megengine/optimizer/sgd.py b/python_module/megengine/optimizer/sgd.py deleted file mode 100644 index a1f807b3..00000000 --- a/python_module/megengine/optimizer/sgd.py +++ /dev/null @@ -1,73 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from typing import Iterable, Union - -from ..core import Buffer, Parameter -from .internal import add_update_fastpath as add_update -from .optimizer import Optimizer - - -class SGD(Optimizer): - r"""Implements stochastic gradient descent. - - Nesterov momentum is based on the formula from - `"On the importance of initialization and momentum in deep learning" `_ . - - :param params: iterable of parameters to optimize or dicts defining - parameter groups. - :param lr: learning rate. - :param momentum: momentum factor. Default: 0.0 - :param weight_decay: weight decay (L2 penalty). Default: 0.0 - """ - - def __init__( - self, - params: Union[Iterable[Parameter], dict], - lr: float, - momentum: float = 0.0, - weight_decay: float = 0.0, - ): - assert lr >= 0.0, "Invalid learning rate: {}".format(lr) - assert momentum >= 0.0, "Invalid momentum value: {}".format(momentum) - assert weight_decay >= 0.0, "Invalid weight_decay value: {}".format( - weight_decay - ) - - defaults = dict(lr=lr, momentum=momentum, weight_decay=weight_decay) - super().__init__(params, defaults) - - def _create_state(self, param_group): - if param_group["momentum"] != 0.0: - for param in param_group["params"]: - self._add_state(param, "momentum_buffer") - - def _updates(self, param_group): - lr = param_group["lr"] - weight_decay = param_group["weight_decay"] - momentum = param_group["momentum"] - - for param in param_group["params"]: - if not isinstance(param.grad, Buffer): - raise TypeError( - "grad must be a Buffer, maybe you forget to call backward()?" - ) - - if not param.requires_grad: - continue - - grad = param.grad - if weight_decay != 0.0: - grad = add_update(grad, param, beta=weight_decay) - - if momentum: - v = self._state[param]["momentum_buffer"] - update_v = add_update(v, grad, alpha=momentum) - add_update(param, update_v, beta=-lr) - else: - add_update(param, grad, beta=-lr) diff --git a/python_module/megengine/quantization/__init__.py b/python_module/megengine/quantization/__init__.py deleted file mode 100644 index 9c8a0e0d..00000000 --- a/python_module/megengine/quantization/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -from .fake_quant import FakeQuantize -from .internal_fake_quant import * -from .observer import HistogramObserver, Observer -from .qconfig import ( - QConfig, - calibration_qconfig, - ema_fakequant_qconfig, - ema_lowbit_fakequant_qconfig, - min_max_fakequant_qconfig, - tqt_quant_qconfig, -) -from .utils import QuantMode diff --git a/python_module/megengine/quantization/fake_quant.py b/python_module/megengine/quantization/fake_quant.py deleted file mode 100644 index 7260b9db..00000000 --- a/python_module/megengine/quantization/fake_quant.py +++ /dev/null @@ -1,145 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import copy -import math - -import numpy as np - -from .. import functional as F -from .._internal.dtype import _metadata_dict, get_quantized_dtype -from ..core import Buffer, Function, Parameter -from ..jit import sideeffect -from ..module import Module -from .utils import QuantMode, Round, fake_quant_tensor, get_qparam_dict - - -class _FakeQuantize(Module): - r""" - A Basic Fake Quant module. - - :param dtype: A string indicating the target quantization type of input. - :param narrow_range: Whether the absolute value of ``qmin`` is the same as ``qmax``, - instead of 1 greater. Usually True for weight and False for activation. - :param enable: Whether do ``normal_forward`` or ``fake_quant_forward``. - """ - - def __init__(self, dtype: str, narrow_range: bool = False, enable: bool = True): - super().__init__() - if not dtype in _metadata_dict.keys(): - raise ValueError( - "unknown dtype: {}, only support {}".format( - dtype, _metadata_dict.keys() - ) - ) - self.dtype = dtype - self.narrow_range = narrow_range - self.qmin = ( - -_metadata_dict[dtype].qmax if narrow_range else _metadata_dict[dtype].qmin - ) - self.qmax = _metadata_dict[dtype].qmax - self.enabled = enable - - def enable(self): - self.enabled = True - - def disable(self): - self.enabled = False - - def fake_quant_forward(self, inp, q_dict=None): - return inp - - def normal_foward(self, inp, q_dict=None): - return inp - - def forward(self, inp, q_dict=None): - if self.enabled: - return self.fake_quant_forward(inp, q_dict=q_dict) - else: - return self.normal_foward(inp, q_dict=q_dict) - - -class TQT_Function(Function): - def __init__(self, lowerbound, upperbound): - super().__init__() - self.lowerbound = lowerbound - self.upperbound = upperbound - - def forward(self, inp, scale): - t = 2 ** scale - # t = F.maximum(t, 1e-4) - inp_scaled = inp / t - inp_clipped = F.maximum(F.minimum(inp_scaled, self.upperbound), self.lowerbound) - inp_rounded = F.round(inp_clipped) - inp_flq = inp_rounded * t - self.save_for_backward(inp_scaled, inp_rounded, t) - return inp_flq - - def backward(self, grad_inp_flq): - (inp_scaled, inp_rounded, t) = self.saved_tensors - mask_clip = (inp_scaled < -0.5 + self.lowerbound) + ( - inp_scaled > self.upperbound + 0.5 - ) # mask for accumulating the gradients of |data_scaled|>L - mask_quant = F.abs( - mask_clip - 1 - ) # mask for accumulating the gradients with |data_scaled|<=L - grad_quant = ( - grad_inp_flq * mask_quant * (inp_rounded - inp_scaled) - ) # gradient within |data_scaled|<=L - grad_clip = ( - grad_inp_flq * mask_clip * inp_rounded - ) # gradient with | data_scaled|>L - grad_s = grad_clip.sum() + grad_quant.sum() - # dL/ds = dL/dt * t * ln(2) - grad_s = grad_s * t * math.log(2) - grad_inp = grad_inp_flq * mask_quant - return grad_inp, grad_s - - -class TQT(_FakeQuantize): - r""" - TQT: https://arxiv.org/abs/1903.08066 Trained Quantization Thresholds - for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks. - """ - - def __init__(self, dtype: str, narrow_range: bool = False, enable: bool = True): - super().__init__(dtype, narrow_range, enable) - self.scale = Parameter(0.0, dtype=np.float32) - - def fake_quant_forward(self, inp, q_dict=None): - # when enable, TQT will do fakequant forward, finetune the scale - return TQT_Function(self.qmin, self.qmax)(inp, self.scale) - - def normal_foward(self, inp, q_dict=None): - if q_dict["enable_observer"]: - # when disable, TQT will do normal forward, initialize scale weight - tmp_scale = F.maximum(F.abs(q_dict["min_val"]), F.abs(q_dict["max_val"])) - tmp_scale = F.log(tmp_scale / 127) / F.log(2) - F.add_update(self.scale, tmp_scale, alpha=0.0, beta=1.0, bias=0.0) - return inp - - def get_qparams(self): - q_dict = get_qparam_dict(QuantMode.TQT) - q_dict["scale"] = 2 ** self.scale - return q_dict - - def get_dtype(self): - q_dict = self.get_qparams() - scale = None if "scale" not in q_dict else q_dict["scale"].numpy()[0] - zero_point = ( - None if "zero_point" not in q_dict else q_dict["zero_point"].numpy()[0] - ) - return get_quantized_dtype(self.dtype, scale, zero_point) - - -class FakeQuantize(_FakeQuantize): - r""" - A module to do quant and dequant according to observer's scale and zero_point. - """ - - def fake_quant_forward(self, inp, q_dict=None): - return fake_quant_tensor(inp, self.qmin, self.qmax, q_dict) diff --git a/python_module/megengine/quantization/internal_fake_quant.py b/python_module/megengine/quantization/internal_fake_quant.py deleted file mode 100644 index df15a916..00000000 --- a/python_module/megengine/quantization/internal_fake_quant.py +++ /dev/null @@ -1,19 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import copy -import math -from functools import partial - -import numpy as np - -from .. import functional as F -from ..core import Function -from .fake_quant import _FakeQuantize -from .observer import MinMaxObserver -from .qconfig import QConfig - diff --git a/python_module/megengine/quantization/observer.py b/python_module/megengine/quantization/observer.py deleted file mode 100644 index 6aa3a406..00000000 --- a/python_module/megengine/quantization/observer.py +++ /dev/null @@ -1,422 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import math -from abc import abstractmethod -from enum import Enum - -import numpy as np - -from .. import functional as F -from .._internal.dtype import _metadata_dict, get_quantized_dtype -from ..core import Buffer -from ..jit import sideeffect -from ..module import Module -from .utils import QuantMode, Round, get_qparam_dict - - -class Observer(Module): - r""" - A base class for Observer Module. - - :param dtype: a string indicating to collect scale and zero_point of which dtype - :param narrow_range: Whether the absolute value of ``qmin`` is the same as ``qmax``, - instead of 1 greater. Usually True for weight and False for activation. - """ - - def __init__(self, dtype: str, narrow_range: bool = False): - super().__init__() - if dtype not in _metadata_dict.keys(): - raise ValueError( - "unknown dtype: {}, only support {}".format( - dtype, _metadata_dict.keys() - ) - ) - self.dtype = dtype - self.narrow_range = narrow_range - self.qmin = ( - -_metadata_dict[dtype].qmax if narrow_range else _metadata_dict[dtype].qmin - ) - self.qmax = _metadata_dict[dtype].qmax - self.enabled = True - - def get_dtype(self): - q_dict = self.get_qparams() - numpy_scale = None if "scale" not in q_dict else q_dict["scale"].numpy()[0] - numpy_zero_point = ( - None if "zero_point" not in q_dict else q_dict["zero_point"].numpy()[0] - ) - return get_quantized_dtype(self.dtype, numpy_scale, numpy_zero_point) - - def enable(self): - self.enabled = True - - def disable(self): - self.enabled = False - - def train(self, mode: bool = True, recursive: bool = True) -> None: - super().train(mode, recursive) - if mode: - self.enable() - else: - self.disable() - - @abstractmethod - def forward(self, x): - pass - - @abstractmethod - def get_qparams(self, **kwargs): - pass - - -class MinMaxObserver(Observer): - def __init__( - self, - mode=QuantMode.SYMMERTIC, - eps=0.00001, - dtype="qint8", - narrow_range: bool = False, - ): - super().__init__(dtype, narrow_range) - self.mode = mode - self.min_val = Buffer(np.finfo(np.float32).max, dtype=np.float32) - self.max_val = Buffer(np.finfo(np.float32).min, dtype=np.float32) - self.scale_limit = eps - - def _calculate_qparams(self, inp_min_val, inp_max_val): - min_val = F.minimum(0.0, inp_min_val) - max_val = F.maximum(0.0, inp_max_val) - q_dict = get_qparam_dict(self.mode) - q_dict["min_val"] = inp_min_val - q_dict["max_val"] = inp_max_val - q_dict["enable_observer"] = self.enable - if self.mode == QuantMode.SYMMERTIC: - symmetric_max_vals = F.maximum(-min_val, max_val) - # use maximun to avoid scale too small at the begin - q_dict["scale"] = F.maximum( - symmetric_max_vals / ((self.qmax - self.qmin) / 2), self.scale_limit - ) - # zero_point = self.zero_point - else: - # use maximun to avoid scale too small at the begin - q_dict["scale"] = F.maximum( - (max_val - min_val) / (self.qmax - self.qmin), self.scale_limit, - ) - # caculate zero_point - q_dict["zero_point"] = self.qmin - Round()((min_val / q_dict["scale"])) - - return q_dict - - def get_qparams(self): - return self._calculate_qparams(self.min_val, self.max_val) - - def forward(self, x_orig): - if self.enabled: - # stop gradient - x = F.zero_grad(x_orig) - # find max and min - F.add_update( - self.min_val, - F.minimum(self.min_val, x.min()), - alpha=0.0, - beta=1.0, - bias=0.0, - ) - F.add_update( - self.max_val, - F.maximum(self.max_val, x.max()), - alpha=0.0, - beta=1.0, - bias=0.0, - ) - return x_orig - - -class ExponentialMovingAverageObserver(MinMaxObserver): - def __init__( - self, - momentum=0.9, - mode=QuantMode.SYMMERTIC, - eps=0.00001, - dtype="qint8", - narrow_range: bool = False, - ): - super().__init__(mode, eps, dtype, narrow_range) - self.momentum = Buffer(momentum) - self.runtime_momentum = Buffer(0.0) - - def set_momentum(self, momentum): - self.momentum.set_value(momentum) - - def forward(self, x_orig): - if self.enabled: - # stop gradient - x = F.zero_grad(x_orig) - # Exponential Moving Average - tmp_min = ( - self.min_val * self.runtime_momentum - + (1 - self.runtime_momentum) * x.min() - ) - tmp_max = ( - self.max_val * self.runtime_momentum - + (1 - self.runtime_momentum) * x.max() - ) - F.add_update(self.min_val, tmp_min, alpha=0.0, beta=1.0, bias=0.0) - F.add_update(self.max_val, tmp_max, alpha=0.0, beta=1.0, bias=0.0) - F.add_update( - self.runtime_momentum, self.momentum, alpha=0.0, beta=1.0, bias=0.0 - ) - return x_orig - - -class HistogramObserver(MinMaxObserver): - def __init__( - self, - bins=2048, - upsample_rate=128, - mode=QuantMode.SYMMERTIC, - eps=0.00001, - dtype="qint8", - narrow_range: bool = False, - ): - super().__init__(mode, eps, dtype, narrow_range) - self.bins = bins - self.upsample_rate = upsample_rate - self.dst_nbins = _metadata_dict[dtype].qmax - _metadata_dict[dtype].qmin + 1 - self.histogram = Buffer([-1] + [0.0] * (bins - 1)) - - def _non_linear_param_search(self): - r"""Non-linear parameter search. - An approximation for L2 error minimization for selecting min/max. - By selecting new min/max, we filter out outliers in input distribution. - """ - - np_min_val = self.min_val.numpy()[0] - np_max_val = self.max_val.numpy()[0] - np_histogram = self.histogram.numpy() - assert len(np_histogram) == self.bins, "bins mistmatch" - bin_width = (np_max_val - np_min_val) / self.bins - - def _get_norm(delta_begin, delta_end, density, norm_type): - r""" - Compute the norm of the values uniformaly distributed between - delta_begin and delta_end. - norm = density * (integral_{begin, end} x^2) - = density * (end^3 - begin^3) / 3 - """ - assert norm_type == "L2", "Only L2 norms are currently supported" - norm = 0.0 - if norm_type == "L2": - norm = ( - delta_end * delta_end * delta_end - - delta_begin * delta_begin * delta_begin - ) / 3 - return density * norm - - def _compute_quantization_error(next_start_bin, next_end_bin, norm_type): - r""" - Compute the quantization error if we use start_bin to end_bin as the - min and max to do the quantization. - """ - - norm = 0.0 - dst_bin_width = ( - bin_width * (next_end_bin - next_start_bin + 1) / self.dst_nbins - ) - if dst_bin_width == 0.0: - return 0.0 - for src_bin in range(self.bins): - # distances from the beginning of first dst_bin to the beginning and - # end of src_bin - src_bin_begin = (src_bin - next_start_bin) * bin_width - src_bin_end = src_bin_begin + bin_width - - # which dst_bins the beginning and end of src_bin belong to? - dst_bin_of_begin = min( - self.dst_nbins - 1, - max(0.0, math.floor(src_bin_begin / dst_bin_width)), - ) - dst_bin_of_end = min( - self.dst_nbins - 1, - max(0.0, math.floor(src_bin_end / dst_bin_width)), - ) - dst_bin_of_begin_center = ( - dst_bin_of_begin * dst_bin_width + dst_bin_width / 2 - ) - - density = np_histogram[src_bin] / bin_width - if dst_bin_of_begin == dst_bin_of_end: - # if src_bin is entirely within 1 dst_bin - delta_begin = src_bin_begin - dst_bin_of_begin_center - delta_end = src_bin_end - dst_bin_of_begin_center - norm = norm + _get_norm(delta_begin, delta_end, density, norm_type) - else: - delta_begin = src_bin_begin - dst_bin_of_begin_center - delta_end = dst_bin_width / 2 - norm = norm + _get_norm(delta_begin, delta_end, density, norm_type) - - norm = norm + (dst_bin_of_end - dst_bin_of_begin - 1) * _get_norm( - -dst_bin_width / 2, dst_bin_width / 2, density, norm_type - ) - - dst_bin_of_end_center = ( - dst_bin_of_end * dst_bin_width + dst_bin_width / 2 - ) - - delta_begin = -dst_bin_width / 2 - delta_end = src_bin_end - dst_bin_of_end_center - norm = norm + _get_norm(delta_begin, delta_end, density, norm_type) - return norm - - # cumulative sum - total = sum(np_histogram) - cSum = np.cumsum(np_histogram, axis=0) - - stepsize = 1e-5 # granularity - alpha = 0.0 # lower bound - beta = 1.0 # upper bound - start_bin = 0 - end_bin = self.bins - 1 - norm_min = float("inf") - - while alpha < beta: - # Find the next step - next_alpha = alpha + stepsize - next_beta = beta - stepsize - - # find the left and right bins between the quantile bounds - l = start_bin - r = end_bin - while l < end_bin and cSum[l] < next_alpha * total: - l = l + 1 - while r > start_bin and cSum[r] > next_beta * total: - r = r - 1 - - # decide the next move - next_start_bin = start_bin - next_end_bin = end_bin - if (l - start_bin) > (end_bin - r): - # move the start bin - next_start_bin = l - alpha = next_alpha - else: - # move the end bin - next_end_bin = r - beta = next_beta - - if next_start_bin == start_bin and next_end_bin == end_bin: - continue - - # calculate the quantization error using next_start_bin and next_end_bin - norm = _compute_quantization_error(next_start_bin, next_end_bin, "L2") - - if norm > norm_min: - break - norm_min = norm - start_bin = next_start_bin - end_bin = next_end_bin - - new_min = self.min_val + bin_width * start_bin - new_max = self.min_val + bin_width * (end_bin + 1) - return new_min, new_max - - def get_qparams(self): - new_min, new_max = self._non_linear_param_search() - return self._calculate_qparams(new_min, new_max) - - def _combine_histograms( - self, orig_hist, new_hist, upsample_rate, downsample_rate, start_idx, Nbins - ): - # First up-sample the histogram with new data by a factor of L - # This creates an approximate probability density thats piecwise constant - upsampled_histogram = new_hist.repeat(upsample_rate) - # Now insert the upsampled histogram into the output - # histogram, which is initialized with zeros. - # The offset at which the histogram is introduced is determined - # by the start index as the output histogram can cover a wider range - histogram_with_output_range = np.zeros((Nbins * downsample_rate)) - histogram_with_output_range[ - start_idx : Nbins * upsample_rate + start_idx - ] = upsampled_histogram - # Compute integral histogram, double precision is needed to ensure - # that there are no overflows - integral_histogram = np.cumsum(histogram_with_output_range, 0)[ - downsample_rate - 1 :: downsample_rate - ] - # Finally perform interpolation - shifted_integral_histogram = np.zeros((Nbins)) - shifted_integral_histogram[1:Nbins] = integral_histogram[0:-1] - interpolated_histogram = ( - integral_histogram - shifted_integral_histogram - ) / upsample_rate - orig_hist = orig_hist + interpolated_histogram - return orig_hist - - def _adjust_min_max(self, combined_min, combined_max, upsample_rate): - # We ensure that: - # (combined_max - combined_min)/(downsample_rate*Nbins) = (max - min)/(upsample_rate*Nbins) - # This allows us to have a common grid of resolution s, where we can align - # the input histogram - # start_idx maps min_val to the histogram bin index. - np_min_val = self.min_val.numpy()[0] - np_max_val = self.max_val.numpy()[0] - - hist_bin_width = (np_max_val - np_min_val) / (self.bins * upsample_rate) - downsample_rate = int( - np.ceil((combined_max - combined_min) / (self.bins * hist_bin_width)) - ) - e = downsample_rate * (self.bins * hist_bin_width) - ( - combined_max - combined_min - ) - combined_max = combined_max + e / 2 - combined_min = combined_min - e / 2 - start_idx = int(np.round((np_min_val - combined_min) / hist_bin_width)) - - return combined_min, combined_max, downsample_rate, start_idx - - @sideeffect - def sideeffect_forward(self, x_orig): - x = x_orig.numpy() - min_val = self.min_val.numpy()[0] - max_val = self.max_val.numpy()[0] - histogram = self.histogram.numpy() - new_min = x.min() - new_max = x.max() - if histogram[0] == -1: - new_histogram, _ = np.histogram(x, self.bins, (new_min, new_max)) - else: - new_min = min(new_min, min_val) - new_max = max(new_max, max_val) - # combine the existing histogram and new histogram into 1 histogram - # We do this by first upsampling the histogram to a dense grid - # and then downsampling the histogram efficiently - (new_min, new_max, downsample_rate, start_idx,) = self._adjust_min_max( - new_min, new_max, self.upsample_rate - ) - - new_histogram, _ = np.histogram(x, self.bins, (new_min, new_max)) - new_histogram = new_histogram.astype(np.float64) - if new_min == min_val and new_max == max_val: - new_histogram += histogram - else: - new_histogram = self._combine_histograms( - new_histogram, - histogram, - self.upsample_rate, - downsample_rate, - start_idx, - self.bins, - ) - - self.histogram.set_value(new_histogram) - self.min_val.set_value(new_min) - self.max_val.set_value(new_max) - - def forward(self, x_orig): - self.sideeffect_forward(x_orig) - return x_orig diff --git a/python_module/megengine/quantization/qconfig.py b/python_module/megengine/quantization/qconfig.py deleted file mode 100644 index 6606c1a5..00000000 --- a/python_module/megengine/quantization/qconfig.py +++ /dev/null @@ -1,109 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -#' -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from functools import partial - -from ..module import Module -from .fake_quant import TQT, FakeQuantize -from .observer import ( - ExponentialMovingAverageObserver, - HistogramObserver, - MinMaxObserver, -) - - -class QConfig: - r""" - A config class indicating how to do quantize toward :class:`~.QATModule`'s - ``activation`` and ``weight``. See :meth:`~.QATModule.set_qconfig` for detail usage. - - :param weight_observer: interface to instantiate an :class:`~.Observer` indicating - how to collect scales and zero_point of wegiht. - :param act_observer: similar to ``weight_observer`` but toward activation. - :param weight_fake_quant: interface to instantiate a :class:`~.FakeQuantize` indicating - how to do fake_quant calculation. - :param act_observer: similar to ``weight_fake_quant`` but toward activation. - - Examples: - - .. code-block:: - - # Default EMA QConfig for QAT. - ema_fakequant_qconfig = QConfig( - weight_observer=partial(MinMaxObserver, dtype="qint8", narrow_range=True), - act_observer=partial(ExponentialMovingAverageObserver, dtype="qint8", narrow_range=False), - weight_fake_quant=partial(FakeQuantize, dtype="qint8", narrow_range=True), - act_fake_quant=partial(FakeQuantize, dtype="qint8", narrow_range=False), - ) - - Each parameter is a ``class`` rather than an instance. And we recommand using ``functools.partial`` - to add initialization parameters of the ``class``, so that don't need to provide parameters in - :meth:`~.QATModule.set_qconfig`. - - Usually we set ``narrow_range`` of weight related paramters to ``True`` and of activation related - parameters to ``False``. For the result of multiplication and addition as ``a * b + c * d``, if - four variables are all -128 of dtype ``qint8``, then the result will be ``2^15`` and cause overflow. - Weights are commonly calculated in this way, so needed to narrow the range. - """ - - def __init__( - self, weight_observer, act_observer, weight_fake_quant, act_fake_quant - ): - if isinstance(act_observer, Module) or isinstance(weight_observer, Module): - raise ValueError( - "QConfig must not receive observer instance, please pass observer" - " class generator using `partial(Observer, ...)` instead. Use" - " partial(MyObserver, x=1) to override arguments to constructor if needed" - ) - self.weight_observer = weight_observer - self.act_observer = act_observer - self.weight_fake_quant = weight_fake_quant - self.act_fake_quant = act_fake_quant - - -tqt_quant_qconfig = QConfig( - weight_observer=partial( - ExponentialMovingAverageObserver, dtype="qint8", narrow_range=True - ), - act_observer=partial( - ExponentialMovingAverageObserver, dtype="qint8", narrow_range=False - ), - weight_fake_quant=partial(TQT, dtype="qint8", narrow_range=True), - act_fake_quant=partial(TQT, dtype="qint8", narrow_range=False), -) - -min_max_fakequant_qconfig = QConfig( - weight_observer=partial(MinMaxObserver, dtype="qint8", narrow_range=True), - act_observer=partial(MinMaxObserver, dtype="qint8", narrow_range=False), - weight_fake_quant=partial(FakeQuantize, dtype="qint8", narrow_range=True), - act_fake_quant=partial(FakeQuantize, dtype="qint8", narrow_range=False), -) - -ema_fakequant_qconfig = QConfig( - weight_observer=partial(MinMaxObserver, dtype="qint8", narrow_range=True), - act_observer=partial( - ExponentialMovingAverageObserver, dtype="qint8", narrow_range=False - ), - weight_fake_quant=partial(FakeQuantize, dtype="qint8", narrow_range=True), - act_fake_quant=partial(FakeQuantize, dtype="qint8", narrow_range=False), -) - -ema_lowbit_fakequant_qconfig = QConfig( - weight_observer=partial(MinMaxObserver, dtype="qint4", narrow_range=False), - act_observer=partial( - ExponentialMovingAverageObserver, dtype="qint4", narrow_range=False - ), - weight_fake_quant=partial(FakeQuantize, dtype="qint4", narrow_range=False), - act_fake_quant=partial(FakeQuantize, dtype="qint4", narrow_range=False), -) - -calibration_qconfig = QConfig( - weight_observer=partial(MinMaxObserver, dtype="qint8", narrow_range=True), - act_observer=partial(HistogramObserver, dtype="qint8", narrow_range=False), - weight_fake_quant=None, - act_fake_quant=None, -) diff --git a/python_module/megengine/quantization/quantize.py b/python_module/megengine/quantization/quantize.py deleted file mode 100644 index 5dab2ae4..00000000 --- a/python_module/megengine/quantization/quantize.py +++ /dev/null @@ -1,191 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from copy import copy, deepcopy -from typing import Callable, Dict, Tuple - -from .. import module as Float -from ..module import Module -from ..module import qat as QAT -from ..module import quantized as Quantized -from ..module.qat import QATModule -from ..module.quantized import QuantizedModule -from .fake_quant import TQT -from .qconfig import QConfig, ema_fakequant_qconfig - - -def _get_quantable_module_names(): - def is_quantable(key: str): - value = getattr(Quantized, key) - return ( - isinstance(value, type) - and issubclass(value, QuantizedModule) - and value != QuantizedModule - ) - - # source should have all quantable modules' names - quantable_module_names = [key for key in dir(Quantized) if is_quantable(key)] - return quantable_module_names - - -def _get_convert_dict() -> Tuple[ - Dict[Module, QATModule], Dict[QATModule, QuantizedModule] -]: - quantable_module_names = _get_quantable_module_names() - - quantable_modules = [getattr(Float, key) for key in quantable_module_names] - qat_modules = [getattr(QAT, key) for key in quantable_module_names] - quantized_modules = [getattr(Quantized, key) for key in quantable_module_names] - - float2qat_dict = dict(zip(quantable_modules, qat_modules)) - qat2quantized_dict = dict(zip(qat_modules, quantized_modules)) - return float2qat_dict, qat2quantized_dict - - -_float2qat_dict, _qat2quantized_dict = _get_convert_dict() - - -def quantize(module: Module, inplace: bool = True, mapping: dict = None): - r""" - Recursively convert :class:`~.QATModule` to :class:`~.QuantizedModule` - through :meth:`~.Module.apply`. - - :param module: root module to do convert recursively. - :param inplace: whether to convert submodules in-place. - :param mapping: a dict indicating how to convert custom modules from QATModule to - QuantizedModule. Will be combined with internal default convert mapping dict. - """ - - if not inplace: - module = deepcopy(module) - - convert_dict = copy(_qat2quantized_dict) - if mapping is not None: - convert_dict.update(mapping) - qat_modules = tuple(convert_dict.keys()) - - def is_qat(mod: Module): - return isinstance(mod, qat_modules) - - # must use list to avoid replacement influencing successor modules - for key, submodule, parent in list( - module._flatten(with_key=True, with_parent=True, predicate=is_qat) - ): - new_mod = convert_dict[type(submodule)].from_qat_module(submodule) - if isinstance(parent, Float.Sequential): - # cannnot use setattr to be compatible with Sequential's ``__setitem__`` - parent[int(key.split(".")[-1])] = new_mod - else: - setattr(parent, key.split(".")[-1], new_mod) - - return module - - -def quantize_qat( - module: Module, - inplace: bool = True, - qconfig: QConfig = ema_fakequant_qconfig, - mapping: dict = None, -): - r""" - Recursively convert float :class:`~.Module` to :class:`~.QATModule` - through :meth:`~.Module.apply` and set qconfig relatively. - - :param module: root module to do convert recursively. - :param inplace: whether to convert submodules in-place. - :param qconfig: an instance of :class:`~.QConfig` to be set as submodules' qconfig. - default is ``ema_fakequant_qconfig``. - :param mapping: a dict indicating how to convert custom modules from Module to QATModule. - Will be combined with internal default convert mapping dict. - """ - - if not inplace: - module = deepcopy(module) - - convert_dict = copy(_float2qat_dict) - if mapping is not None: - convert_dict.update(mapping) - quantable_modules = tuple(convert_dict.keys()) - - def is_quantable(mod: Module): - return isinstance(mod, quantable_modules) - - # must use list to avoid replacement influencing successor modules - for key, submodule, parent in list( - module._flatten(with_key=True, with_parent=True, predicate=is_quantable) - ): - # only convert top quantable module. - if is_quantable(parent) or submodule.quantize_disabled: - continue - - new_mod = convert_dict[type(submodule)].from_float_module(submodule) - if isinstance(parent, Float.Sequential): - # cannnot use setattr to be compatible with Sequential's ``__setitem__`` - parent[int(key.split(".")[-1])] = new_mod - else: - setattr(parent, key.split(".")[-1], new_mod) - - propagate_qconfig(module, qconfig) - return module - - -def _propagate(module: Module, func_str: str, *args, **kargs): - def fn(mod: Module): - if isinstance(mod, QATModule): - getattr(mod, func_str)(*args, **kargs) - - module.apply(fn) - - -def propagate_qconfig(module: QATModule, qconfig: QConfig): - r""" - Recursively set ``module``'s qconfig through :meth:`~.Module.apply`. - - :param module: root module to traverse recursively. - :param qconfig: a instance of :class:`~.QConfig` to be set as submodules' qconfig. - """ - _propagate(module, "set_qconfig", qconfig) - - -def disable_fake_quant(module: Module): - r""" - Recursively disable ``module`` fake quantization in QATModule through :meth:`~.Module.apply` - - :param module: root module to do disable fake quantization recursively. - """ - - _propagate(module, "set_fake_quant", False) - - -def disable_observer(module: Module): - r""" - Recursively disable ``module`` observer in QATModule through :meth:`~.Module.apply` - - :param module: root module to do disable observer recursively. - """ - - _propagate(module, "set_observer", False) - - -def enable_fake_quant(module: Module): - r""" - Recursively enable ``module`` fake quantization in QATModule through :meth:`~.Module.apply` - - :param module: root module to do enable fake quantization recursively. - """ - - _propagate(module, "set_fake_quant", True) - - -def enable_observer(module: Module): - r""" - Recursively enable ``module`` observer in QATModule through :meth:`~.Module.apply` - - :param module: root module to do enable observer recursively. - """ - - _propagate(module, "set_observer", True) diff --git a/python_module/megengine/quantization/utils.py b/python_module/megengine/quantization/utils.py deleted file mode 100644 index 2b940c9d..00000000 --- a/python_module/megengine/quantization/utils.py +++ /dev/null @@ -1,116 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -from enum import Enum -from functools import partial, update_wrapper, wraps -from typing import Dict - -from .. import functional as F -from .._internal.dtype import _metadata_dict -from ..core import Function, Tensor - - -class Round(Function): - """ - The functional round have no grad and can not use for quantization-aware-training. - We use Function and STE(Straight-Through Estimator) to implement backward propagation. - """ - - def forward(self, x): - return x.round() - - def backward(self, output_grads): - return output_grads - - -def register_method_to_class(cls): - def decorator(func): - @wraps(func) - def wrapper(self, *args, **kwargs): - return func(self, *args, **kwargs) - - if isinstance(func, partial): - update_wrapper(func, func.func) - setattr(cls, func.__name__, wrapper) - return func - - return decorator - - -class QuantMode(Enum): - """Quantization mode enumerate class. - """ - - SYMMERTIC = 1 - ASYMMERTIC = 2 - TQT = 3 - - -qparam_dict = { - QuantMode.SYMMERTIC: {"mode": QuantMode.SYMMERTIC, "scale": None,}, - QuantMode.ASYMMERTIC: { - "mode": QuantMode.ASYMMERTIC, - "scale": None, - "zero_point": None, - }, - QuantMode.TQT: {"mode": QuantMode.TQT, "scale": None,}, -} - - -def get_qparam_dict(mode: QuantMode): - """Return the quantization parameters dictory according to the mode. - """ - return qparam_dict.get(mode, None) - - -def fake_quant_tensor(inp: Tensor, qmin: int, qmax: int, q_dict: Dict) -> Tensor: - """Apply fake quantization to the inp tensor. - - :param inp: the input tensor which need to be faked. - :param qmin: the minimum value which the integer limit to. - :param qmax: the maximum value which the integer limit to. - :param q_dict: the quantization parameter dict. - - """ - scale = q_dict["scale"] - zero_point = 0 - if q_dict["mode"] == QuantMode.ASYMMERTIC: - zero_point = q_dict["zero_point"] - # Quant - oup = Round()(inp / scale) + zero_point - # Clip - oup = F.minimum(F.maximum(oup, qmin), qmax) - # Dequant - oup = (oup - zero_point) * scale - return oup - - -def fake_quant_bias(bias: Tensor, inp: Tensor, w_qat: Tensor) -> Tensor: - """Apply fake quantization to bias, the special scale from input tensor - and weight tensor, the quantized type set to qint32 also. - - :param bias: the bias tensor which need to be faked. - :param inp: the input tensor which contain the quantization parameters. - :param qmax: the weight tensor which contain the quantization parameters. - - .. warning:: - Only work for symmetric quantization method now. - - """ - b_qat = bias - if hasattr(inp, "q_dict") and b_qat is not None: - if inp.q_dict["scale"] is not None and w_qat.q_dict["scale"] is not None: - # use the same mode with weight. - b_dict = get_qparam_dict(w_qat.q_dict["mode"]) - b_dict["scale"] = inp.q_dict["scale"] * w_qat.q_dict["scale"] - # TODO: add zero_point for ASYMMERTIC mode. - qmax = _metadata_dict["qint32"].qmax - qmin = _metadata_dict["qint32"].qmin - b_qat = fake_quant_tensor(b_qat, qmin, qmax, b_dict) - - return b_qat diff --git a/python_module/megengine/random/__init__.py b/python_module/megengine/random/__init__.py deleted file mode 100644 index 86c8d797..00000000 --- a/python_module/megengine/random/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from .distribution import gaussian, uniform -from .rng import manual_seed - -# pylint: disable=undefined-variable -del distribution, rng # type: ignore[name-defined] diff --git a/python_module/megengine/random/distribution.py b/python_module/megengine/random/distribution.py deleted file mode 100644 index 55977f9c..00000000 --- a/python_module/megengine/random/distribution.py +++ /dev/null @@ -1,102 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from typing import Iterable, Optional - -import megengine._internal as mgb -from megengine._internal import CompGraph, CompNode - -from ..core.graph import _use_default_if_none -from ..core.tensor import Tensor, wrap_io_tensor -from .rng import _random_seed_generator - -__all__ = ["gaussian", "uniform"] - - -@wrap_io_tensor -def gaussian( - shape: Iterable[int], - mean: float = 0, - std: float = 1, - comp_node: Optional[CompNode] = None, - comp_graph: Optional[CompGraph] = None, -) -> Tensor: - r"""Random variable with Gaussian distribution $N(\mu, \sigma)$ - - :param shape: Output tensor shape - :param mean: The mean or expectation of the distribution - :param std: The standard deviation of the distribution (variance = $\sigma ^ 2$) - :param comp_node: The comp node output on, default to None - :param comp_graph: The graph in which output is, default to None - :return: The output tensor - - Examples: - - .. testcode:: - - import megengine as mge - import megengine.random as rand - - x = rand.gaussian((2, 2), mean=0, std=1) - print(x.numpy()) - - .. testoutput:: - :options: +SKIP - - [[-0.20235455 -0.6959438 ] - [-1.4939808 -1.5824696 ]] - - """ - comp_node, comp_graph = _use_default_if_none(comp_node, comp_graph) - seed = _random_seed_generator().__next__() - return mgb.opr.gaussian_rng( - shape, seed=seed, mean=mean, std=std, comp_node=comp_node, comp_graph=comp_graph - ) - - -@wrap_io_tensor -def uniform( - shape: Iterable[int], - low: float = 0, - high: float = 1, - comp_node: Optional[CompNode] = None, - comp_graph: Optional[CompGraph] = None, -) -> Tensor: - r"""Random variable with uniform distribution $U(0, 1)$ - - :param shape: Output tensor shape - :param low: Lower range - :param high: Upper range - :param comp_node: The comp node output on, default to None - :param comp_graph: The graph in which output is, default to None - :return: The output tensor - - Examples: - - .. testcode:: - - import megengine as mge - import megengine.random as rand - - x = rand.uniform((2, 2)) - print(x.numpy()) - - .. testoutput:: - :options: +SKIP - - [[0.76901674 0.70496535] - [0.09365904 0.62957656]] - - """ - assert low < high, "Uniform is not defined when low >= high" - - comp_node, comp_graph = _use_default_if_none(comp_node, comp_graph) - seed = _random_seed_generator().__next__() - return low + (high - low) * mgb.opr.uniform_rng( - shape, seed=seed, comp_node=comp_node, comp_graph=comp_graph - ) diff --git a/python_module/megengine/random/rng.py b/python_module/megengine/random/rng.py deleted file mode 100644 index 54da30bd..00000000 --- a/python_module/megengine/random/rng.py +++ /dev/null @@ -1,27 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import time - -from numpy.random import MT19937 - -_rng = None - - -def _random_seed_generator(): - if _rng is None: - from ..distributed.util import get_rank - - manual_seed(seed=int(time.time()) + get_rank()) - while True: - yield _rng.random_raw() - - -def manual_seed(seed: int): - global _rng # pylint: disable=global-statement - _rng = MT19937(seed=seed) diff --git a/python_module/megengine/test/__init__.py b/python_module/megengine/test/__init__.py deleted file mode 100644 index 44ed54c2..00000000 --- a/python_module/megengine/test/__init__.py +++ /dev/null @@ -1,67 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np - - -def assertTensorClose( - v0, v1, *, max_err: float = 1e-6, allow_special_values: bool = False, name=None -): - """ - :param allow_special_values: whether to allow :attr:`v0` and :attr:`v1` to contain inf and nan values. - :param max_err: relative error - """ - __tracebackhide__ = True # pylint: disable=unused-variable - - assert ( - v0.dtype == v1.dtype - ), "Two Tensor must have same dtype, but the inputs are {} and {}".format( - v0.dtype, v1.dtype - ) - v0 = np.ascontiguousarray(v0, dtype=np.float32).copy() - v1 = np.ascontiguousarray(v1, dtype=np.float32).copy() - if allow_special_values: - # check nan and rm it - v0_nan_mask = np.isnan(v0) - if np.any(v0_nan_mask): - assert np.array_equiv(v0_nan_mask, np.isnan(v1)), (v0, v1) - v0[v0_nan_mask] = 0 - v1[v0_nan_mask] = 0 - # check inf and rm it - v0_inf_mask = v0 == float("inf") - if np.any(v0_inf_mask): - assert np.array_equiv(v0_inf_mask, v1 == float("inf")), (v0, v1) - v0[v0_inf_mask] = 0 - v1[v0_inf_mask] = 0 - # check -inf and rm it - v0_inf_mask = v0 == float("-inf") - if np.any(v0_inf_mask): - assert np.array_equiv(v0_inf_mask, v1 == float("-inf")), (v0, v1) - v0[v0_inf_mask] = 0 - v1[v0_inf_mask] = 0 - else: - assert np.isfinite(v0.sum()) and np.isfinite(v1.sum()), (v0, v1) - - assert v0.shape == v1.shape, "Two tensor must have same shape({} v.s. {})".format( - v0.shape, v1.shape - ) - vdiv = np.max([np.abs(v0), np.abs(v1), np.ones_like(v0)], axis=0) - err = np.abs(v0 - v1) / vdiv - check = err > max_err - if check.sum(): - idx = tuple(i[0] for i in np.nonzero(check)) - if name is None: - name = "tensor" - else: - name = "tensor {}".format(name) - raise AssertionError( - "{} not equal: " - "shape={} nonequal_idx={} v0={} v1={} err={}".format( - name, v0.shape, idx, v0[idx], v1[idx], err[idx] - ) - ) diff --git a/python_module/megengine/utils/__init__.py b/python_module/megengine/utils/__init__.py deleted file mode 100644 index 6b7b2d3a..00000000 --- a/python_module/megengine/utils/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -from megengine._internal.plugin import load_tensor_binary - - -def prod(iterable): - result = 1 - for i in iterable: - result *= i - return result diff --git a/python_module/megengine/utils/hook.py b/python_module/megengine/utils/hook.py deleted file mode 100644 index 9864a94a..00000000 --- a/python_module/megengine/utils/hook.py +++ /dev/null @@ -1,23 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import weakref - - -class HookHandler: - hook_num = 0 - - def __init__(self, source_dict, hook): - self.id = HookHandler.hook_num - HookHandler.hook_num += 1 - source_dict[self.id] = hook - self.source_ref = weakref.ref(source_dict) - - def remove(self): - source_dict = self.source_ref() - if source_dict is not None and self.id in source_dict: - del source_dict[self.id] diff --git a/python_module/megengine/utils/http_download.py b/python_module/megengine/utils/http_download.py deleted file mode 100644 index add2a649..00000000 --- a/python_module/megengine/utils/http_download.py +++ /dev/null @@ -1,66 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import hashlib -import os -import shutil -from tempfile import NamedTemporaryFile - -import requests -from tqdm import tqdm - -from ..logger import get_logger - -logger = get_logger(__name__) - -CHUNK_SIZE = 1024 -HTTP_CONNECTION_TIMEOUT = 5 - - -class HTTPDownloadError(BaseException): - """The class that represents http request error""" - - -def download_from_url(url: str, dst: str, http_read_timeout=120): - """ - Downloads file from given url to ``dst`` - - :param url: source URL - :param dst: saving path - :param http_read_timeout: how many seconds to wait for data before giving up - """ - dst = os.path.expanduser(dst) - dst_dir = os.path.dirname(dst) - - resp = requests.get( - url, timeout=(HTTP_CONNECTION_TIMEOUT, http_read_timeout), stream=True - ) - if resp.status_code != 200: - raise HTTPDownloadError("An error occured when downloading from {}".format(url)) - - md5 = hashlib.md5() - total_size = int(resp.headers.get("Content-Length", 0)) - bar = tqdm( - total=total_size, unit="iB", unit_scale=True, ncols=80 - ) # pylint: disable=blacklisted-name - try: - with NamedTemporaryFile("w+b", delete=False, suffix=".tmp", dir=dst_dir) as f: - logger.info("Download file to temp file %s", f.name) - for chunk in resp.iter_content(CHUNK_SIZE): - if not chunk: - break - bar.update(len(chunk)) - f.write(chunk) - md5.update(chunk) - bar.close() - shutil.move(f.name, dst) - finally: - # ensure tmp file is removed - if os.path.exists(f.name): - os.remove(f.name) - return md5.hexdigest() diff --git a/python_module/megengine/utils/max_recursion_limit.py b/python_module/megengine/utils/max_recursion_limit.py deleted file mode 100644 index d7bce6e8..00000000 --- a/python_module/megengine/utils/max_recursion_limit.py +++ /dev/null @@ -1,78 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import platform -import sys -import threading - -# Windows do not imp resource package -if platform.system() != "Windows": - import resource - - -class AlternativeRecursionLimit: - r"""A reentrant context manager for setting global recursion limits. - """ - - def __init__(self, new_py_limit): - self.new_py_limit = new_py_limit - self.count = 0 - self.lock = threading.Lock() - - self.orig_py_limit = 0 - self.orig_rlim_stack_soft = 0 - self.orig_rlim_stack_hard = 0 - - def __enter__(self): - with self.lock: - if self.count == 0: - self.orig_py_limit = sys.getrecursionlimit() - if platform.system() != "Windows": - ( - self.orig_rlim_stack_soft, - self.orig_rlim_stack_hard, - ) = resource.getrlimit(resource.RLIMIT_STACK) - # FIXME: https://bugs.python.org/issue34602, python3 release version - # on Macos always have this issue, not all user install python3 from src - try: - resource.setrlimit( - resource.RLIMIT_STACK, - (self.orig_rlim_stack_hard, self.orig_rlim_stack_hard), - ) - except ValueError as exc: - if platform.system() != "Darwin": - raise exc - - # increase recursion limit - sys.setrecursionlimit(self.new_py_limit) - self.count += 1 - - def __exit__(self, type, value, traceback): - with self.lock: - self.count -= 1 - if self.count == 0: - sys.setrecursionlimit(self.orig_py_limit) - - if platform.system() != "Windows": - try: - resource.setrlimit( - resource.RLIMIT_STACK, - (self.orig_rlim_stack_soft, self.orig_rlim_stack_hard), - ) - except ValueError as exc: - if platform.system() != "Darwin": - raise exc - - -_max_recursion_limit_context_manager = AlternativeRecursionLimit(2 ** 31 - 1) - - -def max_recursion_limit(): - r"""Sets recursion limit to the max possible value - """ - return _max_recursion_limit_context_manager diff --git a/python_module/megengine/utils/net_stats.py b/python_module/megengine/utils/net_stats.py deleted file mode 100644 index fa35d114..00000000 --- a/python_module/megengine/utils/net_stats.py +++ /dev/null @@ -1,279 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from functools import partial - -import numpy as np -import tabulate - -import megengine as mge -import megengine._internal as mgb -import megengine.module as m -import megengine.module.qat as qatm -import megengine.module.quantized as qm - -try: - mge.logger.MegEngineLogFormatter.max_lines = float("inf") -except AttributeError as e: - raise ValueError("set logger max lines failed") - -logger = mge.get_logger(__name__) - - -CALC_FLOPS = {} - - -def _register_modules(*modules): - def callback(impl): - for module in modules: - CALC_FLOPS[module] = impl - return impl - - return callback - - -@_register_modules( - m.Conv2d, - m.ConvTranspose2d, - m.LocalConv2d, - qm.Conv2d, - qm.ConvRelu2d, - qm.ConvBn2d, - qm.ConvBnRelu2d, - qatm.Conv2d, - qatm.ConvRelu2d, - qatm.ConvBn2d, - qatm.ConvBnRelu2d, -) -def count_convNd(module, input, output): - bias = 1 if module.bias is not None else 0 - group = module.groups - ic = input[0].shape[1] - oc = output[0].shape[1] - goc = oc // group - gic = ic // group - N = output[0].shape[0] - HW = np.prod(output[0].shape[2:]) - # N x Cout x H x W x (Cin x Kw x Kh + bias) - return N * HW * goc * (gic * np.prod(module.kernel_size) + bias) - - -@_register_modules(m.ConvTranspose2d) -def count_deconvNd(module, input, output): - return np.prod(input[0].shape) * output[0].shape[1] * np.prod(module.kernel_size) - - -@_register_modules(m.Linear, qatm.Linear, qm.Linear) -def count_linear(module, input, output): - return np.prod(output[0].shape) * module.in_features - - -# does not need import qat and quantized module since they inherit from float module. -hook_modules = ( - m.Conv2d, - m.ConvTranspose2d, - m.LocalConv2d, - m.BatchNorm2d, - m.Linear, -) - - -def net_stats(model, input_size, bar_length_max=20, log_params=True, log_flops=True): - def dict2table(list_of_dict, header): - table_data = [header] - for d in list_of_dict: - row = [] - for h in header: - v = "" - if h in d: - v = d[h] - row.append(v) - table_data.append(row) - return table_data - - def sizeof_fmt(num, suffix="B"): - for unit in ["", "Ki", "Mi", "Gi", "Ti", "Pi", "Ei", "Zi"]: - if abs(num) < 1024.0: - return "{:3.3f} {}{}".format(num, unit, suffix) - num /= 1024.0 - sign_str = "-" if num < 0 else "" - return "{}{:.1f} {}{}".format(sign_str, num, "Yi", suffix) - - def get_byteswidth(tensor): - dtype = tensor.dtype - if mgb.dtype.is_quantize(dtype): - return 1 - elif mgb.dtype.is_bfloat16(dtype): - return 2 - else: - return 4 - - def print_flops_stats(flops): - flops_list = [i["flops_num"] for i in flops] - max_flops_num = max(flops_list + [0]) - # calc total flops and set flops_cum - total_flops_num = 0 - for d in flops: - total_flops_num += int(d["flops_num"]) - d["flops_cum"] = sizeof_fmt(total_flops_num, suffix="OPs") - - for i in flops: - f = i["flops_num"] - i["flops"] = sizeof_fmt(f, suffix="OPs") - r = i["ratio"] = f / total_flops_num - i["percentage"] = "{:.2f}%".format(r * 100) - bar_length = int(f / max_flops_num * bar_length_max) - i["bar"] = "#" * bar_length - - header = [ - "name", - "class_name", - "input_shapes", - "output_shapes", - "flops", - "flops_cum", - "percentage", - "bar", - ] - - total_flops_str = sizeof_fmt(total_flops_num, suffix="OPs") - total_var_size = sum(sum(s[1] for s in i["output_shapes"]) for i in flops) - flops.append( - dict(name="total", flops=total_flops_str, output_shapes=total_var_size) - ) - - logger.info( - "flops stats: \n" + tabulate.tabulate(dict2table(flops, header=header)) - ) - - return total_flops_num - - def print_params_stats(params): - total_param_dims, total_param_size = 0, 0 - for d in params: - total_param_dims += int(d["param_dim"]) - total_param_size += int(d["size"]) - d["size"] = sizeof_fmt(d["size"]) - d["size_cum"] = sizeof_fmt(total_param_size) - - for d in params: - ratio = d["param_dim"] / total_param_dims - d["ratio"] = ratio - d["percentage"] = "{:.2f}%".format(ratio * 100) - - # construct bar - max_ratio = max([d["ratio"] for d in params]) - for d in params: - bar_length = int(d["ratio"] / max_ratio * bar_length_max) - d["size_bar"] = "#" * bar_length - - param_size = sizeof_fmt(total_param_size) - params.append(dict(name="total", param_dim=total_param_dims, size=param_size,)) - - header = [ - "name", - "shape", - "mean", - "std", - "param_dim", - "bits", - "size", - "size_cum", - "percentage", - "size_bar", - ] - - logger.info( - "param stats: \n" + tabulate.tabulate(dict2table(params, header=header)) - ) - - return total_param_size - - def net_stats_hook(module, input, output, name=""): - class_name = str(module.__class__).split(".")[-1].split("'")[0] - - flops_fun = CALC_FLOPS.get(type(module)) - if callable(flops_fun): - flops_num = flops_fun(module, input, output) - - if not isinstance(output, (list, tuple)): - output = [output] - - flops.append( - dict( - name=name, - class_name=class_name, - input_shapes=[i.shape for i in input], - output_shapes=[o.shape for o in output], - flops_num=flops_num, - flops_cum=0, - ) - ) - - if hasattr(module, "weight") and module.weight is not None: - w = module.weight - value = w.numpy() - param_dim = np.prod(w.shape) - param_bytes = get_byteswidth(w) - params.append( - dict( - name=name + "-w", - shape=w.shape, - param_dim=param_dim, - bits=param_bytes * 8, - size=param_dim * param_bytes, - size_cum=0, - mean="{:.2g}".format(value.mean()), - std="{:.2g}".format(value.std()), - ) - ) - - if hasattr(module, "bias") and module.bias is not None: - b = module.bias - value = b.numpy() - param_dim = np.prod(b.shape) - param_bytes = get_byteswidth(b) - params.append( - dict( - name=name + "-b", - shape=b.shape, - param_dim=param_dim, - bits=param_bytes * 8, - size=param_dim * param_bytes, - size_cum=0, - mean="{:.2g}".format(value.mean()), - std="{:.2g}".format(value.std()), - ) - ) - - # multiple inputs to the network - if not isinstance(input_size[0], tuple): - input_size = [input_size] - - params = [] - flops = [] - hooks = [] - - for (name, module) in model.named_modules(): - if isinstance(module, hook_modules): - hooks.append( - module.register_forward_hook(partial(net_stats_hook, name=name)) - ) - - inputs = [mge.zeros(in_size, dtype=np.float32) for in_size in input_size] - model.eval() - model(*inputs) - for h in hooks: - h.remove() - - total_flops, total_params = 0, 0 - if log_params: - total_params = print_params_stats(params) - if log_flops: - total_flops = print_flops_stats(flops) - - return total_params, total_flops diff --git a/python_module/megengine/utils/profile_analyze.py b/python_module/megengine/utils/profile_analyze.py deleted file mode 100755 index 8041c0d8..00000000 --- a/python_module/megengine/utils/profile_analyze.py +++ /dev/null @@ -1,424 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import argparse -import collections -import json -import re -import textwrap - -import numpy as np -from tabulate import tabulate - -from megengine.utils.profile_analyzer import ( - NonExistNum, - ProfileAnalyzer, - TimeFuncHelper, -) - - -def _tabulate_ml(tab, **kwargs): - """Tabulate profile output with multi-line support.""" - new_tab = [] - new_tab_is_row = [] - for row in tab: - col_lines = [str(i).split("\n") for i in row] - max_nr_line = max(map(len, col_lines)) - new_tab_is_row.append(True) - if max_nr_line > 1: - new_tab_is_row.extend([False] * (max_nr_line - 1)) - for i in col_lines: - if len(i) < max_nr_line: - i.extend([""] * (max_nr_line - len(i))) - new_tab.extend(zip(*col_lines)) - else: - new_tab.append(row) - - assert len(new_tab_is_row) == len(new_tab) - ret = [i + "\n" for i in tabulate(new_tab, **kwargs).split("\n")] - for idx, val in enumerate(new_tab_is_row): - if not val: - ret[idx * 2 + 2] = "" - return "".join(ret)[:-1] - - -def _tabulate_confluence(tab, **kwargs): - """Tabulate profile output.""" - kwargs.pop("tablefmt", None) - s = tabulate(tab, tablefmt="orgtbl", **kwargs) - lines = s.split("\n") - lines[1] = lines[1].replace("+", "|") - return "\n".join(lines) - - -def main(passed_args=None): # pylint: disable=too-many-statements - """Analyses profile info from :mod:`~.utils.profile_analyzer` . - - Run this file with ``--help`` to get more usage. - """ - parser = argparse.ArgumentParser( - description="analyze analyzer result", - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - ) - parser.add_argument("dump") - parser.add_argument( - "-t", - "--top", - type=int, - default=3, - help="number of most time-consuming operators to print", - ) - parser.add_argument( - "--type", action="append", help="filter oprs in the top list by type" - ) - parser.add_argument( - "--aggregate-by", - default=None, - choices=["type"], - help="aggragate profiling result by", - ) - parser.add_argument( - "--opr-name", help="filter oprs in the top list by regex of name" - ) - parser.add_argument( - "--input-dtype", type=str, help="filter oprs in the top list by input dtype" - ) - parser.add_argument( - "--top-end-key", - default="end", - choices=["end", "kern"], - help="how time in top is calculated; end corresponds " - "to total device time, and kern corresponds to only " - "wait time", - ) - parser.add_argument( - "--aggregate", - default=None, - help="aggregate operations", - choices=["max", "min", "sum", "mean"], - ) - parser.add_argument( - "--order-by", - default="time", - help="sort result according to given column; the param can be " - " or +, meaning sorting in descending or " - "ascending order respectively", - ) - parser.add_argument( - "--copy-time", action="store_true", help="show copy time related result" - ) - parser.add_argument( - "--min-time", - type=float, - default=float("-inf"), - help="minimal time of a result to be printed", - ) - parser.add_argument( - "--max-time", - type=float, - default=float("inf"), - help="maximal time of a result to be printed", - ) - parser.add_argument( - "--show-host", action="store_true", help="show host profiling info" - ) - parser.add_argument( - "--dump-only-opr", - action="store_true", - help="only dump operator info as plaintext; useful " - "for diff between two filtered profile results", - ) - parser.add_argument( - "--confluence", - "--wiki", - action="store_true", - help="output confluence-markdown-compatible table", - ) - parser.add_argument( - "--print-only", - choices={"summary", "device", "host"}, - help="print only chosen info", - ) - - args = parser.parse_args(passed_args) - - opr_filters = [] - if args.type: - opr_filters.append(lambda o, a, b: o["type"] in args.type) - if args.opr_name: - opr_filters.append( - lambda o, a, b, r=re.compile(args.opr_name): r.match(o["name"]) - ) - if args.input_dtype: - opr_filters.append( - lambda o, a, b: any( - [i["mem_plan"]["layout"]["dtype"] == args.input_dtype for i in a] - ) - ) - if not opr_filters: - - def opr_filter(o, a, b): # pylint: disable=unused-argument - return True - - else: - - def opr_filter(o, a, b): - return all(i(o, a, b) for i in opr_filters) - - with open(args.dump) as fin: - dump = json.load(fin) - - analyzer = ProfileAnalyzer(dump, opr_filter) - analyzer_tot = ProfileAnalyzer(dump, lambda _, __, ___: True) - - def summary(): - device_end_func = TimeFuncHelper.eval_time_func("device", "end", np.max) - device_kern_func = TimeFuncHelper.eval_time_func("device", "kern", np.max) - host_end_func = TimeFuncHelper.eval_time_func("host", "end", np.max) - - def get_tot_time(func): - rec = analyzer_tot.select(func, aggregate=np.sum) - if not rec: - return "N/A" - rec = rec[0] - return rec.time - - tab = [] - tot_dev_time = get_tot_time(device_end_func) - tot_host_time = get_tot_time(host_end_func) - tab.append(("total device time", tot_dev_time)) - tab.append(("total host time", tot_host_time)) - if args.copy_time: - - def fmt(a, b): - a = a[0] - b = b[0] - return "tot={:.4f} avg={:.4f}".format(a.time, b.time) - - tab.append( - ( - "copy time", - fmt( - analyzer.select( - device_end_func, - lambda opr: opr.opr_info["type"] == "Copy", - aggregate=np.sum, - ), - analyzer.select( - device_end_func, - lambda opr: opr.opr_info["type"] == "Copy", - aggregate=np.mean, - ), - ), - ) - ) - tab.append( - ( - "copy wait time", - fmt( - analyzer.select( - device_kern_func, - lambda opr: opr.opr_info["type"] == "Copy", - aggregate=np.sum, - ), - analyzer.select( - device_kern_func, - lambda opr: opr.opr_info["type"] == "Copy", - aggregate=np.mean, - ), - ), - ) - ) - - if args.confluence: - tab_str = _tabulate_confluence(tab, headers=["name", "value"]) - else: - tab_str = tabulate(tab) - - return tab_str, tot_dev_time, tot_host_time - - def prof_details(prof_type, tot_time): - tab = [] - - def func( - opr, - *, - f0=TimeFuncHelper.eval_time_func(prof_type, args.top_end_key, np.max) - ): - t = f0(opr) - if t is not None and (t < args.min_time or t > args.max_time): - return None - return t - - records = analyzer.select( - func, - aggregate=args.aggregate, - aggregate_by=args.aggregate_by, - top_k=args.top, - sort_by=args.order_by, - ) - - if args.dump_only_opr: - ret = [] - for i in records: - ret.append(" ".join(i.info.values())) - return "\n".join(ret) - - def format_shapes(shapes, layouts=None, sep="\n"): - if isinstance(shapes, NonExistNum) or shapes is None: - return repr(shapes) - if layouts is None: - layouts = [None] * len(shapes) - - comp = [] - for i, j in zip(shapes, layouts): - i = "{" + ",".join(map(str, i)) + "}" - if j: - i += "\n -[" + ",".join(map(str, j)) + "]" - comp.append(i) - return sep.join(comp) - - def fix_num_and_find_unit(x, base): - if isinstance(x, NonExistNum) or ( - isinstance(x, float) and not np.isfinite(x) - ): - return x, "" - unit = iter(["", "K", "M", "G", "T", "P"]) - while x >= base: - x /= base - next(unit) - return x, next(unit) - - def get_number_with_unit(num, unit, base, sep="\n"): - num, unit_prefix = fix_num_and_find_unit(num, base) - if isinstance(unit, list): - unit = unit[int(unit_prefix != "")] - return ("{:.2f}" + sep + "{}{}").format(num, unit_prefix, unit) - - if args.confluence: - rows = [] - cum_time = 0 - - max_time = max([r.time for r in records]) - max_bandwidth = max([r.bandwidth for r in records]) - max_flops = max( - [r.flops for r in records if not isinstance(r.flops, NonExistNum)] - ) - - bar_length = 15 - for idx, record in enumerate(records): - cum_time += record.time - - opr_info = [("opr " + k, v) for k, v in record.info.items()] - - row = collections.OrderedDict( - [ - ("#", idx), - ("time", "{:.3}".format(record.time)), - ("ratio", "{:.1f}%".format(record.time / tot_time * 100)), - ("time bar", "#" * int(record.time / max_time * bar_length)), - ("cum-time", cum_time), - ("cum-time ratio", cum_time / tot_time), - ] - + opr_info - + [ - ( - "computation (MFLO)", - "{:.1f}".format(record.computation / 1000 ** 2), - ), - ("MFLOPS", "{:.1f}".format(record.flops / 1000 ** 2)), - ( - "MFLOPS-bar", - "" - if isinstance(record.flops, NonExistNum) - else ("#" * int(record.flops / max_flops * bar_length)), - ), - ("memory (MB)", "{:.1f}".format(record.memory / 1024 ** 2)), - ( - "bandwidth (MiB/s)", - "{:.1f}".format(record.bandwidth / 1024 ** 2), - ), - ( - "bandwidth bar", - "#" * int(record.bandwidth / max_bandwidth * bar_length), - ), - ( - "in_shapes", - format_shapes( - record.in_shapes, record.in_layouts, sep=", " - ), - ), - ("out_shapes", format_shapes(record.out_shapes, sep=", ")), - ] - ) - rows.append(row) - headers = list(rows[0].keys()) - tab = [[row[i] for i in headers] for row in rows] - - return _tabulate_confluence(tab, headers=headers) - - else: - cum_time = 0 - for idx, record in enumerate(records): - cum_time += record.time - tab.append( - ( - "#{}\n{:.3}\n{:.1f}%".format( - idx, record.time, record.time / tot_time * 100 - ), - "{:.3}\n{:.1f}%".format(cum_time, cum_time / tot_time * 100), - "\n".join( - "\n- ".join(textwrap.wrap(str(i), width=30)) - for i in record.info.values() - ), - get_number_with_unit(record.computation, "FLO", 1000), - get_number_with_unit(record.flops, "FLOPS", 1000), - get_number_with_unit(record.memory, ["byte", "iB"], 1024), - get_number_with_unit( - record.bandwidth, ["byte/s", "iB/s"], 1024 - ), - format_shapes(record.in_shapes, record.in_layouts), - format_shapes(record.out_shapes), - ) - ) - return _tabulate_ml( - tab, - headers=[ - "{} self time".format(prof_type), - "cumulative", - "operator info", - "computation", - "FLOPS", - "memory", - "bandwidth", - "in_shapes", - "out_shapes", - ], - tablefmt="fancy_grid", - ) - - summary_tab, tot_dev_time, tot_host_time = summary() - if args.print_only: - print( - { - "summary": lambda: summary_tab, - "device": lambda: prof_details("device", tot_dev_time), - "host": lambda: prof_details("host", tot_host_time), - }[args.print_only]() - ) - else: - print(summary_tab) - print() - print(prof_details("device", tot_dev_time)) - if args.show_host: - print() - print(prof_details("host", tot_host_time)) - - -if __name__ == "__main__": - main() diff --git a/python_module/megengine/utils/profile_analyzer.py b/python_module/megengine/utils/profile_analyzer.py deleted file mode 100644 index 75cc0c0c..00000000 --- a/python_module/megengine/utils/profile_analyzer.py +++ /dev/null @@ -1,401 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import collections -import copy -import functools -from typing import Callable, List, Optional, Union - -import numpy as np - - -class NonExistNum: - """An object that behaves like a number but means a field does not exist; It is - always greater than any real number - """ - - def __truediv__(self, _): - return self - - def __add__(self, rhs): - return rhs - - def __radd__(self, lhs): - return lhs - - def __neg__(self): - return self - - def __gt__(self, rhs): - if isinstance(rhs) is NonExistNum: - return id(self) > id(rhs) - return True - - def __ge__(self, rhs): - return self > rhs or self == rhs - - def __lt__(self, rhs): - if isinstance(rhs) is NonExistNum: - return id(self) < id(rhs) - return False - - def __le__(self, rhs): - return self < rhs or self == rhs - - def __eq__(self, rhs): - return self is rhs - - def __format__(self, spec): - return "N/A" - - def __repr__(self): - return "N/A" - - -class OprProfRst: - """Opr profiling result dumped from megengine profiler.""" - - opr_info = None - """A dict containing operator info: name, id and type.""" - - time_dict = None - """A mapping from ``"host"`` or ``"device"`` to list of profiling - results.""" - - footprint = None - """A mapping from ``"memory"`` or ``"computation"`` to the actual number - of corresponding operations""" - - def __init__(self, entry: dict): - """Opr profiling initialization, which sets up name, type and id of opr_info. - - :param entry: profiling json exec_graph items - """ - assert isinstance(entry, dict) - self.opr_info = collections.OrderedDict() - for key in ["name", "type", "id"]: - self.opr_info[key] = entry[key] - self.time_dict = collections.defaultdict(list) - self.footprint = collections.defaultdict(NonExistNum) - - def update_device_prof_info(self, dev_time: dict): - """Updates device profiling info - - :param dev_time: device time for single opr, - is an attribute of profiling result. - """ - assert isinstance(dev_time, dict) - self.time_dict["device"].append(copy.deepcopy(dev_time)) - - def update_host_prof_info(self, host_time: dict): - """Updates host profiling info - - :param host_time: host time for single opr, - is an attribute of profiling result. - """ - assert isinstance(host_time, dict) - self.time_dict["host"].append(copy.deepcopy(host_time)) - - def update_footprint(self, footprint: dict): - """Updates opr footprint - - :param footprint: footprint for single opr, - is an attribute of profiling result. - """ - assert isinstance(footprint, dict) - self.footprint.update(footprint) - - -class Record: - """A record of analyzing result""" - - __slot__ = [ - "time", - "info", - "computation", - "memory", - "in_shapes", - "in_layouts", - "out_shapes", - "flops", - "bandwidth", - "opr_id", - ] - - def __init__(self, time: float, info: dict, footprint: dict): - """Initializes single record - - :param time: opr running time, evaluated by applying users providing - function to OprProfRst. - :param info: opr information, could be original opr information or - aggregate infomation if aggregating enabled. - :param footprint: contains footprint information, for now, we have - ``"computation"``, ``"memory"``, ``"in_shapes"``, ``"out_shapes"``. - """ - - assert isinstance(footprint, dict) - self.time = time - self.info = collections.OrderedDict(copy.deepcopy(info)) - self.computation = footprint["computation"] or NonExistNum() - self.memory = footprint["memory"] - self.in_shapes = footprint["in_shapes"] - self.in_layouts = footprint.get("in_layouts") - self.out_shapes = footprint["out_shapes"] - self.flops = self.computation / self.time - self.bandwidth = self.memory / self.time - self.opr_id = info.get("id") - if isinstance(self.opr_id, str) and self.opr_id != "N/A": - self.opr_id = int(self.opr_id) - - def get_column_by_name(self, name: str = None): - """extracts column value by its column name - - :param name: column name, None for time. - """ - - if name is None: - name = "time" - return getattr(self, name) - - -class ProfileAnalyzer: - def __init__(self, obj: dict, opr_filter: Callable = lambda opr, inp, out: True): - """Initializes ProfileAnalyzer - - :param obj: dict dumped from json str. - :param opr_filter: function that filter oprs. - """ - self._opr_set = dict() # type: dict - assert isinstance(obj, dict) - varz = obj["graph_exec"]["var"] - for opr_id, entry in obj["graph_exec"]["operator"].items(): - inp = [varz[i] for i in entry["input"]] - out = [varz[i] for i in entry["output"]] - if opr_filter(entry, inp, out): - self._opr_set[opr_id] = OprProfRst(entry) - - for opr_id, entry in obj["profiler"]["device"].items(): - if opr_id not in self._opr_set: - continue - opr = self._opr_set[opr_id] - for _, time in entry.items(): - opr.update_device_prof_info(time) - - for opr_id, entry in obj["profiler"]["host"].items(): - if opr_id not in self._opr_set: - continue - opr = self._opr_set[opr_id] - for _, time in entry.items(): - opr.update_host_prof_info(time) - - for opr_id, entry in obj["profiler"].get("opr_footprint", {}).items(): - if opr_id not in self._opr_set: - continue - opr = self._opr_set[opr_id] - opr.update_footprint(entry) - - def _aggregate( - self, records: List[Record], aop: Union[str, Callable], atype: Optional[str] - ) -> List[Record]: - """Aggregate operation - - :param records: selected records - :param aop: aggregate operation, if aop is str, we would replace it - with associated numpy function wth aop name" - :param atype: the type aggregated by, None for aggregating all into single - record. - """ - if aop is None: - assert atype is None, "must specify aggregate op" - return records - if isinstance(aop, str): - aop = getattr(np, aop) - type2stat = collections.defaultdict(lambda: [[], [], []]) # type: dict - for item in records: - if atype == "type": - d = type2stat[item.info["type"]] - else: - d = type2stat["all"] - d[0].append(item.time) - d[1].append(item.computation) - d[2].append(item.memory) - - rst = [] - for opr_type in type2stat.keys(): - time, computation, memory = type2stat[opr_type] - nr_oprs = len(time) - time_rst = aop(time) - comp_rst = aop(computation) - mem_rst = aop(memory) - - item = Record( - time_rst, - {"type": opr_type, "count": nr_oprs, "id": "N/A"}, - { - "computation": comp_rst, - "memory": mem_rst, - "in_shapes": None, - "out_shapes": None, - }, - ) - rst.append(item) - return rst - - def _sort(self, records: List[Record], sort_by: str) -> List[Record]: - """sort operation - - :param records: the records after aggregate operation. - :param sort_by: keyword for sorting the list - """ - if sort_by is None: - return records - if sort_by.startswith("+"): - sort_by = sort_by[1:] - key = lambda record: record.get_column_by_name(sort_by) - else: - key = lambda record: -record.get_column_by_name(sort_by) - records.sort(key=key) - return records - - def select( - self, - time_func: Callable, - opr_filter: Callable = lambda opr: True, - aggregate: Callable = None, - aggregate_by: str = None, - sort_by: str = None, - top_k: int = 0, - ) -> List[Record]: - """Select operation - - :param time_func: time_func provided by user, would apply to every - OprProfRst - :param opr_filter: filter satisfied operatiors. - :param aggregate: function that apply to list of records which are - aggregated by atype - :param aggregate_by: the type aggregated by - :param sort_by: keyword for sorting all records. - :param top_k: specify the maximum number of records. - :return: the records that go through select, aggregate, sort. - """ - - records = [] - for opr in self._opr_set.values(): - if opr_filter(opr): - time = time_func(opr) - if time is None: - continue - item = Record(time, opr.opr_info, opr.footprint) - records.append(item) - - records = self._aggregate(records, aggregate, aggregate_by) - if not records: - return records - return self._sort(records, sort_by)[0 : len(records) if top_k == 0 else top_k] - - -class TimeFuncHelper: - """Time Function Helper for users.""" - - @staticmethod - def _eval_time(prof_type, end_key, func, opr_prof): - """Eval time - - :type prof_type: str - :param prof_type: 'host' or 'device' - :type end_key: str - :param end_key: 'kern' or 'end' - :type func: function - :param func: apply to list of all ``thread`` of ``gpu`` time. - :type opr_prof: `class OprProfRst` - :param opr_prof: operator profiling result - :rtype: float - :return: time - """ - - if prof_type not in opr_prof.time_dict: - return None - time = [time[end_key] - time["start"] for time in opr_prof.time_dict[prof_type]] - return func(time) - - @staticmethod - def eval_time_func(prof_type: str, end_key: str, func: Callable) -> float: - """Eval oprerator profile time. - - :param prof_type: 'host' or 'device' - :param end_key: 'kern' or 'end' - :param func: apply to list of all ``thread`` of ``gpu`` time. - :return: Eval time results - """ - return functools.partial(TimeFuncHelper._eval_time, prof_type, end_key, func) - - @staticmethod - def _min_start( - prof_type, end_key, func, opr_prof - ): # pylint: disable=unused-argument - """Eval minimum start time - - :type prof_type: str - :param prof_type: 'host' or 'device' - :type end_key: str - :param end_key: 'kern' or 'end' - :type func: function - :param func: apply to list of all ``thread`` of ``gpu`` time. - :type opr_prof: `class OprProfRst` - :param opr_prof: operator profiling result - :rtype: float - :return: time - """ - if prof_type not in opr_prof.time_dict: - return None - time = [time["start"] for time in opr_prof.time_dict[prof_type]] - return np.min(time) - - @staticmethod - def min_start_func( - prof_type: str, end_key: str, func: Callable - ) -> float: # pylint: disable=unused-argument - """Eval oprerator profile min start time - - :param prof_type: 'host' or 'device' - :param end_key: 'kern' or 'end' - :param func: apply to list of all ``thread`` of ``gpu`` time. - :return: Eval time results - """ - return functools.partial(TimeFuncHelper._min_start, prof_type, end_key, func) - - @staticmethod - def _max_end(prof_type, end_key, func, opr_prof): # pylint: disable=unused-argument - """Eval maximum end time - - :type prof_type: str - :param prof_type: 'host' or 'device' - :type end_key: str - :param end_key: 'kern' or 'end' - :type func: function - :param func: apply to list of all ``thread`` of ``gpu`` time. - :type opr_prof: `class OprProfRst` - :param opr_prof: operator profiling result - :rtype: float - :return: time - """ - if prof_type not in opr_prof.time_dict: - return None - time = [time["end"] for time in opr_prof.time_dict[prof_type]] - return np.max(time) - - @staticmethod - def max_end_func(prof_type: str, end_key: str, func: Callable) -> float: - """Eval oprerator profile max end time - - :param prof_type: 'host' or 'device' - :param end_key: 'kern' or 'end' - :param func: apply to list of all ``thread`` of ``gpu`` time. - :return: Eval time results - """ - return functools.partial(TimeFuncHelper._max_end, prof_type, end_key, func) diff --git a/python_module/megengine/utils/types.py b/python_module/megengine/utils/types.py deleted file mode 100644 index 465ca03c..00000000 --- a/python_module/megengine/utils/types.py +++ /dev/null @@ -1,37 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import collections -import functools - - -def get_ndtuple(value, *, n, allow_zero=True): - r"""Converts possibly 1D tuple to nd tuple - - :type allow_zero: bool - :param allow_zero: whether to allow zero tuple value""" - if not isinstance(value, collections.Iterable): - value = int(value) - value = tuple([value for i in range(n)]) - else: - assert len(value) == n, "tuple len is not equal to n: {}".format(value) - spatial_axis = map(int, value) - value = tuple(spatial_axis) - if allow_zero: - minv = 0 - else: - minv = 1 - assert min(value) >= minv, "invalid value: {}".format(value) - return value - - -_single = functools.partial(get_ndtuple, n=1, allow_zero=True) -_pair = functools.partial(get_ndtuple, n=2, allow_zero=True) -_pair_nonzero = functools.partial(get_ndtuple, n=2, allow_zero=False) -_triple = functools.partial(get_ndtuple, n=3, allow_zero=True) -_quadruple = functools.partial(get_ndtuple, n=4, allow_zero=True) diff --git a/python_module/megengine/version.py b/python_module/megengine/version.py deleted file mode 100644 index 3d187266..00000000 --- a/python_module/megengine/version.py +++ /dev/null @@ -1 +0,0 @@ -__version__ = "0.5.0" diff --git a/python_module/requires-style.txt b/python_module/requires-style.txt deleted file mode 100644 index 899aac52..00000000 --- a/python_module/requires-style.txt +++ /dev/null @@ -1,4 +0,0 @@ -black==19.10b0 -isort==4.3.21 -pylint==2.4.3 -mypy==0.750 diff --git a/python_module/requires-test.txt b/python_module/requires-test.txt deleted file mode 100644 index 9e732684..00000000 --- a/python_module/requires-test.txt +++ /dev/null @@ -1,3 +0,0 @@ -pytest==5.3.0 -pytest-sphinx>=0.2.2 -pytest-json-report>=1.2.1 diff --git a/python_module/requires.txt b/python_module/requires.txt deleted file mode 100644 index 5ebb52a3..00000000 --- a/python_module/requires.txt +++ /dev/null @@ -1,7 +0,0 @@ -numpy>=1.17 -opencv-python -pyarrow -requests -tabulate -tqdm -redispy diff --git a/python_module/setup.py b/python_module/setup.py deleted file mode 100644 index 414f4fcf..00000000 --- a/python_module/setup.py +++ /dev/null @@ -1,118 +0,0 @@ -# -*- coding: utf-8 -*- -# This file is part of MegBrain. -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - -import os -import re -import pathlib -import platform -from distutils.file_util import copy_file -from setuptools import setup, find_packages, Extension -from setuptools.command.build_ext import build_ext as _build_ext - -class PrecompiledExtesion(Extension): - def __init__(self, name): - super().__init__(name, sources=[]) - -class build_ext(_build_ext): - - def build_extension(self, ext): - if not isinstance(ext, PrecompiledExtesion): - return super().build_extension(ext) - - if not self.inplace: - fullpath = self.get_ext_fullpath(ext.name) - extdir = pathlib.Path(fullpath) - extdir.parent.mkdir(parents=True, exist_ok=True) - - modpath = self.get_ext_fullname(ext.name).split('.') - if platform.system() == "Windows": - modpath[-1] += '.pyd' - else: - modpath[-1] += '.so' - modpath = str(pathlib.Path(*modpath).resolve()) - - copy_file(modpath, fullpath, verbose=self.verbose, dry_run=self.dry_run) - -package_name = 'MegEngine' - -v = {} -with open("megengine/version.py") as fp: - exec(fp.read(), v) -__version__ = v['__version__'] - -email = 'megengine@megvii.com' -local_version = os.environ.get('LOCAL_VERSION') -if local_version: - __version__ = '{}+{}'.format(__version__, local_version) - -packages = find_packages(exclude=['test']) -package_data = [ - str(f.relative_to('megengine')) - for f in pathlib.Path('megengine', '_internal', 'include').glob('**/*') -] -package_data += [ - str(f.relative_to('megengine')) - for f in pathlib.Path('megengine', '_internal', 'lib').glob('**/*') -] -package_data += [ - os.path.join('module', 'pytorch', 'torch_mem_fwd.cpp') -] - -with open('requires.txt') as f: - requires = f.read().splitlines() -with open('requires-style.txt') as f: - requires_style = f.read().splitlines() -with open('requires-test.txt') as f: - requires_test = f.read().splitlines() - -setup_kwargs = dict( - name=package_name, - version=__version__, - description='Framework for numerical evaluation with ' - 'auto-differentiation', - author='Megvii Engine Team', - author_email=email, - packages=packages, - package_data={ - 'megengine': package_data, - }, - ext_modules=[PrecompiledExtesion('megengine._internal._mgb')], - install_requires=requires, - extras_require={ - 'dev': requires_style + requires_test, - 'ci': requires_test, - }, - cmdclass={'build_ext': build_ext}, -) - - -setup_kwargs.update(dict( - classifiers=[ - 'Development Status :: 3 - Alpha', - 'Intended Audience :: Developers', - 'Intended Audience :: Education', - 'Intended Audience :: Science/Research', - 'License :: OSI Approved :: Apache Software License', - 'Programming Language :: C++', - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.5', - 'Programming Language :: Python :: 3.6', - 'Programming Language :: Python :: 3.7', - 'Programming Language :: Python :: 3.8', - 'Topic :: Scientific/Engineering', - 'Topic :: Scientific/Engineering :: Mathematics', - 'Topic :: Scientific/Engineering :: Artificial Intelligence', - 'Topic :: Software Development', - 'Topic :: Software Development :: Libraries', - 'Topic :: Software Development :: Libraries :: Python Modules', - ], - license='Apache 2.0', - keywords='megengine deep learning', - data_files = [("megengine", [ - "../LICENSE", - "../ACKNOWLEDGMENTS", - ])] -)) - -setup(**setup_kwargs) diff --git a/python_module/src/cpp/bfloat16.cpp b/python_module/src/cpp/bfloat16.cpp deleted file mode 100644 index 712e5219..00000000 --- a/python_module/src/cpp/bfloat16.cpp +++ /dev/null @@ -1,296 +0,0 @@ -/** - * \file python_module/src/cpp/bfloat16.cpp - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \brief numpy dtypes for bfloat16 - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - -#include "megbrain/common.h" -#include "megbrain/dtype.h" - -#include -#include - -#define NO_IMPORT_ARRAY 1 -#include "./numpy_incl.h" - -#pragma GCC diagnostic ignored "-Wmissing-field-initializers" - -namespace { - -struct BFloat16Type { - static int npy_typenum; - mgb::dt_bfloat16 value; - - struct PyObj; - struct NpyType; - - template - struct NpyCast; -}; - -int BFloat16Type::npy_typenum; - -/* ==================== BFloat16Type::NpyCast ==================== */ - -template -struct BFloat16Type::NpyCast { - static void apply(void* from_, void* to_, npy_intp n, void* /*fromarr*/, - void* /*toarr*/) { - auto from = static_cast(from_); - auto to = static_cast(to_); - for (npy_intp i = 0; i < n; ++i) { - float cur = static_cast(from[i]); - to[i].value = cur; - } - } -}; - -template -struct BFloat16Type::NpyCast { - static void apply(void* from_, void* to_, npy_intp n, void* /*fromarr*/, - void* /*toarr*/) { - auto from = static_cast(from_); - auto to = static_cast(to_); - for (npy_intp i = 0; i < n; ++i) { - to[i] = from[i].value; - } - } -}; - -/* ==================== BFloat16Type::PyObj ==================== */ -struct BFloat16Type::PyObj { - PyObject_HEAD BFloat16Type obj; - - static PyTypeObject py_type; - - static PyObject* from_bfloat16(BFloat16Type val) { - auto p = reinterpret_cast(py_type.tp_alloc(&py_type, 0)); - p->obj.value = val.value; - return reinterpret_cast(p); - } - - static PyObject* py_new(PyTypeObject* type, PyObject* args, PyObject* kwds); - static PyObject* py_repr(PyObject* obj); - static PyObject* py_richcompare(PyObject* a, PyObject* b, int op); -}; -PyTypeObject BFloat16Type::PyObj::py_type; - -PyObject* BFloat16Type::PyObj::py_new(PyTypeObject* type, PyObject* args, - PyObject* kwds) { - PyObj* self; - Py_ssize_t size; - - self = (PyObj*)type->tp_alloc(type, 0); - - size = PyTuple_GET_SIZE(args); - if (size > 1) { - PyErr_SetString(PyExc_TypeError, "BFloat16Type Only has 1 parameter"); - return NULL; - } - PyObject* x = PyTuple_GET_ITEM(args, 0); - if (PyObject_IsInstance(x, (PyObject*)&py_type)) { - Py_INCREF(x); - return x; - } - - if (!PyFloat_Check(x)) { - PyErr_SetString(PyExc_TypeError, - "BFloat16Type must be initialized wit float"); - return NULL; - } - - const float s = PyFloat_AsDouble(x); - - self->obj.value = s; - - return (PyObject*)self; -} - -PyObject* BFloat16Type::PyObj::py_repr(PyObject* obj) { - float fval = static_cast(((PyObj*)obj)->obj.value); - return PyUnicode_FromString(mgb::ssprintf("%f", fval).c_str()); -} - -PyObject* BFloat16Type::PyObj::py_richcompare(PyObject* a, PyObject* b, - int op) { - mgb_assert(PyObject_IsInstance(a, (PyObject*)&py_type)); - auto bval = PyFloat_AsDouble(b); - if (bval == -1 && PyErr_Occurred()) { - return NULL; - } - double aval = ((PyObj*)a)->obj.value; -#define OP(py, op) \ - case py: { \ - if (aval op bval) { \ - Py_RETURN_TRUE; \ - } else { \ - Py_RETURN_FALSE; \ - } \ - } - switch (op) { - OP(Py_LT, <) - OP(Py_LE, <=) - OP(Py_EQ, ==) - OP(Py_NE, !=) - OP(Py_GT, >) - OP(Py_GE, >=) - }; -#undef OP - return Py_NotImplemented; -} - -/* ==================== BFloat16Type::NpyType ==================== */ -struct BFloat16Type::NpyType { - static PyArray_ArrFuncs funcs; - static PyArray_Descr descr; - - static bool init(); - - static void copyswap(void* dst, void* src, int swap, void* /*arr*/) { - if (src) { - mgb_assert(!swap); - memcpy(dst, src, sizeof(BFloat16Type)); - } - } - static PyObject* getitem(void* data, void* ap) { - return BFloat16Type::PyObj::from_bfloat16( - *static_cast(data)); - } - static int setitem(PyObject* op, void* ov, void* ap); -}; - -PyArray_ArrFuncs BFloat16Type::NpyType::funcs; -PyArray_Descr BFloat16Type::NpyType::descr; - -int BFloat16Type::NpyType::setitem(PyObject* op, void* ov, void* ap) { - if (PyLong_Check(op)) { - int a = PyLong_AsLong(op); - static_cast(ov)->value = a; - } else if (PyFloat_Check(op)) { - float a = PyFloat_AsDouble(op); - static_cast(ov)->value = a; - } else if (PyObject_IsInstance( - op, (PyObject*)(&(BFloat16Type::PyObj::py_type)))) { - static_cast(ov)->value = ((PyObj*)op)->obj.value; - } else { - PyErr_SetString(PyExc_ValueError, - "input type must be int/float/bfloat16"); - return -1; - } - return 0; -} - -bool BFloat16Type::NpyType::init() { - descr = {PyObject_HEAD_INIT(0) & BFloat16Type::PyObj::py_type, - 'V', // kind - 'f', // type - '=', // byteorder - NPY_NEEDS_PYAPI | NPY_USE_GETITEM | NPY_USE_SETITEM, - 1, // type num - sizeof(BFloat16Type), - alignof(BFloat16Type), - NULL, - NULL, - NULL, - &funcs}; - Py_TYPE(&descr) = &PyArrayDescr_Type; - PyArray_InitArrFuncs(&funcs); - funcs.copyswap = copyswap; - funcs.getitem = getitem; - funcs.setitem = setitem; - npy_typenum = PyArray_RegisterDataType(&descr); - -#define REGISTER_CAST(From, To, From_descr, To_typenum, safe) \ - { \ - PyArray_Descr* from_descr = (From_descr); \ - if (PyArray_RegisterCastFunc(from_descr, (To_typenum), \ - NpyCast::apply) < 0) { \ - return false; \ - } \ - if (safe && PyArray_RegisterCanCast(from_descr, (To_typenum), \ - NPY_NOSCALAR) < 0) { \ - return false; \ - } \ - } -#define REGISTER_INT_CASTS(bits) \ - REGISTER_CAST(npy_int##bits, BFloat16Type, \ - PyArray_DescrFromType(NPY_INT##bits), \ - BFloat16Type::npy_typenum, 1) \ - REGISTER_CAST(BFloat16Type, npy_int##bits, &descr, NPY_INT##bits, 0) \ - REGISTER_CAST(npy_uint##bits, BFloat16Type, \ - PyArray_DescrFromType(NPY_UINT##bits), \ - BFloat16Type::npy_typenum, 1) \ - REGISTER_CAST(BFloat16Type, npy_uint##bits, &descr, NPY_UINT##bits, 0) - - REGISTER_INT_CASTS(8) - REGISTER_INT_CASTS(16) - REGISTER_INT_CASTS(32) - REGISTER_INT_CASTS(64) - REGISTER_CAST(BFloat16Type, float, &descr, NPY_FLOAT, 0) - REGISTER_CAST(float, BFloat16Type, PyArray_DescrFromType(NPY_FLOAT), - BFloat16Type::npy_typenum, 0) - REGISTER_CAST(BFloat16Type, double, &descr, NPY_DOUBLE, 1) - REGISTER_CAST(double, BFloat16Type, PyArray_DescrFromType(NPY_DOUBLE), - BFloat16Type::npy_typenum, 0) - return true; -} - -} // anonymous namespace - -bool init_pytype_bfloat16() { - auto& py_type = BFloat16Type::PyObj::py_type; - py_type = {PyVarObject_HEAD_INIT(NULL, 0)}; - py_type.tp_name = "megbrain._mgb.pybfloat16"; - py_type.tp_basicsize = sizeof(BFloat16Type::PyObj); - py_type.tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE; - py_type.tp_doc = "bfloat16 type"; - py_type.tp_new = BFloat16Type::PyObj::py_new; - py_type.tp_str = BFloat16Type::PyObj::py_repr; - py_type.tp_repr = BFloat16Type::PyObj::py_repr; - py_type.tp_richcompare = BFloat16Type::PyObj::py_richcompare; - py_type.tp_base = &PyGenericArrType_Type; - return PyType_Ready(&py_type) >= 0; -} - -void register_pytype_bfloat16(PyObject* d, PyObject* m) { - Py_INCREF(&BFloat16Type::PyObj::py_type); - PyDict_SetItemString(d, "bfloat16_pytype", - (PyObject*)&BFloat16Type::PyObj::py_type); - PyModule_AddObject(m, "bfloat16_pytype", - (PyObject*)&BFloat16Type::PyObj::py_type); -} - -//! called from swig init -void _init_bfloat16_types(PyObject* m) { - if (m == NULL) - return; - PyObject* d = PyModule_GetDict(m); - PyArray_Descr* dtype; - if (!init_pytype_bfloat16()) - return; - if (!BFloat16Type::NpyType::init()) - return; - dtype = PyArray_DescrFromType(BFloat16Type::npy_typenum); - if (!dtype) - return; - { - PyObject* pytype = (PyObject*)(&BFloat16Type::PyObj::py_type); - Py_INCREF(pytype); - PyDict_SetItemString(d, "pybfloat16", pytype); - } - Py_INCREF(dtype); - PyDict_SetItemString(d, "bfloat16", (PyObject*)dtype); - register_pytype_bfloat16(d, m); - return; -} - -int mgb::npy_num_bfloat16() { - return BFloat16Type::npy_typenum; -} - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/craniotome.cpp b/python_module/src/cpp/craniotome.cpp deleted file mode 100644 index af9d17b0..00000000 --- a/python_module/src/cpp/craniotome.cpp +++ /dev/null @@ -1,435 +0,0 @@ -/** - * \file python_module/src/cpp/craniotome.cpp - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - -#include "./craniotome.h" -#include "./python_helper.h" -#include "megbrain/comp_node_env.h" -#include "megbrain/graph/grad_impl.h" -#include "megbrain/serialization/sereg.h" - -using namespace mgb; -using namespace opr; - -MGB_DYN_TYPE_OBJ_FINAL_IMPL(CraniotomeDesc); -MGB_DYN_TYPE_OBJ_FINAL_IMPL(Craniotome); - - -bool CraniotomeDesc::is_same_st(const mgb::Hashable &rhs) const { - auto rp = static_cast(rhs).py_self(); - size_t ref0 = rp->ob_refcnt; - bool ret; - { - PYTHON_GIL; - Py_INCREF(rp); - ret = _is_same(rp); - } - size_t ref1 = rp->ob_refcnt; - mgb_assert(ref0 == ref1, - "reference count changed from %zu to %zu", - ref0, ref1); - return ret; -} - -size_t CraniotomeDesc::hash() const { - return _hash(); -} - - -PyObject* CraniotomeDesc::py_self() const { - if (!m_py_self) { - PYTHON_GIL; - PyObject* dst = PyList_New(0); - mgb_assert(dst); - PyObjRefKeeper dst_ref{dst}; - - Py_INCREF(dst); - _setup_self(dst); - mgb_assert(dst->ob_refcnt == 1); - - mgb_assert(PyList_Size(dst) == 1); - m_py_self = PyList_GetItem(dst, 0); - } - - return m_py_self; -} - -class Craniotome::FuncDelCallbackInvoker final - : public UserDataContainer::UserData { - MGB_TYPEINFO_OBJ_DECL; - SmallVector m_oprs; - -public: - ~FuncDelCallbackInvoker() { - Craniotome* cur_opr = nullptr; - MGB_MARK_USED_VAR(cur_opr); - MGB_TRY { - std::vector arr; - for (auto i : m_oprs) { - cur_opr = i; - mgb_assert(i->m_on_graph_compile_called); - i->m_desc->_on_graph_compile_or_func_del(arr); - i->m_on_graph_compile_called = false; - } - } - MGB_HANDLE_EXCEPTION_DTOR( - ssprintf("craniotome opr %s", cur_opr->cname()).c_str()); - } - void add(Craniotome* opr) { m_oprs.push_back(opr); } -}; -MGB_TYPEINFO_OBJ_IMPL(Craniotome::FuncDelCallbackInvoker); - -Craniotome::Craniotome( - mgb::ComputingGraph *graph, std::unique_ptr desc, - const VarNodeArray &inputs, const OperatorNodeConfig &config): - Super{graph, config, desc->_get_opr_type_name().c_str(), inputs}, - m_node_flag{desc->_node_flag()}, - m_desc{std::move(desc)} -{ - for (auto i: inputs) - add_input({i}); - m_nr_dev_value_inp = input().size() - m_desc->_get_nr_dev_comp_order_deps(); - m_desc->_get_all_io_vars = [this]() { - SymbolVarArray ret; - ret.reserve(input().size() + output().size()); - for (auto i: input()) - ret.push_back(i); - for (auto i: output()) - ret.push_back(i); - return ret; - }; - - auto nr_out = m_desc->_get_nr_outputs(); - if (nr_out > 1) { - for (size_t i = 0, it = nr_out; i < it; ++ i) - add_output(ssprintf("o%zu", i)); - } else { - mgb_assert(nr_out == 1, - "could not create an operator with %zu outputs: %s", - nr_out, cname()); - add_output(None); - } - if (output_no_sys_mem_alloc()) { - for (auto i: output()) - i->add_flag(VarNode::Flag::NO_SYS_MEM_ALLOC); - } - if (m_node_flag & NodeFlag::ALLOW_EMPTY_OUTPUT) { - for (auto i: output()) - i->add_flag(VarNode::Flag::ALLOW_EMPTY_SHAPE); - } - add_equivalence_component(m_desc.get()); - - // init comp node early because desc may access it - this->init_output_comp_node(); - m_desc->owner_opr = this; -} - -Craniotome::~Craniotome() noexcept { - if (m_on_graph_compile_called) { - m_desc->_on_graph_compile_or_func_del({}); - m_on_graph_compile_called = false; - } -} - -Craniotome::NodeProp* Craniotome::do_make_node_prop() const { - auto ret = Super::do_make_node_prop(); - if (m_node_flag & NodeFlag::DISALLOW_DUPLICATE) { - ret->add_flag(NodeProp::Flag::NO_AUTOMATIC_DUP); - } - if (m_nr_dev_value_inp < input().size()) { - using DT = NodeProp::DepType; - SmallVector
dep_types(input().size(), DT::DEV_VALUE); - for (size_t i = m_nr_dev_value_inp; i < dep_types.size(); ++i) { - dep_types[i] = DT::DEV_COMP_ORDER; - } - ret->reset_dep_type(input(), dep_types); - } - return ret; -} - -void Craniotome::scn_do_execute() { - m_prev_inferred_shape.invalidate(); - auto &&env = CompNodeEnv::from_comp_node(comp_node()); - env.activate(); - std::vector inpval(m_nr_dev_value_inp); - std::vector outval(output().size()); - auto dest_cn = comp_node(); - for (size_t i = 0; i < inpval.size(); ++ i) { - auto ivar = input(i); - if (ivar->comp_node() == dest_cn) { - inpval[i].setup(input(i)->dev_tensor(), false); - } else { - auto tensor = input(i)->dev_tensor(); - tensor.comp_node(dest_cn); - inpval[i].setup(tensor, false); - } - } - - TensorShapeArray orig_shape; - std::vector orig_ptr; - if (output_no_sys_mem_alloc()) { - for (size_t i = 0; i < outval.size(); ++ i) - outval[i].assign(output(i)); - } else { - for (size_t i = 0; i < outval.size(); ++ i) { - outval[i].assign(output(i)->dev_tensor()); - orig_shape.push_back(output(i)->shape()); - orig_ptr.push_back(output(i)->dev_tensor().raw_ptr()); - } - } - m_desc->_execute(inpval, outval); - mgb_assert(outval.size() == output().size()); - if (!output_no_sys_mem_alloc()) { - for (size_t i = 0; i < outval.size(); ++ i) { - mgb_assert(output(i)->shape().eq_shape(orig_shape[i]) && - orig_ptr[i] == output(i)->dev_tensor().raw_ptr(), - "%s: shape or ptr of output %zu changed", - cname(), i); - } - } -} - -void Craniotome::get_output_var_shape(const TensorShapeArray &inp_shape, - TensorShapeArray &out_shape) const { - TensorShapeVec cvt_ishp(inp_shape.size()); - for (size_t i = 0; i < cvt_ishp.size(); ++ i) - cvt_ishp[i] = npy::shape2vec(inp_shape[i]); - auto cvt_oshp = m_desc->_infer_shape(cvt_ishp); - mgb_assert(cvt_oshp.size() == output().size()); - out_shape.resize(cvt_oshp.size()); - for (size_t i = 0; i < cvt_oshp.size(); ++ i) - out_shape[i] = npy::vec2shape(cvt_oshp[i]); -} - -MGB_IMPL_OPR_GRAD(Craniotome) { - if (wrt_idx >= opr.nr_dev_value_inp()) { - return nullptr; - } - SymbolVarArray isv(opr.nr_dev_value_inp()), osv(opr.output().size()), - ogsv(out_grad.size()); - for (size_t i = 0; i < isv.size(); ++i) - isv[i] = opr.input(i); - for (size_t i = 0; i < osv.size(); ++i) - osv[i] = opr.output(i); - for (size_t i = 0; i < out_grad.size(); ++i) - ogsv[i] = out_grad[i]; - - auto ret = cg::to_var_node_array(const_cast(opr.desc()) - ._grad(wrt_idx, isv, osv, ogsv)); - - auto update_shape = [&opr](size_t i, VarNode* var) { - auto inp = opr.input(i); - if (var && cg::is_static_var_shape(inp) && - !cg::is_static_var_shape(var)) { - var = SymbolVar{var}.reshape(SymbolVar{inp}.symshape()).node(); - } - return var; - }; - if (ret.size() != 1) { - mgb_assert(ret.size() == opr.input().size()); - for (size_t i = 0; i < ret.size(); ++i) { - ret[i] = update_shape(i, ret[i]); - } - return ret; - } - return update_shape(wrt_idx, ret[0]); -} - -void Craniotome::add_input_layout_constraint() { - for (auto i : input()) - i->add_layout_constraint_contiguous(); - - if (!m_on_graph_compile_called) { - // check used outputs and call _on_graph_compile - auto graph = owner_graph(); - auto&& out = output(); - std::vector used_outputs; - used_outputs.reserve(out.size()); - for (size_t i = 0; i < out.size(); ++i) { - if (!graph->var_receiver_in_current_comp_seq(out[i]).empty()) { - used_outputs.push_back(i); - } - } - mgb_assert(!used_outputs.empty()); - m_desc->_on_graph_compile_or_func_del(used_outputs); - auto seq = graph->current_comp_seq(); - if (seq) { - seq->user_data() - .get_user_data_or_create() - ->add(this); - } else { - mgb_assert(graph->options().eager_evaluation); - } - m_on_graph_compile_called = true; - } -} - -SymbolVarArray Craniotome::make( - std::unique_ptr desc, - const SymbolVarArray &inputs, - const OperatorNodeConfig &config) { - VarNodeArray inp_vn(inputs.size()); - for (size_t i = 0; i < inputs.size(); ++ i) - inp_vn[i] = inputs[i].node(); - ComputingGraph *graph; - if (!inputs.empty()) { - graph = inp_vn[0]->owner_graph(); - } else { - graph = &desc->_get_comp_graph().get(); - mgb_assert(graph); - } - auto opr = graph->insert_opr( - std::make_unique( - graph, std::move(desc), inp_vn, config)); - SymbolVarArray rst; - for (auto i: opr->output()) - rst.push_back(i); - return rst; -} - -void Craniotome::init_output_static_infer_desc() { - if (!(m_node_flag & NodeFlag::DYNAMIC_OUTPUT_SHAPE)) { - Super::init_output_static_infer_desc(); - } else if (input().empty()) { - using namespace cg::static_infer; - auto &&mgr = owner_graph()->static_infer_manager(); - for (size_t idx = 0; idx < output().size(); ++ idx) { - auto infer = [this, idx](TensorShape &dest, const InpVal &) { - if (!m_prev_inferred_shape.valid()) { - auto &&shp = m_prev_inferred_shape.emplace(); - shp.resize(output().size()); - get_output_var_shape({}, shp); - } - dest = m_prev_inferred_shape->at(idx); - return true; - }; - mgr.register_shape_infer(output(idx), - {SourceType::MUTABLE, {}, infer}); - } - } -} - -void Craniotome::init_output_dtype() { - PYTHON_GIL; - - auto input_dtypes = PyList_New(input().size()), - ret = PyList_New(output().size()); - mgb_assert(input_dtypes); - PyObjRefKeeper input_dtypes_ref{input_dtypes}; - - mgb_assert(ret); - PyObjRefKeeper ret_ref{ret}; - - for (size_t i = 0; i < input().size(); ++ i) { - auto err = PyList_SetItem(input_dtypes, i, - npy::dtype_mgb2np(input(i)->dtype())); - mgb_assert(!err); - } - - // it seems that we need to incref before passing it to swig director method - Py_INCREF(input_dtypes); - Py_INCREF(ret); - if (!m_desc->_init_output_dtype(input_dtypes, ret)) { - Super::init_output_dtype(); - return; - } - - mgb_assert(PyList_Check(ret), - "_init_output_dtype should return list"); - mgb_assert(PyList_Size(ret) == static_cast(output().size()), - "_init_output_dtype list size not equal to number of outputs"); - for (size_t i = 0; i < output().size(); ++ i) { - auto cur = PyList_GetItem(ret, i); - mgb_assert(cur, "failed to get dtype for output %zu", i); - output(i)->dtype(npy::dtype_np2mgb(cur)); - } - - mgb_assert(input_dtypes->ob_refcnt == 1); - mgb_assert(ret->ob_refcnt == 1); -} - -// serialization -namespace { - - void craniotome_dumper( - serialization::OprDumpContext &ctx, - const cg::OperatorNodeBase &opr) { - - auto &&desc = opr.cast_final_safe().desc(); - auto result = PyList_New(0); - mgb_assert(result); - PyObjRefKeeper result_ref{result}; - - Py_INCREF(result); - desc._setup_serialize_params(result); - mgb_assert(result->ob_refcnt == 1); - - auto sz = PyList_Size(result); - mgb_assert(sz >= 1 && sz <= 2); - - auto name_obj = PyList_GetItem(result, 0); - mgb_assert(name_obj && PyUnicode_Check(name_obj)); - Py_ssize_t name_size; - const char *name_str = PyUnicode_AsUTF8AndSize(name_obj, &name_size); - mgb_assert(name_str); - - char *param_str = nullptr; - Py_ssize_t param_size = 0; - if (sz == 2) { - auto param_obj = PyList_GetItem(result, 1); - mgb_assert(param_obj && PyBytes_Check(param_obj)); - auto err = PyBytes_AsStringAndSize( - param_obj, ¶m_str, ¶m_size); - mgb_assert(!err); - } - - - ctx.dump_buf_with_len(name_str, name_size); - if (param_str) { - ctx.dump_buf_with_len(param_str, param_size); - } - } - - cg::OperatorNodeBase* craniotome_shallow_copy( - const serialization::OprShallowCopyContext &ctx, - const cg::OperatorNodeBase &opr, const VarNodeArray &inputs, - const OperatorNodeConfig &config) { - - MGB_MARK_USED_VAR(ctx); - auto &&orig_desc = opr.cast_final_safe().desc(); - std::unique_ptr desc; - mgb_assert(!orig_desc._set_copy_result); - orig_desc._set_copy_result = [&desc](CraniotomeDesc *r) { - mgb_assert(!desc); - desc.reset(r); - }; - orig_desc._copy(); - mgb_assert(desc); - orig_desc._set_copy_result = {}; - - mgb_assert(&orig_desc != desc.get()); - return Craniotome::make(std::move(desc), - {inputs.begin(), inputs.end()}, config).at(0).node( - )->owner_opr(); - } - - class _RegDumper { - public: - _RegDumper() { - serialization::OprRegistry::add_using_dynamic_loader( - Craniotome::typeinfo(), "Craniotome", - craniotome_dumper); - MGB_REG_OPR_SHALLOW_COPY_IMPL( - Craniotome, craniotome_shallow_copy); - } - }; - _RegDumper _reg_dumper; - -} // anonymous namespace - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/craniotome.h b/python_module/src/cpp/craniotome.h deleted file mode 100644 index d4754cb0..00000000 --- a/python_module/src/cpp/craniotome.h +++ /dev/null @@ -1,193 +0,0 @@ -/** - * \file python_module/src/cpp/craniotome.h - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \brief extend megbrain operators in python - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - -#pragma once - -#include "megbrain/graph/operator_node.h" -#include "./megbrain_wrap.h" - -using TensorShapeVec = std::vector>; -using SymbolVarArray = mgb::SymbolVarArray; - -namespace mgb { -namespace opr { -class Craniotome; -} // namespace opr -} // namespace mgb - -class CraniotomeDesc: public mgb::Hashable { - MGB_DYN_TYPE_OBJ_FINAL_DECL; - - mutable PyObject *m_py_self = nullptr; - - bool is_same_st(const mgb::Hashable &rhs) const override; - - size_t hash() const override; - - public: - struct NodeFlag { - static constexpr uint32_t - DYNAMIC_OUTPUT_SHAPE = 1 << 0, - DISALLOW_DUPLICATE = 1 << 1, - ALLOW_EMPTY_OUTPUT = 1 << 2, - DISABLE_SYS_MEM_ALLOC = 1 << 3; - }; - virtual ~CraniotomeDesc() = default; - - mgb::opr::Craniotome* owner_opr = nullptr; - - //! get final py object that implements this interface - PyObject* py_self() const ; - - //! store self in \p result which is a list - virtual void _setup_self(PyObject *result) const = 0; - - virtual bool _is_same(PyObject *rhs) const = 0; - - virtual uint32_t _node_flag() const = 0; - - virtual size_t _hash() const = 0; - - virtual std::string _get_opr_type_name() = 0; - - virtual size_t _get_nr_outputs() = 0; - - virtual void _execute( - const std::vector &inputs, - std::vector &outputs) = 0; - - /*! - * \brief infer output shape if DYNAMIC_OUTPUT_SHAPE is not set - */ - virtual TensorShapeVec _infer_shape( - const TensorShapeVec &inp_shape) = 0; - - virtual SymbolVarArray _grad( - size_t wrt_idx, - const SymbolVarArray &inputs, - const SymbolVarArray &outputs, - const SymbolVarArray &out_grad) = 0; - - virtual size_t _get_nr_dev_comp_order_deps() = 0; - - mgb::thin_function _get_all_io_vars; - - /*! - * \brief get output dtypes from input dtypes - * \param[in] input_dtypes python list of input - * \param[out] result initialized as an empty python list, and should - * be filled with output dtypes - * \return whether user has set the dtype - */ - virtual bool _init_output_dtype( - PyObject *input_dtypes, PyObject *result) = 0; - - /*! - * \brief get computing graph when no input var is provided - */ - virtual CompGraph _get_comp_graph() = 0; - - /*! - * \brief copy this CraniotomeDesc - * - * The implementation must call _set_copy_result() to return the result; - * this is used to bypass some swig issues. - */ - virtual void _copy() const = 0; - mutable mgb::thin_function _set_copy_result; - - /*! - * \brief setup params for serialization - * \param output an allocated list. One or two elements should be - * inserted in it after this function returns: the first element - * should be a string, indicating the id to be passed to - * opr_maker_loader; the second element, if exists, must be a byte - * object containing extra param that should be written to file. - */ - virtual void _setup_serialize_params(PyObject *output) const = 0; - - /*! - * \brief callback invoked when the graph is compiled or when func is - * destructed - * - * If the graph is compiled but not executed, this function might not be - * called - * - * \param used_outputs an array indices indicating the used output vars; - * this argument being empty means that the previously compiled - * func is destructed - */ - virtual void _on_graph_compile_or_func_del( - const std::vector& used_outputs) = 0; -}; - - -namespace mgb { -namespace opr { - -MGB_DEFINE_OPR_CLASS(Craniotome, cg::SingleCNOutshapePureByInshapeOprBase) // { - class FuncDelCallbackInvoker; - using NodeFlag = CraniotomeDesc::NodeFlag; - - bool m_on_graph_compile_called = false; - const uint32_t m_node_flag; - - //! DEV_COMP_ORDER inputs are at the tail of input array; this is the - //! number of DEV_VALUE inputs, and also the index of the first - //! DEV_COMP_ORDER input - size_t m_nr_dev_value_inp; - - std::unique_ptr m_desc; - - //! previously inferred shape; used when there is no input and - //! m_is_dynamic_output_shape is set to true - Maybe m_prev_inferred_shape; - - void scn_do_execute() override; - void get_output_var_shape(const TensorShapeArray &inp_shape, - TensorShapeArray &out_shape) const override; - - void add_input_layout_constraint() override; - - void init_output_static_infer_desc() override; - void init_output_dtype() override; - NodeProp* do_make_node_prop() const override; - - bool output_no_sys_mem_alloc() const { - return m_node_flag & (NodeFlag::DYNAMIC_OUTPUT_SHAPE | - NodeFlag::DISABLE_SYS_MEM_ALLOC); - } - - public: - Craniotome(mgb::ComputingGraph *graph, - std::unique_ptr desc, - const VarNodeArray &inputs, const OperatorNodeConfig &config); - - ~Craniotome() noexcept; - - static SymbolVarArray make( - std::unique_ptr desc, - const SymbolVarArray &inputs, - const OperatorNodeConfig &config = {}); - - const CraniotomeDesc& desc() const { - return *m_desc; - } - - size_t nr_dev_value_inp() const { - return m_nr_dev_value_inp; - } -}; - -} // namespace opr -} // namespace mgb - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/function_replace.cpp b/python_module/src/cpp/function_replace.cpp deleted file mode 100644 index 6fc0fb63..00000000 --- a/python_module/src/cpp/function_replace.cpp +++ /dev/null @@ -1,174 +0,0 @@ -/** - * \file python_module/src/cpp/function_replace.cpp - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \brief replace functions in megbrain core - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - -#include "./megbrain_wrap.h" -#include "./python_helper.h" - -#include "megbrain/utils/debug.h" -#include "megbrain/common.h" -#include "megbrain/system.h" - -#include -#include -#include - -#include - -#ifdef WIN32 -#include -#include -#else -#include -#endif - -namespace { - -PyObject *logger = nullptr; - -#if MGB_ENABLE_DEBUG_UTIL -void throw_fork_cuda_exc() { - // set python error state, so when returning to parent process that calls - // fork(), an exception could be raised - // - // call chain: - // python -> fork() -> pthread_atfork -> CudaCheckOnFork -> - // ForkAfterCudaError::throw_ - mgb_log_warn("try to raise python exception for fork after cuda"); - PyErr_SetString(PyExc_SystemError, "fork after cuda has been initialized"); -} -#endif - -class Init { - static Init inst; - Init() { -#if MGB_ENABLE_DEBUG_UTIL - mgb::debug::ForkAfterCudaError::throw_ = throw_fork_cuda_exc; -#endif - } -}; -Init Init::inst; - -int fork_exec_impl(const std::string& arg0, const std::string& arg1, - const std::string& arg2) { -#ifdef WIN32 - STARTUPINFO si; - PROCESS_INFORMATION pi; - ZeroMemory(&si, sizeof(si)); - si.cb = sizeof(si); - ZeroMemory(&pi, sizeof(pi)); - auto args_str = " " + arg1 + " " + arg2; - - // Start the child process. - if (!CreateProcess(arg0.c_str(), // exe name - const_cast(args_str.c_str()), // Command line - NULL, // Process handle not inheritable - NULL, // Thread handle not inheritable - FALSE, // Set handle inheritance to FALSE - 0, // No creation flags - NULL, // Use parent's environment block - NULL, // Use parent's starting directory - &si, // Pointer to STARTUPINFO structure - &pi) // Pointer to PROCESS_INFORMATION structure - ) { - mgb_log_warn("CreateProcess failed (%lu).\n", GetLastError()); - fprintf(stderr, "[megbrain] failed to execl %s [%s, %s]\n", - arg0.c_str(), arg1.c_str(), arg2.c_str()); - __builtin_trap(); - } - return pi.dwProcessId; -#else - auto pid = fork(); - if (!pid) { - execl(arg0.c_str(), arg0.c_str(), arg1.c_str(), arg2.c_str(), nullptr); - fprintf(stderr, "[megbrain] failed to execl %s [%s, %s]: %s\n", - arg0.c_str(), arg1.c_str(), arg2.c_str(), - std::strerror(errno)); - std::terminate(); - } - mgb_assert(pid > 0, "failed to fork: %s", std::strerror(errno)); - return pid; -#endif -} - -} // anonymous namespace - -// called from swig/misc.i -void _timed_func_set_fork_exec_path(const char *arg0, const char *arg1) { - using namespace std::placeholders; - mgb::sys::TimedFuncInvoker::ins().set_fork_exec_impl( - std::bind(fork_exec_impl, std::string{arg0}, std::string{arg1}, - _1)); -} - -void _timed_func_exec_cb(const char *user_data) { - mgb::sys::TimedFuncInvoker::ins().fork_exec_impl_mainloop(user_data); -} - -void _register_logger(PyObject *l) { - logger = l; -} - -namespace { -void py_log_handler(mgb::LogLevel level, - const char *file, const char *func, int line, const char *fmt, - va_list ap) { - if (global_finalized()) { - return; - } - - using mgb::LogLevel; - - MGB_MARK_USED_VAR(file); - MGB_MARK_USED_VAR(func); - MGB_MARK_USED_VAR(line); - - if (!logger) - return; - - PYTHON_GIL; - - const char *py_type; - switch (level) { - case LogLevel::DEBUG: - py_type = "debug"; - break; - case LogLevel::INFO: - py_type = "info"; - break; - case LogLevel::WARN: - py_type = "warning"; - break; - case LogLevel::ERROR: - py_type = "error"; - break; - default: - throw std::runtime_error("bad log level"); - } - - std::string msg = mgb::svsprintf(fmt, ap); - PyObject *py_msg = Py_BuildValue("s", msg.c_str()); - PyObject_CallMethod(logger, - const_cast(py_type), const_cast("O"), - py_msg); - Py_DECREF(py_msg); -} - -class LogHandlerSetter { - static LogHandlerSetter ins; - public: - LogHandlerSetter() { - mgb::set_log_handler(py_log_handler); - } -}; -LogHandlerSetter LogHandlerSetter::ins; -} // anobymous namespace - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/intbx.cpp b/python_module/src/cpp/intbx.cpp deleted file mode 100644 index 104a2353..00000000 --- a/python_module/src/cpp/intbx.cpp +++ /dev/null @@ -1,364 +0,0 @@ -/** - * \file python_module/src/cpp/intbx.cpp - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \brief numpy dtypes for low bit - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - -#include "megbrain/common.h" - -#include -#include - -#define NO_IMPORT_ARRAY 1 -#include "./numpy_incl.h" - -#pragma GCC diagnostic ignored "-Wmissing-field-initializers" - -namespace { - -template -struct LowBitType { - static_assert(N < 8, "low bit only supports less than 8 bits"); - static int npy_typenum; - //! numerical value (-3, -1, 1, 3) - int8_t value; - - struct PyObj; - struct NpyType; - - const static int32_t max_value = (1 << N) - 1; - - //! check whether val is (-3, -1, 1, 3) and set python error - static bool check_value_set_err(int val) { - int t = val + max_value; - if ((t & 1) || t < 0 || t > (max_value << 1)) { - PyErr_SetString(PyExc_ValueError, - mgb::ssprintf("low bit dtype number error: " - "value=%d; allowed {-3, -1, 1, 3}", - val) - .c_str()); - return false; - } - - return true; - } - - template - struct NpyCast; -}; - -template -int LowBitType::npy_typenum; - -/* ==================== LowBitType::NpyCast ==================== */ - -template -template -struct LowBitType::NpyCast> { - static void apply(void* from_, void* to_, npy_intp n, void* /*fromarr*/, - void* /*toarr*/) { - auto from = static_cast(from_); - auto to = static_cast*>(to_); - for (npy_intp i = 0; i < n; ++i) { - int cur = static_cast(from[i]); - if (!LowBitType::check_value_set_err(cur)) - return; - to[i].value = cur; - } - } -}; - -template -template -struct LowBitType::NpyCast, T> { - static void apply(void* from_, void* to_, npy_intp n, void* /*fromarr*/, - void* /*toarr*/) { - auto from = static_cast*>(from_); - auto to = static_cast(to_); - for (npy_intp i = 0; i < n; ++i) { - to[i] = from[i].value; - } - } -}; - -/* ==================== LowBitType::PyObj ==================== */ -template -struct LowBitType::PyObj { - PyObject_HEAD LowBitType obj; - - static PyTypeObject py_type; - - static PyObject* from_lowbit(LowBitType val) { - auto p = reinterpret_cast(py_type.tp_alloc(&py_type, 0)); - p->obj.value = val.value; - return reinterpret_cast(p); - } - - static PyObject* py_new(PyTypeObject* type, PyObject* args, PyObject* kwds); - static PyObject* py_repr(PyObject* obj); - static PyObject* py_richcompare(PyObject* a, PyObject* b, int op); -}; -template -PyTypeObject LowBitType::PyObj::py_type; - -template -PyObject* LowBitType::PyObj::py_new(PyTypeObject* type, PyObject* args, - PyObject* kwds) { - PyObj* self; - Py_ssize_t size; - - self = (PyObj*)type->tp_alloc(type, 0); - - size = PyTuple_GET_SIZE(args); - if (size > 1) { - PyErr_SetString(PyExc_TypeError, "LowBitType Only has 1 parameter"); - return NULL; - } - PyObject* x = PyTuple_GET_ITEM(args, 0); - if (PyObject_IsInstance(x, (PyObject*)&py_type)) { - Py_INCREF(x); - return x; - } - - if (!PyLong_Check(x)) { - PyErr_SetString(PyExc_TypeError, - "LowBitType must be initialized wit int"); - return NULL; - } - - const long s = PyLong_AsLong(x); - - self->obj.value = s; - - return (PyObject*)self; -} - -template -PyObject* LowBitType::PyObj::py_repr(PyObject* obj) { - return PyUnicode_FromFormat("%d", ((PyObj*)obj)->obj.value); -} - -template -PyObject* LowBitType::PyObj::py_richcompare(PyObject* a, PyObject* b, - int op) { - mgb_assert(PyObject_IsInstance(a, (PyObject*)&py_type)); - auto bval = PyFloat_AsDouble(b); - if (bval == -1 && PyErr_Occurred()) { - return NULL; - } - double aval = ((PyObj*)a)->obj.value; -#define OP(py, op) \ - case py: { \ - if (aval op bval) { \ - Py_RETURN_TRUE; \ - } else { \ - Py_RETURN_FALSE; \ - } \ - } - switch (op) { - OP(Py_LT, <) - OP(Py_LE, <=) - OP(Py_EQ, ==) - OP(Py_NE, !=) - OP(Py_GT, >) - OP(Py_GE, >=) - }; -#undef OP - return Py_NotImplemented; -} - -/* ==================== LowBitType::NpyType ==================== */ -template -struct LowBitType::NpyType { - static PyArray_ArrFuncs funcs; - static PyArray_Descr descr; - - static bool init(); - - static void copyswap(void* dst, void* src, int swap, void* /*arr*/) { - if (src) { - mgb_assert(!swap); - memcpy(dst, src, sizeof(LowBitType)); - } - } - static PyObject* getitem(void* data, void* ap) { - return LowBitType::PyObj::from_lowbit( - *static_cast*>(data)); - } - static int setitem(PyObject* op, void* ov, void* ap); - static int fill(void* data_, npy_intp length, void* arr); -}; - -template -PyArray_ArrFuncs LowBitType::NpyType::funcs; -template -PyArray_Descr LowBitType::NpyType::descr; - -template -int LowBitType::NpyType::setitem(PyObject* op, void* ov, void* ap) { - if (!PyLong_Check(op)) { - PyErr_SetString(PyExc_ValueError, "input type must be int"); - return -1; - } - - int a = PyLong_AsLong(op); - if (!check_value_set_err(a)) - return -1; - - static_cast*>(ov)->value = a; - return 0; -} - -template -int LowBitType::NpyType::fill(void* data_, npy_intp length, void* arr) { - auto data = static_cast*>(data_); - int8_t delta = data[1].value - data[0].value, r = data[1].value; - if (!check_value_set_err(data[0].value) || - !check_value_set_err(data[1].value)) - return -1; - for (npy_intp i = 2; i < length; i++) { - r += delta; - if (r > max_value) - r = -max_value; - else if (r < -max_value) - r = max_value; - data[i].value = r; - } - return 0; -} - -template -bool LowBitType::NpyType::init() { - descr = {PyObject_HEAD_INIT(0) & LowBitType::PyObj::py_type, - 'V', // kind - 'r', // type - '=', // byteorder - NPY_NEEDS_PYAPI | NPY_USE_GETITEM | NPY_USE_SETITEM, - 0, // type num - sizeof(LowBitType), - alignof(LowBitType), - NULL, - NULL, - NULL, - &funcs}; - Py_TYPE(&descr) = &PyArrayDescr_Type; - PyArray_InitArrFuncs(&funcs); - funcs.copyswap = copyswap; - funcs.getitem = getitem; - funcs.setitem = setitem; - funcs.fill = fill; - npy_typenum = PyArray_RegisterDataType(&descr); - -#define REGISTER_CAST(From, To, From_descr, To_typenum, safe) \ - { \ - PyArray_Descr* from_descr = (From_descr); \ - if (PyArray_RegisterCastFunc(from_descr, (To_typenum), \ - NpyCast::apply) < 0) { \ - return false; \ - } \ - if (safe && PyArray_RegisterCanCast(from_descr, (To_typenum), \ - NPY_NOSCALAR) < 0) { \ - return false; \ - } \ - } -#define REGISTER_INT_CASTS(bits) \ - REGISTER_CAST(npy_int##bits, LowBitType, \ - PyArray_DescrFromType(NPY_INT##bits), \ - LowBitType::npy_typenum, 1) \ - REGISTER_CAST(LowBitType, npy_int##bits, &descr, NPY_INT##bits, 0) \ - REGISTER_CAST(npy_uint##bits, LowBitType, \ - PyArray_DescrFromType(NPY_UINT##bits), \ - LowBitType::npy_typenum, 1) \ - REGISTER_CAST(LowBitType, npy_uint##bits, &descr, NPY_UINT##bits, 0) - - REGISTER_INT_CASTS(8) - REGISTER_INT_CASTS(16) - REGISTER_INT_CASTS(32) - REGISTER_INT_CASTS(64) - REGISTER_CAST(LowBitType, float, &descr, NPY_FLOAT, 0) - REGISTER_CAST(float, LowBitType, PyArray_DescrFromType(NPY_FLOAT), - LowBitType::npy_typenum, 0) - REGISTER_CAST(LowBitType, double, &descr, NPY_DOUBLE, 1) - REGISTER_CAST(double, LowBitType, PyArray_DescrFromType(NPY_DOUBLE), - LowBitType::npy_typenum, 0) - return true; -} - -} // anonymous namespace - -#define DEFINE_INTBX(n) using IntB##n = LowBitType; -FOREACH_MGB_LOW_BIT(DEFINE_INTBX) -#undef DEFINE_INTBX - -#define MGB_STR_HELPER(n) #n - -#define DEFINE_INIT_PYTYPE(n) \ - bool init_pytype_intb##n() { \ - auto& py_type = IntB##n::PyObj::py_type; \ - py_type = {PyVarObject_HEAD_INIT(NULL, 0)}; \ - py_type.tp_name = "megbrain._mgb.pyintb" MGB_STR_HELPER(n); \ - py_type.tp_basicsize = sizeof(IntB##n::PyObj); \ - py_type.tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE; \ - py_type.tp_doc = "an low bit int type"; \ - py_type.tp_new = IntB##n::PyObj::py_new; \ - py_type.tp_str = IntB##n::PyObj::py_repr; \ - py_type.tp_repr = IntB##n::PyObj::py_repr; \ - py_type.tp_richcompare = IntB##n::PyObj::py_richcompare; \ - py_type.tp_base = &PyGenericArrType_Type; \ - return PyType_Ready(&py_type) >= 0; \ - } -FOREACH_MGB_LOW_BIT(DEFINE_INIT_PYTYPE) -#undef DEFINE_INIT_PYTYPE - -#define DEFINE_REGISTER_FUNC(n) \ - void register_pytype_intb##n(PyObject* d, PyObject* m) { \ - Py_INCREF(&IntB##n::PyObj::py_type); \ - PyDict_SetItemString(d, "intb" MGB_STR_HELPER(n) "_pytype", \ - (PyObject*)&IntB##n::PyObj::py_type); \ - PyModule_AddObject(m, "intb" MGB_STR_HELPER(n) "_pytype", \ - (PyObject*)&IntB##n::PyObj::py_type); \ - } -FOREACH_MGB_LOW_BIT(DEFINE_REGISTER_FUNC) -#undef DEFINE_REGISTER_FUNC - -//! called from swig init -void _init_intbx_types(PyObject* m) { - if (m == NULL) - return; - PyObject* d = PyModule_GetDict(m); - PyArray_Descr* dtype; -#define DEFINE_INIT_INTBX_TYPE(n) \ - if (!init_pytype_intb##n()) \ - return; \ - if (!IntB##n::NpyType::init()) \ - return; \ - dtype = PyArray_DescrFromType(IntB##n::npy_typenum); \ - if (!dtype) \ - return; \ - { \ - PyObject* pytype = (PyObject*)(&IntB##n::PyObj::py_type); \ - Py_INCREF(pytype); \ - PyDict_SetItemString(d, "pyintb" MGB_STR_HELPER(n), pytype); \ - } \ - Py_INCREF(dtype); \ - PyDict_SetItemString(d, "intb" MGB_STR_HELPER(n), (PyObject*)dtype); \ - register_pytype_intb##n(d, m); - FOREACH_MGB_LOW_BIT(DEFINE_INIT_INTBX_TYPE) -#undef DEFINE_INIT_INTBX_TYPE - return; -} - -#define DEFINE_NPY_INTBX(n) \ - int mgb::npy_num_intb##n() { return IntB##n::npy_typenum; } -FOREACH_MGB_LOW_BIT(DEFINE_NPY_INTBX) -#undef DEFINE_NPY_INTBX -/*int mgb::npy_num_intb2() { - return IntB2::npy_typenum; -}*/ - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/megbrain_config.cpp b/python_module/src/cpp/megbrain_config.cpp deleted file mode 100644 index fa31950e..00000000 --- a/python_module/src/cpp/megbrain_config.cpp +++ /dev/null @@ -1,517 +0,0 @@ -/** - * \file python_module/src/cpp/megbrain_config.cpp - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - -#include "./megbrain_config.h" -#include "./python_helper.h" - -#include "megbrain/graph/event.h" -#include "megbrain/utils/debug.h" -#include "megbrain/comp_node_env.h" -#include "megbrain/serialization/opr_registry.h" - -#include -#include -#include -#include - -#ifdef WIN32 -#include -#include -#else -#include -#endif - -#if MGB_ENABLE_OPR_MM -#include "megbrain/opr/mm_handler.h" -#endif - -#if MGB_CUDA -#include -#endif - -#ifdef WIN32 -#define F_OK 0 -#define RTLD_LAZY 0 -#define RTLD_GLOBAL 0 -#define RTLD_NOLOAD 0 -#define access(a, b) false -#define SPLITER ';' -#define ENV_PATH "Path" -#define NVCC_EXE "nvcc.exe" -static void* dlopen(const char* file, int) { - return static_cast(LoadLibrary(file)); -} - -static void* dlerror() { - const char* errmsg = "dlerror not aviable in windows"; - return const_cast(errmsg); -} - -static void* dlsym(void* handle, const char* name) { - FARPROC symbol = GetProcAddress((HMODULE)handle, name); - return reinterpret_cast(symbol); -} - -static int check_file_exist(const char* path, int mode) { - return _access(path, mode); -} -#else -#define SPLITER ':' -#define ENV_PATH "PATH" -#define NVCC_EXE "nvcc" -static int check_file_exist(const char* path, int mode) { - return access(path, mode); -} -#endif - -using namespace mgb; - -namespace { - std::unordered_map - set_priority_on_opr_inserted_handle; - std::mutex set_priority_on_opr_inserted_handle_mtx; - -} // anonymous namespace - -bool _config::set_comp_graph_option( - CompGraph &cg, const std::string &name, int val_int) { - -#define SET_CG_OPTION(name_chk) \ - do { \ - static_assert( \ - std::is_same::value || \ - std::is_same::value || \ - std::is_same::value || \ - std::is_same::value || \ - std::is_same::value, \ - "not bool/int opt"); \ - if (name == #name_chk) { \ - auto ret = opt.name_chk; \ - opt.name_chk = val_int; \ - return ret; \ - } \ - } while(0) - - auto &&opt = cg.get().options(); - SET_CG_OPTION(seq_opt.enable_mem_plan_opt); - SET_CG_OPTION(seq_opt.enable_mem_reuse_alloc); - SET_CG_OPTION(seq_opt.enable_seq_comp_node_opt); - SET_CG_OPTION(force_dynamic_alloc); - SET_CG_OPTION(enable_grad_var_static_reshape); - SET_CG_OPTION(async_exec_level); - SET_CG_OPTION(graph_opt.jit); - SET_CG_OPTION(graph_opt.tensorrt); - SET_CG_OPTION(graph_opt_level); - SET_CG_OPTION(allreduce_pack_max_size); - SET_CG_OPTION(allreduce_pack_ignore_first); - SET_CG_OPTION(var_sanity_check_first_run); - SET_CG_OPTION(no_profiling_on_shape_change); - SET_CG_OPTION(allocate_static_mem_after_graph_compile); - SET_CG_OPTION(log_level); - SET_CG_OPTION(enable_sublinear_memory_opt); - SET_CG_OPTION(sublinear_mem_config.lb_memory); - SET_CG_OPTION(sublinear_mem_config.genetic_nr_iter); - SET_CG_OPTION(sublinear_mem_config.genetic_pool_size); - SET_CG_OPTION(sublinear_mem_config.thresh_nr_try); - SET_CG_OPTION(sublinear_mem_config.num_worker); - SET_CG_OPTION(enable_var_mem_defragment); - SET_CG_OPTION(eager_evaluation); - SET_CG_OPTION(enable_memory_swap); - throw MegBrainError(ssprintf( - "invalid computing graph option name: %s", name.c_str())); -#undef SET_CG_OPTION -} - -bool _config::comp_graph_is_eager(CompGraph &cg) { - return cg.get().options().eager_evaluation; -} - -void _config::add_extra_vardep(const SymbolVar &var, const SymbolVar &dep) { - auto og = var.node()->owner_graph(); - mgb_assert(og == dep.node()->owner_graph()); - og->options().extra_vardeps[var.node()].push_back(dep.node()); -} - -void _config::begin_set_opr_priority(CompGraph& cg, int priority) { - SyncEventConnecter::ReceiverHandler* handle; - { - MGB_LOCK_GUARD(set_priority_on_opr_inserted_handle_mtx); - handle = &set_priority_on_opr_inserted_handle[&cg.get()]; - } - mgb_assert(!*handle, "multiple calls to _begin_set_opr_priority()"); - - auto on_opr_inserted = [priority](const cg::event::OprInserted& event) { - if (!event.exc && priority) { - int& pri = event.opr->node_prop().attribute().priority; - if (!pri) - pri = priority; - else - pri = std::min(pri, priority); - } - }; - *handle = cg.get().event().register_receiver( - on_opr_inserted); -} - -void _config::end_set_opr_priority(CompGraph &cg) { - MGB_LOCK_GUARD(set_priority_on_opr_inserted_handle_mtx); - auto nr = set_priority_on_opr_inserted_handle.erase(&cg.get()); - mgb_assert(nr, "end_set_opr_priority called " - "before begin_set_opr_priority"); -} - -void _config::begin_set_exc_opr_tracker(CompGraph &cg, PyObject *tracker) { - OprPyTracker::begin_set_tracker(cg.get(), tracker); -} - -void _config::end_set_exc_opr_tracker(CompGraph &cg) { - OprPyTracker::end_set_tracker(cg.get()); -} - -PyObject* _config::get_opr_tracker(CompGraph &cg, size_t var_id) { - auto var = cg.get().find_var_by_id(var_id); - if (!var) - Py_RETURN_NONE; - return OprPyTracker::get_tracker(var->owner_opr()).as_tuple(); -} - -void _config::set_opr_sublinear_memory_endpoint(const SymbolVar &var) { - MGB_MARK_USED_VAR(var); -#if MGB_ENABLE_SUBLINEAR - auto opr = var.node()->owner_opr(); - opr->owner_graph()->options().opr_attribute.sublinear_memory_endpoint. - insert(opr); -#endif -} - -void _config::set_fork_cuda_warning_flag(int flag) { -#if MGB_ENABLE_DEBUG_UTIL - debug::set_fork_cuda_warning_flag(flag); -#else - MGB_MARK_USED_VAR(flag); -#endif -} - -bool _config::is_cuda_ctx_set() { -#if MGB_CUDA - CUcontext ctx; - return cuCtxGetCurrent(&ctx) == CUDA_SUCCESS && ctx; -#else - return false; -#endif -} - -std::string _config::get_cuda_gencode() { -#if MGB_CUDA - std::set used; - int nr_dev; - auto err = cudaGetDeviceCount(&nr_dev); - if (err == cudaErrorNoDevice) { - return {}; - } - MGB_CUDA_CHECK(err); - for (int i = 0; i < nr_dev; ++ i) { - cudaDeviceProp prop; - MGB_CUDA_CHECK(cudaGetDeviceProperties(&prop, i)); - std::string cur{std::to_string(prop.major)}; - cur += std::to_string(prop.minor); - used.insert(cur); - } - - std::string ret; - for (auto &&i: used) { - if (!ret.empty()) - ret.append(" "); - ret.append(i); - } - return ret; -#else - mgb_throw(MegBrainError, "cuda disabled at compile time"); -#endif -} - -namespace { - -std::string find_content_in_file(const std::string& file_name, - const std::string& content) { - std::ifstream fin(file_name.c_str()); - std::string read_str; - while (std::getline(fin, read_str)) { - auto idx = read_str.find(content); - if (idx != std::string::npos) { - fin.close(); - return read_str.substr(idx); - } - } - fin.close(); - return {}; -} - -std::vector split_env(const char* env) { - std::string e(env); - std::istringstream stream(e); - std::vector ret; - std::string path; - while (std::getline(stream, path, SPLITER)) { - ret.emplace_back(path); - } - return ret; -} - -//! this function will find file_name in each path in envs. It accepts add -//! intermediate path between env and file_name -std::string find_file_in_envs_with_intmd( - const std::vector& envs, const std::string& file_name, - const std::vector& itmedias = {}) { - for (auto&& env : envs) { - auto ret = getenv(env.c_str()); - if (ret) { - for (auto&& path : split_env(ret)) { - auto file_path = std::string(path) + "/" + file_name; - if (!check_file_exist(file_path.c_str(), F_OK)) { - return file_path; - } - if (!itmedias.empty()) { - for (auto&& inter_path : itmedias) { - file_path = std::string(path) + "/" + inter_path + "/" + - file_name; - if (!check_file_exist(file_path.c_str(), F_OK)) { - return file_path; - } - } - } - } - } - } - return std::string{}; -} - -std::string get_nvcc_root_path() { - auto nvcc_root_path = find_file_in_envs_with_intmd({ENV_PATH}, NVCC_EXE); - if (nvcc_root_path.empty()) { - mgb_throw(MegBrainError, - "nvcc not found. Add your nvcc to your environment Path"); - } else { - auto idx = nvcc_root_path.rfind('/'); - return nvcc_root_path.substr(0, idx + 1); - } -} - -size_t get_local_cuda_version() { - auto nvcc_root_path = get_nvcc_root_path(); - auto ver_path = nvcc_root_path + "../version.txt"; - if (check_file_exist(ver_path.c_str(), F_OK)) { - mgb_throw(MegBrainError, "No such file : %s\n", ver_path.c_str()); - } - auto str_cuda_version = find_content_in_file(ver_path, "CUDA Version"); - if (str_cuda_version.empty()) { - mgb_throw(MegBrainError, "can not read version information from : %s\n", - ver_path.c_str()); - } - size_t cuda_major = 0; - size_t cuda_minor = 0; - sscanf(str_cuda_version.c_str(), "CUDA Version %zu.%zu,", &cuda_major, - &cuda_minor); - return cuda_major * 1000 + cuda_minor * 10; -} - -void check_cudnn_existence() { - auto cudnn_header_path = find_file_in_envs_with_intmd( - {"PC_CUDNN_INCLUDE_DIRS", "CUDNN_ROOT_DIR", "CUDA_TOOLKIT_INCLUDE", - "CUDNN_LIBRARY", "CUDA_PATH"}, - "cudnn.h", {"../include", "include"}); - if (cudnn_header_path.empty()) { - mgb_log_warn( - "cudnn.h not found. Please make sure cudnn install at " - "${CUDNN_ROOT_DIR}"); - } else { // check cudnn lib exist - auto str_cudnn_major = - find_content_in_file(cudnn_header_path, "#define CUDNN_MAJOR"); - auto str_cudnn_minor = - find_content_in_file(cudnn_header_path, "#define CUDNN_MINOR"); - auto str_cudnn_patch = find_content_in_file(cudnn_header_path, - "#define CUDNN_PATCHLEVEL"); - - if (str_cudnn_major.empty() || str_cudnn_minor.empty() || - str_cudnn_patch.empty()) { - mgb_log_warn( - "can not find cudnn version information in %s.\n You may " - "Update cudnn\n", - cudnn_header_path.c_str()); - return; - } - - size_t cudnn_major = 0, cudnn_minor = 0, cudnn_patch = 0; - sscanf(str_cudnn_major.c_str(), "#define CUDNN_MAJOR %zu", - &cudnn_major); - sscanf(str_cudnn_minor.c_str(), "#define CUDNN_MINOR %zu", - &cudnn_minor); - sscanf(str_cudnn_patch.c_str(), "#define CUDNN_PATCHLEVEL %zu", - &cudnn_patch); - -#ifdef WIN32 - std::string cudnn_lib_name = - "cudnn64_" + std::to_string(cudnn_major) + ".dll"; -#else - std::string cudnn_lib_name = - "libcudnn.so." + std::to_string(cudnn_major) + "." + - std::to_string(cudnn_minor) + "." + std::to_string(cudnn_patch); -#endif - - auto cudnn_lib_path = find_file_in_envs_with_intmd( - {"CUDNN_ROOT_DIR", "CUDNN_LIBRARY", "CUDA_PATH", ENV_PATH}, - cudnn_lib_name, {"lib64", "lib/x64"}); - if (cudnn_lib_path.empty()) { - mgb_log_warn( - "%s not found. Please make sure cudnn install at " - "${CUDNN_LIBRARY}", - cudnn_lib_name.c_str()); - } - } -} -} // namespace - -std::vector _config::get_cuda_include_path() { -#if MGB_CUDA - auto nvcc_path = get_nvcc_root_path(); - auto cudart_header_path = nvcc_path + "../include/cuda_runtime.h"; - //! double check path_to_nvcc/../include/cuda_runtime.h exists - auto ret = check_file_exist(cudart_header_path.c_str(), F_OK); - if (ret) { - mgb_throw(MegBrainError, - "%s not found. Please make sure your cuda toolkit install " - "right", - cudart_header_path.c_str()); - } else { - return {nvcc_path + "..", nvcc_path + "../include"}; - } -#else - mgb_throw(MegBrainError, "cuda disabled at compile time"); -#endif -} - -std::vector _config::get_cuda_lib_path() { -#if MGB_CUDA - auto nvcc_path = get_nvcc_root_path(); -#ifdef WIN32 - auto cuda_version = get_local_cuda_version(); - auto cuda_major = cuda_version / 1000; - auto cuda_minor = cuda_version % 10; - auto cudart_lib_path = nvcc_path + "cudart64_" + - std::to_string(cuda_major * 10 + cuda_minor) + - ".dll"; -#else - auto cudart_lib_path = nvcc_path + "../lib64/libcudart.so"; -#endif - //! double check cudart_lib_path exists - auto ret = check_file_exist(cudart_lib_path.c_str(), F_OK); - if (ret) { - mgb_throw(MegBrainError, - "%s not found. Please make sure your cuda toolkit install " - "right", - cudart_lib_path.c_str()); - } else { -#ifdef WIN32 - //! cudart64_101.dll locates at cuda/bin - return {nvcc_path + "../lib/x64", nvcc_path}; -#else - return {nvcc_path + "../lib64"}; -#endif - } -#else - mgb_throw(MegBrainError, "cuda disabled at compile time"); -#endif -} - -int _config::get_cuda_version() { -#if MGB_CUDA - int version; - MGB_CUDA_CHECK(cudaRuntimeGetVersion(&version)); - return version; -#else - mgb_throw(MegBrainError, "cuda disabled at compile time"); -#endif -} - -bool _config::is_local_cuda_env_ok() { - check_cudnn_existence(); - if (get_nvcc_root_path().empty()) { - return false; - } - return true; -} - -bool _config::is_compiled_with_cuda() { -#if MGB_CUDA - return true; -#else - return false; -#endif -} - -void _config::load_opr_library(const char* self_path, const char* lib_path) { - static bool self_global = false; - static std::mutex self_global_mtx; - { - MGB_LOCK_GUARD(self_global_mtx); - if (!self_global) { - auto hdl = dlopen(self_path, RTLD_LAZY | RTLD_GLOBAL); - mgb_assert(hdl, "failed to set mgb to global: %s", dlerror()); - self_global = true; - } - } - if (lib_path) { - auto hdl = dlopen(lib_path, RTLD_LAZY); - mgb_assert(hdl, "failed to load libray %s: %s", lib_path, dlerror()); - } -} - -std::vector> _config::dump_registered_oprs() { -#if MGB_ENABLE_DEBUG_UTIL - return serialization::OprRegistry::dump_registries(); -#else - return {}; -#endif -} - -#if MGB_ENABLE_OPR_MM -/*! see definition : src/cpp/megbrain_config.h. - * Create mm server. port 0 is permitted, leave zmqrpc to decide which port - * should be used. - */ -int _config::create_mm_server(const std::string& server_addr, int port) { - return create_zmqrpc_server(server_addr, port); -} - -void _config::group_barrier(const std::string& server_addr, - int port, uint32_t size, uint32_t rank) { - mgb_assert(rank < size, "invalid rank %d", rank); - auto group_mgr = std::make_shared( - ssprintf("%s:%d", server_addr.c_str(), port)); - uint32_t rsp = group_mgr->group_barrier(size, rank); - mgb_assert(rsp != 0, "rank already registered: %d", rank); - mgb_assert(size == rsp, "inconsistent size: %d, expect %d", size, rsp); -} - -#else - -int _config::create_mm_server(const std::string& server_addr, int port) { - mgb_throw(mgb::MegBrainError, "OPR_MM suppport disable at compile time"); - return 0; -} - -void _config::group_barrier(const std::string& server_addr, - int port, uint32_t size, uint32_t rank) { - mgb_throw(mgb::MegBrainError, "OPR_MM suppport disable at compile time"); -} - -#endif - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/megbrain_config.h b/python_module/src/cpp/megbrain_config.h deleted file mode 100644 index ed4f4c9d..00000000 --- a/python_module/src/cpp/megbrain_config.h +++ /dev/null @@ -1,78 +0,0 @@ -/** - * \file python_module/src/cpp/megbrain_config.h - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - -#ifndef SWIG - -#pragma once - -#include "megbrain_build_config.h" -#include "./megbrain_wrap.h" -#include -using mgb::cg::SymbolVar; -#endif - -//! wrap by a class so swig can put the functions in a namespace -class _config { - public: - static bool set_comp_graph_option( - CompGraph &cg, const std::string &name, int val_int); - - static bool comp_graph_is_eager(CompGraph &cg); - - static void add_extra_vardep( - const SymbolVar &var, const SymbolVar &dep); - - static void begin_set_opr_priority( - CompGraph &cg, int priority); - static void end_set_opr_priority(CompGraph &cg); - - static void begin_set_exc_opr_tracker( - CompGraph &cg, PyObject *tracker); - static void end_set_exc_opr_tracker(CompGraph &cg); - - //! return (opr_msg, fwd tracker, grad tracker) or None - static PyObject* get_opr_tracker(CompGraph &cg, size_t var_id); - - static void set_opr_sublinear_memory_endpoint(const SymbolVar &var); - - static void set_fork_cuda_warning_flag(int flag); - - static bool is_cuda_ctx_set(); - - //! get cuda gencode strings for local devices - static std::string get_cuda_gencode(); - - //! get cuda lib paths. - static std::vector get_cuda_lib_path(); - - //! get cuda include paths. - static std::vector get_cuda_include_path(); - - //! get cuda version - static int get_cuda_version(); - - //! check local cuda env. The method returns true if CUDA's nvcc - //! compiler and cudnn installs in PATH. - static bool is_local_cuda_env_ok(); - - static bool is_compiled_with_cuda(); - - static void load_opr_library( - const char* self_path, const char* lib_path); - - static std::vector> - dump_registered_oprs(); - - static int create_mm_server(const std::string& server_addr, int port); - - static void group_barrier(const std::string& server_addr, - int port, uint32_t size, uint32_t rank); -}; - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/megbrain_pubapi.cpp b/python_module/src/cpp/megbrain_pubapi.cpp deleted file mode 100644 index 6665723c..00000000 --- a/python_module/src/cpp/megbrain_pubapi.cpp +++ /dev/null @@ -1,334 +0,0 @@ -/** - * \file python_module/src/cpp/megbrain_pubapi.cpp - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - -#include "./megbrain_pubapi.h" -#include "./megbrain_pubapi_internal.h" - -#include "megbrain/tensor.h" -#include "megbrain/graph/var_node.h" -#include "megbrain/comp_node_env.h" - -namespace { - -class DeleteDispatcher final : public mgb::CompNodeDepedentObject { - mgb::thin_function m_deleter; - mgb::CompNode m_comp_node; - std::atomic done; - - std::shared_ptr on_comp_node_finalize() override { - bool _ = false; - if (done.compare_exchange_strong(_, true)) { - m_deleter(); - } - return {}; - } -public: - explicit DeleteDispatcher(mgb::thin_function&& deleter, - mgb::CompNode cn) - : m_deleter(std::move(deleter)), m_comp_node(cn) { - done.store(false); - } - - void trigger() { - bool _ = false; - if (done.compare_exchange_strong(_, true)) { - if (!is_finalized()) { - m_comp_node.add_callback(std::move(m_deleter)); - } else { - m_deleter(); - } - } - } -}; - -} // namespace - -using namespace mgb; - -pubapi::DeviceTensor::DataType mgb::dtype_mgb2pubapi(DType dtype) { - using DevDType = pubapi::DeviceTensor::DataType; - switch (dtype.enumv()) { -#define o(s, t) \ - case DTypeEnum::s: \ - return DevDType::t - o(Float32, FLOAT32); - o(Float16, FLOAT16); - o(Int32, INT32); - o(Int16, INT16); - o(Int8, INT8); - o(Uint8, UINT8); -#undef o - default: - mgb_throw(MegBrainError, "dtype %s not implemented for pubapi", - dtype.name()); - } -} - -struct pubapi::DeviceTensor::_Impl { - -static TensorShape desc_shape_to_tensor_shape(const DeviceTensor::Desc &desc) { - TensorShape shape; - mgb_assert(desc.ndim && desc.ndim <= TensorShape::MAX_NDIM, - "invalid ndim: %zu", desc.ndim); - shape.ndim = desc.ndim; - for (size_t i = 0; i < desc.ndim; ++ i) { - shape[i] = desc.shape[i]; - } - return shape; -} - -#if MGB_CUDA -class CudaCurrentDeviceRestore { - int m_orig_dev = -1; - - public: - - CudaCurrentDeviceRestore(CompNode cn) { - if (cn.device_type() == CompNode::DeviceType::CUDA) { - MGB_CUDA_CHECK(cudaGetDevice(&m_orig_dev)); - } - } - - ~CudaCurrentDeviceRestore() { - if (m_orig_dev != -1) { - cudaSetDevice(m_orig_dev); - } - } -}; -#else -class CudaCurrentDeviceRestore { - public: - CudaCurrentDeviceRestore(CompNode) { - } -}; -#endif - -static void sync(const DeviceTensor *self, bool strong) { - CompNode cn; - if (self->m_dev_nd) { - cn = static_cast(self->m_dev_nd)->comp_node(); - } else { - mgb_assert(self->m_varptr); - cn = static_cast(self->m_varptr)->comp_node(); - } - CudaCurrentDeviceRestore cuda_dev_restore{cn}; - cn.sync(); -#if MGB_CUDA - if (strong && cn.device_type() == CompNode::DeviceType::CUDA) { - cn.activate(); - MGB_CUDA_CHECK(cudaDeviceSynchronize()); - } -#endif -} - -static const char* dtype_name(DataType dtype) { - switch (dtype) { -#define on(c) \ - case DataType::c: \ - return #c - on(FLOAT32); - on(FLOAT16); - on(INT32); - on(INT16); - on(INT8); - on(UINT8); -#undef on - default: - mgb_throw(MegBrainError, "invalid pubapi dtype enum: %d", - static_cast(dtype)); - } -} - -static void copy( - DeviceTensor *self, const Desc &other, CopyDirection direction) { - mgb_assert(self->desc.dtype == other.dtype, "dtype mismatch: %s vs %s", - self->dtype_name(), dtype_name(other.dtype)); - mgb_assert(self->m_varptr || self->m_dev_nd); - const DeviceTensorND *dv; - if (direction == CopyDirection::OTHER_TO_SELF) { - mgb_assert(!self->m_readonly, "can not copy into readonly tensor"); - auto shape = desc_shape_to_tensor_shape(other); - if (self->m_varptr) { - auto var = static_cast(self->m_varptr); - dv = &var->shape_alloc(shape).dev_tensor(); - } else { - dv = static_cast(self->m_dev_nd); - mgb_assert(dv->shape().eq_shape(shape), - "copy dest tensor shape is %s, but source shape is %s", - dv->shape().to_string().c_str(), shape.to_string().c_str()); - } - mgb_assert(self->desc.dtype == dtype_mgb2pubapi(dv->dtype())); - self->desc.dev_ptr = dv->raw_ptr(); - self->desc.ndim = dv->shape().ndim; - self->desc.shape = dv->shape().shape; - if (!other.dev_ptr) { - // used in resize() - return; - } - } else { - mgb_assert(direction == CopyDirection::SELF_TO_OTHER); - if (self->m_varptr) { - dv = &static_cast(self->m_varptr)->dev_tensor(); - } else { - dv = static_cast(self->m_dev_nd); - } - } - - mgb_assert(dv->layout().is_contiguous()); - auto size = dv->layout().span().dist_byte(); - auto cn = dv->comp_node(); - CudaCurrentDeviceRestore cuda_dev_restore{cn}; - - void *dst = dv->raw_ptr(), *src = other.dev_ptr; - if (direction == CopyDirection::SELF_TO_OTHER) { - std::swap(dst, src); - } - -#if !MGB_CUDA - mgb_assert(other.type != Type::CUDA, "cuda disabled at compile time"); -#endif - - auto &&desc = self->desc; - if (other.type == desc.type) { -#if MGB_CUDA - if (desc.type == Type::CUDA) { - int dev = desc.cuda_ctx.device; - if (dev == -1) { - MGB_CUDA_CHECK(cudaGetDevice(&dev)); - } - mgb_assert(dev == other.cuda_ctx.device, - "DeviceTensor copy must be on the same device; " - "got %d vs %d", dev, other.cuda_ctx.device); - } -#endif - cn.peer_copy_to(cn, dst, src, size); - } else { - if ((desc.type == Type::CPU && other.type == Type::CUDA && - direction == CopyDirection::SELF_TO_OTHER) || - (other.type == Type::CPU && desc.type == Type::CUDA && - direction == CopyDirection::OTHER_TO_SELF)) { - cn.copy_to_device(dst, src, size); - } else { - mgb_assert((desc.type == Type::CUDA && other.type == Type::CPU && - direction == CopyDirection::SELF_TO_OTHER) || - (other.type == Type::CUDA && desc.type == Type::CPU && - direction == CopyDirection::OTHER_TO_SELF)); - cn.copy_to_host(dst, src, size); - } - } -} - -static void forward_other_memory( - const DeviceTensor *self, - const Desc &other, CallbackOnce deleter) { - mgb_assert(self->desc.dtype == other.dtype, "dtype mismatch: %s vs %s", - self->dtype_name(), dtype_name(other.dtype)); - auto deleter_wrap = [deleter]() mutable { deleter.consume(); }; - thin_function deleter_dispatch; - if (self->desc.type == Type::CPU) { - CompNode cn{}; - if (self->m_varptr) { - cn = static_cast(self->m_varptr)->comp_node(); - } else { - cn = static_cast(self->m_dev_nd)->comp_node(); - } - deleter_dispatch = [d = new DeleteDispatcher(deleter_wrap, cn)](void*) { - d->trigger(); - delete d; - }; - } else { - deleter_dispatch = [deleter_wrap](void*) mutable { deleter_wrap(); }; - } - auto shape = desc_shape_to_tensor_shape(other); - if (self->m_varptr) { - auto var = static_cast(self->m_varptr); - DeviceTensorStorage storage; - storage.reset(var->comp_node(), - shape.total_nr_elems() * var->dtype().size(), - {static_cast(other.dev_ptr), deleter_dispatch}); - DeviceTensorND tensor; - tensor.reset(storage, {shape, var->dtype()}); - var->reset_dev_tensor_from_tensor(tensor); - } else { - DeviceTensorND& tensor = *static_cast(self->m_dev_nd); - DeviceTensorStorage storage; - size_t dtype_size = tensor.layout().dtype.size(); - storage.reset(tensor.comp_node(), - shape.total_nr_elems() * dtype_size, - {static_cast(other.dev_ptr), deleter_dispatch}); - tensor.reset(storage, {shape, tensor.layout().dtype}); - } -} - -static void forward_to( - const DeviceTensor *self, - void **dest, CallbackOnce* deleter) { - auto orig_dv_ptr = static_cast(self->m_dev_nd); - *dest = orig_dv_ptr->ptr(); - mgb_assert(*dest == self->desc.dev_ptr); - deleter->user_data = new DeviceTensorStorage(*orig_dv_ptr); - deleter->fptr = [](void* ptr) { - delete reinterpret_cast(ptr); - }; -} - -static void init_tensor(pubapi::DeviceTensor& dest, DeviceTensorND* tensor, - VarNode* var, bool readonly) { - memset(&dest, 0, sizeof(pubapi::DeviceTensor)); - { - static FuncTable functable{&sync, ©, &forward_other_memory, - &dtype_name, &forward_to}; - dest.m_functable = &functable; - } - dest._version0 = dest._version1 = CURRENT_VERSION; - - mgb_assert((!!tensor) ^ (!!var)); - auto cn = tensor ? tensor->comp_node() : var->comp_node(); - using Type = pubapi::DeviceTensor::Type; - switch (cn.device_type()) { - case CompNode::DeviceType::CPU: - dest.desc.type = Type::CPU; - break; -#if MGB_CUDA - case CompNode::DeviceType::CUDA: - dest.desc.type = Type::CUDA; - break; -#endif - default: - mgb_throw(MegBrainError, "bad comp node type: %d", - static_cast(cn.device_type())); - } - dest.desc.dtype = dtype_mgb2pubapi(tensor ? tensor->dtype() : var->dtype()); - if (tensor) { - dest.desc.dev_ptr = tensor->raw_ptr(); - dest.desc.shape = tensor->shape().shape; - dest.desc.ndim = tensor->shape().ndim; - dest.size_bytes = tensor->layout().span().dist_byte(); - } -#if MGB_CUDA - if (dest.desc.type == Type::CUDA) { - auto&& env = CompNodeEnv::from_comp_node(cn).cuda_env(); - dest.desc.cuda_ctx.device = env.device; - dest.desc.cuda_ctx.stream = env.stream; - } -#endif - dest.m_readonly = readonly; - dest.m_dev_nd = tensor; - dest.m_varptr = var; -} - -}; // pubapi::DeviceTensor::Impl - -void mgb::init_pubapi_dev_tensor(pubapi::DeviceTensor& dest, - DeviceTensorND* tensor, VarNode* var, - bool readonly) { - pubapi::DeviceTensor::_Impl::init_tensor(dest, tensor, var, readonly); -} - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/megbrain_pubapi.h b/python_module/src/cpp/megbrain_pubapi.h deleted file mode 100644 index fa33f815..00000000 --- a/python_module/src/cpp/megbrain_pubapi.h +++ /dev/null @@ -1,185 +0,0 @@ -/** - * \file python_module/src/cpp/megbrain_pubapi.h - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \brief public API for exposing megbrain internal data structures - * - * This is a pure header without compile-time dependencies. - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - */ - -#pragma once - -#include -#include - -namespace mgb { -namespace pubapi { - - /*! - * \brief a general callback that would be invoked exactly once - * - * During the invoke, the functor shoule release related memory - */ - struct CallbackOnce { - void (*fptr)(void *); - void *user_data; - - //! invoke the callback and clean up the scene - void consume() { - fptr(user_data); - fptr = nullptr; - user_data = nullptr; - } - }; - - //! tensor on a computing device - class DeviceTensor { - public: - static constexpr uint32_t CURRENT_VERSION = 20190725; - - //! device type - enum class Type: uint32_t { - CPU, CUDA - }; - enum class DataType: uint32_t { - FLOAT32, FLOAT16, INT32, INT16, INT8, UINT8 - }; - enum class CopyDirection { - SELF_TO_OTHER, OTHER_TO_SELF - }; - struct CudaContext { - int device; //! set to -1 in copy() to use current device - void *stream; //!< set to nullptr for default stream - }; - - //! tensor descriptor - struct Desc { - Type type; - DataType dtype; - void *dev_ptr; //!< pointer to actual device buffer - const size_t *shape; //!< pointer to shape array - size_t ndim; - //! only valid if type == Type::CUDA - CudaContext cuda_ctx; - }; - - uint32_t _version0; //!< for consistency check - // note: fields starting with underscore are for internal use only - - Desc desc; - size_t size_bytes; - - /*! - * \brief synchonize with the calling thread - * - * This must be called before forwarding memory for direct use - * - * \param strong whether to synchronoze the whole device (true), or - * just the computing node (false). Currently it only affects - * how cuda sync is performed. - */ - void sync(bool strong = false) const { - m_functable->sync(this, strong); - } - - /*! - * \brief copy to/from another buffer - * - * Note: the copy is performed on the comp node on which this tensor - * resides and is always async. - * - * If \p direction is OTHER_TO_SELF and shape of this changes, then - * the corresponding dev_ptr would also be updated. - * - * \param other the other buffer involved in the copy; if - * \p direction is SELF_TO_OTHER, then only its type and - * dev_ptr would be used - * \param direction specify the direction to perform the copy - */ - void copy(const Desc &other, CopyDirection direction) { - m_functable->copy(this, other, direction); - } - - /*! - * \brief resize this tensor to given shape - */ - void resize(size_t ndim, const size_t *shape) { - Desc tmp; - tmp.dev_ptr = nullptr; - tmp.ndim = ndim; - tmp.shape = shape; - copy(tmp, CopyDirection::OTHER_TO_SELF); - } - - //! name of dtype of this tensor - const char* dtype_name() const { return dtype_name(desc.dtype); } - - //! name of given dtype - const char* dtype_name(DataType dtype) const { - return m_functable->dtype_name(dtype); - } - - /*! - * \brief forward memory from \p other directly to the underlying - * storage - * - * This can only be used when there is a corresponding VarNode for - * this DeviceTensor. (e.g. for the outputs of Craniotome oprs) - */ - void forward_other_memory( - const Desc &other, CallbackOnce deleter) const { - m_functable->forward_other_memory(this, other, deleter); - } - - /*! - * \brief forward device buffer to \p dest directly and create a - * tensor storage shared memory with m_dv_nd, it would be deleted - * when calling deleter, so refcnt to data ptr could be managed - * correctly. - */ - void forward_to( - void **dest, CallbackOnce* deleter) const { - m_functable->forward_to(this, dest, deleter); - } - - struct _Impl; - private: - // note: we use a func table to avoid symbol visibility problems and - // linking hazards when built with other code base - struct FuncTable { - void (*sync)(const DeviceTensor*, bool); - void (*copy)(DeviceTensor*, const Desc&, CopyDirection); - void (*forward_other_memory)(const DeviceTensor*, const Desc&, - CallbackOnce); - const char* (*dtype_name)(DataType); - void (*forward_to)(const DeviceTensor*, void**, CallbackOnce*); - }; - bool m_readonly; - void* m_dev_nd; - void* m_varptr; - FuncTable* m_functable; - public: - uint32_t _version1; - }; - - /*! - * \brief reinterpret_cast raw pointer or pointer integer to mgb object and - * check version - * \return object pointer if the version is correct; nullptr if failed - */ - template - T* as_versioned_obj(S &&val) { - T *obj = reinterpret_cast(val); - if (obj->_version0 != T::CURRENT_VERSION || - obj->_version1 != T::CURRENT_VERSION) { - return nullptr; - } - return obj; - } -} // namespace pubapi -} // namespace mgb - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/megbrain_pubapi_internal.h b/python_module/src/cpp/megbrain_pubapi_internal.h deleted file mode 100644 index 7581d60e..00000000 --- a/python_module/src/cpp/megbrain_pubapi_internal.h +++ /dev/null @@ -1,31 +0,0 @@ -/** - * \file python_module/src/cpp/megbrain_pubapi_internal.h - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \brief internal helpers related to pubapi. Implemented in pubapi.cpp - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - -#pragma once - -#include "megbrain_pubapi.h" -#include "megbrain/graph.h" - -namespace mgb { - /*! - * \brief fill fields in \p dest with information from other tensors - * - * Note that exactly one of \p tensor and \p var must be non-null - */ - void init_pubapi_dev_tensor( - pubapi::DeviceTensor &dest, - DeviceTensorND *tensor, VarNode *var, bool readonly); - - //! convert megbrain dtype to pubapi dtype - pubapi::DeviceTensor::DataType dtype_mgb2pubapi(DType dtype); -} - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/megbrain_serialize.cpp b/python_module/src/cpp/megbrain_serialize.cpp deleted file mode 100644 index d6e371bb..00000000 --- a/python_module/src/cpp/megbrain_serialize.cpp +++ /dev/null @@ -1,180 +0,0 @@ -/** - * \file python_module/src/cpp/megbrain_serialize.cpp - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - -#include "./megbrain_serialize.h" -#include "./python_helper.h" - -#include "megbrain/opr/basic_arith.h" - -using namespace mgb; -using namespace serialization; - -TensorValueDumperContext::~TensorValueDumperContext() noexcept = default; -TensorValueLoaderContext::~TensorValueLoaderContext() noexcept = default; - -PyObject* TensorValueDumperContext::_value() { - return npy::ndarray_from_tensor(m_value, npy::ShareType::TRY_SHARE); -} - -void TensorValueDumperContext::_write(PyObject *bytes) { - mgb_assert(PyBytes_Check(bytes)); - auto arr_len = PyBytes_Size(bytes); - auto arr_buf = PyBytes_AsString(bytes); - m_fout.write(arr_buf, arr_len); -} - -std::vector TensorValueLoaderContext::_get_shape() const { - mgb_assert(m_layout.is_contiguous()); - return npy::shape2vec(m_layout); -} - -PyObject* TensorValueLoaderContext::_get_dtype() const { - return npy::dtype_mgb2np(m_layout.dtype); -} - -PyObject* TensorValueLoaderContext::_read(size_t n) { - // Creates a PyBytes with uninitialized content - PyObject* bytes = PyBytes_FromStringAndSize(nullptr, n); - m_fin.read(PyBytes_AsString(bytes), n); - return bytes; -} - -std::string _get_info_for_strip(const SymbolVarArray &dest_vars) { - std::unordered_set opr_types, dtype_names, elemwise_modes; - - auto on_opr = [&](cg::OperatorNodeBase *opr) { - if (GraphDumper::should_remove_in_dump(opr)) - return; - opr_types.insert(opr->dyn_typeinfo()->name); - for (auto i: opr->output()) - dtype_names.insert(i->dtype().name()); - if (opr->same_type()) { - auto mode = opr->cast_final().param().mode; - elemwise_modes.insert( - megdnn::Elemwise::ModeTrait::from_mode(mode).name); - } - }; - cg::DepOprIter opr_iter{on_opr}; - for (auto i: dest_vars) - opr_iter.add(i.node()->owner_opr()); - - auto to_json = [](const std::unordered_set &v) { - std::vector vs(v.begin(), v.end()); - std::sort(vs.begin(), vs.end()); - auto ret = json::Array::make(); - for (auto &&i: vs) - ret->add(json::String::make(i)); - return ret; - }; - - return json::Object::make({ - {"opr_types", to_json(opr_types)}, - {"dtypes", to_json(dtype_names)}, - {"elemwise_modes", to_json(elemwise_modes)}, - })->to_string(); -} - -void _serialize_comp_graph_to_file( - const char *fpath, bool append, GraphDumpFormat format, - const SymbolVarArray &output_vars, - int keep_var_name, bool keep_param_name, bool keep_opr_priority, - _TensorValueDumperCallback *tensor_value_dumper, - std::vector &stat, - std::vector &inputs, - std::vector &outputs, - std::vector ¶ms) { - - auto dumper = GraphDumper::make( - OutputFile::make_fs(fpath, append ? 'a' : 'w'), format); - GraphDumper::DumpConfig config{keep_var_name, keep_param_name, - keep_opr_priority}; - - if (tensor_value_dumper) { - config.tensor_value_dumper = [f=tensor_value_dumper]( - OutputFile &fout, const cg::OperatorNodeBase &opr, - const HostTensorND &value) { - mgb_assert(value.layout().is_contiguous()); - TensorValueDumperContext ctx{fout, opr, value}; - f->call(ctx); - }; - } - - auto rst = dumper->dump(output_vars, config); - inputs = std::move(rst.inputs); - outputs = std::move(rst.outputs); - params = std::move(rst.params); - stat = {rst.nr_opr, rst.tot_bytes, rst.tensor_value_bytes, - rst.content_hash}; -} - -CompGraph _load_comp_graph_from_file( - const char* fpath, _CompNodeMapperCallback* cn_mapper, - _TensorValueLoaderCallback* tensor_value_loader, - std::vector>& output_var_map, - SymbolVarArray& output_var_list) { - auto file = InputFile::make_fs(fpath); - auto format = GraphLoader::identify_graph_dump_format(*file); - mgb_throw_if(!format.valid(), SerializationError, - "unknown model format (input is likely not a MegBrain model)"); - auto loader = GraphLoader::make(std::move(file), format.val()); - GraphLoader::LoadConfig config; - if (cn_mapper) { - config.comp_node_mapper = [f = cn_mapper](CompNode::Locator& locator) { - locator = CompNode::Locator::parse(f->call(locator.to_string())); - }; - } - if (tensor_value_loader) { - config.tensor_value_loader = [f = tensor_value_loader]( - void* ptr, - const TensorLayout& layout, - InputFile& fin) { - TensorValueLoaderContext ctx{layout, fin}; - PyObjRefKeeper value = f->call(ctx); - mgb_assert(value.get()->ob_refcnt > 0); - if (ptr) { - HostTensorStorage storage; - // Unmanaged shared_ptr - storage.reset(CompNode::default_cpu(), - layout.span().dist_byte(), - {std::shared_ptr(), - reinterpret_cast(ptr)}); - HostTensorND tensor; - tensor.reset(storage, layout); - npy::np2tensor(value.get(), npy::Meth::copy_into(&tensor), - layout.dtype); - } - }; - } - auto rst = loader->load(config); - output_var_map = {rst.output_var_map.begin(), rst.output_var_map.end()}; - output_var_list = std::move(rst.output_var_list); - - std::unordered_map tensor2name; - for (const auto& pair : rst.tensor_map) { - tensor2name[pair.second.get()] = &pair.first; - } - auto cb = [&tensor2name, graph=rst.graph](cg::OperatorNodeBase* opr) { - if (!opr->same_type()) - return; - - auto& h2d = opr->cast_final_safe(); - auto it = tensor2name.find(h2d.host_data().get()); - mgb_throw_if(it == tensor2name.end(), GraphError, - "unbound Host2DeviceCopy in loaded graph"); - h2d.output(0)->name(*it->second); - mark_as_input(graph.get(), h2d.output(0)); - }; - cg::DepOprIter iter{cb}; - for (const auto& var : output_var_list) { - iter.add(var.node()->owner_opr()); - } - return CompGraph::make_from_shared_ptr(rst.graph); -} - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/megbrain_serialize.h b/python_module/src/cpp/megbrain_serialize.h deleted file mode 100644 index f320c680..00000000 --- a/python_module/src/cpp/megbrain_serialize.h +++ /dev/null @@ -1,159 +0,0 @@ -/** - * \file python_module/src/cpp/megbrain_serialize.h - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - - -#ifndef SWIG - -#pragma once - -#include "megbrain/serialization/serializer.h" -#include "./megbrain_wrap.h" -using mgb::cg::SymbolVar; -using mgb::cg::SymbolVarArray; -#endif - -#ifdef SWIG -%feature("autodoc", -"An object that is passed to the callback in \ -:func:`.serialize_comp_graph_to_file`.") TensorValueDumperContext; -%feature("autodoc", -"An object that is passed to the callback in \ -:func:`.load_comp_graph_from_file`.") TensorValueLoaderContext; -%feature("director") _TensorValueDumperCallback; -%feature("director") _TensorValueLoaderCallback; -%feature("director") _CompNodeMapperCallback; - -%template(_VectorPairStringSymbolVar) std::vector>; -%typemap(directorout) PyObjRefKeeper { - Py_XINCREF($input); - $result = PyObjRefKeeper($input); -} -#endif -class TensorValueDumperContext { -#ifndef SWIG - mgb::serialization::OutputFile &m_fout; - const mgb::cg::OperatorNodeBase &m_opr; - const mgb::HostTensorND &m_value; -#endif - - public: - TensorValueDumperContext() = delete; - TensorValueDumperContext(const TensorValueDumperContext&) = delete; - TensorValueDumperContext& operator = ( - const TensorValueDumperContext&) = delete; - -#ifndef SWIG - TensorValueDumperContext( - mgb::serialization::OutputFile &fout, - const mgb::cg::OperatorNodeBase &opr, - const mgb::HostTensorND &value): - m_fout{fout}, m_opr{opr}, m_value{value} - { - } -#endif - ~TensorValueDumperContext() noexcept; - - const char* _name() const { - return m_opr.cname(); - } - - const char* _type() const { - return m_opr.dyn_typeinfo()->name; - } - - PyObject* _value(); - - void _write(PyObject *bytes); - - void _write_default() { - mgb::serialization::GraphDumpConfig::default_tensor_value_dumper( - m_fout, m_opr, m_value); - } - -#ifdef SWIG -%include "./megbrain_serialize_TensorValueDumperContext.py" -#endif - -}; - -class TensorValueLoaderContext { -#ifndef SWIG - const mgb::TensorLayout &m_layout; - mgb::serialization::InputFile &m_fin; -#endif - - public: - TensorValueLoaderContext() = delete; - TensorValueLoaderContext(const TensorValueLoaderContext&) = delete; - TensorValueLoaderContext& operator=(const TensorValueLoaderContext&) = - delete; - -#ifndef SWIG - TensorValueLoaderContext(const mgb::TensorLayout &layout, - mgb::serialization::InputFile &fin) - : m_layout(layout), m_fin(fin) {} -#endif - ~TensorValueLoaderContext() noexcept; - - std::vector _get_shape() const; - PyObject* _get_dtype() const; - - // Returns bytes - PyObject* _read(size_t n); - -#ifdef SWIG -%include "./megbrain_serialize_TensorValueLoaderContext.py" -#endif -}; - -class _TensorValueDumperCallback { - public: - virtual ~_TensorValueDumperCallback() = default; - virtual void call(TensorValueDumperContext &ctx) = 0; -}; - -class _TensorValueLoaderCallback { - public: - virtual ~_TensorValueLoaderCallback() = default; - virtual PyObjRefKeeper call(TensorValueLoaderContext &ctx) = 0; -}; - -class _CompNodeMapperCallback { - public: - virtual ~_CompNodeMapperCallback() = default; - virtual std::string call(const std::string &desc) = 0; -}; - -#ifdef SWIG -%include "megbrain/serialization/dump_format.h" -#else -#include "megbrain/serialization/dump_format.h" -#endif - -void _serialize_comp_graph_to_file( - const char *fpath, bool append, - mgb::serialization::GraphDumpFormat format, - const SymbolVarArray &output_vars, - int keep_var_name, bool keep_param_name, bool keep_opr_priority, - _TensorValueDumperCallback *tensor_value_dumper, - std::vector &stat, - std::vector &inputs, - std::vector &outputs, - std::vector ¶ms); - -std::string _get_info_for_strip(const SymbolVarArray &dest_vars); - -CompGraph _load_comp_graph_from_file( - const char *fpath, _CompNodeMapperCallback *cn_mapper, - _TensorValueLoaderCallback *tensor_value_loader, - /* Outputs */ - std::vector> &output_var_map, - SymbolVarArray &output_var_list); - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/megbrain_serialize_TensorValueDumperContext.py b/python_module/src/cpp/megbrain_serialize_TensorValueDumperContext.py deleted file mode 100644 index d8028678..00000000 --- a/python_module/src/cpp/megbrain_serialize_TensorValueDumperContext.py +++ /dev/null @@ -1,44 +0,0 @@ -%pythoncode { - @property - def name(self): - """name of the param - - :type: str - """ - return str(self._name()) - - @property - def type(self): - """operator type - - :type: str - """ - return str(self._type()) - - @property - def value(self): - """numerical value of the param - - :type: :class:`numpy.ndarray` - """ - return self._value() - - def write(self, buf): - """write raw data to the output file - - :param buf: value to be written - :type buf: :class:`bytes` - :return: self - """ - assert type(buf) is bytes, 'bad value: {!r}'.format(type(buf)) - self._write(buf) - - def write_default(self): - """dump the numerical value in default format - - :return: self - """ - self._write_default() - return self - -} diff --git a/python_module/src/cpp/megbrain_serialize_TensorValueLoaderContext.py b/python_module/src/cpp/megbrain_serialize_TensorValueLoaderContext.py deleted file mode 100644 index a2ff798c..00000000 --- a/python_module/src/cpp/megbrain_serialize_TensorValueLoaderContext.py +++ /dev/null @@ -1,19 +0,0 @@ -%pythoncode { - @property - def shape(self): - return self._get_shape() - - @property - def dtype(self): - return self._get_dtype() - - def read(self, size): - """read raw data from the input file - - :param size: number of bytes to be read - :type size: :class:`int` - :return: bytes - """ - return self._read(size) - -} diff --git a/python_module/src/cpp/megbrain_wrap.cpp b/python_module/src/cpp/megbrain_wrap.cpp deleted file mode 100644 index bcb85ae1..00000000 --- a/python_module/src/cpp/megbrain_wrap.cpp +++ /dev/null @@ -1,1055 +0,0 @@ -/** - * \file python_module/src/cpp/megbrain_wrap.cpp - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - -#include "./megbrain_wrap.h" -#include "./python_helper.h" -#include "./megbrain_pubapi_internal.h" - -#include "megbrain/version.h" -#include "megbrain/tensor.h" -#include "megbrain/comp_node_env.h" -#include "megbrain/opr/io.h" -#include "megbrain/opr/utility.h" -#include "megbrain/gopt/inference.h" -#include "megbrain/utils/thread.h" -#include "megbrain/utils/timer.h" - -#include -using namespace mgb; - -namespace { - bool g_global_finalize_called = false; - - /*! - * \brief record the vars produced from user-created Host2DeviceCopy - * - * Note that the vars are mapped by address of underlying HostTensorND, so - * in the case of partial execution, vars in the parent graph can be - * retrieved from oprs in the sub graphs. - */ - class UserInputVars final : public UserDataContainer::UserData { - MGB_TYPEINFO_OBJ_DECL; - - //! we keep this mapping to handle multi-part compiling, where new - //! graphs would be created and the var in the original graph is needed - ThinHashMap m_tensor2var; - - public: - void register_var(SymbolVar x) { - m_tensor2var[x.node()->owner_opr() - ->cast_final_safe() - .host_data() - .get()] = x.node(); - } - - //! get the corresponding var from an opr if it has been registered; - //! return nullptr otherwise - VarNode* check(cg::OperatorNodeBase* opr) const { - if (opr->same_type()) { - auto ptr = opr->cast_final() - .host_data() - .get(); - auto iter = m_tensor2var.find(ptr); - return iter == m_tensor2var.end() ? nullptr : iter->second; - } - return nullptr; - } - - static UserInputVars& get(ComputingGraph* graph) { - return *graph->options() - .user_data.get_user_data_or_create(); - } - }; - - __attribute__((constructor)) - void global_init() { - CompNode::enable_affinity_for_cpu(true); - } -} // anonymous namespace - -MGB_TYPEINFO_OBJ_IMPL(UserInputVars); - -/* ================= SharedND ================= */ - -bool SharedND::sync(mgb::DeviceTensorND &dv) { - if (m_copy_sync) { - dv.sync(); - return true; - } - return false; -} - -void SharedND::_set_init_shape(const std::vector &shape) { - mgb_assert(m_dev_tensor && m_dev_tensor->empty()); - m_dev_tensor->resize(npy::vec2shape(shape)); -} - -void SharedND::_resize(const std::vector &shape) { - auto tshp = npy::vec2shape(shape); - if (m_dev_tensor) { - m_dev_tensor->resize(tshp); - } else { - mgb_assert(m_var); - m_var->shape_alloc(tshp); - } -} - -void SharedND::_reset_zero() { - fill_zero_dev_tensor(*m_dev_tensor); -} - -void SharedND::_copy_from_npyarr(PyObject *npyarr) { - auto do_copy = [&](DeviceTensorND *dest, VarNode *var) { - DType dtype = dest ? dest->dtype() : var->dtype(); - mgb_assert(dtype.valid()); - auto hv = npy::np2tensor(npyarr, npy::Meth::borrow(), dtype); - if (var) { - // only setup by assign(), by craniotome - var->shape_alloc(hv.shape()); - dest = &var->mutable_dev_tensor(); - } - if (!sync(dest->copy_from(hv))) { - m_async_copy_refkeeper = hv; - } else { - m_async_copy_refkeeper = {}; - } - }; - if (m_var) { - mgb_assert(!m_dev_tensor); - do_copy(nullptr, m_var); - } else { - mgb_assert(m_dev_tensor); - do_copy(m_dev_tensor.get(), nullptr); - } -} - -PyObject* SharedND::_get_npyarr() { - mgb_assert(m_dev_tensor); - if (m_dev_tensor->empty()) - Py_RETURN_NONE; - HostTensorND hv; - hv.comp_node(CompNode::default_cpu()) - .copy_from(*m_dev_tensor) - .sync(); - return npy::ndarray_from_tensor(hv, npy::ShareType::TRY_SHARE); -} - -PyObject* SharedND::_get_dtype() { - mgb_assert(m_dev_tensor); - return npy::dtype_mgb2np(m_dev_tensor->dtype()); -} - -void SharedND::_copy_from_value_proxy(CompGraphCallbackValueProxy &value) { - if (value.eager_copy()) { - mgb_log_warn("copy from eager-copied CompGraphCallbackValueProxy into" - " SharedND; consider using callback_lazycopy; traceback:\n%s", - PyStackExtracter::run().c_str()); - } - - if (m_var) { - mgb_assert(!m_dev_tensor); - auto &&src = value.dev_tensor(); - m_var->shape_alloc(src.shape()). - mutable_dev_tensor().copy_from(src); - } else { - mgb_assert(m_dev_tensor); - sync(m_dev_tensor->copy_from(value.dev_tensor())); - } -} - -void SharedND::_share_from_value_proxy(CompGraphCallbackValueProxy& value) { - if (value.eager_copy()) { - mgb_log_warn( - "share value from eager-copied CompGraphCallbackValueProxy into" - " SharedND; consider using callback_lazycopy; traceback:\n%s", - PyStackExtracter::run().c_str()); - } - - if (m_var) { - mgb_assert(!m_dev_tensor); - m_var->reset_dev_tensor_from_tensor(value.dev_tensor()); - } else { - mgb_assert(m_dev_tensor); - *m_dev_tensor = value.dev_tensor(); - } -} - -SharedND SharedND::_from_symvar(SymbolVar symvar) { - auto opr = symvar.node()->owner_opr(); - if (auto vsnd = opr->try_cast_final()) { - return SharedND(vsnd->dev_data()); - } - if (auto snd = opr->try_cast_final()) { - return SharedND(snd->dev_data()); - } - mgb_throw(MegBrainError, "cannot convert from %s", opr->dyn_typeinfo()->name); -} - -uintptr_t SharedND::_pubapi_dev_tensor_ptr(int version) { - DeviceTensorND *dv; - if (m_dev_tensor) { - mgb_assert(!m_var); - dv = m_dev_tensor.get(); - } else { - mgb_assert(m_var); - dv = nullptr; - } - void *ret; - if (version == 0) { - if (dv) { - ret = dv->raw_ptr(); - } else { - ret = m_var->dev_tensor().raw_ptr(); - } - } else { - init_pubapi_dev_tensor(m_pubapi_dev_tensor, dv, m_var, false); - ret = &m_pubapi_dev_tensor; - } - return reinterpret_cast(ret); -} - -SymbolVar SharedND::_as_sym_var(CompGraph &cg, const std::string &name, - bool volatile_) { - mgb_assert(m_dev_tensor); - OperatorNodeConfig config; - if (!name.empty()) - config.name(name); - if (volatile_) { - return opr::VolatileSharedDeviceTensor::make(cg.get(), m_dev_tensor, - config); - } else { - return opr::SharedDeviceTensor::make(cg.get(), m_dev_tensor, config); - } -} - -std::vector SharedND::_get_shape(){ - if (m_var) { - mgb_assert(!m_dev_tensor); - return npy::shape2vec(m_var->shape()); - } - mgb_assert(m_dev_tensor); - return npy::shape2vec(m_dev_tensor->shape()); -} - -void SharedND::copy_to_sub_from_shared( - int axis, ptrdiff_t begin, ptrdiff_t end, ptrdiff_t step, - const SharedND &rhs) { - mgb_assert(m_dev_tensor && rhs.m_dev_tensor); - auto sub = m_dev_tensor->sub( - Slice(begin, end, step).apply(m_dev_tensor->layout(), axis)); - sub.copy_from_fixlayout(*rhs.m_dev_tensor).sync(); - -} - -void SharedND::copy_from_shared_sub(const SharedND &rhs, - int axis, ptrdiff_t begin, ptrdiff_t end, ptrdiff_t step) { - mgb_assert(m_dev_tensor && rhs.m_dev_tensor); - if (axis == -3) { - sync(m_dev_tensor->copy_from_fixlayout(*rhs.m_dev_tensor)); - } else if (axis == -2) { - sync(m_dev_tensor->copy_from(*rhs.m_dev_tensor)); - } else { - auto sub = rhs.m_dev_tensor->sub( - Slice(begin, end, step).apply( - rhs.m_dev_tensor->layout(), axis)); - sync(m_dev_tensor->copy_from(sub)); - } -} - -void SharedND::_check_before_share_memory(const SharedND& rhs) { - mgb_assert(rhs.m_dev_tensor); - mgb_assert(m_dev_tensor); - mgb_assert(rhs.m_dev_tensor->dtype() == m_dev_tensor->dtype()); - mgb_assert(rhs.m_dev_tensor->comp_node() == m_dev_tensor->comp_node()); -} - -void SharedND::_share_memory_from(const SharedND& rhs, size_t begin) { - _check_before_share_memory(rhs); - m_dev_tensor->reset( - rhs.m_dev_tensor->storage().sub(m_dev_tensor->dtype().size() * begin), - m_dev_tensor->layout()); -} - -void SharedND::_reset_dev_tensor(const SharedND &rhs) { - _check_before_share_memory(rhs); - *m_dev_tensor = *(rhs.m_dev_tensor); -} - -/* ================= _HostSharedND ================= */ - -void _HostSharedND::ensure_own_storage() { - if (!m_own_storage) { - mgb_assert(m_tensor); - HostTensorND val{m_tensor->comp_node(), m_tensor->dtype()}; - if (!m_tensor->empty()) { - val.resize(m_tensor->shape()); - } - *m_tensor = std::move(val); - m_own_storage = true; - } -} - -void _HostSharedND::_resize(const std::vector &shape) { - ensure_own_storage(); - m_tensor->resize(npy::vec2shape(shape)); -} - -void _HostSharedND::_copy_from_npyarr(PyObject *npyarr, bool borrow) { - mgb_assert(m_tensor); - mgb_assert(m_tensor->dtype().valid()); - if (!m_borrow_on_cpu && - m_tensor->comp_node().device_type() == CompNode::DeviceType::CPU) { - borrow = false; - } - if (borrow) { - auto val = npy::np2tensor( - npyarr, npy::Meth::borrow(m_tensor->comp_node()), - m_tensor->dtype()); - m_own_storage = false; - *m_tensor = std::move(val); - } else { - ensure_own_storage(); - npy::np2tensor(npyarr, - npy::Meth::copy_into(m_tensor.get()), m_tensor->dtype()); - } -} - -SymbolVar _HostSharedND::_as_sym_var(CompGraph &cg, bool enable_static_infer, - const std::string &name) { - if (m_tensor->empty()) - cg.get().options().allocate_static_mem_after_graph_compile = false; - - OperatorNodeConfig config; - if (!name.empty()) - config.name(name); - - SymbolVar ret; - if (enable_static_infer) { - ret = opr::Host2DeviceCopy::make(cg.get(), m_tensor, config); - } else { - ret = opr::Host2DeviceCopy::make_no_value_infer(cg.get(), m_tensor, - config); - } - UserInputVars::get(&cg.get()).register_var(ret); - return ret; -} - -_HostSharedND _HostSharedND::make_proxy(SymbolVar var) { - auto &&opr = var.node()->owner_opr()-> - cast_final_safe(); - _HostSharedND rst{var.node()->comp_node(), var.dtype()}; - rst.m_tensor = opr.host_data(); - rst.m_proxied_opr = &opr; - return rst; -} - -std::string _HostSharedND::__repr__() const { - if (m_proxied_opr) { - return ssprintf("", - this, m_proxied_opr->cname()); - } - return ssprintf("", this); -} - -PyObject* _HostSharedND::_get_dtype() { - mgb_assert(m_tensor); - return npy::dtype_mgb2np(m_tensor->dtype()); -} - -/* ================= CompGraphCallbackValueProxy ================= */ - -CompGraphCallbackValueProxy -CompGraphCallbackValueProxy::make_raw_host_value_proxy( - const mgb::HostTensorND &hv) { - CompGraphCallbackValueProxy ret; - ret.m_use_raw_hv = true; - ret.m_hv = hv; - ret.m_is_active = true; - return ret; -} - -void CompGraphCallbackValueProxy::setup( - const mgb::DeviceTensorND &val, bool eager_copy) { - - while (__atomic_load_n(&m_is_active, __ATOMIC_SEQ_CST)) { - // wait for previous callback to finish - std::this_thread::yield(); - } - - mgb_assert(!m_use_raw_hv && val.shape_valid()); - m_eager_copy = eager_copy; - m_dev_value = val; - if (eager_copy) { - m_value_used = false; - do_copy(); - } else { - m_value_used = true; - } - - __atomic_store_n(&m_is_active, true, __ATOMIC_SEQ_CST); -} - -void CompGraphCallbackValueProxy::do_copy() { - mgb_assert(!m_use_raw_hv && m_dev_value.shape_valid()); - m_hv.copy_from(m_dev_value); - auto cn = m_hv.comp_node(); - if (!m_copy_event) - m_copy_event = cn.create_event(); - m_copy_event->record(); -} - -#if defined(WIN32) -#include -#include -#undef CONST -#define usleep Sleep -#endif -void CompGraphCallbackValueProxy::sync() const { - mgb_assert(!m_use_raw_hv); - RealTimer t0; - double next_warn_time = 2, warn_time_delta = 1; - while (!m_copy_event->finished()) { - //! sleep 1ms or sleep 1us no difference for performance on win32 - usleep(1); - if (t0.get_secs() >= next_warn_time) { - mgb_log_warn("wait d2h copy for more than %.3f secs", - t0.get_secs()); - next_warn_time += warn_time_delta; - warn_time_delta += 1; - } - } -} - -void CompGraphCallbackValueProxy::on_finished() { - mgb_assert(m_is_active && !m_use_raw_hv); - m_dev_value = {}; - if (m_hv.shape_valid()) { - m_hv.resize({}); // resize to reuse buffer - } - __atomic_store_n(&m_is_active, false, __ATOMIC_SEQ_CST); - if (!m_value_used) { - mgb_log_warn("computing graph callback did not read the value"); - } -} - -PyObject* CompGraphCallbackValueProxy::_get_npyarr() { - mgb_assert(m_is_active); - - if (!m_use_raw_hv) { - mgb_assert(m_dev_value.shape_valid()); - if (!m_hv.shape_valid()) { - do_copy(); - sync(); - } - } - m_value_used = true; - return npy::ndarray_from_tensor(m_hv, npy::ShareType::TRY_SHARE); -} - -PyObject* CompGraphCallbackValueProxy::_get_dtype() { - mgb_assert(m_is_active); - - if (m_use_raw_hv) - return npy::dtype_mgb2np(m_hv.dtype()); - - mgb_assert(m_dev_value.shape_valid()); - return npy::dtype_mgb2np(m_dev_value.dtype()); -} - -std::vector CompGraphCallbackValueProxy::_get_shape() { - mgb_assert(m_is_active); - - if (m_use_raw_hv) - return npy::shape2vec(m_hv.shape()); - - mgb_assert(m_dev_value.shape_valid()); - return npy::shape2vec(m_dev_value.shape()); -} - -uintptr_t CompGraphCallbackValueProxy::_pubapi_dev_tensor_ptr(int version) { - mgb_assert(m_is_active && !m_use_raw_hv); - mgb_assert(m_dev_value.shape_valid()); - void *ret; - if (version == 0) { - ret = m_dev_value.raw_ptr(); - } else { - init_pubapi_dev_tensor( - m_pubapi_dev_tensor, &m_dev_value, nullptr, true); - ret = &m_pubapi_dev_tensor; - } - return reinterpret_cast(ret); -} - -mgb::CompNode CompGraphCallbackValueProxy::_get_comp_node() { - mgb_assert(m_is_active && !m_use_raw_hv); - mgb_assert(m_dev_value.shape_valid()); - return m_dev_value.comp_node(); -} - -/* ================= AsyncExec ================= */ - -class AsyncExec::Core { - public: - Core(std::unique_ptr f): - m_func(std::move(f)) - { - } - - mgb::cg::AsyncExecutable* func() const { - return m_func.get(); - } - - struct CallbackParam { - std::vector value; - _CompGraphCallback *cb; - }; - - void dispatch_callback(const CallbackParam ¶m) { - m_worker.add_task(param); - } - - void wait_callback_finish() { - m_worker.wait_all_task_finish(); - } - - private: - std::unique_ptr m_func; - - class Worker final: public AsyncQueueSC { - public: - void process_one_task(const CallbackParam &task) { - for (auto &tmp_value: task.value) { - tmp_value.sync(); - } - task.cb->call_pycb(); - } - }; - Worker m_worker; -}; - -AsyncExec::AsyncExec(std::unique_ptr f): - m_core(std::make_shared(std::move(f))) -{ -} - -AsyncExec::~AsyncExec() { - if (m_core) - _wait(); -} - -AsyncExec::Core* AsyncExec::core() const { - return m_core.get(); -} - -void AsyncExec::_execute() { - m_core->func()->execute(); -} - -std::string AsyncExec::_to_json_str() { - auto jv = m_core->func()->to_json(); - return jv->to_string(); -} - -void AsyncExec::_wait() { - m_core->wait_callback_finish(); - m_core->func()->wait(); -} - -double AsyncExec::_get_prev_exec_time() { - return m_core->func()->get_prev_exec_time(); -} - -SymbolVarArray AsyncExec::_find_mutable_input() { - ThinHashSet used_set; - UserInputVars* user_vars = nullptr; - auto cb = [&](cg::OperatorNodeBase* opr) { - if (!user_vars) { - ComputingGraph* g; - if (m_multi_part_par_graph) - g = m_multi_part_par_graph.get(); - else - g = opr->owner_graph(); - user_vars = &UserInputVars::get(g); - } - if (auto var = user_vars->check(opr)) { - used_set.insert(var); - } - return true; - }; - m_core->func()->iter_opr_seq(cb); - for (auto i : m_core->func()->get_rt_static_source_deps()) { - cb(i.dest->owner_opr()); - } - SymbolVarArray ret; - ret.reserve(used_set.size()); - ret.insert(ret.begin(), used_set.begin(), used_set.end()); - return ret; -} - -void AsyncExec::clear_device_memory() { - _wait(); - m_core->func()->clear_device_memory(); -} - -std::vector> -AsyncExec::_update_static_alloc_plan_and_get_size() { - std::vector> ret; - for (auto&& i : m_core->func()->update_static_alloc_plan_and_get_size()) { - ret.emplace_back(i.first, i.second); - } - return ret; -} - -/* ================= _CompGraphCallback ================= */ - -void _CompGraphCallback::set_async_exec(const AsyncExec &ae) { - mgb_assert(!m_ae_core); - m_ae_core = ae.core(); -} - -void _CompGraphCallback::set_eager_copy(bool flag) { - mgb_assert(!m_cb_created); - m_eager_copy = flag; -} - -std::function &)> _CompGraphCallback::make_multi_input_callback() { - mgb_assert(!m_cb_created); - m_cb_created = true; - - // shared_ptr would delete this afterwards - std::shared_ptr <_CompGraphCallback> self(this); - - auto cb = [self](SmallVector &data) { - for (size_t i = self->m_value_proxies.size(); i < data.size(); ++i) { - self->m_value_proxies.emplace_back(); - } - if (self->m_eager_copy) { - mgb_assert(self->m_ae_core); - for (size_t i = 0; i < self->m_value_proxies.size(); ++i) { - self->m_value_proxies[i].setup(data[i], true); - } - self->m_ae_core->dispatch_callback( - AsyncExec::Core::CallbackParam{self->m_value_proxies, self.get()} - ); - } else { - for (size_t i = 0; i < self->m_value_proxies.size(); ++i) - self->m_value_proxies[i].setup(data[i], false); - self->call_pycb(); - } - }; - - return cb; -} - -std::function _CompGraphCallback::make_callback() { - this->m_value_proxies.emplace_back(); - mgb_assert(!m_cb_created); - m_cb_created = true; - - // shared_ptr would delete this afterwards - std::shared_ptr <_CompGraphCallback> self(this); - - auto cb = [self](mgb::DeviceTensorND &data) { - if (self->m_eager_copy) { - mgb_assert(self->m_ae_core); - self->m_value_proxies[0].setup(data, true); - self->m_ae_core->dispatch_callback( - AsyncExec::Core::CallbackParam{self->m_value_proxies, self.get()} - ); - } else { - self->m_value_proxies[0].setup(data, false); - self->call_pycb(); - } - }; - - return cb; -} - -void _CompGraphCallback::call_pycb() { - try { - call(m_value_proxies); - } catch (...) { - for(auto &m_value_proxy: m_value_proxies) { - m_value_proxy.on_finished(); - } - throw; - } - for(auto &m_value_proxy: m_value_proxies) { - m_value_proxy.on_finished(); - } -} - -/* ================= CompGraph ================= */ - -class CompGraph::PyUserData final: public UserDataContainer::UserData, - public NonCopyableObj { - MGB_TYPEINFO_OBJ_DECL; - - PyObject *m_obj; - - public: - - PyUserData() { - PYTHON_GIL; - m_obj = PyDict_New(); - mgb_assert(m_obj, "failed to create python object"); - } - - ~PyUserData() { - PYTHON_GIL; - Py_DECREF(m_obj); - } - - PyObject* get() const { - return m_obj; - } -}; -MGB_TYPEINFO_OBJ_IMPL(CompGraph::PyUserData); - -mgb::ComputingGraph& CompGraph::get() const { - if (m_comp_graph_own) - return *m_comp_graph_own; - auto &&val = m_comp_graph_borrow.lock(); - mgb_assert(val, "CompGraph has been destructed"); - return *val; -} - -void CompGraph::clear_device_memory() { - if (!m_comp_graph_own) - return; - m_comp_graph_own->clear_device_memory(); -} - -PyObject* CompGraph::_user_data() { - auto ct = get().options().user_data.get_user_data_or_create(); - auto ret = ct->get(); - PYTHON_GIL; - Py_INCREF(ret); - return ret; -} - -void CompGraph::_add_output_spec( - mgb::cg::SymbolVar &var, _CompGraphCallback *callback) { - - cg::ComputingGraph::Callback cb; - if (callback) { - cb = callback->make_callback(); - m_raw_callbacks.push_back({callback, m_out_specs.size() - 1}); - } - if (m_out_specs.empty()) { - m_out_specs.emplace_back(); - } - m_out_specs.back().push_back({var, cb}); -} - -AsyncExec CompGraph::_do_compile(bool copy, bool optimize_for_inference) { - mgb_assert(m_out_specs.size() == 1, "got %zu output specs for compile", - m_out_specs.size()); - auto&& spec = m_out_specs[0]; - if (optimize_for_inference) { - SymbolVarArray vars; - vars.reserve(spec.size()); - for (auto&& i : spec) { - vars.push_back(i.first); - } - vars = gopt::optimize_for_inference(vars, {}); - mgb_assert(vars.size() == spec.size()); - for (size_t i = 0; i < vars.size(); ++i) { - spec[i].first = vars[i]; - } - } - - std::unique_ptr async_executable; - if (get().options().eager_evaluation || - (copy && get().current_comp_seq())) { - // need to copy a new comp graph - SymbolVarArray vars; - vars.reserve(spec.size()); - for (auto&& i : spec) { - vars.emplace_back(i.first); - } - - // copy graph - auto new_graph = mgb::ComputingGraph::make(); - SymbolVarArray new_vars = - replace_vars_comp_graph(std::move(vars), new_graph.get()); - mgb_assert(new_vars.size() == spec.size()); - - // register input - auto h2d = find_h2d(new_vars); - for (auto&& i : h2d) { - UserInputVars::get(new_graph.get()).register_var(i); - } - - mgb::ComputingGraph::OutputSpec new_spec; - new_spec.reserve(spec.size()); - for (size_t i = 0; i < spec.size(); ++i) { - new_spec.emplace_back(mgb::ComputingGraph::OutputSpecItem{ - new_vars[i], spec[i].second}); - } - async_executable = new_graph->compile(new_spec); - } else { - async_executable = get().compile(spec); - } - - AsyncExec ret{std::move(async_executable)}; - - for (auto&& i : m_raw_callbacks) { - mgb_assert(!i.second); - i.first->set_async_exec(ret); - } - _clear_output_spec(); - return ret; -} - -std::vector CompGraph::_do_compile_multi_part() { - // last spec is empty due to an extra call to _add_multi_part_endpoint() - mgb_assert(m_out_specs.size() > 1 && m_out_specs.back().empty(), - "got %zu output specs for multi-part compile", - m_out_specs.size()); - m_out_specs.pop_back(); - std::vector ret; - ret.reserve(m_out_specs.size()); - auto graph = get().shared_from_this(); - for (auto&& i : graph->compile_multi_part(m_out_specs)) { - ret.emplace_back(std::move(i)); - } - for (auto&& i : ret) { - i.set_multi_part_par_graph(graph); - } - for (auto&& i : m_raw_callbacks) { - i.first->set_async_exec(ret.at(i.second)); - } - _clear_output_spec(); - return ret; -} - -/* ================= SharedScalar ================= */ - -SharedScalar::SharedScalar(PyObject *val): - m_val{std::make_shared()} -{ - _set(val); -} - -HostTensorND& SharedScalar::val_as_host_nd() { - if (m_val_as_host_nd.empty()) { - HostTensorStorage storage; - storage.reset(CompNode::default_cpu(), m_val->dtype().size(), - {m_val, static_cast( - const_cast(m_val->storage()))}); - m_val_as_host_nd.reset(storage, {TensorShape{1}, m_val->dtype()}); - } - return m_val_as_host_nd; -} - -void SharedScalar::_set(PyObject *val) { - auto tensor = npy::np2tensor(val, npy::Meth::borrow(), {}); - mgb_assert(tensor.layout().is_scalar(), - "value given to SharedScalar must be scalar; got shape %s", - tensor.shape().to_string().c_str()); - if (m_dtype_locked) { - mgb_assert(tensor.dtype() == m_val->dtype(), - "dtype for SharedScalar has been locked as %s, " - "but attempt to set it to %s", m_val->dtype().name(), - tensor.dtype().name()); - } - m_val->set_raw(tensor.dtype(), tensor.raw_ptr()); - - if (!m_dev_val.empty()) { - auto &&hv = val_as_host_nd(); - for (auto &&i: m_dev_val) - i.second->copy_from_fixlayout(hv); - } -} - -PyObject* SharedScalar::_get() { - HostTensorND hv{CompNode::default_cpu(), TensorShape{1}, m_val->dtype()}; - memcpy(hv.raw_ptr(), m_val->storage(), m_val->dtype().size(1)); - return npy::ndarray_from_tensor(hv, npy::ShareType::TRY_SHARE); -} - -SymbolVar SharedScalar::_as_sym_var(CompGraph &cg, mgb::CompNode &cn) { - m_dtype_locked = true; - auto &&dv = m_dev_val[cn]; - auto &&hv = val_as_host_nd(); - if (!dv) { - dv = std::make_shared(cn); - dv->copy_from(hv); - } - return opr::SharedDeviceTensor::make(cg.get(), dv, - ssprintf("SharedScalar@%p", m_val.get())); -} - -/* =============== Operator =============== */ - -const std::unique_ptr Operator::sm_opr_footprint_ptr{ - std::make_unique()}; - -/* ================= misc ================= */ - -SymbolVar fill_retain_dtype(SymbolVar var, PyObject *value) { - auto tensor = npy::np2tensor(value, npy::Meth::borrow(), {}); - mgb_assert(tensor.shape().is_scalar(), - "value for fill_retain_dtype must be scalar; got shape %s", - tensor.shape().to_string().c_str()); - switch (tensor.dtype().enumv()) { -#define cb(_dt) case DTypeTrait<_dt>::enumv: \ - static_assert(sizeof(DTypeTrait<_dt>::ctype) <= sizeof(int), \ - "bad dtype size"); \ - return var.fill_retain_dtype(static_cast( \ - *tensor.ptr::ctype>())); - MEGDNN_FOREACH_COMPUTING_DTYPE_INT(cb) -#undef cb - case DTypeEnum::Float32: - return var.fill_retain_dtype(*tensor.ptr()); - case DTypeEnum::Float16: - return var.fill_retain_dtype( - static_cast(*tensor.ptr())); - case DTypeEnum::BFloat16: - return var.fill_retain_dtype( - static_cast(*tensor.ptr())); - // TODO: What does this mean? - case DTypeEnum::Quantized8Asymm: - case DTypeEnum::QuantizedS32: - case DTypeEnum::QuantizedS8: - case DTypeEnum::Quantized4Asymm: - case DTypeEnum::QuantizedS4: - case DTypeEnum::Byte: - case DTypeEnum::QuantizedS16: - case DTypeEnum::Bool: - break; -#define cb(low_bit, size) \ - case DTypeEnum::low_bit##size: \ - break; -MEGDNN_FOREACH_LOWBIT_DTYPE(cb) -#undef cb - - } - throw ConversionError(ssprintf( - "unsupported value dtype: %s", tensor.dtype().name())); -} - -PyObject* get_symvar_inferred_value(mgb::SymbolVar symvar) { - auto var = symvar.node(); - auto&& mgr = var->owner_graph()->static_infer_manager(); - using IT = cg::static_infer::InferType; - auto it = mgr.get_infer_type(var); - if (!(it.value & (IT::CONST | IT::RT_STATIC))) - Py_RETURN_NONE; - - auto val = mgr.infer_value_fallible(var); - if (!val) - Py_RETURN_NONE; - - auto hv = HostTensorND::make_proxy(*val); - return npy::ndarray_from_tensor(hv, npy::ShareType::MUST_UNSHARE); -} - -void _mgb_global_finalize() { - CompNode::finalize(); - g_global_finalize_called = true; -} - -bool global_finalized() { - return g_global_finalize_called; -} - -std::vector _get_mgb_version() { - return {MGB_MAJOR, MGB_MINOR, MGB_PATCH, MGB_IS_DEV}; -} - -SymbolVarArray _grad(SymbolVar target, SymbolVarArray wrts, - bool warn_mid_wrt, int use_virtual_grad, - bool return_zero_for_nodep) { - if (use_virtual_grad == -1) { - use_virtual_grad = std::abs( - target.node()->owner_graph()->options().graph_opt_level) >= 2; - } - - if (use_virtual_grad) { - mgb_assert(return_zero_for_nodep, - "can't return a null var when using virtual grad opr"); - SymbolVarArray ret; - ret.reserve(wrts.size()); - for (auto&& wrt : wrts) { - ret.push_back(opr::VirtualGrad::make(target, wrt)); - } - return ret; - } - return cg::grad(target, wrts, warn_mid_wrt, return_zero_for_nodep); -} - -SymbolVar _inter_graph_trans_var( - CompGraph &dest_graph, SymbolVar src) { - auto &&graph = dest_graph.get(); - auto trans = mgb::cg::InterGraphVarTransformer::get(graph); - mgb_assert(trans, "trans func on graph %p has not been setup", &graph); - return trans->trans(src.node()); -} - -SymbolVar _get_graph_optimizer_replaced_var(SymbolVar src) { - return gopt::GraphOptimizer::var_replace_lookup(src.node()); -} - -void mark_as_input(ComputingGraph* cg, SymbolVar var) { - VarNode* node = var.node(); - mgb_assert(node->owner_graph() == cg); - mgb_assert(node->owner_opr()->same_type()); - UserInputVars::get(cg).register_var(var); -} - -namespace { - -void add_update_impl(const DeviceTensorND& dest, - const DeviceTensorND& delta_nobrd, - float alpha, float beta, float bias) { - auto&& cn = dest.comp_node(); - using DT = CompNode::DeviceType; - mgb_assert(cn == delta_nobrd.comp_node() && - (cn.device_type() == DT::CUDA || cn.device_type() == DT::CPU || - cn.device_type() == DT::ROCM)); - mgb_assert(dest.dtype() == delta_nobrd.dtype()); - auto&& delta = delta_nobrd.sub(SubTensorSpec::make_from_offset_elem( - delta_nobrd.layout().broadcast(dest.shape()), 0)); - cn.activate(); - if (!static_cast(alpha) && beta == 1 && - !static_cast(bias)) { - dest.copy_from_fixlayout(delta); - } else { - auto&& handle = MegDNNHandle::get( - CompNodeEnv::from_comp_node(cn)).handle(); - auto&& op = handle->create_operator(); - op->param() = {alpha, beta, bias}; - op->exec(dest.as_megdnn(), delta.as_megdnn()); - if (cn.device_type() == DT::CPU && cn != CompNode::default_cpu()) { - CompNodeEnv::from_comp_node(cn).cpu_env().dispatch( - [p = op.release()] { delete p; } - ); - } - } -} - -} // anonymous namespace - -void _add_update_fastpath(SharedND& dest_, SharedND& delta_, - float alpha, float beta, float bias) { - auto&& dest = dest_.dev_tensor(); - auto&& delta = delta_.dev_tensor(); - add_update_impl(*dest, *delta, alpha, beta, bias); -} - -void _add_update_fastpath(SharedND& dest_, CompGraphCallbackValueProxy& delta_, - float alpha, float beta, float bias) { - auto&& dest = dest_.dev_tensor(); - auto&& delta = delta_.dev_tensor(); - add_update_impl(*dest, delta, alpha, beta, bias); -} - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/megbrain_wrap.h b/python_module/src/cpp/megbrain_wrap.h deleted file mode 100644 index 106457ee..00000000 --- a/python_module/src/cpp/megbrain_wrap.h +++ /dev/null @@ -1,491 +0,0 @@ -/** - * \file python_module/src/cpp/megbrain_wrap.h - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \brief wrappers for basic functionalities - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - -#pragma once - -#include "./python_helper.h" -#include "./megbrain_pubapi.h" - -#include "megbrain/graph.h" -#include "megbrain/opr/io.h" - -#include "megbrain/plugin/opr_footprint.h" - -#include -#include - -class CompGraph; -class CompGraphCallbackValueProxy; - -/*! - * \brief proxy a mgb::DeviceTensorND or a SymbolVar - */ -class SharedND { - mgb::pubapi::DeviceTensor m_pubapi_dev_tensor; - - std::shared_ptr m_dev_tensor; - mgb::HostTensorND m_async_copy_refkeeper; - mgb::VarNode *m_var = nullptr; - bool m_copy_sync = true; - - bool sync(mgb::DeviceTensorND &dv); - inline void _check_before_share_memory(const SharedND& rhs); - - public: - SharedND() = default; - - SharedND(mgb::CompNode node, PyObject* dtype): - m_dev_tensor(std::make_shared( - node, npy::dtype_np2mgb(dtype))) - { } - - SharedND(const std::shared_ptr& dv) - : m_dev_tensor(dv) {} - - //! set init shape; can be only called once - void _set_init_shape(const std::vector &shape); - - //! resize to given shape - void _resize(const std::vector &shape); - - //! reset dev_tensor to zeros - void _reset_zero(); - - /*! - * \brief assign to proxy given dev tensor; used by craniotome - */ - void assign(const mgb::DeviceTensorND &dv) { - mgb_assert(!m_dev_tensor && !m_var); - m_dev_tensor = std::make_shared(dv); - } - - /*! - * \brief assign to proxy a var node; used by craniotome - */ - void assign(mgb::VarNode *var) { - mgb_assert(!m_dev_tensor && !m_var); - m_var = var; - } - - /*! - * \brief share memory from another SharedND; only used in ParamPack - */ - void _share_memory_from(const SharedND& rhs, size_t begin); - - /*! - * \brief reset dev_tensor to another SharedNd's - */ - void _reset_dev_tensor(const SharedND& rhs); - - uintptr_t _pubapi_dev_tensor_ptr(int version); - - mgb::SymbolVar _as_sym_var(CompGraph &cg, const std::string &name, - bool volatile_); - - mgb::CompNode _get_comp_node() { - return m_dev_tensor->comp_node(); - } - - void _set_copy_sync(bool flag) { - m_copy_sync = flag; - } - - //! get dev buffer from shared nd - const std::shared_ptr& dev_tensor() { - return m_dev_tensor; - } - - void _copy_from_npyarr(PyObject *npyarr); - void _copy_from_value_proxy(CompGraphCallbackValueProxy &value); - void _share_from_value_proxy(CompGraphCallbackValueProxy &value); - static SharedND _from_symvar(mgb::SymbolVar symvar); - - //! get numpy ndarray that contains a copy of the value; return new ref - PyObject* _get_npyarr(); - PyObject* _get_dtype(); - std::vector _get_shape(); - - /*! - * \brief copy to sub of this from another SharedND - * \param axis axis for sub, or -1 to work on flattened array - */ - void copy_to_sub_from_shared( - int axis, ptrdiff_t begin, ptrdiff_t end, ptrdiff_t step, - const SharedND &rhs); - - /*! - * \brief copy from sub of another SharedND to this - * \param axis axis for sub, or -1 to work on flattened array, -2 to - * copy whole tensor, -3 to copy whole tensor fixlayout - */ - void copy_from_shared_sub(const SharedND &rhs, - int axis, ptrdiff_t begin, ptrdiff_t end, ptrdiff_t step); -}; - -/*! - * \brief wraps around shared pointer to mgb::HostTensorND - */ -class _HostSharedND { - bool m_own_storage = false, m_borrow_on_cpu = false; - std::shared_ptr m_tensor; - //! set to non-null if this _HostSharedND is set to proxy a var - mgb::opr::Host2DeviceCopy* m_proxied_opr = nullptr; - - void ensure_own_storage(); - - public: - _HostSharedND() = default; - - _HostSharedND(const _HostSharedND &rhs): - m_own_storage{false}, - m_tensor{rhs.m_tensor}, - m_proxied_opr{rhs.m_proxied_opr} - { - } - - _HostSharedND(mgb::CompNode node, mgb::DType dtype): - m_own_storage{true}, - m_tensor{std::make_shared(node, dtype)} - { - } - - _HostSharedND(mgb::CompNode node, PyObject* dtype): - _HostSharedND(node, npy::dtype_np2mgb(dtype)) - { - } - - _HostSharedND& operator = (const _HostSharedND &) = delete; - - /*! - * \brief make a _HostSharedND by proxing a var produced by - * Host2DeviceCopy - */ - static _HostSharedND make_proxy(mgb::SymbolVar var); - - mgb::SymbolVar _as_sym_var(CompGraph &cg, bool enable_static_infer, - const std::string &name); - - void _resize(const std::vector &shape); - void _copy_from_npyarr(PyObject *npyarr, bool borrow); - - void _enable_borrow_on_cpu(bool flag) { - m_borrow_on_cpu = flag; - } - - std::string __repr__() const; - PyObject* _get_dtype(); -}; - -/*! - * \brief proxy a value to be passed to computing graph callback - */ -class CompGraphCallbackValueProxy { - mgb::pubapi::DeviceTensor m_pubapi_dev_tensor; - bool m_is_active = false; //! setup called but on_finished not called - bool m_use_raw_hv = false; - bool m_value_used, m_eager_copy; - mgb::HostTensorND m_hv; - std::shared_ptr m_copy_event; - - //! original dev value - mgb::DeviceTensorND m_dev_value; - - //! perform D2H copy - void do_copy(); - - public: - static CompGraphCallbackValueProxy make_raw_host_value_proxy( - const mgb::HostTensorND &hv); - - bool eager_copy() const { - return m_eager_copy; - } - - mgb::DeviceTensorND& dev_tensor() { - return m_dev_value; - } - - void setup(const mgb::DeviceTensorND &val, bool eager_copy); - void sync() const; - - /*! - * \brief called after python callback returned - */ - void on_finished(); - - //! get numpy ndarray that contains a copy of the value; return new ref - PyObject* _get_npyarr(); - PyObject* _get_dtype(); - std::vector _get_shape(); - - uintptr_t _pubapi_dev_tensor_ptr(int version); - - mgb::CompNode _get_comp_node(); -}; - -class AsyncExec { - public: - class Core; - - AsyncExec() = default; - - ~AsyncExec(); - - AsyncExec(std::unique_ptr f); - - void _execute(); - void _wait(); - double _get_prev_exec_time(); - - void clear_device_memory(); - - std::vector> - _update_static_alloc_plan_and_get_size(); - - std::string _to_json_str(); - - /*! - * \brief find all Host2DeviceCopy input vars that are mutable (i.e. - * used as func args) - */ - mgb::SymbolVarArray _find_mutable_input(); - - Core* core() const; - - void set_multi_part_par_graph(std::shared_ptr g) { - m_multi_part_par_graph = std::move(g); - } - - private: - std::shared_ptr m_core; - //! parent graph in multi-part compiling - std::shared_ptr m_multi_part_par_graph; -}; - -/*! - * \brief callback wrapper for computing graph - */ -class _CompGraphCallback { - bool m_cb_created = false, m_eager_copy = false; - AsyncExec::Core* m_ae_core = nullptr; - std::vector m_value_proxies; - - public: - /*! - * \brief set AsyncExec associated with this callback; if it is set, - * eager value copy would be enabled - */ - void set_async_exec(const AsyncExec &ae); - - /*! - * \brief set whether enabling eager copy - * - * If eager copy is enabled, host to device copy would start immediately - * and asynchronously when this callback is executed by megbrain - */ - void set_eager_copy(bool flag); - - virtual ~_CompGraphCallback() = default; - - std::function &)> make_multi_input_callback(); - std::function make_callback(); - - /*! - * \brief call python callback - */ - void call_pycb(); - - /*! - * \brief python callback to be overwritten - */ - virtual void call(std::vector&) = 0; -}; - -/*! - * \brief wrap around shared mgb::ComputingGraph - */ -class CompGraph { - class PyUserData; - - mgb::SmallVector m_out_specs; - //! (callback, output spec part) - mgb::SmallVector> m_raw_callbacks; - - std::shared_ptr m_comp_graph_own; - std::weak_ptr m_comp_graph_borrow; - - explicit CompGraph(const std::shared_ptr& cg) - : m_comp_graph_own{cg} {} - - explicit CompGraph(const std::weak_ptr &cg): - m_comp_graph_borrow{cg} - {} - - public: - - CompGraph(): - m_comp_graph_own(mgb::ComputingGraph::make()) - {} - - // A mgb::cg::ComputingGraph may be wrapped in a CompGraph in two ways: - // 1. Borrowing a ComputingGraph. - // 2. Own a shared_ptr of ComputingGraph. - // We make constructors private and use factory function instead to make - // it explicit at the call site. (So-called "Named Constructor") - - /*! - * \brief Wrap a ComputingGraph by borrowing a reference. - */ - static CompGraph make_from_weak_ptr( - const std::weak_ptr& cg) { - return CompGraph{cg}; - } - - /*! - * \brief Wrap a ComputingGraph by owning one of its reference. - */ - static CompGraph make_from_shared_ptr( - const std::shared_ptr& cg) { - return CompGraph{cg}; - } - - CompGraph(const mgb::cg::SymbolVarArray& dest_symbol_vars) { - m_comp_graph_own = mgb::ComputingGraph::make(); - mgb::cg::replace_vars_comp_graph(dest_symbol_vars, - m_comp_graph_own.get()); - } - - void clear_device_memory(); - - //! get underlying ComputingGraph instance - mgb::ComputingGraph& get() const; - - CompGraph& share_device_memory_with(CompGraph &other) { - get().share_device_memory_with(other.get()); - return *this; - } - - //! get a dict to store arbitrary user data - PyObject* _user_data(); - - AsyncExec _do_compile(bool copy, bool optimize_for_inference); - std::vector _do_compile_multi_part(); - - /*! - * \brief add an output spec - * \param callback callback to be invoked; or nullptr for computing - * output var only - */ - void _add_output_spec(mgb::cg::SymbolVar &var, - _CompGraphCallback *callback); - - //! mark currently added output specs as a part in multi-part compile - void _add_multi_part_endpoint() { - m_out_specs.emplace_back(); - } - - void _clear_output_spec() { - m_raw_callbacks.clear(); - m_out_specs.resize(1); - m_out_specs[0].clear(); - } - - size_t _release() { - if (m_comp_graph_own) { - auto ret = m_comp_graph_own.use_count(); - m_comp_graph_own.reset(); - return ret; - } - m_comp_graph_borrow.reset(); - return 0; - } - -}; - -//! wrap shared_ptr -class SharedScalar { - bool m_dtype_locked = false; - std::shared_ptr m_val; - mgb::HostTensorND m_val_as_host_nd; - mgb::CompNode::UnorderedMap> m_dev_val; - - mgb::HostTensorND& val_as_host_nd(); - - public: - SharedScalar(PyObject *val); - void _set(PyObject *val); - PyObject* _get(); - mgb::SymbolVar _as_sym_var(CompGraph &cg, mgb::CompNode &cn); - - void _lock_dtype() { - m_dtype_locked = true; - } - - bool _dtype_locked() { - return m_dtype_locked; - } - - const std::shared_ptr& get_val() const { - return m_val; - } -}; - -/*! - * \brief wrap around shared mgb::cg::OperatorNodeBase - */ -class Operator { - mgb::cg::OperatorNodeBase* m_operator_node; - std::string m_params; - - static const std::unique_ptr sm_opr_footprint_ptr; - -public: - Operator() : m_operator_node(nullptr){}; - Operator(mgb::cg::OperatorNodeBase* operator_node) - : m_operator_node(operator_node), - m_params(std::move( - (sm_opr_footprint_ptr->calc_footprint(m_operator_node)).param->to_string())) - {} - - size_t id() const { return m_operator_node->id(); } - - const std::string& name() const { return m_operator_node->name(); } - - const std::string& params() const { return m_params; } - - const std::shared_ptr get_owner_graph() const { - return m_operator_node->owner_graph()->shared_from_this(); - } - - const mgb::SymbolVarArray inputs() const { - return mgb::cg::to_symbol_var_array(m_operator_node->input()); - } - - const mgb::SymbolVarArray outputs() const { - return mgb::cg::to_symbol_var_array(m_operator_node->output()); - } - - mgb::cg::OperatorNodeBase* node() const { return m_operator_node; } -}; - -//! get inferred value as numpy ndarray or None -PyObject* get_symvar_inferred_value(mgb::SymbolVar var); - -mgb::SymbolVar fill_retain_dtype(mgb::SymbolVar var, PyObject* value); - -//! whether _mgb_global_finalize() has been called -bool global_finalized(); - -#ifndef SWIG -void mark_as_input(mgb::cg::ComputingGraph* cg, mgb::cg::SymbolVar var); -#endif - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/numpy_incl.h b/python_module/src/cpp/numpy_incl.h deleted file mode 100644 index 02a2b693..00000000 --- a/python_module/src/cpp/numpy_incl.h +++ /dev/null @@ -1,37 +0,0 @@ -/** - * \file python_module/src/cpp/numpy_incl.h - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \brief import numpy array with proper settings - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ -#pragma once - -#define PY_ARRAY_UNIQUE_SYMBOL mgb_numpy_array_api -#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION -#include - -#define FOREACH_MGB_LOW_BIT(cb) \ - cb(1) \ - cb(2) \ - cb(4) \ - -#define FOREACH_MGB_DTYPE_PAIR(cb) \ - cb(IntB1, npy_num_intb1()) \ - cb(IntB2, npy_num_intb2()) \ - cb(IntB4, npy_num_intb4()) \ - cb(BFloat16, npy_num_bfloat16()) - -namespace mgb { - //! numpy type num for intb2 type -#define DEFINE_NPY_INTBX(n) \ - int npy_num_intb##n(); -FOREACH_MGB_LOW_BIT(DEFINE_NPY_INTBX) -#undef DEFINE_NPY_INTBX - int npy_num_bfloat16(); -} - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/opr_defs.cpp b/python_module/src/cpp/opr_defs.cpp deleted file mode 100644 index 1fd4dbbb..00000000 --- a/python_module/src/cpp/opr_defs.cpp +++ /dev/null @@ -1,313 +0,0 @@ -/** - * \file python_module/src/cpp/opr_defs.cpp - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - -#include "./opr_defs.h" -#include "./opr_helper.h" -#include "./python_helper.h" - -#if MGB_ENABLE_OPR_MM -#include "megbrain/opr/mm_handler.h" -#endif - -#include "megbrain/opr/io.h" -#include "megbrain/serialization/extern_c_opr_io.h" - -using namespace mgb; -using namespace mgb::opr; - -SymbolVar _Opr::_axis_add_remove(SymbolVar src, - const std::vector& axis, bool is_add, - const OperatorNodeConfig &config) { - using ADR = mgb::opr::AxisAddRemove; - std::vector desc; - mgb_assert(!axis.empty()); - for (auto i: axis) { - if (is_add) { - desc.emplace_back(ADR::AxisDesc::make_add(i)); - } else { - desc.emplace_back(ADR::AxisDesc::make_remove(i)); - } - } - return ADR::make(src, desc, config); -} - -SymbolVarArray _Opr::param_pack_split( - SymbolVar src, const std::vector>& shapes, - const OperatorNodeConfig& config) { - auto size = shapes.size(); - mgb::TensorShapeArray shapearr(size); - for (size_t i = 0; i < size; i++) { - shapearr[i] = npy::vec2shape(shapes[i]); - } - - auto cn = src.node()->comp_node(); - auto offsets = megdnn::ParamPackConcat::gen_offsets( - shapearr, cn.get_mem_addr_alignment(), src.dtype().size()); - - return mgb::opr::ParamPackSplit::make(src, offsets, shapearr, config); -} - -#if MGB_ENABLE_OPR_MM -#include "megbrain/opr/lock.h" -#include "megbrain/opr/io_remote.h" - -SymbolVar _Opr::lock_acquire(SymbolVar var, size_t lock_id, size_t group_id, - const OperatorNodeConfig &config) { - return mgb::opr::LockAcquire::make(var, {lock_id, group_id}, config); -} - -SymbolVar _Opr::lock_release(SymbolVar var, size_t lock_id, size_t group_id, - const OperatorNodeConfig &config) { - return mgb::opr::LockRelease::make(var, {lock_id, group_id}, config); -} - -SymbolVar _Opr::remote_send( - const std::string& server_addr, const int port, - const std::string& key, SymbolVar var, - const bool is_grad, - const OperatorNodeConfig& config) { - return RemoteSend::make(key, var, - std::make_shared(ssprintf( - "%s:%d", server_addr.c_str(), port)), - is_grad, config); -} - -SymbolVar _Opr::remote_recv(const std::string& server_addr, const int port, - const std::string& key, CompGraph& graph, - const std::vector& shape, PyObject* dtype, - const OperatorNodeConfig& config) { - const TensorShape ishape = npy::vec2shape(shape); - const DType idtype = npy::dtype_np2mgb(dtype); - - return RemoteRecv::make(key, graph.get(), - std::make_shared( - ssprintf("%s:%d", server_addr.c_str(), port)), - config, ishape, idtype); -} - -SymbolVar _Opr::collective_comm_with_input( - SymbolVar inpvar, const std::string& key, const size_t nr_devices, - const bool is_root, const int rank, const bool local_grad, - const std::string& server_addr, const int port, PyObject* params, - PyObject* dtype, const std::string& backend, SharedND* output_buf, - const OperatorNodeConfig& config, const SharedScalar& disable) { - SymbolVarArray inputs(1, inpvar); - ComputingGraph* graph = inpvar.node()->owner_graph(); - auto group_mgr = std::make_shared( - ssprintf("%s:%d", server_addr.c_str(), port)); - SmallVector> dev_buffer_arr(1, nullptr); - if (output_buf) - dev_buffer_arr[0] = output_buf->dev_tensor(); - CollectiveComm::Param param = load_collective_comm_params(params, graph); - mgb::DType _dtype = DType(); - if (dtype != Py_None) { - _dtype = npy::dtype_np2mgb(dtype); - } - return CollectiveComm::make(inputs, graph, key, nr_devices, is_root, rank, - local_grad, group_mgr, dev_buffer_arr, param, - _dtype, backend, config, disable.get_val())[0]; -} - -SymbolVar _Opr::collective_comm_without_input( - CompGraph& cg, const std::string& key, const size_t nr_devices, - const bool is_root, const int rank, const bool local_grad, - const std::string& server_addr, const int port, PyObject* params, - PyObject* dtype, const std::string& backend, SharedND* output_buf, - const OperatorNodeConfig& config, const SharedScalar& disable) { - SymbolVarArray inputs; - auto& graph = cg.get(); - auto group_mgr = std::make_shared( - ssprintf("%s:%d", server_addr.c_str(), port)); - SmallVector> dev_buffer_arr(1, nullptr); - if (output_buf) - dev_buffer_arr[0] = output_buf->dev_tensor(); - CollectiveComm::Param param = load_collective_comm_params(params, &graph); - mgb::DType _dtype = DType(); - if (dtype != Py_None) { - _dtype = npy::dtype_np2mgb(dtype); - } - return CollectiveComm::make(inputs, &graph, key, nr_devices, is_root, rank, - local_grad, group_mgr, dev_buffer_arr, param, - _dtype, backend, config, disable.get_val())[0]; -} - -#else -namespace { - [[noreturn]] void on_opr_mm() { - mgb_throw(MegBrainError, "opr-mm disabled at compile time"); - } -} -SymbolVar _Opr::lock_acquire(SymbolVar var, size_t lock_id, size_t group_id, - const OperatorNodeConfig &config) { - on_opr_mm(); -} - -SymbolVar _Opr::lock_release(SymbolVar var, size_t lock_id, size_t group_id, - const OperatorNodeConfig &config) { - on_opr_mm(); -} - - -SymbolVar _Opr::remote_send( - const std::string& server_addr, const int port, - const std::string& key, SymbolVar var, - const bool is_grad, - const OperatorNodeConfig& config) { - on_opr_mm(); -} - -SymbolVar _Opr::remote_recv(const std::string& server_addr, const int port, - const std::string& key, CompGraph& graph, - const std::vector& shape, PyObject* dtype, - const OperatorNodeConfig& config) { - on_opr_mm(); -} - -SymbolVar _Opr::collective_comm_with_input( - SymbolVar inpvar, const std::string& key, const size_t nr_devices, - const bool is_root, const int rank, const bool local_grad, - const std::string& server_addr, const int port, PyObject* params, - PyObject* dtype, const std::string& backend, SharedND* output_buf, - const OperatorNodeConfig& config, const SharedScalar& disable) { - on_opr_mm(); -} - -SymbolVar _Opr::collective_comm_without_input( - CompGraph& cg, const std::string& key, const size_t nr_devices, - const bool is_root, const int rank, const bool local_grad, - const std::string& server_addr, const int port, PyObject* params, - PyObject* dtype, const std::string& backend, SharedND* output_buf, - const OperatorNodeConfig& config, const SharedScalar& disable) { - on_opr_mm(); -} - -#endif // MGB_ENABLE_OPR_MM - -SymbolVarArray _Opr::extern_c_opr_placeholder( - const SymbolVarArray& inputs, - const std::vector>& output_shapes, - PyObject* output_dtypes, const char* dump_name, PyObject* data_bytes, - const OperatorNodeConfig& config) { - mgb_assert(PyBytes_Check(data_bytes)); - if (output_dtypes != Py_None) { - mgb_assert(PyTuple_Check(output_dtypes)); - mgb_assert(output_shapes.size() == - static_cast(PyTuple_Size(output_dtypes))); - } - - TensorShapeArray cpp_output_shapes(output_shapes.size()); - for (size_t i = 0; i < output_shapes.size(); ++i) { - cpp_output_shapes[i] = npy::vec2shape(output_shapes[i]); - } - SmallVector cpp_output_dtypes; - if (output_dtypes != Py_None) { - size_t dtype_size = PyTuple_Size(output_dtypes); - for (size_t i = 0; i < dtype_size; ++i) { - cpp_output_dtypes.push_back( - npy::dtype_np2mgb(PyTuple_GetItem(output_dtypes, i))); - } - } - - auto opr = opr::ExternCOprRunner::make_placeholder( - inputs, cpp_output_shapes, dump_name, PyBytes_AsString(data_bytes), - PyBytes_Size(data_bytes), config, cpp_output_dtypes); - SymbolVarArray ret; - ret.reserve(opr->output().size()); - for (auto i: opr->output()) - ret.emplace_back(i); - return ret; -} - -#if MGB_ENABLE_TENSOR_RT - -#include "megbrain/tensorrt/tensorrt_runtime_opr.h" - -SymbolVarArray _Opr::tensor_rt_runtime(const SymbolVarArray& inputs, - PyObject* data_bytes, - const OperatorNodeConfig& config) { - mgb_assert(PyBytes_Check(data_bytes)); - auto size = PyBytes_Size(data_bytes); - mgb_assert(size, "trt data bytes should not be empty"); - return opr::TensorRTRuntimeOpr::make(PyBytes_AsString(data_bytes), - size, inputs, - config); -} -#else -SymbolVarArray _Opr::tensor_rt_runtime(const SymbolVarArray& inputs, - PyObject* data_bytes, - const OperatorNodeConfig& config) { - mgb_throw(MegBrainError, "TensorRT disabled at compile time"); -} -#endif - -#if MGB_ATLAS - -#include "megbrain/opr/atlas_runtime_op.h" - -SymbolVarArray _Opr::atlas_runtime(const SymbolVarArray& inputs, - PyObject* data_bytes, - const OperatorNodeConfig& config) { - mgb_assert(PyBytes_Check(data_bytes)); - auto size = PyBytes_Size(data_bytes); - mgb_assert(size, "atlas data bytes should not be empty"); - - return opr::AtlasRuntimeOpr::make(PyBytes_AsString(data_bytes), size, - inputs, config); -} -#else -SymbolVarArray _Opr::atlas_runtime(const SymbolVarArray& inputs, - PyObject* data_bytes, - const OperatorNodeConfig& config) { - mgb_throw(MegBrainError, "Atlas disabled at compile time"); -} -#endif - -SymbolVar _Opr::timestamp(SymbolVar input, PyObject* dest, size_t dest_off, - const OperatorNodeConfig& config) { - auto tensor = std::make_shared( - npy::np2tensor(dest, npy::Meth::must_borrow(), dtype::Float32{})); - return opr::Timestamp::make(input, std::move(tensor), dest_off, config); -} - -SymbolVar _Opr::virtual_loss(const SymbolVarArray& ys, - const SymbolVarArray& y_grads, - const OperatorNodeConfig& config) { - return opr::VirtualLoss::make(ys, y_grads, {}, config); -} - -SymbolVar _Opr::virtual_dep(const SymbolVarArray& symvars, - const OperatorNodeConfig& config) { - return opr::VirtualDep::make(symvars, config); -} - - -#if MGB_CAMBRICON -#include "megbrain/cambricon/cambricon_runtime_opr.h" - -SymbolVarArray _Opr::cambricon_runtime(PyObject* data_bytes, const char* symbol, - const SymbolVarArray& inputs, - bool tensor_dim_mutable, - const OperatorNodeConfig& config) { - mgb_assert(PyBytes_Check(data_bytes)); - auto size = PyBytes_Size(data_bytes); - mgb_assert(size, "cambricon data bytes should not be empty"); - return opr::CambriconRuntimeOpr::make(PyBytes_AsString(data_bytes), size, - symbol, inputs, tensor_dim_mutable, - config); -} -#else -SymbolVarArray _Opr::cambricon_runtime(PyObject* data_bytes, const char* symbol, - const SymbolVarArray& inputs, - bool tensor_dim_mutable, - const OperatorNodeConfig& config) { - mgb_throw(MegBrainError, "Cambricon disabled at compile time"); -} -#endif - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/opr_defs.h b/python_module/src/cpp/opr_defs.h deleted file mode 100644 index 29a70c5f..00000000 --- a/python_module/src/cpp/opr_defs.h +++ /dev/null @@ -1,170 +0,0 @@ -/** - * \file python_module/src/cpp/opr_defs.h - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \brief extra opr definitions - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - -#ifndef SWIG -#pragma once - -#include "./megbrain_wrap.h" -#include "./opr_helper.h" - -#if MGB_ENABLE_OPR_MM -#include "megbrain/opr/collective_comm.h" -#endif -#include "megbrain/opr/basic_arith.h" -#include "megbrain/opr/tensor_manip.h" -using mgb::SymbolVar; -using mgb::SymbolVarArray; -using mgb::OperatorNodeConfig; - -#endif - -class _Opr { - -public: - -// basic arith - -static SymbolVar add_update(SymbolVar dest, SymbolVar delta, - const SharedScalar &alpha, const SharedScalar &beta, - const SharedScalar &bias, const SharedScalar &disable, - const OperatorNodeConfig &config) { - return mgb::opr::AddUpdate::make(dest, delta, - {alpha.get_val(), beta.get_val(), bias.get_val(), disable.get_val()}, - config); -} - -// tensor manip - -static SymbolVarArray param_pack_split( - SymbolVar src, const std::vector>& shapes, - const OperatorNodeConfig& config); - -static SymbolVar dimshuffle(SymbolVar src, - const std::vector &pattern, size_t ndim, - const OperatorNodeConfig &config) { - return mgb::opr::Dimshuffle::make(src, pattern, ndim, config); -} - -static SymbolVar _axis_add_remove(SymbolVar src, - const std::vector& axis, bool is_add, - const OperatorNodeConfig &config); - -static SymbolVar callback_injector(SymbolVar src, _CompGraphCallback &callback, - const OperatorNodeConfig &config) { - return mgb::opr::CallbackInjector::make(src, callback.make_callback()); -} - -static SymbolVar callback_injector(SymbolVarArray src, _CompGraphCallback &callback, - const OperatorNodeConfig &config) { - return mgb::opr::CallbackInjector::make(src, callback.make_multi_input_callback()); -} - -static SymbolVar set_grad(SymbolVar src, _SetGradCallback &grad_getter, - const OperatorNodeConfig &config) { - return mgb::opr::SetGrad::make(src, grad_getter.make_callback(), config); -} - -// multi machine - -static SymbolVar lock_acquire(SymbolVar var, size_t lock_id, size_t group_id, - const OperatorNodeConfig &config); - -static SymbolVar lock_release(SymbolVar var, size_t lock_id, size_t group_id, - const OperatorNodeConfig &config); - -static SymbolVar remote_send( - const std::string& server_addr, const int port, - const std::string& key, SymbolVar var, - const bool is_grad, - const OperatorNodeConfig& config); - -static SymbolVar remote_recv(const std::string& server_addr, const int port, - const std::string& key, - CompGraph& graph, - const std::vector& shape, PyObject* dtype, - const OperatorNodeConfig& config); - -static SymbolVar collective_comm_with_input( - SymbolVar inpvar, const std::string& key, const size_t nr_devices, - const bool is_root, const int rank, const bool local_grad, - const std::string& server_addr, const int port, PyObject* params, - PyObject* dtype, const std::string& backend, SharedND* output_buf, - const OperatorNodeConfig& config, const SharedScalar& disable); - -static SymbolVar collective_comm_without_input( - CompGraph& graph, const std::string& key, const size_t nr_devices, - const bool is_root, const int rank, const bool local_grad, - const std::string& server_addr, const int port, PyObject* params, - PyObject* dtype, const std::string& backend, SharedND* output_buf, - const OperatorNodeConfig& config, const SharedScalar& disable); - -// misc -static SymbolVarArray extern_c_opr_placeholder( - const SymbolVarArray& inputs, - const std::vector>& output_shapes, - PyObject* dtypes, - const char* dump_name, PyObject* data_bytes, - const OperatorNodeConfig& config); - -static SymbolVarArray tensor_rt_runtime(const SymbolVarArray& inputs, - PyObject* data_bytes, - const OperatorNodeConfig& config); - - - -static SymbolVar timestamp(SymbolVar input, PyObject* dest, size_t dest_off, - const OperatorNodeConfig& config); - -static SymbolVar virtual_loss(const SymbolVarArray& ys, - const SymbolVarArray& y_grads, - const OperatorNodeConfig& config); - -static SymbolVar virtual_dep(const SymbolVarArray& symvars, - const OperatorNodeConfig& config); - -static SymbolVarArray atlas_runtime(const SymbolVarArray& inputs, - PyObject* data_bytes, - const OperatorNodeConfig& config); - - -static SymbolVarArray cambricon_runtime(PyObject* data_bytes, - const char* symbol, - const SymbolVarArray& inputs, - bool tensor_dim_mutable, - const OperatorNodeConfig& config); - -#ifdef SWIG -%pythoncode { - -@classmethod -def _make_axis_vec(cls, axis): - ret = _VectorInt() - if isinstance(axis, collections.Iterable): - for i in axis: - ret.push_back(i) - else: - ret.push_back(axis) - return ret - -@classmethod -def add_axis(cls, src, axis, config): - return cls._axis_add_remove(src, cls._make_axis_vec(axis), True, config) - -@classmethod -def remove_axis(cls, src, axis, config): - return cls._axis_add_remove(src, cls._make_axis_vec(axis), False, config) - -} // %pythoncode -#endif // SWIG - -}; - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/opr_helper.cpp b/python_module/src/cpp/opr_helper.cpp deleted file mode 100644 index 0baa47c2..00000000 --- a/python_module/src/cpp/opr_helper.cpp +++ /dev/null @@ -1,192 +0,0 @@ -/** - * \file python_module/src/cpp/opr_helper.cpp - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - -#include "./opr_helper.h" -#include "./megbrain_wrap.h" -#include "megbrain/opr/indexing.h" -#include "megbrain/opr/io.h" -#include "megbrain/serialization/opr_load_dump.h" - -using namespace mgb; - -namespace { - class OprParamsLoadContext final: public serialization::OprLoadContextRawPOD { - PyObject *m_params; - ComputingGraph *m_graph; - size_t m_nr_used_params = 0, m_param_size = 0; - size_t m_item_bytes_consumed = 0; - - void read_raw(void *dest, size_t size) override final { - mgb_assert(m_nr_used_params < m_param_size); - auto item = PyList_GetItem(m_params, m_nr_used_params); - mgb_assert(item, "failed to get item %zu", m_nr_used_params); - mgb_assert(PyBytes_Check(item), "list item must be bytes"); - auto item_size = PyBytes_Size(item); - mgb_assert(size < (SIZE_MAX >> 3)); - mgb_assert(m_item_bytes_consumed + size <= size_t(item_size)); - auto item_buf = PyBytes_AsString(item); - mgb_assert(item_size > 0 && item_buf); - memcpy(dest, item_buf + m_item_bytes_consumed, size); - m_item_bytes_consumed += size; - if (m_item_bytes_consumed == size_t(item_size)) { - ++ m_nr_used_params; - m_item_bytes_consumed = 0; - } - } - - std::shared_ptr load_tensor() override { - mgb_assert(0); - } - - std::shared_ptr load_tensor_shared() override { - mgb_assert(0); - } - - const serialization::GraphLoadConfig& config() const override { - mgb_assert(0); - } - - public: - OprParamsLoadContext(PyObject *params, ComputingGraph *graph): - m_params{params}, m_graph{graph} - { - mgb_assert(PyList_Check(params), "params must be a list"); - m_param_size = PyList_Size(params); - } - - ~OprParamsLoadContext() { - mgb_assert(m_nr_used_params == m_param_size, - "number of params mismatch"); - } - - ComputingGraph& graph() override { - return *m_graph; - } - }; -} // anonymous namespace - -_SplitPartCallback::callback_t _SplitPartCallback::make_callback() { - mgb_assert(!m_cb_created); - m_cb_created = true; - - std::shared_ptr<_SplitPartCallback> cb_ptr(this); - - auto cb = [cb_ptr](size_t sz) { - return cb_ptr->call(sz); - }; - - return cb; -} - -_SetGradCallback::callback_t _SetGradCallback::make_callback() { - mgb_assert(!m_cb_created); - m_cb_created = true; - - if (empty()) { - return {}; - } - - std::shared_ptr<_SetGradCallback> cb_ptr(this); - - auto cb = [cb_ptr](const opr::SetGrad& opr) { - auto graph = CompGraph::make_from_weak_ptr( - opr.owner_graph()->shared_from_this()); - return cb_ptr->call(graph); - }; - - return cb; -} - -_TimeoutCallback::callback_t _TimeoutCallback::make_callback() { - mgb_assert(!m_cb_created); - m_cb_created = true; - - std::shared_ptr<_TimeoutCallback> cb_ptr(this); - auto cb = [cb_ptr]() { - return cb_ptr->call(); - }; - return cb; -} - -mgb::SymbolVar _create_subtensor_like_opr( - const std::string &name, - const SymbolVarArray& inputs, - const std::vector &idx, - const mgb::OperatorNodeConfig &config) { -#define CHK1(_name, _opr) \ - if (name == _name) { \ - mgb_assert(inputs.size() == 1); \ - return opr::_opr::make(inputs[0], idx, config); \ - } -#define CHK2(_name, _opr) \ - if (name == _name) { \ - mgb_assert(inputs.size() == 2); \ - return opr::_opr::make(inputs[0], inputs[1], idx, config); \ - } - - CHK1("subtensor", Subtensor); - CHK2("set_subtensor", SetSubtensor); - CHK2("incr_subtensor", IncrSubtensor); - CHK1("mavi", IndexingMultiAxisVec); - CHK2("set_mavi", IndexingSetMultiAxisVec); - CHK2("incr_mavi", IndexingIncrMultiAxisVec); - CHK1("mesh_indexing", MeshIndexing); - CHK1("batched_mesh_indexing", BatchedMeshIndexing); - CHK2("incr_mesh_indexing", IncrMeshIndexing); - CHK2("set_mesh_indexing", SetMeshIndexing); - CHK2("batched_incr_mesh_indexing", BatchedIncrMeshIndexing); - CHK2("batched_set_mesh_indexing", BatchedSetMeshIndexing); - - mgb_throw(MegBrainError, "bad subtensor opr name: %s", name.c_str()); - -#undef CHK1 -#undef CHK2 -} - -SymbolVar _make_immutable(CompGraph &comp_graph, PyObject *npyarr, - PyObject *dtype, const mgb::cg::OperatorNodeConfig &config) { - - auto cn = config.get_single_comp_node(); - mgb_assert(cn.valid(), "invalid comp node given to make_tensor"); - DType dtype_mgb; - if (dtype && dtype != Py_None) - dtype_mgb = npy::dtype_np2mgb(dtype); - auto hv = npy::np2tensor(npyarr, npy::Meth::borrow(cn), dtype_mgb); - return opr::ImmutableTensor::make(comp_graph.get(), hv, config); -} - -SymbolVarArray _create_opr( - const char *name, const SymbolVarArray &inputs, - PyObject *params, const OperatorNodeConfig &config) { - mgb_assert(!inputs.empty()); - auto registry = serialization::OprRegistry::find_by_name(name); - mgb_assert(registry, "operator %s not found", name); - OprParamsLoadContext ctx{params, inputs[0].node()->owner_graph()}; - VarNodeArray vinputs(inputs.size()); - for (size_t i = 0; i < inputs.size(); ++ i) - vinputs[i] = inputs[i].node(); - auto opr = registry->loader(ctx, vinputs, config); - - SymbolVarArray ret; - for (auto i: opr->output()) { - if (!i->contain_flag(VarNode::Flag::VOLATILE_CONTENT)) - ret.push_back(i); - } - return ret; -} - -#if MGB_ENABLE_OPR_MM -mgb::opr::CollectiveComm::Param load_collective_comm_params( - PyObject* params, mgb::ComputingGraph* graph) { - OprParamsLoadContext ctx{params, graph}; - return ctx.read_param(); -} -#endif - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/opr_helper.h b/python_module/src/cpp/opr_helper.h deleted file mode 100644 index 15b49d5a..00000000 --- a/python_module/src/cpp/opr_helper.h +++ /dev/null @@ -1,71 +0,0 @@ -/** - * \file python_module/src/cpp/opr_helper.h - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \brief helper for wrapping special oprs - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - -#pragma once - -#include "./megbrain_wrap.h" - -#include "megbrain/opr/tensor_manip.h" -#include "megbrain/opr/utility.h" -#if MGB_ENABLE_OPR_MM -#include "megbrain/opr/collective_comm.h" -#endif -using AxisIndexer = mgb::opr::indexing::AxisIndexer; - -/*! - * \brief wrapping callbacks used for opr::Split::Options::make_callback - */ -class _SplitPartCallback { - bool m_cb_created = false; - - public: - virtual ~_SplitPartCallback() = default; - virtual std::vector call(size_t tot_size) = 0; - - using callback_t = mgb::opr::Split::Options::callback_t; - callback_t make_callback(); -}; - -class _SetGradCallback { - bool m_cb_created = false; - - public: - virtual ~_SetGradCallback() = default; - virtual mgb::SymbolVar call(CompGraph &graph) = 0; - virtual bool empty() = 0; - - using callback_t = mgb::opr::SetGrad::GradGetter; - callback_t make_callback(); -}; - -/*! - * \brief wrapping callbacks used for subclasses of opr::RemoteIOBase - */ -class _TimeoutCallback { - bool m_cb_created = false; - - public: - virtual ~_TimeoutCallback() = default; - /*! - * \brief Will be overrided by swig generated code, calls into Python. - */ - virtual bool call() = 0; - - using callback_t = mgb::thin_function; - callback_t make_callback(); -}; - -#if MGB_ENABLE_OPR_MM -mgb::opr::CollectiveComm::Param load_collective_comm_params( - PyObject* params, mgb::ComputingGraph* graph); -#endif - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/plugin.cpp b/python_module/src/cpp/plugin.cpp deleted file mode 100644 index 5ca4df44..00000000 --- a/python_module/src/cpp/plugin.cpp +++ /dev/null @@ -1,243 +0,0 @@ -/** - * \file python_module/src/cpp/plugin.cpp - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \brief helpers for debugging - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - -#include "./plugin.h" -#include "./python_helper.h" - -#include "megbrain/system.h" - -#include -#include -#include - -#ifdef WIN32 -#include -#else -#include -#include -#endif -#include - -/* ================= _InfkernFinderImpl ================= */ -size_t _InfkernFinderImpl::sm_id = 0; - -_InfkernFinderImpl::_InfkernFinderImpl(CompGraph &cg, bool record_input_value): - m_id{sm_id ++}, - m_comp_graph{cg.get().shared_from_this()}, - m_finder{m_comp_graph.get(), record_input_value} -{ -} - -size_t _InfkernFinderImpl::_write_to_file(const char *fpath) { - auto opr = m_finder.write_to_file(fpath); - if (opr) - return opr->id() + 1; - return 0; -} - -size_t _InfkernFinderImpl::_get_input_values_prepare(size_t opr_id) { - m_inp_val = m_finder.get_input_values(opr_id); - return m_inp_val.size(); -} - -const char* _InfkernFinderImpl::_get_input_values_var_name(size_t idx) { - return m_inp_val.at(idx).first->cname(); -} - -size_t _InfkernFinderImpl::_get_input_values_var_idx(size_t idx) { - return m_inp_val.at(idx).first->id(); -} - -size_t _InfkernFinderImpl::_get_input_values_run_id(size_t idx) { - return m_inp_val.at(idx).second.run_id; -} - -CompGraphCallbackValueProxy _InfkernFinderImpl::_get_input_values_val(size_t idx) { - return CompGraphCallbackValueProxy::make_raw_host_value_proxy( - m_inp_val.at(idx).second.val); -} - -std::string _InfkernFinderImpl::__repr__() { - return mgb::ssprintf( - "_InfkernFinderImpl(%zu,graph=%p)", m_id, m_comp_graph.get()); -} - -/* ================= _FastSignal ================= */ - -class _FastSignal::Impl { - using HandlerCallback = std::function; - bool m_worker_started = false; - std::mutex m_mtx; - std::thread m_worker_hdl; -#ifdef WIN32 - SECURITY_ATTRIBUTES win_sa = {sizeof(SECURITY_ATTRIBUTES), NULL, TRUE}; - HANDLE pipe_r, pipe_w; - DWORD bytes_r_w; -#else - int m_pfd[2]; //! pipe fds; write signal handlers, -1 for exit -#endif - std::unordered_map m_handler_callbacks; - - void worker() { - std::ostringstream oss; - oss << std::this_thread::get_id() << std::endl; - mgb_log("fast signal worker started in thread %s", oss.str().c_str()); - mgb::sys::set_thread_name("fastsgl"); - int signum; - for (;;) { -#ifdef WIN32 - if (ReadFile(pipe_r, &signum, sizeof(int), &bytes_r_w, NULL) == - NULL) { -#else - if (read(m_pfd[0], &signum, sizeof(int)) != sizeof(int)) { -#endif - if (errno == EINTR) - continue; - mgb_log_error("fast signal worker: " - "failed to read from self pipe: %s", - strerror(errno)); - return; - } - std::exception_ptr exc_ptr; - if (signum == -1) - return; - try { - HandlerCallback *cb; - { - MGB_LOCK_GUARD(m_mtx); - cb = &m_handler_callbacks.at(signum); - } - (*cb)(); - } MGB_CATCH_ALL_EXCEPTION("fast signal worker", exc_ptr); - } - } - - void setup() { - if (m_worker_started) - return; - -#ifdef WIN32 - if (!CreatePipe(&pipe_r, &pipe_w, &win_sa, 0)) { - throw mgb::MegBrainError(mgb::ssprintf("failed to create pipe: %s", - strerror(errno))); - } -#else - if (pipe(m_pfd)) { - throw mgb::MegBrainError(mgb::ssprintf("failed to create pipe: %s", - strerror(errno))); - } -#endif - std::thread t(std::bind(&Impl::worker, this)); - m_worker_hdl.swap(t); - m_worker_started = true; - } - - void write_pipe(int v) { - mgb_assert(m_worker_started); -#ifdef WIN32 - if (WriteFile(pipe_w, &v, sizeof(int), &bytes_r_w, NULL) == NULL) { -#else - if (write(m_pfd[1], &v, sizeof(int)) != sizeof(int)) { -#endif - mgb_log_error("fast signal: failed to write to self pipe: %s", - strerror(errno)); - } - } - - public: - bool worker_started() const { - return m_worker_started; - } - - void register_handler(int signum, PyObject *func) { - setup(); - - { - PYTHON_GIL; - mgb_assert(PyCallable_Check(func)); - Py_INCREF(func); - } - auto deleter = [](PyObject *f){ - PYTHON_GIL; - Py_DECREF(f); - }; - std::shared_ptr funcptr(func, deleter); - - auto callback = [funcptr]() { - PYTHON_GIL; - auto func = funcptr.get(); - auto ret = PyObject_CallObject(func, nullptr); - mgb_assert(ret, "failed to call pyobj %p; repr=%s", - func, PyUnicode_AsUTF8(PyObject_Repr(func))); - Py_DECREF(ret); - }; - - MGB_LOCK_GUARD(m_mtx); - m_handler_callbacks[signum] = callback; - } - - void shutdown() { - MGB_LOCK_GUARD(m_mtx); - if (!m_worker_started) - return; - write_pipe(-1); - m_worker_hdl.join(); -#ifdef WIN32 - CloseHandle(pipe_r); - CloseHandle(pipe_w); -#else - close(m_pfd[0]); - close(m_pfd[1]); -#endif - m_handler_callbacks.clear(); - m_worker_started = false; - } - - void signal_hander(int signum) { - write_pipe(signum); - } - - ~Impl() { - shutdown(); - } -}; - -_FastSignal::Impl _FastSignal::sm_impl; - -void _FastSignal::signal_hander(int signum) { - if (sm_impl.worker_started()) - sm_impl.signal_hander(signum); -} - -void _FastSignal::register_handler(int signum, PyObject *func) { -#ifdef WIN32 - //! up to now we can only use CTRL_C_EVENT to unix signal.SIGUSR1/2 - //FIXME: how to coherence signal number at python side - // https://docs.microsoft.com/en-gb/cpp/c-runtime-library/reference/signal?view=vs-2017 - mgb_assert(signum == CTRL_C_EVENT, "only allow register CTRL_C_EVENT as unix signal.SIGUSR1/2 now"); - signal(signum, signal_hander); -#else - struct sigaction action; - memset(&action, 0, sizeof(action)); - action.sa_handler = &signal_hander; - int ret = sigaction(signum, &action, nullptr); - mgb_assert(!ret, "sigaction failed: %s", strerror(errno)); -#endif - - sm_impl.register_handler(signum, func); -} - -void _FastSignal::shutdown() { - sm_impl.shutdown(); -} - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} - diff --git a/python_module/src/cpp/plugin.h b/python_module/src/cpp/plugin.h deleted file mode 100644 index 5253a0bb..00000000 --- a/python_module/src/cpp/plugin.h +++ /dev/null @@ -1,135 +0,0 @@ -/** - * \file python_module/src/cpp/plugin.h - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \brief helpers for debugging - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - - -#ifndef SWIG - -#pragma once - -#include "./megbrain_wrap.h" - -#include "megbrain/plugin/profiler.h" -#include "megbrain/plugin/infkern_finder.h" -#include "megbrain/plugin/num_range_checker.h" -#include "megbrain/plugin/opr_io_dump.h" - -#endif // SWIG - -#include - -class _CompGraphProfilerImpl { -#ifndef SWIG - std::shared_ptr m_comp_graph; - mgb::GraphProfiler m_profiler; -#endif - - public: - _CompGraphProfilerImpl(CompGraph &cg): - m_comp_graph{cg.get().shared_from_this()}, - m_profiler{m_comp_graph.get()} - { - } - - std::string _get_result() { - auto json = m_profiler.to_json_full( - m_comp_graph->current_comp_seq()); - return json->to_string(); - } -}; - -class _NumRangeCheckerImpl { -#ifndef SWIG - std::shared_ptr m_comp_graph; - mgb::NumRangeChecker m_checker; -#endif - - public: - _NumRangeCheckerImpl(CompGraph &cg, float range): - m_comp_graph{cg.get().shared_from_this()}, - m_checker{m_comp_graph.get(), range} - { - } -}; - -class _TextOprIODumpImpl { -#ifndef SWIG - std::shared_ptr m_comp_graph; - mgb::TextOprIODump m_dump; -#endif - - public: - _TextOprIODumpImpl(CompGraph &cg, const char *fpath): - m_comp_graph{cg.get().shared_from_this()}, - m_dump{m_comp_graph.get(), fpath} - { - } - - void _print_addr(bool flag) { - m_dump.print_addr(flag); - } - - void _max_size(size_t size) { - m_dump.max_size(size); - } -}; - -class _BinaryOprIODumpImpl { -#ifndef SWIG - std::shared_ptr m_comp_graph; - mgb::BinaryOprIODump m_dump; -#endif - - public: - _BinaryOprIODumpImpl(CompGraph &cg, const char *fpath): - m_comp_graph{cg.get().shared_from_this()}, - m_dump{m_comp_graph.get(), fpath} - { - } -}; - -class _InfkernFinderImpl { -#ifndef SWIG - static size_t sm_id; - const size_t m_id; - std::shared_ptr m_comp_graph; - mgb::InfkernFinder m_finder; - mgb::InfkernFinder::InputValueRecord::FullRecord m_inp_val; -#endif - - public: - _InfkernFinderImpl(CompGraph &cg, bool record_input_value); - - size_t _write_to_file(const char *fpath); - - size_t _get_input_values_prepare(size_t opr_id); - const char* _get_input_values_var_name(size_t idx); - size_t _get_input_values_var_idx(size_t idx); - size_t _get_input_values_run_id(size_t idx); - CompGraphCallbackValueProxy _get_input_values_val(size_t idx); - - std::string __repr__(); - -}; - -class _FastSignal { -#ifndef SWIG - class Impl; - static Impl sm_impl; - - static void signal_hander(int signum); -#endif - public: - static void register_handler(int signum, PyObject *func); - static void shutdown(); -}; - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} - diff --git a/python_module/src/cpp/python_helper.cpp b/python_module/src/cpp/python_helper.cpp deleted file mode 100644 index 1cc97d7a..00000000 --- a/python_module/src/cpp/python_helper.cpp +++ /dev/null @@ -1,911 +0,0 @@ -/** - * \file python_module/src/cpp/python_helper.cpp - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \brief helper utilities for python integration - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - -#include "./python_helper.h" -#include "megbrain/graph/exc_extra_info.h" -#include "megbrain/graph/event.h" -#include "megbrain/graph/cg.h" -#include "megbrain/utils/mempool.h" - -#include "./numpy_incl.h" - -/* - * demangle typeid, see - * http://stackoverflow.com/questions/281818/unmangling-the-result-of-stdtype-infoname - */ -#ifdef __GNUG__ -#include -#include -#include - -namespace { - -std::string demangle_typeid(const char* name) { - - int status = -4; // some arbitrary value to eliminate the compiler warning - - // enable c++11 by passing the flag -std=c++11 to g++ - std::unique_ptr res { - abi::__cxa_demangle(name, nullptr, nullptr, &status), - std::free - }; - - return (status==0) ? res.get() : name ; -} -} -#else - -namespace { -// does nothing if not g++ -std::string demangle_typeid(const char* name) { - return name; -} -} - -#endif - -using namespace mgb; -using namespace cg; - -PyStackExtracter* PyStackExtracter::ins = nullptr; - -namespace { - - std::string repr_pyobj(PyObject *obj) { - if (!obj) - return ""; - PYTHON_GIL; - auto str = PyObject_Repr(obj); - if (!str) - return ssprintf("", obj); - std::string ret{PyUnicode_AsUTF8(str)}; - Py_DECREF(str); - return ret; - } - - template - std::string typeid_name(const T &t) { - return demangle_typeid(typeid(t).name()); - } - -} // anonymous namespace - -/* ============== OprPyTracker ============== */ - -class OprPyTracker::TrackerStorage final : public UserDataContainer::UserData, - public NonCopyableObj { - MGB_TYPEINFO_OBJ_DECL; - - PyObject* m_cur_tracker = nullptr; - size_t m_refcnt_to_add = 0; - SyncEventConnecter::ReceiverHandler m_opr_insert_handler; - ThinHashMap m_opr2tracker; - -public: - explicit TrackerStorage(ComputingGraph& graph) { - auto on_new_opr = [this](const event::OprInserted& ev) { - if (!ev.is_dedup && !ev.exc) { - if (m_cur_tracker) { - ++m_refcnt_to_add; - m_opr2tracker[ev.opr] = m_cur_tracker; - } - } - }; - m_opr_insert_handler = - graph.event().register_receiver(on_new_opr); - } - - ~TrackerStorage() { - if (m_cur_tracker) { - // manage refcnt of cur tracker - disable(); - } - PYTHON_GIL; - for (auto&& i : m_opr2tracker) { - Py_DecRef(i.second); - } - } - - //! get the instance - static TrackerStorage& inst(ComputingGraph& graph) { - auto make = [&graph]() { - return std::make_shared(graph); - }; - return *graph.options() - .user_data.get_user_data_or_create( - make); - } - - //! get the tracker associated with an opr, or nullptr - PyObject* get(OperatorNodeBase* opr) const { - auto iter = m_opr2tracker.find(opr); - return iter == m_opr2tracker.end() ? nullptr : iter->second; - } - - void enable(PyObject* obj) { - mgb_assert(!m_cur_tracker, - "multiple calls to begin_set_tracker() on the same graph"); - m_cur_tracker = obj; - } - - void disable() { - mgb_assert(m_cur_tracker, - "call end_set_tracker() before begin_set_tracker()"); - if (m_refcnt_to_add) { - PYTHON_GIL; - for (size_t i = 0; i < m_refcnt_to_add; ++i) { - Py_IncRef(m_cur_tracker); - } - } - m_cur_tracker = nullptr; - } -}; -MGB_TYPEINFO_OBJ_IMPL(OprPyTracker::TrackerStorage); - -void OprPyTracker::begin_set_tracker(ComputingGraph& graph, PyObject* obj) { - TrackerStorage::inst(graph).enable(obj); -} - -void OprPyTracker::end_set_tracker(ComputingGraph& graph) { - TrackerStorage::inst(graph).disable(); -} - -OprPyTracker::TrackerResult OprPyTracker::get_tracker(mgb::MegBrainError& exc) { - auto ptr = dynamic_cast(exc.extra_info()); - if (!ptr) - return {}; - return get_tracker(ptr->opr()); -} - -OprPyTracker::TrackerResult OprPyTracker::get_tracker( - mgb::cg::OperatorNodeBase* opr) { - TrackerResult ret; - mgb_assert(opr); - ret.exc_opr = opr; - opr = cg::get_opr_root_source_opr(opr); - ret.unopt_opr = opr; - - auto&& storage = TrackerStorage::inst(*opr->owner_graph()); - ret.tracker = storage.get(opr); - - { - auto&& grad_info = opr->node_prop().attribute().grad_tracker; - if (grad_info.valid()) { - ret.opr_grad_src = cg::get_opr_root_source_opr(grad_info->orig_opr); - ret.tracker_grad_src = storage.get(ret.opr_grad_src); - } - } - - return ret; -} - -PyObject* OprPyTracker::TrackerResult::as_tuple(const char *leading_msg) const { - std::string msg; - if (leading_msg) - msg = leading_msg; - - auto print_opr = [&](const char *otype, cg::OperatorNodeBase *opr) { - if (!opr) - return; - - msg += ssprintf("\n%s: id=%zu name=%s type=%s\n", - otype, opr->id(), opr->cname(), - typeid_name(*opr).c_str()); - msg += " input variables: \n"; - size_t idx = 0; - for (auto i: opr->input()) { - msg += ssprintf(" %zu: ", idx ++); - msg += cg::dump_var_info({i}); - msg += "\n"; - } - - msg += " output variables: \n"; - idx = 0; - for (auto i: opr->output()) { - msg += ssprintf(" %zu: ", idx ++); - msg += cg::dump_var_info({i}); - msg += "\n"; - } - }; - - print_opr("Associated operator", exc_opr); - if (unopt_opr != exc_opr) { - print_opr("Unoptimized equivalent of associated operator", unopt_opr); - } - print_opr("Associated operator created by taking grad of", opr_grad_src); - - PYTHON_GIL; - PyObject *py_msg = PyUnicode_FromString(msg.c_str()), - *py_tuple = PyTuple_Pack(3, py_msg, - tracker ? tracker : Py_None, - tracker_grad_src ? tracker_grad_src : Py_None); - Py_DECREF(py_msg); - return py_tuple; -} - -std::string blame(mgb::cg::OperatorNodeBase* opr) { - mgb_assert(PyMGBExceptionMaker::py_exc_class, - "Python exception class is not set yet"); - PyObject* args = OprPyTracker::get_tracker(opr).as_tuple(); - - PYTHON_GIL; - - PyObject* py_exc = PyObject_CallObject(PyMGBExceptionMaker::py_exc_class, args); - Py_DECREF(args); - mgb_assert(py_exc); - - PyObject* py_str = PyObject_Str(py_exc); - Py_DECREF(py_exc); - mgb_assert(py_str); - - int err = PyUnicode_READY(py_str); - if (err) { - Py_DECREF(py_str); - mgb_assert(!err); - } - - Py_ssize_t c_str_size; - const char* c_str = PyUnicode_AsUTF8AndSize(py_str, &c_str_size); - if (!c_str) { - Py_DECREF(py_str); - mgb_assert(c_str); - } - std::string ret(c_str, c_str_size); - Py_DECREF(py_str); - return ret; -} - -/* ============== PyMGBExceptionMaker ============== */ -PyObject *PyMGBExceptionMaker::py_exc_class = nullptr; - -void PyMGBExceptionMaker::setup_py_exception(std::exception &exc) { - mgb_assert(py_exc_class); - if (auto cbexc = dynamic_cast(&exc)) { - cbexc->restore(); - return; - } - - std::string msg; - try { - msg = ssprintf("MegBrain core throws exception: %s\n%s", - typeid_name(exc).c_str(), exc.what()); - - auto mgbexc = dynamic_cast(&exc); - OprPyTracker::TrackerResult tracker; - if (mgbexc) { - tracker = OprPyTracker::get_tracker(*mgbexc); - } - - PYTHON_GIL; - PyObject *py_exc_arg = tracker.as_tuple(msg.c_str()); - PyErr_SetObject(py_exc_class, py_exc_arg); - Py_DECREF(py_exc_arg); - } catch (std::exception &newexc) { - auto newmsg = ssprintf( - "caught exception during handling exception: %s\n%s\n" - "original message: %s", - typeid_name(newexc).c_str(), newexc.what(), - msg.c_str()); - PyErr_SetString(PyExc_RuntimeError, newmsg.c_str()); - } catch (...) { - auto newmsg = ssprintf( - "caught unknown exception during handling exception\n" - "original message: %s", msg.c_str()); - PyErr_SetString(PyExc_RuntimeError, newmsg.c_str()); - } -} - -/* ============== PyExceptionForward ============== */ - -PyExceptionForward::~PyExceptionForward() { - PYTHON_GIL; - PyObjRefKeeper::deleter(m_type); - PyObjRefKeeper::deleter(m_value); - PyObjRefKeeper::deleter(m_traceback); -} - -void PyExceptionForward::restore() { - PyErr_Restore(m_type, m_value, m_traceback); - m_type = m_value = m_traceback = nullptr; -} - -void PyExceptionForward::throw_() { - PyObject *etype, *obj, *trace; - PyErr_Fetch(&etype, &obj, &trace); - PyErr_NormalizeException(&etype, &obj, &trace); - - std::string msg{"python exception"}; - bool succ = false; - if (etype && obj && trace) { - auto run = [&]() { -#define DEF(name, expr) \ - PyObjRefKeeper name{expr}; \ - if (!name.get()) \ - return - DEF(mod, PyImport_ImportModule("traceback")); - DEF(result, PyObject_CallMethod(mod.get(), "format_exception", - "(OOO)", etype, obj, trace)); - if (!PyList_Check(result.get())) - return; - auto size = PyList_Size(result.get()); - msg.append(":\n"); - for (Py_ssize_t i = 0; i < size; ++i) { - msg.append(" "); - msg.append(PyUnicode_AsUTF8(PyList_GetItem(result.get(), i))); - } - msg.pop_back(); // remove last \n - succ = true; -#undef DEF - }; - run(); - } - if (!succ) { - PyObject* obj_str_py; - if (obj && (obj_str_py = PyObject_Repr(obj))) { - msg.append(" with message "); - msg.append(PyUnicode_AsUTF8(obj_str_py)); - Py_DECREF(obj_str_py); - } else { - msg.append(" with unknown message"); - } - } - // throwing exception may cause abort due to unknown reasons; so we first - // log the message - mgb_log_error("caught exception from python callback: %s", msg.c_str()); - fflush(stdout); - fflush(stderr); - throw PyExceptionForward{etype, obj, trace, msg}; -} - -/* ============== namespace npy ============== */ - -namespace { - -int to_mgb_supported_dtype_raw(int dtype) { - if (dtype == NPY_INT64) - return NPY_INT32; - if (dtype == NPY_FLOAT64) - return NPY_FLOAT32; - return dtype; -} - -#define FOREACH_NPY_DTYPE_PAIR(cb) \ - cb(Uint8, NPY_UINT8) \ - cb(Int8, NPY_INT8) \ - cb(Int16, NPY_INT16) \ - cb(Int32, NPY_INT32) \ - cb(Float16, NPY_FLOAT16) \ - cb(Float32, NPY_FLOAT32) - -#define FOREACH_NPY_MGB_DTYPE_PAIR(cb) \ - FOREACH_NPY_DTYPE_PAIR(cb) \ - FOREACH_MGB_DTYPE_PAIR(cb) - - - -//! convert megbrain dtype to numpy dtype -int dtype_mgb2np_raw(DType dtype) { - mgb_assert(dtype.valid(), "attempt to convert from invalid dtype"); - switch (dtype.enumv()) { -#define cb(_m, _n) \ - case DTypeEnum::_m: \ - return _n; - FOREACH_NPY_MGB_DTYPE_PAIR(cb) -#undef cb - default: - break; - } - throw ConversionError(ssprintf( - "can not convert dtype %s to numpy dtype", dtype.name())); -} - -struct PyArrayDescrDeleter { - void operator()(PyArray_Descr* obj) { - Py_XDECREF(obj); - } -}; - -//! Convert MegBrain DType to NumPy DType descriptor, the caller receives a new -//! reference to the descriptor. -std::unique_ptr dtype_mgb2np_descr( - DType dtype) { - PYTHON_GIL; - mgb_assert(dtype.valid(), "attempt to convert from invalid dtype"); - auto build_mgb_dtype_dict = - [](const char* name, - const std::vector>& data) { - PyObject* metadata = PyDict_New(); - PyObject* mgb_dtype_metadata = PyDict_New(); - PyDict_SetItemString(mgb_dtype_metadata, "name", - PyUnicode_FromString(name)); - for (const auto& d : data) { - PyDict_SetItemString(mgb_dtype_metadata, d.first, d.second); - } - PyDict_SetItemString(metadata, "mgb_dtype", mgb_dtype_metadata); - return metadata; - }; - if (dtype.has_param()) { - PyArray_Descr* type_descr; - switch (dtype.enumv()) { - case DTypeEnum::Quantized8Asymm: { - auto& param = dtype.param(); - type_descr = PyArray_DescrNewFromType(NPY_UINT8); - type_descr->metadata = build_mgb_dtype_dict( - DTypeTrait::name, - {{"scale", PyFloat_FromDouble(param.scale)}, - {"zero_point", PyLong_FromLong(param.zero_point)}}); - break; - } - case DTypeEnum::QuantizedS8: { - auto& param = dtype.param(); - type_descr = PyArray_DescrNewFromType(NPY_INT8); - type_descr->metadata = build_mgb_dtype_dict( - DTypeTrait::name, - {{"scale", PyFloat_FromDouble(param.scale)}}); - break; - } - case DTypeEnum::Quantized4Asymm: { - auto& param = dtype.param(); - type_descr = PyArray_DescrNewFromType(NPY_UINT8); - type_descr->metadata = build_mgb_dtype_dict( - DTypeTrait::name, - {{"scale", PyFloat_FromDouble(param.scale)}, - {"zero_point", PyLong_FromLong(param.zero_point)}}); - break; - } - case DTypeEnum::QuantizedS4: { - auto& param = dtype.param(); - type_descr = PyArray_DescrNewFromType(NPY_INT8); - type_descr->metadata = build_mgb_dtype_dict( - DTypeTrait::name, - {{"scale", PyFloat_FromDouble(param.scale)}}); - break; - } - case DTypeEnum::QuantizedS32: { - auto& param = dtype.param(); - type_descr = PyArray_DescrNewFromType(NPY_INT32); - type_descr->metadata = build_mgb_dtype_dict( - DTypeTrait::name, - {{"scale", PyFloat_FromDouble(param.scale)}}); - break; - } - default: - mgb_throw(ConversionError, "unhandled parameterized DType %s", - dtype.name()); - } - return std::unique_ptr(type_descr); - } - PyArray_Descr* basic_descr = PyArray_DescrFromType(dtype_mgb2np_raw(dtype)); - mgb_assert(basic_descr != nullptr, - "failed to convert expected dtype to numpy type descriptor"); - return std::unique_ptr(basic_descr); -} - -DType dtype_np2mgb_raw(int npt) { - switch (npt) { -#define cb(_m, _n) \ - case _n: \ - return dtype::_m(); - FOREACH_NPY_DTYPE_PAIR(cb) -#undef cb - } -#define cb(_m, _n) \ - if (_n == npt) return dtype::_m(); - FOREACH_MGB_DTYPE_PAIR(cb) -#undef cb - - PYTHON_GIL; - std::string msg; - auto py_obj = PyArray_TypeObjectFromType(npt); - if (!py_obj) { - msg = ssprintf("unknown numpy dtype enum %d", npt); - } else { - msg = ssprintf("unsupported numpy dtype %s", - repr_pyobj(py_obj).c_str()); - } - Py_DECREF(py_obj); - throw ConversionError(msg); -} - -DType dtype_np2mgb_descr(PyArray_Descr* descr) { - PYTHON_GIL; - auto handle_parameterized_dtype = [](PyObject* metadata) -> DType { - mgb_assert(PyDict_Check(metadata), - "Invalid parameterized DType metadata: should be a dict"); - PyObject* dtype_name_py = PyDict_GetItemString(metadata, "name"); - mgb_assert( - PyUnicode_Check(dtype_name_py), - "Invalid parameterized DType metadata: name should be a str"); - std::string dtype_name(PyUnicode_AsUTF8(dtype_name_py)); - if (dtype_name == "Quantized8Asymm") { - PyObject* scale_py = PyDict_GetItemString(metadata, "scale"); - PyObject* zero_point_py = - PyDict_GetItemString(metadata, "zero_point"); - mgb_assert(scale_py && zero_point_py, - "Invalid Quantized8Asymm metadata: missing scale or " - "zero_point."); - mgb_assert( - PyFloat_Check(scale_py), - "Invalid Quantized8Asymm metadata: scale should be float"); - mgb_assert(PyLong_Check(zero_point_py), - "Invalid Quantized8Asymm metadata: zero_point should be " - "integer"); - auto zero_point = PyLong_AS_LONG(zero_point_py); - mgb_assert(zero_point >= 0 && zero_point < 256, - "Invalid Quantized8Asymm metadata: zero_point should be " - "in [0, 256)"); - return dtype::Quantized8Asymm( - static_cast(PyFloat_AS_DOUBLE(scale_py)), - static_cast(zero_point)); - } - if (dtype_name == "Quantized4Asymm") { - PyObject* scale_py = PyDict_GetItemString(metadata, "scale"); - PyObject* zero_point_py = - PyDict_GetItemString(metadata, "zero_point"); - mgb_assert(scale_py && zero_point_py, - "Invalid Quantized4Asymm metadata: missing scale or " - "zero_point."); - mgb_assert( - PyFloat_Check(scale_py), - "Invalid Quantized4Asymm metadata: scale should be float"); - mgb_assert(PyLong_Check(zero_point_py), - "Invalid Quantized4Asymm metadata: zero_point should be " - "integer"); - auto zero_point = PyLong_AS_LONG(zero_point_py); - mgb_assert(zero_point >= 0 && zero_point < 15, - "Invalid Quantized4Asymm metadata: zero_point should be " - "in [0, 15)"); - return dtype::Quantized4Asymm( - static_cast(PyFloat_AS_DOUBLE(scale_py)), - static_cast(zero_point)); - } - if (dtype_name == "QuantizedS32" || dtype_name == "QuantizedS8" || - dtype_name == "QuantizedS4") { - PyObject* scale_py = PyDict_GetItemString(metadata, "scale"); - mgb_assert(scale_py, "Invalid metadata: missing scale"); - mgb_assert(PyFloat_Check(scale_py), - "Invalid metadata: scale should be float"); - float scale = static_cast(PyFloat_AS_DOUBLE(scale_py)); - if (dtype_name == "QuantizedS32") { - return dtype::QuantizedS32(scale); - } else if (dtype_name == "QuantizedS8"){ - return dtype::QuantizedS8(scale); - } else { - return dtype::QuantizedS4(scale); - } - } - throw ConversionError( - ssprintf("Unknown parameterized DType: %s", dtype_name.c_str()) - .c_str()); - }; - PyObject* dtype_metadata; - if (descr->metadata && PyDict_Check(descr->metadata) && - (dtype_metadata = PyDict_GetItemString(descr->metadata, "mgb_dtype"))) { - return handle_parameterized_dtype(dtype_metadata); - } - return dtype_np2mgb_raw(descr->type_num); -} - -HostTensorND lowbit_ndarray_to_host_tensor( - CompNode comp_node, TensorLayout &layout, PyArrayObject *input) { - auto src_ptr = reinterpret_cast(PyArray_DATA(input)); - if (!layout.ndim) { - // numpy scalar - mgb_assert(src_ptr, "can not convert from null numpy array"); - layout.init_contiguous_stride({1}); - } else { - mgb_assert(layout.ndim && layout.ndim <= TensorShape::MAX_NDIM, - "unsupported ndim %zu", layout.ndim); - for (size_t i = 0; i < layout.ndim; ++ i) { - layout.shape[i] = PyArray_SHAPE(input)[i]; - layout.stride[i] = PyArray_STRIDE(input, i); - mgb_assert(layout.shape[i], "zero shape not supported"); - } - mgb_assert(layout.is_contiguous()); - } - HostTensorND ret{comp_node, layout}; - lowbit_memcpy_byte2compact(layout.dtype, ret.raw_ptr(), src_ptr, - layout.total_nr_elems()); - return ret; -} - -/*! - * \brief convert a python object to tensor and try to borrow memory if the - * original object is a contiguous numpy array - * \param dtype see np2tensor - * \return the megbrain tensor, and whether memory is borrowed - */ -std::pair np2tensor_try_borrow( - PyObject *obj, CompNode dest_cn, DType dtype) { - mgb_assert(dest_cn.valid()); - - PYTHON_GIL; - - PyArray_Descr* expected_descr = nullptr; - if (dtype.valid()) { - // The reference to expected_descr will be stealed later. - expected_descr = dtype_mgb2np_descr(dtype).release(); - } - - // make result from PyArrayObject; its reference would be stolen - auto make_from_arr = [&](PyArrayObject *input, bool is_borrow) { - PyObjRefKeeper ref_obj_cvt{reinterpret_cast(input)}; - - TensorLayout layout; - layout.dtype = dtype_np2mgb_descr(PyArray_DESCR(input)); - if (dtype.valid()) - mgb_assert(dtype == layout.dtype); - layout.ndim = PyArray_NDIM(input); - - if (layout.dtype.is_low_bit()) { - auto ret = lowbit_ndarray_to_host_tensor(dest_cn, layout, input); - // decref(input) would be handled by ref_obj_cvt - return std::make_pair(ret, false); - } - - auto data = reinterpret_cast(PyArray_DATA(input)); - if (!layout.ndim) { - // numpy scalar - mgb_assert(data, "can not convert from null numpy array"); - layout.init_contiguous_stride({1}); - } else { - mgb_assert(layout.ndim && layout.ndim <= TensorShape::MAX_NDIM, - "unsupported ndim %zu", layout.ndim); - auto dsize = layout.dtype.size(); - bool is_empty = false; - for (size_t i = 0; i < layout.ndim; ++ i) { - layout.shape[i] = PyArray_SHAPE(input)[i]; - layout.stride[i] = PyArray_STRIDE(input, i); - if (!layout.shape[i]) { - is_empty = true; - } - mgb_assert(layout.stride[i] % dsize == 0, - "bad stride %zd", layout.stride[i]); - layout.stride[i] /= dsize; - } - mgb_assert(is_empty || layout.is_contiguous()); - } - HostTensorStorage storage; - auto input_ptr = ref_obj_cvt.make_shared(data); - storage.reset(dest_cn, layout.span().high_byte, input_ptr); - HostTensorND ret; - ret.reset(storage, layout); - return std::make_pair(ret, is_borrow); - }; - - PyArrayObject *obj_as_arr = nullptr; - do { - // check contiguous and dtype, and borrow mem if ok - if (!PyArray_Check(obj)) - break; - obj_as_arr = reinterpret_cast(obj); - int typenum = PyArray_DTYPE(obj_as_arr)->type_num; - // We have to check dtype.valid() and typenum first to avoid - // accidentally trigger ConversionError on incompatible dtypes which can - // be automatically converted into comptaible ones (e.g. float64). - if (dtype.valid() && - (expected_descr->type_num != typenum || - dtype_np2mgb_descr(PyArray_DTYPE(obj_as_arr)) != dtype)) - break; - if (typenum != to_mgb_supported_dtype_raw(typenum)) { - mgb_assert(!dtype.valid() && expected_descr == nullptr); - expected_descr = - PyArray_DescrFromType(to_mgb_supported_dtype_raw(typenum)); - break; - } - if (PyArray_ISCARRAY_RO(obj_as_arr)) { - Py_INCREF(obj_as_arr); - return make_from_arr(obj_as_arr, true); - } - } while(0); - - constexpr auto NP_FLAGS = NPY_ARRAY_C_CONTIGUOUS | NPY_ARRAY_FORCECAST; - PyObject *obj_cvt; - if (obj_as_arr) { - obj_cvt = PyArray_FromArray(obj_as_arr, expected_descr, NP_FLAGS); - } else { - obj_cvt = PyArray_FromAny(obj, expected_descr, 0, 0, NP_FLAGS, nullptr); - } - - if (obj_cvt) { - // convert to mgb supported dtype - auto arr = reinterpret_cast(obj_cvt); - int dt0 = PyArray_TYPE(arr), dt1 = to_mgb_supported_dtype_raw(dt0); - if (dt0 != dt1) { - mgb_assert(expected_descr == nullptr); - expected_descr = PyArray_DescrFromType(dt1); - mgb_assert(expected_descr); - auto obj_cvt_new = PyArray_FromAny( - obj_cvt, expected_descr, 0, 0, NP_FLAGS, nullptr); - Py_DECREF(obj_cvt); - obj_cvt = obj_cvt_new; - } - } - - if (!obj_cvt) { - if (PyErr_Occurred()) { - PyExceptionForward::throw_(); - } - throw ConversionError(ssprintf("can not convert to numpy array from %s", - repr_pyobj(obj).c_str())); - } - - return make_from_arr(reinterpret_cast(obj_cvt), false); -} - -//! hold a reference to HostTensorND -class HostTensorNDRefHolder final: public NonCopyableObj { - HostTensorND m_val; - static MemPool sm_mem_pool; - - friend class MemPool; - - HostTensorNDRefHolder(const HostTensorND &v): - m_val{v} - { - } - - public: - - static HostTensorNDRefHolder* alloc(const HostTensorND &v) { - return sm_mem_pool.alloc(v); - } - - static void free(HostTensorNDRefHolder *p) { - return sm_mem_pool.free(p); - } -}; -MemPool HostTensorNDRefHolder::sm_mem_pool; - -void ndarray_shared_from_tensor_py_capsule_dtor(PyObject *cap) { - auto ptr = PyCapsule_GetPointer(cap, "HostTensorND"); - mgb_assert(ptr, "not a PyCapsule: %s", repr_pyobj(cap).c_str()); - HostTensorNDRefHolder::free(static_cast(ptr)); -} - -} // anonymous namespace - -PyObject* npy::ndarray_from_tensor( - const HostTensorND &val, ShareType share_type) { - if (!val.layout().is_contiguous() && !val.shape().is_empty()) { - mgb_assert(share_type != ShareType::MUST_SHARE); - HostTensorND contig; - contig.copy_from(val); - return ndarray_from_tensor(contig, ShareType::TRY_SHARE); - } - PYTHON_GIL; - npy_intp dims[TensorLayout::MAX_NDIM]; - for (size_t i = 0; i < val.layout().ndim; ++ i) - dims[i] = val.shape()[i]; - PyObject* ret = nullptr; - - auto alloc_new_ret = [&]() { - mgb_assert(!ret); - ret = PyArray_NewFromDescr( - &PyArray_Type, dtype_mgb2np_descr(val.dtype()).release(), - val.layout().ndim, dims, nullptr, nullptr, 0, nullptr); - mgb_assert(ret, "failed to allocate array"); - mgb_assert(PyArray_Check(ret)); - return PyArray_DATA(reinterpret_cast(ret)); - }; - if (val.dtype().is_low_bit()) { - mgb_assert(share_type != ShareType::MUST_SHARE, - "can not share memory for lowbit dtype"); - lowbit_memcpy_compact2byte(val.dtype(), alloc_new_ret(), val.raw_ptr(), - val.layout().total_nr_elems()); - } else if (share_type == ShareType::MUST_UNSHARE) { - memcpy(alloc_new_ret(), val.raw_ptr(), val.layout().span().dist_byte()); - } else { - // share data - ret = PyArray_NewFromDescr( - &PyArray_Type, dtype_mgb2np_descr(val.dtype()).release(), - val.layout().ndim, dims, nullptr, - const_cast(val.raw_ptr()), 0, nullptr); - mgb_assert(ret, "failed to alloc ndarray"); - auto capsule = PyCapsule_New(HostTensorNDRefHolder::alloc(val), - "HostTensorND", ndarray_shared_from_tensor_py_capsule_dtor); - mgb_assert(capsule, "failed to create PyCapsule"); - auto err = PyArray_SetBaseObject( - reinterpret_cast(ret), capsule); - mgb_assert(!err); - } - return ret; -} - -HostTensorND npy::np2tensor(PyObject* obj, const Meth& meth, DType dtype) { - auto ret_full = np2tensor_try_borrow(obj, meth.dest_cn_, dtype); - if (meth.dest_tensor_) { - meth.dest_tensor_->copy_from(ret_full.first); - return *meth.dest_tensor_; - } - if (meth.must_borrow_) { - mgb_assert(ret_full.second, - "can not borrow from numpy array as contig array with dtype " - "%s; src=%s", - dtype.name(), repr_pyobj(obj).c_str()); - } - return ret_full.first; -} - -PyObject* npy::dtype_mgb2np(mgb::DType dtype) { - PYTHON_GIL; - // According to - // https://docs.scipy.org/doc/numpy/reference/c-api.array.html#c.PyArray_TypeObjectFromType - // the following is equivalent to PyArray_TypeObjectFromType for built-in - // types. - auto descr = dtype_mgb2np_descr(dtype); - if (descr == nullptr) { - return nullptr; - } - if (dtype.has_param()) { - return reinterpret_cast(descr.release()); - } - PyObject* typeobj = reinterpret_cast(descr->typeobj); - Py_XINCREF(typeobj); - return typeobj; -} - -mgb::DType npy::dtype_np2mgb(PyObject *obj) { - mgb_assert(obj && obj != Py_None, - "can not convert null PyObject to numpy dtype"); - // see - // http://stackoverflow.com/questions/8477122/numpy-c-api-convert-type-object-to-type-number - PYTHON_GIL; - - PyArray_Descr* dtype; - if(!PyArray_DescrConverter(obj, &dtype)) { - throw ConversionError(ssprintf("can not convert to np.dtype from %s", - repr_pyobj(obj).c_str())); - } - - mgb::DType result = dtype_np2mgb_descr(dtype); - Py_DECREF(dtype); - return result; -} - -PyObject* npy::to_mgb_supported_dtype(PyObject* dtype) { - PYTHON_GIL; - - PyArray_Descr* descr; - if (!PyArray_DescrConverter(dtype, &descr)) { - throw ConversionError(ssprintf("can not convert to np.dtype from %s", - repr_pyobj(dtype).c_str())); - } - mgb_assert(!descr->metadata, - "unexpected metadata in dtype: " - "dtype_obj=%s metadata=%s", - repr_pyobj(dtype).c_str(), repr_pyobj(descr->metadata).c_str()); - int type_num = to_mgb_supported_dtype_raw(descr->type_num); - return PyArray_TypeObjectFromType(type_num); -} - -TensorShape npy::vec2shape(const std::vector &vec) { - TensorShape shape; - mgb_assert(vec.size() <= TensorShape::MAX_NDIM, - "dim too large: %zd (max %zd)", - vec.size(), TensorShape::MAX_NDIM); - shape.ndim = vec.size(); - for (size_t i = 0; i < vec.size(); i ++) { - if (!vec[i]) { - shape.ndim = 0; - break; - } - shape[i] = vec[i]; - } - mgb_assert(shape.ndim, "shape should not be empty"); - return shape; -} - -void mgb_init_numpy() { - import_array1( ); -} - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/cpp/python_helper.h b/python_module/src/cpp/python_helper.h deleted file mode 100644 index 1d443905..00000000 --- a/python_module/src/cpp/python_helper.h +++ /dev/null @@ -1,229 +0,0 @@ -/** - * \file python_module/src/cpp/python_helper.h - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * \brief helper utilities for python integration - * - * \copyright Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - * - */ - -#pragma once - -#include "megbrain/graph.h" - -#include -#include - -class GILManager { - PyGILState_STATE gstate; - - public: - GILManager(): - gstate(PyGILState_Ensure()) - { - } - - ~GILManager() { - PyGILState_Release(gstate); - } -}; -#define PYTHON_GIL GILManager __gil_manager - -//! wraps a shared_ptr and decr PyObject ref when destructed -class PyObjRefKeeper { - std::shared_ptr m_ptr; - -public: - static void deleter(PyObject* p) { - if (p) { - PYTHON_GIL; - Py_DECREF(p); - } - } - - PyObjRefKeeper() = default; - PyObjRefKeeper(PyObject* p) : m_ptr{p, deleter} {} - - PyObject* get() const { return m_ptr.get(); } - - //! create a shared_ptr as an alias of the underlying ptr - template - std::shared_ptr make_shared(T* ptr) const { - return {m_ptr, ptr}; - } -}; - -class PyStackExtracter { - static PyStackExtracter *ins; - - public: - virtual ~PyStackExtracter() = default; - - virtual std::string extract() = 0; - - static void reg(PyStackExtracter *p) { - ins = p; - } - - static std::string run() { - return ins->extract(); - } -}; - -//! exception to be thrown when python callback fails -class PyExceptionForward : public std::exception { - PyObject *m_type, *m_value, *m_traceback; - std::string m_msg; - - PyExceptionForward(PyObject* type, PyObject* value, PyObject* traceback, - const std::string& msg) - : m_type{type}, - m_value{value}, - m_traceback{traceback}, - m_msg{msg} {} - -public: - PyExceptionForward(const PyExceptionForward&) = delete; - PyExceptionForward& operator=(const PyExceptionForward&) = delete; - ~PyExceptionForward(); - - PyExceptionForward(PyExceptionForward&& rhs) - : m_type{rhs.m_type}, - m_value{rhs.m_value}, - m_traceback{rhs.m_traceback}, - m_msg{std::move(rhs.m_msg)} { - rhs.m_type = rhs.m_value = rhs.m_traceback = nullptr; - } - - //! throw PyExceptionForward from current python error state - static void throw_() __attribute__((noreturn)); - - //! restore python error - void restore(); - - const char* what() const noexcept override { return m_msg.c_str(); } -}; - -/*! - * \brief make python exception - */ -class PyMGBExceptionMaker { - static PyObject *py_exc_class; - friend std::string blame(mgb::cg::OperatorNodeBase* opr); - - public: - static void setup_py_exception(std::exception &exc); - - static void _reg_exception_class(PyObject *cls) { - py_exc_class = cls; - } - -}; - -//! associate a python object with an operator -class OprPyTracker final : public mgb::NonCopyableObj { - class TrackerStorage; - OprPyTracker() = delete; - -public: - /*! - * \brief set current tracker; all operators created later would share - * this tracker - * - * Note that a py reference would be kept - */ - static void begin_set_tracker(mgb::cg::ComputingGraph& graph, - PyObject* obj); - - static void end_set_tracker(mgb::cg::ComputingGraph& graph); - - struct TrackerResult { - mgb::cg::OperatorNodeBase - //! operator that directly causes the exception - *exc_opr = nullptr, - //! operator constructed by user (non-optimized exc_opr) - *unopt_opr = nullptr, - //! the grad source if opr is constructed by taking grad - *opr_grad_src = nullptr; - PyObject *tracker = nullptr, *tracker_grad_src = nullptr; - - //! format as python tuple - PyObject* as_tuple(const char* leading_msg = nullptr) const; - }; - - //! get tracker from exception - static TrackerResult get_tracker(mgb::MegBrainError& exc); - - //! get tracker from operator - static TrackerResult get_tracker(mgb::cg::OperatorNodeBase* opr); -}; - -std::string blame(mgb::cg::OperatorNodeBase* opr); - -//! numpy utils -namespace npy { - //! convert tensor shape to raw vector - static inline std::vector shape2vec(const mgb::TensorShape &shape) { - return {shape.shape, shape.shape + shape.ndim}; - } - - //! change numpy dtype to megbrain supported dtype - PyObject* to_mgb_supported_dtype(PyObject *dtype); - - //! convert raw vector to tensor shape - mgb::TensorShape vec2shape(const std::vector &vec); - - //! convert megbrain dtype to numpy dtype object; return new reference - PyObject* dtype_mgb2np(mgb::DType dtype); - - //! convert numpy dtype object or string to megbrain dtype - mgb::DType dtype_np2mgb(PyObject *obj); - - //! buffer sharing type - enum class ShareType { - MUST_SHARE, //!< must be shared - MUST_UNSHARE, //!< must not be shared - TRY_SHARE //!< share if possible - }; - - //! get ndarray from HostTensorND - PyObject* ndarray_from_tensor(const mgb::HostTensorND &val, - ShareType share_type); - - //! specify how to convert numpy array to tensor - struct Meth { - bool must_borrow_ = false; - mgb::HostTensorND *dest_tensor_ = nullptr; - mgb::CompNode dest_cn_; - - //! make a Meth that allows borrowing numpy array memory - static Meth borrow( - mgb::CompNode dest_cn = mgb::CompNode::default_cpu()) { - return {false, nullptr, dest_cn}; - } - - //! make a Meth that requires the numpy array to be borrowed - static Meth must_borrow( - mgb::CompNode dest_cn = mgb::CompNode::default_cpu()) { - return {true, nullptr, dest_cn}; - } - - //! make a Meth that requires copying the value into another - //! tensor - static Meth copy_into(mgb::HostTensorND *tensor) { - return {false, tensor, tensor->comp_node()}; - } - }; - /*! - * \brief convert an object to megbrain tensor - * \param meth specifies how the conversion should take place - * \param dtype desired dtype; it can be set as invalid to allow arbitrary - * dtype - */ - mgb::HostTensorND np2tensor(PyObject *obj, const Meth &meth, - mgb::DType dtype); -} - -// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/python/genopr.py b/python_module/src/python/genopr.py deleted file mode 100755 index 28ce7e28..00000000 --- a/python_module/src/python/genopr.py +++ /dev/null @@ -1,293 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -# This file is part of MegBrain. -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - -from io import StringIO -import re -import argparse -import subprocess -import os -import textwrap - -def camel2underscore( - name, *, - first_cap_re=re.compile('([A-Z])([A-Z][a-z]+)'), - all_cap_re = re.compile('([a-z])([A-Z]+)')): - if name.isupper(): - return name.lower() - s1 = first_cap_re.sub(r'\1_\2', name) - return all_cap_re.sub(r'\1_\2', s1).lower() - -class Doc: - """wrap an identifier and doc""" - _id = None - - def __init__(self, id_, doc, typestr=None, default=None): - self._id = id_ - self.doc = doc - self.typestr = typestr - self.default = default - - def __str__(self): - return self._id - - -class OprGenerator: - _fout = None - _cur_indent = '' - - def __init__(self): - self._fout = StringIO() - - def _indent(self): - self._cur_indent += ' ' * 4 - - def _unindent(self): - self._cur_indent = self._cur_indent[:-4] - - def _write(self, content, *fmt, indent=0): - if indent < 0: - self._unindent() - - self._fout.write(self._cur_indent) - if fmt: - content = content % fmt - self._fout.write(content) - self._fout.write('\n') - - if indent > 0: - self._indent() - - def _gen_signature(self, inputs, params, *, have_config=True, - has_out_dtype=False): - assert inputs - sig = [] - for inp in inputs: - name = str(inp) - if name.startswith('*'): - assert name[1:].isidentifier() - assert inp is inputs[-1] - else: - assert name.isidentifier() - if isinstance(inp, Doc) and inp.default is not None: - name += '={}'.format(inp.default) - sig.append(name) - if not str(inputs[-1]).startswith('*'): - sig.append('*') - for i, _ in params: - sig.append('{}=None'.format(i)) - - if have_config: - sig.extend(['name=None', 'comp_node=None', 'config=None']) - if 'comp_graph' not in map(str, inputs): - sig.append('comp_graph=None') - if has_out_dtype: - assert 'dtype' not in map(str, inputs) - sig.append('dtype=None') - - if params: - sig.append('**kwargs') - - if sig[-1] == '*': - sig.pop() - return ', '.join(sig) - - def _write_canonize_inputs(self, inputs, canonize_input_vars, - canonize_input_vars_args=None, - has_out_dtype=False): - self._write_gen_config(has_out_dtype) - inputs = list(map(str, inputs)) - if canonize_input_vars_args is None: - if inputs[0][0] == '*': - arg = inputs[0][1:] - else: - arg = '[{}]'.format(', '.join(inputs)) - else: - arg = canonize_input_vars_args - self._write('all_inputs = _helper.%s(%s, ' - 'comp_graph=comp_graph, config=config)', - canonize_input_vars, arg) - - def _write_gen_config(self, has_out_dtype=False): - if not has_out_dtype: - self._write('config = _helper.gen_config(name, comp_node, config)') - else: - self._write('config = _helper.gen_config(name, comp_node, config, dtype)') - - def _write_make_params(self, params, body): - for pname, ptype in params: - self._write('%s = _helper.cvt_to_opr_param_def(%s, ' - '_opr_param_defs.%s, kwargs)', - pname, pname, ptype) - self._write('assert not kwargs, "extra kwargs: {}".format(kwargs)') - - for i in body: - self._write(i) - - self._write('all_params = []') - for pname, _ in params: - self._write('all_params.append(%s.serialize())', - pname) - - def _write_doc(self, inputs, params, desc): - self._write('"""') - if isinstance(desc, Doc): - assert desc._id is None - self._write(desc.doc) - elif desc: - for i in textwrap.wrap(desc, 75): - self._write(i) - - self._write('') - for i in inputs: - name = str(i) - typestr = ':class:`.SymbolVar`' - if name[0] == '*': - name = name[1:] - typestr = 'list of ' + typestr - if isinstance(i, Doc): - self._write(':param %s: %s', name, i.doc) - if i.typestr is not None: - typestr = i.typestr - if typestr: - if not isinstance(i, Doc): - self._write(':param %s: ', name) - self._write(':type %s: %s', name, typestr) - - for pname, ptype in params: - self._write(':param %s: ', pname) - self._write(':type %s: :class:`~megbrain.opr_param_defs.%s`', - pname, ptype) - - self._write(':param comp_node: see doc for *config*') - self._write(':param name: see doc for *config*') - self._write( - ':param config: give a :class:`.OperatorNodeConfig` object to set ' - 'operator name and comp node. This can also be achieved by passing ' - '*comp_node* and *name* separately.') - - if 'comp_graph' not in map(str, inputs): - self._write( - ':param comp_graph: If all inputs are immediate numbers, ' - '*comp_graph* and *comp_node* must be provided ' - 'so the input values can be put on appropriate ' - 'computing graph and computing node') - self._write('"""') - - def _write_return(self, name, outputs): - self._write('outputs = _mgb._create_opr(' - '"%s", all_inputs, all_params, config)', name) - if outputs: - self._write('outputs = [outputs[i] for i in %s]', - list(map(int, outputs))) - self._write('return _helper.cvt_opr_result(outputs, ' - '**cvt_result_kwargs)') - - def decl_opr(self, name, *, inputs, params, desc=None, pyname=None, - canonize_input_vars='canonize_input_vars', - canonize_input_vars_args=None, body=[], - outputs=None, version=0, has_out_dtype=False): - """ - :param inputs: name of variable inputs; a name starting with `*' means - a list of vars - :type inputs: list of str - :param params: (param name, param type) pairs; it can be a single - string representing the param type, and param name defaults to - 'param' - :type params: list of pair of str, or str - :param pyname: python function name - :param body: extra statements to be placed before calling _create_opr - :param outputs: the indices of output vars to be selected from raw opr - result - """ - if isinstance(params, str): - params = [('param', params)] - assert params - - if pyname is None: - pyname = camel2underscore(name) - - self._write('def %s(%s):', pyname, - self._gen_signature(inputs, params, - has_out_dtype=has_out_dtype), indent=1) - self._write_doc(inputs, params, desc) - self._write_canonize_inputs( - inputs, canonize_input_vars, - canonize_input_vars_args=canonize_input_vars_args, - has_out_dtype=has_out_dtype) - self._write('cvt_result_kwargs = {}') - self._write_make_params(params, body) - if version: - name += 'V{}'.format(version) - self._write_return(name, outputs) - self._write('', indent=-1) - - def decl_raw_opr(self, name, *, inputs, inputs_cvt=[], body=None, - desc=None, local_defs=[], have_config=True): - """declare a raw operator that is forwarded to _mgb._Opr; if *body* is - given, a custom implemented can be provided - - :param inputs_cvt: list of (input name, cvt) pairs, where cvt is name - of the function to convert that input - :param body: list of statements to produce output, or None - :param local_defs: list of statements to be prepended before generated - code - """ - self._write('def %s(%s):', name, - self._gen_signature(inputs, [], have_config=have_config), - indent=1) - self._write_doc(inputs, [], desc) - if have_config: - self._write_gen_config() - for i in local_defs: - self._write(i) - for k, v in inputs_cvt: - self._write('%s = %s(%s)', k, v, k) - self._write('cvt_result_kwargs = {}') - if body is None: - self._write('output = _mgb._Opr.%s(%s, config)', - name, ', '.join(map(str, inputs))) - else: - for i in body: - self._write(i) - self._write( - 'return _helper.cvt_opr_result(output, **cvt_result_kwargs)') - self._write('', indent=-1) - - def get_str(self): - return self._fout.getvalue() - - -def main(): - parser = argparse.ArgumentParser( - description='generate operator function def code from decl file') - parser.add_argument('inputs', nargs='+') - args = parser.parse_args() - - gen = OprGenerator() - exec_globals = { - 'decl_opr': gen.decl_opr, - 'decl_raw_opr': gen.decl_raw_opr, - 'Doc': Doc, - 'camel2underscore': camel2underscore, - } - for i in args.inputs: - print('generate oprs from {}'.format(i)) - with open(i) as fin: - exec(fin.read(), exec_globals) - - git_commit = subprocess.Popen(['git', 'rev-parse', 'HEAD'], - stdout=subprocess.PIPE).communicate()[0].strip() - git_commit = git_commit.decode('utf-8') - - file_rela = lambda *paths: os.path.join(os.path.dirname(__file__), *paths) - outfile = lambda name: file_rela('../../megengine/_internal', name) - with open(file_rela('opr_template.py')) as fin: - with open(outfile('opr.py'), 'w') as fout: - fout.write(fin.read(). - replace('{%body%}', gen.get_str()). - replace('{%git_commit%}', git_commit)) - -if __name__ == '__main__': - main() diff --git a/python_module/src/python/opr_template.py b/python_module/src/python/opr_template.py deleted file mode 100644 index 29ea0a9d..00000000 --- a/python_module/src/python/opr_template.py +++ /dev/null @@ -1,425 +0,0 @@ -# -*- coding: utf-8 -*- -# This file is part of MegBrain. -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. - -"""This python module contains functions to apply the operators defined by -megbrain. - -.. note:: - Most of the functions are automatically generated, and their signature have - the form contain a ``param`` argument (or more than one arguments such as - :func:`convolution` that has ``param`` and ``execution_polity``) and also - accept keyword arguments. In such case, it can be called by either - providing a param object of appropriate type, or by passing the arguments - needed by the constructor of param object to the keyword arguments. - Furthermore, for a param that needs an enumeration member, the enum name - can be used to refer to the enum object. - - For example, the following statements are equivalent:: - - elemwise([a, b], mode='max') - elemwise([a, b], mode=opr_param_defs.Elemwise.Mode.MAX) - elemwise([a, b], param=opr_param_defs.Elemwise('max')) -""" - -from . import mgb as _mgb -from . import helper as _helper -from . import opr_param_defs as _opr_param_defs - -import sys -import enum -import collections -import json - -__git_commit__ = "{%git_commit%}" - -{%body%} - -class _ElemMeta(type): - def __getattr__(self, name): - def run(*inputs, **kwargs): - return elemwise(inputs, mode=name, **kwargs) - if name.startswith('__'): - return - return run - - -class elem(metaclass=_ElemMeta): - """ - Helper class for easily applying element-wise operator. Request for getting - member method would be translated to a call to :func:`elemwise` with mode - set to the method name. Example:: - - elem.exp(a) # elemwise(a, mode='exp') - elem.max(a, b) # elemwise([a, b], mode='max') - - """ - - -def add_update( - dest, delta, - alpha=_mgb.SharedScalar(1), beta=_mgb.SharedScalar(1), - bias=_mgb.SharedScalar(0), disable=_mgb.SharedScalar(0), *, - name=None, comp_node=None, config=None, comp_graph=None): - """update *dest* by `dest := dest*alpha + delta*beta + bias` - - :param dest: target var to be updated; must be created from - :func:`make_shared` - :type dest: :class:`.SymbolVar` - :param disable: AddUpdate will not be executed if disable is set to 1, - this is used for dynamic param-updating. The value of this SharedScalar - can only be set to 0/1 of type `int` - :type disable: :class:`.SharedScalar` - """ - def as_ss(x): - if not isinstance(x, _mgb.SharedScalar): - x = _mgb.SharedScalar(x) - return x - - assert isinstance(dest, _mgb.SymbolVar) - config = _helper.gen_config(name, comp_node, config) - dest, delta = _helper.canonize_input_vars( - [dest, delta], comp_graph=comp_graph, config=config) - - assert isinstance(disable, _mgb.SharedScalar) - - alpha, beta, bias = map(as_ss, [alpha, beta, bias]) - return _mgb._Opr.add_update(dest, delta, alpha, beta, bias, disable, config) - -def reduce_(src, mode, axis=None, keepdims=False, *, - name=None, comp_node=None, config=None, comp_graph=None): - """reduce along given axis; if axis is None, reduce to scalar - - :param mode: reduction mode - :type mode: :class:`~megengine._internal.opr_param_defs.Reduce.Mode` compatible - :param axis: axis along which to reduce input var - :type axis: int - :param keepdims: whether to keep an axis of shape 1 in the result - :type keepdims: False - """ - assert isinstance(src, _mgb.SymbolVar) - config = _helper.gen_config(name, comp_node, config) - inputs = [src] - kwargs = {'mode': mode} - remove_axis = False - if axis is None: - inputs.append(1) - assert not keepdims, 'can not set axis=None and keepdims=True' - else: - remove_axis = not keepdims - kwargs['axis'] = axis - - ret = reduce_general(inputs, config=config, comp_graph=comp_graph, - **kwargs) - if remove_axis: - ret = _mgb._Opr.remove_axis(ret, axis, _mgb.make_opr_config()) - return _helper.cvt_opr_result(ret) - -def _reduce_like(impl, src, axis, keepdims, - name, comp_node, config, comp_graph): - config = _helper.gen_config(name, comp_node, config) - remove_axis = False - if axis is None: - assert not keepdims, 'can not set axis=None and keepdims=True' - src = src.flatten() - axis = 0 - else: - assert isinstance(axis, int) and axis >= 0, ( - 'bad axis: {!r}'.format(axis)) - remove_axis = not keepdims - - ret = impl(src, axis=axis, config=config, comp_graph=comp_graph) - if remove_axis: - ret = _mgb._Opr.remove_axis(ret, axis, _mgb.make_opr_config()) - return _helper.cvt_opr_result(ret) - -def dimshuffle(src, pattern, ndim=0, *, - name=None, comp_node=None, config=None): - """swap shapes and strides according to given pattern - - :param pattern: a list of integers, where each element is the input axis of - that output axis. An element could also be 'x' for creating a new axis - with shape 1 - :param ndim: number of input dimensions; 0 to be inferred from pattern; - this is required only for grad - """ - config = _helper.gen_config(name, comp_node, config) - if not isinstance(pattern, (list, tuple)): - raise TypeError('could not convert {} to dimshuffle pattern'.format( - pattern)) - pattern_mgb = _mgb._VectorInt() - for i in pattern: - if i == 'x': - pattern_mgb.push_back(-1) - else: - i = int(i) - assert i >= 0 - pattern_mgb.push_back(i) - return _mgb._Opr.dimshuffle(src, pattern_mgb, int(ndim), config) - -def param_pack_split(src, shapes, *, - name=None, comp_node=None, config=None): - """ - split param into a list of tensor for given shape - ParamPackSplit operator has a input: ``src`` and would - have a ``output``. output[i] indicates the address of tensor which part of - ``src`` would transfer its elements into. - - Example: a input tensor with size 32, the shapes: ``[(1, 2, 4), (4, 2, 2), - (4, 2, 1)]``, the output tensor would be a list of address with size 3. - output[0] indicates the address of tensor with shapes[0]:(1, 2, 4), - output[1] indicates the address of tensor with shapes[1]:(4, 2, 2), - output[2] indicates the address of tensor with shapes[2]:(4, 2, 1). - - :param src: The concatenated input tensor. - :type src: :class:`SymbolVar` - :param shapes: Shapes of output tensors - :type shapes: list of list of int - """ - config = _helper.gen_config(name, comp_node, config) - - if not isinstance(shapes, (list, tuple)): - raise TypeError('could not convert {} to tensor shapes'.format( - shapes)) - - shapes_mgb = _mgb._VectorTensorShape() - - for s in shapes: - s = tuple(map(int, s)) - assert min(s) > 0 - shapes_mgb.push_back(s) - - return _mgb._Opr.param_pack_split(src, shapes_mgb, config) - -class _modify_subtensor_helper: - def __init__(self, dest, val, *, name=None, comp_node=None, config=None): - self.dest = dest - self.val = val - self.config = _helper.gen_config(name, comp_node, config) - - def __getitem__(self, idx): - inp = _mgb._VectorSymbolVar() - dest, desc = _helper.cvt_getitem_to_idx_desc( - self.dest, idx, allow_newaxis=False) - assert desc is not None, 'no __getitem__ entries given' - inp.push_back(dest) - inp.push_back(self.val) - return _mgb._create_subtensor_like_opr( - self._opr_name, inp, desc, self.config) - -class set_subtensor(_modify_subtensor_helper): - """a proxy object which supports ``__getitem__`` to set subtensor. - ``c = set_subtensor(a, b)[idx]`` is equivalent to the numpy - expression:: - - c = a.copy() - c[idx] = b - - """ - _opr_name = 'set_subtensor' - - -class incr_subtensor(_modify_subtensor_helper): - """a proxy object which supports ``__getitem__`` to increase subtensor. - ``c = incr_subtensor(a, b)[idx]`` is equivalent to the numpy - expression:: - - c = a.copy() - c[idx] += b - """ - _opr_name = 'incr_subtensor' - -class mesh_indexing: - """ Extract elements from given tensor by the coordinates which is - Cartesian product of given index; example:: - - mesh_indexing(x)[:, [2, 3], :, [2, 3, 4]] - """ - - def __init__(self, src, *, name=None, comp_node=None, config=None): - self.src = src - self.config = _helper.gen_config(name, comp_node, config) - - - def __getitem__(self, idx): - inp, desc = _helper.cvt_getitem_to_idx_desc(self.src, idx) - if desc is None: - return inp - return _mgb._create_subtensor_like_opr( - 'mesh_indexing', [inp], desc, self.config) - -class batched_mesh_indexing: - """ Similar to :class:`mesh_indexing`, while the k-th position of - slices is a 2-dim matrix `matrix[k]`. - The `matrix[k] is a list of index. The i-th row `matrix[k][i]` - represents the index of the associated k-th position slice when - `batch_idx == i` ; example:: - - batched_mesh_indexing(x)[:, [[1, 2], [2, 3]], 1:-1:-1] - - .. warning:: - The first dimension of slices must be (start, stop, step) like, - cannot be any of SymbolVar, numpy.array, Python list. - And the shape of other indexs must be (n, x) while n is the length - of first dimension of tensor after applying [start:stop:step] - - """ - - def __init__(self, src, *, name=None, comp_node=None, config=None): - self.src = src - self.config = _helper.gen_config(name, comp_node, config) - - - def __getitem__(self, idx): - inp, desc = _helper.cvt_getitem_to_idx_desc(self.src, idx) - if desc is None: - return inp - return _mgb._create_subtensor_like_opr( - 'batched_mesh_indexing', [inp], desc, self.config) - -class incr_mesh_indexing(_modify_subtensor_helper): - _opr_name = 'incr_mesh_indexing' - -class set_mesh_indexing(_modify_subtensor_helper): - _opr_name = 'set_mesh_indexing' - -class batched_incr_mesh_indexing(_modify_subtensor_helper): - _opr_name = 'batched_incr_mesh_indexing' - -class batched_set_mesh_indexing(_modify_subtensor_helper): - _opr_name = 'batched_set_mesh_indexing' - -class advanced_indexing: - """wrapper for numpy-like advanced indexing, where a non-slice index can be - a vector; example:: - - advanced_indexing(x)[:, [2, 3]] - - """ - def __init__(self, src, *, name=None, comp_node=None, config=None): - self.src = src - self.config = _helper.gen_config(name, comp_node, config) - - def __getitem__(self, idx): - inp, desc = _helper.cvt_getitem_to_idx_desc(self.src, idx) - if desc is None: - return inp - return _mgb._create_subtensor_like_opr( - 'mavi', [inp], desc, self.config) - -class set_advanced_indexing(_modify_subtensor_helper): - """:class:`set_subtensor` equivalent with advanced-indexing support""" - _opr_name = 'set_mavi' - - -class incr_advanced_indexing(_modify_subtensor_helper): - """:class:`incr_subtensor` equivalent with advanced-indexing support""" - _opr_name = 'incr_mavi' - - -def mean(inp, axis, keepdims): - """average value along an axis""" - if hasattr(inp.dtype, 'metadata'): - return reduce_(inp, 'MEAN', axis, keepdims) - else: - s = reduce_(inp, 'SUM', axis, keepdims) - if axis is None: - cnt = inp.shape.prod() - else: - cnt = inp.axis_shape(axis) - return s / cnt - -def square(inp): - """*inp* squared""" - return inp ** 2 - -def sqrt(inp): - """square root""" - return inp ** 0.5 - - -class _LoopDescMakerCallback(_mgb._LoopDescMakerCallback): - def __init__(self, func): - super().__init__() - assert isinstance(func, collections.Callable) - self._func = func - self.__disown__() - - def call(self, desc): - self._func(desc) - - -def make_loop(desc_maker, *, - swap_interval=-5, name=None, comp_node=None, config=None): - """Create a loop operator. The loop operator works in the following way: - - 1. Copy variables specified by :meth:`.LoopDesc.add_input` from the parent - graph into the sub graph. - 2. Evaluates the loop condition. - 3. If the absolute value of the loop condition is no more than 1e-6, go to - 5. - 4. Update variables in the sub graph using rules specified by - :meth:`.LoopDesc.assign` and then go to 2 again. - 5. Copy values of output variables given by :meth:`.LoopDesc.add_output` - into the parent graph and exit. - - The loop operator could be thought of as a digital circuit, where the sub - graph (which must be purely functional) is the combinational logic part and - the :meth:`.LoopDesc.assign` rules serve as the flip-flops. - - :type desc_maker: callable - :param desc_maker: a function to create the loop descriptor; it would - receive a :class:`.LoopDesc` object and should call methods on it to - describe the sub graph. This function may be called multiple times, and - it should behave exactly the same in every call. - - :type swap_interval: int - :param swap_interval: number of loop executions between swapping saved - mutable states to host; larger *swap_interval* requires more memory and - less copy stall. If *swap_interval* is negative, then statically - inferred loop time would be used if possible; otherwise its absolute - value would be used as swap interval. - - :rtype: list of :class:`.SymbolVar` - :return: the output vars, corresponding to each - :meth:`.LoopDesc.add_output` call. - """ - config = _helper.gen_config(name, comp_node, config) - return _mgb._make_loop(_LoopDescMakerCallback(desc_maker), swap_interval, - config) - -def symvar_from_shared_nd(sv, comp_graph, name=None): - """get a symbol var in a computing graph that represents a shared (i.e. - pre-allocated) value on device - - :param sv: the shared value - :type sv: :class:`.SharedND` - :param comp_graph: the computing graph to which this symvar should belong - :type graph: :class:`.CompGraph` - :param name: the name of resulting symvar - :type name: str or None - :rtype: :class:`.SymbolVar` - """ - assert isinstance(sv, _mgb.SharedND) - return sv.symvar(comp_graph, name) - -def zero_grad(sv, **kwargs): - return set_grad(sv, None, **kwargs) - -# for backward pickle compatiblility -def _make_enum_unpickle(new_enum): - """create a class that can be used for unpickling old enum values""" - class OldEnum: - def __new__(cls, value): - return new_enum[value] - return OldEnum - - - -ConvMode = _make_enum_unpickle(_opr_param_defs.Convolution.Mode) -PoolingMode = _make_enum_unpickle(_opr_param_defs.Pooling.Mode) -ROIPoolingMode = _make_enum_unpickle(_opr_param_defs.ROIPooling.Mode) -WarpPerspectiveBorderMode = _make_enum_unpickle( - _opr_param_defs.WarpPerspective.BorderMode) -WarpPerspectiveInterpMode = _make_enum_unpickle( - _opr_param_defs.WarpPerspective.InterpolationMode) diff --git a/python_module/src/swig/callback.i b/python_module/src/swig/callback.i deleted file mode 100644 index d5953a13..00000000 --- a/python_module/src/swig/callback.i +++ /dev/null @@ -1,215 +0,0 @@ -/* - * $File: callback.i - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * $Copyright: Copyright (c) 2014-2017 Megvii Inc. All rights reserved. - */ - -%feature("autodoc", -"""It is used to be passed as arguments to callbacks (used in -:meth:`.CompGraph.compile`, :func:`.callback_injector`, and -:meth:`.CraniotomeBase.execute`). Object of this type could also be directly -passed to :meth:`.SharedND.set_value`, to bypass some host and device -communication. Note that the underlying buffer may be reused after the callback -returns, so reference to this object should not be passed outside of the -callback, and :meth:`get_value` should be called immediately if the numerical -value is needed.""") -CompGraphCallbackValueProxy; - -class CompGraphCallbackValueProxy { - public: - - PyObject* _get_npyarr(); - PyObject* _get_dtype(); - std::vector _get_shape(); - - uintptr_t _pubapi_dev_tensor_ptr(int version); - CompNode _get_comp_node(); - - %pythoncode{ - - @property - def shape(self): - """get shape of the var - - :type: tuple of int - """ - return tuple(map(int, self._get_shape())) - - @property - def comp_node(self): - """get comp node of the var - - :type: :class:`.CompNode` - """ - return self._get_comp_node() - - @property - def dtype(self): - """get data type of the var - - :type: :class:`.numpy.dtype` - """ - return self._get_dtype() - - def get_value(self, *, borrow_mem=False): - """get value as numpy array - - :param borrow_mem: whether to forward internal buffer with - zero-copy; if True, the content in returned buffer would be - modified directly by asynchronous graph execution. - """ - ret = self._get_npyarr() - if not borrow_mem: - ret = ret.copy() - return ret - - @property - def dev_ptr(self): - """this method is DEPRECATED; use :meth:`pubapi_dev_tensor_ptr` - instead""" - return self._pubapi_dev_tensor_ptr(0) - - @property - def pubapi_dev_tensor_ptr(self): - """get a pointer to the corresponding mgb::pubapi::DeviceTensor object - - :rtype: int - :return: the address as an integer - """ - return self._pubapi_dev_tensor_ptr(1) - } -}; -%template(_VectorCompGraphCallbackValueProxy) - std::vector; - -%feature("director") _CompGraphCallback; -class _CompGraphCallback { - public: - _CompGraphCallback(); - - void set_eager_copy(bool flag); - - virtual ~_CompGraphCallback(); - virtual void call(std::vector &value) = 0; -}; - -%feature("director") _SplitPartCallback; -class _SplitPartCallback { - public: - _SplitPartCallback(); - virtual ~_SplitPartCallback(); - - virtual std::vector call(size_t tot_size) = 0; -}; - -%feature("director") _SetGradCallback; -class _SetGradCallback { - public: - _SetGradCallback(); - virtual ~_SetGradCallback(); - - virtual SymbolVar call(CompGraph &graph) = 0; - virtual bool empty() = 0; -}; - -%feature("director") _TimeoutCallback; -class _TimeoutCallback { - public: - _TimeoutCallback(); - virtual ~_TimeoutCallback(); - - virtual bool call() = 0; -}; - -%pythoncode{ -import collections -import inspect -from .mgb_helper import callback_lazycopy - -class _CompGraphCallbackPyWrapper(_CompGraphCallback): - """wraps around a callable to be used as comp graph callback""" - - def __init__(self, f): - super().__init__() - if isinstance(f, callback_lazycopy): - f = f.func - self.set_eager_copy(False) - else: - self.set_eager_copy(True) - assert isinstance(f, collections.Callable) - self._func = f - self.__disown__() - - def call(self, value): - if value.size() == 1: - self._func(value[0]) - else: - self._func(value) - - -_CompGraphCallbackPyWrapperNoEager = lambda f: ( - _CompGraphCallbackPyWrapper(callback_lazycopy(f))) - -class _SplitPartCallbackPyWrapper(_SplitPartCallback): - def __init__(self, f): - super().__init__() - assert isinstance(f, collections.Callable) - self._func = f - self.__disown__() - - def call(self, size): - return tuple(map(int, self._func(size))) - - -class _SetGradCallbackPyWrapper(_SetGradCallback): - def __init__(self, f): - super().__init__() - if f is None: - self._func = None - else: - assert isinstance(f, collections.Callable) - nr_arg = len(list(filter( - lambda x: ( - x.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD and - x.default == inspect.Parameter.empty), - inspect.signature(f).parameters.values()))) - if not nr_arg: - f = lambda graph, f0=f: f0() - else: - assert nr_arg == 1, 'bad callback for SetGrad: {}'.format(f) - self._func = f - - self.__disown__() - - def call(self, graph): - if self._func is None: - return SymbolVar() - - ret = self._func(graph) - if ret is None: - ret = SymbolVar() - else: - assert isinstance(ret, SymbolVar), ( - 'bad return value for var maker: {!r}'.format(ret)) - return ret - - def empty(self): - return self._func is None - - -class _TimeoutCallbackPyWrapper(_TimeoutCallback): - def __init__(self, f): - super().__init__() - assert isinstance(f, collections.Callable) - self._func = f - self.__disown__() - - def call(self): - return bool(self._func()) - - -} // %pythoncode - -// vim: ft=swig diff --git a/python_module/src/swig/comp_graph.i b/python_module/src/swig/comp_graph.i deleted file mode 100644 index 80c05586..00000000 --- a/python_module/src/swig/comp_graph.i +++ /dev/null @@ -1,87 +0,0 @@ -/* - * $File: comp_graph.i - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * $Copyright: Copyright (c) 2014-2017 Megvii Inc. All rights reserved. - */ - - -%pythoncode{ -from .mgb_helper import copy_output, FuncOutputSaver -import json -} // pythoncode - -%feature("autodoc", """a callable object compiled from :class:`CompGraph`. - -.. note:: - - Only the most recently compiled AsyncExec object can be used. -""") AsyncExec; -%feature("autodoc", """explicitly release the underlying staticially allocated -device memory""") AsyncExec::clear_device_memory; -class AsyncExec { - public: - AsyncExec() = delete; - - void _execute(); - void _wait(); - double _get_prev_exec_time(); - std::string _to_json_str(); - SymbolVarArray _find_mutable_input(); - std::vector> - _update_static_alloc_plan_and_get_size(); - - void clear_device_memory(); - - %include "comp_graph_impl_AsyncExec.py" -}; - -%template(_VectorAsyncExec) std::vector; - -%feature("autodoc", """use device memory manager in another computing graph to -manage memory of this graph, so their memories can be shared. This is safe only -when :class:`AsyncExec` compiled from these graphs do not run concurrently.""") -CompGraph::share_device_memory_with; -%feature("valuewrapper") CompGraph; -class CompGraph { - public: - CompGraph(); - - AsyncExec _do_compile(bool copy, bool optimize_for_inference); - std::vector _do_compile_multi_part(); - void _add_output_spec(SymbolVar &var, _CompGraphCallback *callback); - void _add_multi_part_endpoint(); - void _clear_output_spec(); - size_t _release(); - - CompGraph& share_device_memory_with(CompGraph &other); - - PyObject* _user_data(); - void clear_device_memory(); - - %extend { - size_t _id() const { - return $self->get().id(); - } - - size_t _get_ptr_addr() const { - return reinterpret_cast(&$self->get()); - } - - std::string get_mem_allocation_info() const { - return self->get().get_mem_allocation_info(); - } - - std::string __repr__() const { - auto &&graph = $self->get(); - return mgb::ssprintf("", graph.id(), &graph); - } - } - - %include "comp_graph_impl_CompGraph.py" -}; - -%include "comp_graph_tools.i" - -// vim: ft=swig diff --git a/python_module/src/swig/comp_graph_impl_AsyncExec.py b/python_module/src/swig/comp_graph_impl_AsyncExec.py deleted file mode 100644 index 28836c13..00000000 --- a/python_module/src/swig/comp_graph_impl_AsyncExec.py +++ /dev/null @@ -1,229 +0,0 @@ -%pythoncode { - -_var2output_saver = None -"""map from var id to corresponding output saver; setup by -:meth:`.CompGraph.compile`""" - -_expand_single_output = False -"""whether output contains only a single element and it should not be wrapped -by a list; setup by :meth:`.CompGraph.compile`""" - -__output_savers = None -"""list of (symvar, output saver)""" - -__inputs = None -"""list of (symvar, _HostSharedND.make_proxy(symvar)) pairs""" - -__allow_args_input = None - -__warned_unused_keys = None - -__auto_wait_enabled = True - -callbefore_func = None - -callback_func = None - -def __normalize_var_list(self, vlist): - if len(vlist) == 1: - vlist, = vlist - if isinstance(vlist, SymbolVar): - return [vlist] - ret = [] - for i in vlist: - assert isinstance(i, SymbolVar) - ret.append(i) - return ret - -def _setup_args(self, args, kwargs): - if kwargs: - assert not args, 'should not provide both args and kwargs' - for symvar, hsv in self.__inputs: - val = kwargs.pop(symvar.name, None) - assert val is not None, ( - 'missing input at runtime: {}'.format(symvar)) - hsv.set_value(val, borrow=self.__auto_wait_enabled) - - if kwargs: - keys = set(kwargs.keys()) - if keys != self.__warned_unused_keys: - from .logconf import get_logger - logger = get_logger() - logger.warning( - 'extra kwargs provided for megbrain AsyncExec: {}'.format( - keys)) - self.__warned_unused_keys = keys - return - - assert not args or self.__allow_args_input, ( - 'pass non-keyword args to function compiled without' - ' inputs spec') - assert len(args) == len(self.__inputs), ( - 'inputs do not match: args={} needed={}'.format( - args, [i[0] for i in self.__inputs])) - for (symvar, hsv), val in zip(self.__inputs, args): - hsv.set_value(val, borrow=self.__auto_wait_enabled) - -def enable_borrow_on_cpu(self, flag=True): - """whether to allow borrow input tensor memory on CPU; if set to True, then - the user should ensure that memory buffers of input tensors are unchanged. - - This is set to False by default. - """ - for _, i in self.__inputs: - i.enable_borrow_on_cpu(flag) - -def __call__(self, *args, **kwargs): - """Execute the function; either one of positional arguments or keyword - arguments must be given. Set :attr:`inputs` to change the order of - positional arguments. The keys in keyword arguments are the names of input - symvars - - :return: if auto wait is disabled, the return value would be - :class:`FuncOutputSaver` objects corresponding to the vars marked by - :class:`copy_output`; if auto wait is enabled, the numerical values as - :class:`numpy.ndarray` would be returned. - """ - if self.callbefore_func: - if not callable(self.callbefore_func): - raise TypeError( - "callbefore func must be callable: {}".format(self.callbefore_func)) - self.callbefore_func() - self._setup_args(args, kwargs) - self._execute() - if self.callback_func: - if not callable(self.callback_func): - raise TypeError( - "callback func must be callable: {}".format(self.callback_func)) - self.callback_func() - if self.__auto_wait_enabled: - self.wait() - - if not self.__output_savers: - return - ret = [] - if self.__auto_wait_enabled: - for _, i in self.__output_savers: - ret.append(i.get()) - else: - for _, i in self.__output_savers: - ret.append(i) - if self._expand_single_output: - ret, = ret - return ret - -def wait(self): - """wait for previous async exec to finish; wait is needed (i.e. the - function runs in async mode) only when there is no output callback (i.e. - all outputs are given by dest symvar only), or :meth:`disable_auto_wait` is - called explicitly. - - :return: self""" - self._wait() - return self - -@property -def prev_exec_time(self): - """previous execution time in seconds""" - return self._get_prev_exec_time() - -@property -def inputs(self): - """get input vars needed at runtime, in the order as the values that should - be passed to :meth:`__call__` - - :setter: Set the order of input vars, which must be created by - :func:`.make_arg`. None could also given, and in such case only keyword - arguments would be allowed for :meth:`__call__` - - :type: tuple of :class:`.SymbolVar` - """ - return tuple(i[0] for i in self.__inputs) - -@inputs.setter -def inputs(self, *inputs): - if self.__inputs is None: - needed = tuple(self._find_mutable_input()) - used_names = set() - for i in needed: - assert i.name not in used_names, ( - 'duplicated input name: {}'.format(i.name)) - used_names.add(i.name) - self.__inputs = [(i, _HostSharedND.make_proxy(i)) for i in needed] - - if len(inputs) == 1 and inputs[0] is None: - self.__allow_args_input = False - return - - inputs = self.__normalize_var_list(inputs) - inpvar2proxy = dict(self.__inputs) - self.__allow_args_input = True - reordered = [] - for i in inputs: - proxy = inpvar2proxy.pop(i, None) - if proxy is None: - raise TypeError('extra input var provided: {}; needed: {}'.format( - i, self.inputs)) - reordered.append((i, proxy)) - - assert not inpvar2proxy, 'inputs not provided: {}'.format( - list(inpvar2proxy.keys())) - - self.__inputs = reordered - -@property -def available_outputs(self): - """get output vars that could be used to set :attr:`outputs`. The order may - be unstable - - :type: tuple of :class:`.SymbolVar`""" - return tuple(self._var2output_saver.keys()) - -@property -def outputs(self): - """get output vars whose corresponding values would be returned by - :meth:`__call__` - - :setter: set the order of output vars to be returned. Duplicated vars could - be included, but all the vars must have been provided to - :meth`.CompGraph.compile`. - - :type: tuple of :class:`.SymbolVar`""" - if not self.__output_savers: - return - - if self._expand_single_output: - (var, saver), = self.__output_savers - return var - return tuple(var for var, saver in self.__output_savers) - -@outputs.setter -def outputs(self, *outputs): - olist = [] - for var in self.__normalize_var_list(outputs): - saver = self._var2output_saver.get(var) - assert saver is not None, 'var {} is not set to be output var'.format( - var) - olist.append((var, saver)) - self.__output_savers = olist - -def dump(self): - """dump internal graph and execution sequence as - json-serializable object""" - return json.loads(self._to_json_str()) - -def disable_auto_wait(self): - """if there is output callback function, then by default when - :meth:`__call__` is invoked, it would not return until all computation is - finished. This behavior can be changed by disabling auto wait, so the - function returns as early as possible.""" - self.__auto_wait_enabled = False - -def update_static_alloc_plan_and_get_size(self): - """update static memory allocation plan without actual allocation - - :return: a dict that maps from comp node to size of allocation in bytes - """ - return {k: v for k, v in self._update_static_alloc_plan_and_get_size()} - -} diff --git a/python_module/src/swig/comp_graph_impl_CompGraph.py b/python_module/src/swig/comp_graph_impl_CompGraph.py deleted file mode 100644 index 5f87a532..00000000 --- a/python_module/src/swig/comp_graph_impl_CompGraph.py +++ /dev/null @@ -1,191 +0,0 @@ -%pythoncode{ - -@property -def id(self): - """an integer increasing ID""" - return self._id() - -def __eq__(self, rhs): - return isinstance(rhs, CompGraph) and self.id == rhs.id - -def __hash__(self): - return self.id - -@property -def user_data(self): - """get a dict that is associated with this computing graph to store - arbitrary user data""" - return self._user_data() - -def _process_output_spec(self, inputs, outspec): - """process user-provided output spec and add to the output staging list of - this graph - - :return: a callable ``f(func)` to update compiled :class:`.AsyncExec` status - """ - assert outspec - - if isinstance(outspec, copy_output): - outspec = [outspec] - expand_single_output = True - else: - expand_single_output = False - - var2output_saver = {} - output_vars = [] - - for spec in outspec: - if isinstance(spec, copy_output): - var = spec.symvar - output_vars.append(var) - if var in var2output_saver: - continue - - callback = FuncOutputSaver(spec.borrow_mem) - var2output_saver[var] = callback - elif isinstance(spec, SymbolVar): - var = spec - callback = None - else: - var, callback = spec - assert isinstance(var, SymbolVar) - if callback is not None: - callback = _CompGraphCallbackPyWrapper(callback) - self._add_output_spec(var, callback) - - def update(func): - assert isinstance(func, AsyncExec) - func.inputs = inputs - if output_vars: - func._var2output_saver = var2output_saver - func._expand_single_output = expand_single_output - func.outputs = output_vars - - return update - -def compile(self, inputs, outspec, *, copy=False, optimize_for_inference=False): - """Compile the graph to get a callable function for numerical evaluation - - .. warning:: - - If ``compile()`` is called multiple times, only the most recent result - function can be used. - - :type inputs: iterable of :class:`.SymbolVar` or None - :param inputs: specifying the positional parameters to be passed to the - generated function, or use None for keyword params only - :type outspec: iterable of *single_outspec* - :param outspec: specifying how the compiled function should - return outputs. Each *single_outspec* may be one of the - following forms: - - * a pair of (var, callback), the callback would be called during - function execution with a :class:`.CompGraphCallbackValueProxy` - argument corresponding to the given symbolvar. Additionally, - *callback* may be wrapped by :class:`.callback_lazycopy`; see the - its document for details. - * a single :class:`.SymbolVar`, to ensure this var is computed (so - the non-pure operators on its dependency path could take effect) - * a :class:`.copy_output` object, so the var's value would be - copied to the return value of compiled function. If there is one - such spec, the function would be synchronous. - :param copy: whether to copy the graph - :param optimize_for_inference: whether to run - :func:`.optimize_for_inference` on the output vars before compiling - :rtype: :class:`.AsyncExec` - """ - - self._clear_output_spec() - ret_update = self._process_output_spec(inputs, outspec) - ret = self._do_compile(copy, optimize_for_inference) - ret_update(ret) - return ret - -def compile_outonly(self, outputs, *, inputs=None): - """compile for only output; (almost) equavalent to - ``self.compile(inputs, [copy_output(i) for i in outputs])`` - - :type outputs: :class:`.SymbolVar` or list of - :class:`.SymbolVar` - :param outputs: the output symbol vars - """ - if isinstance(outputs, SymbolVar): - outputs = copy_output(outputs) - else: - assert isinstance(outputs, collections.Iterable), ( - '{} not iterable'.format(outputs)) - outputs = [copy_output(i) for i in outputs] - - return self.compile(inputs, outputs) - -def compile_multi_part(self, io_specs): - """Compile multiple functions for partial execution. Each function would - only execute the oprs necessary to compute current outspec, and intermediate - results from previous functions are reused. The functions would share - underlying device storage with this graph. - - .. warning:: - - Each individual partial function would have a newly created computing - graph. Therefore plugins attached on this graph would not be effective - on the partial functions. - - :param io_specs: input/output specifications as a list of - ``(inputs, outspec)`` pairs. Each pair is defined as the params of - :meth:`compile`. - :return: a list of :class:`.AsyncExec` objects as the functions - corresponding to each part - """ - self._clear_output_spec() - updaters = [] - for inputs, outspec in io_specs: - updaters.append(self._process_output_spec(inputs, outspec)) - self._add_multi_part_endpoint() - funcs = self._do_compile_multi_part() - for i, j in zip(funcs, updaters): - j(i) - return funcs - -def make_shared(self, comp_node, *, dtype=None, - shape=None, value=None, name=None, volatile=False): - """make a shared value belonging to this comp graph; see - :func:`.make_shared`""" - from . import make_shared - return make_shared(comp_node, dtype=dtype, shape=shape, value=value, - comp_graph=self, name=name, volatile=volatile) - -def make_immutable(self, comp_node, value, *, dtype=None, name=None): - """make an immutable value belonging to this comp graph; see - :func:`.make_immutable`""" - from . import make_immutable - return make_immutable(comp_node, self, value, dtype=dtype, name=name) - -def make_arg(self, comp_node, *, dtype=np.float32, shape=None, name=None, - value=None): - """make a runtime argument belonging to this comp graph; see - :func:`.make_arg`""" - from . import make_arg - return make_arg(comp_node, self, dtype=dtype, shape=shape, name=name, - value=value) - -def set_option(self, name, val): - """set comp graph option; see :func:`.set_comp_graph_option`""" - from .config import set_comp_graph_option - return set_comp_graph_option(self, name, val) - -def is_eager(self): - """return True if comp_graph is in eager mode""" - from .config import comp_graph_is_eager - return comp_graph_is_eager(self) - -def release(self): - """explicitly release the underlying computing graph storage; this is - mostly useful in eager evaluation mode, since doing so would release the - underlying device storage - - :return: original reference count before release - :rtype: int - """ - return int(self._release()) - -} diff --git a/python_module/src/swig/comp_graph_tools.i b/python_module/src/swig/comp_graph_tools.i deleted file mode 100644 index 7f6262df..00000000 --- a/python_module/src/swig/comp_graph_tools.i +++ /dev/null @@ -1,55 +0,0 @@ -%{ -#include "megbrain/gopt/framework.h" -%} - -%inline { - - SymbolVarArray _get_owner_opr_inputs(SymbolVar var) { - mgb_assert(var.node()); - return mgb::cg::to_symbol_var_array(var.node()->owner_opr()->input()); - } - - std::string _get_owner_opr_type(SymbolVar var) { - mgb_assert(var.node()); - return var.node()->owner_opr()->dyn_typeinfo()->name; - } - - std::string _get_opr_type(Operator opr) { - return opr.node()->dyn_typeinfo()->name; - } - - SymbolVarArray _replace_vars(const SymbolVarArray& repl_src, - const SymbolVarArray& repl_dst, - const SymbolVarArray& vars) { - mgb::ThinHashMap varmap; - for (size_t i = 0; i < repl_src.size(); ++i) { - varmap[repl_src[i]] = repl_dst[i]; - } - return mgb::cg::replace_vars(vars, varmap); - } - - typedef std::vector OperatorArray; - SymbolVarArray _replace_oprs(const OperatorArray& repl_src, - const OperatorArray& repl_dst, - const SymbolVarArray& vars) { - mgb::ThinHashMap - oprmap; - for (size_t i = 0; i < repl_src.size(); ++i) { - oprmap[repl_src[i].node()] = repl_dst[i].node(); - } - return mgb::cg::replace_oprs(vars, oprmap); - } - - void _set_priority_to_id(const SymbolVarArray& dest_vars) { - auto on_opr = [](mgb::cg::OperatorNodeBase* opr) { - if (opr->node_prop().attribute().priority == 0) { - opr->node_prop().attribute().priority = opr->id(); - } - }; - mgb::cg::DepOprIter dep_iter{on_opr}; - for (const SymbolVar& var : dest_vars) { - dep_iter.add(var); - } - } -} -// vim: ft=swig foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/swig/comp_node.i b/python_module/src/swig/comp_node.i deleted file mode 100644 index 708cd34f..00000000 --- a/python_module/src/swig/comp_node.i +++ /dev/null @@ -1,165 +0,0 @@ -/* - * $File: comp_node.i - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * $Copyright: Copyright (c) 2014-2017 Megvii Inc. All rights reserved. - */ - -%{ -using mgb::CompNode; -static CompNode::DeviceType str2device_type( - const std::string &str, bool allow_unspec) { - using T = CompNode::DeviceType; - if (str == "CPU") { - return T::CPU; - } else if (str == "CUDA" || str == "GPU") { - return T::CUDA; - } else { - mgb_assert(allow_unspec && str == "XPU", "bad device type: %s; which " - "must be either CPU, GPU or XPU", str.c_str()); - return T::UNSPEC; - } -} -%} - -class CompNode { - public: - static CompNode load(const char* id); - - %extend { - static std::vector _parse_locator(const std::string &id) { - auto logi = CompNode::Locator::parse(id); - return { - static_cast(logi.type), logi.device, logi.stream, - }; - } - static void _set_device_map(const std::string &type, - int from, int to) { - CompNode::Locator::set_device_map( - str2device_type(type, false), from, to); - } - - static size_t _get_device_count(const std::string &type, bool warn) { - return CompNode::get_device_count(str2device_type(type, true), warn); - } - - static void _set_unspec_device_type(const std::string &type) { - CompNode::Locator::set_unspec_device_type( - str2device_type(type, false)); - } - - static void _try_coalesce_all_free_memory() { - CompNode::try_coalesce_all_free_memory(); - } - - bool _check_eq(const CompNode &rhs) const { - return (*$self) == rhs; - } - - std::vector _get_locator() const { - auto logi = $self->locator_logical(), phys = $self->locator(); - return { - static_cast(logi.type), logi.device, logi.stream, - static_cast(phys.type), phys.device, - phys.stream, - }; - } - - std::string __getstate__() { - return $self->to_string_logical(); - } - - std::string __str__() { - return $self->to_string(); - } - - std::string __repr__() { - return mgb::ssprintf("CompNode(\"%s\" from \"%s\")", - $self->to_string().c_str(), - $self->to_string_logical().c_str()); - } - - size_t _get_mem_align_() const { - return $self->get_mem_addr_alignment(); - } - - size_t __hash__() { - return mgb::hash(*$self); - } - - std::pair _get_mem_status_bytes() { - return $self->get_mem_status_bytes(); - } - } - - %pythoncode { - DEVICE_TYPE_MAP = { - 0: 'XPU', - 1: 'CUDA', - 2: 'CPU' - } - - cn_thread_local = threading.local() - """used to save map location when calling :func:`mge.load()`""" - - def __setstate__(self, state): - """:func:`mge.load()` and :func:`deepcopy()` call this function, - The latter will not produce the map_location attribute""" - if "map_location" in CompNode.cn_thread_local.__dict__.keys(): - state = CompNode.cn_thread_local.map_location(state) - self.this = CompNode_load(state).this - - def __eq__(self, rhs): - return isinstance(rhs, CompNode) and self._check_eq(rhs) - - @property - def mem_align(self): - """memory alignment in bytes""" - return self._get_mem_align_() - - @property - def locator_logical(self) -> [str, int, int]: - """logical locator: a tuple containing (type, device, stream)""" - t, d, s = self._get_locator()[:3] - return self.DEVICE_TYPE_MAP[t], d, s - - @property - def locator_physical(self) -> [str, int, int]: - """physical locator: a tuple containing (type, device, stream)""" - t, d, s = self._get_locator()[3:] - return self.DEVICE_TYPE_MAP[t], d, s - - @property - def mem_status_bytes(self) -> [int, int]: - """get (total, free) memory on the computing device in bytes. - - Free memory includes memory chunks that buffered by the memory manager. - - Please note that the results are the same for different CompNode within same device. - """ - return self._get_mem_status_bytes() - } -}; -%template(_VectorCompNode) std::vector; -%template(_VectorCompNodeAndSize) std::vector>; - -%pythoncode { - -def as_comp_node(desc): - """create a :class:`.CompNode` by desc - - :type desc: str or :class:`.CompNode` - :param desc: if str, an id describing the comp node, like 'gpu0', 'gpu1'. A - special id 'gpux' represents the logical default comp node. Otherwise - it should already be a :class:`.CompNode`. - """ - if isinstance(desc, str): - return CompNode_load(desc) - assert isinstance(desc, CompNode), ( - 'could not convert {} to CompNode'.format(desc)) - return desc - -} - -// vim: ft=swig diff --git a/python_module/src/swig/craniotome.i b/python_module/src/swig/craniotome.i deleted file mode 100644 index adff8c4d..00000000 --- a/python_module/src/swig/craniotome.i +++ /dev/null @@ -1,88 +0,0 @@ -/* - * $File: craniotome.i - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * $Copyright: Copyright (c) 2014-2017 Megvii Inc. All rights reserved. - */ - -%{ -#include "craniotome.h" -%} - -typedef std::vector> TensorShapeVec; -%template(_VectorTensorShape) std::vector>; - -%feature("director") CraniotomeDesc; -class CraniotomeDesc { - - public: - virtual ~CraniotomeDesc() = default; - - virtual void _setup_self(PyObject *result) const = 0; - - virtual bool _is_same(PyObject *rhs) const = 0; - - virtual uint32_t _node_flag() const = 0; - - virtual size_t _hash() const = 0; - - virtual std::string _get_opr_type_name() = 0; - - virtual size_t _get_nr_outputs() = 0; - - virtual void _execute( - const std::vector &inputs, - std::vector &outputs) = 0; - - virtual TensorShapeVec _infer_shape( - const TensorShapeVec &inp_shape) = 0; - - virtual SymbolVarArray _grad( - size_t wrt_idx, - const SymbolVarArray &inputs, - const SymbolVarArray &outputs, - const SymbolVarArray &out_grad) = 0; - - virtual size_t _get_nr_dev_comp_order_deps() = 0; - - SymbolVarArray _get_all_io_vars(); - - virtual bool _init_output_dtype( - PyObject *input_dtypes, PyObject *result) = 0; - - virtual CompGraph _get_comp_graph() = 0; - - virtual void _copy() const = 0; - void _set_copy_result(CraniotomeDesc *result); - - virtual void _setup_serialize_params(PyObject *output) const = 0; - - virtual void _on_graph_compile_or_func_del( - const std::vector& used_outputs) = 0; - - %extend { - CompNode _get_comp_node() { - mgb_assert($self->owner_opr); - return $self->owner_opr->comp_node(); - } - - size_t _get_opr_id() { - mgb_assert($self->owner_opr); - return $self->owner_opr->id(); - } - } -}; - -%inline { - static SymbolVarArray make_opr_from_craniotome_desc( - CraniotomeDesc *desc, - const SymbolVarArray inputs, - const OperatorNodeConfig &config) { - - return mgb::opr::Craniotome::make( - std::unique_ptr(desc), inputs, config); - } -} - -// vim: ft=swig diff --git a/python_module/src/swig/loop.i b/python_module/src/swig/loop.i deleted file mode 100644 index 0a094d2d..00000000 --- a/python_module/src/swig/loop.i +++ /dev/null @@ -1,134 +0,0 @@ -/* - * $File: loop.i - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * $Copyright: Copyright (c) 2014-2017 Megvii Inc. All rights reserved. - */ - -%{ -#include "megbrain/opr/loop.h" -#include -using LoopDesc = mgb::opr::Loop::Desc; -%} - -%feature("autodoc", -"""An object used by callbacks for :func:`.make_loop` to describe the sub graph in -loop operator. See docs of :func:`.make_loop` for more explanation. -""") LoopDesc; - -%feature("autodoc", -"""forward a variable belonging to the parent graph into the sub graph - -:type input: :class:`.SymbolVar` -:param input: a variable in the parent graph -:type has_assign: bool -:param has_assign: whether this input var would be assigned later -:rtype: :class:`.SymbolVar` -:return: the corresponding variable in the sub graph -""") LoopDesc::add_input; - -%feature("autodoc", -"""instructs that value of a variable in the sub graph should be replaced by -the new value at the end of each loop. - -:type dest: :class:`.SymbolVar` -:param dest: the variable to be updated. It must be a return value of - :meth:`add_input`. -:type val: :class:`.SymbolVar` -:param val: the new value -:return: self to be chained -""") LoopDesc::assign; - -%feature("autodoc", -"""set a variable to indicate whether the loop should be repeated. - -:type cond: :class:`.SymbolVar` -:param cond: loop would be repeated if the absolute value of *cond* is more - than 1e-6; It must evaluates to a scalar. -:return: self to be chained -""") LoopDesc::set_loop_condition; - -%feature("autodoc", -"""get the loop counter, which would indicate current loop count, starting from zero. - -:rtype: :class:`.SymbolVar` -:return: the loop counter -""") LoopDesc::get_counter_var; - -%feature("autodoc", -"""mark a variable to be copied as output value of the loop operator. - -:type var: :class:`.SymbolVar` -:param var: a variable in sub graph whose value should be copied into the - parent graph -:type mode: str -:param mode: output mode; possible values are: - - * ``'last'``: only the last value would be recorded - * ``'all'``: all the value would be recorded; shape of the variable should - not change during looping, and the output var would be prepended with an - extra leading dimension to index the loop count. - * ``'sum'``: sum of all values of this variable during looping would be - copied to output - * ``'product'``: product of all values of this variable during looping - would be copied to output -:rtype: int -:return: call id, starting at 0 and increasing continuously -""") LoopDesc::add_output; - -class LoopDesc { - public: - LoopDesc() = delete; - ~LoopDesc() = delete; - - SymbolVar add_input(SymbolVar input, bool has_assign = false); - LoopDesc& assign(SymbolVar dest, SymbolVar val); - LoopDesc& set_loop_condition(SymbolVar cond); - SymbolVar get_counter_var(); - - %extend { - size_t add_output(SymbolVar& var, std::string mode) { - using Desc = mgb::opr::Loop::Desc; - auto get_mode = [&]() { - using OM = Desc::OutputMode; - for (char &i: mode) - i = std::tolower(i); - if (mode == "last") - return OM::LAST; - if (mode == "all") - return OM::ALL; - if (mode == "sum") - return OM::SUM; - throw mgb::MegBrainError( - mgb::ssprintf("unrecognized loop mode: %s", - mode.c_str())); - }; - return $self->add_output(var, get_mode()); - } - } - -}; - -%feature("director") _LoopDescMakerCallback; -%inline { - class _LoopDescMakerCallback { - public: - virtual ~_LoopDescMakerCallback() = default; - virtual void call(LoopDesc &desc) = 0; - }; - - static SymbolVarArray _make_loop( - _LoopDescMakerCallback* callback, int swap_interval, - const OperatorNodeConfig &config) { - - std::shared_ptr<_LoopDescMakerCallback> callbackptr{callback}; - - auto desc_maker = [callbackptr](mgb::opr::Loop::Desc &loop_desc) { - callbackptr->call(loop_desc); - }; - return mgb::opr::Loop::make(desc_maker, swap_interval, config); - } -} // %inline - -// vim: ft=swig diff --git a/python_module/src/swig/mgb.i b/python_module/src/swig/mgb.i deleted file mode 100644 index 3a2c871d..00000000 --- a/python_module/src/swig/mgb.i +++ /dev/null @@ -1,71 +0,0 @@ -/* - * $File: mgb.i - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * $Copyright: Copyright (c) 2014-2017 Megvii Inc. All rights reserved. - */ - -%include "symbol_var_array.i" - -%include "mgb_exception.i" -%module(directors="1") mgb -%{ -#define SWIG_FILE_WITH_INIT 1 -void mgb_init_numpy(); // implemented in python_helper.cpp -void _init_intbx_types(PyObject *m); // implemented in intbx.cpp -void _init_bfloat16_types(PyObject *m); // implemented in bfloat16.cpp -%} - -%init %{ - mgb_init_numpy(); - _init_intbx_types(m); - _init_bfloat16_types(m); -%} - -%include "std_vector.i" -%include "std_pair.i" -%include "stdint.i" -%template(_VectorSizeT) std::vector; -%template(_VectorInt) std::vector; -%template(_VectorString) std::vector; -%template(_PairStringSizeT) std::pair; -%template(_PairSizeTSizeT) std::pair; -%template(_VectorPairSizeTString) std::vector>; - -%pythoncode %{ -import numpy as np -import os -import threading -intb1 = _mgb.intb1 -intb2 = _mgb.intb2 -intb4 = _mgb.intb4 -bfloat16 = _mgb.bfloat16 -%} - -%{ -#include "megbrain/comp_node.h" -#include "megbrain/tensor.h" -#include "megbrain/graph.h" - -#include "megbrain_wrap.h" -#include "megbrain_config.h" -#include "megbrain_serialize.h" -#include "plugin.h" -%} - -%include "megbrain_build_config.h" -%include "comp_node.i" -%include "comp_graph.i" -%include "symbol_var.i" -%include "shared_nd.i" -%include "../cpp/megbrain_config.h" -%include "callback.i" -%include "operator.i" -%include "craniotome.i" -%include "misc.i" -%include "loop.i" -%include "../cpp/megbrain_serialize.h" -%include "../cpp/plugin.h" - -// vim: ft=swig diff --git a/python_module/src/swig/mgb_exception.i b/python_module/src/swig/mgb_exception.i deleted file mode 100644 index 8a6ee7c5..00000000 --- a/python_module/src/swig/mgb_exception.i +++ /dev/null @@ -1,40 +0,0 @@ -/* - * $File: mgb_exception.i - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * $Copyright: Copyright (c) 2014-2017 Megvii Inc. All rights reserved. - */ - - -%include "std_string.i" -%include "std_except.i" -%include "pyabc.i" - -%{ -#include "python_helper.h" -%} - -namespace PyMGBExceptionMaker { - void _reg_exception_class(PyObject *cls); -} - -%feature("director:except") { - if ($error) - PyExceptionForward::throw_(); -} - -%include "exception.i" -%allowexception; -%exception { - try { - $action - } catch (std::exception &e) { - PyMGBExceptionMaker::setup_py_exception(e); - SWIG_fail; - } catch(...) { - SWIG_exception(SWIG_UnknownError, "Unknown exception"); - } -} - -// vim: ft=swig diff --git a/python_module/src/swig/misc.i b/python_module/src/swig/misc.i deleted file mode 100644 index d3f18946..00000000 --- a/python_module/src/swig/misc.i +++ /dev/null @@ -1,151 +0,0 @@ -/* - * $File: misc.i - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * $Copyright: Copyright (c) 2014-2017 Megvii Inc. All rights reserved. - */ - - -%{ -#include "megbrain/utils/persistent_cache.h" -#include "megbrain/serialization/helper.h" -#include "megbrain/gopt/inference.h" -#include "megbrain/plugin/opr_footprint.h" -using _PyStackExtracter = PyStackExtracter; -using _PersistentCache = mgb::PersistentCache; -using _PersistentCacheBlob = _PersistentCache::Blob; -using _MaybePersistentCacheBlob = mgb::Maybe<_PersistentCacheBlob>; -using _OptimizeForInferenceOptions = mgb::gopt::OptimizeForInferenceOptions; -%} - -%feature("director") _PyStackExtracter; -class _PyStackExtracter { - public: - virtual ~_PyStackExtracter() = default; - virtual std::string extract() = 0; - static void reg(_PyStackExtracter *p); -}; - -// from Blob to python bytes -%typemap(in) const _PersistentCacheBlob& { - mgb_assert(PyBytes_Check($input)); - $1->ptr = PyBytes_AsString($input); - $1->size = PyBytes_Size($input); -} -%typemap(directorin) const _PersistentCacheBlob& { - $input = PyBytes_FromStringAndSize( - static_cast($1.ptr), $1.size); -} -%typemap(directorout) _MaybePersistentCacheBlob { - mgb_assert($1->ob_refcnt >= 2, "persistent cache result refcnt too small"); - if ($1 == Py_None) { - $result = mgb::None; - } else { - mgb_assert(PyBytes_Check($input)); - _PersistentCacheBlob blob; - blob.ptr = PyBytes_AsString($1); - blob.size = PyBytes_Size($1); - $result = blob; - } -} - -%feature("director") _PersistentCache; -class _PersistentCache { - public: - virtual ~_PersistentCache() = default; - - virtual void put(const std::string &category, - const _PersistentCacheBlob &key, - const _PersistentCacheBlob &value) = 0; - - virtual _MaybePersistentCacheBlob get( - const std::string &category, - const _PersistentCacheBlob &key) = 0; - - %extend { - static void reg(_PersistentCache *p) { - _PersistentCache::set_impl({p, [](_PersistentCache*){}}); - } - } -}; - -struct _OptimizeForInferenceOptions { -#define SET(n) void enable_##n(); - SET(f16_io_f32_comp); - SET(f16_io_comp); - SET(fuse_conv_bias_nonlinearity); - SET(fuse_conv_bias_with_z); -#undef SET -#define SET(_trans, _trans_capital) \ - void enable_##_trans(); \ - - SET(nchw4, NCHW4); - SET(nhwcd4, NHWCD4); - SET(nchw88, NCHW88); - SET(nchw44, NCHW44); - SET(nchw44_dot, NCHW44_DOT); - SET(nchw32, NCHW32); - SET(chwn4, CHWN4); -#undef SET -}; - -%inline { - static SymbolVarArray _optimize_for_inference( - const SymbolVarArray& dest_vars, - const _OptimizeForInferenceOptions& opt) { - return mgb::gopt::optimize_for_inference(dest_vars, opt); - } - - // defined in function_replace.cpp - void _register_logger(PyObject *logger); - void _timed_func_set_fork_exec_path(const char *arg0, const char *arg1); - void _timed_func_exec_cb(const char *user_data); - - // defined in megbrain_wrap.cpp - void _mgb_global_finalize(); - std::vector _get_mgb_version(); - SymbolVarArray _grad(SymbolVar target, SymbolVarArray wrts, - bool warn_mid_wrt, int use_virtual_grad, - bool return_zero_for_nodep); - SymbolVar _inter_graph_trans_var( - CompGraph &dest_graph, SymbolVar src); - SymbolVar _get_graph_optimizer_replaced_var(SymbolVar src); - void _add_update_fastpath(SharedND& dest, SharedND& delta, - float alpha, float beta, float bias); - void _add_update_fastpath(SharedND& dest, - CompGraphCallbackValueProxy& delta, - float alpha, float beta, float bias); - - static SymbolVar _current_grad_target(CompGraph &graph) { - return mgb::cg::current_grad_target(graph.get()); - } - - uint32_t _get_dtype_num(PyObject *dtype) { - return static_cast(npy::dtype_np2mgb(dtype).enumv()); - } - - PyObject* _get_serialized_dtype(PyObject *dtype) { - PYTHON_GIL; - std::string sdtype; - auto write = [&sdtype](const void* data, size_t size) { - auto pos = sdtype.size(); - sdtype.resize(pos + size); - memcpy(&sdtype[pos], data, size); - }; - mgb::serialization::serialize_dtype(npy::dtype_np2mgb(dtype), write); - return PyBytes_FromStringAndSize(sdtype.data(), sdtype.size()); - } - - size_t max_size_t() { - return std::numeric_limits::max(); - } - - std::string _get_opr_fp_graph_exec( - CompGraph& cg, const SymbolVarArray& outputs) { - auto json = mgb::OprFootprint::get_opr_fp_graph_exec(cg.get(), outputs); - return json->to_string(); - } -} - -// vim: ft=swig diff --git a/python_module/src/swig/operator.i b/python_module/src/swig/operator.i deleted file mode 100644 index e7bd0911..00000000 --- a/python_module/src/swig/operator.i +++ /dev/null @@ -1,148 +0,0 @@ -/* - * $File: operator.i - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * $Copyright: Copyright (c) 2014-2017 Megvii Inc. All rights reserved. - */ - -%{ -#include "opr_helper.h" -#include "opr_defs.h" - -using ::mgb::cg::OperatorNodeConfig; - -using _AxisIndexer = AxisIndexer; - -static inline PyObject* _to_mgb_supported_dtype(PyObject* dtype) { - return ::npy::to_mgb_supported_dtype(dtype); -} - -%} - -%feature("autodoc", "Extra configuration for an operator") OperatorNodeConfig; -class OperatorNodeConfig { - public: - OperatorNodeConfig(); - void name(const std::string &name); - void comp_node(const CompNode &node); - - %extend { - void comp_node_arr(const std::vector &arr) { - OperatorNodeConfig::CompNodeArray tarr(arr.begin(), arr.end()); - $self->comp_node_arr(tarr); - } - - CompNode require_comp_node() { - mgb_assert($self->comp_node().size() == 1, - "comp_node is required for the config"); - return $self->comp_node()[0]; - } - - void output_dtype(PyObject* dtype) { - $self->output_dtype(npy::dtype_np2mgb(dtype)); - } - } -}; - -%feature("autodoc", - "representing a operator node in a computing graph") Operator; -class Operator { -public: - %extend { - size_t _get_id() const { - return $self->id(); - } - - const std::string& _get_name() const { - return $self->name(); - } - - const std::string& _get_params() const { - return $self->params(); - } - - SymbolVarArray _get_inputs() { - return $self->inputs(); - } - - SymbolVarArray _get_outputs() { - return $self->outputs(); - } - - CompGraph _get_owner_graph() { - const auto& cg = $self->get_owner_graph(); - return CompGraph::make_from_shared_ptr(cg); - } - - %include "operator.py" - } -}; - -%template(_VectorOperator) std::vector; - -class _AxisIndexer { -public: - static _AxisIndexer make_interval(int axis, SymbolVar begin, SymbolVar end, - SymbolVar step); - - static _AxisIndexer make_index(int axis, SymbolVar idx); -}; -%template(_VectorAxisIndexer) std::vector<_AxisIndexer>; - -%inline { - // all defined in opr_helper.cpp - SymbolVarArray _create_opr( - const char *name, const SymbolVarArray &inputs, PyObject *params, - const OperatorNodeConfig &config); - - SymbolVar _create_subtensor_like_opr( - const std::string &name, - const SymbolVarArray& inputs, - const std::vector<_AxisIndexer> &idx, - const OperatorNodeConfig &config); - - SymbolVar _make_immutable( - CompGraph &comp_graph, PyObject *npyarr, PyObject *dtype, - const OperatorNodeConfig &config); -} - -PyObject* _to_mgb_supported_dtype(PyObject *dtype); - -%include "../cpp/opr_defs.h" - -%pythoncode { - -def make_opr_config(name=None, comp_node=None, output_dtype=None): - """make :class:`.OperatorNodeConfig` from given name or comp_node - - :type name: None or str - :param name: name for the operator - :type comp_node: None or comp_node-compatible or iterable of - comp_node-compatible - :param comp_node: a single comp_node, or iterable of comp_nodes - :type dtype: None or numpy-dtype compatible - :param dtype: the specified dtype the operator. - """ - rst = OperatorNodeConfig() - if comp_node is not None: - if isinstance(comp_node, str): - rst.comp_node(as_comp_node(comp_node)) - elif isinstance(comp_node, collections.Iterable): - vec = _VectorCompNode() - for i in comp_node: - vec.push_back(as_comp_node(i)) - rst.comp_node_arr(vec) - else: - rst.comp_node(as_comp_node(comp_node)) - if name is not None: - assert isinstance(name, str) - rst.name(name) - if output_dtype is not None: - rst.output_dtype(output_dtype) - - return rst - -} // %pythoncode - -// vim: ft=swig foldmethod=marker foldmarker=f{{{,f}}} diff --git a/python_module/src/swig/operator.py b/python_module/src/swig/operator.py deleted file mode 100644 index a1eb095e..00000000 --- a/python_module/src/swig/operator.py +++ /dev/null @@ -1,37 +0,0 @@ -%pythoncode { - -__owner_graph = None - -@property -def owner_graph(self): - """get the owner graph; note that a reference would be kept in this var""" - if self.__owner_graph is None: - self.__owner_graph = self._get_owner_graph() - return self.__owner_graph - -@property -def id(self): - """an integer identifier for this opr that is unique in the computing - graph""" - return int(self._get_id()) - -@property -def name(self): - return self._get_name() - -@property -def params(self): - import json - return json.loads(self._get_params()) - -@property -def inputs(self): - return tuple(self._get_inputs()) - -@property -def outputs(self): - return tuple(self._get_outputs()) - -def __repr__(self): - return 'Operator(id={},name={})'.format(self.id, self.name) -} diff --git a/python_module/src/swig/shared_nd.i b/python_module/src/swig/shared_nd.i deleted file mode 100644 index 03e3dced..00000000 --- a/python_module/src/swig/shared_nd.i +++ /dev/null @@ -1,125 +0,0 @@ -/* - * $File: shared_nd.i - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * $Copyright: Copyright (c) 2014-2017 Megvii Inc. All rights reserved. - */ - -%pythoncode { -from .mgb_helper import SharedNDLazyInitializer -} // pythoncode - -%feature("autodoc", """a value stored on computing device and can be modified -by special operators in the graph""") SharedND; -class SharedND { - public: - SharedND(CompNode comp_node, PyObject *dtype); - - void _set_init_shape(const std::vector &shape); - void _resize(const std::vector &shape); - void _reset_zero(); - - PyObject* _get_npyarr(); - PyObject* _get_dtype(); - std::vector _get_shape(); - - void _copy_from_npyarr(PyObject *npyarr); - void _copy_from_value_proxy(CompGraphCallbackValueProxy &value); - void _share_from_value_proxy(CompGraphCallbackValueProxy &value); - static SharedND _from_symvar(SymbolVar symvar); - - void _set_copy_sync(bool flag); - uintptr_t _pubapi_dev_tensor_ptr(int version); - - void copy_to_sub_from_shared( - int axis, ptrdiff_t begin, ptrdiff_t end, ptrdiff_t step, - const SharedND &rhs); - - void copy_from_shared_sub(const SharedND &rhs, - int axis, ptrdiff_t begin, ptrdiff_t end, ptrdiff_t step); - - CompNode _get_comp_node(); - - SymbolVar _as_sym_var(CompGraph &graph, const std::string &name, - bool volatile_); - - void _share_memory_from(const SharedND &rhs, size_t begin); - - void _reset_dev_tensor(const SharedND& rhs); - - %include "shared_nd_SharedND.py" -}; -%template(_VectorSharedND) std::vector; - -class _HostSharedND { - public: - _HostSharedND(CompNode node, PyObject *dtype); - static _HostSharedND make_proxy(SymbolVar var); - - SymbolVar _as_sym_var(CompGraph &cg, bool enable_static_infer, - const std::string &name); - PyObject* _get_dtype(); - void _resize(const std::vector &shape); - void _copy_from_npyarr(PyObject *npyarr, bool borrow); - void _enable_borrow_on_cpu(bool flag); - std::string __repr__() const; - - %include "shared_nd_HostSharedND.py" -}; - - -%feature("autodoc", -"""a scalar value that can be modified after it has been created; -compared to :class:`SharedND`, it has the advantage that no comp node needs to -be specified.""") SharedScalar; -class SharedScalar { - public: - SharedScalar(PyObject *val); - void _set(PyObject *val); - PyObject* _get(); - bool _dtype_locked(); - void _lock_dtype(); - SymbolVar _as_sym_var(CompGraph &cg, CompNode &cn); - - %pythoncode { - - def lock_dtype(self): - """lock dtype so further set() calls must pass the same dtyped - value""" - self._lock_dtype() - - @property - def dtype_locked(self): - """whether dtype is locked""" - return self._dtype_locked() - - def set(self, val): - self._set(val) - - def get(self): - """get the value stored in this SharedScalar""" - return self._get()[0] - - def __getstate__(self): - state = self.__dict__.copy() - del state['this'] - state['__shared_scalar_value'] = self.get() - state['__shared_scalar_dtype_locked'] = self.dtype_locked - return state - - def __setstate__(self, state): - val = SharedScalar(state.pop('__shared_scalar_value')) - if state.pop('__shared_scalar_dtype_locked', True): - val._lock_dtype() - self.this = val.this - for k, v in state.items(): - self.__dict__[k] = v - - def __repr__(self): - return 'SharedScalar({})'.format(self.get()) - } -}; - - -// vim: ft=swig diff --git a/python_module/src/swig/shared_nd_HostSharedND.py b/python_module/src/swig/shared_nd_HostSharedND.py deleted file mode 100644 index f94ff798..00000000 --- a/python_module/src/swig/shared_nd_HostSharedND.py +++ /dev/null @@ -1,67 +0,0 @@ -%pythoncode{ - -__dtype = None - -def as_sym_var(self, cg, enable_static_infer, name=None): - """get symvar to represent value of this HostSharedND in a - computing graph - - :type cg: :class:`.CompGraph` - :param cg: computing graph - :type enable_static_infer: :class:`bool` - :param enable_static_infer: whether to enable static value - inference for this symvar; if set to True, the value must - be set up before calling :meth:`as_sym_var`. - """ - if name is None: - name = '' - return self._as_sym_var(cg, enable_static_infer, name) - -def symvar(self, comp_graph, name=None, *, enable_static_infer=None): - return self.as_sym_var(comp_graph, enable_static_infer, name) - -def enable_borrow_on_cpu(self, flag): - """whether to allow borrow memory in :meth:`set_value` if - the underlying comp ndoe is on CPU""" - self._enable_borrow_on_cpu(flag) - -def _set_value_print_warn( - self, reason, *, - disabled=os.getenv('MGB_DISABLE_SET_VALUE_WARN') is not None): - if disabled: - return - from .logconf import get_logger - logger = get_logger() - logger.warning('set {} from incompatible object is slow: {}'.format( - self, reason)) - -def set_value(self, w, *, borrow=False): - """set value to given numpy array - - :param borrow: if set to True, the memory of *w* may be - borrowed, and *w* must remain unmodified during usage of - this object - :type borrow: bool - :return: self - """ - if self.__dtype is None: - self.__dtype = self._get_dtype() - - if not isinstance(w, np.ndarray): - wtype = type(w) - w = np.ascontiguousarray(w, self.__dtype) - if w.size >= 1024: - self._set_value_print_warn( - 'not an ndarray object: {}'.format(wtype)) - elif w.size >= 1024: - if w.dtype != self.__dtype: - self._set_value_print_warn( - 'dtype mismatch: expect {}, get {}'.format( - self.__dtype, w.dtype)) - elif not w.flags['C_CONTIGUOUS']: - self._set_value_print_warn('non-contiguous ndarray') - - self._copy_from_npyarr(w, borrow) - return self - -} diff --git a/python_module/src/swig/shared_nd_SharedND.py b/python_module/src/swig/shared_nd_SharedND.py deleted file mode 100644 index da024c7a..00000000 --- a/python_module/src/swig/shared_nd_SharedND.py +++ /dev/null @@ -1,196 +0,0 @@ -%pythoncode{ - -__lazy_initializer = None - -def __apply_lazy_initializer(self): - """ __lazy_initializer released by self.set_value()""" - if self.__lazy_initializer is not None: - self.set_value(self.__lazy_initializer.get_value()) - -@property -def shape(self): - """get shape of unerlying data""" - if self.__lazy_initializer is not None: - val = self.__lazy_initializer.get_shape() - else: - val = self._get_shape() - return tuple(map(int, val)) - -@property -def comp_node(self): - return self._get_comp_node() - -@property -def dtype(self): - return self._get_dtype() - -@property -def lazy_initializer(self): - """object to specify how to initialize this SharedND, or None - if not set - - Please not that the initializer could be called at any time. - - :type: :class:`.SharedNDLazyInitializer` - """ - return self.__lazy_initializer - -@lazy_initializer.setter -def lazy_initializer(self, init): - assert not len(self._get_shape()), ( - 'can not set initializer for initialized SharedND') - assert isinstance(init, SharedNDLazyInitializer) - self.__lazy_initializer = init - -def set_value(self, w, *, sync=True, inplace=False, share=False): - """set value from a numpy array or from outputs in callback - - .. warning:: - - If sync is false, a reference to input is kept and the caller is - responsible to ensure that the input would not be modified after - this function returns. - - :param w: value to be set - :type w: :class:`numpy.ndarray`-compatible, :class:`SharedND` or - :class:`.CompGraphCallbackValueProxy` - :param sync: whether to sync device before returns - :type sync: bool - :param inplace: whether to copy in-place from another :class:`.SharedND`, - guaranteed no memory allocating; if True, this SharedND must have the - same shape as *w*, and buffer for this :class:`SharedND` would not be - re-allocated. - :param share: directly share the buffer in a - :class:`.CompGraphCallbackValueProxy` with zero copy - :return: self - """ - - if self is w: - return self - - if share: - assert isinstance(w, CompGraphCallbackValueProxy) - self._share_from_value_proxy(w) - return self - - self._set_copy_sync(sync) - if isinstance(w, CompGraphCallbackValueProxy): - self._copy_from_value_proxy(w) - return self - - if isinstance(w, SharedND): - w.__apply_lazy_initializer() - ax_type = -2 - if inplace: - ax_type = -3 - self.copy_from_shared_sub(w, ax_type, -1, -1, -1) - return self - assert not inplace, 'inplace only implemented for copying from SharedND' - - if self.__lazy_initializer is not None: - del self.__lazy_initializer - self._copy_from_npyarr(w) - return self - -def get_value(self): - """get value as numpy array - :return: numpy array, or None if value is empty""" - self.__apply_lazy_initializer() - return self._get_npyarr() - -def resize(self, *shape): - """resize the SharedND to given shape and allocate memory, without - initializing data; usually :meth:`pubapi_dev_tensor_ptr` is then called to - get the buffer address and pass it to some other library - - :return: self - """ - if len(shape) == 1 and isinstance(shape[0], collections.Iterable): - shape = shape[0] - self._resize(shape) - return self - -def reset_zero(self): - """reset dev_tensor to zeros""" - self._reset_zero() - -def copy_to(self, dest): - """copy value to numpy array - - :type dest: :class:`np.ndarray` - :param dest: destination array to write value of this var to, - which must match shape, have float32 dtype and be - contiguous - """ - check_cont_ndarray(dest) - wflat = dest.reshape(-1) - assert wflat.ctypes.data == dest.ctypes.data - self._copy_to_flatten(wflat) - return dest - -@property -def dev_ptr(self): - """this method is DEPRECATED; use :meth:`pubapi_dev_tensor_ptr` instead""" - return self._pubapi_dev_tensor_ptr(0) - -@property -def pubapi_dev_tensor_ptr(self): - """get a pointer to the corresponding mgb::pubapi::DeviceTensor object - - :rtype: int - :return: the address as an integer - """ - return self._pubapi_dev_tensor_ptr(1) - -def symvar(self, comp_graph, name=None, *, volatile=False): - """convert to SymbolVar to be put into a computing graph - - :param volatile: whether shape/ptr is allowed to change - """ - self.__apply_lazy_initializer() - assert self.shape, "initial shape must be available" - if name is None: - name = '' - return self._as_sym_var(comp_graph, name, volatile) - -def __getstate__(self): - state = self.__dict__.copy() - del state['this'] - state['value'] = self.get_value() - state['comp_node'] = self.comp_node - state['dtype'] = self.dtype - return state - -def __setstate__(self, state): - val = state.pop('value') - dtype = state.pop('dtype', 'float32') - snd = SharedND(state.pop('comp_node'), dtype) - if val is not None: - assert val.dtype == dtype - snd.set_value(val) - self.this = snd.this - for k, v in state.items(): - self.__dict__[k] = v - -def share_memory_from(self, rhs, offset): - """ - share memory from another SharedND, self and rhs must be initialized - :param rhs: another sharedND used to share memory - :type rhs: :class:`SharedND` - - :param offset: offset in rhs sharedND - :type offset: int - """ - assert self != rhs - self._share_memory_from(rhs, offset) - -def reset_dev_tensor(self, rhs): - """ - reset devive tensor to another SharedND, self and rhs must be initialized. - :param rhs: another sharedND whose device tensor to be reset to. - :type rhs: :class:`SharedND` - """ - assert self != rhs - self._reset_dev_tensor(rhs) - -} diff --git a/python_module/src/swig/symbol_var.i b/python_module/src/swig/symbol_var.i deleted file mode 100644 index 2c86f206..00000000 --- a/python_module/src/swig/symbol_var.i +++ /dev/null @@ -1,113 +0,0 @@ -/* - * $File: symbol_var.i - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * $Copyright: Copyright (c) 2014-2017 Megvii Inc. All rights reserved. - */ - -%{ -using mgb::cg::SymbolVar; -%} - -%feature("autodoc", -"representing a symbolic variable in a computing graph") SymbolVar; - -class SymbolVar { -public: - SymbolVar flatten(); - SymbolVar rename(const std::string &name); - bool allow_shape_change(); - - %extend { - - SymbolVar fill_retain_dtype(PyObject *val) { - return fill_retain_dtype(*$self, val); - } - - CompGraph _get_owner_graph() { - mgb_assert($self->node()); - auto cg = $self->node()->owner_graph()->shared_from_this(); - return CompGraph::make_from_shared_ptr(cg); - } - - Operator _get_owner_opr() { - mgb_assert($self->node()); - return Operator{$self->node()->owner_opr()}; - } - - CompNode _get_comp_node() { - mgb_assert($self->node()); - return $self->node()->comp_node(); - } - - const std::string& _get_name() const { - mgb_assert($self->node()); - return $self->node()->name(); - } - - size_t _get_id() const { - mgb_assert($self->node()); - return $self->node()->id(); - } - - std::vector _get_imm_shape() { - mgb_assert($self->node()); - return npy::shape2vec($self->node()->shape()); - } - - PyObject* _get_inferred_value() { - return get_symvar_inferred_value(*$self); - } - - bool _is_valid() const { - return $self->node(); - } - - PyObject* _get_dtype() const { - return npy::dtype_mgb2np($self->dtype()); - } - - CompGraphCallbackValueProxy _eager_eval_get_value() const { - CompGraphCallbackValueProxy ret; - ret.setup($self->eager_eval_get_value(), false); - return ret; - } - - void _reeval_if_eager_eval() { - auto &&var = $self->node(); - mgb_assert(var); - auto &&cg = var->owner_graph(); - if (cg->options().eager_evaluation) { - mgb_assert(var->owner_opr()->inserted_in_graph()); - cg->insert_opr(std::unique_ptr( - var->owner_opr())); - } - } - - bool _is_shared_device_tensor() { - if ($self->node() - ->owner_opr() - ->same_type()) - return true; - return false; - } - - %include "symbol_var_SymbolVar.py" - - } - -}; - -typedef std::vector SymbolVarArray; -%template(_VectorSymbolVar) std::vector; - -// SymbolVarArray compatibility; see symbol_var_array.i for more details -%typemap(out) SymbolVarArray { - $result = swig::from(static_cast&>($1)); -} -%typemap(directorin) const SymbolVarArray& { - $input = swig::from(static_cast&>($1)); -} - -// vim: ft=swig diff --git a/python_module/src/swig/symbol_var_SymbolVar.py b/python_module/src/swig/symbol_var_SymbolVar.py deleted file mode 100644 index 96fb3383..00000000 --- a/python_module/src/swig/symbol_var_SymbolVar.py +++ /dev/null @@ -1,216 +0,0 @@ -%pythoncode { - -__owner_graph = None -__owner_opr = None - -@property -def owner_graph(self): - """get the owner graph; note that a reference would be kept in this var""" - if self.__owner_graph is None: - self.__owner_graph = self._get_owner_graph() - return self.__owner_graph - -@property -def owner_opr(self): - """get the owner opr; get owner graph explicitly so it can keep a reference - to its owner graph""" - if self.__owner_opr is None: - self.__owner_opr = self._get_owner_opr() - - self.__owner_opr.owner_graph - return self.__owner_opr - -@property -def comp_node(self): - return self._get_comp_node() - -@property -def name(self): - return self._get_name() - -@property -def id(self): - """an integer identifier for this var that is unique in the computing - graph""" - return int(self._get_id()) - -@property -def imm_shape(self): - """shape as immediate number - - :type: tuple of int - """ - return tuple(map(int, self._get_imm_shape())) - -@property -def inferred_value(self): - """get statically inferred value of this var, or None if - inference failed - - :type: :class:`numpy.ndarray` or None""" - return self._get_inferred_value() - -@property -def valid(self): - """whether this symvar is valid (i.e. has corresponding var node in - graph)""" - return self._is_valid() - -@property -def volatile(self): - """whether the shape is volatile""" - return not self._is_shared_device_tensor() - -@property -def dtype(self): - """get underling data type - :rtype: :class:`numpy.dtype`""" - return self._get_dtype() - -def __hash__(self): - return hash((self.owner_graph, self.id)) - -def __eq__(self, rhs): - return (isinstance(rhs, SymbolVar) and - self.owner_graph == rhs.owner_graph and - self.id == rhs.id) - -def _binary_opr(self, mode, rhs): - from .opr import elemwise - return elemwise([self, rhs], mode=mode) - -def _binary_opr_lhs(self, mode, lhs): - from .opr import elemwise - return elemwise([lhs, self], mode=mode) - -def __add__(self, rhs): - return self._binary_opr('ADD', rhs) -def __radd__(self, lhs): - return self._binary_opr_lhs('ADD', lhs) - -def __sub__(self, rhs): - return self._binary_opr('SUB', rhs) -def __rsub__(self, lhs): - return self._binary_opr_lhs('SUB', lhs) - -def __mul__(self, rhs): - return self._binary_opr('MUL', rhs) -def __rmul__(self, lhs): - return self._binary_opr_lhs('MUL', lhs) - -def __matmul__(self, rhs): - from .opr import matrix_mul - return matrix_mul(self, rhs) -def __rmatmul__(self, rhs): - from .opr import matrix_mul - return matrix_mul(rhs, self) - -def __lshift__(self, rhs): - return self._binary_opr('SHL', rhs) -def __rshift__(self, rhs): - return self._binary_opr('SHR', rhs) - -def __truediv__(self, rhs): - return self._binary_opr('TRUE_DIV', rhs) -def __rtruediv__(self, lhs): - return self._binary_opr_lhs('TRUE_DIV', lhs) - -def __floordiv__(self, rhs): - return self._binary_opr('FLOOR_DIV', rhs) -def __rfloordiv__(self, rhs): - return self._binary_opr_lhs('FLOOR_DIV', rhs) - -def __mod__(self, rhs): - return self._binary_opr('MOD', rhs) -def __rmod__(self, rhs): - return self._binary_opr_lhs('MOD', rhs) - -def __pow__(self, rhs): - return self._binary_opr('POW', rhs) -def __rpow__(self, lhs): - return self._binary_opr_lhs('POW', lhs) - -def __lt__(self, rhs): - return self._binary_opr('LT', rhs) -def __gt__(self, lhs): - return self._binary_opr_lhs('LT', lhs) - -def __le__(self, rhs): - return self._binary_opr('LEQ', rhs) -def __ge__(self, lhs): - return self._binary_opr_lhs('LEQ', lhs) - -def __neg__(self): - from .opr import elemwise - return elemwise([self], mode='NEGATE') - -def __getitem__(self, idx): - from .helper import cvt_getitem_to_idx_desc - inpvar, desc = cvt_getitem_to_idx_desc(self, idx) - if desc is None: - return inpvar - return _create_subtensor_like_opr('subtensor', [inpvar], desc, make_opr_config()) - -def reshape(self, *shp): - from .opr import reshape - return reshape(self, shp) - -def broadcast(self, *shp): - from .opr import broadcast - return broadcast(self, shp) - -def sum(self, axis=None, keepdims=False): - from .opr import reduce_ - return reduce_(self, 'SUM', axis, keepdims) - -def max(self, axis=None, keepdims=False): - from .opr import reduce_ - return reduce_(self, 'MAX', axis, keepdims) - -def min(self, axis=None, keepdims=False): - from .opr import reduce_ - return reduce_(self, 'MIN', axis, keepdims) - -def prod(self, axis=None, keepdims=False): - from .opr import reduce_ - return reduce_(self, 'PRODUCT', axis, keepdims) - -def mean(self, axis=None, keepdims=False): - from .opr import mean - return mean(self, axis, keepdims) - -def dimshuffle(self, *pattern, **kwargs): - from .opr import dimshuffle - ndim = kwargs.pop('ndim', 0) - assert not kwargs - return dimshuffle(self, pattern=pattern, ndim=ndim) - -def astype(self, target_dtype): - """see :func:`typecvt`""" - from .opr import typecvt - return typecvt(self, target_dtype) - -@property -def shape(self): - from .opr import get_var_shape - return get_var_shape(self) - -def axis_shape(self, axis): - from .opr import get_var_shape - return get_var_shape(self, axis=axis) - -@property -def eager_val(self): - """get value in eager evaluation mode""" - return self._eager_eval_get_value() if self.owner_graph.is_eager() else None - - -def __iter__(self): - """add __iter__ to avoid implicit iteration by calling - __getitem__""" - raise NotImplementedError('SymbolVar var could not be itered') - -def __repr__(self): - return 'SymbolVar(id={},name={})'.format(self.id, self.name) - -} diff --git a/python_module/src/swig/symbol_var_array.i b/python_module/src/swig/symbol_var_array.i deleted file mode 100644 index 75b68dd0..00000000 --- a/python_module/src/swig/symbol_var_array.i +++ /dev/null @@ -1,46 +0,0 @@ -/* - * $File: symbol_var_array.i - * - * This file is part of MegBrain, a deep learning framework developed by Megvii. - * - * $Copyright: Copyright (c) 2014-2017 Megvii Inc. All rights reserved. - */ - -/* - * In megbrain, SymbolVarArray is SmallVector. - * - * I do no want to convert between std::vector<> and mgb::SmallVector in the - * C++ wrappers; neither do I want to write a SmallVector<> interface file as - * good as swig's std::vector<> implementation. - * - * So the goal becomes making swig generate python wrapper for std::vector<>, - * but call SymbolVarArray in the generated C++ file. - * - * A logical solution is to derive SymbolVarArray from std::vector<> only in - * the .i file so swig can use the correct name; however the generated python - * class becomes uniterable. So our hack here is to specialize std::vector to - * use SymbolVarArray in the generated C++ file. - * - * This file must be included before instantiation of std::vector. - */ -%{ -#include -#include "megbrain/graph/symbol_var.h" -using SymbolVar = mgb::cg::SymbolVar; -using SymbolVarArray = mgb::cg::SymbolVarArray; -namespace std { -template -class vector : public SymbolVarArray { -public: - using SymbolVarArray::SymbolVarArray; - using allocator_type = alloc; - - allocator_type get_allocator() const { - mgb_throw(mgb::MegBrainError, "get_allocator() should not be called"); - return {}; - } -}; -} -%} - -// vim: ft=swig diff --git a/python_module/src/version.ld b/python_module/src/version.ld deleted file mode 100644 index ac6e0802..00000000 --- a/python_module/src/version.ld +++ /dev/null @@ -1,17 +0,0 @@ -{ -global: - MGB_VSYM_*; - MEGDNN_VSYM_*; - mgb_get_extern_c_opr_api_versioned; - PyInit__mgb; - extern "C++" { - *mgb::*; - *megdnn::*; - *megcore::*; - megcore*; - }; - megcore*; - -local: - *; -}; diff --git a/python_module/test/.gitignore b/python_module/test/.gitignore deleted file mode 100644 index 8b29bf97..00000000 --- a/python_module/test/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -log.txt -*json -massif.out* diff --git a/python_module/test/README.md b/python_module/test/README.md deleted file mode 100644 index 797e8c5f..00000000 --- a/python_module/test/README.md +++ /dev/null @@ -1,18 +0,0 @@ -# MegEngine Tests - -* unit: This directory has same layout as megengine directory. -* regression: Small tests to check whether old issue is fixed. -* integration: Tests involve multiple parts of megengine, tests that longer than 1min should be an manual test. -* pytorch_comparison: Special directory for torch-related test -* helpers - - Test utilities should placed in this directory - - `from helpers import ...` in your test code - - -## Default running setup - -Execute `run.sh` to test default set of tests. - -- No torch related test -- No internet related test -- No doc related test diff --git a/python_module/test/__init__.py b/python_module/test/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/python_module/test/conftest.py b/python_module/test/conftest.py deleted file mode 100644 index cf1a5359..00000000 --- a/python_module/test/conftest.py +++ /dev/null @@ -1,34 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import os -import sys - -sys.path.append(os.path.join(os.path.dirname(__file__))) - - -def pytest_json_modifyreport(json_report): - events = [] - timestamp = 0 - for item in json_report["tests"]: - for stage in ["setup", "call", "teardown"]: - if stage in item: - events.append( - { - "name": item["nodeid"], - "ph": "X", - "ts": timestamp, - "dur": item[stage]["duration"] * 1e6, - "cat": stage, - "pid": stage, - "tid": item["nodeid"], - } - ) - timestamp += events[-1]["dur"] - json_report["traceEvents"] = events - del json_report["collectors"] - del json_report["tests"] diff --git a/python_module/test/helpers/__init__.py b/python_module/test/helpers/__init__.py deleted file mode 100644 index 63d89aed..00000000 --- a/python_module/test/helpers/__init__.py +++ /dev/null @@ -1,172 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from typing import List, Tuple - -import numpy as np - -import megengine._internal as mgb -import megengine.functional as F -from megengine import Graph, jit -from megengine.module import Linear, Module -from megengine.test import assertTensorClose - -from .env import modified_environ - - -class MLP(Module): - def __init__(self): - super().__init__() - self.dense0 = Linear(28, 50) - self.dense1 = Linear(50, 20) - - def forward(self, x): - x = self.dense0(x) - x = F.relu(x) - x = self.dense1(x) - return x - - -def has_gpu(num=1): - try: - mgb.comp_node("gpu{}".format(num - 1)) - except mgb.MegBrainError: - return False - - return True - - -def randomNp(*args): - for arg in args: - assert isinstance(arg, int) - return np.random.random(args) - - -def randomTorch(*args): - import torch # pylint: disable=import-outside-toplevel - - for arg in args: - assert isinstance(arg, int) - return torch.tensor(randomNp(*args), dtype=torch.float32) - - -def graph_mode(*modes): - if not set(modes).issubset({"eager", "static"}): - raise ValueError("graph mode must be in (eager, static)") - - def decorator(func): - def wrapper(*args, **kwargs): - if "eager" in set(modes): - func(*args, **kwargs) - if "static" in set(modes): - with Graph() as cg: - cg.set_option("eager_evaluation", False) - func(*args, **kwargs) - - return wrapper - - return decorator - - -def _default_compare_fn(x, y): - assertTensorClose(x.numpy(), y) - - -def opr_test( - cases, - func, - mode=("eager", "static", "dynamic_shape"), - compare_fn=_default_compare_fn, - ref_fn=None, - **kwargs -): - """ - mode: the list of test mode which are eager, static and dynamic_shape - will test all the cases if None. - func: the function to run opr. - compare_fn: the function to compare the result and expected, use assertTensorClose if None. - ref_fn: the function to generate expected data, should assign output if None. - cases: the list which have dict element, the list length should be 2 for dynamic shape test. - and the dict should have input, - and should have output if ref_fn is None. - should use list for multiple inputs and outputs for each case. - kwargs: The additional kwargs for opr func. - - simple examples: - - dtype = np.float32 - cases = [{"input": [10, 20]}, {"input": [20, 30]}] - opr_test(cases, - F.eye, - ref_fn=lambda n, m: np.eye(n, m).astype(dtype), - dtype=dtype) - - """ - - def check_results(results, expected): - if not isinstance(results, Tuple): - results = (results,) - for r, e in zip(results, expected): - compare_fn(r, e) - - def get_trace_fn(func, enabled, symbolic): - jit.trace.enabled = enabled - return jit.trace(func, symbolic=symbolic) - - def get_param(cases, idx): - case = cases[idx] - inp = case.get("input", None) - outp = case.get("output", None) - if inp is None: - raise ValueError("the test case should have input") - if not isinstance(inp, List): - inp = (inp,) - else: - inp = tuple(inp) - if ref_fn is not None and callable(ref_fn): - outp = ref_fn(*inp) - if outp is None: - raise ValueError("the test case should have output or reference function") - if not isinstance(outp, List): - outp = (outp,) - else: - outp = tuple(outp) - - return inp, outp - - if not set(mode).issubset({"eager", "static", "dynamic_shape"}): - raise ValueError("opr test mode must be in (eager, static, dynamic_shape)") - - if len(cases) == 0: - raise ValueError("should give one case at least") - - if "dynamic_shape" in set(mode): - if len(cases) != 2: - raise ValueError("should give 2 cases for dynamic shape test") - - if not callable(func): - raise ValueError("the input func should be callable") - - inp, outp = get_param(cases, 0) - - def run(*args, **kwargs): - return func(*args, **kwargs) - - if "eager" in set(mode): - f = get_trace_fn(run, False, False) - results = f(*inp, **kwargs) - check_results(results, outp) - - if "static" in set(mode) or "dynamic_shape" in set(mode): - f = get_trace_fn(run, True, True) - results = f(*inp, **kwargs) - check_results(results, outp) - if "dynamic_shape" in set(mode): - inp, outp = get_param(cases, 1) - results = f(*inp, **kwargs) - check_results(results, outp) diff --git a/python_module/test/helpers/env.py b/python_module/test/helpers/env.py deleted file mode 100644 index f5dae64d..00000000 --- a/python_module/test/helpers/env.py +++ /dev/null @@ -1,56 +0,0 @@ -# -*- coding: utf-8 -*- -# The MIT License (MIT) -# -# Copyright (c) 2018 Laurent LAPORTE -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. -import contextlib -import os - - -# modified_environ codes come from https://github.com/laurent-laporte-pro/stackoverflow-q2059482/blob/master/demo/environ_ctx.py -@contextlib.contextmanager -def modified_environ(*remove, **update): - """ - Temporarily updates the ``os.environ`` dictionary in-place. - - The ``os.environ`` dictionary is updated in-place so that the modification - is sure to work in all situations. - - :param remove: Environment variables to remove. - :param update: Dictionary of environment variables and values to add/update. - """ - env = os.environ - update = update or {} - remove = remove or [] - - # List of environment variables being updated or removed. - stomped = (set(update.keys()) | set(remove)) & set(env.keys()) - # Environment variables and values to restore on exit. - update_after = {k: env[k] for k in stomped} - # Environment variables and values to remove on exit. - remove_after = frozenset(k for k in update if k not in env) - - try: - env.update(update) - [env.pop(k, None) for k in remove] - yield - finally: - env.update(update_after) - [env.pop(k) for k in remove_after] diff --git a/python_module/test/helpers/torch_util.py b/python_module/test/helpers/torch_util.py deleted file mode 100644 index 659033c5..00000000 --- a/python_module/test/helpers/torch_util.py +++ /dev/null @@ -1,33 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np -import torch - -from megengine.core import tensor -from megengine.utils import prod - - -def _uniform(shape): - return np.random.random(shape).astype(np.float32) - - -def init_with_same_value(mge_param, torch_param, initializer=_uniform): - mge_shape = mge_param.shape - torch_shape = torch_param.shape - assert prod(mge_shape) == prod(torch_shape) - weight = initializer(mge_shape) - mge_param.set_value(weight) - torch_param.data = torch.Tensor(weight.reshape(torch_shape)) - - -def gen_same_input(shape, initializer=_uniform): - data = initializer(shape) - mge_input = tensor(data) - torch_input = torch.Tensor(data) - return mge_input, torch_input diff --git a/python_module/test/integration/manual/README.md b/python_module/test/integration/manual/README.md deleted file mode 100644 index aad1ed6f..00000000 --- a/python_module/test/integration/manual/README.md +++ /dev/null @@ -1,178 +0,0 @@ -# Regression test -* [How to run](#how-to-run) -* [Correctness](#correctness) -* [Performance](#performance) -* [Debug tools](#debug-tools) -* [To do list](#to-do-list) - -## How to run - -1. Run correctness regression test by - -``` -rlaunch --cpu=4 --memory=15000 --gpu=1 -- python3 verify_correctness.py -``` - -2. Run performance regression test by - -``` -rlaunch --cpu=4 --memory=15000 --gpu=1 -- python3 run_resnet50_perf.py -``` - -Compare with the [reference result](#performance) to verify the performance change. - -3. [Temporary]: Run dynamic graph test - -``` -cd python_module/megengine/examples/cifar10/resnet_example -rlaunch --cpu=4 --memory=15000 --gpu=1 -- MGE_DISABLE_TRACE=1 python3 main.py --mode train --backend megengine-dynamic -``` - -Be sure to run a few epochs to verify the CPU/GPU memory usage and the result tends to converge. The complete run takes around 2 hours. - -## Correctness - -Pre-trained Resnet18 model on cifar10 dataset is used. - -The test set contains -* forward run with static graph -* forward run with dynamic graph -* forward + backward + parameter update with static graph -* forward + backward + parameter update with dynamic graph - -Sample output: - -``` -Running fwd static ... -Success -Running fwd dynamic ... -Success -Running train static ... -Success -Running train dynamic ... -Failed!!! -import megengine operator -[INFO] load /home/zhangfan/.local/lib/python3.6/site-packages/megengine/examples/cifar10/resnet_example/checkpoint/pytorch_init.pth done -calculated loss: [2.3731833, 34.4626] -expect: [ 2.3731833 34.460594 ] -``` - -## Performance - -Test cases run Resnet 50 training with batch size = 64. - -Run `python3 resnet50_perf.py --help` for valid options. - -Example script: - -* Run `python3 run_resnet50_perf.py` -* You may want to submit the job to a remote server by `rlaunch --cpu=16 --memory=100384 --gpu=8 -- python3 run_resnet50_perf.py` -* Sample output -``` -************************************** -Run ResNet 50 performance test with batch size = 64 -************************************** -Run static graph with default opt level -Finish with GPU Usage 6710MiB -Wall time per iter 283 ms -Run status: finished -************************************** -Run static graph with conv fastrun -Finish with GPU Usage 6540MiB -Wall time per iter 265 ms -Run status: finished -************************************** -Run static graph with conv fastrun and JIT -Finish with GPU Usage 6540MiB -Wall time per iter 267 ms -Run status: finished -************************************** -Run static graph with JIT, conv fastrun and without running step -Finish with GPU Usage 6540MiB -Wall time per iter 223 ms -Run status: finished -************************************** -``` - -## Debug tools - -You can pass `--run-debug-tool` to script `run_resnet50_perf.py`. Opr-level profiling result and valgrind will be invoked. - -### How much overhead time will it take due to usage of the profiler - -Please compare the same job with/without profiler. The timing statistic reported by profiler does not include the overhead time from itself. - -### How can I get more information from profiler? - -Refer to the main function in `megengine.utils.profile_analyze`. - -### How can I profile main memory usage? - -Valgrind massif tool can be used. The script also prints memory usage summary on screen as: - -``` - - GB -1.836^ # - | @@#::::::@::: - | @@@ #::::::@::: - | ::::::::::::@:::::::::@:@@@ #::::::@::: - | ::::: :::::: @ ::: ::: @:@@@ #::::::@::: - | @@::::: :::::: @ ::: ::: @:@@@ #::::::@::: - | ::@@::::: :::::: @ ::: ::: @:@@@ #::::::@::: - | @:: @@::::: :::::: @ ::: ::: @:@@@ #::::::@::: - | @@@:: @@::::: :::::: @ ::: ::: @:@@@ #::::::@::: - | :@@@:: @@::::: :::::: @ ::: ::: @:@@@ #::::::@::: - | @::@@@:: @@::::: :::::: @ ::: ::: @:@@@ #::::::@::: - | @@::@@@:: @@::::: :::::: @ ::: ::: @:@@@ #::::::@::: - | @:@@::@@@:: @@::::: :::::: @ ::: ::: @:@@@ #::::::@::: - | :@ @@::@@@:: @@::::: :::::: @ ::: ::: @:@@@ #::::::@::: - | ::::@ @@::@@@:: @@::::: :::::: @ ::: ::: @:@@@ #::::::@::: - | :::: :@ @@::@@@:: @@::::: :::::: @ ::: ::: @:@@@ #::::::@::: - | :@: :: :@ @@::@@@:: @@::::: :::::: @ ::: ::: @:@@@ #::::::@::: - | :@@: :: :@ @@::@@@:: @@::::: :::::: @ ::: ::: @:@@@ #::::::@::: - | @@:@@: :: :@ @@::@@@:: @@::::: :::::: @ ::: ::: @:@@@ #::::::@::: - | @@ :@@: :: :@ @@::@@@:: @@::::: :::::: @ ::: ::: @:@@@ #::::::@::: - 0 +----------------------------------------------------------------------->Gi - 0 19.39 - -``` -You can change "--run-iter" value to adjust iters to profile. -The detailed profiling is printed to `massif.out.ms_print`. - -### How can I understand the profiler result? - -The dumped profiling file `prof.json` can be interpolated by [megengine/utils/profile_analyze.py](../../utils/profile_analyze.py). -The following information is printed from the profiler: - -``` ------------------ -------- -total device time 0.318062 -total host time 0.275643 ------------------ -------- - -╒════════════════════╤══════════════╤═══════════════════════════╤═══════════════╤═════════╤══════════╤═════════════╤═════════════╤══════════════╕ -│ device self time │ cumulative │ operator info │ computation │ FLOPS │ memory │ bandwidth │ in_shapes │ out_shapes │ -╞════════════════════╪══════════════╪═══════════════════════════╪═══════════════╪═════════╪══════════╪═════════════╪═════════════╪══════════════╡ -│ #0 │ 0.114 │ Elemwise │ 6.53 │ 57.40 │ 51.63 │ 454.02 │ None │ None │ -│ 0.114 │ 35.8% │ 1481 │ GFLO │ GFLOPS │ GiB │ GiB/s │ │ │ -│ 35.8% │ │ N/A │ │ │ │ │ │ │ -├────────────────────┼──────────────┼───────────────────────────┼───────────────┼─────────┼──────────┼─────────────┼─────────────┼──────────────┤ -│ #1 │ 0.176 │ ConvolutionBackwardFilter │ 523.15 │ 8.35 │ 5.28 │ 84.24 │ None │ None │ -│ 0.0627 │ 55.5% │ 53 │ GFLO │ TFLOPS │ GiB │ GiB/s │ │ │ -│ 19.7% │ │ N/A │ │ │ │ │ │ │ -├────────────────────┼──────────────┼───────────────────────────┼───────────────┼─────────┼──────────┼─────────────┼─────────────┼──────────────┤ -│ #2 │ 0.221 │ ConvolutionBackwardData │ 508.05 │ 11.31 │ 5.05 │ 112.42 │ None │ None │ -│ 0.0449 │ 69.6% │ 52 │ GFLO │ TFLOPS │ GiB │ GiB/s │ │ │ -│ 14.1% │ │ N/A │ │ │ │ │ │ │ -├────────────────────┼──────────────┼───────────────────────────┼───────────────┼─────────┼──────────┼─────────────┼─────────────┼──────────────┤ -``` -Please read [megengine/utils/profile_analyze.py](../../utils/profile_analyze.py) for more usages. - -## To do list - -* Change numerical tolerance after XPU-280 is done -* Add scripts to facilitate log analysis -* Profile GPU memory -* Incorporate with QA system -* Add more regression tests diff --git a/python_module/test/integration/manual/resnet50_perf.py b/python_module/test/integration/manual/resnet50_perf.py deleted file mode 100644 index 9f36e8f0..00000000 --- a/python_module/test/integration/manual/resnet50_perf.py +++ /dev/null @@ -1,210 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import argparse -import json -import os -import subprocess -import sys -import time - -import numpy as np - -import megengine as mge -import megengine.distributed as dist -import megengine.functional as F -from megengine._internal.plugin import CompGraphProfiler -from megengine.core import Graph, tensor -from megengine.core.graph import get_default_graph -from megengine.functional.debug_param import ( - get_conv_execution_strategy, - set_conv_execution_strategy, -) -from megengine.jit import trace -from megengine.module import BatchNorm2d, Conv2d, Linear, MaxPool2d, Module -from megengine.optimizer import SGD - -sys.path.append(os.path.join(os.path.dirname(__file__), "..", "..", "..", "examples")) - - -def init_profiler(comp_graph=get_default_graph()): - profiler = CompGraphProfiler(comp_graph) - return profiler - - -def dump_profiler(profiler, filename): - with open(filename, "w") as fout: - json.dump(profiler.get(), fout, indent=2) - - -def print_gpu_usage(): - stdout = subprocess.getoutput("nvidia-smi") - for line in stdout.split("\n"): - for item in line.split(" "): - if "MiB" in item: - print("Finish with GPU Usage", item) - break - - -def run_perf( - batch_size=64, - warm_up=True, - dump_prof=None, - opt_level=2, - conv_fastrun=False, - run_step=True, - track_bn_stats=True, - warm_up_iter=20, - run_iter=100, - num_gpu=None, - device=0, - server=None, - port=None, - scale_batch_size=False, - eager=False, -): - - # pylint: disable = import-outside-toplevel - from resnet50 import Resnet50 - - if conv_fastrun: - set_conv_execution_strategy("PROFILE") - - if num_gpu: - dist.init_process_group(args.server, args.port, num_gpu, device, device) - if scale_batch_size: - batch_size = batch_size // num_gpu - print("Run with data parallel, batch size = {} per GPU".format(batch_size)) - - data = tensor(np.random.randn(batch_size, 3, 224, 224).astype("float32")) - label = tensor(np.random.randint(1000, size=[batch_size,], dtype=np.int32)) - - net = Resnet50(track_bn_stats=track_bn_stats) - opt = SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=1e-4) - - def train_func(data, label): - logits = net(data) - loss = F.cross_entropy_with_softmax(logits, label) - - if num_gpu: - loss = loss / num_gpu - - opt.zero_grad() - opt.backward(loss) - return loss - - train_func = trace( - train_func, - symbolic=(not eager), - opt_level=opt_level, - profiling=not (dump_prof is None), - ) - - if warm_up: - print("Warm up ...") - for _ in range(warm_up_iter): - opt.zero_grad() - train_func(data, label) - if run_step: - opt.step() - print_gpu_usage() - print("Running train ...") - start = time.time() - for _ in range(run_iter): - opt.zero_grad() - train_func(data, label) - if run_step: - opt.step() - - time_used = time.time() - start - - if dump_prof: - with open(dump_prof, "w") as fout: - json.dump(train_func.get_profile(), fout, indent=2) - - return time_used / run_iter - - -def str2bool(v): - if isinstance(v, bool): - return v - if v.lower() in ("yes", "true", "t", "y", "1"): - return True - elif v.lower() in ("no", "false", "f", "n", "0"): - return False - else: - raise argparse.ArgumentTypeError("Boolean value expected.") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser( - description="Running regression test on Resnet 50", - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - ) - parser.add_argument("--batch-size", type=int, default=64, help="batch size ") - parser.add_argument( - "--warm-up", type=str2bool, default=True, help="whether to warm up" - ) - parser.add_argument( - "--dump-prof", - type=str, - default=None, - help="pass the json file path to dump the profiling result", - ) - parser.add_argument("--opt-level", type=int, default=2, help="graph opt level") - parser.add_argument( - "--conv-fastrun", - type=str2bool, - default=False, - help="whether to use conv fastrun mode", - ) - parser.add_argument( - "--run-step", - type=str2bool, - default=True, - help="whether to run optimizer.step()", - ) - parser.add_argument( - "--track-bn-stats", - type=str2bool, - default=True, - help="whether to track bn stats", - ) - parser.add_argument( - "--warm-up-iter", type=int, default=20, help="number of iters to warm up" - ) - parser.add_argument( - "--run-iter", type=int, default=100, help="number of iters to collect wall time" - ) - parser.add_argument("--server", default="0.0.0.0") - parser.add_argument("--port", type=int, default=2222) - parser.add_argument( - "--scale-batch-size", - type=str2bool, - default=False, - help="whether to divide batch size by number of GPUs", - ) - parser.add_argument( - "--eager", type=str2bool, default=False, help="whether to use eager mode" - ) - - # Data parallel related - parser.add_argument("--num-gpu", type=int, default=None) - parser.add_argument("--device", type=int, default=0) - args = parser.parse_args() - - print(vars(args)) - - os.environ["MGB_JIT_BACKEND"] = "NVRTC" - - t = run_perf(**vars(args)) - - print("**********************************") - print("Wall time per iter {:.0f} ms".format(t * 1000)) - print("**********************************") - get_default_graph().clear_device_memory() diff --git a/python_module/test/integration/manual/run_resnet50_perf.py b/python_module/test/integration/manual/run_resnet50_perf.py deleted file mode 100644 index a8a24d99..00000000 --- a/python_module/test/integration/manual/run_resnet50_perf.py +++ /dev/null @@ -1,162 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import argparse -import os -import pathlib -import subprocess - -from megengine.utils.profile_analyze import main as profiler - -home = pathlib.Path(__file__).parent.absolute() -script_path = os.path.join(str(home), "resnet50_perf.py") -script_path = "python3 " + script_path - -prof_path = "prof.json" - -log_path = "log.txt" - - -def print_log(msg: str, log: str = log_path): - print(msg) - with open(log, "a") as f: - print(msg, file=f) - - -def run_cmd(cmd: str, log: str = log_path) -> bool: - stdout = subprocess.getoutput(cmd) - token = "Wall time" - gpu_msg = "GPU Usage" - run_finished = False - for line in stdout.split("\n"): - if token in line: - print(line) - print_log("Run status: finished") - run_finished = True - if gpu_msg in line: - print(line) - if not run_finished: - print_log("Run status: failed") - with open(log, "a") as f: - print(stdout, file=f) - - return run_finished - - -if __name__ == "__main__": - - parser = argparse.ArgumentParser(description="ResNet50 train performance") - parser.add_argument( - "--run-debug-tool", action="store_true", help="run profiler and valgrind" - ) - parser.add_argument( - "--run-parallel", action="store_true", help="run data parallel performance" - ) - parser.add_argument("--run-eager", action="store_false", help="run eager graph") - args = parser.parse_args() - - f = open(log_path, "w") - f.close() - - print_log("**************************************") - print_log("Run ResNet 50 performance test with batch size = 64") - - print_log("**************************************") - print_log("Run static graph with default opt level") - cmd = script_path - run_cmd(cmd) - - print_log("**************************************") - print_log("Run static graph with conv fastrun") - cmd = script_path + " --conv-fastrun=yes" - run_cmd(cmd) - - print_log("**************************************") - print_log("Run static graph with conv fastrun and JIT") - cmd = script_path + " --conv-fastrun=yes --opt-level=3" - run_cmd(cmd) - - print_log("**************************************") - print_log("Run static graph with JIT, conv fastrun and without running step") - cmd = script_path + " --conv-fastrun=yes --opt-level=3 --run-step=no" - run_cmd(cmd) - - if args.run_eager: - print_log("**************************************") - print_log("Run static graph with default opt level and batch-size=8") - cmd = script_path + " --batch-size=8" - run_cmd(cmd) - print_log("**************************************") - print_log("Run eager graph with default opt level and batch-size=8") - cmd = script_path - run_cmd("MGE_DISABLE_TRACE=1 " + cmd + " --eager=yes") - - if args.run_debug_tool: - - print_log("**************************************") - print_log("Run with dump_prof") - cmd = script_path + " --dump-prof=" + prof_path - if run_cmd(cmd): - print("Printing profiling result") - profiler([prof_path, "--aggregate-by=type", "--aggregate=sum", "-t 10"]) - - print_log("**************************************") - print_log("Run with valgrind massif") - massif_out = "massif.out" - # Use 0.01% as valgrind massif threashold - # A smaller value reports more details but it may take longer time to analyze the log - # Change it accordingly. - mem_threshold = 0.01 - cmd = ( - "valgrind --tool=massif --threshold={} --massif-out-file=".format( - mem_threshold - ) - + massif_out - + " " - ) - cmd = cmd + script_path + " --warm-up=no --run-iter=20" - run_cmd(cmd) - ms_print_file = "massif.out.ms_print" - cmd = ( - "ms_print --threshold={} ".format(mem_threshold) - + massif_out - + " > " - + ms_print_file - ) - os.system(cmd) - cmd = "head -n 33 " + ms_print_file - os.system(cmd) - print_log("Read {} for detailed massif output".format(ms_print_file)) - - if args.run_parallel: - print_log("**************************************") - tmp_out = "/dev/null" - # Change server and port to run at your system - server = "localhost" - port = "2222" - for num_gpu in (2, 4, 8): - print_log("Run with {} GPUs".format(num_gpu)) - - cmd = script_path + " --num-gpu={} --server={} --port={} ".format( - num_gpu, server, port - ) - for i in range(num_gpu - 1): - irank = num_gpu - 1 - i - os.system( - cmd - + " --device={}".format(irank) - + " 1>{} 2>{} &".format(tmp_out, tmp_out) - ) - if not run_cmd(cmd): - break - - print_log("**************************************") - print_log("**************************************") - print("Finish run, summary:") - cmd = 'grep "Run with\|Wall time\|Run status\|Error\|GPU Usage" ' + log_path - os.system(cmd) diff --git a/python_module/test/integration/mnist_model_with_test.mge b/python_module/test/integration/mnist_model_with_test.mge deleted file mode 100644 index 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0 else 1 - pred_label = np.argmax(pred_output) - if pred_label == label: - correct += 1 - return float(correct) / len(data) - - -class XORNet(Module): - def __init__(self): - self.mid_layers = 14 - self.num_class = 2 - super().__init__() - - self.fc0 = Linear(self.num_class, self.mid_layers, bias=True) - self.fc1 = Linear(self.mid_layers, self.mid_layers, bias=True) - - self.fc2 = Linear(self.mid_layers, self.num_class, bias=True) - - def forward(self, x): - x = self.fc0(x) - x = tanh(x) - x = self.fc1(x) - x = tanh(x) - x = self.fc2(x) - return x - - -@pytest.mark.slow -def test_training_converge(): - net = XORNet() - opt = SGD( - net.parameters(requires_grad=True), lr=0.01, momentum=0.9, weight_decay=5e-4 - ) - - @trace - def train(data, label): - pred = net(data) - opt.zero_grad() - loss = cross_entropy_with_softmax(pred, label) - opt.backward(loss) - return loss - - @trace - def infer(data): - return net(data) - - train_dataset = minibatch_generator() - losses = [] - - for data, label in itertools.islice(train_dataset, 2000): - # opt.zero_grad() - loss = train(data, label) - loss = loss[0][0] - opt.step() - losses.append(loss.numpy()) - - assert np.mean(losses[-100:]) < 0.1, "Final training Loss must be low enough" - - ngrid = 10 - x = np.linspace(-1.0, 1.0, ngrid) - xx, yy = np.meshgrid(x, x) - xx = xx.reshape((ngrid * ngrid, 1)) - yy = yy.reshape((ngrid * ngrid, 1)) - data = np.concatenate((xx, yy), axis=1).astype(np.float32) - - pred = infer(data).numpy() - assert calculate_precision(data, pred) == 1.0, "Test precision must be high enough" diff --git a/python_module/test/integration/test_correctness.py b/python_module/test/integration/test_correctness.py deleted file mode 100644 index 70a9861f..00000000 --- a/python_module/test/integration/test_correctness.py +++ /dev/null @@ -1,199 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import os -import re -import subprocess -import sys - -import numpy as np - -import megengine as mge -import megengine.functional as F -from megengine import jit, tensor -from megengine.functional.debug_param import set_conv_execution_strategy -from megengine.jit import SublinearMemoryConfig -from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module -from megengine.optimizer import SGD -from megengine.test import assertTensorClose - - -def get_gpu_name(): - try: - gpu_info = subprocess.check_output( - ["nvidia-smi", "--query-gpu=gpu_name", "--format=csv,noheader"] - ) - gpu_info = gpu_info.decode("ascii").split("\n")[0] - except: - gpu_info = "None" - return gpu_info - - -def get_cpu_name(): - cpu_info = "None" - try: - cpu_info = subprocess.check_output(["cat", "/proc/cpuinfo"]).decode("ascii") - for line in cpu_info.split("\n"): - if "model name" in line: - return re.sub(".*model name.*:", "", line, 1).strip() - except: - pass - return cpu_info - - -def get_xpu_name(): - if mge.is_cuda_available(): - return get_gpu_name() - else: - return get_cpu_name() - - -class MnistNet(Module): - def __init__(self, has_bn=False): - super().__init__() - self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True) - self.pool0 = AvgPool2d(2) - self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True) - self.pool1 = AvgPool2d(2) - self.fc0 = Linear(20 * 4 * 4, 500, bias=True) - self.fc1 = Linear(500, 10, bias=True) - self.bn0 = None - self.bn1 = None - if has_bn: - self.bn0 = BatchNorm2d(20) - self.bn1 = BatchNorm2d(20) - - def forward(self, x): - x = self.conv0(x) - if self.bn0: - x = self.bn0(x) - x = F.relu(x) - x = self.pool0(x) - x = self.conv1(x) - if self.bn1: - x = self.bn1(x) - x = F.relu(x) - x = self.pool1(x) - x = F.flatten(x, 1) - x = self.fc0(x) - x = F.relu(x) - x = self.fc1(x) - return x - - -def train(data, label, net, opt): - - pred = net(data) - loss = F.cross_entropy_with_softmax(pred, label) - opt.backward(loss) - return loss - - -def update_model(model_path): - """ - Update the dumped model with test cases for new reference values. - - The model with pre-trained weights is trained for one iter with the test data attached. - The loss and updated net state dict is dumped. - - .. code-block:: python - - from test_correctness import update_model - update_model('mnist_model_with_test.mge') # for gpu - update_model('mnist_model_with_test_cpu.mge') # for cpu - - """ - net = MnistNet(has_bn=True) - checkpoint = mge.load(model_path) - net.load_state_dict(checkpoint["net_init"]) - lr = checkpoint["sgd_lr"] - opt = SGD(net.parameters(), lr=lr) - - data = tensor(dtype=np.float32) - label = tensor(dtype=np.int32) - data.set_value(checkpoint["data"]) - label.set_value(checkpoint["label"]) - - opt.zero_grad() - loss = train(data, label, net=net, opt=opt) - opt.step() - - xpu_name = get_xpu_name() - - checkpoint.update( - {"net_updated": net.state_dict(), "loss": loss.numpy(), "xpu": xpu_name} - ) - mge.save(checkpoint, model_path) - - -def run_test( - model_path, use_jit, use_symbolic, sublinear_memory_config=None, max_err=None, -): - - """ - Load the model with test cases and run the training for one iter. - The loss and updated weights are compared with reference value to verify the correctness. - - Dump a new file with updated result by calling update_model - if you think the test fails due to numerical rounding errors instead of bugs. - Please think twice before you do so. - - """ - net = MnistNet(has_bn=True) - checkpoint = mge.load(model_path) - net.load_state_dict(checkpoint["net_init"]) - lr = checkpoint["sgd_lr"] - opt = SGD(net.parameters(), lr=lr) - - data = tensor(dtype=np.float32) - label = tensor(dtype=np.int32) - data.set_value(checkpoint["data"]) - label.set_value(checkpoint["label"]) - - if max_err is None: - max_err = 1e-5 - - train_func = train - if use_jit: - train_func = jit.trace( - train_func, - symbolic=use_symbolic, - sublinear_memory_config=sublinear_memory_config, - ) - - opt.zero_grad() - loss = train_func(data, label, net=net, opt=opt) - opt.step() - - assertTensorClose(loss.numpy(), checkpoint["loss"], max_err=max_err) - - for param, param_ref in zip( - net.state_dict().items(), checkpoint["net_updated"].items() - ): - assert param[0] == param_ref[0] - assertTensorClose(param[1], param_ref[1], max_err=max_err) - - -def test_correctness(): - - if mge.is_cuda_available(): - model_name = "mnist_model_with_test.mge" - else: - model_name = "mnist_model_with_test_cpu.mge" - model_path = os.path.join(os.path.dirname(__file__), model_name) - set_conv_execution_strategy("HEURISTIC_REPRODUCIBLE") - - run_test(model_path, False, False) - run_test(model_path, True, False) - run_test(model_path, True, True) - - # sublinear - config = SublinearMemoryConfig(genetic_nr_iter=10) - run_test( - model_path, True, True, sublinear_memory_config=config, max_err=1e-5, - ) diff --git a/python_module/test/integration/test_distributed.py b/python_module/test/integration/test_distributed.py deleted file mode 100644 index ba2e3080..00000000 --- a/python_module/test/integration/test_distributed.py +++ /dev/null @@ -1,97 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import multiprocessing as mp -import platform -import subprocess -import sys - -import numpy as np -import pytest - - -def worker(master_ip, master_port, world_size, rank, dev, trace): - import megengine.distributed as dist - import megengine.functional as F - from megengine import is_cuda_available - from megengine import jit - from megengine.module import Linear, Module - from megengine.optimizer import SGD - - if not is_cuda_available(): - return - - class MLP(Module): - def __init__(self): - super().__init__() - self.fc0 = Linear(3 * 224 * 224, 500) - self.fc1 = Linear(500, 10) - - def forward(self, x): - x = self.fc0(x) - x = F.relu(x) - x = self.fc1(x) - return x - - dist.init_process_group( - master_ip=master_ip, master_port=3456, world_size=world_size, rank=rank, dev=dev - ) - net = MLP() - - opt = SGD(net.parameters(requires_grad=True), lr=0.02) - - data = np.random.random((64, 3 * 224 * 224)).astype(np.float32) - label = np.random.randint(0, 10, size=(64,)).astype(np.int32) - - jit.trace.enabled = trace - - @jit.trace() - def train_func(data, label): - pred = net(data) - loss = F.cross_entropy_with_softmax(pred, label) - opt.backward(loss) - return loss - - for i in range(5): - opt.zero_grad() - loss = train_func(data, label) - opt.step() - - -def start_workers(worker, world_size, trace=False): - def run_subproc(rank): - cmd = "from test.integration.test_distributed import worker\n" - cmd += "worker('localhost', 3456, {}, {}, {}, {})".format( - world_size, rank, rank, "True" if trace else "False" - ) - cmd = [sys.executable, "-c", cmd] - ret = subprocess.run( - cmd, stdout=sys.stdout, stderr=sys.stderr, universal_newlines=True - ) - assert ret.returncode == 0, "subprocess failed" - - procs = [] - for rank in range(world_size): - p = mp.Process(target=run_subproc, args=(rank,)) - p.start() - procs.append(p) - - for p in procs: - p.join() - assert p.exitcode == 0 - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -def test_distributed(): - start_workers(worker, 2, trace=True) - start_workers(worker, 2, trace=False) diff --git a/python_module/test/integration/test_equivalence.py b/python_module/test/integration/test_equivalence.py deleted file mode 100644 index e215450f..00000000 --- a/python_module/test/integration/test_equivalence.py +++ /dev/null @@ -1,184 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import copy -import itertools -import os -from typing import Callable - -import numpy as np -import pytest - -import megengine as mge -import megengine.module.init as init -from megengine.core import tensor -from megengine.functional import cross_entropy_with_softmax, relu -from megengine.jit import trace -from megengine.module import Linear, Module -from megengine.optimizer import SGD, Optimizer -from megengine.test import assertTensorClose - -batch_size = 64 -data_shape = (batch_size, 2) -label_shape = (batch_size,) - - -def minibatch_generator(): - while True: - inp_data = np.zeros((batch_size, 2)) - label = np.zeros(batch_size, dtype=np.int32) - - for i in range(batch_size): - # [x0, x1], sampled from U[-1, 1] - inp_data[i, :] = np.random.rand(2) * 2 - 1 - label[i] = 0 if np.prod(inp_data[i]) < 0 else 1 - - yield inp_data.astype(np.float32), label.astype(np.int32) - - -class SimpleNet(Module): - def __init__(self): - self.mid_layers = 14 - self.num_class = 2 - super().__init__() - - self.fc0 = Linear(self.num_class, self.mid_layers, bias=True) - fan_in, _ = init.calculate_fan_in_and_fan_out(self.fc0.weight) - init.normal_(self.fc0.weight, std=np.sqrt(float(1.0) / fan_in)) - init.zeros_(self.fc0.bias) - - self.fc1 = Linear(self.mid_layers, self.mid_layers, bias=True) - fan_in, _ = init.calculate_fan_in_and_fan_out(self.fc1.weight) - init.normal_(self.fc1.weight, std=np.sqrt(float(1.0) / fan_in)) - init.zeros_(self.fc1.bias) - - self.fc2 = Linear(self.mid_layers, self.num_class, bias=True) - fan_in, _ = init.calculate_fan_in_and_fan_out(self.fc2.weight) - init.normal_(self.fc2.weight, std=np.sqrt(float(1.0) / fan_in)) - init.zeros_(self.fc2.bias) - - def forward(self, x): - x = self.fc0(x) - x = relu(x) # Should use tanh but it's not stable now. - x = self.fc1(x) - x = relu(x) # Should use tanh but it's not stable now. - x = self.fc2(x) - return x - - -def generate_eager_step(net: Module, opt_factory: Callable[[Module], Optimizer]): - data_inp = tensor(np.zeros(data_shape), dtype=np.float32) - label_inp = tensor(np.zeros(label_shape), dtype=np.int32) - opt = opt_factory(net) - - def step(data, label): - opt.zero_grad() - data_inp.set_value(data) - label_inp.set_value(label) - - pred = net(data_inp) - loss = cross_entropy_with_softmax(pred, label_inp) - opt.backward(loss) - opt.step() - - return loss.numpy()[0] - - return step - - -def generate_static_step(net: Module, opt_factory: Callable[[Module], Optimizer]): - data = tensor(np.zeros(data_shape), dtype=np.float32) - label = tensor(np.zeros(label_shape), dtype=np.int32) - opt = opt_factory(net) - - # Save state to reset parameters later. - state = copy.deepcopy(net.state_dict()) - - # Evaluate network in eager mode once. - pred = net(data) - loss = cross_entropy_with_softmax(pred, label) - opt.zero_grad() - grads = opt.backward(loss) - - f = mge.graph.compile(loss, grads) - - def step(data, label): - opt.zero_grad() - out = f(data=data, label=label) - opt.step() - loss = out[0][0] - return loss - - # Reset parameters. - net.load_state_dict(state) - return step - - -def generate_trace_step( - net: Module, opt_factory: Callable[[Module], Optimizer], enable: bool -): - opt = opt_factory(net) - - @trace - def train(data, label): - pred = net(data) - loss = cross_entropy_with_softmax(pred, label) - opt.zero_grad() - opt.backward(loss) - return loss - - train.enabled = enable - - def step(data, label): - out = train(data, label) - opt.step() - loss = out[0][0] - return loss - - return step - - -def assert_network_equvilence(nets): - net_state = [net.state_dict() for net in nets] - - for state in net_state[1:]: - assert len(net_state[0]) == len(state) - - for k, v in net_state[0].items(): - for state in net_state[1:]: - assert k in state - assertTensorClose(v, state[k]) - - -@pytest.mark.slow -def test_eager_equvilence(): - eager_net = SimpleNet() - trace_enable_net = copy.deepcopy(eager_net) - trace_disable_net = copy.deepcopy(eager_net) - - opt_factory = lambda net: SGD( - net.parameters(requires_grad=True), lr=0.01, momentum=0.01 - ) - - estep = generate_eager_step(eager_net, opt_factory) - te_step = generate_trace_step(trace_enable_net, opt_factory, True) - td_step = generate_trace_step(trace_disable_net, opt_factory, False) - - assert_network_equvilence([eager_net, trace_enable_net, trace_disable_net]) - - # Use hard code number as limit, may increase if needed. - for data, label in itertools.islice(minibatch_generator(), 200): - eloss = estep(data, label) - te_loss = te_step(data, label) - td_loss = td_step(data, label) - - assertTensorClose(eloss, te_loss) - assertTensorClose(eloss, td_loss) - assert_network_equvilence( - [eager_net, trace_enable_net, trace_disable_net,] - ) diff --git a/python_module/test/integration/test_fastrun.py b/python_module/test/integration/test_fastrun.py deleted file mode 100644 index 8cce8c59..00000000 --- a/python_module/test/integration/test_fastrun.py +++ /dev/null @@ -1,12 +0,0 @@ -import numpy as np - -import megengine as mge -from megengine.functional.debug_param import set_conv_execution_strategy -from megengine.module.conv import Conv2d - - -def test_fastrun(): - set_conv_execution_strategy("PROFILE") - x = Conv2d(1, 1, kernel_size=1, bias=True) - a = mge.tensor(np.random.randn(1, 1, 1, 1).astype(np.float32)) - a = x(a) diff --git a/python_module/test/integration/test_parampack.py b/python_module/test/integration/test_parampack.py deleted file mode 100644 index c9acc47c..00000000 --- a/python_module/test/integration/test_parampack.py +++ /dev/null @@ -1,274 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import itertools - -import numpy as np -import pytest - -import megengine as mge -from megengine.core import tensor -from megengine.functional import cross_entropy_with_softmax, tanh -from megengine.jit import trace -from megengine.module import Linear, Module, ParamPack -from megengine.optimizer import SGD - -batch_size = 64 -data_shape = (batch_size, 2) -label_shape = (batch_size,) - - -def minibatch_generator(): - while True: - inp_data = np.zeros((batch_size, 2)) - label = np.zeros(batch_size, dtype=np.int32) - for i in range(batch_size): - # [x0, x1], sampled from U[-1, 1] - inp_data[i, :] = np.random.rand(2) * 2 - 1 - label[i] = 0 if np.prod(inp_data[i]) < 0 else 1 - yield inp_data.astype(np.float32), label.astype(np.int32) - - -def calculate_precision(data: np.ndarray, pred: np.ndarray) -> float: - """ Calculate precision for given data and prediction. - - :type data: [[x, y], ...] - :param data: Input data - :type pred: [[x_pred, y_pred], ...] - :param pred: Network output data - """ - correct = 0 - assert len(data) == len(pred) - for inp_data, pred_output in zip(data, pred): - label = 0 if np.prod(inp_data) < 0 else 1 - pred_label = np.argmax(pred_output) - if pred_label == label: - correct += 1 - return float(correct) / len(data) - - -class XORNet(Module): - def __init__(self): - self.mid_layers = 14 - self.num_class = 2 - super().__init__() - - self.fc0 = Linear(self.num_class, self.mid_layers, bias=True) - self.fc1 = Linear(self.mid_layers, self.mid_layers, bias=True) - - self.fc2 = Linear(self.mid_layers, self.num_class, bias=True) - - def forward(self, x): - x = self.fc0(x) - x = tanh(x) - x = self.fc1(x) - x = tanh(x) - x = self.fc2(x) - return x - - -@pytest.mark.slow -def test_static_graph_parampack(): - net = XORNet() - net = ParamPack( - net, nr_ignore_first=0, max_size_per_group=10, max_nr_params_per_group=100 - ) - opt = SGD( - net.parameters(requires_grad=True), lr=0.01, momentum=0.9, weight_decay=5e-4 - ) - - @trace(symbolic=True) - def train(data, label): - pred = net(data) - opt.zero_grad() - loss = cross_entropy_with_softmax(pred, label) - opt.backward(loss) - return loss - - @trace(symbolic=True) - def infer(data): - return net(data) - - train_dataset = minibatch_generator() - losses = [] - - for data, label in itertools.islice(train_dataset, 2000): - loss = train(data, label) - loss = loss[0][0] - opt.step() - losses.append(loss.numpy()) - - assert np.mean(losses[-100:]) < 0.1, "Final training Loss must be low enough" - - ngrid = 10 - x = np.linspace(-1.0, 1.0, ngrid) - xx, yy = np.meshgrid(x, x) - xx = xx.reshape((ngrid * ngrid, 1)) - yy = yy.reshape((ngrid * ngrid, 1)) - data = np.concatenate((xx, yy), axis=1).astype(np.float32) - - pred = infer(data).numpy() - assert calculate_precision(data, pred) == 1.0, "Test precision must be high enough" - - -@pytest.mark.slow -def test_nopack_parampack(): - net = XORNet() - net = ParamPack(net, max_size_per_group=0, max_nr_params_per_group=0) - opt = SGD( - net.parameters(requires_grad=True), lr=0.01, momentum=0.9, weight_decay=5e-4 - ) - - @trace(symbolic=True) - def train(data, label): - pred = net(data) - opt.zero_grad() - loss = cross_entropy_with_softmax(pred, label) - opt.backward(loss) - return loss - - @trace(symbolic=True) - def infer(data): - return net(data) - - train_dataset = minibatch_generator() - losses = [] - - for data, label in itertools.islice(train_dataset, 2000): - loss = train(data, label) - loss = loss[0][0] - opt.step() - losses.append(loss.numpy()) - assert np.mean(losses[-100:]) < 0.1, "Final training Loss must be low enough" - - ngrid = 10 - x = np.linspace(-1.0, 1.0, ngrid) - xx, yy = np.meshgrid(x, x) - xx = xx.reshape((ngrid * ngrid, 1)) - yy = yy.reshape((ngrid * ngrid, 1)) - data = np.concatenate((xx, yy), axis=1).astype(np.float32) - - pred = infer(data).numpy() - assert calculate_precision(data, pred) == 1.0, "Test precision must be high enough" - - -@pytest.mark.slow -def test_dynamic_graph_parampack(): - net = XORNet() - net = ParamPack( - net, nr_ignore_first=0, max_size_per_group=10, max_nr_params_per_group=100 - ) - opt = SGD( - net.parameters(requires_grad=True), lr=0.01, momentum=0.9, weight_decay=5e-4 - ) - - @trace(symbolic=False) - def train(data, label): - pred = net(data) - opt.zero_grad() - loss = cross_entropy_with_softmax(pred, label) - opt.backward(loss) - return loss - - @trace(symbolic=False) - def infer(data): - return net(data) - - train_dataset = minibatch_generator() - losses = [] - - for data, label in itertools.islice(train_dataset, 2000): - loss = train(data, label) - loss = loss[0][0] - opt.step() - losses.append(loss.numpy()) - - assert np.mean(losses[-100:]) < 0.1, "Final training Loss must be low enough" - - ngrid = 10 - x = np.linspace(-1.0, 1.0, ngrid) - xx, yy = np.meshgrid(x, x) - xx = xx.reshape((ngrid * ngrid, 1)) - yy = yy.reshape((ngrid * ngrid, 1)) - data = np.concatenate((xx, yy), axis=1).astype(np.float32) - - pred = infer(data).numpy() - assert calculate_precision(data, pred) == 1.0, "Test precision must be high enough" - - -@pytest.mark.slow -def test_correctness_parampack(): - net1 = XORNet() - net2 = XORNet() - params1 = net1.parameters() - params2 = net2.parameters() - for param1, param2 in zip(params1, params2): - param1.set_value(param2.numpy()) - net1 = ParamPack( - net1, nr_ignore_first=0, max_size_per_group=10, max_nr_params_per_group=100 - ) - opt1 = SGD( - net1.parameters(requires_grad=True), lr=0.01, momentum=0.9, weight_decay=5e-4 - ) - - opt2 = SGD( - net2.parameters(requires_grad=True), lr=0.01, momentum=0.9, weight_decay=5e-4 - ) - - @trace(symbolic=False) - def train1(data, label): - pred = net1(data) - opt1.zero_grad() - loss = cross_entropy_with_softmax(pred, label) - opt1.backward(loss) - return loss - - @trace(symbolic=False) - def train2(data, label): - pred = net2(data) - opt2.zero_grad() - loss = cross_entropy_with_softmax(pred, label) - opt2.backward(loss) - return loss - - @trace(symbolic=False) - def infer1(data): - return net1(data) - - @trace(symbolic=False) - def infer2(data): - return net2(data) - - train_dataset = minibatch_generator() - - for data, label in itertools.islice(train_dataset, 2000): - train1(data, label) - opt1.step() - - train2(data, label) - opt2.step() - - data, _ = next(train_dataset) - pred1 = infer1(data).numpy() - pred2 = infer2(data).numpy() - assert np.allclose(pred1, pred2) - - -def test_parampack_group_func(): - net = XORNet() - net = ParamPack( - net, - nr_ignore_first=1, - max_size_per_group=10, - max_nr_params_per_group=100, - group_func=lambda n, p: "weight" in n, - ) - for p in net.parameters(requires_grad=True): - assert p.pack_group_key is not None - for n, p in net.named_parameters(requires_grad=True): - assert p.pack_group_key is not None diff --git a/python_module/test/regression/.gitignore b/python_module/test/regression/.gitignore deleted file mode 100644 index 328c8678..00000000 --- a/python_module/test/regression/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -data -log diff --git a/python_module/test/regression/__init__.py b/python_module/test/regression/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/python_module/test/regression/test_MGE-103.py b/python_module/test/regression/test_MGE-103.py deleted file mode 100644 index e0d387c1..00000000 --- a/python_module/test/regression/test_MGE-103.py +++ /dev/null @@ -1,34 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np -import pytest - -import megengine as mge -from megengine.core import tensor -from megengine.jit import trace -from megengine.module import BatchNorm2d -from megengine.test import assertTensorClose - - -@pytest.mark.regression -def test_batchnorm_change_batchsize(): - data_shape = (2, 3, 8, 8) - real_shape = (4, 3, 8, 8) - data = np.random.random(data_shape).astype(np.float32) - d = np.random.random(real_shape).astype(np.float32) - - bn = BatchNorm2d(3) - f = trace(bn) - f(data) - - y1 = f(d) - - y0 = bn(tensor(d)) - - assertTensorClose(y0.numpy(), y1.numpy()) diff --git a/python_module/test/regression/test_MGE-22.py b/python_module/test/regression/test_MGE-22.py deleted file mode 100644 index b719b9be..00000000 --- a/python_module/test/regression/test_MGE-22.py +++ /dev/null @@ -1,39 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np -import pytest - -from megengine.core import tensor -from megengine.module import Linear, Module -from megengine.optimizer import SGD - - -class Blur(Module): - def __init__(self, dim1=16, dim2=128, dim3=1): - super().__init__() - - self.fc1 = Linear(dim1, dim2) - self.fc2 = Linear(dim2, dim3) - - def forward(self, x): - x = self.fc1(x) - x = self.fc2(x) - return x.mean(axis=1, keepdims=True) - - -@pytest.mark.regression -def test_blur(): - net = Blur() - data = tensor(np.random.random((32, 16)).astype("float32")) - - opt = SGD(net.parameters(requires_grad=True), lr=0.1) - opt.zero_grad() - - loss = net(data) - opt.backward(loss.sum()) diff --git a/python_module/test/regression/test_MGE-323.py b/python_module/test/regression/test_MGE-323.py deleted file mode 100644 index 47e73f2a..00000000 --- a/python_module/test/regression/test_MGE-323.py +++ /dev/null @@ -1,21 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np - -import megengine as mge - - -def test_mge_323(): - # Regression: set_value does not update eager_val - x = mge.tensor([0]) - _ = x * 2 - x.set_value([1, 1]) - np.testing.assert_array_equal(x.numpy(), [1, 1]) - assert x.shape == (2,) - np.testing.assert_array_equal(x * 2, [2, 2]) diff --git a/python_module/test/regression/test_MGE-81.py b/python_module/test/regression/test_MGE-81.py deleted file mode 100644 index f32b3232..00000000 --- a/python_module/test/regression/test_MGE-81.py +++ /dev/null @@ -1,36 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np - -import megengine as mge -import megengine.functional as F -import megengine.module as M -from megengine.core import tensor - - -def test_mge_81(): - np.random.seed(0) - N, D = 3, 4 - x = mge.Parameter(value=np.random.normal(size=(N, D)).astype(np.float32)) - y = mge.Parameter(value=np.random.normal(size=(N, D)).astype(np.float32)) - z = mge.Parameter(value=np.random.normal(size=(N, D)).astype(np.float32)) - a = x * y - b = a + z - c = F.sum(b) - grad_x = F.grad(c, x, use_virtual_grad=False) - grad_y = F.grad(c, y, use_virtual_grad=False) - grad_z = F.grad(c, z, use_virtual_grad=False) - print(grad_x.numpy()) - print(grad_y.numpy()) - print(grad_z.numpy()) - m = M.BatchNorm2d(4) - input = tensor(np.zeros((64, 4, 32, 32), dtype=np.float32)) - _ = m(input) - m = M.BatchNorm2d(4, affine=False) - _ = m(input) diff --git a/python_module/test/run.sh b/python_module/test/run.sh deleted file mode 100755 index 7f2d9038..00000000 --- a/python_module/test/run.sh +++ /dev/null @@ -1,19 +0,0 @@ -#!/bin/bash -e - -ignore_list="--ignore test/unit/module/test_pytorch.py \ - --ignore test/pytorch_comparison \ - --ignore test/unit/hub/test_hub.py \ - --ignore test/unit/data \ - --ignore test/integration/manual \ - --ignore megengine/module/pytorch \ - --ignore test/unit/module/test_external.py" -test_dirs="megengine test" - -pushd $(dirname "${BASH_SOURCE[0]}")/.. >/dev/null - python3 -m pytest -xv -m 'isolated_distributed' \ - --json-report --json-report-file=time_python_test.json \ - $ignore_list $test_dirs - python3 -m pytest -xv -m 'not internet and not isolated_distributed' \ - --json-report --json-report-file=time_python_test.json \ - $ignore_list $test_dirs -popd >/dev/null diff --git a/python_module/test/unit/__init__.py b/python_module/test/unit/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/python_module/test/unit/core/__init__.py b/python_module/test/unit/core/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/python_module/test/unit/core/test_dynamic_profiling.py b/python_module/test/unit/core/test_dynamic_profiling.py deleted file mode 100644 index 53e603b8..00000000 --- a/python_module/test/unit/core/test_dynamic_profiling.py +++ /dev/null @@ -1,56 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import json - -import numpy as np - -import megengine.functional as F -from megengine import graph, tensor - - -def test_dynamic_profiling(): - with graph.Graph(): - sz = 16 - - cg = graph.get_default_graph() - - x = tensor(np.arange(0, sz, dtype=np.float32)) - y = F.relu(x) - - str1 = cg.get_mem_allocation_info() - if str1 == "": - return - json_str1 = json.loads(str1) - - z = F.add_update(x, y) - - json_str2 = json.loads(cg.get_mem_allocation_info()) - - diff = lambda l1, l2: [x for x in l1 if x not in l2] - - jdiff = diff(json_str2, json_str1) - assert ( - len(jdiff) == 1 - ), "add_update operator should produce only one opr internally" - - dest_key = list(jdiff[0].keys())[0] - assert ( - jdiff[0][dest_key]["output"][0]["memory"] == sz * 4 - ), "output of add_update operator has wrong allocated size" - - # check add_update is inplace or not - dest_ptr = jdiff[0][dest_key]["output"][0]["dev_ptr"] - - found = False - for li in json_str1: - if "0" in li.keys(): - src_ptr = li["0"]["output"][0]["dev_ptr"] - found = dest_ptr == src_ptr - - assert found == True, "add_update is not inplace" diff --git a/python_module/test/unit/core/test_function.py b/python_module/test/unit/core/test_function.py deleted file mode 100644 index c58978bb..00000000 --- a/python_module/test/unit/core/test_function.py +++ /dev/null @@ -1,169 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import copy - -import numpy as np - -import megengine.functional as F -from megengine.core import Function, tensor -from megengine.jit import trace -from megengine.test import assertTensorClose - - -def test_a_plus_b(): - data_shape = (1, 9, 2, 6) - av = np.random.random(data_shape).astype(np.float32) - bv = np.random.random(data_shape).astype(np.float32) - a = tensor(av) - b = tensor(bv) - - class MulFunc(Function): - def forward(self, a, b): - return a * b - - def backward(self, grad_o): - return (grad_o * b * 2, grad_o * a * 3) - - c = MulFunc()(a, b).sum() - assertTensorClose(c.numpy(), (av * bv).sum()) - assertTensorClose(F.grad(c, a, use_virtual_grad=False).numpy(), bv * 2) - assertTensorClose(F.grad(c, b, use_virtual_grad=False).numpy(), av * 3) - - -def test_skip_invalid_grad(): - data_shape = (1, 9, 2, 6) - av = np.random.random(data_shape).astype(np.float32) - bv = np.random.random(data_shape).astype(np.float32) - a = tensor(av) - b = tensor(bv) - cookie = tensor(np.random.random(data_shape).astype(np.float32)) - - class EqWithFakeGrad(Function): - def forward(self, a, b): - return a == b - - def backward(self, grad_o): - _ = grad_o - return cookie, cookie - - c = EqWithFakeGrad()(a, b).sum() - assertTensorClose(c.numpy(), (av == bv).sum().astype(np.float32)) - assertTensorClose(F.grad(c, a, use_virtual_grad=False).numpy(), cookie) - assertTensorClose(F.grad(c, b, use_virtual_grad=False).numpy(), cookie) - - -def test_ste(): - class STE(Function): - def forward(self, x): - maxv, minv = x.max(), x.min() - scale = F.maximum(maxv, -minv) / 127 - return F.round(x / scale) * scale - - def backward(self, grad_y): - return grad_y - - data_shape = (1, 9, 2, 6) - av = np.random.random(data_shape).astype(np.float32) - a = tensor(av) - q = STE()(a) - q_2 = (q * 2.0).sum() - assertTensorClose( - F.grad(q_2, a, use_virtual_grad=False).numpy(), - np.broadcast_to(np.array([2.0], dtype=np.float32), data_shape), - ) - - -def test_deepcopy(): - class Sigmoid(Function): - def __init__(self, param): - super().__init__() - self.param = param - - def forward(self, x): - y = 1 / (1 + F.exp(-x)) - self.save_for_backward(y) - return y - - def backward(self, grad_y): - (y,) = self.saved_tensors - return grad_y * y * (1 - y) - - origin = Sigmoid(0) - new = copy.deepcopy(Sigmoid(0)) - assert new.param == origin.param - - -def test_save_context(): - class Sigmoid(Function): - def forward(self, x): - y = 1 / (1 + F.exp(-x)) - self.save_for_backward(y) - return y - - def backward(self, grad_y): - (y,) = self.saved_tensors - return grad_y * y * (1 - y) - - def run_saved_context(a, net=None): - return net(a) - - def run(use_trace, symbolic): - a = tensor(np.array([1926.0817], dtype=np.float32)) - net = Sigmoid() - func_run = run_saved_context - if use_trace: - func_run = trace(run_saved_context, symbolic=symbolic) - s = func_run(a, net=net) - s2 = F.sigmoid(a) - assertTensorClose(s.numpy(), s2.numpy()) - assertTensorClose( - F.grad(s, a, use_virtual_grad=False).numpy(), - F.grad(s2, a, use_virtual_grad=False).numpy(), - ) - - run(False, False) - run(True, False) - run(True, True) - - -def test_none_in_out_grad(): - class Test(Function): - def forward(self, a, b): - return a, b - - def backward(self, grad_a, grad_b): - assert grad_b is None - return (grad_a, 0) - - a = tensor(np.array([1.0], dtype=np.float32)) - b = tensor(np.array([2.0], dtype=np.float32)) - aa, bb = Test()(a, b) - assertTensorClose( - F.grad(aa, a, use_virtual_grad=False).numpy(), np.array([1.0], dtype=np.float32) - ) - assertTensorClose( - F.grad(aa, b, use_virtual_grad=False).numpy(), np.array([0.0], dtype=np.float32) - ) - - -def test_zero_grad(): - class StopGradient(Function): - def forward(self, a): - return a - - def backward(self, *_): - return None - - a = tensor(np.array([1.0], dtype=np.float32)) - b = a * 3.0 - c = a * 4.0 - loss = StopGradient()(b) + c - assertTensorClose( - F.grad(loss, a, use_virtual_grad=False).numpy(), - np.array([4.0], dtype=np.float32), - ) diff --git a/python_module/test/unit/core/test_graph.py b/python_module/test/unit/core/test_graph.py deleted file mode 100644 index 5f5fbe95..00000000 --- a/python_module/test/unit/core/test_graph.py +++ /dev/null @@ -1,74 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np -import pytest -from helpers import MLP - -import megengine._internal as mgb -import megengine.functional as F -from megengine.core import Graph -from megengine.module import Linear, Module -from megengine.optimizer import SGD -from megengine.test import assertTensorClose - - -def test_compile_multi_times_eager(): - return # XXX: rewrite or remove this test - data = Input("data", shape=(2, 28)) - label = Input("label", shape=(2,), dtype=np.int32) - - mlp = MLP() - opt = SGD(mlp.parameters(requires_grad=True), lr=0.01) - - pred0 = mlp(data) - pred = F.softmax(pred0) - loss = F.square_loss(pred, label.reshape(2, 1)) - opt.zero_grad() - grads = opt.backward(loss) - opt.step() - - f0 = compile(pred, None) - f1 = compile([pred, loss], grads, copy=False) - for _ in range(3): - data = np.random.random((2, 28)).astype(np.float32) - label = np.random.randint(0, 10, (2,)).astype(np.float32) - out0 = f0(data=data) - out1 = f1(data=data, label=label) - assertTensorClose(out0[0], out1[0]) - - -def test_compile_multi_times_static(): - return # XXX: rewrite or remove this test - with Graph() as cg: - cg.set_option("eager_evaluation", False) - data = Input("data", shape=(2, 28)) - label = Input("label", shape=(2,), dtype=np.int32) - - mlp = MLP() - opt = SGD(mlp.parameters(requires_grad=True), lr=0.01) - - pred0 = mlp(data) - pred = F.softmax(pred0) - loss = F.square_loss(pred, label.reshape(2, 1)) - opt.zero_grad() - grads = opt.backward(loss) - opt.step() - - f0 = compile(pred, None) - f1 = compile([pred, loss], grads, copy=True) - - data = np.random.random((2, 28)).astype(np.float32) - label = np.random.randint(0, 10, (2,)).astype(np.float32) - out0 = f0(data=data) - out1 = f1(data=data, label=label) - assertTensorClose(out0[0], out1[0]) - - _ = compile([pred, loss], grads, copy=False) - with pytest.raises(mgb.MegBrainError): - f0(data=data) diff --git a/python_module/test/unit/core/test_index.py b/python_module/test/unit/core/test_index.py deleted file mode 100644 index 4121c08f..00000000 --- a/python_module/test/unit/core/test_index.py +++ /dev/null @@ -1,311 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np - -from megengine import Tensor, tensor -from megengine.jit import trace -from megengine.test import assertTensorClose - - -def check_equal(np_tensor, mge_tensor): - assertTensorClose(np_tensor, mge_tensor.numpy()) - - -def test_index(): - a_shape = (10, 10, 10, 10, 10) - a = np.random.random(a_shape).astype(dtype=np.float32) - b = tensor(a) - test_set = {} - test_set["a"] = np.random.random(a_shape).astype(dtype=np.float32) - test_set["b"] = tensor(test_set["a"]) - test_set["c"] = tensor(test_set["a"]) - - def check_id_2(np_idx, mge_idx): - # print('start :', mge_idx) - def get_b(symbolic, *args): - # print('get_b:', args) - def get_func(inp): - for i in mge_idx: - if isinstance(i, (list, Tensor)): - return inp.ai[mge_idx] - return inp[mge_idx] - - func = trace(get_func, symbolic=symbolic) - return func(*args) - - def set_b(symbolic, *args): - # print('set_b:', args) - def set_func(inp, val): - for i in mge_idx: - if isinstance(i, (list, Tensor)): - return inp.set_ai(val)[mge_idx] - return inp.set_subtensor(val)[mge_idx] - - func = trace(set_func, symbolic=symbolic) - return func(*args) - - sub_a = a[np_idx] - for symbolic in [True, False]: - sub_b = get_b(symbolic, b) - check_equal(sub_a, sub_b) - # do not support set - # print(mge_idx) - if not mge_idx: - continue - go_flag = False - for i in mge_idx: - if i is np.newaxis: - go_flag = True - break - if go_flag: - continue - if not symbolic: - test_set["b"] = set_b(symbolic, test_set["b"], sub_b) - check_equal(test_set["a"], test_set["b"]) - else: - test_set["a"][np_idx] = sub_a - test_set["c"] = set_b(symbolic, test_set["c"], sub_b) - check_equal(test_set["a"], test_set["c"]) - - def check_idx(*idx): - check_id_2(idx, idx) - - def tensor_wrap(*idx): - check_idx(*idx) - tensor_idx = [] - numpy_idx = [] - for i in idx: - numpy_idx.append(np.asarray(i).astype(np.int32)) - tensor_idx.append(tensor(numpy_idx[-1])) - a_idx = tuple(numpy_idx) - b_idx = tuple(tensor_idx) - check_id_2(a_idx, b_idx) - - def test_one_dim(): - check_idx(-7) - check_idx(-7, -7) - check_idx(-7, -7, -7) - check_idx(-7, -7, -7, -7) - check_idx(-7, -7, -7, -7, -7) - check_idx(7, 7, 7, 7, 7) - check_idx(7, 7, 7, 7) - check_idx(7, 7, 7) - check_idx(7, 7) - check_idx(7) - check_idx() - - def test_slice(): - - check_idx(slice(1, 7)) - check_idx(slice(1, 7)) - check_idx(slice(7, None)) - check_idx(slice(1, 7, 2)) - check_idx(slice(-7, 5)) - check_idx(slice(7, None, 7)) - check_idx(slice(None, 7, 7)) - - def test_new_axis(): - check_idx(7, np.newaxis, 7) - check_idx(7, np.newaxis, slice(3, 7)) - - def test_ellipsis(): - check_idx(..., 7) - - check_idx(7, ...) - - check_idx(7, ..., 7, 7) - - check_idx(7, ..., 7, -7) - - check_idx(7, ..., slice(1, 7), -7) - - def test_integer_array(): - - index = [[6, 7, 8], [9, 7, 4], [1, 1, 1], [3, 5, 6], [7, 8, 1]] - - tensor_wrap(index[0]) - tensor_wrap(index[0], index[1]) - tensor_wrap(index[0], index[1], index[2]) - tensor_wrap(index[0], index[1], index[2], index[3]) - tensor_wrap(index[0], index[1], index[2], index[3], index[4]) - - # multi dimension - index = [ - [6, 7, 8, 8, 9, 7], - [9, 7, 4, 1, 8, 2], - [1, 1, 1, 0, 3, 3], - [3, 5, 6, 1, 6, 3], - [7, 8, 1, 1, 8, 2], - ] - - tensor_wrap(index[0]) - tensor_wrap(index[0], index[1]) - tensor_wrap(index[0], index[1], index[2]) - tensor_wrap(index[0], index[1], index[2], index[3]) - tensor_wrap(index[0], index[1], index[2], index[3], index[4]) - - # braodcast - # index = [ - # [6, 7, 8, 8, 9, 7], # 2 * 3 - # [2], # 1 - # [1, 1, 1], # 1 * 3 - # [6, 2], # 2 * 1 - # [7, 8, 1, 1, 8, 2], # 2 * 3 - # ] - - # tensor_wrap(index[0]) - # tensor_wrap(index[0], index[1]) - # tensor_wrap(index[0], index[1], index[2]) - # tensor_wrap(index[0], index[1], index[2], index[3]) - # tensor_wrap(index[0], index[1], index[2], index[3], index[4]) - - def test_multi_dim(): - check_equal(a[7][7, 7, 7], b[7][7, 7, 7]) - check_equal(a[7, 7, 7][7], b[7, 7, 7][7]) - - check_equal(a[7][7][7, 7], b[7][7][7, 7]) - check_equal(a[7][7, 7][7], b[7][7, 7][7]) - check_equal(a[7, 7][7][7], b[7, 7][7][7]) - - check_equal(a[7, 7, 7][7, 7], b[7, 7, 7][7, 7]) - check_equal(a[7, 7][7, 7, 7], b[7, 7][7, 7, 7]) - - check_equal(a[7][1:7:2], b[7][1:7:2]) - - check_equal(a[7][7:], b[7][7:]) - - check_equal(a[7][-7:-1], b[7][-7:-1]) - - check_equal(a[7][-1:-7:-1], b[7][-1:-7:-1]) - - check_equal(a[7:8][:], b[7:8][:]) - check_equal(a[7][:], b[7][:]) - - check_equal(a[7][:][7], b[7][:][7]) - check_equal(a[:][7][7], b[:][7][7]) - - check_equal(a[7][7], b[7][7]) - check_equal(a[7][7][7], b[7][7][7]) - check_equal(a[7][7][7][7], b[7][7][7][7]) - check_equal(a[7][7][7][7][7], b[7][7][7][7][7]) - - def test_hard(): - check_idx(slice(None, None), [6, 7, 8], slice(0, 7), [6, 7, 8]) - check_idx(slice(None, None), slice(0, 7), [6, 7, 8], [6, 7, 8]) - # check_idx(slice(None, None), slice(0, 7), [[6], [7], [8]], [[6, 7, 8]]) - # check_idx(slice(None, None), [[6, 7, 8]], slice(1, 3), [[6], [7], [8]]) - # check_idx(slice(None, None), [[6, 7, 8]], [[6], [7], [8]], slice(1, 3)) - check_idx([6, 7, 8], [6, 7, 8], 7, slice(2, 7)) - # check_idx(Ellipsis, 1, [[[6]]], [[[0]]], slice(1, 4, 2)) - # check_idx(slice(2, 4, 2), 1, Ellipsis, 0, [[[7]], [[3]]]) - # check_idx(slice(7, 10, 2), 3, Ellipsis, [[[5, 0]]], [[[3, 1]]]) - # check_idx(slice(7, 9, 1), [[[4]]], 8, Ellipsis, [[[6]], [[9]]]) - - def test_super_random(): - from random import randint - from random import random as rand - - def true_or_false(ture_prob): - return rand() < ture_prob - - def random_list(limit, size, one_base=False): - if one_base: - return [randint(1, limit) for _ in range(0, size)] - else: - # 0 <= x < limit - return [randint(0, limit - 1) for _ in range(0, size)] - - def generate_random_int_matrix(limit, shape): - if len(shape) == 0: - return [] - if len(shape) == 1: - return random_list(limit, shape[0]) - return [ - generate_random_int_matrix(limit, shape[1:]) for _ in range(0, shape[0]) - ] - - def generate_boardcast_shape(limit_shape): - # new_len = randint(1, len(limit_shape)) - new_len = len(limit_shape) - return [(1 if true_or_false(0.3) else i) for i in limit_shape[:new_len]] - - def g_slice(size): - start = randint(0, size) - if start == size: - start = None - end = randint(1 if start is None else start + 1, size + 1) - if end == size + 1: - end = None - return slice(start, end, 1 if true_or_false(0.3) else 2) - - def g_int(size): - return randint(0, size - 1) - - def g_inedx(limit_shape): - new_len = randint(len(limit_shape) // 2, len(limit_shape)) - output = [] - # [5] -> (0 ~ 4) - - cur_dim, cur_new = len(limit_shape), 0 - use_int_array = False - i = 0 - while len(output) < new_len: - flag = rand() - single_idx = None - old_dim, old_new, old_use_int_array = cur_dim, cur_new, use_int_array - if flag < 0.3: - single_idx = g_int(limit_shape[i]) - cur_dim -= 1 - elif flag < 0.5: - single_idx = g_slice(limit_shape[i]) - elif flag < 0.9: - if not use_int_array: - board_cast_dim = random_list(10, 1, one_base=True) - cur_dim += len(board_cast_dim) - use_int_array = True - cur_dim -= 1 - integer_array_shape = generate_boardcast_shape(board_cast_dim) - single_idx = generate_random_int_matrix( - limit_shape[i], integer_array_shape - ) - else: - cur_dim += 1 - cur_new += 1 - single_idx = np.newaxis - # MAX_DIM < 7 - if cur_dim > 7 or cur_new + len(limit_shape) > 7: - cur_dim, cur_new, use_int_array = ( - old_dim, - old_new, - old_use_int_array, - ) - continue - if not single_idx is np.newaxis: - i += 1 - output.append(single_idx) - # print('[cur_dim]: ', cur_dim, output) - - if cur_dim < 7 and rand() < 0.3 and new_len < len(limit_shape): - output.insert(randint(0, len(output)), Ellipsis) - - return tuple(output) - - for i in range(0, 17): - idx = g_inedx(a_shape) - # print('[task {}] {}'.format(i, idx)) - check_idx(*idx) - - test_one_dim() - test_multi_dim() - test_slice() - test_new_axis() - test_ellipsis() - test_integer_array() - test_hard() - test_super_random() diff --git a/python_module/test/unit/core/test_recoverable.py b/python_module/test/unit/core/test_recoverable.py deleted file mode 100644 index 86735d1e..00000000 --- a/python_module/test/unit/core/test_recoverable.py +++ /dev/null @@ -1,40 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np -import pytest - -import megengine as mge -import megengine._internal as mgb -from megengine.core import tensor -from megengine.test import assertTensorClose - - -def test_recoverable(): - a = tensor() - b = tensor() - a_np = np.random.random((4, 3)).astype("float32") - b_np = np.random.random((3, 7)).astype("float32") - a.set_value(a_np) - b.set_value(b_np) - - # Do some normal computation. - a2 = a * 2 - ab = a @ b - - # Raise a computation error. - with pytest.raises(mgb.MegBrainError): - _ = a * b - - # Variable a2 and ab should be still usable after error happened. - assertTensorClose(a2.numpy(), a_np * 2) - assertTensorClose(ab.numpy(), a_np @ b_np) - - # Should allow computation as well. - ab2 = ab ** 2 - assertTensorClose(ab2.numpy(), (a_np @ b_np) ** 2) diff --git a/python_module/test/unit/core/test_release_memory.py b/python_module/test/unit/core/test_release_memory.py deleted file mode 100644 index cf2ef0ac..00000000 --- a/python_module/test/unit/core/test_release_memory.py +++ /dev/null @@ -1,56 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import time - -import numpy as np -import pytest -from helpers import has_gpu - -import megengine as mge -import megengine.functional as F -from megengine.optimizer import SGD - - -@pytest.mark.skip -@pytest.mark.slow -def test_release_memory(): - mnist_datasets = load_mnist_datasets() - data_train, label_train = mnist_datasets["train"] - - batch_size = 15000 - data_shape = (batch_size, 1, 28, 28) - label_shape = (batch_size,) - - data = nn.Input("data", shape=data_shape, dtype=np.float32) - label = nn.Input( - "label", shape=label_shape, dtype=np.int32, value=np.zeros(label_shape) - ) - - net = MnistNet() - opt = SGD(net.parameters(), lr=0.01) - - pred = F.softmax(net(data)) - loss = F.cross_entropy(pred, label) - - opt.zero_grad() - opt.backward(loss) - add_updates = opt.step() - - mge.graph._default_graph.get_default().clear_device_memory() - - f = mge.graph.compile(loss, add_updates) - - for _ in range(3): - train_loss = 0.0 - for i in range(0, data_train.shape[0], batch_size): - opt.zero_grad() - data = data_train[i : i + batch_size, :, :, :] - label = label_train[i : i + batch_size] - loss = f(data=data, label=label)[0] - train_loss += loss[0] diff --git a/python_module/test/unit/core/test_reshape_broadcast.py b/python_module/test/unit/core/test_reshape_broadcast.py deleted file mode 100644 index 87469906..00000000 --- a/python_module/test/unit/core/test_reshape_broadcast.py +++ /dev/null @@ -1,108 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np -import pytest - -from megengine import tensor -from megengine.test import assertTensorClose - - -def test_reshape_tuple(): - inp = tensor(np.arange(1, 17, dtype=np.int32).reshape(4, 4)) - out = tensor(np.arange(100, 116, dtype=np.int32).reshape(1, 16)) - out = out.reshape(inp.shape) - - assertTensorClose(out.numpy(), np.arange(100, 116, dtype=np.int32).reshape(4, 4)) - - -def test_reshape_asterisk(): - inp = tensor(np.arange(1, 17, dtype=np.int32).reshape(4, 4)) - out = tensor(np.arange(100, 116, dtype=np.int32).reshape(1, 16)) - out = out.reshape(*inp.shape) - - assertTensorClose(out.numpy(), np.arange(100, 116, dtype=np.int32).reshape(4, 4)) - - -def test_reshape_shapeof(): - inp = tensor(np.arange(1, 17, dtype=np.int32).reshape(4, 4)) - out = tensor(np.arange(100, 116, dtype=np.int32).reshape(1, 16)) - out = out.reshape(inp.shapeof()) - - assertTensorClose(out.numpy(), np.arange(100, 116, dtype=np.int32).reshape(4, 4)) - - -def test_reshape_tensor(): - out = tensor(np.arange(100, 116, dtype=np.int32).reshape(1, 16)) - out = out.reshape(tensor([4, 4])) - - assertTensorClose(out.numpy(), np.arange(100, 116, dtype=np.int32).reshape(4, 4)) - - -def test_reshape_tensor_fused(): - out = tensor(np.arange(100, 116, dtype=np.int32).reshape(1, 16)) - out = out.reshape(tensor([4, 4]), 1) - - assertTensorClose(out.numpy(), np.arange(100, 116, dtype=np.int32).reshape(4, 4, 1)) - - -def test_reshape_fused(): - out = tensor(np.arange(100, 116, dtype=np.int32).reshape(1, 16)) - out = out.reshape(tensor(2), 2, tensor(4), 1) - - assertTensorClose( - out.numpy(), np.arange(100, 116, dtype=np.int32).reshape(2, 2, 4, 1) - ) - - -def test_reshape_wrong_tuple(): - out = tensor(np.arange(100, 116, dtype=np.int32).reshape(1, 16)) - with pytest.raises(ValueError): - out = out.reshape((2, 2), 4) - - -def test_reshape_wrong_tuple2(): - out = tensor(np.arange(100, 116, dtype=np.int32).reshape(1, 16)) - with pytest.raises(AssertionError): - out = out.reshape(4, (2, 2)) - - -def test_broadcast_tuple(): - inp = tensor(np.arange(1, 17, dtype=np.int32).reshape(4, 4)) - out = tensor(np.arange(100, 104, dtype=np.int32).reshape(1, 4)) - - out = out.broadcast(inp.shape) - - tmp = np.array([[100, 101, 102, 103]], dtype=np.int32) - out2 = np.repeat(tmp, 4, axis=0) - - assertTensorClose(out.numpy(), out2) - - -def test_broadcast_asterisk(): - inp = tensor(np.arange(1, 17, dtype=np.int32).reshape(4, 4)) - out = tensor(np.arange(100, 104, dtype=np.int32).reshape(1, 4)) - - out = out.broadcast(*inp.shape) - - tmp = np.array([[100, 101, 102, 103]], dtype=np.int32) - out2 = np.repeat(tmp, 4, axis=0) - - assertTensorClose(out.numpy(), out2) - - -def test_broadcast_shapeof(): - inp = tensor(np.arange(1, 17, dtype=np.int32).reshape(4, 4)) - out = tensor(np.arange(100, 104, dtype=np.int32).reshape(1, 4)) - - out = out.broadcast(inp.shapeof()) - - tmp = np.array([[100, 101, 102, 103]], dtype=np.int32) - out2 = np.repeat(tmp, 4, axis=0) - - assertTensorClose(out.numpy(), out2) diff --git a/python_module/test/unit/core/test_serialization.py b/python_module/test/unit/core/test_serialization.py deleted file mode 100644 index 85c30eb0..00000000 --- a/python_module/test/unit/core/test_serialization.py +++ /dev/null @@ -1,47 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import pickle -from tempfile import TemporaryFile - -import numpy as np - -from megengine.core import Buffer, Parameter, tensor -from megengine.test import assertTensorClose - - -def test_tensor_serialization(): - def tensor_eq(a, b): - assert a.dtype == b.dtype - assert a.device == b.device - assert a.requires_grad == b.requires_grad - assertTensorClose(a, b) - - with TemporaryFile() as f: - data = np.random.randint(low=0, high=7, size=[233]) - a = tensor(data, device="xpux", dtype=np.int32) - pickle.dump(a, f) - f.seek(0) - b = pickle.load(f) - tensor_eq(a, b) - - with TemporaryFile() as f: - a = Parameter(np.random.random(size=(233, 2)).astype(np.float32)) - pickle.dump(a, f) - f.seek(0) - b = pickle.load(f) - assert isinstance(b, Parameter) - tensor_eq(a, b) - - with TemporaryFile() as f: - a = Buffer(np.random.random(size=(2, 233)).astype(np.float32)) - pickle.dump(a, f) - f.seek(0) - b = pickle.load(f) - assert isinstance(b, Buffer) - tensor_eq(a, b) diff --git a/python_module/test/unit/core/test_tensor.py b/python_module/test/unit/core/test_tensor.py deleted file mode 100644 index 3ac0a4c8..00000000 --- a/python_module/test/unit/core/test_tensor.py +++ /dev/null @@ -1,91 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np -import pytest - -import megengine as mge -import megengine._internal as mgb - - -def test_wrong_dtype(): - with pytest.raises(TypeError): - mge.tensor(np.zeros((5, 5), dtype=np.float64)) - - with pytest.raises(TypeError): - mge.Parameter(np.zeros((5, 5), dtype=np.int64)) - - -def test_tensor_routine(): - mge.tensor(np.zeros((1, 2), dtype=np.int32)) - - mge.tensor([1]) - - mge.tensor(1.5) - - -def test_tensor_set_dtype(): - def check_dtype_value(tensor, dtype_scale, value): - if mgb.dtype.is_quantize(tensor.dtype): - if np.abs(mgb.dtype.get_scale(tensor.dtype) - dtype_scale) > 1e-5: - raise AssertionError( - "compare scale failed expect {} got {}".format( - dtype_scale, mgb.dtype.get_scale(tensor.dtype) - ) - ) - if np.abs(tensor.numpy()[0][0] - value) > 1e-5: - raise AssertionError( - "compare value failed expect {} got {}".format( - tensor.numpy()[0][0], value - ) - ) - - t = mge.Parameter(np.ones((3, 4), dtype="float32")) - t.dtype = mgb.dtype.qint8(0.1) - check_dtype_value(t, 0.1, 10) - - t = mge.Parameter(np.ones((3, 4), dtype=mgb.dtype.qint8(1))) - t.dtype = mgb.dtype.qint8(0.3) - check_dtype_value(t, 0.3, 3) - - t = mge.Buffer(np.ones((3, 4), dtype="float32")) - t.dtype = mgb.dtype.qint8(0.1) - check_dtype_value(t, 0.1, 10) - - t = mge.Buffer(np.ones((3, 4), dtype=mgb.dtype.qint8(1))) - t.dtype = mgb.dtype.qint8(0.3) - check_dtype_value(t, 0.3, 3) - - t = mge.Buffer(np.ones((3, 4), dtype="float32")) - s = t + 1 - s.dtype = mgb.dtype.qint8(0.2) - check_dtype_value(s, 0.2, 10) - - t.dtype = mgb.dtype.qint8(0.3) - s = t + 1 - s.dtype = mgb.dtype.qint8(0.1) - check_dtype_value(s, 0.1, 18) - s.dtype = "float32" - check_dtype_value(s, 0, 1.8) - - -def test_tensor_name(): - p = mge.Parameter(np.ones((3, 4), dtype="float32")) - assert "shared" in p.name - with pytest.raises(ValueError): - p.name = "Parameter0" - - b = mge.Buffer(np.ones((3, 4), dtype="float32")) - assert "shared" in b.name - with pytest.raises(ValueError): - b.name = "Buffer0" - - s = b + 1 - assert "ADD" in s.name - s.name = "WeightAdd1" - assert s.name == "WeightAdd1" diff --git a/python_module/test/unit/core/test_zeros_ones.py b/python_module/test/unit/core/test_zeros_ones.py deleted file mode 100644 index 4d14653b..00000000 --- a/python_module/test/unit/core/test_zeros_ones.py +++ /dev/null @@ -1,35 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np -import pytest - -import megengine as mge -from megengine.test import assertTensorClose - - -def test_zeros(): - assertTensorClose( - mge.zeros((2, 2), dtype=np.int32).numpy(), np.zeros((2, 2), dtype=np.int32) - ) - - assertTensorClose( - mge.zeros(mge.tensor([2, 2], dtype=np.int32), dtype=np.int32).numpy(), - np.zeros((2, 2), dtype=np.int32), - ) - - -def test_ones(): - assertTensorClose( - mge.ones((2, 2), dtype=np.int32).numpy(), np.ones((2, 2), dtype=np.int32) - ) - - assertTensorClose( - mge.ones(mge.tensor([2, 2], dtype=np.int32), dtype=np.int32).numpy(), - np.ones((2, 2), dtype=np.int32), - ) diff --git a/python_module/test/unit/data/__init__.py b/python_module/test/unit/data/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/python_module/test/unit/data/test_dataloader.py b/python_module/test/unit/data/test_dataloader.py deleted file mode 100644 index 6bb0f3e3..00000000 --- a/python_module/test/unit/data/test_dataloader.py +++ /dev/null @@ -1,183 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import os -import time - -import numpy as np -import pytest - -from megengine.data.collator import Collator -from megengine.data.dataloader import DataLoader -from megengine.data.dataset import ArrayDataset -from megengine.data.sampler import RandomSampler, SequentialSampler -from megengine.data.transform import PseudoTransform, Transform - - -def init_dataset(): - sample_num = 100 - rand_data = np.random.randint(0, 255, size=(sample_num, 1, 32, 32), dtype=np.uint8) - label = np.random.randint(0, 10, size=(sample_num,), dtype=int) - dataset = ArrayDataset(rand_data, label) - return dataset - - -def test_dataloader_init(): - dataset = init_dataset() - with pytest.raises(ValueError): - dataloader = DataLoader(dataset, num_workers=2, divide=True) - with pytest.raises(ValueError): - dataloader = DataLoader(dataset, num_workers=-1) - with pytest.raises(ValueError): - dataloader = DataLoader(dataset, timeout=-1) - with pytest.raises(ValueError): - dataloader = DataLoader(dataset, num_workers=0, divide=True) - - dataloader = DataLoader(dataset) - assert isinstance(dataloader.sampler, SequentialSampler) - assert isinstance(dataloader.transform, PseudoTransform) - assert isinstance(dataloader.collator, Collator) - - dataloader = DataLoader( - dataset, sampler=RandomSampler(dataset, batch_size=6, drop_last=False) - ) - assert len(dataloader) == 17 - dataloader = DataLoader( - dataset, sampler=RandomSampler(dataset, batch_size=6, drop_last=True) - ) - assert len(dataloader) == 16 - - -def test_dataloader_serial(): - dataset = init_dataset() - dataloader = DataLoader( - dataset, sampler=RandomSampler(dataset, batch_size=4, drop_last=False) - ) - for (data, label) in dataloader: - assert data.shape == (4, 1, 32, 32) - assert label.shape == (4,) - - -def test_dataloader_parallel(): - # set max shared memory to 100M - os.environ["MGE_PLASMA_MEMORY"] = "100000000" - - dataset = init_dataset() - dataloader = DataLoader( - dataset, - sampler=RandomSampler(dataset, batch_size=4, drop_last=False), - num_workers=2, - divide=False, - ) - for (data, label) in dataloader: - assert data.shape == (4, 1, 32, 32) - assert label.shape == (4,) - - dataloader = DataLoader( - dataset, - sampler=RandomSampler(dataset, batch_size=4, drop_last=False), - num_workers=2, - divide=True, - ) - for (data, label) in dataloader: - assert data.shape == (4, 1, 32, 32) - assert label.shape == (4,) - - -def test_dataloader_parallel_timeout(): - dataset = init_dataset() - - class TimeoutTransform(Transform): - def __init__(self): - pass - - def apply(self, input): - time.sleep(10) - return input - - dataloader = DataLoader( - dataset, - sampler=RandomSampler(dataset, batch_size=4, drop_last=False), - transform=TimeoutTransform(), - num_workers=2, - timeout=2, - ) - with pytest.raises(RuntimeError, match=r".*timeout.*"): - data_iter = iter(dataloader) - batch_data = next(data_iter) - - -def test_dataloader_parallel_worker_exception(): - dataset = init_dataset() - - class FakeErrorTransform(Transform): - def __init__(self): - pass - - def apply(self, input): - y = x + 1 - return input - - dataloader = DataLoader( - dataset, - sampler=RandomSampler(dataset, batch_size=4, drop_last=False), - transform=FakeErrorTransform(), - num_workers=2, - ) - with pytest.raises(RuntimeError, match=r"worker.*died"): - data_iter = iter(dataloader) - batch_data = next(data_iter) - - -def _multi_instances_parallel_dataloader_worker(): - dataset = init_dataset() - - for divide_flag in [True, False]: - train_dataloader = DataLoader( - dataset, - sampler=RandomSampler(dataset, batch_size=4, drop_last=False), - num_workers=2, - divide=divide_flag, - ) - val_dataloader = DataLoader( - dataset, - sampler=RandomSampler(dataset, batch_size=10, drop_last=False), - num_workers=2, - divide=divide_flag, - ) - for idx, (data, label) in enumerate(train_dataloader): - assert data.shape == (4, 1, 32, 32) - assert label.shape == (4,) - if idx % 5 == 0: - for val_data, val_label in val_dataloader: - assert val_data.shape == (10, 1, 32, 32) - assert val_label.shape == (10,) - - -def test_dataloader_parallel_multi_instances(): - # set max shared memory to 100M - os.environ["MGE_PLASMA_MEMORY"] = "100000000" - - _multi_instances_parallel_dataloader_worker() - - -def test_dataloader_parallel_multi_instances_multiprocessing(): - # set max shared memory to 100M - os.environ["MGE_PLASMA_MEMORY"] = "100000000" - - import multiprocessing as mp - - # mp.set_start_method("spawn") - processes = [] - for i in range(4): - p = mp.Process(target=_multi_instances_parallel_dataloader_worker) - p.start() - processes.append(p) - - for p in processes: - p.join() diff --git a/python_module/test/unit/data/test_dataset.py b/python_module/test/unit/data/test_dataset.py deleted file mode 100644 index d68d3784..00000000 --- a/python_module/test/unit/data/test_dataset.py +++ /dev/null @@ -1,43 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import os -import sys - -import numpy as np -import pytest - -from megengine.data.dataset import ArrayDataset, Dataset, MapDataset, StreamDataset - - -def test_abstract_cls(): - with pytest.raises(TypeError): - Dataset() - with pytest.raises(TypeError): - MapDataset() - with pytest.raises(TypeError): - StreamDataset() - - -def test_array_dataset(): - size = (10,) - data_shape = (3, 256, 256) - label_shape = (1,) - data = np.random.randint(0, 255, size + data_shape) - label = np.random.randint(0, 9, size + label_shape) - dataset = ArrayDataset(data, label) - assert dataset[0][0].shape == data_shape - assert dataset[0][1].shape == label_shape - assert len(dataset) == size[0] - - -def test_array_dataset_dim_error(): - data = np.random.randint(0, 255, (10, 3, 256, 256)) - label = np.random.randint(0, 9, (1,)) - with pytest.raises(ValueError): - ArrayDataset(data, label) diff --git a/python_module/test/unit/data/test_sampler.py b/python_module/test/unit/data/test_sampler.py deleted file mode 100644 index bc399e86..00000000 --- a/python_module/test/unit/data/test_sampler.py +++ /dev/null @@ -1,81 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import copy -import os -import sys - -import numpy as np -import pytest - -from megengine.data.dataset import ArrayDataset -from megengine.data.sampler import RandomSampler, ReplacementSampler, SequentialSampler - - -def test_sequential_sampler(): - indices = list(range(100)) - sampler = SequentialSampler(ArrayDataset(indices)) - assert indices == list(each[0] for each in sampler) - - -def test_RandomSampler(): - indices = list(range(20)) - indices_copy = copy.deepcopy(indices) - sampler = RandomSampler(ArrayDataset(indices_copy)) - sample_indices = sampler - assert indices != list(each[0] for each in sample_indices) - assert indices == sorted(list(each[0] for each in sample_indices)) - - -def test_random_sampler_seed(): - seed = [0, 1] - indices = list(range(20)) - indices_copy1 = copy.deepcopy(indices) - indices_copy2 = copy.deepcopy(indices) - indices_copy3 = copy.deepcopy(indices) - sampler1 = RandomSampler(ArrayDataset(indices_copy1), seed=seed[0]) - sampler2 = RandomSampler(ArrayDataset(indices_copy2), seed=seed[0]) - sampler3 = RandomSampler(ArrayDataset(indices_copy3), seed=seed[1]) - assert indices != list(each[0] for each in sampler1) - assert indices != list(each[0] for each in sampler2) - assert indices != list(each[0] for each in sampler3) - assert indices == sorted(list(each[0] for each in sampler1)) - assert indices == sorted(list(each[0] for each in sampler2)) - assert indices == sorted(list(each[0] for each in sampler3)) - assert list(each[0] for each in sampler1) == list(each[0] for each in sampler2) - assert list(each[0] for each in sampler1) != list(each[0] for each in sampler3) - - -def test_ReplacementSampler(): - num_samples = 30 - indices = list(range(20)) - weights = list(range(20)) - sampler = ReplacementSampler( - ArrayDataset(indices), num_samples=num_samples, weights=weights - ) - assert len(list(each[0] for each in sampler)) == num_samples - - -def test_sampler_drop_last_false(): - batch_size = 5 - drop_last = False - indices = list(range(24)) - sampler = SequentialSampler( - ArrayDataset(indices), batch_size=batch_size, drop_last=drop_last - ) - assert len([each for each in sampler]) == len(sampler) - - -def test_sampler_drop_last_true(): - batch_size = 5 - drop_last = True - indices = list(range(24)) - sampler = SequentialSampler( - ArrayDataset(indices), batch_size=batch_size, drop_last=drop_last - ) - assert len([each for each in sampler]) == len(sampler) diff --git a/python_module/test/unit/data/test_transform.py b/python_module/test/unit/data/test_transform.py deleted file mode 100644 index d438d8b5..00000000 --- a/python_module/test/unit/data/test_transform.py +++ /dev/null @@ -1,108 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np - -from megengine.data.transform import * - -data_shape = (100, 100, 3) -label_shape = (4,) -ToMode_target_shape = (3, 100, 100) -CenterCrop_size = (90, 70) -CenterCrop_target_shape = CenterCrop_size + (3,) -RandomResizedCrop_size = (50, 50) -RandomResizedCrop_target_shape = RandomResizedCrop_size + (3,) - - -def generate_data(): - return [ - ( - (np.random.rand(*data_shape) * 255).astype(np.uint8), - np.random.randint(10, size=label_shape), - ) - for _ in range(*label_shape) - ] - - -def test_ToMode(): - t = ToMode(mode="CHW") - aug_data = t.apply_batch(generate_data()) - aug_data_shape = [(a.shape, b.shape) for a, b in aug_data] - target_shape = [(ToMode_target_shape, label_shape)] * 4 - assert aug_data_shape == target_shape - - -def test_CenterCrop(): - t = CenterCrop(output_size=CenterCrop_size) - aug_data = t.apply_batch(generate_data()) - aug_data_shape = [(a.shape, b.shape) for a, b in aug_data] - target_shape = [(CenterCrop_target_shape, label_shape)] * 4 - assert aug_data_shape == target_shape - - -def test_ColorJitter(): - t = ColorJitter() - aug_data = t.apply_batch(generate_data()) - aug_data_shape = [(a.shape, b.shape) for a, b in aug_data] - target_shape = [(data_shape, label_shape)] * 4 - assert aug_data_shape == target_shape - - -def test_RandomHorizontalFlip(): - t = RandomHorizontalFlip(prob=1) - aug_data = t.apply_batch(generate_data()) - aug_data_shape = [(a.shape, b.shape) for a, b in aug_data] - target_shape = [(data_shape, label_shape)] * 4 - assert aug_data_shape == target_shape - - -def test_RandomVerticalFlip(): - t = RandomVerticalFlip(prob=1) - aug_data = t.apply_batch(generate_data()) - aug_data_shape = [(a.shape, b.shape) for a, b in aug_data] - target_shape = [(data_shape, label_shape)] * 4 - assert aug_data_shape == target_shape - - -def test_RandomResizedCrop(): - t = RandomResizedCrop(output_size=RandomResizedCrop_size) - aug_data = t.apply_batch(generate_data()) - aug_data_shape = [(a.shape, b.shape) for a, b in aug_data] - target_shape = [(RandomResizedCrop_target_shape, label_shape)] * 4 - assert aug_data_shape == target_shape - - -def test_Normalize(): - t = Normalize() - aug_data = t.apply_batch(generate_data()) - aug_data_shape = [(a.shape, b.shape) for a, b in aug_data] - target_shape = [(data_shape, label_shape)] * 4 - assert aug_data_shape == target_shape - - -def test_RandomCrop(): - t = RandomCrop((150, 120), padding_size=10, padding_value=[1, 2, 3]) - aug_data = t.apply_batch(generate_data()) - aug_data_shape = [(a.shape, b.shape) for a, b in aug_data] - target_shape = [((150, 120, 3), label_shape)] * 4 - assert aug_data_shape == target_shape - - -def test_Compose(): - t = Compose( - [ - CenterCrop(output_size=CenterCrop_size), - RandomHorizontalFlip(prob=1), - ToMode(mode="CHW"), - ] - ) - aug_data = t.apply_batch(generate_data()) - aug_data_shape = [(a.shape, b.shape) for a, b in aug_data] - print(aug_data_shape) - target_shape = [((3, 90, 70), label_shape)] * 4 - assert aug_data_shape == target_shape diff --git a/python_module/test/unit/distributed/test_functional.py b/python_module/test/unit/distributed/test_functional.py deleted file mode 100644 index 8bbaf7e0..00000000 --- a/python_module/test/unit/distributed/test_functional.py +++ /dev/null @@ -1,467 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -import multiprocessing as mp -import platform - -import numpy as np -import pytest - -import megengine as mge -import megengine.distributed as dist -from megengine.core import Parameter, tensor - - -def _init_process_group_wrapper(world_size, rank, dev, backend, q): - if rank == 0: - dist.init_process_group("localhost", 0, world_size, rank, dev, backend) - q.put(dist.get_master_port()) - else: - port = q.get() - dist.init_process_group("localhost", port, world_size, rank, dev, backend) - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -@pytest.mark.isolated_distributed -def test_reduce_sum(): - world_size = 2 - - def worker(rank, data, backend, expect, port_queue): - if mge.get_device_count("gpu") < world_size: - return - _init_process_group_wrapper(world_size, rank, rank, backend, port_queue) - inp = tensor(data) - output = dist.functional.reduce_sum(inp) - if rank == 0: - assert np.allclose(output.numpy(), expect) - else: - assert np.allclose(output.numpy(), 0) - - def check(shape, backend): - port_queue = mp.Queue() - x = np.random.rand(*shape).astype("float32") - y = np.random.rand(*shape).astype("float32") - z = x + y - p0 = mp.Process(target=worker, args=(0, x, backend, z, port_queue)) - p1 = mp.Process(target=worker, args=(1, y, backend, None, port_queue)) - - p0.start() - p1.start() - - p0.join(10) - p1.join(10) - - assert p0.exitcode == 0 and p1.exitcode == 0 - - for shape in [(2, 3), (8, 10), (99, 77)]: - for backend in ["nccl"]: - check(shape, backend) - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -@pytest.mark.isolated_distributed -def test_gather(): - world_size = 2 - - def worker(rank, data, backend, expect, port_queue): - if mge.get_device_count("gpu") < world_size: - return - _init_process_group_wrapper(world_size, rank, rank, backend, port_queue) - inp = tensor(data) - output = dist.functional.gather(inp) - if rank == 0: - assert np.allclose(output.numpy(), expect) - else: - assert np.allclose(output.numpy(), 0) - - def check(shape, backend): - port_queue = mp.Queue() - x = np.random.rand(*shape).astype("float32") - y = np.random.rand(*shape).astype("float32") - z = np.concatenate((x, y)) - p0 = mp.Process(target=worker, args=(0, x, backend, z, port_queue)) - p1 = mp.Process(target=worker, args=(1, y, backend, None, port_queue)) - - p0.start() - p1.start() - - p0.join(10) - p1.join(10) - - assert p0.exitcode == 0 and p1.exitcode == 0 - - for shape in [(2, 3), (8, 10), (99, 77)]: - for backend in ["nccl"]: - check(shape, backend) - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -@pytest.mark.isolated_distributed -def test_broadcast(): - world_size = 2 - - def worker(rank, data, backend, expect, port_queue): - if mge.get_device_count("gpu") < world_size: - return - _init_process_group_wrapper(world_size, rank, rank, backend, port_queue) - inp = tensor(data) - output = dist.functional.broadcast(inp) - assert np.allclose(output.numpy(), expect) - - def check(shape, backend): - port_queue = mp.Queue() - x = np.random.rand(*shape).astype("float32") - y = x + 1 - p0 = mp.Process(target=worker, args=(0, x, backend, x, port_queue)) - p1 = mp.Process(target=worker, args=(1, y, backend, x, port_queue)) - - p0.start() - p1.start() - - p0.join(10) - p1.join(10) - - assert p0.exitcode == 0 and p1.exitcode == 0 - - for shape in [(2, 3), (8, 10), (99, 77)]: - for backend in ["nccl"]: - check(shape, backend) - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -@pytest.mark.isolated_distributed -def test_scatter(): - world_size = 2 - - def worker(rank, data, backend, expect, port_queue): - if mge.get_device_count("gpu") < world_size: - return - _init_process_group_wrapper(world_size, rank, rank, backend, port_queue) - inp = tensor(data) - output = dist.functional.scatter(inp) - assert np.allclose(output.numpy(), expect) - - def check(shape, backend): - port_queue = mp.Queue() - x = np.random.rand(*shape).astype("float32") - y = x + 1 - p0 = mp.Process( - target=worker, args=(0, x, backend, x[: shape[0] // 2], port_queue) - ) - p1 = mp.Process( - target=worker, args=(1, y, backend, x[shape[0] // 2 :], port_queue) - ) - - p0.start() - p1.start() - - p0.join(10) - p1.join(10) - - assert p0.exitcode == 0 and p1.exitcode == 0 - - for shape in [(2, 3), (8, 10), (100, 77)]: - for backend in ["nccl"]: - check(shape, backend) - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -@pytest.mark.isolated_distributed -def test_all_to_all(): - world_size = 2 - - def worker(rank, data, backend, expect, port_queue): - if mge.get_device_count("gpu") < world_size: - return - _init_process_group_wrapper(world_size, rank, rank, backend, port_queue) - inp = tensor(data) - output = dist.functional.all_to_all(inp) - assert np.allclose(output.numpy(), expect) - - def check(shape, backend): - port_queue = mp.Queue() - x = np.random.rand(*shape).astype("float32") - y = np.random.rand(*shape).astype("float32") - a = np.concatenate((x[: shape[0] // 2], y[: shape[0] // 2])) - b = np.concatenate((x[shape[0] // 2 :], y[shape[0] // 2 :])) - p0 = mp.Process(target=worker, args=(0, x, backend, a, port_queue)) - p1 = mp.Process(target=worker, args=(1, y, backend, b, port_queue)) - - p0.start() - p1.start() - - p0.join(10) - p1.join(10) - - assert p0.exitcode == 0 and p1.exitcode == 0 - - for shape in [(2, 3), (8, 10), (100, 77)]: - for backend in ["nccl"]: - check(shape, backend) - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -@pytest.mark.isolated_distributed -def test_all_gather(): - world_size = 2 - - def worker(rank, data, backend, expect, port_queue): - if mge.get_device_count("gpu") < world_size: - return - _init_process_group_wrapper(world_size, rank, rank, backend, port_queue) - inp = tensor(data) - output = dist.functional.all_gather(inp) - assert np.allclose(output.numpy(), expect) - - def check(shape, backend): - port_queue = mp.Queue() - x = np.random.rand(*shape).astype("float32") - y = np.random.rand(*shape).astype("float32") - z = np.concatenate((x, y)) - p0 = mp.Process(target=worker, args=(0, x, backend, z, port_queue)) - p1 = mp.Process(target=worker, args=(1, y, backend, z, port_queue)) - - p0.start() - p1.start() - - p0.join(10) - p1.join(10) - - assert p0.exitcode == 0 and p1.exitcode == 0 - - for shape in [(2, 3), (8, 10), (99, 77)]: - for backend in ["nccl"]: - check(shape, backend) - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -@pytest.mark.isolated_distributed -def test_reduce_scatter_sum(): - world_size = 2 - - def worker(rank, data, backend, expect, port_queue): - if mge.get_device_count("gpu") < world_size: - return - _init_process_group_wrapper(world_size, rank, rank, backend, port_queue) - inp = tensor(data) - output = dist.functional.reduce_scatter_sum(inp) - assert np.allclose(output.numpy(), expect) - - def check(shape, backend): - port_queue = mp.Queue() - x = np.random.rand(*shape).astype("float32") - y = np.random.rand(*shape).astype("float32") - z = x + y - p0 = mp.Process( - target=worker, args=(0, x, backend, z[: shape[0] // 2], port_queue) - ) - p1 = mp.Process( - target=worker, args=(1, y, backend, z[shape[0] // 2 :], port_queue) - ) - - p0.start() - p1.start() - - p0.join(10) - p1.join(10) - - assert p0.exitcode == 0 and p1.exitcode == 0 - - for shape in [(2, 4), (8, 10), (88, 44)]: - for backend in ["nccl"]: - check(shape, backend) - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -@pytest.mark.isolated_distributed -def test_all_reduce_sum(): - world_size = 2 - - def worker(rank, data, backend, expect, port_queue): - if mge.get_device_count("gpu") < world_size: - return - _init_process_group_wrapper(world_size, rank, rank, backend, port_queue) - inp = tensor(data) - output = dist.functional.all_reduce_sum(inp) - assert np.allclose(output.numpy(), expect) - - def check(shape, backend): - port_queue = mp.Queue() - x = np.random.rand(*shape).astype("float32") - y = np.random.rand(*shape).astype("float32") - z = x + y - p0 = mp.Process(target=worker, args=(0, x, backend, z, port_queue)) - p1 = mp.Process(target=worker, args=(1, y, backend, z, port_queue)) - - p0.start() - p1.start() - - p0.join(10) - p1.join(10) - - assert p0.exitcode == 0 and p1.exitcode == 0 - - for shape in [(2, 3), (8, 10), (99, 77)]: - for backend in ["nccl"]: - check(shape, backend) - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -@pytest.mark.isolated_distributed -def test_all_reduce_max(): - world_size = 2 - - def worker(rank, data, backend, expect, port_queue): - if mge.get_device_count("gpu") < world_size: - return - _init_process_group_wrapper(world_size, rank, rank, backend, port_queue) - inp = tensor(data) - output = dist.functional.all_reduce_max(inp) - assert np.allclose(output.numpy(), expect) - - def check(shape, backend): - port_queue = mp.Queue() - x = np.random.rand(*shape).astype("float32") - y = np.random.rand(*shape).astype("float32") - z = np.maximum(x, y) - p0 = mp.Process(target=worker, args=(0, x, backend, z, port_queue)) - p1 = mp.Process(target=worker, args=(1, y, backend, z, port_queue)) - - p0.start() - p1.start() - - p0.join(10) - p1.join(10) - - assert p0.exitcode == 0 and p1.exitcode == 0 - - for shape in [(2, 3), (8, 10), (99, 77)]: - for backend in ["nccl"]: - check(shape, backend) - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -@pytest.mark.isolated_distributed -def test_all_reduce_min(): - world_size = 2 - - def worker(rank, data, backend, expect, port_queue): - if mge.get_device_count("gpu") < world_size: - return - _init_process_group_wrapper(world_size, rank, rank, backend, port_queue) - inp = tensor(data) - output = dist.functional.all_reduce_min(inp) - assert np.allclose(output.numpy(), expect) - - def check(shape, backend): - port_queue = mp.Queue() - x = np.random.rand(*shape).astype("float32") - y = np.random.rand(*shape).astype("float32") - z = np.minimum(x, y) - p0 = mp.Process(target=worker, args=(0, x, backend, z, port_queue)) - p1 = mp.Process(target=worker, args=(1, y, backend, z, port_queue)) - - p0.start() - p1.start() - - p0.join(10) - p1.join(10) - - assert p0.exitcode == 0 and p1.exitcode == 0 - - for shape in [(2, 3), (8, 10), (99, 77)]: - for backend in ["nccl"]: - check(shape, backend) - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -@pytest.mark.isolated_distributed -def test_bcast_param(): - world_size = 2 - - def worker(rank, data, backend, expect, port_queue): - if mge.get_device_count("gpu") < world_size: - return - _init_process_group_wrapper(world_size, rank, rank, backend, port_queue) - inp = Parameter(data) - dist.functional.bcast_param(inp) - assert np.allclose(inp.numpy(), expect) - - def check(shape, backend): - port_queue = mp.Queue() - x = np.random.rand(*shape).astype("float32") - y = x + 1 - p0 = mp.Process(target=worker, args=(0, x, backend, x, port_queue)) - p1 = mp.Process(target=worker, args=(1, y, backend, x, port_queue)) - - p0.start() - p1.start() - - p0.join(10) - p1.join(10) - - assert p0.exitcode == 0 and p1.exitcode == 0 - - for shape in [(2, 3), (8, 10), (99, 77)]: - for backend in ["nccl"]: - check(shape, backend) diff --git a/python_module/test/unit/distributed/test_util.py b/python_module/test/unit/distributed/test_util.py deleted file mode 100644 index 04b34244..00000000 --- a/python_module/test/unit/distributed/test_util.py +++ /dev/null @@ -1,187 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import multiprocessing as mp -import platform -import queue -from time import sleep - -import pytest - -import megengine as mge -import megengine._internal as mgb -import megengine.distributed as dist - -_LOCALHOST = "127.0.0.1" - - -def _assert_q_empty(q): - try: - res = q.get(timeout=1) - except Exception as e: - assert isinstance(e, queue.Empty) - else: - assert False, "queue is not empty" - - -def _assert_q_val(q, val): - ret = q.get() - assert ret == val - - -def _init_process_group_wrapper(world_size, rank, dev, backend, q): - if rank == 0: - dist.init_process_group(_LOCALHOST, 0, world_size, rank, dev, backend) - q.put(dist.get_master_port()) - else: - port = q.get() - dist.init_process_group(_LOCALHOST, port, world_size, rank, dev, backend) - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -@pytest.mark.isolated_distributed -def test_create_mm_server(): - def worker(): - if not mge.is_cuda_available(): - return - port = mgb.config.create_mm_server("0.0.0.0", 0) - assert port > 0 - res = mgb.config.create_mm_server("0.0.0.0", port) - assert res == -1 - - p = mp.Process(target=worker) - - p.start() - - p.join(10) - - assert p.exitcode == 0 - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -@pytest.mark.isolated_distributed -def test_init_process_group(): - world_size = 2 - - def worker(rank, backend, q): - if not mge.is_cuda_available(): - return - _init_process_group_wrapper(world_size, rank, rank, backend, q) - assert dist.is_distributed() == True - assert dist.get_master_ip() == _LOCALHOST - assert dist.get_master_port() > 0 - assert dist.get_world_size() == world_size - assert dist.get_rank() == rank - assert dist.get_backend() == backend - - def check(backend): - Q = mp.Queue() - p0 = mp.Process(target=worker, args=(0, backend, Q)) - p1 = mp.Process(target=worker, args=(1, backend, Q)) - - p0.start() - p1.start() - - p0.join(10) - p1.join(10) - - assert p0.exitcode == 0 and p1.exitcode == 0 - - check("nccl") - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -@pytest.mark.isolated_distributed -def test_group_barrier(): - world_size = 2 - ip = "127.0.0.1" - backend = "nccl" - - def worker(rank, q): - if not mge.is_cuda_available(): - return - _init_process_group_wrapper(world_size, rank, rank, backend, q) - dist.group_barrier() - if rank == 0: - dist.group_barrier() - q.put(0) # to be observed in rank 1 - else: - _assert_q_empty(q) # q.put(0) is not executed in rank 0 - dist.group_barrier() - _assert_q_val(q, 0) # q.put(0) executed in rank 0 - - Q = mp.Queue() - p0 = mp.Process(target=worker, args=(0, Q)) - p1 = mp.Process(target=worker, args=(1, Q)) - - p0.start() - p1.start() - - p0.join(10) - p1.join(10) - - assert p0.exitcode == 0 and p1.exitcode == 0 - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -@pytest.mark.isolated_distributed -def test_synchronized(): - world_size = 2 - backend = "nccl" - - @dist.synchronized - def func(rank, q): - q.put(rank) - - def worker(rank, q): - if not mge.is_cuda_available(): - return - _init_process_group_wrapper(world_size, rank, rank, backend, q) - dist.group_barrier() - if rank == 0: - func(0, q) # q.put(0) - q.put(2) - else: - _assert_q_val(q, 0) # func executed in rank 0 - _assert_q_empty(q) # q.put(2) is not executed - func(1, q) - _assert_q_val( - q, 1 - ) # func in rank 1 executed earlier than q.put(2) in rank 0 - _assert_q_val(q, 2) # q.put(2) executed in rank 0 - - Q = mp.Queue() - p0 = mp.Process(target=worker, args=(0, Q)) - p1 = mp.Process(target=worker, args=(1, Q)) - - p0.start() - p1.start() - - p0.join(10) - p1.join(10) - - assert p0.exitcode == 0 and p1.exitcode == 0 diff --git a/python_module/test/unit/functional/__init__.py b/python_module/test/unit/functional/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/python_module/test/unit/functional/test_elemwise.py b/python_module/test/unit/functional/test_elemwise.py deleted file mode 100644 index c02bd58b..00000000 --- a/python_module/test/unit/functional/test_elemwise.py +++ /dev/null @@ -1,65 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np - -import megengine.functional as F -from megengine import tensor -from megengine.test import assertTensorClose - - -def test_abs(): - assertTensorClose( - F.abs(tensor([-3.0, -4.0, -5.0])).numpy(), - np.abs(np.array([-3.0, -4.0, -5.0], dtype=np.float32)), - ) - - assertTensorClose(F.abs(-3.0), np.abs(np.float32(-3.0))) - - -def test_multiply(): - assertTensorClose( - F.multiply(-3.0, -4.0), np.multiply(np.float32(-3.0), np.float32(-4.0)) - ) - - assertTensorClose( - F.multiply(tensor([3.0, 4.0]), 4.0).numpy(), - np.multiply(np.array([3.0, 4.0], dtype=np.float32), 4.0), - ) - - assertTensorClose( - F.multiply(4.0, tensor([3.0, 4.0])).numpy(), - np.multiply(4.0, np.array([3.0, 4.0], dtype=np.float32)), - ) - - assertTensorClose( - F.multiply(tensor([3.0, 4.0]), tensor([3.0, 4.0])).numpy(), - np.multiply( - np.array([3.0, 4.0], dtype=np.float32), - np.array([3.0, 4.0], dtype=np.float32), - ), - ) - - -def test_clamp(): - """Fix an issue when `lower` or `upper` is 0, it will be recognized as `False` and - `F.clamp` will fall into wrong conditions unexpectedly. - """ - x = np.linspace(-6, 6, dtype="float32") - assertTensorClose(F.clamp(tensor(x) + 3, 0, 6).numpy(), np.clip(x + 3, 0, 6)) - assertTensorClose(F.clamp(tensor(x) - 3, -6, 0).numpy(), np.clip(x - 3, -6, 0)) - - -def test_isnan(): - for case in [[1, float("nan"), 0]]: - assertTensorClose(F.isnan(tensor(case)), np.isnan(case).astype("uint8")) - - -def test_isinf(): - for case in [[1, float("inf"), 0]]: - assertTensorClose(F.isinf(tensor(case)), np.isinf(case).astype("uint8")) diff --git a/python_module/test/unit/functional/test_functional.py b/python_module/test/unit/functional/test_functional.py deleted file mode 100644 index a3739b3d..00000000 --- a/python_module/test/unit/functional/test_functional.py +++ /dev/null @@ -1,496 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np -import pytest -from helpers import opr_test - -import megengine._internal as mgb -import megengine.functional as F -from megengine import Buffer, Parameter, is_cuda_available, jit, tensor -from megengine.test import assertTensorClose - - -def test_flatten(): - data0_shape = (2, 3, 4, 5) - data1_shape = (4, 5, 6, 7) - data0 = np.random.random(data0_shape).astype(np.float32) - data1 = np.random.random(data1_shape).astype(np.float32) - - def compare_fn(x, y): - assert x.numpy().shape == y - - output0 = (2 * 3 * 4 * 5,) - output1 = (4 * 5 * 6 * 7,) - cases = [{"input": data0, "output": output0}, {"input": data1, "output": output1}] - opr_test(cases, F.flatten, compare_fn=compare_fn) - - output0 = (2, 3 * 4 * 5) - output1 = (4, 5 * 6 * 7) - cases = [{"input": data0, "output": output0}, {"input": data1, "output": output1}] - opr_test(cases, F.flatten, compare_fn=compare_fn, start_axis=1) - - output0 = (2, 3, 4 * 5) - output1 = (4, 5, 6 * 7) - cases = [{"input": data0, "output": output0}, {"input": data1, "output": output1}] - opr_test(cases, F.flatten, compare_fn=compare_fn, start_axis=2) - - output0 = (2, 3 * 4, 5) - output1 = (4, 5 * 6, 7) - cases = [{"input": data0, "output": output0}, {"input": data1, "output": output1}] - opr_test(cases, F.flatten, compare_fn=compare_fn, start_axis=1, end_axis=2) - - -def test_where(): - maskv0 = np.array([[1, 0], [0, 1]], dtype=np.int32) - xv0 = np.array([[1, np.inf], [np.nan, 4]], dtype=np.float32) - yv0 = np.array([[5, 6], [7, 8]], dtype=np.float32) - - maskv1 = np.array([[1, 0, 1], [1, 0, 0], [1, 1, 0]], dtype=np.int32) - xv1 = np.array([[1, np.inf, 2], [0, np.nan, 4], [1, 5, 7]], dtype=np.float32) - yv1 = np.array([[5, 6, 9], [2, 7, 8], [2, 1, 9]], dtype=np.float32) - - cases = [ - {"input": [maskv0, xv0, yv0]}, - {"input": [maskv1, xv1, yv1]}, - ] - opr_test(cases, F.where, ref_fn=np.where) - - maskv2 = np.array([1, 1, 1], dtype=np.int32) - xv2 = np.array([1, 3, 2], dtype=np.float32) - yv2 = np.array([5, 6, 9], dtype=np.float32) - - maskv3 = np.array([0, 0, 0], dtype=np.int32) - xv3 = np.array([1, 3, 2], dtype=np.float32) - yv3 = np.array([5, 6, 9], dtype=np.float32) - - cases = [ - {"input": [maskv2, xv2, yv2]}, - {"input": [maskv3, xv3, yv3]}, - ] - opr_test(cases, F.where, ref_fn=np.where) - - -def test_eye(): - dtype = np.float32 - cases = [{"input": [10, 20]}, {"input": [20, 30]}] - opr_test(cases, F.eye, ref_fn=lambda n, m: np.eye(n, m).astype(dtype), dtype=dtype) - - -def test_concat(): - def get_data_shape(length: int): - return (length, 2, 3) - - data1 = np.random.random(get_data_shape(5)).astype("float32") - data2 = np.random.random(get_data_shape(6)).astype("float32") - data3 = np.random.random(get_data_shape(7)).astype("float32") - - def run(data1, data2): - return F.concat([data1, data2]) - - cases = [{"input": [data1, data2]}, {"input": [data1, data3]}] - opr_test(cases, run, ref_fn=lambda x, y: np.concatenate([x, y])) - - -def test_matrix_mul(): - shape1 = (2, 3) - shape2 = (3, 4) - shape3 = (4, 5) - data1 = np.random.random(shape1).astype("float32") - data2 = np.random.random(shape2).astype("float32") - data3 = np.random.random(shape3).astype("float32") - - cases = [{"input": [data1, data2]}, {"input": [data2, data3]}] - opr_test(cases, F.matrix_mul, ref_fn=np.matmul) - - -def test_batched_matrix_mul(): - batch_size = 10 - shape1 = (batch_size, 2, 3) - shape2 = (batch_size, 3, 4) - shape3 = (batch_size, 4, 5) - data1 = np.random.random(shape1).astype("float32") - data2 = np.random.random(shape2).astype("float32") - data3 = np.random.random(shape3).astype("float32") - - cases = [{"input": [data1, data2]}, {"input": [data2, data3]}] - for i in range(0, batch_size): - - def compare_fn(x, y): - x.numpy()[i, ...] == y - - opr_test( - cases, - F.batched_matrix_mul, - compare_fn=compare_fn, - ref_fn=lambda x, y: np.matmul(x[i, ...], y[i, ...]), - ) - - -def test_sort(): - data1_shape = (10, 3) - data2_shape = (12, 2) - data1 = np.random.random(data1_shape).astype(np.float32) - data2 = np.random.random(data2_shape).astype(np.float32) - output0 = [np.sort(data1), np.argsort(data1).astype(np.int32)] - output1 = [np.sort(data2), np.argsort(data2).astype(np.int32)] - - cases = [ - {"input": data1, "output": output0}, - {"input": data2, "output": output1}, - ] - opr_test(cases, F.sort) - - -def test_round(): - data1_shape = (15,) - data2_shape = (25,) - data1 = np.random.random(data1_shape).astype(np.float32) - data2 = np.random.random(data2_shape).astype(np.float32) - - cases = [{"input": data1}, {"input": data2}] - opr_test(cases, F.round, ref_fn=np.round) - - -def test_broadcast_to(): - input1_shape = (20, 30) - output1_shape = (30, 20, 30) - data1 = np.random.random(input1_shape).astype(np.float32) - - input2_shape = (10, 20) - output2_shape = (20, 10, 20) - data2 = np.random.random(input2_shape).astype(np.float32) - - def compare_fn(x, y): - assert x.numpy().shape == y - - cases = [ - {"input": [data1, output1_shape], "output": output1_shape}, - {"input": [data2, output2_shape], "output": output2_shape}, - ] - opr_test(cases, F.broadcast_to, compare_fn=compare_fn) - - -def test_linspace(): - cases = [ - {"input": [1, 9, 9]}, - {"input": [3, 10, 8]}, - ] - opr_test( - cases, - F.linspace, - ref_fn=lambda start, end, step: np.linspace(start, end, step, dtype=np.float32), - ) - - cases = [ - {"input": [9, 1, 9]}, - {"input": [10, 3, 8]}, - ] - opr_test( - cases, - F.linspace, - ref_fn=lambda start, end, step: np.linspace(start, end, step, dtype=np.float32), - ) - - -def test_arange(): - cases = [ - {"input": [1, 9, 1]}, - {"input": [2, 10, 2]}, - ] - opr_test( - cases, - F.arange, - ref_fn=lambda start, end, step: np.arange(start, end, step, dtype=np.float32), - ) - - cases = [ - {"input": [9, 1, -1]}, - {"input": [10, 2, -2]}, - ] - opr_test( - cases, - F.arange, - ref_fn=lambda start, end, step: np.arange(start, end, step, dtype=np.float32), - ) - - cases = [ - {"input": [9.3, 1.2, -0.5]}, - {"input": [10.3, 2.1, -1.7]}, - ] - opr_test( - cases, - F.arange, - ref_fn=lambda start, end, step: np.arange(start, end, step, dtype=np.float32), - ) - - -def test_add_update(): - shape = (2, 3) - v = np.random.random(shape).astype(np.float32) - b = Buffer(v) - - u = F.add_update(b, 1) - assertTensorClose(u.numpy(), v + 1) - u = F.add_update(b, 1) - assertTensorClose(u.numpy(), v + 2) - - x = np.ones((2, 2), dtype=np.float32) - y = x * 0.5 - dest = tensor(x) - delta = tensor(y) - r = F.add_update(dest, delta, alpha=tensor(0.9), beta=0.1, bias=0.1) - assertTensorClose(r.numpy(), x * 0.9 + y * 0.1 + 0.1) - - -def test_add_update_params(): - b = np.random.random((2, 3)).astype(np.float32) - y = Buffer(b) - - @jit.trace - def f(x): - return F.add_update(y, x) - - f(np.zeros((2, 3)).astype(np.float32)) - - z = Buffer(np.zeros((2, 3)).astype(np.float32)) - F.add_update(y, z, beta=0.1) - - res = f(np.ones((2, 3)).astype(np.float32)) - assertTensorClose(res, b + 1) - - -def test_cross_entropy_with_softmax(): - data1_shape = (1, 2) - label1_shape = (1,) - data2_shape = (1, 3) - label2_shape = (1,) - - data1 = np.array([1, 0.5], dtype=np.float32).reshape(data1_shape) - label1 = np.array([1], dtype=np.int32).reshape(label1_shape) - expect1 = F.cross_entropy(F.softmax(tensor(data1)), tensor(label1)).numpy() - - data2 = np.array([0.3, 0.4, 0.3], dtype=np.float32).reshape(data2_shape) - label2 = np.array([1], dtype=np.int32).reshape(label2_shape) - expect2 = F.cross_entropy(F.softmax(tensor(data2)), tensor(label2)).numpy() - - cases = [ - {"input": [data1, label1], "output": expect1,}, - {"input": [data2, label2], "output": expect2,}, - ] - opr_test(cases, F.cross_entropy_with_softmax) - - -def test_cross_entropy(): - data1_shape = (1, 2) - label1_shape = (1,) - data2_shape = (1, 3) - label2_shape = (1,) - - data1 = np.array([0.5, 0.5], dtype=np.float32).reshape(data1_shape) - label1 = np.array([1], dtype=np.int32).reshape(label1_shape) - expect1 = np.array([-np.log(0.5)], dtype=np.float32) - - data2 = np.array([0.3, 0.4, 0.3], dtype=np.float32).reshape(data2_shape) - label2 = np.array([1], dtype=np.int32).reshape(label2_shape) - expect2 = np.array([-np.log(0.4)], dtype=np.float32) - - cases = [ - {"input": [data1, label1], "output": expect1,}, - {"input": [data2, label2], "output": expect2,}, - ] - opr_test(cases, F.cross_entropy) - - -def test_binary_cross_entropy(): - data1_shape = (2, 2) - label1_shape = (2, 2) - data2_shape = (2, 3) - label2_shape = (2, 3) - - def sigmoid(x): - return 1 / (1 + np.exp(-x)) - - def compare_fn(x, y): - assertTensorClose(x.numpy(), y, max_err=5e-4) - - np.random.seed(123) - data1 = sigmoid(np.random.uniform(size=data1_shape).astype(np.float32)) - label1 = np.random.uniform(size=label1_shape).astype(np.float32) - expect1 = np.array([0.6361], dtype=np.float32) - - np.random.seed(123) - data2 = sigmoid(np.random.uniform(size=data2_shape).astype(np.float32)) - label2 = np.random.uniform(size=label2_shape).astype(np.float32) - expect2 = np.array([0.6750], dtype=np.float32) - - cases = [ - {"input": [data1, label1], "output": expect1,}, - {"input": [data2, label2], "output": expect2,}, - ] - opr_test(cases, F.binary_cross_entropy, compare_fn=compare_fn) - - -def test_hinge_loss(): - np.random.seed(123) - # case with L1 norm - cases = [] - for shape in [(2, 2), (2, 3)]: - data = np.random.uniform(size=shape).astype(np.float32) - label = 2 * np.random.randint(0, 1, size=shape).astype(np.int32) - 1 - expect = np.clip(0, np.inf, 1 - data * label).sum(axis=1).mean() - cases.append({"input": [data, label], "output": tensor(expect)}) - - opr_test(cases, F.hinge_loss) - - # cases with L2 norm - cases = [] - for shape in [(2, 2), (2, 3)]: - data = np.random.uniform(size=shape).astype(np.float32) - label = 2 * np.random.randint(0, 1, size=shape).astype(np.int32) - 1 - expect = ((np.clip(0, np.inf, 1 - data * label) ** 2).sum(axis=1)).mean() - cases.append({"input": [data, label], "output": tensor(expect)}) - - def hinge_loss_with_l2_norm(pred, label): - return F.hinge_loss(pred, label, "L2") - - opr_test(cases, hinge_loss_with_l2_norm) - - -def test_smooth_l1_loss(): - np.random.seed(123) - cases = [] - for shape in [(2, 2), (2, 3)]: - data = np.random.uniform(size=shape).astype(np.float32) - label = np.random.uniform(size=shape).astype(np.float32) - diff = np.abs(data - label) - expect = np.where(diff < 1, 0.5 * diff ** 2, diff - 0.5).mean() - cases.append({"input": [data, label], "output": tensor(expect)}) - - opr_test(cases, F.smooth_l1_loss) - - -@pytest.mark.skip -def test_conv_bias(): - inp_scale = 0.01 - w_scale = 0.02 - outp_scale = 0.1 - inp_dtype = mgb.dtype.qint8(inp_scale) - w_dtype = mgb.dtype.qint8(w_scale) - b_dtype = mgb.dtype.qint32(inp_scale * w_scale) - out_dtype = mgb.dtype.qint8(outp_scale) - - def run( - N, - IC, - OC, - IH, - IW, - KH, - KW, - PH, - PW, - SH, - SW, - has_bias=True, - nonlinear_mode="IDENTITY", - ): - inp_v = np.random.normal(size=(N, IC, IH, IW)) - w_v = np.random.normal(size=(OC, IC, KW, KW)) - b_v = np.random.normal(size=(1, OC, 1, 1)) - inp_scale = mgb.dtype.get_scale(inp_dtype) - w_scale = mgb.dtype.get_scale(w_dtype) - b_scale = mgb.dtype.get_scale(b_dtype) - - inpv = mgb.dtype.convert_to_qint8(inp_v * inp_scale, inp_dtype) - wv = mgb.dtype.convert_to_qint8(w_v * w_scale, w_dtype) - bv = mgb.dtype.convert_to_qint32(b_v * b_scale, b_dtype) - - inp_int8 = tensor(inpv, dtype=inp_dtype) - w_int8 = Parameter(wv, dtype=w_dtype) - b_int32 = Parameter(bv, dtype=b_dtype) - - inp_fp32 = inp_int8.astype("float32") - w_fp32 = w_int8.astype("float32") - b_fp32 = b_int32.astype("float32") - - jit.trace.enabled = True - b_symbolic = True - - def convert_to_nchw4(var): - return var.reshape( - var.shapeof(0), var.shapeof(1) // 4, 4, var.shapeof(2), var.shapeof(3) - ).dimshuffle(0, 1, 3, 4, 2) - - @jit.trace(symbolic=b_symbolic) - def run_conv2d(inp, w, b): - O = F.conv2d( - inp, w, b if has_bias else None, stride=(SH, SW), padding=(PH, PW), - ) - if nonlinear_mode == "RELU": - return F.relu(O) - else: - return O - - @jit.trace(symbolic=b_symbolic) - def run_conv_bias(inp, w, b, format="NCHW"): - b = b if has_bias else np.zeros_like(b) - if format == "NCHW4": - inp = convert_to_nchw4(inp) - w = convert_to_nchw4(w) - b = F.flatten(b) - return F.conv_bias_activation( - inp, - w, - b, - stride=(SH, SW), - padding=(PH, PW), - dtype=out_dtype, - nonlinear_mode=nonlinear_mode, - ) - - format = "NCHW4" if is_cuda_available() else "NCHW" - - expected = run_conv2d(inp_fp32, w_fp32, b_fp32) - expected = expected.astype(out_dtype).astype("float32") - result = run_conv_bias(inp_int8, w_int8, b_int32, format=format).astype( - "float32" - ) - if format == "NCHW4": - result = result.dimshuffle(0, 1, 4, 2, 3) - expected = F.flatten(expected) - result = F.flatten(result) - assertTensorClose(result.numpy(), expected.numpy()) - - if not is_cuda_available(): - run(1, 4, 4, 24, 33, 1, 1, 2, 3, 1, 1, False) - run(10, 12, 24, 46, 46, 1, 1, 2, 1, 3, 1, False) - run(10, 36, 8, 46, 26, 2, 2, 2, 1, 1, 2, False) - - run(1, 4, 4, 24, 33, 1, 1, 2, 3, 1, 1) - run(10, 12, 24, 46, 46, 1, 1, 2, 1, 3, 1) - run(10, 36, 8, 46, 26, 2, 2, 2, 1, 1, 2) - - run(10, 36, 8, 46, 26, 2, 2, 2, 1, 1, 2, False, "RELU") - run(10, 36, 8, 46, 26, 2, 2, 2, 1, 1, 2, True, "RELU") - - -def test_softplus(): - x = np.arange(1000).astype(np.float32) - out = F.softplus(tensor(x)) - mask = x <= 20 - with np.errstate(over="ignore"): - expected = np.where(mask, np.log(1 + np.exp(x)), x) - assertTensorClose(out, expected) - beta = 2 - out = F.softplus(tensor(x), beta=beta, threshold=30) - mask = beta * x <= 30 - # ignore overflow - with np.errstate(over="ignore"): - expected = np.where(mask, np.log(1 + np.exp(x * beta)) / beta, x) - assertTensorClose(out, expected) diff --git a/python_module/test/unit/functional/test_interpolate.py b/python_module/test/unit/functional/test_interpolate.py deleted file mode 100644 index 2da981ff..00000000 --- a/python_module/test/unit/functional/test_interpolate.py +++ /dev/null @@ -1,60 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np -import pytest - -import megengine.functional as F -from megengine import tensor -from megengine.test import assertTensorClose - - -def test_linear_interpolate(): - inp = tensor(np.arange(1, 3, dtype=np.float32).reshape(1, 1, 2)) - - out = F.interpolate(inp, scale_factor=2.0, mode="LINEAR") - out2 = F.interpolate(inp, 4, mode="LINEAR") - - assertTensorClose( - out.numpy(), np.array([[[1.0, 1.25, 1.75, 2.0]]], dtype=np.float32) - ) - assertTensorClose( - out2.numpy(), np.array([[[1.0, 1.25, 1.75, 2.0]]], dtype=np.float32) - ) - - -def test_many_batch_interpolate(): - inp = tensor(np.arange(1, 9, dtype=np.float32).reshape(2, 1, 2, 2)) - - out = F.interpolate(inp, [4, 4]) - out2 = F.interpolate(inp, scale_factor=2.0) - - assertTensorClose(out.numpy(), out2.numpy()) - - -def test_assign_corner_interpolate(): - inp = tensor(np.arange(1, 5, dtype=np.float32).reshape(1, 1, 2, 2)) - - out = F.interpolate(inp, [4, 4], align_corners=True) - out2 = F.interpolate(inp, scale_factor=2.0, align_corners=True) - - assertTensorClose(out.numpy(), out2.numpy()) - - -def test_error_shape_linear_interpolate(): - inp = tensor(np.arange(1, 5, dtype=np.float32).reshape(1, 1, 2, 2)) - - with pytest.raises(ValueError): - F.interpolate(inp, scale_factor=2.0, mode="LINEAR") - - -def test_inappropriate_scale_linear_interpolate(): - inp = tensor(np.arange(1, 3, dtype=np.float32).reshape(1, 1, 2)) - - with pytest.raises(ValueError): - F.interpolate(inp, scale_factor=[2.0, 3.0], mode="LINEAR") diff --git a/python_module/test/unit/functional/test_math.py b/python_module/test/unit/functional/test_math.py deleted file mode 100644 index 6dc5c82c..00000000 --- a/python_module/test/unit/functional/test_math.py +++ /dev/null @@ -1,168 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - -from functools import partial - -import numpy as np -from helpers import opr_test - -import megengine.functional as F -from megengine.test import assertTensorClose - - -def common_test_reduce(opr, ref_opr): - data1_shape = (5, 6, 7) - data2_shape = (2, 9, 12) - data1 = np.random.random(data1_shape).astype(np.float32) - data2 = np.random.random(data2_shape).astype(np.float32) - cases = [{"input": data1}, {"input": data2}] - - if opr not in (F.argmin, F.argmax): - # test default axis - opr_test(cases, opr, ref_fn=ref_opr) - # test all axises in range of input shape - for axis in range(-3, 3): - # test keepdims False - opr_test(cases, opr, ref_fn=lambda x: ref_opr(x, axis=axis), axis=axis) - # test keepdims True - opr_test( - cases, - opr, - ref_fn=lambda x: ref_opr(x, axis=axis, keepdims=True), - axis=axis, - keepdims=True, - ) - else: - # test defaut axis - opr_test(cases, opr, ref_fn=lambda x: ref_opr(x).astype(np.int32)) - # test all axises in range of input shape - for axis in range(0, 3): - opr_test( - cases, - opr, - ref_fn=lambda x: ref_opr(x, axis=axis).astype(np.int32), - axis=axis, - ) - - -def test_sum(): - common_test_reduce(opr=F.sum, ref_opr=np.sum) - - -def test_prod(): - common_test_reduce(opr=F.prod, ref_opr=np.prod) - - -def test_mean(): - common_test_reduce(opr=F.mean, ref_opr=np.mean) - - -def test_min(): - common_test_reduce(opr=F.min, ref_opr=np.min) - - -def test_max(): - common_test_reduce(opr=F.max, ref_opr=np.max) - - -def test_argmin(): - common_test_reduce(opr=F.argmin, ref_opr=np.argmin) - - -def test_argmax(): - common_test_reduce(opr=F.argmax, ref_opr=np.argmax) - - -def test_sqrt(): - d1_shape = (15,) - d2_shape = (25,) - d1 = np.random.random(d1_shape).astype(np.float32) - d2 = np.random.random(d2_shape).astype(np.float32) - - cases = [{"input": d1}, {"input": d2}] - opr_test(cases, F.sqrt, ref_fn=np.sqrt) - - -def test_normalize(): - - cases = [ - {"input": np.random.random((2, 3, 12, 12)).astype(np.float32)} for i in range(2) - ] - - def np_normalize(x, p=2, axis=None, eps=1e-12): - if axis is None: - norm = np.sum(x ** p) ** (1.0 / p) - else: - norm = np.sum(x ** p, axis=axis, keepdims=True) ** (1.0 / p) - return x / np.clip(norm, a_min=eps, a_max=np.inf) - - # Test L-2 norm along all dimensions - opr_test(cases, F.normalize, ref_fn=np_normalize) - - # Test L-1 norm along all dimensions - opr_test(cases, partial(F.normalize, p=1), ref_fn=partial(np_normalize, p=1)) - - # Test L-2 norm along the second dimension - opr_test(cases, partial(F.normalize, axis=1), ref_fn=partial(np_normalize, axis=1)) - - # Test some norm == 0 - cases[0]["input"][0, 0, 0, :] = 0 - cases[1]["input"][0, 0, 0, :] = 0 - opr_test(cases, partial(F.normalize, axis=3), ref_fn=partial(np_normalize, axis=3)) - - -def test_logsumexp(): - x = np.arange(10).astype(np.float32) - expected = np.log(np.sum(np.exp(x))) - cases = [{"input": x, "output": expected}] - compare_fn = partial(assertTensorClose, allow_special_values=True) - # large value check - n = 100 - x = np.full(n, 10000, dtype=np.float32) - expected = 10000 + np.log(n) - cases.append({"input": x, "output": expected.astype(np.float32)}) - opr_test(cases, F.logsumexp, axis=0, compare_fn=compare_fn) - - # special value check - x = np.array([np.inf], dtype=np.float32) - expected = x - cases = [{"input": x, "output": expected}] - - x = np.array([-np.inf, 0.0], dtype=np.float32) - expected = np.zeros(1).astype(np.float32) - cases.append({"input": x, "output": expected}) - opr_test(cases, F.logsumexp, axis=0, compare_fn=compare_fn) - - x = np.array([np.nan], dtype=np.float32) - expected = x - cases = [{"input": x, "output": expected}] - - x = np.array([-np.inf, 1], dtype=np.float32) - expected = np.array([1.0], dtype=np.float32) - cases.append({"input": x, "output": expected}) - - opr_test(cases, F.logsumexp, axis=0, compare_fn=compare_fn) - - # keepdims check - x = np.array([[1e10, 1e-10], [-1e10, -np.inf]], dtype=np.float32) - expected = np.array([[1e10], [-1e10]], dtype=np.float32) - cases = [{"input": x, "output": expected}] - x = np.array([[1e10, -1e-10, 1e-10], [1e10, 1e-10, np.inf]], dtype=np.float32) - expected = np.array([[1e10], [np.inf]], dtype=np.float32) - cases.append({"input": x, "output": expected}) - opr_test(cases, F.logsumexp, axis=1, keepdims=True, compare_fn=compare_fn) - - # multiple axes check - x = np.array([[1e10, 1e-10], [-1e10, -np.inf]], dtype=np.float32) - expected = np.array([1e10], dtype=np.float32) - cases = [{"input": x, "output": expected}] - x = np.array([[1e10, -1e-10, 1e-10], [1e10, 1e-10, np.inf]], dtype=np.float32) - expected = np.array([np.inf], dtype=np.float32) - cases.append({"input": x, "output": expected}) - opr_test(cases, F.logsumexp, axis=(0, 1), keepdims=False, compare_fn=compare_fn) diff --git a/python_module/test/unit/functional/test_onehot.py b/python_module/test/unit/functional/test_onehot.py deleted file mode 100644 index 3edbe5de..00000000 --- a/python_module/test/unit/functional/test_onehot.py +++ /dev/null @@ -1,26 +0,0 @@ -import numpy as np -import pytest - -import megengine.functional as F -from megengine import tensor -from megengine.test import assertTensorClose - - -def test_onehot_low_dimension(): - inp = tensor(np.arange(1, 4, dtype=np.int32)) - out = F.one_hot(inp, num_classes=4) - - assertTensorClose( - out.numpy(), np.eye(4, dtype=np.int32)[np.arange(1, 4, dtype=np.int32)] - ) - - -def test_onehot_high_dimension(): - arr = np.array( - [[3, 2, 4, 4, 2, 4, 0, 4, 4, 1], [4, 1, 1, 3, 2, 2, 4, 2, 4, 3]], dtype=np.int32 - ) - - inp = tensor(arr) - out = F.one_hot(inp, 10) - - assertTensorClose(out.numpy(), np.eye(10, dtype=np.int32)[arr]) diff --git a/python_module/test/unit/hub/__init__.py b/python_module/test/unit/hub/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/python_module/test/unit/hub/test_hub.py b/python_module/test/unit/hub/test_hub.py deleted file mode 100644 index 5fdb32ec..00000000 --- a/python_module/test/unit/hub/test_hub.py +++ /dev/null @@ -1,38 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import pytest -from helpers import modified_environ - -from megengine.hub import hub - - -@pytest.mark.internet -def test_hub_http_basic(tmp_path): - # Override XDG_CACHE_HOME to make sure test won't have side effect for system. - with modified_environ(XDG_CACHE_HOME=str(tmp_path)): - # Use pytorch's URL due to we don't have public address now. - repo_info, entry = "pytorch/vision:v0.4.2", "alexnet" - - assert len(hub.list(repo_info)) > 0 - - assert entry in hub.list(repo_info) - - assert hub.help(repo_info, entry) - - assert isinstance(hub.load(repo_info, entry), object) - - -@pytest.mark.internet -def test_github_load_with_commit_id(tmp_path): - # Override XDG_CACHE_HOME to make sure test won't have side effect for system. - with modified_environ(XDG_CACHE_HOME=str(tmp_path)): - # Use pytorch's URL due to we don't have public address now. - repo_info, commit, entry = "pytorch/vision", "d2c763e1", "alexnet" - - assert isinstance(hub.load(repo_info, entry, commit=commit), object) diff --git a/python_module/test/unit/jit/__init__.py b/python_module/test/unit/jit/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/python_module/test/unit/jit/test_jit.py b/python_module/test/unit/jit/test_jit.py deleted file mode 100644 index b14b1dfc..00000000 --- a/python_module/test/unit/jit/test_jit.py +++ /dev/null @@ -1,257 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import contextlib -import os -import tempfile - -import numpy as np -import pytest - -import megengine as mge -import megengine._internal as mgb -import megengine.functional as F -import megengine.module as M -from megengine import functional as F -from megengine import jit, tensor -from megengine.core.tensor import Tensor -from megengine.jit import SublinearMemoryConfig -from megengine.test import assertTensorClose - - -@contextlib.contextmanager -def mkstemp(): - fd, path = tempfile.mkstemp() - try: - os.close(fd) - yield path - finally: - os.remove(path) - - -def load_and_compile(fpath): - cg, _, outputs = mgb.load_comp_graph_from_file(fpath) - inputs = mgb.cgtools.get_dep_vars(outputs, "Host2DeviceCopy") - inputs = sorted(inputs, key=lambda i: i.name) - outputs = list(map(mgb.copy_output, outputs)) - if len(outputs) == 1: - (outputs,) = outputs - return cg.compile(inputs, outputs) - - -def test_symbolic(): - @jit.trace(symbolic=False) - def f(x): - return Tensor(mgb.opr.assert_equal(x._symvar, x._symvar + 1)) - - with pytest.raises(mgb.exc.MegBrainError): - f.trace(0) - - @jit.trace(symbolic=True) - def f(x): - return Tensor(mgb.opr.assert_equal(x._symvar, x._symvar + 1)) - - f.trace(0) - - -def test_add_update_semantic(): - for symbolic in [False, True]: - x = tensor(0) - - @jit.trace(symbolic=symbolic) - def f(): - F.add_update(x, 1) - return x + 1 - - np.testing.assert_equal(f().numpy(), [2]) - np.testing.assert_equal(f().numpy(), [3]) - - -def test_dump(): - @jit.trace(symbolic=True) - def f(x, y): - return x * y - - f.trace(0, 0) - - with mkstemp() as out: - f.dump(out) - g = load_and_compile(out) - - np.testing.assert_allclose(g([1, 2, 3], [1, 2, 3]), [1, 4, 9]) - - -def test_goptions(): - @jit.trace(symbolic=True, opt_level=0) - def f(x): - return x / x - - @jit.trace(symbolic=True, opt_level=1) - def g(x): - return x / x - - out = f([0.0]).numpy() - # out is nan - if out == out: - raise - - # with gopt, x / x returns 1 - out = g([0.0]).numpy() - assert out == 1 - - -def test_json_prof(): - @jit.trace(symbolic=True, profiling=True) - def f(x): - return x * x - - f([0.0]) - - out = f.get_profile() - assert out.get("profiler") - - -def test_capture_dump(): - p = tensor(7) - - @jit.trace(symbolic=True) - def f(x): - return x * p - - f.trace(0) - - with mkstemp() as out: - f.dump(out) - g = load_and_compile(out) - - np.testing.assert_allclose(g([1, 2, 3]), [7, 14, 21]) - - -def test_dump_volatile(): - p = tensor(7) - - @jit.trace(symbolic=True) - def f(x): - return x * p - - f.trace(0) - - with mkstemp() as out: - f.dump(out) - cg, _, outputs = mgb.load_comp_graph_from_file(out) - - (out,) = outputs - assert mgb.cgtools.get_type(mgb.cgtools.get_inputs(out)[1]) == "SharedDeviceTensor" - - -def test_graph_traversal(): - net = M.Conv2d(3, 4, 3, 1, 1, groups=1, bias=False) - net.eval() - - @jit.trace(symbolic=True) - def fun(data): - return net(data) - - data = np.random.random([1, 3, 224, 224]).astype(np.float32) - fun.trace(data) - - with mkstemp() as out: - fun.dump(out) - *_, outputs = mgb.load_comp_graph_from_file(out) - - _, map_vars, var2oprs, *_ = mgb.cgtools.graph_traversal(outputs) - input_var = map_vars[1] - _, var_idx = var2oprs[input_var.id][0] - - assert var_idx == 0 - - -def test_network_visitor(): - @jit.trace(symbolic=True) - def f(x): - # this line will produce shape_of, subtensor and concat op - # after pruning, they will be deleted - target_shape = (x.shape[0], -1) - - return x.reshape(*target_shape) - - f.trace(tensor(np.random.random([2, 3, 4, 5]).astype(np.float32))) - - with mkstemp() as out: - f.dump(out) - *_, outputs = mgb.load_comp_graph_from_file(out) - - all_oprs = mgb.cgtools.get_oprs_seq(outputs) - pruned_oprs = mgb.cgtools.get_oprs_seq(outputs, prune_reshape=True) - - assert len(all_oprs) == len(pruned_oprs) + 3 - - -def test_shape_tracing(): - for symbolic in [False, True]: - - @jit.trace(symbolic=symbolic) - def f(x): - a, b = x.shape - return a * b - - assert f(np.zeros([4, 3], dtype="float32")).item() == 12 - assert f(np.zeros([6, 4], dtype="float32")).item() == 24 - - -def test_shape_infer(): - @jit.trace(symbolic=True) - def f(x): - a, b = x.shape - return sum(x[i] for i in range(a)) - - x = np.random.randn(3, 10).astype("float32") - assertTensorClose(f(x), x.sum(0)) - x = np.random.randn(4, 10).astype("float32") - assertTensorClose(f(x), x[:3].sum(0)) - - -def test_dump_bn_fused(): - class ConvBNReLU(M.Sequential): - def __init__(self): - super(ConvBNReLU, self).__init__( - M.Conv2d(3, 4, 3, 1, 1, groups=1, bias=False), - M.BatchNorm2d(4), - M.ReLU(), - ) - - net = ConvBNReLU() - net.eval() - - @jit.trace(symbolic=True) - def fun(data): - return net(data) - - data = np.random.random([1, 3, 224, 224]).astype(np.float32) - fun.trace(data) - with mkstemp() as out: - fun.dump(out, optimize_for_inference=True) - cg, _, outputs = mgb.load_comp_graph_from_file(out) - - (out,) = outputs - inputs = mgb.cgtools.get_inputs(out) - assert len(inputs) == 2 and ( - mgb.cgtools.get_type(inputs[0]) == "MultipleDeviceTensorHolder" - and mgb.cgtools.get_type(inputs[1]) == "ConvolutionForward" - ) - - -# Simply verify the options passed down -def test_sublinear(): - config = SublinearMemoryConfig(genetic_nr_iter=10) - - @jit.trace(symbolic=True, sublinear_memory_config=config) - def f(x): - return x + x - - f([0.0]) diff --git a/python_module/test/unit/module/.gitattributes b/python_module/test/unit/module/.gitattributes deleted file mode 100644 index 816f72d6..00000000 --- a/python_module/test/unit/module/.gitattributes +++ /dev/null @@ -1 +0,0 @@ -*.mlu binary diff --git a/python_module/test/unit/module/AtlasRuntimeOprTest.basic.om b/python_module/test/unit/module/AtlasRuntimeOprTest.basic.om deleted file mode 100644 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      {bSs7xyRdP-$(QLC#x1{zHk zW;bkJ(j}^)I9_TP-8bnnnlv10{tJ>)>ywsN4!ELn{sWScid2Ewl%uY(oF8&p~4t1$sh1#lh^h;&u z&i21!UL+G&iZPKeMC1{|{%lq4_=Ua-6~{@{JXHI3QpNXt?OUcc55;Sw5L&ZoY&VEh z_D!q5mhN2bn^VpIZ1(NScZ)TI*>lhvi)v(?Z_uiVI?jpKzfqa+t=22wEgp#Bp8@|X t-z^>hdK_~j{-0$mT={PC0Qhwqb?NZG^4;Qrpey0{khpZvpY6NF{~yJK!y*6x diff --git a/python_module/test/unit/module/__init__.py b/python_module/test/unit/module/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/python_module/test/unit/module/test_activation.py b/python_module/test/unit/module/test_activation.py deleted file mode 100644 index afca1fde..00000000 --- a/python_module/test/unit/module/test_activation.py +++ /dev/null @@ -1,24 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np - -import megengine as mge -from megengine.module import LeakyReLU -from megengine.test import assertTensorClose - - -def test_leaky_relu(): - data = np.array([-8, -12, 6, 10]).astype(np.float32) - negative_slope = 0.1 - - leaky_relu = LeakyReLU(negative_slope) - output = leaky_relu(mge.tensor(data)) - - np_output = np.maximum(0, data) + negative_slope * np.minimum(0, data) - assertTensorClose(output.numpy(), np_output, max_err=0) diff --git a/python_module/test/unit/module/test_batchnorm.py b/python_module/test/unit/module/test_batchnorm.py deleted file mode 100644 index cd651444..00000000 --- a/python_module/test/unit/module/test_batchnorm.py +++ /dev/null @@ -1,406 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import multiprocessing as mp -import platform - -import numpy as np -import pytest - -import megengine as mge -import megengine.distributed as dist -from megengine.core import tensor -from megengine.module import BatchNorm1d, BatchNorm2d, SyncBatchNorm -from megengine.test import assertTensorClose - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -@pytest.mark.isolated_distributed -def test_syncbn(): - nr_chan = 8 - data_shape = (3, nr_chan, 4, 16) - momentum = 0.9 - eps = 1e-5 - running_mean = np.zeros((1, nr_chan, 1, 1), dtype=np.float32) - running_var = np.ones((1, nr_chan, 1, 1), dtype=np.float32) - steps = 4 - nr_ranks = 2 - - def worker(rank, data, yv_expect, running_mean, running_var): - if mge.get_device_count("gpu") < nr_ranks: - return - dist.init_process_group("localhost", 2333, nr_ranks, rank, rank) - bn = SyncBatchNorm(nr_chan, momentum=momentum, eps=eps) - data_tensor = tensor() - for i in range(steps): - data_tensor.set_value(data[i]) - yv = bn(data_tensor) - - assertTensorClose(yv_expect, yv.numpy(), max_err=5e-6) - assertTensorClose(running_mean, bn.running_mean.numpy(), max_err=5e-6) - assertTensorClose(running_var, bn.running_var.numpy(), max_err=5e-6) - - xv = [] - for i in range(steps): - xv.append(np.random.normal(loc=2.3, size=data_shape).astype(np.float32)) - xv_transposed = np.transpose(xv[i], [0, 2, 3, 1]).reshape( - (data_shape[0] * data_shape[2] * data_shape[3], nr_chan) - ) - - mean = np.mean(xv_transposed, axis=0).reshape(1, nr_chan, 1, 1) - - var_biased = np.var(xv_transposed, axis=0).reshape((1, nr_chan, 1, 1)) - sd = np.sqrt(var_biased + eps) - - var_unbiased = np.var(xv_transposed, axis=0, ddof=1).reshape((1, nr_chan, 1, 1)) - running_mean = running_mean * momentum + mean * (1 - momentum) - running_var = running_var * momentum + var_unbiased * (1 - momentum) - - yv_expect = (xv[i] - mean) / sd - - data = [] - for i in range(nr_ranks): - data.append([]) - for j in range(steps): - data[i].append(xv[j][:, :, :, i * 8 : i * 8 + 8]) - - procs = [] - for rank in range(nr_ranks): - p = mp.Process( - target=worker, - args=( - rank, - data[rank], - yv_expect[:, :, :, rank * 8 : rank * 8 + 8], - running_mean, - running_var, - ), - ) - p.start() - procs.append(p) - - for p in procs: - p.join(10) - assert p.exitcode == 0 - - -def test_batchnorm(): - nr_chan = 8 - data_shape = (3, nr_chan, 4) - momentum = 0.9 - bn = BatchNorm1d(nr_chan, momentum=momentum) - running_mean = np.zeros((1, nr_chan, 1), dtype=np.float32) - running_var = np.ones((1, nr_chan, 1), dtype=np.float32) - data = tensor() - for i in range(3): - xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32) - mean = np.mean(np.mean(xv, axis=0, keepdims=True), axis=2, keepdims=True) - xv_transposed = np.transpose(xv, [0, 2, 1]).reshape( - (data_shape[0] * data_shape[2], nr_chan) - ) - - var_biased = np.var(xv_transposed, axis=0).reshape((1, nr_chan, 1)) - sd = np.sqrt(var_biased + bn.eps) - - var_unbiased = np.var(xv_transposed, axis=0, ddof=1).reshape((1, nr_chan, 1)) - running_mean = running_mean * momentum + mean * (1 - momentum) - running_var = running_var * momentum + var_unbiased * (1 - momentum) - - data.set_value(xv) - yv = bn(data) - yv_expect = (xv - mean) / sd - - assertTensorClose(yv_expect, yv.numpy(), max_err=5e-6) - assertTensorClose( - running_mean.reshape(-1), bn.running_mean.numpy().reshape(-1), max_err=5e-6 - ) - assertTensorClose( - running_var.reshape(-1), bn.running_var.numpy().reshape(-1), max_err=5e-6 - ) - - # test set 'training' flag to False - mean_backup = bn.running_mean.numpy() - var_backup = bn.running_var.numpy() - bn.training = False - xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32) - data.set_value(xv) - yv1 = bn(data) - yv2 = bn(data) - assertTensorClose(yv1.numpy(), yv2.numpy(), max_err=0) - assertTensorClose(mean_backup, bn.running_mean.numpy(), max_err=0) - assertTensorClose(var_backup, bn.running_var.numpy(), max_err=0) - yv_expect = (xv - running_mean) / np.sqrt(running_var + bn.eps) - assertTensorClose(yv_expect, yv1.numpy(), max_err=5e-6) - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -def test_syncbn1d(): - nr_chan = 8 - data_shape = (3, nr_chan, 4) - momentum = 0.9 - bn = SyncBatchNorm(nr_chan, momentum=momentum) - running_mean = np.zeros((1, nr_chan, 1), dtype=np.float32) - running_var = np.ones((1, nr_chan, 1), dtype=np.float32) - data = tensor() - for i in range(3): - xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32) - mean = np.mean(np.mean(xv, axis=0, keepdims=True), axis=2, keepdims=True) - xv_transposed = np.transpose(xv, [0, 2, 1]).reshape( - (data_shape[0] * data_shape[2], nr_chan) - ) - - var_biased = np.var(xv_transposed, axis=0).reshape((1, nr_chan, 1)) - sd = np.sqrt(var_biased + bn.eps) - - var_unbiased = np.var(xv_transposed, axis=0, ddof=1).reshape((1, nr_chan, 1)) - running_mean = running_mean * momentum + mean * (1 - momentum) - running_var = running_var * momentum + var_unbiased * (1 - momentum) - - data.set_value(xv) - yv = bn(data) - yv_expect = (xv - mean) / sd - - assertTensorClose(yv_expect, yv.numpy(), max_err=5e-6) - assertTensorClose( - running_mean.reshape(-1), bn.running_mean.numpy().reshape(-1), max_err=5e-6 - ) - assertTensorClose( - running_var.reshape(-1), bn.running_var.numpy().reshape(-1), max_err=5e-6 - ) - - # test set 'training' flag to False - mean_backup = bn.running_mean.numpy() - var_backup = bn.running_var.numpy() - bn.training = False - xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32) - data.set_value(xv) - yv1 = bn(data) - yv2 = bn(data) - assertTensorClose(yv1.numpy(), yv2.numpy(), max_err=0) - assertTensorClose(mean_backup, bn.running_mean.numpy(), max_err=0) - assertTensorClose(var_backup, bn.running_var.numpy(), max_err=0) - yv_expect = (xv - running_mean) / np.sqrt(running_var + bn.eps) - assertTensorClose(yv_expect, yv1.numpy(), max_err=5e-6) - - -def test_batchnorm2d(): - nr_chan = 8 - data_shape = (3, nr_chan, 16, 16) - momentum = 0.9 - bn = BatchNorm2d(nr_chan, momentum=momentum) - running_mean = np.zeros((1, nr_chan, 1, 1), dtype=np.float32) - running_var = np.ones((1, nr_chan, 1, 1), dtype=np.float32) - data = tensor() - for i in range(3): - xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32) - xv_transposed = np.transpose(xv, [0, 2, 3, 1]).reshape( - (data_shape[0] * data_shape[2] * data_shape[3], nr_chan) - ) - - mean = np.mean(xv_transposed, axis=0).reshape(1, nr_chan, 1, 1) - - var_biased = np.var(xv_transposed, axis=0).reshape((1, nr_chan, 1, 1)) - sd = np.sqrt(var_biased + bn.eps) - - var_unbiased = np.var(xv_transposed, axis=0, ddof=1).reshape((1, nr_chan, 1, 1)) - running_mean = running_mean * momentum + mean * (1 - momentum) - running_var = running_var * momentum + var_unbiased * (1 - momentum) - - data.set_value(xv) - yv = bn(data) - yv_expect = (xv - mean) / sd - - assertTensorClose(yv_expect, yv.numpy(), max_err=5e-6) - assertTensorClose(running_mean, bn.running_mean.numpy(), max_err=5e-6) - assertTensorClose(running_var, bn.running_var.numpy(), max_err=5e-6) - - # test set 'training' flag to False - mean_backup = bn.running_mean.numpy() - var_backup = bn.running_var.numpy() - bn.training = False - xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32) - data.set_value(xv) - yv1 = bn(data) - yv2 = bn(data) - assertTensorClose(yv1.numpy(), yv2.numpy(), max_err=0) - assertTensorClose(mean_backup, bn.running_mean.numpy(), max_err=0) - assertTensorClose(var_backup, bn.running_var.numpy(), max_err=0) - yv_expect = (xv - running_mean) / np.sqrt(running_var + bn.eps) - assertTensorClose(yv_expect, yv1.numpy(), max_err=5e-6) - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -def test_syncbn2d(): - nr_chan = 8 - data_shape = (3, nr_chan, 16, 16) - momentum = 0.9 - bn = SyncBatchNorm(nr_chan, momentum=momentum) - running_mean = np.zeros((1, nr_chan, 1, 1), dtype=np.float32) - running_var = np.ones((1, nr_chan, 1, 1), dtype=np.float32) - data = tensor() - for i in range(3): - xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32) - xv_transposed = np.transpose(xv, [0, 2, 3, 1]).reshape( - (data_shape[0] * data_shape[2] * data_shape[3], nr_chan) - ) - - mean = np.mean(xv_transposed, axis=0).reshape(1, nr_chan, 1, 1) - - var_biased = np.var(xv_transposed, axis=0).reshape((1, nr_chan, 1, 1)) - sd = np.sqrt(var_biased + bn.eps) - - var_unbiased = np.var(xv_transposed, axis=0, ddof=1).reshape((1, nr_chan, 1, 1)) - running_mean = running_mean * momentum + mean * (1 - momentum) - running_var = running_var * momentum + var_unbiased * (1 - momentum) - - data.set_value(xv) - yv = bn(data) - yv_expect = (xv - mean) / sd - - assertTensorClose(yv_expect, yv.numpy(), max_err=5e-6) - assertTensorClose(running_mean, bn.running_mean.numpy(), max_err=5e-6) - assertTensorClose(running_var, bn.running_var.numpy(), max_err=5e-6) - - # test set 'training' flag to False - mean_backup = bn.running_mean.numpy() - var_backup = bn.running_var.numpy() - bn.training = False - xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32) - data.set_value(xv) - yv1 = bn(data) - yv2 = bn(data) - assertTensorClose(yv1.numpy(), yv2.numpy(), max_err=0) - assertTensorClose(mean_backup, bn.running_mean.numpy(), max_err=0) - assertTensorClose(var_backup, bn.running_var.numpy(), max_err=0) - yv_expect = (xv - running_mean) / np.sqrt(running_var + bn.eps) - assertTensorClose(yv_expect, yv1.numpy(), max_err=5e-6) - - -def test_batchnorm_no_stats(): - nr_chan = 8 - data_shape = (3, nr_chan, 4) - bn = BatchNorm1d(8, track_running_stats=False) - data = tensor() - for i in range(4): - if i == 2: - bn.training = False - xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32) - mean = np.mean(np.mean(xv, axis=0, keepdims=True), axis=2, keepdims=True) - var = np.var( - np.transpose(xv, [0, 2, 1]).reshape( - (data_shape[0] * data_shape[2], nr_chan) - ), - axis=0, - ).reshape((1, nr_chan, 1)) - sd = np.sqrt(var + bn.eps) - - data.set_value(xv) - yv = bn(data) - yv_expect = (xv - mean) / sd - - assertTensorClose(yv_expect, yv.numpy(), max_err=5e-6) - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -def test_syncbn_no_stats(): - nr_chan = 8 - data_shape = (3, nr_chan, 4) - bn = SyncBatchNorm(8, track_running_stats=False) - data = tensor() - for i in range(4): - if i == 2: - bn.training = False - xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32) - mean = np.mean(np.mean(xv, axis=0, keepdims=True), axis=2, keepdims=True) - var = np.var( - np.transpose(xv, [0, 2, 1]).reshape( - (data_shape[0] * data_shape[2], nr_chan) - ), - axis=0, - ).reshape((1, nr_chan, 1)) - sd = np.sqrt(var + bn.eps) - - data.set_value(xv) - yv = bn(data) - yv_expect = (xv - mean) / sd - - assertTensorClose(yv_expect, yv.numpy(), max_err=5e-6) - - -def test_batchnorm2d_no_stats(): - nr_chan = 8 - data_shape = (3, nr_chan, 16, 16) - bn = BatchNorm2d(8, track_running_stats=False) - data = tensor() - for i in range(4): - if i == 2: - bn.training = False - xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32) - xv_transposed = np.transpose(xv, [0, 2, 3, 1]).reshape( - (data_shape[0] * data_shape[2] * data_shape[3], nr_chan) - ) - - mean = np.mean(xv_transposed, axis=0).reshape(1, nr_chan, 1, 1) - var = np.var(xv_transposed, axis=0).reshape((1, nr_chan, 1, 1)) - sd = np.sqrt(var + bn.eps) - - data.set_value(xv) - yv = bn(data) - yv_expect = (xv - mean) / sd - - assertTensorClose(yv_expect, yv.numpy(), max_err=5e-6) - - -@pytest.mark.skipif( - platform.system() == "Darwin", reason="do not imp GPU mode at macos now" -) -@pytest.mark.skipif( - platform.system() == "Windows", reason="do not imp GPU mode at Windows now" -) -def test_syncbn2d_no_stats(): - nr_chan = 8 - data_shape = (3, nr_chan, 16, 16) - bn = SyncBatchNorm(8, track_running_stats=False) - data = tensor() - for i in range(4): - if i == 2: - bn.training = False - xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32) - xv_transposed = np.transpose(xv, [0, 2, 3, 1]).reshape( - (data_shape[0] * data_shape[2] * data_shape[3], nr_chan) - ) - - mean = np.mean(xv_transposed, axis=0).reshape(1, nr_chan, 1, 1) - var = np.var(xv_transposed, axis=0).reshape((1, nr_chan, 1, 1)) - sd = np.sqrt(var + bn.eps) - - data.set_value(xv) - yv = bn(data) - yv_expect = (xv - mean) / sd - - assertTensorClose(yv_expect, yv.numpy(), max_err=5e-6) diff --git a/python_module/test/unit/module/test_conv.py b/python_module/test/unit/module/test_conv.py deleted file mode 100644 index f67a8aaf..00000000 --- a/python_module/test/unit/module/test_conv.py +++ /dev/null @@ -1,110 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import itertools - -import numpy as np - -from megengine import Parameter, tensor -from megengine.module import ConvTranspose2d, LocalConv2d -from megengine.test import assertTensorClose - - -def test_conv_transpose2d(): - SH, SW = 3, 1 - PH, PW = 2, 0 - N, IC, IH, IW = 4, 5, 8, 6 - KH, KW = 3, 4 - OC = 3 - BIAS = True - - def getsize(inp, kern, stride): - return (inp - 1) * stride + kern - - OH = getsize(IH, KH, SH) - OW = getsize(IW, KW, SW) - - inp = np.random.normal(size=(N, IC, IH, IW)).astype(np.float32) - out = np.zeros((N, OC, OH, OW), dtype=np.float32) - weight = np.random.normal(size=(IC, OC, KH, KW)).astype(np.float32) - bias = np.random.normal(size=(1, OC, 1, 1)).astype(np.float32) - - # naive calculation use numpy - for n, ic, ih, iw in itertools.product(*map(range, [N, IC, IH, IW])): - oh, ow = ih * SH, iw * SW - out[n, :, oh : oh + KH, ow : ow + KW] += inp[n, ic, ih, iw] * weight[ic] - out = out[:, :, PH : OH - PH, PW : OW - PW] - if BIAS: - out += bias - - # megengine conv_transpose2d calculation - conv_transpose2d = ConvTranspose2d(IC, OC, (KH, KW), (SH, SW), (PH, PW), bias=BIAS) - conv_transpose2d.weight = Parameter(weight, dtype=np.float32) - if BIAS: - conv_transpose2d.bias = Parameter(bias, dtype=np.float32) - y = conv_transpose2d(tensor(inp)) - - assertTensorClose(out, y.numpy(), max_err=2e-6) - - -def test_local_conv2d(): - batch_size = 10 - in_channels = 4 - out_channels = 8 - input_height = 8 - input_width = 8 - kernel_size = 3 - stride = 1 - padding = 1 - dilation = 1 - groups = 1 - local_conv2d = LocalConv2d( - in_channels=in_channels, - out_channels=out_channels, - input_height=input_height, - input_width=input_width, - kernel_size=kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - groups=groups, - ) - inputs = np.random.normal( - size=(batch_size, in_channels, input_height, input_width) - ).astype(np.float32) - output_height = (input_height + padding * 2 - kernel_size) // stride + 1 - output_width = (input_width + padding * 2 - kernel_size) // stride + 1 - weights = np.random.normal( - size=( - groups, - output_height, - output_width, - in_channels // groups, - kernel_size, - kernel_size, - out_channels // groups, - ) - ).astype(np.float32) - local_conv2d.weight = Parameter(weights) - outputs = local_conv2d(tensor(inputs)) - # naive calculation use numpy - # only test output_height == input_height, output_width == input_width, group == 1 - inputs = np.pad(inputs, ((0, 0), (0, 0), (1, 1), (1, 1))) - expected = np.zeros( - (batch_size, out_channels, output_height, output_width), dtype=np.float32, - ) - for n, oc, oh, ow in itertools.product( - *map(range, [batch_size, out_channels, output_height, output_width]) - ): - ih, iw = oh * stride, ow * stride - expected[n, oc, ih, iw] = np.sum( - inputs[n, :, ih : ih + kernel_size, iw : iw + kernel_size] - * weights[0, oh, ow, :, :, :, oc] - ) - - assertTensorClose(outputs.numpy(), expected, max_err=1e-5) diff --git a/python_module/test/unit/module/test_external.py b/python_module/test/unit/module/test_external.py deleted file mode 100644 index 44f5cf21..00000000 --- a/python_module/test/unit/module/test_external.py +++ /dev/null @@ -1,70 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import os - -import numpy as np - -import megengine as mge -from megengine import tensor -from megengine.module import Module -from megengine.module.external import AtlasSubgraph, CambriconSubgraph - - -class CambriconModule(Module): - def __init__(self, data): - super().__init__() - self.cambricon = CambriconSubgraph(data, "subnet0", True) - - def forward(self, inputs): - out = self.cambricon(inputs) - return out - - -def test_cambricon_module(): - model = "CambriconRuntimeOprTest.MutableBatchSize.mlu" - model = os.path.join(os.path.dirname(__file__), model) - with open(model, "rb") as f: - data = f.read() - m = CambriconModule(data) - inputs = [] - inputs.append(tensor(dtype=np.float16, device="cambricon0")) - inputs[0].set_value(np.random.normal(size=(1, 64, 32, 32)).astype(np.float16)) - - def inference(inps): - pred = m(inps) - return pred - - pred = inference(inputs) - - -class AtlasModule(Module): - def __init__(self, data): - super().__init__() - self.atlas = AtlasSubgraph(data) - - def forward(self, inputs): - out = self.atlas(inputs) - return out - - -def test_atlas_module(): - model = "AtlasRuntimeOprTest.basic.om" - model = os.path.join(os.path.dirname(__file__), model) - with open(model, "rb") as f: - data = f.read() - m = AtlasModule(data) - inputs = [] - inputs.append(tensor(dtype=np.float32, device="atlas0")) - inputs[0].set_value(np.random.normal(size=(4, 3, 16, 16)).astype(np.float32)) - - def inference(inps): - pred = m(inps) - return pred - - pred = inference(inputs) diff --git a/python_module/test/unit/module/test_init.py b/python_module/test/unit/module/test_init.py deleted file mode 100644 index 06bc4339..00000000 --- a/python_module/test/unit/module/test_init.py +++ /dev/null @@ -1,27 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import pytest - -from megengine.module import Conv2d, Linear -from megengine.module.init import calculate_fan_in_and_fan_out - - -def test_calculate_fan_in_and_fan_out(): - l = Linear(in_features=3, out_features=8) - fanin, fanout = calculate_fan_in_and_fan_out(l.weight) - assert fanin == 3 - assert fanout == 8 - - with pytest.raises(ValueError): - calculate_fan_in_and_fan_out(l.bias) - - l = Conv2d(in_channels=2, out_channels=3, kernel_size=(5, 7)) - fanin, fanout = calculate_fan_in_and_fan_out(l.weight) - assert fanin == 2 * 5 * 7 - assert fanout == 3 * 5 * 7 diff --git a/python_module/test/unit/module/test_module.py b/python_module/test/unit/module/test_module.py deleted file mode 100644 index 0766f6ee..00000000 --- a/python_module/test/unit/module/test_module.py +++ /dev/null @@ -1,464 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import os -import tempfile -from collections import OrderedDict -from io import BytesIO - -import numpy as np -import pytest -from helpers import MLP - -import megengine as mge -import megengine._internal as mgb -import megengine.functional as F -from megengine.core import Buffer, Parameter, Tensor, tensor -from megengine.module import ( - BatchNorm1d, - BatchNorm2d, - Conv2d, - Linear, - Module, - Sequential, -) -from megengine.quantization.quantize import quantize, quantize_qat -from megengine.test import assertTensorClose - - -class MyModule(Module): - class InnerModule(Module): - def __init__(self): - super().__init__() - self.bn = BatchNorm2d(4) - - def forward(self, x): - return self.bn(x) - - def __init__(self): - super().__init__() - self.i = self.InnerModule() - self.bn = BatchNorm2d(4) - self.param = Parameter(np.ones(1, dtype=np.float32)) - self.buff = Buffer(np.ones(1, dtype=np.float32)) - - def forward(self, x): - x = self.i(x) - x = self.bn(x) - return x - - -def test_module_api(): - m = MyModule() - assert list(m.children()) == [m.bn, m.i] - assert list(m.named_children()) == [("bn", m.bn), ("i", m.i)] - assert list(m.modules()) == [m, m.bn, m.i, m.i.bn] - assert list(m.named_modules()) == [ - ("", m), - ("bn", m.bn), - ("i", m.i), - ("i.bn", m.i.bn), - ] - assert list(m.named_modules(prefix="x")) == [ - ("x", m), - ("x.bn", m.bn), - ("x.i", m.i), - ("x.i.bn", m.i.bn), - ] - assert list(m.buffers()) == [ - m.bn.running_mean, - m.bn.running_var, - m.buff, - m.i.bn.running_mean, - m.i.bn.running_var, - ] - assert list(m.buffers(recursive=False)) == [m.buff] - assert list(m.named_buffers()) == [ - ("bn.running_mean", m.bn.running_mean), - ("bn.running_var", m.bn.running_var), - ("buff", m.buff), - ("i.bn.running_mean", m.i.bn.running_mean), - ("i.bn.running_var", m.i.bn.running_var), - ] - assert list(m.parameters()) == [ - m.bn.bias, - m.bn.weight, - m.i.bn.bias, - m.i.bn.weight, - m.param, - ] - assert list(m.named_parameters()) == [ - ("bn.bias", m.bn.bias), - ("bn.weight", m.bn.weight), - ("i.bn.bias", m.i.bn.bias), - ("i.bn.weight", m.i.bn.weight), - ("param", m.param), - ] - m.eval() - assert ( - m.training == False - and m.bn.training == False - and m.i.training == False - and m.i.bn.training == False - ) - m.bn.train() - assert m.training == False and m.bn.training == True and m.i.bn.training == False - m.eval() - m.i.train() - assert ( - m.training == False - and m.bn.training == False - and m.i.training == True - and m.i.bn.training == True - ) - m.eval() - m.train() - assert m.training == True and m.bn.training == True and m.i.bn.training == True - - def fn(m): - m.training = False - - m.apply(fn) - assert m.bn.training == False and m.i.bn.training == False - - -def test_module_api_reuse_submodule(): - m = MyModule() - m.h = m.i # pylint: disable=attribute-defined-outside-init - assert list(m.modules()) == [m, m.bn, m.i, m.i.bn] - assert list(m.named_modules()) == [ - ("", m), - ("bn", m.bn), - ("h", m.i), - ("h.bn", m.i.bn), - ] - - -def test_module_api_iterable_stability(): - m = MyModule() - l = list(m.modules()) - for _ in range(100): - assert list(m.modules()) == l - - -def test_module_api_hooks(): - net = MyModule() - pre_hook_num = 0 - post_hook_num = 0 - hooks = [] - - def pre_hook(module, inputs): - nonlocal pre_hook_num - pre_hook_num += 1 - modified_inputs = tuple(inp + 1 for inp in inputs) - return modified_inputs - - def post_hook(module, inputs, outputs): - nonlocal post_hook_num - post_hook_num += 1 - outputs += 1 - return outputs - - net.apply(lambda module: hooks.append(module.register_forward_pre_hook(pre_hook))) - net.apply(lambda module: hooks.append(module.register_forward_hook(post_hook))) - - shape = (1, 4, 1, 1) - x = tensor(np.zeros(shape, dtype=np.float32)) - y = net(x) - - assert pre_hook_num == 4 - assert post_hook_num == 4 - mean1 = Parameter(np.zeros(shape), dtype=np.float32) - bn1 = F.batch_norm2d( - x + 3, mean1, Parameter(np.ones(shape), dtype=np.float32), training=True - ) - assertTensorClose( - net.i.bn.running_mean, mean1, - ) - mean2 = Parameter(np.zeros(shape), dtype=np.float32) - bn2 = F.batch_norm2d( - bn1 + 3, mean2, Parameter(np.ones(shape), dtype=np.float32), training=True - ) - assertTensorClose( - net.bn.running_mean, mean2, - ) - assertTensorClose(bn2 + 2, y) - - assert len(hooks) == 8 - for handler in hooks: - handler.remove() - y = net(x) - assert pre_hook_num == 4 - assert post_hook_num == 4 - - -class MyModule2(Module): - class InnerModule(Module): - def __init__(self): - super().__init__() - self.bn = BatchNorm2d(4) - self.test_bool_key = {True: 1, False: 0} - - def forward(self, x): - x = self.bn(x) - - def __init__(self): - super().__init__() - self.bn = BatchNorm2d(4) - self.a = [ - BatchNorm2d(4), - {"x": BatchNorm2d(4), "y": [BatchNorm2d(4), self.InnerModule()], "z": 0}, - (self.InnerModule(),), - ] - - def forward(self, x): - return x - - -def test_expand_structure(): - m = MyModule2() - assert list(m.named_modules()) == [ - ("", m), - ("a.0", m.a[0]), - ("a.1.x", m.a[1]["x"]), - ("a.1.y.0", m.a[1]["y"][0]), - ("a.1.y.1", m.a[1]["y"][1]), - ("a.1.y.1.bn", m.a[1]["y"][1].bn), - ("a.2.0", m.a[2][0]), - ("a.2.0.bn", m.a[2][0].bn), - ("bn", m.bn), - ] - - -def test_flatten_others(): - def be_others(obj): - return not isinstance(obj, (Tensor, Module)) - - m = MyModule2() - assert len(list(m._flatten(with_key=True, predicate=be_others))) == 0 - - -def test_flatten_with_parent(): - m = MyModule2() - assert list(m.named_modules(with_parent=True)) == [ - ("", m, None), - ("a.0", m.a[0], m), - ("a.1.x", m.a[1]["x"], m), - ("a.1.y.0", m.a[1]["y"][0], m), - ("a.1.y.1", m.a[1]["y"][1], m), - ("a.1.y.1.bn", m.a[1]["y"][1].bn, m.a[1]["y"][1]), - ("a.2.0", m.a[2][0], m), - ("a.2.0.bn", m.a[2][0].bn, m.a[2][0]), - ("bn", m.bn, m), - ] - assert list(m.modules(with_parent=True)) == [ - (m, None), - (m.a[0], m), - (m.a[1]["x"], m), - (m.a[1]["y"][0], m), - (m.a[1]["y"][1], m), - (m.a[1]["y"][1].bn, m.a[1]["y"][1]), - (m.a[2][0], m), - (m.a[2][0].bn, m.a[2][0]), - (m.bn, m), - ] - - -class MyModule3(Module): - class InnerModule(Module): - def __init__(self): - super().__init__() - self.bn = BatchNorm2d(4) - - def forward(self, x): - x = self.bn(x) - - def __init__(self): - super().__init__() - self.bn = BatchNorm2d(4) - self.seq = Sequential(BatchNorm2d(4), self.InnerModule(),) - - def forward(self, x): - return x - - -def test_module_api_with_sequential(): - m = MyModule3() - assert list(m.named_modules()) == [ - ("", m), - ("bn", m.bn), - ("seq", m.seq), - ("seq.0", m.seq[0]), - ("seq.1", m.seq[1]), - ("seq.1.bn", m.seq[1].bn), - ] - - -def test_sequential_named_children(): - modules = OrderedDict() - modules["name0"] = Linear(20, 10) - modules["name1"] = Linear(10, 5) - modules["name2"] = Linear(5, 1) - m = Sequential(modules) - l = list(m.named_children()) - assert l[0][0] == "name0" - assert l[1][0] == "name1" - assert l[2][0] == "name2" - - -def test_state_dict(): - data_shape = (2, 28) - data = tensor() - data.set_value(np.random.random(data_shape)) - mlp = MLP() - pred0 = mlp(data) - - with BytesIO() as fout: - mge.save(mlp.state_dict(), fout) - fout.seek(0) - state_dict = mge.load(fout) - state_dict["extra"] = None - mlp1 = MLP() - mlp1.load_state_dict(state_dict, strict=False) - pred1 = mlp1(data) - assertTensorClose(pred0.numpy(), pred1.numpy(), max_err=5e-6) - with pytest.raises(KeyError): - mlp1.load_state_dict(state_dict) - del state_dict["extra"] - del state_dict["dense0.bias"] - with pytest.raises(KeyError): - mlp1.load_state_dict(state_dict) - - -class AssertModule(Module): - def __init__(self): - super().__init__() - self.error_tensor_key = {True: tensor(), False: 0} - - def forward(self, x): - return x - - -def test_assert_message(): - m = AssertModule() - with pytest.raises( - AssertionError, match="keys for Tensor and Module must be str, error key: True" - ): - list(m._flatten()) - - -class Simple(Module): - def __init__(self): - super().__init__() - self.conv0 = Conv2d(1, 1, kernel_size=3, bias=False) - self.conv1 = Conv2d(1, 1, kernel_size=3, bias=False) - self.conv1.weight = self.conv0.weight - - def forward(self, inputs): - pass - - -def test_shared_param(): - net = Simple() - assert net.conv0.weight is net.conv1.weight - data = tensor(np.random.random((1, 1, 8, 8)).astype(np.float32)) - assertTensorClose(net.conv0(data).numpy(), net.conv1(data).numpy()) - with BytesIO() as f: - mge.save(net, f) - f.seek(0) - net1 = mge.load(f) - assert net1.conv0.weight is net1.conv1.weight - assertTensorClose(net1.conv0(data).numpy(), net1.conv1(data).numpy()) - - with BytesIO() as f: - mge.save(net.conv0, f) - f.seek(0) - conv0 = mge.load(f) - - with BytesIO() as f: - mge.save(net.conv1, f) - f.seek(0) - conv1 = mge.load(f) - - assert conv0.weight is not conv1.weight - assertTensorClose(conv0(data).numpy(), conv1(data).numpy()) - - -def test_pickle_module(): - data_shape = (2, 28) - data = tensor() - data.set_value(np.random.random(data_shape)) - mlp = MLP() - # pickle before forward - with BytesIO() as fout: - mge.save(mlp, fout) - fout.seek(0) - mlp1 = mge.load(fout) - pred0 = mlp1(data) - - pred1 = mlp(data) - - # pickle after forward - with BytesIO() as fout: - mge.save(mlp, fout) - fout.seek(0) - mlp1 = mge.load(fout) - pred2 = mlp1(data) - - assertTensorClose(pred0.numpy(), pred1.numpy(), max_err=5e-6) - assertTensorClose(pred0.numpy(), pred2.numpy(), max_err=5e-6) - - -def test_dump_model(): - data_shape = (2, 28) - data = tensor() - data.set_value(np.random.random(data_shape)) - mlp = MLP() - pred = mlp(data) - f = tempfile.NamedTemporaryFile(delete=False) - f_name = f.name - try: - mge.dump(pred, f_name) - finally: - f.close() - os.unlink(f_name) - - -def test_load_quantized(): - data_shape = (2, 28) - data = tensor(np.random.random(data_shape), dtype="float32") - data = data.astype(mgb.dtype.qint8(0.1)) - mlp = MLP() - quantize_qat(mlp) - quantize(mlp) - mlp.dense0.weight = Parameter( - mlp.dense0.weight.astype(mgb.dtype.qint8(0.001)).numpy() - ) - mlp.dense1.weight = Parameter( - mlp.dense1.weight.astype(mgb.dtype.qint8(0.0002)).numpy() - ) - mlp.eval() - pred0 = mlp(data) - - with BytesIO() as fout: - mge.save(mlp.state_dict(), fout) - fout.seek(0) - checkpoint = mge.load(fout) - # change mlp weight. - mlp.dense0.weight = Parameter( - mlp.dense0.weight.astype(mgb.dtype.qint8(0.00001)).numpy() - ) - mlp.dense1.weight = Parameter( - mlp.dense1.weight.astype(mgb.dtype.qint8(0.2)).numpy() - ) - mlp.load_state_dict(checkpoint) - pred1 = mlp(data) - - assertTensorClose( - pred0.astype("float32").numpy(), pred1.astype("float32").numpy(), max_err=5e-6 - ) diff --git a/python_module/test/unit/module/test_pytorch.py b/python_module/test/unit/module/test_pytorch.py deleted file mode 100644 index d7b3ae9a..00000000 --- a/python_module/test/unit/module/test_pytorch.py +++ /dev/null @@ -1,140 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np -import torch -from helpers import randomTorch - -import megengine as mge -import megengine._internal as mgb -import megengine.functional -import megengine.optimizer as optimizer -from megengine import get_default_device, set_default_device -from megengine.core import Parameter, tensor -from megengine.jit import trace -from megengine.module import Module as MGEModule -from megengine.module.pytorch import PyTorchModule -from megengine.test import assertTensorClose - - -def test_pytorch_forward(): - class APlusB(torch.nn.Module): - def __init__(self): - super(APlusB, self).__init__() - - def forward(self, a, b): - return a + b - - a = randomTorch(15, 15) - b = randomTorch(15, 15) - - def get_pytorch_forward(): - return APlusB()(a, b) - - def get_mge_forward(): - mge_module = PyTorchModule(APlusB()) - mge_a = tensor(a.numpy(), dtype=np.float32) - mge_b = tensor(b.numpy(), dtype=np.float32) - return mge_module(mge_a, mge_b) - - assertTensorClose(get_pytorch_forward().numpy(), get_mge_forward().numpy()) - - -def test_pytorch_backward(): - class APlusB(torch.nn.Module): - def __init__(self): - super(APlusB, self).__init__() - - def forward(self, a, b): - return a + b - - a = randomTorch(15, 15) - b = randomTorch(15, 15) - - def get_pytorch_backward(): - parameter_a = a.clone() - parameter_a.requires_grad = True - c = APlusB()(parameter_a, b) - d = APlusB()(c, b) - e = torch.sum(d) - e.backward() - return parameter_a.grad - - def get_mge_backward(): - mge_module = PyTorchModule(APlusB()) - mge_a = Parameter(a.numpy(), dtype=np.float32) - mge_b = tensor(b.numpy(), dtype=np.float32) - mge_c = mge_module(mge_a, mge_b) - mge_d = mge_module(mge_c, mge_b) - mge_e = mge.functional.sum(mge_d) - return mge.functional.grad(mge_e, mge_a, use_virtual_grad=False) - - assertTensorClose(get_pytorch_backward().numpy(), get_mge_backward().numpy()) - - -def test_pytorch_mixed(): - - init_param = (np.array([2.0], dtype=np.float32), np.array([3.0], dtype=np.float32)) - lr = 1.0 - - class Mixed(MGEModule): - class SubModule(torch.nn.Module): - def __init__(self): - super().__init__() - self.multiplier = torch.nn.Parameter(torch.tensor(init_param[0])) - - def forward(self, inp): - return inp * self.multiplier - - def __init__(self): - super().__init__() - self.torch_module = PyTorchModule(self.SubModule()) - self.multiplier = Parameter(init_param[1], dtype=np.float32) - - def forward(self, inp): - return self.torch_module(inp) * self.multiplier - - def run(step, enable_trace, use_symbolic): - def train_func(data, net=None, opt=None): - pred = net(data) - opt.backward(pred) - return pred - - if enable_trace: - train_func = trace(train_func, symbolic=use_symbolic) - - net = Mixed() - data = tensor() - opt = optimizer.SGD(net.parameters(), lr=lr) - - saved_param = init_param - for i in range(step): - opt.zero_grad() - data.set_value([i + 1.0]) - output = train_func(data, net=net, opt=opt) - opt.step() - - expect_param = ( - saved_param[0] - lr * saved_param[1] * data.numpy(), - saved_param[1] - lr * saved_param[0] * data.numpy(), - ) - assertTensorClose( - output.numpy(), saved_param[0] * saved_param[1] * data.numpy() - ) - torch_param = net.torch_module._torch_params[0].detach().cpu() - assertTensorClose(torch_param.numpy(), expect_param[0]) - assertTensorClose(net.multiplier.numpy(), expect_param[1]) - saved_param = expect_param - - run(1, False, False) - run(1, True, True) - run(1, True, False) - - run(2, False, False) - run(2, True, True) - run(2, True, False) diff --git a/python_module/test/unit/module/test_qat.py b/python_module/test/unit/module/test_qat.py deleted file mode 100644 index 6b6c5a86..00000000 --- a/python_module/test/unit/module/test_qat.py +++ /dev/null @@ -1,85 +0,0 @@ -from itertools import product - -import numpy as np - -from megengine import tensor -from megengine.module import ( - Conv2d, - ConvBn2d, - ConvRelu2d, - DequantStub, - Module, - QuantStub, -) -from megengine.quantization.quantize import disable_fake_quant, quantize_qat -from megengine.test import assertTensorClose - - -def test_qat_convbn2d(): - in_channels = 32 - out_channels = 64 - kernel_size = 3 - for groups, bias in product([1, 4], [True, False]): - module = ConvBn2d( - in_channels, out_channels, kernel_size, groups=groups, bias=bias - ) - module.train() - qat_module = quantize_qat(module, inplace=False) - disable_fake_quant(qat_module) - inputs = tensor(np.random.randn(4, in_channels, 32, 32).astype(np.float32)) - normal_outputs = module(inputs) - qat_outputs = qat_module(inputs) - assertTensorClose(normal_outputs, qat_outputs, max_err=5e-6) - assertTensorClose( - module.bn.running_mean, qat_module.bn.running_mean, max_err=5e-8 - ) - assertTensorClose( - module.bn.running_var, qat_module.bn.running_var, max_err=5e-7 - ) - module.eval() - normal_outputs = module(inputs) - qat_module.eval() - qat_outputs = qat_module(inputs) - assertTensorClose(normal_outputs, qat_outputs, max_err=5e-6) - - -def test_qat_conv(): - - in_channels = 32 - out_channels = 64 - kernel_size = 3 - - class TestNet(Module): - def __init__(self, groups, bias): - super().__init__() - self.quant = QuantStub() - self.dequant = DequantStub() - self.conv = Conv2d( - in_channels, out_channels, kernel_size, groups=groups, bias=bias - ) - self.conv_relu = ConvRelu2d( - out_channels, in_channels, kernel_size, groups=groups, bias=bias - ) - - def forward(self, inp): - out = self.quant(inp) - out = self.conv(out) - out = self.conv_relu(out) - out = self.dequant(out) - return out - - inputs = tensor(np.random.randn(4, in_channels, 32, 32).astype(np.float32)) - for groups, bias in product([1, 4], [True, False]): - net = TestNet(groups, bias) - net.train() - qat_net = quantize_qat(net, inplace=False) - disable_fake_quant(qat_net) - normal_outputs = net(inputs) - qat_outputs = qat_net(inputs) - assertTensorClose(normal_outputs, qat_outputs) - - net.eval() - normal_outputs = net(inputs) - qat_net.eval() - qat_outputs = qat_net(inputs) - assertTensorClose(normal_outputs, qat_outputs) diff --git a/python_module/test/unit/module/test_tensor.py b/python_module/test/unit/module/test_tensor.py deleted file mode 100644 index 86c5726e..00000000 --- a/python_module/test/unit/module/test_tensor.py +++ /dev/null @@ -1,87 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import copy - -import numpy as np -import pytest - -import megengine as mge -import megengine.functional as F -from megengine.core import Buffer, Graph, Parameter -from megengine.module import Conv2d -from megengine.test import assertTensorClose - - -def test_set_value(): - v0 = np.random.random((2, 3)).astype(np.float32) - param = Parameter(v0) - v1 = np.random.random((2, 3)).astype(np.float32) - param.set_value(v1) - assertTensorClose(param.numpy(), v1, max_err=5e-6) - v2 = np.random.random((3, 3)).astype(np.float32) - # TODO: add this - # with pytest.raises(ValueError): - # param.set_value(v2) - assertTensorClose(param.numpy(), v1, max_err=5e-6) - - -def test_fill(): - a = Buffer(np.zeros((2, 3), dtype=np.float32)) - a.fill(3) - assertTensorClose(a.numpy(), np.full((2, 3), 3, dtype=np.float32)) - a.fill(124.568) - assertTensorClose(a.numpy(), np.full((2, 3), 124.568, dtype=np.float32)) - - -# TODO: remove or rewrite following test -# def test_attach(): -# p_ = np.random.random((2, 3)).astype(np.float32) - -# with Graph() as g: -# g.set_option('eager_evaluation', False) -# p = Parameter(p_) -# v = p * 2 -# f = compile(v, None) - -# out, = f() -# assertTensorClose(out, p_ * 2) - -# F.add_update(p, p) -# out, = f() -# assertTensorClose(out, p_ * 4) - -# TODO: remove or rewrite following test -# def test_module_attach(): -# v = np.random.random((1, 3, 64, 64)).astype(np.float32) -# net = Conv2d(3, 16, 3) - -# with Graph() as g: -# g.set_option('eager_evaluation', False) - -# data0 = Input("data") -# f = compile(net(data0), None) - -# out0, = f(data=v) - -# data1 = Input("data", value=v) -# out1 = net(data1) - -# assertTensorClose(out0, out1.numpy()) - - -def test_shape_warning(): - with Graph() as cg: - cg.set_option("eager_evaluation", False) - b = Buffer(np.ones((2, 3)).astype(np.float32)) - with pytest.warns(None) as record: - print(b.shape) - if len(record) != 0: - raise ValueError( - "Getting the shape of a constant Tensor should throw no Warning" - ) diff --git a/python_module/test/unit/optimizer/__init__.py b/python_module/test/unit/optimizer/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/python_module/test/unit/optimizer/test_lr_scheduler.py b/python_module/test/unit/optimizer/test_lr_scheduler.py deleted file mode 100644 index e185f179..00000000 --- a/python_module/test/unit/optimizer/test_lr_scheduler.py +++ /dev/null @@ -1,23 +0,0 @@ -from bisect import bisect_right - -import numpy as np -from helpers import MLP - -from megengine.optimizer import SGD, MultiStepLR -from megengine.test import assertTensorClose - - -def test_multi_step_lr(): - mlp = MLP() - opt = SGD(mlp.parameters(), lr=0.01, momentum=0.9) - scheduler = MultiStepLR(opt, [3, 6, 8]) - - lr = np.array(0.01, dtype=np.float32) - for i in range(10): - for group in opt.param_groups: - assertTensorClose( - np.array(group["lr"], dtype=np.float32), - (lr * 0.1 ** bisect_right([3, 6, 8], i)).astype(np.float32), - max_err=5e-6, - ) - scheduler.step() diff --git a/python_module/test/unit/optimizer/test_optimizer.py b/python_module/test/unit/optimizer/test_optimizer.py deleted file mode 100644 index e172df79..00000000 --- a/python_module/test/unit/optimizer/test_optimizer.py +++ /dev/null @@ -1,258 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from io import BytesIO - -import numpy as np -from helpers import MLP, graph_mode - -import megengine.functional as F -from megengine import load, optimizer, save -from megengine.core import TensorDict, tensor -from megengine.jit import trace -from megengine.test import assertTensorClose - - -def get_input(): - batch_size, input_dim = 2, 28 - data_shape, label_shape = (batch_size, input_dim), (batch_size,) - data, label = tensor(dtype=np.float32), tensor(dtype=np.int32) - data.set_value(np.random.random(data_shape).astype(np.float32)) - label.set_value(np.random.randint(0, 10, label_shape)) - return data, data_shape, label, label_shape - - -@graph_mode("eager", "static") -def test_optimizer_serialization(): - data, data_shape, label, label_shape = get_input() - mlp = MLP() - opt = optimizer.SGD(mlp.parameters(), lr=0.01, momentum=0.9) - slots = TensorDict() - for param in mlp.parameters(): - slots[param] = np.zeros(param.shape).astype(np.float32) - - pred = mlp(data) - loss = F.square_loss(pred, label.reshape(-1, 1)) - opt.zero_grad() - opt.backward(loss) - opt.step() - for param in mlp.parameters(): - slots[param] = slots[param] * 0.9 + param.grad.numpy() - - with BytesIO() as fout: - save(opt.state_dict(), fout) - fout.seek(0) - state_dict = load(fout) - opt1 = optimizer.SGD(mlp.parameters(), lr=0.02, momentum=0.8) - opt1.load_state_dict(state_dict) - - data.set_value(np.random.random(data_shape).astype(np.float32)) - label.set_value(np.random.randint(0, 10, label_shape)) - pred = mlp(data) - loss = F.square_loss(pred, label.reshape(-1, 1)) - opt1.zero_grad() - opt1.backward(loss) - orig_params = TensorDict() - for param in mlp.parameters(): - orig_params[param] = np.copy(param.numpy()) - opt1.step() - for param in mlp.parameters(): - orig_param = orig_params[param] - slots[param] = slots[param] * 0.9 + param.grad.numpy() - assertTensorClose(param.numpy(), orig_param - 0.01 * slots[param]) - - -def _test_optimizer(opt_str, test_case, check_class, update_lr=False): - iter_num = 3 - data, data_shape, label, label_shape = get_input() - - net = MLP() - opt = getattr(optimizer, opt_str)(net.parameters(), **test_case) - check_func = check_class(net, **test_case) - - step = 0 - - # eager graph - for i in range(iter_num): - if update_lr and i == 1: # change learning rate - for group in opt.param_groups: - group["lr"] += 0.01 - check_func.lr += 0.01 - data.set_value(np.random.random(data_shape).astype(np.float32)) - label.set_value(np.random.randint(0, 10, label_shape)) - pred = net(data) - loss = F.square_loss(pred, label.reshape(-1, 1)) - opt.zero_grad() - opt.backward(loss) - ori_params = TensorDict() - for param in net.parameters(): - ori_params[param] = np.copy(param.numpy()) - opt.step() - step += 1 - check_func(ori_params, net.parameters(), step) - - # static graph - @trace - def train_func(data, label): - pred = net(data) - loss = F.square_loss(pred, label.reshape(-1, 1)) - opt.backward(loss) - - for i in range(iter_num): - if update_lr and i == 1: # change learning rate - for group in opt.param_groups: - group["lr"] += 0.01 - check_func.lr += 0.01 - opt.zero_grad() - ori_params = TensorDict() - for param in net.parameters(): - ori_params[param] = np.copy(param.numpy()) - train_func( - np.random.random(data_shape).astype(np.float32), - np.random.randint(0, 10, label_shape).astype(np.int32), - ) - opt.step() - step += 1 - check_func(ori_params, net.parameters(), step) - - -def test_sgd(): - class CheckValue: - def __init__(self, net, **kwarg): - self.slots = TensorDict() - for param in net.parameters(): - self.slots[param] = np.zeros(param.shape).astype(np.float32) - for k, v in kwarg.items(): - setattr(self, k, v) - - def __call__(self, ori_params, new_params, step): - for param in new_params: - grad = param.grad.numpy() - if hasattr(self, "momentum"): - self.slots[param] = grad + self.slots[param] * self.momentum - delta = -self.lr * self.slots[param] - else: - delta = -self.lr * grad - assertTensorClose(param.numpy(), ori_params[param] + delta) - - cases = [ - {"momentum": 0.9, "lr": 0.01}, # SGD with momentum - {"lr": 0.01}, # simple SGD - {"weight_decay": 0.1, "lr": 0.01}, # with weight_decay - ] - for case in cases: - _test_optimizer("SGD", case, CheckValue) - _test_optimizer("SGD", case, CheckValue, update_lr=True) - - -def test_adam(): - class CheckValue: - def __init__(self, net, **kwarg): - self.m_slots = TensorDict() - self.v_slots = TensorDict() - for param in net.parameters(): - self.m_slots[param] = np.zeros(param.shape).astype(np.float32) - self.v_slots[param] = np.zeros(param.shape).astype(np.float32) - for k, v in kwarg.items(): - setattr(self, k, v) - - def __call__(self, ori_params, new_params, step): - for param in new_params: - grad = param.grad.numpy() - m = self.m_slots[param] - v = self.v_slots[param] - m *= self.betas[0] - m += (1 - self.betas[0]) * grad - v *= self.betas[1] - v += (1 - self.betas[1]) * grad * grad - delta = (m / (1 - self.betas[0] ** step)) / ( - np.sqrt(v / (1 - self.betas[1] ** step)) + self.eps - ) - assertTensorClose(param.numpy(), ori_params[param] - self.lr * delta) - - cases = [ - {"betas": (0.8, 0.9), "eps": 1e-04, "lr": 0.01}, - { - "betas": (0.8, 0.9), - "eps": 1e-04, - "lr": 0.01, - "weight_decay": 0.1, - }, # with weight_decay - ] - for case in cases: - _test_optimizer("Adam", case, CheckValue) - _test_optimizer("Adam", case, CheckValue, update_lr=True) - - -def test_adagrad(): - class CheckValue: - def __init__(self, net, **kwarg): - self.s_slots = TensorDict() - for param in net.parameters(): - self.s_slots[param] = np.zeros(param.shape).astype(np.float32) - for k, v in kwarg.items(): - setattr(self, k, v) - - def __call__(self, ori_params, new_params, step): - for param in new_params: - grad = param.grad.numpy() - self.s_slots[param] += grad ** 2 - delta = grad / (self.s_slots[param] + self.eps) ** 0.5 - delta *= -(self.lr / (1 + (step - 1) * self.lr_decay)) - assertTensorClose(param.numpy(), ori_params[param] + delta) - - cases = [ - {"lr": 0.01, "eps": 1e-06, "lr_decay": 0.01}, - {"lr": 0.01, "eps": 1e-06, "lr_decay": 0.0}, # without lr_decay - { - "lr": 0.01, - "eps": 1e-06, - "lr_decay": 0.01, - "weight_decay": 0.1, - }, # with weight_decay - ] - for case in cases: - _test_optimizer("Adagrad", case, CheckValue) - _test_optimizer("Adagrad", case, CheckValue, update_lr=True) - - -def test_adadelta(): - class CheckValue: - def __init__(self, net, **kwarg): - self.s_slots = TensorDict() - self.a_slots = TensorDict() - for param in net.parameters(): - self.s_slots[param] = np.zeros(param.shape).astype(np.float32) - self.a_slots[param] = np.zeros(param.shape).astype(np.float32) - for k, v in kwarg.items(): - setattr(self, k, v) - - def __call__(self, ori_params, new_params, step): - for param in new_params: - grad = param.grad.numpy() - self.s_slots[param] = self.s_slots[param] * self.rho + grad ** 2 * ( - 1 - self.rho - ) - delta = ( - grad - * ((self.a_slots[param] + self.eps) ** 0.5) - / (self.s_slots[param] + self.eps) ** 0.5 - ) - self.a_slots[param] = self.a_slots[param] * self.rho + delta ** 2 * ( - 1 - self.rho - ) - delta *= -self.lr - assertTensorClose(param.numpy(), ori_params[param] + delta) - - cases = [ - {"lr": 1.0, "eps": 1e-06, "rho": 0.9}, - {"lr": 1.0, "eps": 1e-06, "rho": 0.9, "weight_decay": 0.9}, # with weight_decay - ] - for case in cases: - _test_optimizer("Adadelta", case, CheckValue) - _test_optimizer("Adadelta", case, CheckValue, update_lr=True) diff --git a/python_module/test/unit/quantization/quantize.py b/python_module/test/unit/quantization/quantize.py deleted file mode 100644 index 236ef9e1..00000000 --- a/python_module/test/unit/quantization/quantize.py +++ /dev/null @@ -1,80 +0,0 @@ -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -from megengine import module as Float -from megengine.module import qat as QAT -from megengine.quantization.quantize import _get_quantable_module_names, quantize_qat - - -def test_get_quantable_module_names(): - # need to make sure names from Quantized and QAT are the same - def _get_qat_module_names(): - def is_qat(key: str): - value = getattr(QAT, key) - return ( - isinstance(value, type) - and issubclass(value, QAT.QATModule) - and value != QAT.QATModule - ) - - # source should have all quantable modules' names - quantable_module_names = [key for key in dir(QAT) if is_qat(key)] - return quantable_module_names - - qat_module_names = _get_qat_module_names() - quantized_module_names = _get_quantable_module_names() - assert set(qat_module_names) == set(quantized_module_names) - - for key in qat_module_names: - value = getattr(Float, key) - assert ( - isinstance(value, type) - and issubclass(value, Float.Module) - and value != Float.Module - ) - - -def test_disable_quantize(): - class Net(Float.Module): - def __init__(self): - super().__init__() - self.conv = Float.ConvBnRelu2d(3, 3, 3) - self.conv.disable_quantize() - - def forward(self, x): - return self.conv(x) - - net = Net() - qat_net = quantize_qat(net, inplace=False) - assert isinstance(qat_net.conv, Float.ConvBnRelu2d) - assert isinstance(qat_net.conv.conv, Float.Conv2d) - - -def test_convert_with_custom_mapping(): - class FloatExample(Float.Module): - def forward(self, x): - return x - - class QATExample(QAT.QATModule): - def forward(self, x): - return x - - @classmethod - def from_float_module(cls, float_module): - return cls() - - class Net(Float.Module): - def __init__(self): - super().__init__() - self.example = FloatExample() - - def forward(self, x): - return self.example(x) - - net = Net() - qat_net = quantize_qat(net, inplace=False, mapping={FloatExample: QATExample}) - assert isinstance(qat_net.example, QATExample) diff --git a/python_module/test/unit/quantization/test_fake_quant.py b/python_module/test/unit/quantization/test_fake_quant.py deleted file mode 100644 index 0fbd9eb1..00000000 --- a/python_module/test/unit/quantization/test_fake_quant.py +++ /dev/null @@ -1,80 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np -import pytest - -import megengine as mge -import megengine._internal as mgb -from megengine.core import tensor -from megengine.quantization.fake_quant import TQT_Function -from megengine.quantization.internal_fake_quant import * -from megengine.test import assertTensorClose - - -class numpy_TQT_Function: - def __init__(self, lowerbound, upperbound): - super().__init__() - self.lowerbound = lowerbound - self.upperbound = upperbound - - def forward(self, inp, scale): - t = 2 ** scale - # t = F.maximum(t, 1e-4) - inp_scaled = inp / t - inp_clipped = np.maximum( - np.minimum(inp_scaled, self.upperbound), self.lowerbound - ) - inp_rounded = np.round(inp_clipped) - inp_flq = inp_rounded * t - self.saved_tensors = (inp_scaled, inp_rounded, t) - return inp_flq - - def backward(self, grad_inp_flq): - (inp_scaled, inp_rounded, t) = self.saved_tensors - mask_clip = (inp_scaled < -0.5 + self.lowerbound) + ( - inp_scaled > self.upperbound + 0.5 - ) # mask for accumulating the gradients of |data_scaled|>L - mask_quant = np.abs( - mask_clip - 1 - ) # mask for accumulating the gradients with |data_scaled|<=L - grad_quant = ( - grad_inp_flq * mask_quant * (inp_rounded - inp_scaled) - ) # gradient within |data_scaled|<=L - grad_clip = ( - grad_inp_flq * mask_clip * inp_rounded - ) # gradient with | data_scaled|>L - grad_s = grad_clip.sum() + grad_quant.sum() - # dL/ds = dL/dt * t * ln(2) - grad_s = grad_s * t * np.log(2) - grad_inp = grad_inp_flq * mask_quant - return grad_inp, grad_s - - -def test_TQT(): - f = TQT_Function(-127, 127) - nf = numpy_TQT_Function(-127, 127) - - def check_inp(a, b, c, a_np, b_np, c_np): - assertTensorClose( - f.forward(a, b).numpy(), nf.forward(a_np, b_np).astype("float32") - ) - c1, c2 = f.backward(c) - c1_np, c2_np = nf.backward(c_np) - assertTensorClose(c1.numpy(), c1_np.astype("float32")) - assertTensorClose(c2.numpy(), c2_np.astype("float32")) - - a = tensor() - b = tensor() - a_np = np.random.random((4, 3)).astype("float32") - b_np = np.random.random((1)).astype("float32") - a.set_value(a_np) - b.set_value(b_np) - check_inp(a, b, b, a_np, b_np, b_np) - - diff --git a/python_module/test/unit/random/test_random.py b/python_module/test/unit/random/test_random.py deleted file mode 100644 index 2e8023e8..00000000 --- a/python_module/test/unit/random/test_random.py +++ /dev/null @@ -1,162 +0,0 @@ -# -*- coding: utf-8 -*- -# MegEngine is Licensed under the Apache License, Version 2.0 (the "License") -# -# Copyright (c) 2014-2020 Megvii Inc. All rights reserved. -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -import numpy as np - -import megengine as mge -import megengine.functional as F -import megengine.jit as jit -import megengine.module as M -import megengine.random as R - - -def test_random_static_diff_result(): - @jit.trace(symbolic=True) - def graph_a(): - return R.uniform(5) + R.gaussian(5) - - @jit.trace(symbolic=True) - def graph_b(): - return R.uniform(5) + R.gaussian(5) - - a = graph_a() - b = graph_b() - assert np.any(a.numpy() != b.numpy()) - - -def test_random_static_same_result(): - @jit.trace(symbolic=True) - def graph_a(): - R.manual_seed(731) - return R.uniform(5) + R.gaussian(5) - - @jit.trace(symbolic=True) - def graph_b(): - R.manual_seed(731) - return R.uniform(5) + R.gaussian(5) - - a = graph_a() - b = graph_b() - assert np.all(a.numpy() == b.numpy()) - - -def test_random_dynamic_diff_result(): - a = R.uniform(5) + R.gaussian(5) - b = R.uniform(5) + R.gaussian(5) - assert np.any(a.numpy() != b.numpy()) - - -def test_random_dynamic_same_result(): - R.manual_seed(0) - a = R.uniform(5) + R.gaussian(5) - R.manual_seed(0) - b = R.uniform(5) + R.gaussian(5) - assert np.all(a.numpy() == b.numpy()) - - -def test_range_uniform_static_diff_result(): - @jit.trace(symbolic=True) - def graph_a(): - return R.uniform(5, low=-2, high=2) - - @jit.trace(symbolic=True) - def graph_b(): - return R.uniform(5, low=-2, high=2) - - a = graph_a() - b = graph_b() - assert np.any(a.numpy() != b.numpy()) - - -def test_range_uniform_static_same_result(): - @jit.trace(symbolic=True) - def graph_a(): - R.manual_seed(731) - return R.uniform(5, low=-2, high=2) - - @jit.trace(symbolic=True) - def graph_b(): - R.manual_seed(731) - return R.uniform(5, low=-2, high=2) - - a = graph_a() - b = graph_b() - assert np.all(a.numpy() == b.numpy()) - - -def test_range_uniform_dynamic_diff_result(): - a = R.uniform(5, low=-2, high=2) - b = R.uniform(5, low=-2, high=2) - assert np.any(a.numpy() != b.numpy()) - - -def test_range_uniform_dynamic_same_result(): - R.manual_seed(0) - a = R.uniform(5, low=-2, high=2) - R.manual_seed(0) - b = R.uniform(5, low=-2, high=2) - assert np.all(a.numpy() == b.numpy()) - - -def test_dropout_dynamic_diff_result(): - x = mge.ones(10) - a = F.dropout(x, 0.5) - b = F.dropout(x, 0.5) - assert np.any(a.numpy() != b.numpy()) - - -def test_dropout_dynamic_same_result(): - x = mge.ones(10) - R.manual_seed(0) - a = F.dropout(x, 0.5) - R.manual_seed(0) - b = F.dropout(x, 0.5) - assert np.all(a.numpy() == b.numpy()) - - -def test_M_dropout_static_diff_result(): - m = M.Dropout(0.5) - - @jit.trace(symbolic=True) - def graph_a(x): - return m(x) - - @jit.trace(symbolic=True) - def graph_b(x): - return m(x) - - x = np.ones(10, dtype="float32") - a = graph_a(x) - a = a.numpy().copy() - b = graph_b(x) - c = graph_a(x) - assert np.any(a != b.numpy()) - assert np.any(a != c.numpy()) - - -def test_M_dropout_static_same_result(): - m = M.Dropout(0.5) - - @jit.trace(symbolic=True) - def graph_a(x): - return m(x) - - @jit.trace(symbolic=True) - def graph_b(x): - return m(x) - - x = np.ones(10, dtype="float32") - R.manual_seed(0) - a = graph_a(x) - a = a.numpy().copy() - R.manual_seed(0) - b = graph_b(x) - R.manual_seed(0) # useless - c = graph_a(x) - assert np.all(a == b.numpy()) - assert np.any(a != c.numpy()) diff --git a/scripts/cmake-build/host_build.sh b/scripts/cmake-build/host_build.sh index 8a8f1508..d522c2ca 100755 --- a/scripts/cmake-build/host_build.sh +++ b/scripts/cmake-build/host_build.sh @@ -9,7 +9,6 @@ function usage() { echo "-t : Build with training mode, default inference only" echo "-m : Build with m32 mode(only for windows build), default m64" echo "-r : remove old build dir before make, default off" - echo "-n : enable new python runtime(valid when training mode with -t, default is legacy runtime)" echo "-h : show usage" echo "append other cmake config by export EXTRA_CMAKE_ARGS=..." echo "example: $0 -d" @@ -23,10 +22,9 @@ MGE_WINDOWS_BUILD_ARCH=x64 MGE_WINDOWS_BUILD_MARCH=m64 MGE_ARCH=x86_64 REMOVE_OLD_BUILD=false -MGE_BUILD_IMPERATIVE_RT=OFF echo "EXTRA_CMAKE_ARGS: ${EXTRA_CMAKE_ARGS}" -while getopts "rhdctmn" arg +while getopts "rhdctm" arg do case $arg in d) @@ -55,10 +53,6 @@ do MGE_WINDOWS_BUILD_MARCH=m32 MGE_ARCH=i386 ;; - n) - echo "Enable imperative python wrapper runtime" - MGE_BUILD_IMPERATIVE_RT=ON - ;; ?) echo "unkonw argument" usage @@ -107,7 +101,6 @@ function cmake_build() { cmake \ -DCMAKE_BUILD_TYPE=$BUILD_TYPE \ -DMGE_INFERENCE_ONLY=$MGE_INFERENCE_ONLY \ - -DMGE_BUILD_IMPERATIVE_RT=${MGE_BUILD_IMPERATIVE_RT} \ -DMGE_WITH_CUDA=$MGE_WITH_CUDA \ -DCMAKE_INSTALL_PREFIX=$INSTALL_DIR \ ${EXTRA_CMAKE_ARGS} \ @@ -244,7 +237,6 @@ function cmake_build_windows() { vcvarsall.bat $MGE_WINDOWS_BUILD_ARCH && cmake -G "Ninja" \ -DMGE_ARCH=$MGE_ARCH \ -DMGE_INFERENCE_ONLY=$MGE_INFERENCE_ONLY \ - -DMGE_BUILD_IMPERATIVE_RT=${MGE_BUILD_IMPERATIVE_RT} \ -DMGE_WITH_CUDA=$MGE_WITH_CUDA \ -DCMAKE_BUILD_TYPE=$BUILD_TYPE \ -DCMAKE_INSTALL_PREFIX:PATH=$INSTALL_DIR \ @@ -257,12 +249,6 @@ function cmake_build_windows() { ${WINDOWS_BUILD_TARGET}" } -if [ ${MGE_BUILD_IMPERATIVE_RT} = "ON" ] && [ ${MGE_INFERENCE_ONLY} = "ON" ]; then - echo "ERR: MGE_BUILD_IMPERATIVE_RT(-n) only valid when enable training mode(-t)" - echo "pls remove -n or add -t" - exit -1 -fi - if [[ $OS =~ "NT" ]]; then if [ ${MGE_ARCH} = "i386" ] && [ ${MGE_INFERENCE_ONLY} = "OFF" ]; then echo "ERR: training mode(-t) only support 64 bit mode" diff --git a/scripts/whl/BUILD_PYTHON_WHL_README.md b/scripts/whl/BUILD_PYTHON_WHL_README.md index 07e55feb..a9510072 100644 --- a/scripts/whl/BUILD_PYTHON_WHL_README.md +++ b/scripts/whl/BUILD_PYTHON_WHL_README.md @@ -7,9 +7,8 @@ ``` 1: enable rootless docker env, refs: https://docs.docker.com/engine/security/rootless/ - 2: cd ./scripts/whl/linux/manylinux2010 - 3: ./build_image.sh cpu - 4: ./build_image.sh cuda + 2: cd ./scripts/whl/manylinux2010 + 3: ./build_image.sh ``` @@ -60,12 +59,10 @@ MegBrain delivers `wheel` package with `manylinux2010` tag defined in [PEP-571](https://www.python.org/dev/peps/pep-0571/). ``` - ./build_wheel.sh cpu - - CUDA_ROOT_DIR=/path/to/cuda \ - CUDNN_ROOT_DIR=/path/to/cudnn \ - TENSORRT_ROOT_DIR=/path/to/tensorrt \ - ./build_wheel.sh cuda + export CUDA_ROOT_DIR=/path/to/cuda + export CUDNN_ROOT_DIR=/path/to/cudnn + export TENSORRT_ROOT_DIR=/path/to/tensorrt + ./scripts/whl/manylinux2010/build_wheel.sh ``` And you can find all of the outputs in `output` directory. @@ -73,10 +70,14 @@ If you just want to build for a specific Python verison, you can use `ALL_PYTHON` environment variable. eg: ``` - ALL_PYTHON=35m ./build_wheel.sh cpu + ALL_PYTHON=35m ./scripts/whl/manylinux2010/build_wheel.sh + ``` + If you just want to build with cpu only version, you can set `BUILD_WHL_CPU_ONLY` environment 'ON'. eg: + + ``` + BUILD_WHL_CPU_ONLY=ON ALL_PYTHON=35m ./scripts/whl/manylinux2010/build_wheel.sh ``` - Please append `imperative` to `build_wheel.sh` to use the new runtime, e.g., `./build_wheel.sh cpu imperative`. ## build for macos ``` ./scripts/whl/macos/macos_build_whl.sh @@ -86,11 +87,6 @@ ``` ALL_PYTHON=3.5.9 ./scripts/whl/macos/macos_build_whl.sh ``` - If you want to build with imperative rt, set env BUILD_IMPERATIVE="ON", eg: - - ``` - ALL_PYTHON=3.5.9 BUILD_IMPERATIVE="ON" ./scripts/whl/macos/macos_build_whl.sh - ``` ## build for windows ``` ./scripts/whl/windows/windows_build_whl.sh @@ -100,10 +96,8 @@ ``` ALL_PYTHON=3.5.4 ./scripts/whl/windows/windows_build_whl.sh ``` - If you want to build windows whl with cuda, also a specific Python verison. eg: + If you just want to build with cpu only version, you can set `BUILD_WHL_CPU_ONLY` environment 'ON'. eg: ``` - WINDOWS_WHL_WITH_CUDA="ON" ALL_PYTHON=3.5.4 ./scripts/whl/windows/windows_build_whl.sh + BUILD_WHL_CPU_ONLY='ON' ALL_PYTHON=3.5.4 ./scripts/whl/windows/windows_build_whl.sh ``` - If you want to build with imperative rt, set env BUILD_IMPERATIVE="ON", eg: - BUILD_IMPERATIVE="ON" WINDOWS_WHL_WITH_CUDA="ON" ALL_PYTHON=3.5.4 ./scripts/whl/windows/windows_build_whl.sh diff --git a/scripts/whl/macos/macos_build_whl.sh b/scripts/whl/macos/macos_build_whl.sh index a10912dd..ef421621 100755 --- a/scripts/whl/macos/macos_build_whl.sh +++ b/scripts/whl/macos/macos_build_whl.sh @@ -84,11 +84,6 @@ function config_python_env() { fi } -if [[ -z ${BUILD_IMPERATIVE} ]] -then - BUILD_IMPERATIVE="OFF" -fi - function do_build() { for ver in ${ALL_PYTHON} do @@ -116,65 +111,38 @@ function do_build() { #change PYTHON_LIBRARY and PYTHON_INCLUDE_DIR, so add #-r to remove build cache after a new ver build, which #will be more slow build than without -r - if [ ${BUILD_IMPERATIVE} = "ON" ]; then - echo "build whl with IMPERATIVE python rt" - ${SRC_DIR}/scripts/cmake-build/host_build.sh -t -n -r - else - echo "build whl with legacy python rt" - ${SRC_DIR}/scripts/cmake-build/host_build.sh -t -r - fi + echo "build whl with legacy python rt" + ${SRC_DIR}/scripts/cmake-build/host_build.sh -t -r #call setup.py BUILD_DIR=${SRC_DIR}/build_dir/host/MGE_WITH_CUDA_OFF/MGE_INFERENCE_ONLY_OFF/Release/build/ cd ${BUILD_DIR} - if [ -d "staging" ]; then - echo "remove old build cache file" - rm -rf staging - fi + rm -rf staging mkdir -p staging - if [ ${BUILD_IMPERATIVE} = "ON" ]; then - echo "build whl with IMPERATIVE python rt" - cp -a imperative/python/{megengine,setup.py,requires.txt,requires-style.txt,requires-test.txt} staging/ - cd ${BUILD_DIR}/staging/megengine/core - rt_file=`ls _imperative_rt.*.so` - echo "rt file is: ${rt_file}" - if [[ -z ${rt_file} ]] - then - echo "ERR: can not find valid rt file" - exit -1 - fi - llvm-strip -s ${rt_file} - mv ${rt_file} _imperative_rt.so - echo "check so valid or not..." - otool_out=`otool -L _imperative_rt.so` - if [[ "${otool_out}" =~ "ython" ]]; then - echo "ERR: invalid _imperative_rt.so which depend on python lib, detail: log" - echo ${otool_out} - exit -1 - else - echo "valid..." - fi + cp -a imperative/python/{megengine,setup.py,requires.txt,requires-style.txt,requires-test.txt} staging/ + cd ${BUILD_DIR}/staging/megengine/core + rt_file=`ls _imperative_rt.*.so` + echo "rt file is: ${rt_file}" + if [[ -z ${rt_file} ]] + then + echo "ERR: can not find valid rt file" + exit -1 + fi + llvm-strip -s ${rt_file} + mv ${rt_file} _imperative_rt.so + echo "check so valid or not..." + otool_out=`otool -L _imperative_rt.so` + if [[ "${otool_out}" =~ "ython" ]]; then + echo "ERR: invalid _imperative_rt.so which depend on python lib, detail: log" + echo ${otool_out} + exit -1 else - echo "build whl with legacy python rt" - - cp -a python_module/{megengine,setup.py,requires.txt,requires-style.txt,requires-test.txt} staging/ - cd ${BUILD_DIR}/staging/megengine/_internal - #FIXME: set lib suffix to dylib may be better, BUT we find after distutils.file_util.copy_file - #will change to .so at macos even we set suffix to dylib, at the same time, macos also support .so - echo "check so valid or not..." - llvm-strip -s _mgb.so - otool_out=`otool -L _mgb.so` - if [[ "${otool_out}" =~ "ython" ]]; then - echo "ERR: invalid _mgb.so which depend on python lib, detail: log" - echo ${otool_out} - exit -1 - else - echo "valid..." - fi + echo "valid..." fi + cd ${BUILD_DIR}/staging ${PYTHON_DIR}/bin/python3 setup.py bdist_wheel cd ${BUILD_DIR}/staging/dist/ diff --git a/scripts/whl/manylinux2010/build_wheel.sh b/scripts/whl/manylinux2010/build_wheel.sh index 02adf235..c4762356 100755 --- a/scripts/whl/manylinux2010/build_wheel.sh +++ b/scripts/whl/manylinux2010/build_wheel.sh @@ -6,7 +6,11 @@ OUTPUTDIR=$(readlink -f ${CWD}/output) USERID=$(id -u) TMPFS_ARGS="--tmpfs /tmp:exec" -IMPERATIVE=${2:-mgb} +BUILD_WHL_CPU_ONLY=${BUILD_WHL_CPU_ONLY} +if [[ -z ${BUILD_WHL_CPU_ONLY} ]] +then + BUILD_WHL_CPU_ONLY="OFF" +fi echo ${BASEDIR} pushd ${BASEDIR}/third_party >/dev/null @@ -16,84 +20,84 @@ popd >/dev/null cd ${CWD} mkdir -p ${OUTPUTDIR} -if [[ -z ${CUDA_ROOT_DIR} ]]; then - echo "Environment variable CUDA_ROOT_DIR not set." - exit -1 -fi -if [[ -z ${CUDNN_ROOT_DIR} ]]; then - echo "Environment variable CUDNN_ROOT_DIR not set." - exit -1 -fi -if [[ -z ${TENSORRT_ROOT_DIR} ]]; then - echo "Environment variable TENSORRT_ROOT_DIR not set." - exit -1 -fi - -## YOU SHOULD MODIFY CUDA VERSION AS BELOW WHEN UPGRADE -REQUIR_CUDA_VERSION="10010" -REQUIR_CUDNN_VERSION="7.6.3" -REQUIR_TENSORRT_VERSION="6.0.1.5" - -CUDA_ROOT_DIR_=${CUDA_ROOT_DIR%*/} -CUDNN_ROOT_DIR_=${CUDNN_ROOT_DIR%*/} -TENSORRT_ROOT_DIR_=${TENSORRT_ROOT_DIR%*/} - -CUDA_VERSION_PATH=${CUDA_ROOT_DIR_}/include/cuda.h -CUDNN_VERSION_PATH=${CUDNN_ROOT_DIR_}/include/cudnn.h -TENSORRT_VERSION_PATH=${TENSORRT_ROOT_DIR_}/include/NvInferVersion.h - -if [ ! -e $CUDA_VERSION_PATH ] ; then - echo file $CUDA_VERSION_PATH is not exist - echo please check the Environment must use CUDA-10.1 NO.$REQUIR_CUDA_VERSION - exit -1 -fi -if [ ! -e $CUDNN_VERSION_PATH ] ; then - echo file $CUDNN_VERSION_PATH is not exist - echo please check the Environment must use CUDNN-V$REQUIR_CUDNN_VERSION - exit -1 -fi -if [ ! -e $TENSORRT_VERSION_PATH ] ; then - echo file $TENSORRT_VERSION_PATH is not exist - echo please check the Environment must use TensorRT-$REQUIR_TENSORRT_VERSION - exit -1 -fi - -CUDA_VERSION_CONTEXT=$(head -85 ${CUDA_VERSION_PATH}) -CUDNN_VERSION_CONTEXT=$(head -62 ${CUDNN_VERSION_PATH}) -TENSORRT_VERSION_CONTEXT=$(tail -12 ${TENSORRT_VERSION_PATH}) - -CUDA_API_VERSION=$(echo $CUDA_VERSION_CONTEXT | grep -Eo "define __CUDA_API_VERSION * +([0-9]+)") -CUDA_VERSION=${CUDA_API_VERSION:0-5} -echo CUDA_VERSION:$CUDA_VERSION - -CUDNN_VERSION_MAJOR=$(echo $CUDNN_VERSION_CONTEXT | grep -Eo "define CUDNN_MAJOR * +([0-9]+)") -CUDNN_VERSION_MINOR=$(echo $CUDNN_VERSION_CONTEXT | grep -Eo "define CUDNN_MINOR * +([0-9]+)") -CUDNN_VERSION_PATCH=$(echo $CUDNN_VERSION_CONTEXT | grep -Eo "define CUDNN_PATCHLEVEL * +([0-9]+)") -CUDNN_VERSION=${CUDNN_VERSION_MAJOR:0-1}.${CUDNN_VERSION_MINOR:0-1}.${CUDNN_VERSION_PATCH:0-1} -echo CUDNN_VERSION:$CUDNN_VERSION - -TENSORRT_VERSION_MAJOR=$(echo $TENSORRT_VERSION_CONTEXT | grep -Eo "NV_TENSORRT_MAJOR * +([0-9]+)") -TENSORRT_VERSION_MINOR=$(echo $TENSORRT_VERSION_CONTEXT | grep -Eo "NV_TENSORRT_MINOR * +([0-9]+)") -TENSORRT_VERSION_PATCH=$(echo $TENSORRT_VERSION_CONTEXT | grep -Eo "NV_TENSORRT_PATCH * +([0-9]+)") -TENSORRT_VERSION_BUILD=$(echo $TENSORRT_VERSION_CONTEXT | grep -Eo "NV_TENSORRT_BUILD * +([0-9]+)") -TENSORRT_VERSION=${TENSORRT_VERSION_MAJOR:0-1}.${TENSORRT_VERSION_MINOR:0-1}.${TENSORRT_VERSION_PATCH:0-1}.${TENSORRT_VERSION_BUILD:0-1} -echo TENSORRT_VERSION:$TENSORRT_VERSION - -if [ $CUDA_VERSION != $REQUIR_CUDA_VERSION ] ; then - echo please check the Environment must use CUDA-10.1 NO.$REQUIR_CUDA_VERSION - exit -1 -fi - -if [ $CUDNN_VERSION != $REQUIR_CUDNN_VERSION ] ; then - echo please check the Environment must use CUDNN-V$REQUIR_CUDNN_VERSION - exit -1 +if [ ${BUILD_WHL_CPU_ONLY} = "OFF" ]; then + if [[ -z ${CUDA_ROOT_DIR} ]]; then + echo "Environment variable CUDA_ROOT_DIR not set." + exit -1 + fi + if [[ -z ${CUDNN_ROOT_DIR} ]]; then + echo "Environment variable CUDNN_ROOT_DIR not set." + exit -1 + fi + if [[ -z ${TENSORRT_ROOT_DIR} ]]; then + echo "Environment variable TENSORRT_ROOT_DIR not set." + exit -1 + fi + + ## YOU SHOULD MODIFY CUDA VERSION AS BELOW WHEN UPGRADE + REQUIR_CUDA_VERSION="10010" + REQUIR_CUDNN_VERSION="7.6.3" + REQUIR_TENSORRT_VERSION="6.0.1.5" + + CUDA_ROOT_DIR_=${CUDA_ROOT_DIR%*/} + CUDNN_ROOT_DIR_=${CUDNN_ROOT_DIR%*/} + TENSORRT_ROOT_DIR_=${TENSORRT_ROOT_DIR%*/} + + CUDA_VERSION_PATH=${CUDA_ROOT_DIR_}/include/cuda.h + CUDNN_VERSION_PATH=${CUDNN_ROOT_DIR_}/include/cudnn.h + TENSORRT_VERSION_PATH=${TENSORRT_ROOT_DIR_}/include/NvInferVersion.h + + if [ ! -e $CUDA_VERSION_PATH ] ; then + echo file $CUDA_VERSION_PATH is not exist + echo please check the Environment must use CUDA-10.1 NO.$REQUIR_CUDA_VERSION + exit -1 + fi + if [ ! -e $CUDNN_VERSION_PATH ] ; then + echo file $CUDNN_VERSION_PATH is not exist + echo please check the Environment must use CUDNN-V$REQUIR_CUDNN_VERSION + exit -1 + fi + if [ ! -e $TENSORRT_VERSION_PATH ] ; then + echo file $TENSORRT_VERSION_PATH is not exist + echo please check the Environment must use TensorRT-$REQUIR_TENSORRT_VERSION + exit -1 + fi + + CUDA_VERSION_CONTEXT=$(head -85 ${CUDA_VERSION_PATH}) + CUDNN_VERSION_CONTEXT=$(head -62 ${CUDNN_VERSION_PATH}) + TENSORRT_VERSION_CONTEXT=$(tail -12 ${TENSORRT_VERSION_PATH}) + + CUDA_API_VERSION=$(echo $CUDA_VERSION_CONTEXT | grep -Eo "define __CUDA_API_VERSION * +([0-9]+)") + CUDA_VERSION=${CUDA_API_VERSION:0-5} + echo CUDA_VERSION:$CUDA_VERSION + + CUDNN_VERSION_MAJOR=$(echo $CUDNN_VERSION_CONTEXT | grep -Eo "define CUDNN_MAJOR * +([0-9]+)") + CUDNN_VERSION_MINOR=$(echo $CUDNN_VERSION_CONTEXT | grep -Eo "define CUDNN_MINOR * +([0-9]+)") + CUDNN_VERSION_PATCH=$(echo $CUDNN_VERSION_CONTEXT | grep -Eo "define CUDNN_PATCHLEVEL * +([0-9]+)") + CUDNN_VERSION=${CUDNN_VERSION_MAJOR:0-1}.${CUDNN_VERSION_MINOR:0-1}.${CUDNN_VERSION_PATCH:0-1} + echo CUDNN_VERSION:$CUDNN_VERSION + + TENSORRT_VERSION_MAJOR=$(echo $TENSORRT_VERSION_CONTEXT | grep -Eo "NV_TENSORRT_MAJOR * +([0-9]+)") + TENSORRT_VERSION_MINOR=$(echo $TENSORRT_VERSION_CONTEXT | grep -Eo "NV_TENSORRT_MINOR * +([0-9]+)") + TENSORRT_VERSION_PATCH=$(echo $TENSORRT_VERSION_CONTEXT | grep -Eo "NV_TENSORRT_PATCH * +([0-9]+)") + TENSORRT_VERSION_BUILD=$(echo $TENSORRT_VERSION_CONTEXT | grep -Eo "NV_TENSORRT_BUILD * +([0-9]+)") + TENSORRT_VERSION=${TENSORRT_VERSION_MAJOR:0-1}.${TENSORRT_VERSION_MINOR:0-1}.${TENSORRT_VERSION_PATCH:0-1}.${TENSORRT_VERSION_BUILD:0-1} + echo TENSORRT_VERSION:$TENSORRT_VERSION + + if [ $CUDA_VERSION != $REQUIR_CUDA_VERSION ] ; then + echo please check the Environment must use CUDA-10.1 NO.$REQUIR_CUDA_VERSION + exit -1 + fi + + if [ $CUDNN_VERSION != $REQUIR_CUDNN_VERSION ] ; then + echo please check the Environment must use CUDNN-V$REQUIR_CUDNN_VERSION + exit -1 + fi + + if [ $TENSORRT_VERSION != $REQUIR_TENSORRT_VERSION ] ; then + echo please check the Environment must use TENSORRT-$REQUIR_TENSORRT_VERSION + exit -1 + fi fi -if [ $TENSORRT_VERSION != $REQUIR_TENSORRT_VERSION ] ; then - echo please check the Environment must use TENSORRT-$REQUIR_TENSORRT_VERSION - exit -1 -fi - -docker run -it --rm $TMPFS_ARGS -e UID=${USERID} -e LOCAL_VERSION=${LOCAL_VERSION} -e ALL_PYTHON=${ALL_PYTHON} -v ${CUDA_ROOT_DIR}:/usr/local/cuda -v ${CUDNN_ROOT_DIR}:/opt/cudnn -v ${TENSORRT_ROOT_DIR}:/opt/tensorrt -v ${BASEDIR}:/home/code -v ${OUTPUTDIR}:/home/output:rw env_manylinux2010:latest /home/code/scripts/whl/manylinux2010/do_build.sh $IMPERATIVE - - +docker run -it --rm $TMPFS_ARGS -e UID=${USERID} -e LOCAL_VERSION=${LOCAL_VERSION} -e BUILD_WHL_CPU_ONLY=${BUILD_WHL_CPU_ONLY} -e ALL_PYTHON=${ALL_PYTHON} -v ${CUDA_ROOT_DIR}:/usr/local/cuda -v ${CUDNN_ROOT_DIR}:/opt/cudnn -v ${TENSORRT_ROOT_DIR}:/opt/tensorrt -v ${BASEDIR}:/home/code -v ${OUTPUTDIR}:/home/output:rw env_manylinux2010:latest /home/code/scripts/whl/manylinux2010/do_build.sh diff --git a/scripts/whl/manylinux2010/do_build.sh b/scripts/whl/manylinux2010/do_build.sh index bf06afd0..10286006 100755 --- a/scripts/whl/manylinux2010/do_build.sh +++ b/scripts/whl/manylinux2010/do_build.sh @@ -5,80 +5,70 @@ then ALL_PYTHON="35m 36m 37m 38" fi -EXTRA_CMAKE_ARGS= -if [[ "$1" == imperative ]]; then - BUILD_IMPERATIVE=ON - SO_NAME=_imperative_rt - SO_PATH=megengine/core -else - BUILD_IMPERATIVE=OFF - SO_NAME=_mgb - SO_PATH=megengine/_internal +BUILD_WHL_CPU_ONLY=${BUILD_WHL_CPU_ONLY} +if [[ -z ${BUILD_WHL_CPU_ONLY} ]] +then + BUILD_WHL_CPU_ONLY="OFF" +fi + +SRC_DIR=$(readlink -f "`dirname $0`/../../../") +BUILD_DIR=${SRC_DIR}/build_dir/host/MGE_WITH_CUDA_OFF/MGE_INFERENCE_ONLY_OFF/Release/build/ +if [ ${BUILD_WHL_CPU_ONLY} = "OFF" ]; then + BUILD_DIR=${SRC_DIR}/build_dir/host/MGE_WITH_CUDA_ON/MGE_INFERENCE_ONLY_OFF/Release/build/ fi +SO_NAME=_imperative_rt +SO_PATH=megengine/core +NEW_LIB_PATH=core/lib for ver in ${ALL_PYTHON} do python_ver=${ver:0:2} - BUILD_DIR=/tmp/build_megengine/python${python_ver} MAJOR=${python_ver:0:1} MINOR=${ver:1} PYTHON_DIR=/opt/python/cp${python_ver}-cp${ver}/ EXT_NAME=${SO_NAME}.cpython-${ver}-x86_64-linux-gnu.so - mkdir -p ${BUILD_DIR} - pushd ${BUILD_DIR} >/dev/null - MGE_CMAKE_FLAGS="-DMGE_WITH_DISTRIBUTED=ON \ - -DMGE_WITH_CUDA=ON \ - -DCMAKE_PREFIX_PATH=${PYTHON_DIR} \ - -DCMAKE_INSTALL_PREFIX=/home/output " - if [[ "$BUILD_IMPERATIVE" == ON ]]; then - MGE_CMAKE_FLAGS+=" -DMGE_BUILD_IMPERATIVE_RT=ON \ - -DPYTHON_EXECUTABLE=${PYTHON_DIR}/bin/python3" - else - MGE_CMAKE_FLAGS+=" -DPYTHON_LIBRARY=${PYTHON_DIR}lib/ \ - -DPYTHON_INCLUDE_DIR=${PYTHON_DIR}include/python${MAJOR}.${MINOR}" - fi - cmake /home/code ${MGE_CMAKE_FLAGS} ${EXTRA_CMAKE_ARGS} - make -j$(nproc) VERBOSE=1 - make install - mkdir -p staging - mkdir -p /home/output/debug - if [[ "$BUILD_IMPERATIVE" == ON ]]; then - cp -a imperative/python/{megengine,setup.py,requires.txt,requires-style.txt,requires-test.txt} staging/ - else - cp -a python_module/{megengine,setup.py,requires.txt,requires-style.txt,requires-test.txt} staging/ - fi - pushd dnn/cuda-stub/ >/dev/null - strip -s libcuda.so - ln -sf libcuda.so libcuda.so.1 - popd >/dev/null - pushd staging >/dev/null - pushd ${SO_PATH} >/dev/null - SO_NAME_EXT=${SO_NAME}.so - objcopy --only-keep-debug ${SO_NAME_EXT} ${EXT_NAME}.dbg - strip -s ${SO_NAME_EXT} - objcopy --add-gnu-debuglink=${EXT_NAME}.dbg ${SO_NAME_EXT} - cp -a ${EXT_NAME}.dbg /home/output/debug - mkdir -p lib/ucx - cp -L /usr/local/cuda/lib*/libnvrtc-builtins.so lib - cp -L ${BUILD_DIR}/third_party/MegRay/third_party/ucx/lib/ucx/*.so lib/ucx/ - strip -s lib/ucx/*.so - popd >/dev/null - ${PYTHON_DIR}/bin/python setup.py bdist_wheel - popd >/dev/null - popd >/dev/null - pushd /home/output >/dev/null - if [[ "$BUILD_IMPERATIVE" == ON ]]; then - NEW_LIB_PATH=core/lib - else - NEW_LIB_PATH=_internal/lib - fi - LD_LIBRARY_PATH=${BUILD_DIR}/dnn/cuda-stub:$LD_LIBRARY_PATH auditwheel repair -L ${NEW_LIB_PATH} ${BUILD_DIR}/staging/dist/Meg*.whl - chown -R ${UID}.${UID} . - popd >/dev/null - rm -rf ${BUILD_DIR} -done + export EXTRA_CMAKE_ARGS="${EXTRA_CMAKE_ARGS} -DCMAKE_BUILD_TYPE=RelWithDebInfo" + export EXTRA_CMAKE_ARGS="${EXTRA_CMAKE_ARGS} -DCMAKE_PREFIX_PATH=${PYTHON_DIR}" + export EXTRA_CMAKE_ARGS="${EXTRA_CMAKE_ARGS} -DPYTHON_EXECUTABLE=${PYTHON_DIR}/bin/python3" + export EXTRA_CMAKE_ARGS="${EXTRA_CMAKE_ARGS} -DPYTHON_LIBRARY=${PYTHON_DIR}lib/" + export EXTRA_CMAKE_ARGS="${EXTRA_CMAKE_ARGS} -DPYTHON_INCLUDE_DIR=${PYTHON_DIR}include/python${MAJOR}.${MINOR}" + + if [ ${BUILD_WHL_CPU_ONLY} = "OFF" ]; then + ${SRC_DIR}/scripts/cmake-build/host_build.sh -c -t -r + else + ${SRC_DIR}/scripts/cmake-build/host_build.sh -t -r + fi + cd ${BUILD_DIR} + rm -rf staging + mkdir -p staging + cp -a imperative/python/{megengine,setup.py,requires.txt,requires-style.txt,requires-test.txt} staging/ -pushd /home/code/dnn/scripts >/dev/null -rm -rf __pycache__ -popd >/dev/null + + if [ ${BUILD_WHL_CPU_ONLY} = "OFF" ]; then + cd ${BUILD_DIR}/dnn/cuda-stub/ + strip -s libcuda.so + ln -sf libcuda.so libcuda.so.1 + fi + + cd ${BUILD_DIR}/staging/${SO_PATH} + SO_NAME_EXT=${SO_NAME}.so + objcopy --only-keep-debug ${SO_NAME_EXT} ${EXT_NAME}.dbg + strip -s ${SO_NAME_EXT} + objcopy --add-gnu-debuglink=${EXT_NAME}.dbg ${SO_NAME_EXT} + mkdir -p lib/ucx + + if [ ${BUILD_WHL_CPU_ONLY} = "OFF" ]; then + cp -L /usr/local/cuda/lib*/libnvrtc-builtins.so lib + cp -L ${BUILD_DIR}/third_party/MegRay/third_party/ucx/lib/ucx/*.so lib/ucx/ + strip -s lib/ucx/*.so + fi + + cd ${BUILD_DIR}/staging/ + ${PYTHON_DIR}/bin/python setup.py bdist_wheel + cd /home/output + LD_LIBRARY_PATH=${BUILD_DIR}/dnn/cuda-stub:$LD_LIBRARY_PATH auditwheel repair -L ${NEW_LIB_PATH} ${BUILD_DIR}/staging/dist/Meg*.whl + chown -R ${UID}.${UID} . + # compat for root-less docker env to remove output at host side + chmod -R 777 . +done diff --git a/scripts/whl/windows/windows_build_whl.sh b/scripts/whl/windows/windows_build_whl.sh index 1e1d553a..d7fd0b86 100755 --- a/scripts/whl/windows/windows_build_whl.sh +++ b/scripts/whl/windows/windows_build_whl.sh @@ -58,9 +58,10 @@ function config_python_env() { PYTHON_INCLUDE_DIR=${PYTHON_DIR}/include } -if [[ -z ${WINDOWS_WHL_WITH_CUDA} ]] +BUILD_WHL_CPU_ONLY=${BUILD_WHL_CPU_ONLY} +if [[ -z ${BUILD_WHL_CPU_ONLY} ]] then - WINDOWS_WHL_WITH_CUDA="OFF" + BUILD_WHL_CPU_ONLY="OFF" fi @@ -86,32 +87,23 @@ function depend_real_copy() { function copy_more_dll() { # for python whl real use - if [ ${BUILD_IMPERATIVE} = "ON" ]; then - echo "config BUILD_IMPERATIVE core lib dir" - CP_WHL_DST=${BUILD_DIR}/staging/megengine/core/lib - else - echo "config legacy python lib dir" - CP_WHL_DST=${BUILD_DIR}/staging/megengine/_internal/lib - fi - rm -rf ${CP_WHL_DST} - mkdir ${CP_WHL_DST} + echo "config BUILD_IMPERATIVE core lib dir" + CP_WHL_DST_IMP=${BUILD_DIR}/staging/megengine/core/lib + rm -rf ${CP_WHL_DST_IMP} + mkdir ${CP_WHL_DST_IMP} + # workround for cpu-only version import failed, use a # empty.file to triger setup.py to create a null empty - echo "empty" > ${CP_WHL_DST}/empty.file + echo "empty" > ${CP_WHL_DST_IMP}/empty.file - if [ ${WINDOWS_WHL_WITH_CUDA} = "ON" ]; then + if [ ${BUILD_WHL_CPU_ONLY} = "OFF" ]; then echo "copy nvidia lib to whl use...." - depend_real_copy ${CP_WHL_DST} + depend_real_copy ${CP_WHL_DST_IMP} fi } -if [[ -z ${BUILD_IMPERATIVE} ]] -then - BUILD_IMPERATIVE="OFF" -fi - function do_build() { for ver in ${ALL_PYTHON} do @@ -144,14 +136,8 @@ function do_build() { #-r to remove build cache after a new ver build, which #will be more slow build than without -r BUILD_ARGS=" -t -r" - if [ ${BUILD_IMPERATIVE} = "ON" ]; then - echo "build whl with IMPERATIVE python rt" - BUILD_ARGS="${BUILD_ARGS} -n " - else - echo "build whl with legacy python rt" - fi - if [ ${WINDOWS_WHL_WITH_CUDA} = "ON" ]; then + if [ ${BUILD_WHL_CPU_ONLY} = "OFF" ]; then echo "build windows whl with cuda" BUILD_ARGS="${BUILD_ARGS} -c " else @@ -161,39 +147,27 @@ function do_build() { echo "host_build.sh BUILD_ARGS: ${BUILD_ARGS}" ${SRC_DIR}/scripts/cmake-build/host_build.sh ${BUILD_ARGS} - #call setup.py BUILD_DIR=${SRC_DIR}/build_dir/host/build/ cd ${BUILD_DIR} - if [ -d "staging" ]; then - echo "remove old build cache file" - rm -rf staging - fi + rm -rf staging mkdir -p staging - - if [ ${BUILD_IMPERATIVE} = "ON" ]; then - echo "build whl with IMPERATIVE python rt" - cp -a imperative/python/{megengine,setup.py,requires.txt,requires-style.txt,requires-test.txt} staging/ - cd ${BUILD_DIR}/staging/megengine/core - rt_file=`ls _imperative_rt.*.pyd` - echo "rt file is: ${rt_file}" - if [[ -z ${rt_file} ]] - then - echo "ERR: can not find valid rt file" - exit -1 - fi - llvm-strip -s ${rt_file} - mv ${rt_file} _imperative_rt.pyd - else - echo "build whl with legacy python rt" - - cp -a python_module/{megengine,setup.py,requires.txt,requires-style.txt,requires-test.txt} staging/ - cd ${BUILD_DIR}/staging/megengine/_internal - llvm-strip -s _mgb.pyd + cp -a imperative/python/{megengine,setup.py,requires.txt,requires-style.txt,requires-test.txt} staging/ + cd ${BUILD_DIR}/staging/megengine/core + rt_file=`ls _imperative_rt.*.pyd` + echo "rt file is: ${rt_file}" + if [[ -z ${rt_file} ]] + then + echo "ERR: can not find valid rt file" + exit -1 fi + llvm-strip -s ${rt_file} + mv ${rt_file} _imperative_rt.pyd + copy_more_dll cd ${BUILD_DIR}/staging + echo "call setup.py now" ${PYTHON_DIR}/python3 setup.py bdist_wheel cp ${BUILD_DIR}/staging/dist/Meg*.whl ${WINDOWS_WHL_HOME}/

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