perf(mge): move Const into C++

GitOrigin-RevId: 31a443cffd

imperative/python/megengine/core/ops/special.py

@@ -1,40 +0,0 @@
-# -*- coding: utf-8 -*-
-# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
-#
-# Copyright (c) 2014-2021 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 .._imperative_rt import make_const
-from .._imperative_rt.core2 import SymbolVar, Tensor
-
-
-class Const:
-    def __init__(self, value=None, *, dtype=None, device=None):
-        self.value = np.asarray(value, dtype=dtype)
-        self.dtype = dtype
-        self.device = device
-
-    def __call__(self, *reference):
-        from ...tensor import Tensor
-
-        device = self.device
-
-        if len(reference) != 0:
-            reference = reference[0]
-            assert isinstance(
-                reference, (SymbolVar, Tensor)
-            ), "Reference should be Tensor or VarNode"
-
-            if device is None:
-                device = reference.device
-
-            if isinstance(reference, SymbolVar):
-                cls = type(reference)
-                rst = cls(make_const(reference.graph, self.value, device, self.dtype))
-                return (rst,)
-
-        return (Tensor(self.value, self.dtype, self.device, True),)

imperative/python/megengine/core/tensor/utils.py

@@ -14,6 +14,7 @@ import numpy as np
 
 from .._imperative_rt import make_const
 from .._imperative_rt.core2 import (
+    Const,
     SymbolVar,
     Tensor,
     _get_convert_inputs,
@@ -28,7 +29,6 @@ from .._imperative_rt.ops import jit_supported
 from .._wrap import as_device
 from ..autodiff.grad import Function
 from ..ops import builtin
-from ..ops.special import Const
 from .amp import _get_amp_high_prec_dtype, _get_amp_low_prec_dtype
 from .dtype import is_dtype_equal, is_quantize
 
@@ -67,7 +67,7 @@ def convert_single_value(v, *, dtype=None, device=None):
         if not is_quantize(v.dtype):
             v = astype(v, dtype)
     else:
-        (v,) = Const(v, dtype=dtype, device=device)()
+        v = Const(v, dtype, device, None)
     return v
 
 
@@ -155,7 +155,7 @@ def astensor1d(x, *reference, dtype=None, device=None):
         if ndim != 0 and ndim != 1:
             raise ValueError("ndim != 1 or 0, get : %d" % ndim)
         if not isinstance(x, (Tensor, SymbolVar)):
-            (x,) = Const(x, dtype=dtype, device=device)(*reference)
+            x = Const(x, dtype, device, reference)
         return x
 
     if not isinstance(x, collections.abc.Sequence):
@@ -166,7 +166,7 @@ def astensor1d(x, *reference, dtype=None, device=None):
         if dtype is not None:
             x = astype(x, dtype)
         return x
-    (x,) = Const(x, dtype=dtype, device=device)(*reference)
+    x = Const(x, dtype, device, reference)
     return x
 
 
@@ -337,7 +337,7 @@ def interpret_subgraph(func, dtype, device):
                 return results
 
         def apply_const(value, dtype=dtype, device=device):
-            return Const(value, dtype=dtype, device=device)()[0]
+            return Const(value, dtype, device, None)
 
         outputs, outputs_has_grad = func(args, apply_expr, apply_const)
         outputs = [

imperative/python/megengine/functional/math.py

@@ -10,10 +10,9 @@ import collections
 import math
 from typing import Iterable, Optional, Sequence, Tuple, Union
 
-from ..core._imperative_rt.core2 import apply, dtype_promotion
+from ..core._imperative_rt.core2 import Const, apply, dtype_promotion
 from ..core._imperative_rt.ops import SubgraphBuilder as _SubgraphBuilder
 from ..core.ops import builtin
-from ..core.ops.special import Const
 from ..core.tensor.array_method import _matmul
 from ..core.tensor.utils import _normalize_axis
 from ..tensor import Tensor
@@ -729,7 +728,7 @@ def topk(
     op = builtin.TopK(mode=mode)
 
     if not isinstance(k, Tensor):
-        (k,) = Const(k, dtype="int32", device=inp.device)()
+        k = Const(k, "int32", inp.device, None)
 
     if len(inp.shape) == 1:
         if kth_only:

imperative/python/megengine/functional/nn.py

@@ -11,7 +11,7 @@ from functools import lru_cache
 from typing import NamedTuple, Optional, Sequence, Tuple, Union
 
 from ..core import _config
-from ..core._imperative_rt.core2 import apply, dtype_promotion
+from ..core._imperative_rt.core2 import Const, apply, dtype_promotion
 from ..core._imperative_rt.ops import SubgraphBuilder as _SubgraphBuilder
 from ..core._imperative_rt.ops import get_global_rng_seed as _get_global_rng_seed
 from ..core.ops import builtin
@@ -26,7 +26,6 @@ from ..core.ops.builtin import (
     Reshape,
     TypeCvt,
 )
-from ..core.ops.special import Const
 from ..core.tensor import amp, megbrain_graph
 from ..core.tensor.array_method import _elwise_apply
 from ..core.tensor.utils import (
@@ -1317,7 +1316,7 @@ def batch_norm(
             raise ValueError("Invalid param_dim {}".format(param_dim))
 
         if x is None:
-            (x,) = Const(value, dtype=inp.dtype, device=inp.device)()
+            x = Const(value, inp.dtype, inp.device, None)
             shape = astensor1d(pshape, inp, dtype="int32", device=inp.device)
             (result,) = apply(builtin.Broadcast(), x, shape)
             return result
@@ -1541,7 +1540,7 @@ def sync_batch_norm(
 
     def _make_full_if_none(x, value):
         if x is None:
-            (x,) = Const(value, dtype=inp.dtype, device=_device)()
+            x = Const(value, inp.dtype, _device, None)
             (result,) = apply(builtin.Broadcast(), x, reduce_shape)
             return result
         elif x.ndim == 1:

imperative/python/megengine/functional/tensor.py

@@ -13,6 +13,7 @@ import numpy as np
 
 from ..core._imperative_rt import CompNode
 from ..core._imperative_rt.core2 import (
+    Const,
     SymbolVar,
     apply,
     broadcast_cpp,
@@ -24,7 +25,6 @@ from ..core._imperative_rt.core2 import (
 from ..core._wrap import as_device
 from ..core.ops import builtin
 from ..core.ops.builtin import Copy, Identity
-from ..core.ops.special import Const
 from ..core.tensor.utils import astensor1d, convert_inputs, get_device, subgraph_fn
 from ..device import get_default_device
 from ..tensor import Tensor
@@ -177,7 +177,7 @@ def full(
         shape = (shape,)
     if device is None:
         device = get_default_device()
-    (x,) = Const(value, dtype=dtype, device=device)()
+    x = Const(value, dtype, device, None)
     if type(shape) in (list, tuple) and len(shape) == 0:
         return x
     return broadcast_to(x, shape)
@@ -325,7 +325,7 @@ def full_like(
              [2 2 2]]
 
     """
-    (x,) = Const(value, dtype=inp.dtype, device=inp.device)(inp)
+    x = Const(value, inp.dtype, inp.device, inp)
     if inp.ndim == 0:
         return x
     return broadcast_to(x, inp.shape)

imperative/python/megengine/functional/tensor_cache.py

@@ -1,4 +1,4 @@
-from ..core.ops.special import Const
+from ..core._imperative_rt.core2 import Const
 from ..jit.tracing import is_tracing
 
 small_tensor_cache = {}
@@ -7,11 +7,11 @@ small_tensor_cache = {}
 def _get_scalar_tensor_with_value(value, dtype=None, device=None):
     global small_tensor_cache
     if is_tracing():
-        (ret,) = Const(value, dtype=dtype, device=device)()
+        ret = Const(value, dtype, device, None)
     else:
         cache_key = (value, dtype, device)
         if cache_key not in small_tensor_cache:
-            (ret,) = Const(value, dtype=dtype, device=device)()
+            ret = Const(value, dtype, device, None)
             small_tensor_cache[cache_key] = ret
         else:
             ret = small_tensor_cache[cache_key]

imperative/python/megengine/traced_module/expr.py

@@ -16,6 +16,7 @@ from importlib import import_module
 from typing import Callable, Dict, Iterable, List, Optional, Sequence, Union
 
 from ..core._imperative_rt import OpDef
+from ..core._imperative_rt.core2 import Const
 from ..core._imperative_rt.core2 import Tensor as RawTensor
 from ..core._imperative_rt.core2 import (
     apply,
@@ -25,7 +26,6 @@ from ..core._imperative_rt.core2 import (
     unset_module_tracing,
 )
 from ..core.ops.builtin import FakeQuant
-from ..core.ops.special import Const
 from ..module import Module
 from ..tensor import Parameter, Tensor
 from ..version import __version__
@@ -764,7 +764,7 @@ class Constant(Expr):
 
     def interpret(self, *inputs):
         if isinstance(self.value, RawTensor):
-            return Const(self.value.numpy())()
+            return (Const(self.value.numpy(), None, None, None),)
         return (self.value,)
 
     def __repr__(self):

imperative/python/src/tensor.cpp

@@ -639,6 +639,7 @@ WRAP_FUNC_PY35(squeeze_cpp);
 WRAP_FUNC_PY35(transpose_cpp);
 WRAP_FUNC_PY35(broadcast_cpp);
 WRAP_FUNC_PY35(reshape_cpp);
+WRAP_FUNC_PY35(Const);
 #undef WRAP_FUNC_PY35
 #define MGE_PY_INTERFACE(NAME, FUNC) \
     { #NAME, (PyCFunction)py35_##FUNC, METH_VARARGS, nullptr }
@@ -777,6 +778,7 @@ void init_tensor(py::module m) {
             MGE_PY_INTERFACE(transpose_cpp, transpose_cpp),
             MGE_PY_INTERFACE(broadcast_cpp, broadcast_cpp),
             MGE_PY_INTERFACE(reshape_cpp, reshape_cpp),
+            MGE_PY_INTERFACE(Const, Const),
             {nullptr, nullptr, 0, nullptr}};
     for (auto&& def : method_defs) {
         if (def.ml_meth != nullptr) {

imperative/python/src/tensor_utils.cpp

@@ -94,7 +94,7 @@ bool is_bool_dtype(PyObject* args) {
 }
 
 py::object _Const(
-        py::handle value, py::handle dtype, py::handle device, py::handle ref) {
+        py::handle value, py::handle dtype, py::handle device, py::handle ref_hdl) {
     py::object val = py::reinterpret_borrow<py::object>(value);
     if (PyArray_Check(value.ptr())) {
         py::tuple strides =
@@ -107,21 +107,56 @@ py::object _Const(
         }
         if (need_squeeze) {
             val = py::reinterpret_borrow<py::array>(value);
+            py::object orig_shp = val.attr("shape");
             val = val.attr("squeeze")();
-            val = val.attr("reshape")(val.attr("shape"));
+            val = val.attr("reshape")(orig_shp);
         }
     }
+    py::object ref;
+    if (py::isinstance<py::tuple>(ref_hdl)) {
+        py::tuple tup = py::reinterpret_borrow<py::tuple>(ref_hdl);
+        if (tup.size()) {
+            ref = tup[0];
+        } else {
+            ref = py::none();
+        }
+    } else {
+        ref = py::reinterpret_borrow<py::object>(ref_hdl);
+    }
     if (py::isinstance<PySymbolVar>(ref)) {
         auto ref_var = ref.cast<PySymbolVar*>();
         auto* graph = ref_var->m_node->owner_graph();
-        auto cn = device.cast<CompNode>();
+        CompNode cn;
+        if (device.ptr() == Py_None) {
+            cn = ref_var->m_node->comp_node();
+        } else {
+            cn = device.cast<CompNode>();
+        }
         OperatorNodeConfig config(cn);
         auto hv = npy::np2tensor(
                 val.ptr(), npy::Meth::borrow(cn), dtype.cast<mgb::DType>());
         auto typeobj = ref.get_type();
         return typeobj(opr::ImmutableTensor::make(*graph, hv, config).node());
     }
-    py::tuple tup = py::make_tuple(val, dtype, device, true, false, py::none());
+    py::object device_obj;
+    if (device.ptr() == Py_None) {
+        device_obj = py::cast(CompNode::load(get_default_device()));
+    } else if (py::isinstance<py::str>(device)) {
+        py::object dmap =
+                getattr(py::reinterpret_borrow<py::object>((PyObject*)py_tensor_type),
+                        "dmap_callback");
+        if (dmap.ptr() != Py_None) {
+            device_obj = dmap(device);
+            py::print(device_obj);
+        } else {
+            device_obj = py::cast(CompNode::load(device.cast<std::string>()));
+        }
+    } else if (py::isinstance<CompNode>(device)) {
+        device_obj = py::reinterpret_borrow<py::object>(device);
+    } else {
+        device_obj = getattr(device, "_cn");
+    }
+    py::tuple tup = py::make_tuple(val, dtype, device_obj, true, false, py::none());
     return TensorWrapper::make(py_tensor_type, tup.ptr(), nullptr);
 }
 
@@ -1107,4 +1142,14 @@ PyObject* reshape_cpp(PyObject* self, PyObject* const* args, size_t nargs) {
     PYEXT17_TRANSLATE_EXC_RET(nullptr)
 }
 
+PyObject* Const(PyObject* self, PyObject* const* args, size_t nargs) {
+    try {
+        return _Const(py::handle(args[0]), py::handle(args[1]), py::handle(args[2]),
+                      py::handle(args[3]))
+                .release()
+                .ptr();
+    }
+    PYEXT17_TRANSLATE_EXC_RET(nullptr)
+}
+
 }  // namespace mgb::imperative::python

imperative/python/src/tensor_utils.h

@@ -20,4 +20,6 @@ PyObject* broadcast_cpp(PyObject* self, PyObject* const* args, size_t nargs);
 
 PyObject* reshape_cpp(PyObject* self, PyObject* const* args, size_t nargs);
 
+PyObject* Const(PyObject* self, PyObject* const* args, size_t nargs);
+
 }  // namespace mgb::imperative::python