feat(mge/utils): add get/set_expand_structure to deal with complex key

GitOrigin-RevId: 4d1b952068

imperative/python/megengine/module/module.py

@@ -21,9 +21,9 @@ from ..utils.naming import auto_naming
 logger = get_logger(__name__)
 
 
-def _expand_structure(key, obj):
+def _expand_structure(prefix, obj):
     if isinstance(obj, (Tensor, Module)):
-        return [(key, obj)]
+        return [(prefix, obj)]
     elif isinstance(obj, (list, tuple, dict)):
         ret = []
         if isinstance(obj, dict):
@@ -37,12 +37,32 @@ def _expand_structure(key, obj):
                     "keys for Tensor and Module must be str, error key: {}".format(k)
                 )
             for kt, vt in sub_ret:
-                ret.extend([(key + "." + kt, vt)])
+                ret.extend([(prefix + "." + kt, vt)])
         return ret
     else:
         return []
 
 
+def _access_structure(obj, key, callback=None):
+    key_list = key.split(".")
+    cur = obj
+    parent = None
+    for k in key_list:
+        parent = cur
+        if isinstance(cur, (Tensor, Module)):
+            cur = getattr(cur, k)
+        elif isinstance(cur, (list, tuple)):
+            k = int(k)
+            cur = cur[k]
+        elif isinstance(cur, dict):
+            cur = cur[k]
+        else:
+            raise ValueError(
+                "Unsupport value type {} to access attribute".format(type(cur))
+            )
+    return callback(parent, k, cur)
+
+
 def _is_parameter(obj):
     return isinstance(obj, Parameter)
 

imperative/python/megengine/module/sequential.py

@@ -18,9 +18,9 @@ class Sequential(Module):
     Alternatively, an ordered dict of modules can also be passed in.
 
     To make it easier to understand, here is a small example:
-    
+
     Examples:
-    
+
     .. testcode::
 
         import numpy as np

imperative/python/megengine/quantization/quantize.py

@@ -7,7 +7,7 @@
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 from copy import copy, deepcopy
 from functools import partial
-from typing import Callable, Dict, Tuple
+from typing import Callable
 
 import numpy as np
 
@@ -19,6 +19,7 @@ from ..module import quantized as Quantized
 from ..module.qat import QATModule
 from ..module.quantized import QuantizedModule
 from ..tensor import Tensor
+from ..utils.module_utils import set_expand_structure
 from .qconfig import QConfig, ema_fakequant_qconfig
 
 
@@ -79,11 +80,7 @@ def quantize(module: Module, inplace: bool = True, mapping: dict = None):
         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)
+        set_expand_structure(parent, key, new_mod)
 
     return module
 
@@ -126,11 +123,7 @@ def quantize_qat(
             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)
+        set_expand_structure(parent, key, new_mod)
 
     propagate_qconfig(module, qconfig)
     return module

imperative/python/megengine/utils/module_utils.py

@@ -0,0 +1,43 @@
+# 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.
+from collections import Iterable
+
+from ..module import Sequential
+from ..module.module import Module, _access_structure
+from ..tensor import Tensor
+
+
+def get_expand_structure(obj: Module, key: str):
+    """
+    Gets Module's attribute compatible with complex key from Module's :meth:`~.named_children`.
+    Supports handling structure containing list or dict.
+    """
+
+    def f(_, __, cur):
+        return cur
+
+    return _access_structure(obj, key, callback=f)
+
+
+def set_expand_structure(obj: Module, key: str, value):
+    """
+    Sets Module's attribute compatible with complex key from Module's :meth:`~.named_children`.
+    Supports handling structure containing list or dict.
+    """
+
+    def f(parent, key, cur):
+        if isinstance(parent, (Tensor, Module)):
+            # cannnot use setattr to be compatible with Sequential's ``__setitem__``
+            if isinstance(cur, Sequential):
+                parent[int(key)] = value
+            else:
+                setattr(parent, key, value)
+        else:
+            parent[key] = value
+
+    _access_structure(obj, key, callback=f)

imperative/python/test/unit/module/test_module.py

@@ -6,8 +6,6 @@
 # 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
 
@@ -29,7 +27,9 @@ from megengine.module import (
     Sequential,
     Softmax,
 )
+from megengine.module.module import _access_structure
 from megengine.quantization.quantize import quantize, quantize_qat
+from megengine.utils.module_utils import get_expand_structure, set_expand_structure
 
 
 class MLP(Module):
@@ -45,146 +45,6 @@ class MLP(Module):
         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):
-    np.testing.assert_allclose(x.numpy(), y, rtol=1e-6)
-
-
-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 np.testing.assert_allclose 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)
-
-
 class MyModule(Module):
     class InnerModule(Module):
         def __init__(self):
@@ -306,13 +166,13 @@ def test_module_api_hooks():
     post_hook_num = 0
     hooks = []
 
-    def pre_hook(module, inputs):
+    def pre_hook(_, 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):
+    def post_hook(_, __, outputs):
         nonlocal post_hook_num
         post_hook_num += 1
         outputs += 1
@@ -376,7 +236,7 @@ class MyModule2(Module):
 
 def test_expand_structure():
     m = MyModule2()
-    assert list(m.named_modules()) == [
+    rst = [
         ("", m),
         ("a.0", m.a[0]),
         ("a.1.x", m.a[1]["x"]),
@@ -387,6 +247,16 @@ def test_expand_structure():
         ("a.2.0.bn", m.a[2][0].bn),
         ("bn", m.bn),
     ]
+    assert list(m.named_modules()) == rst
+
+    for item in rst[1:]:
+        assert get_expand_structure(m, item[0]) == item[1]
+
+    for item in reversed(rst[1:]):
+        if _access_structure(m, item[0], lambda p, k, o: isinstance(p, tuple)):
+            continue
+        set_expand_structure(m, item[0], "TEST_VALUE")
+        assert get_expand_structure(m, item[0]) == "TEST_VALUE"
 
 
 def test_flatten_others():
@@ -603,21 +473,6 @@ def test_pickle_module():
     np.testing.assert_allclose(pred0.numpy(), pred2.numpy(), atol=5e-6)
 
 
-@pytest.mark.skip(reason="under development")
-def test_dump_model():
-    data_shape = (2, 28)
-    data = Tensor(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():
     from megengine.core.tensor import dtype