fix(mge/function): do not deeply copy saved tensor in Function

GitOrigin-RevId: 3c89d1ceaa9f7338b167f80936549a4c364061de

python_module/megengine/core/function.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 copy
 from abc import ABCMeta, abstractmethod
 from typing import Iterable, Tuple, Union
 
@@ -142,6 +143,20 @@ class Function(metaclass=ABCMeta):
         """
         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))
+        self.saved_tensors = tmp
+        return result
+
     def __call__(self, *inputs):
         assert (
             not self._has_saved_state

python_module/megengine/core/tensor.py

@@ -495,7 +495,12 @@ class Tensor:
         )
 
     def __getstate__(self):
-        assert (self.__val is not None) and (self.__sym is None)
+        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(),

python_module/test/unit/core/test_function.py

-# -*- coding: utf-8 -*-
 # MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
 #
 # Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
@@ -6,10 +5,13 @@
 # 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
 
 
@@ -76,6 +78,27 @@ def test_ste():
     )
 
 
+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
+    assert new.saved_tensors == None
+
+
 def test_save_context():
     class Sigmoid(Function):
         def forward(self, x):
@@ -87,14 +110,26 @@ def test_save_context():
             (y,) = self.saved_tensors
             return grad_y * y * (1 - y)
 
-    a = tensor(np.array([1926.0817], dtype=np.float32))
-    s = Sigmoid()(a)
-    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(),
-    )
+    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():