forked from huawei/mindspore2022
158 lines
4.7 KiB
Python
158 lines
4.7 KiB
Python
# Copyright 2020-2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import pytest
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import numpy as np
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import mindspore.nn as nn
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import mindspore.ops.operations as P
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from mindspore.ops import composite as C
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from mindspore import context, Tensor
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from mindspore.common.api import ms_function
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grad_all = C.GradOperation(get_all=True)
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def var_hook_function(grad_out):
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print("grad:", grad_out)
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class GraphVarHook(nn.Cell):
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def __init__(self):
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super(GraphVarHook, self).__init__()
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self.relu = nn.ReLU()
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self.hook = P.HookBackward(var_hook_function)
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def construct(self, x):
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x = x + x
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x = x * x
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x = self.hook(x)
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x = self.relu(x)
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return x
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class MsFuncVarHook(nn.Cell):
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def __init__(self):
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super(MsFuncVarHook, self).__init__()
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self.relu = nn.ReLU()
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self.hook = P.HookBackward(var_hook_function)
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@ms_function
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def construct(self, x):
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x = x + x
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x = x * x
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x = self.hook(x)
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x = self.relu(x)
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return x
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_var_hook_forward():
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input_x = Tensor(np.random.randn(2, 2).astype(np.float32))
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context.set_context(mode=context.PYNATIVE_MODE)
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net1 = MsFuncVarHook()
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out1 = net1(input_x)
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context.set_context(mode=context.GRAPH_MODE)
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net2 = GraphVarHook()
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out2 = net2(input_x)
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assert np.allclose(out1.asnumpy(), out2.asnumpy(), 0.00001, 0.00001)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_var_hook_grad():
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input_x = Tensor(np.random.randn(2, 2).astype(np.float32))
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context.set_context(mode=context.PYNATIVE_MODE)
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net1 = MsFuncVarHook()
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grad_out1 = grad_all(net1)(input_x)
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context.set_context(mode=context.GRAPH_MODE)
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net2 = GraphVarHook()
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grad_out2 = grad_all(net2)(input_x)
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assert np.allclose(grad_out1[0].asnumpy(), grad_out2[0].asnumpy(), 0.00001, 0.00001)
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def cell_hook_function(cell_id, grad_input, grad_output):
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print("cell id:", cell_id)
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print("grad input:", grad_input)
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print("grad output:", grad_output)
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class GraphCellHook(nn.Cell):
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def __init__(self):
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super(GraphCellHook, self).__init__()
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self.relu = nn.ReLU()
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self.relu.register_backward_hook(cell_hook_function)
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def construct(self, x):
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x = x + x
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x = x * x
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x = self.relu(x)
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return x
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class MsFuncCellHook(nn.Cell):
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def __init__(self):
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super(MsFuncCellHook, self).__init__()
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self.relu = nn.ReLU()
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self.relu.register_backward_hook(cell_hook_function)
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@ms_function
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def construct(self, x):
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x = x + x
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x = x * x
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x = self.relu(x)
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return x
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cell_hook_forward():
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input_x = Tensor(np.random.randn(2, 2).astype(np.float32))
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context.set_context(mode=context.PYNATIVE_MODE)
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net1 = MsFuncCellHook()
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out1 = net1(input_x)
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context.set_context(mode=context.GRAPH_MODE)
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net2 = GraphCellHook()
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out2 = net2(input_x)
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assert np.allclose(out1.asnumpy(), out2.asnumpy(), 0.00001, 0.00001)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cell_hook_grad():
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input_x = Tensor(np.random.randn(2, 2).astype(np.float32))
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context.set_context(mode=context.PYNATIVE_MODE)
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net1 = MsFuncCellHook()
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grad_out1 = grad_all(net1)(input_x)
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context.set_context(mode=context.GRAPH_MODE)
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net2 = GraphCellHook()
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grad_out2 = grad_all(net2)(input_x)
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assert np.allclose(grad_out1[0].asnumpy(), grad_out2[0].asnumpy(), 0.00001, 0.00001)
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