forked from mindspore-Ecosystem/mindspore
98 lines
2.8 KiB
Python
98 lines
2.8 KiB
Python
# Copyright 2022 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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""" test ge frontend pass `DropoutForGE` `DropoutGradForGE` """
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import numpy as np
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from tests.st.ge import ge_infer_env # pylint: disable=unused-import
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from mindspore import ops, nn, context, Tensor
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from mindspore.ops.composite import GradOperation
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class DropoutNet(nn.Cell):
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def __init__(self, keep_prob):
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super(DropoutNet, self).__init__()
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self.drop = nn.Dropout(keep_prob)
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self.relu = ops.ReLU()
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def construct(self, x):
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x = self.relu(x)
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return self.relu(self.drop(x))
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class _Grad(nn.Cell):
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def __init__(self, grad, network):
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super().__init__()
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self.network = network
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self.grad = grad
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def construct(self, *inputs):
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return self.grad(self.network)(*inputs)
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class GradOfFirstInput(_Grad):
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"""
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get grad of first input
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"""
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def __init__(self, network, sens_param=True):
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super().__init__(grad=GradOperation(sens_param=sens_param), network=network)
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def ge_drop_out_0_5(shape):
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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net = DropoutNet(0.5)
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net.set_train()
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x = Tensor(np.ones(shape).astype(np.float32))
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out = net(x)
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return out
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def ge_dropout_backward_0_5(shape):
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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net = DropoutNet(0.5)
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grad_net = GradOfFirstInput(net)
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grad_net.set_train()
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x = Tensor(np.ones(shape).astype(np.float32))
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sens = Tensor(np.ones(shape).astype(np.float32))
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out = grad_net(x, sens)
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return out
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def run_ge_dropout():
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"""
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Feature: Test Dropout in GE backend.
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Description: Test Dropout in GE backend.
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Expectation: Dropout result is random, assert result shape.
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"""
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shape = (1, 1, 6, 6)
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out = ge_drop_out_0_5(shape)
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assert out.asnumpy().shape == shape
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def run_ge_dropout_backward():
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"""
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Feature: Test Dropout backward in GE backend.
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Description: Test Dropout backward in GE backend.
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Expectation: Dropout result is random, assert gradient shape.
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"""
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shape = (1, 1, 6, 6)
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out = ge_dropout_backward_0_5(shape)
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assert out.asnumpy().shape == shape
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if __name__ == "__main__":
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run_ge_dropout()
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run_ge_dropout_backward()
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