wehub-resource-sync
92 lines
2.6 KiB
Python
92 lines
2.6 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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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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import unittest
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import numpy as np
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from op_test import is_custom_device
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import paddle
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from paddle import nn
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from paddle.device.cuda.cuda_graphed_layer import CUDAGraphedLayer
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seed = 102
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class Model(nn.Layer):
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def __init__(self, in_size, out_size, dropout=0):
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paddle.seed(seed)
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super().__init__()
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self.linear = nn.Linear(in_size, out_size)
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self.relu = nn.ReLU()
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def forward(self, x):
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x = self.linear(x)
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x = self.relu(x)
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return x
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class DropoutModel(nn.Layer):
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def __init__(self, in_size, out_size, dropout=0.5):
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paddle.seed(seed)
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super().__init__()
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self.linear = nn.Linear(in_size, out_size)
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self.dropout_1 = paddle.nn.Dropout(dropout)
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self.relu = nn.ReLU()
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self.dropout_2 = paddle.nn.Dropout(dropout)
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def forward(self, x):
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x = self.linear(x)
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x = self.dropout_1(x)
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x = self.relu(x)
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x = self.dropout_2(x)
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return x
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@unittest.skipIf(
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not (paddle.is_compiled_with_cuda() or is_custom_device())
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or float(paddle.version.cuda()) < 11.0,
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"only support cuda >= 11.0",
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)
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class TestSimpleModel(unittest.TestCase):
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def train(self, model):
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paddle.seed(seed)
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ans = []
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for _ in range(10):
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x = paddle.randn([3, 10], dtype='float32')
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x.stop_gradient = False
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loss = model(x).mean()
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loss.backward()
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ans.append(x.grad.numpy())
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return np.array(ans)
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def test_layer(self):
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model = Model(10, 20)
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cuda_graphed_model = CUDAGraphedLayer(Model(10, 20))
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dropout_model = DropoutModel(10, 20)
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cuda_graphed_dropout_model = CUDAGraphedLayer(DropoutModel(10, 20))
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np.testing.assert_array_equal(
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self.train(model), self.train(cuda_graphed_model)
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)
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np.testing.assert_array_equal(
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self.train(dropout_model), self.train(cuda_graphed_dropout_model)
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)
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if __name__ == "__main__":
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unittest.main()
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