wehub-resource-sync
112 lines
3.9 KiB
Python
112 lines
3.9 KiB
Python
# Copyright (c) 2019 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 get_places
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import paddle
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from paddle import base
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class SimpleNet(paddle.nn.Layer):
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def __init__(self, vocab_size, hidden_size, dtype):
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super().__init__()
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self.emb = paddle.nn.Embedding(
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vocab_size,
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hidden_size,
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weight_attr='emb.w',
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sparse=True,
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)
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def forward(self, input):
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input_emb = self.emb(input)
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return input_emb, self.emb
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class TestSimpleNet(unittest.TestCase):
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def test_selectedrows_gradient1(self):
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for place in get_places():
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for dtype in ["float32", "float64"]:
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for sort_sum_gradient in [True, False]:
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paddle.disable_static(place)
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base.set_flags(
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{'FLAGS_sort_sum_gradient': sort_sum_gradient}
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)
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# grad_clip = paddle.nn.ClipGradByGlobalNorm(5.0)
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input_word = np.array([[1, 2], [2, 1]]).astype('int64')
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input = paddle.to_tensor(input_word)
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simplenet = SimpleNet(20, 32, dtype)
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adam = paddle.optimizer.SGD(
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learning_rate=0.001,
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parameters=simplenet.parameters(),
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) # grad_clip=grad_clip
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input_emb, emb = simplenet(input)
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input_emb.retain_grads()
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self.assertIsNone(emb.weight.gradient())
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self.assertIsNone(input_emb.gradient())
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input_emb.backward()
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adam.minimize(input_emb)
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self.assertIsNotNone(emb.weight.gradient())
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emb.clear_gradients()
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self.assertIsNone(emb.weight.gradient())
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input_emb.clear_gradient()
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self.assertIsNotNone(input_emb.gradient())
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paddle.enable_static()
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def test_selectedrows_gradient2(self):
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for place in get_places():
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for sort_sum_gradient in [True, False]:
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with base.dygraph.guard(place):
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base.set_flags(
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{'FLAGS_sort_sum_gradient': sort_sum_gradient}
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)
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grad_clip = paddle.nn.ClipGradByGlobalNorm(5.0)
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input_word = np.array([[1, 2], [2, 1]]).astype('int64')
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input = paddle.to_tensor(input_word)
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simplenet = SimpleNet(20, 32, "float32")
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adam = paddle.optimizer.SGD(
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learning_rate=0.001,
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parameters=simplenet.parameters(),
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grad_clip=grad_clip,
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)
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input_emb, emb = simplenet(input)
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input_emb.retain_grads()
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self.assertIsNone(emb.weight.gradient())
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self.assertIsNone(input_emb.gradient())
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input_emb.backward()
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adam.minimize(input_emb)
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self.assertIsNotNone(emb.weight.gradient())
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emb.clear_gradients()
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self.assertIsNone(emb.weight.gradient())
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input_emb.clear_gradient()
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self.assertIsNotNone(input_emb.gradient())
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if __name__ == '__main__':
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unittest.main()
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