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119 lines
4.3 KiB
119 lines
4.3 KiB
# 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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from __future__ import print_function
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import unittest
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import paddle.fluid as fluid
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from paddle.fluid.dygraph.base import to_variable
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from paddle.fluid.dygraph.nn import Embedding
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from paddle.fluid.optimizer import SGDOptimizer
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import numpy as np
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import paddle.fluid.core as core
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import paddle
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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(SimpleNet, self).__init__()
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self.emb = fluid.dygraph.Embedding(
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size=[vocab_size, hidden_size],
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dtype=dtype,
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param_attr='emb.w',
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is_sparse=True)
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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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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for place in 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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fluid.set_flags({
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'FLAGS_sort_sum_gradient': sort_sum_gradient
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})
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# grad_clip = fluid.clip.GradientClipByGlobalNorm(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 = SGDOptimizer(
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learning_rate=0.001,
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parameter_list=simplenet.parameters(
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)) # grad_clip=grad_clip
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input_emb, emb = simplenet(input)
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self.assertTrue(emb.weight.gradient() is None)
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self.assertTrue(input_emb.gradient() is None)
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input_emb.backward()
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adam.minimize(input_emb)
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self.assertTrue(emb.weight.gradient() is not None)
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emb.clear_gradients()
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self.assertTrue(emb.weight.gradient() is None)
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input_emb.clear_gradient()
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self.assertTrue(input_emb.gradient() is not None)
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paddle.enable_static()
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def test_selectedrows_gradient2(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for place in places:
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for sort_sum_gradient in [True, False]:
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with fluid.dygraph.guard(place):
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fluid.set_flags({
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'FLAGS_sort_sum_gradient': sort_sum_gradient
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})
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grad_clip = fluid.clip.GradientClipByGlobalNorm(5.0)
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input_word = np.array([[1, 2], [2, 1]]).astype('int64')
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input = to_variable(input_word)
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simplenet = SimpleNet(20, 32, "float32")
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adam = SGDOptimizer(
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learning_rate=0.001,
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parameter_list=simplenet.parameters(),
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grad_clip=grad_clip)
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input_emb, emb = simplenet(input)
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self.assertTrue(emb.weight.gradient() is None)
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self.assertTrue(input_emb.gradient() is None)
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input_emb.backward()
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adam.minimize(input_emb)
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self.assertTrue(emb.weight.gradient() is not None)
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emb.clear_gradients()
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self.assertTrue(emb.weight.gradient() is None)
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input_emb.clear_gradient()
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self.assertTrue(input_emb.gradient() is not None)
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if __name__ == '__main__':
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unittest.main()
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