Add use_cuda to inplace pass (#17205)
* add use_cuda to inplace pass,test=develop * add test softmax_with_xe_inplace test,test=developfix_api_example
Zeng Jinle
6 years ago
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# 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 paddle
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import paddle.fluid as fluid
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from paddle.fluid import layers
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import numpy as np
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import unittest
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class TestSoftmaxWithXe(unittest.TestCase):
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def setUp(self):
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self.m, self.n = np.random.random_integers(
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low=100, high=2000, size=[2]).astype('int64')
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def softmax_with_xe(self, x, y, place, inplace=True):
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m, n = x.shape
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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with fluid.scope_guard(fluid.Scope()):
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x_d = fluid.layers.data(
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name='x',
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shape=[m, n],
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dtype='float32',
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append_batch_size=False)
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y_d = fluid.layers.data(
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name='y',
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shape=[m, 1],
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dtype='int64',
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append_batch_size=False)
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z_d, s_d = fluid.layers.softmax_with_cross_entropy(
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x_d, y_d, return_softmax=True)
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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build_strategy = fluid.BuildStrategy()
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build_strategy.enable_inplace = inplace
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prog = fluid.CompiledProgram(fluid.default_main_program(
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)).with_data_parallel(
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build_strategy=build_strategy, places=place)
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if inplace and isinstance(place, fluid.CUDAPlace):
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fetch_list = [z_d.name, x_d.name]
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else:
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fetch_list = [z_d.name, s_d.name]
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z, s = exe.run(prog,
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feed={x_d.name: x,
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y_d.name: y},
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fetch_list=fetch_list)
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return z, s
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def main_with_place(self, place):
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x = np.random.random(size=[self.m, self.n]).astype('float32')
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x_range = [(-30, 30), (10, 20), (-1, 1), (2, 3), (0, 0.3), (-200, -100)]
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for a, b in x_range:
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x = ((b - a) * x + a).astype('float32')
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y = np.random.random_integers(
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size=[self.m, 1], low=0, high=self.n - 1).astype('int64')
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z1, s1 = self.softmax_with_xe(x, y, place, False)
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z2, s2 = self.softmax_with_xe(x, y, place, True)
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self.assertTrue((z1 == z2).all())
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self.assertTrue((s1 == s2).all())
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def test_main(self):
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self.main_with_place(fluid.CPUPlace())
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if fluid.core.is_compiled_with_cuda():
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self.main_with_place(fluid.CUDAPlace(0))
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if __name__ == '__main__':
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unittest.main()
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