add cuda generator (#26786)
yaoxuefeng
5 years ago
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GitHub
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commit
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@ -0,0 +1,163 @@
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# Copyright (c) 2018 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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"""Test cloud role maker."""
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from __future__ import print_function
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import os
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import unittest
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import paddle.fluid.generator as generator
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import time # temp for debug
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import paddle.fluid as fluid
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import numpy as np
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import paddle
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import paddle.fluid.core as core
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class TestGeneratorSeed(unittest.TestCase):
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"""
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Test cases for cpu generator seed.
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"""
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def test_gen_dropout_dygraph(self):
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gen = paddle.manual_seed(12343)
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fluid.enable_dygraph()
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gen.manual_seed(111111111)
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st = paddle.get_cuda_rng_state()
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x = fluid.layers.uniform_random(
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[2, 10], dtype="float32", min=0.0, max=1.0)
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x_again = fluid.layers.uniform_random(
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[2, 10], dtype="float32", min=0.0, max=1.0)
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x_third = fluid.layers.uniform_random(
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[2, 10], dtype="float32", min=0.0, max=1.0)
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print("x: {}".format(x.numpy()))
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print("x_again: {}".format(x_again.numpy()))
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x = x + x_again + x_third
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y = fluid.layers.dropout(x, 0.5)
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paddle.set_cuda_rng_state(st)
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x1 = fluid.layers.uniform_random(
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[2, 10], dtype="float32", min=0.0, max=1.0)
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x1_again = fluid.layers.uniform_random(
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[2, 10], dtype="float32", min=0.0, max=1.0)
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x1_third = fluid.layers.uniform_random(
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[2, 10], dtype="float32", min=0.0, max=1.0)
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x1 = x1 + x1_again + x1_third
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y1 = fluid.layers.dropout(x1, 0.5)
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y_np = y.numpy()
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y1_np = y1.numpy()
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if core.is_compiled_with_cuda():
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print(">>>>>>> dropout dygraph >>>>>>>")
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self.assertTrue(np.allclose(y_np, y1_np))
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def test_generator_gaussian_random_dygraph(self):
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"""Test Generator seed."""
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fluid.enable_dygraph()
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paddle.manual_seed(12312321111)
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x = fluid.layers.gaussian_random([120], dtype="float32")
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st1 = paddle.get_cuda_rng_state()
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x1 = fluid.layers.gaussian_random([120], dtype="float32")
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paddle.set_cuda_rng_state(st1)
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x2 = fluid.layers.gaussian_random([120], dtype="float32")
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paddle.manual_seed(12312321111)
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x3 = fluid.layers.gaussian_random([120], dtype="float32")
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x_np = x.numpy()
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x1_np = x1.numpy()
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x2_np = x2.numpy()
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x3_np = x3.numpy()
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if core.is_compiled_with_cuda():
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print(">>>>>>> gaussian random dygraph >>>>>>>")
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self.assertTrue(np.allclose(x1_np, x2_np))
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self.assertTrue(np.allclose(x_np, x3_np))
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def test_generator_randint_dygraph(self):
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"""Test Generator seed."""
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fluid.enable_dygraph()
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gen = paddle.manual_seed(12312321111)
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x = paddle.randint(low=10, shape=[10], dtype="int32")
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st1 = gen.get_state()
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x1 = paddle.randint(low=10, shape=[10], dtype="int32")
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gen.set_state(st1)
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x2 = paddle.randint(low=10, shape=[10], dtype="int32")
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paddle.manual_seed(12312321111)
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x3 = paddle.randint(low=10, shape=[10], dtype="int32")
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x_np = x.numpy()
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x1_np = x1.numpy()
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x2_np = x2.numpy()
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x3_np = x3.numpy()
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if core.is_compiled_with_cuda():
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print(">>>>>>> randint dygraph >>>>>>>")
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self.assertTrue(np.allclose(x1_np, x2_np))
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self.assertTrue(np.allclose(x_np, x3_np))
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def test_gen_TruncatedNormal_initializer(self):
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fluid.disable_dygraph()
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gen = paddle.manual_seed(123123143)
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cur_state = paddle.get_cuda_rng_state()
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startup_program = fluid.Program()
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train_program = fluid.Program()
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with fluid.program_guard(train_program, startup_program):
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# example 1:
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# attr shape is a list which doesn't contain tensor Variable.
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x = fluid.layers.uniform_random(shape=[2, 10])
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result_1 = fluid.layers.fc(
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input=x,
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size=10,
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param_attr=fluid.initializer.TruncatedNormal(
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loc=0.0, scale=2.0))
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result_2 = fluid.layers.fc(
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input=x,
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size=10,
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param_attr=fluid.initializer.TruncatedNormal(
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loc=0.0, scale=2.0))
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exe = fluid.Executor(fluid.CPUPlace())
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exe.run(startup_program)
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out1 = exe.run(train_program,
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feed={},
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fetch_list=[result_1, result_2])
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paddle.manual_seed(123123143)
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with fluid.program_guard(train_program, startup_program):
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exe.run(startup_program)
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out2 = exe.run(train_program,
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feed={},
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fetch_list=[result_1, result_2])
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out1_res1 = np.array(out1[0])
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out1_res2 = np.array(out1[1])
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out2_res1 = np.array(out2[0])
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out2_res2 = np.array(out2[1])
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if core.is_compiled_with_cuda():
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print(">>>>>>> truncated normal static >>>>>>>")
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self.assertTrue(np.allclose(out1_res1, out2_res1))
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self.assertTrue(np.allclose(out1_res2, out2_res2))
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self.assertTrue(not np.allclose(out1_res2, out1_res1))
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if __name__ == "__main__":
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unittest.main()
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