wehub-resource-sync
199 lines
6.5 KiB
Python
199 lines
6.5 KiB
Python
from op_test import is_custom_device
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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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import os
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import shutil
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import tempfile
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import unittest
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import numpy as np
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import paddle
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from paddle import base
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from paddle.base import core
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"Only test cuda Random Generator",
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)
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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.seed(12343)
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base.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 = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
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x_again = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
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x_third = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
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print(f"x: {x.numpy()}")
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print(f"x_again: {x_again.numpy()}")
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x = x + x_again + x_third
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y = paddle.nn.functional.dropout(x, 0.5)
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paddle.set_cuda_rng_state(st)
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x1 = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
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x1_again = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
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x1_third = paddle.uniform([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 = paddle.nn.functional.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() or is_custom_device():
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print(">>>>>>> dropout dygraph >>>>>>>")
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np.testing.assert_allclose(y_np, y1_np, rtol=1e-05)
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def test_generator_gaussian_random_dygraph(self):
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"""Test Generator seed."""
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base.enable_dygraph()
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st = paddle.get_cuda_rng_state()
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x1 = paddle.randn([120], dtype="float32")
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paddle.set_cuda_rng_state(st)
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x2 = paddle.randn([120], dtype="float32")
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paddle.set_cuda_rng_state(st)
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x3 = paddle.randn([120], dtype="float32")
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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() or is_custom_device():
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print(">>>>>>> gaussian random dygraph >>>>>>>")
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np.testing.assert_allclose(x1_np, x2_np, rtol=1e-05)
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np.testing.assert_allclose(x2_np, x3_np, rtol=1e-05)
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def test_generator_randint_dygraph(self):
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"""Test Generator seed."""
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base.enable_dygraph()
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paddle.seed(12312321111)
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x = paddle.randint(low=10, shape=[10], dtype="int32")
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st1 = paddle.get_cuda_rng_state()
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x1 = paddle.randint(low=10, shape=[10], dtype="int32")
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paddle.set_cuda_rng_state(st1)
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x2 = paddle.randint(low=10, shape=[10], dtype="int32")
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paddle.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() or is_custom_device():
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print(">>>>>>> randint dygraph >>>>>>>")
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np.testing.assert_allclose(x_np, x3_np, rtol=1e-05)
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def test_gen_TruncatedNormal_initializer(self):
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base.disable_dygraph()
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gen = paddle.seed(123123143)
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cur_state = paddle.get_cuda_rng_state()
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startup_program = base.Program()
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train_program = base.Program()
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with base.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 = paddle.uniform(shape=[2, 10])
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result_1 = paddle.static.nn.fc(
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x,
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size=10,
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weight_attr=paddle.nn.initializer.TruncatedNormal(
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mean=0.0, std=2.0
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),
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)
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result_2 = paddle.static.nn.fc(
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x,
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size=10,
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weight_attr=paddle.nn.initializer.TruncatedNormal(
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mean=0.0, std=2.0
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),
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)
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exe = base.Executor(base.CPUPlace())
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exe.run(startup_program)
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out1 = exe.run(
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train_program, feed={}, fetch_list=[result_1, result_2]
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)
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paddle.seed(123123143)
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with base.program_guard(train_program, startup_program):
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exe.run(startup_program)
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out2 = exe.run(
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train_program, feed={}, fetch_list=[result_1, result_2]
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)
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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() or is_custom_device():
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print(">>>>>>> truncated normal static >>>>>>>")
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np.testing.assert_allclose(out1_res1, out2_res1, rtol=1e-05)
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np.testing.assert_allclose(out1_res2, out2_res2, rtol=1e-05)
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self.assertTrue(not np.allclose(out1_res2, out1_res1))
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def test_generator_pickle(self):
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output_dir = tempfile.mkdtemp()
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random_file = os.path.join(output_dir, "random.pdmodel")
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base.enable_dygraph()
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x0 = paddle.randn([120], dtype="float32")
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st = paddle.get_cuda_rng_state()
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st_dict = {"random_state": st}
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print("state: ", st[0])
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paddle.save(st_dict, random_file)
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x1 = paddle.randn([120], dtype="float32")
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lt_dict = paddle.load(random_file)
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st = lt_dict["random_state"]
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paddle.set_cuda_rng_state(st)
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x2 = paddle.randn([120], dtype="float32")
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lt_dict = paddle.load(random_file)
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st = lt_dict["random_state"]
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paddle.set_cuda_rng_state(st)
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x3 = paddle.randn([120], dtype="float32")
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x1_np = x1.numpy()
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x2_np = x2.numpy()
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print(">>>>>>> gaussian random dygraph state load/save >>>>>>>")
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np.testing.assert_equal(x1_np, x2_np)
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np.testing.assert_equal(x1_np, x2_np)
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shutil.rmtree(output_dir)
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if __name__ == "__main__":
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unittest.main()
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