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test_cuda_random_seed.py
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Test cloud role maker."""
import os
import shutil
import tempfile
import unittest
import numpy as np
import paddle
from paddle import base
from paddle.base import core
@unittest.skipIf(
not core.is_compiled_with_cuda(), "Only test cuda Random Generator"
)
class TestGeneratorSeed(unittest.TestCase):
"""
Test cases for cpu generator seed.
"""
def test_gen_dropout_dygraph(self):
gen = paddle.seed(12343)
base.enable_dygraph()
gen.manual_seed(111111111)
st = paddle.get_cuda_rng_state()
x = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
x_again = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
x_third = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
print(f"x: {x.numpy()}")
print(f"x_again: {x_again.numpy()}")
x = x + x_again + x_third
y = paddle.nn.functional.dropout(x, 0.5)
paddle.set_cuda_rng_state(st)
x1 = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
x1_again = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
x1_third = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
x1 = x1 + x1_again + x1_third
y1 = paddle.nn.functional.dropout(x1, 0.5)
y_np = y.numpy()
y1_np = y1.numpy()
if core.is_compiled_with_cuda():
print(">>>>>>> dropout dygraph >>>>>>>")
np.testing.assert_allclose(y_np, y1_np, rtol=1e-05)
def test_generator_gaussian_random_dygraph(self):
"""Test Generator seed."""
base.enable_dygraph()
st = paddle.get_cuda_rng_state()
x1 = paddle.randn([120], dtype="float32")
paddle.set_cuda_rng_state(st)
x2 = paddle.randn([120], dtype="float32")
paddle.set_cuda_rng_state(st)
x3 = paddle.randn([120], dtype="float32")
x1_np = x1.numpy()
x2_np = x2.numpy()
x3_np = x3.numpy()
if core.is_compiled_with_cuda():
print(">>>>>>> gaussian random dygraph >>>>>>>")
np.testing.assert_allclose(x1_np, x2_np, rtol=1e-05)
np.testing.assert_allclose(x2_np, x3_np, rtol=1e-05)
def test_generator_randint_dygraph(self):
"""Test Generator seed."""
base.enable_dygraph()
paddle.seed(12312321111)
x = paddle.randint(low=10, shape=[10], dtype="int32")
st1 = paddle.get_cuda_rng_state()
x1 = paddle.randint(low=10, shape=[10], dtype="int32")
paddle.set_cuda_rng_state(st1)
x2 = paddle.randint(low=10, shape=[10], dtype="int32")
paddle.seed(12312321111)
x3 = paddle.randint(low=10, shape=[10], dtype="int32")
x_np = x.numpy()
x1_np = x1.numpy()
x2_np = x2.numpy()
x3_np = x3.numpy()
if core.is_compiled_with_cuda():
print(">>>>>>> randint dygraph >>>>>>>")
np.testing.assert_allclose(x_np, x3_np, rtol=1e-05)
def test_gen_TruncatedNormal_initializer(self):
base.disable_dygraph()
gen = paddle.seed(123123143)
cur_state = paddle.get_cuda_rng_state()
startup_program = base.Program()
train_program = base.Program()
with base.program_guard(train_program, startup_program):
# example 1:
# attr shape is a list which doesn't contain tensor Variable.
x = paddle.uniform(shape=[2, 10])
result_1 = paddle.static.nn.fc(
x,
size=10,
weight_attr=paddle.nn.initializer.TruncatedNormal(
mean=0.0, std=2.0
),
)
result_2 = paddle.static.nn.fc(
x,
size=10,
weight_attr=paddle.nn.initializer.TruncatedNormal(
mean=0.0, std=2.0
),
)
exe = base.Executor(base.CPUPlace())
exe.run(startup_program)
out1 = exe.run(
train_program, feed={}, fetch_list=[result_1, result_2]
)
paddle.seed(123123143)
with base.program_guard(train_program, startup_program):
exe.run(startup_program)
out2 = exe.run(
train_program, feed={}, fetch_list=[result_1, result_2]
)
out1_res1 = np.array(out1[0])
out1_res2 = np.array(out1[1])
out2_res1 = np.array(out2[0])
out2_res2 = np.array(out2[1])
if core.is_compiled_with_cuda():
print(">>>>>>> truncated normal static >>>>>>>")
np.testing.assert_allclose(out1_res1, out2_res1, rtol=1e-05)
np.testing.assert_allclose(out1_res2, out2_res2, rtol=1e-05)
self.assertTrue(not np.allclose(out1_res2, out1_res1))
def test_generator_pickle(self):
output_dir = tempfile.mkdtemp()
random_file = os.path.join(output_dir, "random.pdmodel")
base.enable_dygraph()
x0 = paddle.randn([120], dtype="float32")
st = paddle.get_cuda_rng_state()
st_dict = {"random_state": st}
print("state: ", st[0])
paddle.save(st_dict, random_file)
x1 = paddle.randn([120], dtype="float32")
lt_dict = paddle.load(random_file)
st = lt_dict["random_state"]
paddle.set_cuda_rng_state(st)
x2 = paddle.randn([120], dtype="float32")
lt_dict = paddle.load(random_file)
st = lt_dict["random_state"]
paddle.set_cuda_rng_state(st)
x3 = paddle.randn([120], dtype="float32")
x1_np = x1.numpy()
x2_np = x2.numpy()
x3_np = x3.numpy()
print(">>>>>>> gaussian random dygraph state load/save >>>>>>>")
np.testing.assert_equal(x1_np, x2_np)
np.testing.assert_equal(x1_np, x2_np)
shutil.rmtree(output_dir)
if __name__ == "__main__":
unittest.main()