wehub-resource-sync
402 lines
14 KiB
Python
402 lines
14 KiB
Python
# 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 unittest
|
|
|
|
import numpy as np
|
|
from op_test import is_custom_device
|
|
|
|
import paddle
|
|
from paddle import base
|
|
from paddle.base import core
|
|
from paddle.tensor import random
|
|
|
|
|
|
class TestGeneratorSeed(unittest.TestCase):
|
|
# """
|
|
# Test cases for cpu generator seed.
|
|
# """
|
|
|
|
def test_generator_uniform_random_dygraph(self):
|
|
"""Test Generator seed."""
|
|
|
|
base.enable_dygraph()
|
|
|
|
gen = paddle.seed(12312321111)
|
|
x = paddle.uniform([10], dtype="float32", min=0.0, max=1.0)
|
|
|
|
st1 = gen.get_state()
|
|
x1 = paddle.uniform([10], dtype="float32", min=0.0, max=1.0)
|
|
|
|
gen.set_state(st1)
|
|
print(gen.get_state())
|
|
x2 = paddle.uniform([10], dtype="float32", min=0.0, max=1.0)
|
|
|
|
paddle.seed(12312321111)
|
|
x3 = paddle.uniform([10], dtype="float32", min=0.0, max=1.0)
|
|
|
|
x_np = x.numpy()
|
|
x1_np = x1.numpy()
|
|
x2_np = x2.numpy()
|
|
x3_np = x3.numpy()
|
|
|
|
if (
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
and not core.is_compiled_with_xpu()
|
|
):
|
|
np.testing.assert_allclose(x1_np, x2_np, rtol=1e-05)
|
|
np.testing.assert_allclose(x_np, x3_np, rtol=1e-05)
|
|
|
|
def test_generator_uniform_random_static(self):
|
|
base.disable_dygraph()
|
|
|
|
gen = paddle.seed(123123143)
|
|
|
|
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.
|
|
result_1 = paddle.uniform(shape=[3, 4])
|
|
result_2 = paddle.uniform(shape=[3, 4])
|
|
|
|
exe = base.Executor(base.CPUPlace())
|
|
exe.run(startup_program)
|
|
out1 = exe.run(
|
|
train_program, feed={}, fetch_list=[result_1, result_2]
|
|
)
|
|
# gen.set_state(cur_state)
|
|
gen.manual_seed(123123143)
|
|
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 (
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
and not core.is_compiled_with_xpu()
|
|
):
|
|
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_gen_dropout_dygraph(self):
|
|
base.enable_dygraph()
|
|
|
|
gen = paddle.seed(111111111)
|
|
st = gen.get_state()
|
|
# x = np.arange(1,101).reshape(2,50).astype("float32")
|
|
x = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
|
|
y = paddle.nn.functional.dropout(x, 0.5)
|
|
gen.manual_seed(111111111)
|
|
# gen.set_state(st)
|
|
x1 = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
|
|
y1 = paddle.nn.functional.dropout(x1, 0.5)
|
|
y_np = y.numpy()
|
|
y1_np = y1.numpy()
|
|
|
|
if (
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
and not core.is_compiled_with_xpu()
|
|
):
|
|
print(">>>>>>> dropout dygraph >>>>>>>")
|
|
np.testing.assert_allclose(y_np, y1_np, rtol=1e-05)
|
|
|
|
def test_gen_dropout_static(self):
|
|
base.disable_dygraph()
|
|
|
|
gen = paddle.seed(123123143)
|
|
|
|
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_1 = paddle.uniform(shape=[2, 10])
|
|
y_1 = paddle.nn.functional.dropout(x_1, 0.5)
|
|
exe = base.Executor(base.CPUPlace())
|
|
exe.run(startup_program)
|
|
out1 = exe.run(train_program, feed={}, fetch_list=[y_1])
|
|
# gen.set_state(cur_state)
|
|
gen.manual_seed(123123143)
|
|
out2 = exe.run(train_program, feed={}, fetch_list=[y_1])
|
|
out1_np = np.array(out1[0])
|
|
out2_np = np.array(out2[0])
|
|
|
|
if (
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
and not core.is_compiled_with_xpu()
|
|
):
|
|
print(">>>>>>> dropout static >>>>>>>")
|
|
np.testing.assert_allclose(out1_np, out2_np, rtol=1e-05)
|
|
|
|
def test_generator_gaussian_random_dygraph(self):
|
|
"""Test Generator seed."""
|
|
base.enable_dygraph()
|
|
|
|
gen = paddle.seed(12312321111)
|
|
x = random.gaussian([10], dtype="float32")
|
|
st1 = gen.get_state()
|
|
x1 = random.gaussian([10], dtype="float32")
|
|
gen.set_state(st1)
|
|
x2 = random.gaussian([10], dtype="float32")
|
|
gen.manual_seed(12312321111)
|
|
x3 = random.gaussian([10], dtype="float32")
|
|
x_np = x.numpy()
|
|
x1_np = x1.numpy()
|
|
x2_np = x2.numpy()
|
|
x3_np = x3.numpy()
|
|
|
|
if (
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
and not core.is_compiled_with_xpu()
|
|
):
|
|
print(">>>>>>> gaussian random dygraph >>>>>>>")
|
|
np.testing.assert_allclose(x1_np, x2_np, rtol=1e-05)
|
|
np.testing.assert_allclose(x_np, x3_np, rtol=1e-05)
|
|
|
|
def test_generator_gaussian_random_static(self):
|
|
base.disable_dygraph()
|
|
|
|
gen = paddle.seed(123123143)
|
|
|
|
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.
|
|
result_1 = random.gaussian(shape=[3, 4])
|
|
result_2 = random.gaussian(shape=[3, 4])
|
|
|
|
exe = base.Executor(base.CPUPlace())
|
|
exe.run(startup_program)
|
|
out1 = exe.run(
|
|
train_program, feed={}, fetch_list=[result_1, result_2]
|
|
)
|
|
# gen.set_state(cur_state)
|
|
gen.manual_seed(123123143)
|
|
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 (
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
and not core.is_compiled_with_xpu()
|
|
):
|
|
print(">>>>>>> gaussian random 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_randint_dygraph(self):
|
|
"""Test Generator seed."""
|
|
base.enable_dygraph()
|
|
|
|
gen = paddle.seed(12312321111)
|
|
x = paddle.randint(low=10, shape=[10], dtype="int32")
|
|
st1 = gen.get_state()
|
|
x1 = paddle.randint(low=10, shape=[10], dtype="int32")
|
|
gen.set_state(st1)
|
|
x2 = paddle.randint(low=10, shape=[10], dtype="int32")
|
|
gen.manual_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 (
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
and not core.is_compiled_with_xpu()
|
|
):
|
|
print(">>>>>>> randint dygraph >>>>>>>")
|
|
np.testing.assert_allclose(x1_np, x2_np, rtol=1e-05)
|
|
np.testing.assert_allclose(x_np, x3_np, rtol=1e-05)
|
|
|
|
def test_generator_uniform_random_static_1(self):
|
|
base.disable_dygraph()
|
|
|
|
gen = paddle.seed(123123143)
|
|
|
|
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.
|
|
result_1 = paddle.uniform(shape=[3, 4])
|
|
result_2 = paddle.uniform(shape=[3, 4])
|
|
|
|
exe = base.Executor(base.CPUPlace())
|
|
exe.run(startup_program)
|
|
out1 = exe.run(
|
|
train_program, feed={}, fetch_list=[result_1, result_2]
|
|
)
|
|
# gen.set_state(cur_state)
|
|
gen.manual_seed(123123143)
|
|
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 (
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
and not core.is_compiled_with_xpu()
|
|
):
|
|
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_randint_dygraph_1(self):
|
|
"""Test Generator seed."""
|
|
base.enable_dygraph()
|
|
|
|
gen = paddle.seed(12312321111)
|
|
x = paddle.randint(low=1)
|
|
st1 = gen.get_state()
|
|
x1 = paddle.randint(low=1)
|
|
gen.set_state(st1)
|
|
x2 = paddle.randint(low=1)
|
|
gen.manual_seed(12312321111)
|
|
x3 = paddle.randint(low=1)
|
|
x_np = x.numpy()
|
|
x1_np = x1.numpy()
|
|
x2_np = x2.numpy()
|
|
x3_np = x3.numpy()
|
|
if (
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
and not core.is_compiled_with_xpu()
|
|
):
|
|
np.testing.assert_allclose(x1_np, x2_np, rtol=1e-05)
|
|
np.testing.assert_allclose(x_np, x3_np, rtol=1e-05)
|
|
|
|
def test_generator_ranint_static(self):
|
|
base.disable_dygraph()
|
|
|
|
gen = paddle.seed(123123143)
|
|
|
|
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.
|
|
result_1 = paddle.randint(low=10, shape=[3, 4])
|
|
result_2 = paddle.randint(low=10, shape=[3, 4])
|
|
|
|
exe = base.Executor(base.CPUPlace())
|
|
exe.run(startup_program)
|
|
out1 = exe.run(
|
|
train_program, feed={}, fetch_list=[result_1, result_2]
|
|
)
|
|
# gen.set_state(cur_state)
|
|
gen.manual_seed(123123143)
|
|
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 (
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
and not core.is_compiled_with_xpu()
|
|
):
|
|
print(">>>>>>> randint 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_randperm_dygraph(self):
|
|
"""Test Generator seed."""
|
|
|
|
base.enable_dygraph()
|
|
|
|
gen = paddle.seed(12312321111)
|
|
x = paddle.randperm(10)
|
|
st1 = gen.get_state()
|
|
x1 = paddle.randperm(10)
|
|
gen.set_state(st1)
|
|
x2 = paddle.randperm(10)
|
|
gen.manual_seed(12312321111)
|
|
x3 = paddle.randperm(10)
|
|
x_np = x.numpy()
|
|
x1_np = x1.numpy()
|
|
x2_np = x2.numpy()
|
|
x3_np = x3.numpy()
|
|
|
|
if (
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
and not core.is_compiled_with_xpu()
|
|
):
|
|
print(">>>>>>> randperm dygraph >>>>>>>")
|
|
np.testing.assert_allclose(x1_np, x2_np, rtol=1e-05)
|
|
np.testing.assert_allclose(x_np, x3_np, rtol=1e-05)
|
|
|
|
def test_generator_randperm_static(self):
|
|
base.disable_dygraph()
|
|
|
|
paddle.seed(123123143)
|
|
|
|
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.
|
|
result_1 = paddle.randperm(10)
|
|
result_2 = paddle.randperm(10)
|
|
|
|
exe = base.Executor(base.CPUPlace())
|
|
exe.run(startup_program)
|
|
out1 = exe.run(
|
|
train_program, feed={}, fetch_list=[result_1, result_2]
|
|
)
|
|
|
|
paddle.seed(123123143)
|
|
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 (
|
|
not (core.is_compiled_with_cuda() or is_custom_device())
|
|
and not core.is_compiled_with_xpu()
|
|
):
|
|
print(">>>>>>> randperm 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))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|