wehub-resource-sync
226 lines
8.2 KiB
Python
226 lines
8.2 KiB
Python
# Copyright (c) 2020 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.
|
|
|
|
import unittest
|
|
|
|
import numpy as np
|
|
from op_test import get_device_place, get_devices, get_places
|
|
|
|
import paddle
|
|
|
|
|
|
def _run_ldexp_dynamic(x, y, device='cpu'):
|
|
# dynamic mode
|
|
paddle.disable_static()
|
|
# Set device
|
|
paddle.set_device(device)
|
|
x_ = paddle.to_tensor(x)
|
|
# y is scalar
|
|
if isinstance(y, (int)):
|
|
y_ = y
|
|
# y is tensor
|
|
else:
|
|
y_ = paddle.to_tensor(y)
|
|
res = paddle.ldexp(x_, y_)
|
|
return res.numpy()
|
|
|
|
|
|
def _run_ldexp_static(x, y, device='cpu'):
|
|
# static graph mode
|
|
paddle.enable_static()
|
|
# y is scalar
|
|
if isinstance(y, (int)):
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x_ = paddle.static.data(name="x", shape=x.shape, dtype=x.dtype)
|
|
y_ = y
|
|
res = paddle.ldexp(x_, y_)
|
|
place = paddle.CPUPlace() if device == 'cpu' else get_device_place()
|
|
exe = paddle.static.Executor(place)
|
|
outs = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={'x': x, 'y': y},
|
|
fetch_list=[res],
|
|
)
|
|
return outs[0]
|
|
# y is tensor
|
|
else:
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x_ = paddle.static.data(name="x", shape=x.shape, dtype=x.dtype)
|
|
y_ = paddle.static.data(name="y", shape=y.shape, dtype=y.dtype)
|
|
res = paddle.ldexp(x_, y_)
|
|
place = paddle.CPUPlace() if device == 'cpu' else get_device_place()
|
|
exe = paddle.static.Executor(place)
|
|
outs = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={'x': x, 'y': y},
|
|
fetch_list=[res],
|
|
)
|
|
return outs[0]
|
|
|
|
|
|
def check_dtype(input, desired_dtype):
|
|
if input.dtype != desired_dtype:
|
|
raise ValueError(
|
|
f"The expected data type to be obtained is {desired_dtype}, but got {input.dtype}"
|
|
)
|
|
|
|
|
|
class TestLdexpAPIWithDynamic(unittest.TestCase):
|
|
def setUp(self):
|
|
self.places = get_devices()
|
|
|
|
def test_ldexp_dynamic(self):
|
|
np.random.seed(7)
|
|
for place in self.places:
|
|
# test 1-d float tensor and 1-d int tensor
|
|
dims = (np.random.randint(200, 300),)
|
|
x = (np.random.rand(*dims) * 10).astype(np.float64)
|
|
y = (np.random.randint(-10, 10, dims)).astype(np.int32)
|
|
res = _run_ldexp_dynamic(x, y, place)
|
|
check_dtype(res, np.float64)
|
|
np.testing.assert_allclose(res, np.ldexp(x, y))
|
|
|
|
dims = (np.random.randint(200, 300),)
|
|
x = (np.random.rand(*dims) * 10).astype(np.float32)
|
|
y = (np.random.randint(-10, 10, dims)).astype(np.int32)
|
|
res = _run_ldexp_dynamic(x, y, place)
|
|
check_dtype(res, np.float32)
|
|
np.testing.assert_allclose(res, np.ldexp(x, y))
|
|
|
|
# test 1-d int tensor and 1-d int tensor
|
|
dims = (np.random.randint(200, 300),)
|
|
x = (np.random.randint(-10, 10, dims)).astype(np.int64)
|
|
y = (np.random.randint(-10, 10, dims)).astype(np.int32)
|
|
res = _run_ldexp_dynamic(x, y, place)
|
|
check_dtype(res, np.float32)
|
|
np.testing.assert_allclose(res, np.ldexp(x, y))
|
|
|
|
dims = (np.random.randint(200, 300),)
|
|
x = (np.random.randint(-10, 10, dims)).astype(np.int32)
|
|
y = (np.random.randint(-10, 10, dims)).astype(np.int32)
|
|
res = _run_ldexp_dynamic(x, y, place)
|
|
check_dtype(res, np.float32)
|
|
np.testing.assert_allclose(res, np.ldexp(x, y))
|
|
|
|
# test broadcast
|
|
dims = (
|
|
np.random.randint(1, 10),
|
|
np.random.randint(5, 10),
|
|
np.random.randint(5, 10),
|
|
)
|
|
x = (np.random.rand(*dims) * 10).astype(np.float64)
|
|
y = (np.random.randint(-10, 10, dims[-1])).astype(np.int32)
|
|
res = _run_ldexp_dynamic(x, y)
|
|
check_dtype(res, np.float64)
|
|
np.testing.assert_allclose(res, np.ldexp(x, y))
|
|
|
|
|
|
class TestLdexpAPIWithStatic(unittest.TestCase):
|
|
def setUp(self):
|
|
self.places = get_devices()
|
|
|
|
def test_ldexp_static(self):
|
|
np.random.seed(7)
|
|
for place in self.places:
|
|
dims = (np.random.randint(200, 300),)
|
|
x = (np.random.rand(*dims) * 10).astype(np.float64)
|
|
y = (np.random.randint(-10, 10, dims)).astype(np.int32)
|
|
res = _run_ldexp_static(x, y, place)
|
|
check_dtype(res, np.float64)
|
|
np.testing.assert_allclose(res, np.ldexp(x, y))
|
|
|
|
dims = (np.random.randint(200, 300),)
|
|
x = (np.random.rand(*dims) * 10).astype(np.float32)
|
|
y = (np.random.randint(-10, 10, dims)).astype(np.int32)
|
|
res = _run_ldexp_static(x, y, place)
|
|
check_dtype(res, np.float32)
|
|
np.testing.assert_allclose(res, np.ldexp(x, y))
|
|
|
|
# test 1-d int tensor and 1-d int tensor
|
|
dims = (np.random.randint(200, 300),)
|
|
x = (np.random.randint(-10, 10, dims)).astype(np.int64)
|
|
y = (np.random.randint(-10, 10, dims)).astype(np.int32)
|
|
res = _run_ldexp_static(x, y, place)
|
|
check_dtype(res, np.float32)
|
|
np.testing.assert_allclose(res, np.ldexp(x, y))
|
|
|
|
dims = (np.random.randint(200, 300),)
|
|
x = (np.random.randint(-10, 10, dims)).astype(np.int32)
|
|
y = (np.random.randint(-10, 10, dims)).astype(np.int32)
|
|
res = _run_ldexp_static(x, y, place)
|
|
check_dtype(res, np.float32)
|
|
np.testing.assert_allclose(res, np.ldexp(x, y))
|
|
|
|
# test broadcast
|
|
dims = (
|
|
np.random.randint(1, 10),
|
|
np.random.randint(5, 10),
|
|
np.random.randint(5, 10),
|
|
)
|
|
x = (np.random.rand(*dims) * 10).astype(np.float64)
|
|
y = (np.random.randint(-10, 10, dims[-1])).astype(np.int32)
|
|
res = _run_ldexp_static(x, y)
|
|
check_dtype(res, np.float64)
|
|
np.testing.assert_allclose(res, np.ldexp(x, y))
|
|
|
|
|
|
class TestLdexpError(unittest.TestCase):
|
|
"""TestLdexpError."""
|
|
|
|
def test_errors(self):
|
|
"""test_errors."""
|
|
np.random.seed(7)
|
|
|
|
# test 1-d float and int tensor
|
|
dims = (np.random.randint(200, 300),)
|
|
x = (np.random.rand(*dims) * 10).astype(np.float64)
|
|
y = (np.random.randint(-10, 10, dims)).astype(np.int32)
|
|
self.assertRaises(TypeError, paddle.ldexp, x, paddle.to_tensor(y))
|
|
|
|
# test 1-d float tensor and int
|
|
dims = (np.random.randint(200, 300),)
|
|
x = (np.random.rand(*dims) * 10).astype(np.float64)
|
|
y = (np.random.randint(-10, 10, dims)).astype(np.int32)
|
|
self.assertRaises(TypeError, paddle.ldexp, paddle.to_tensor(x), y)
|
|
|
|
|
|
class TestLdexpAPI_ZeroSize(unittest.TestCase):
|
|
def setUp(self):
|
|
self.places = get_places()
|
|
|
|
def test_ldexp_dynamic(self):
|
|
for place in self.places:
|
|
with paddle.base.dygraph.guard(place):
|
|
dims = [2, 0]
|
|
x = np.random.rand(*dims) * 10
|
|
y = (np.random.randint(-10, 10, dims)).astype(np.int32)
|
|
x_ = paddle.to_tensor(x)
|
|
y_ = paddle.to_tensor(y)
|
|
x_.stop_gradient = False
|
|
y_.stop_gradient = False
|
|
res = paddle.ldexp(x_, y_)
|
|
np.testing.assert_allclose(res, np.ldexp(x, y))
|
|
|
|
loss = paddle.sum(res)
|
|
loss.backward()
|
|
np.testing.assert_allclose(x_.grad.shape, x_.shape)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|