wehub-resource-sync
181 lines
7.1 KiB
Python
181 lines
7.1 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, is_custom_device
|
|
|
|
import paddle
|
|
from paddle import base
|
|
from paddle.framework import in_pir_mode
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestBroadcastToError(unittest.TestCase):
|
|
def test_errors(self):
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
shape = [2, 2]
|
|
if not in_pir_mode():
|
|
x1 = base.create_lod_tensor(
|
|
np.array([[-1]]), [[1]], base.CPUPlace()
|
|
)
|
|
self.assertRaises(
|
|
TypeError, paddle.tensor.broadcast_to, x1, shape
|
|
)
|
|
x2 = paddle.static.data(name='x2', shape=[-1, 4], dtype="bool")
|
|
x2.stop_gradient = False
|
|
self.assertRaises(ValueError, paddle.tensor.broadcast_to, x2, shape)
|
|
x2.stop_gradient = True
|
|
self.assertRaises(TypeError, paddle.tensor.broadcast_to, x2, 1)
|
|
|
|
|
|
# Test python API
|
|
class TestBroadcastToAPI(unittest.TestCase):
|
|
# TODO: add test_with_pir_api
|
|
# base.backward.calc_gradient maybe not support pir
|
|
# AttributeError: 'paddle.base.libpaddle.pir.Program' object has no attribute '_appending_grad_times'
|
|
def test_api(self):
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
input = np.random.random([12, 14]).astype("float32")
|
|
x = paddle.static.data(name='x', shape=[12, 14], dtype="float32")
|
|
|
|
zero_size_input = np.random.random([0, 14]).astype("float32")
|
|
x_zero = paddle.static.data(
|
|
name='x_zero', shape=[0, 14], dtype="float32"
|
|
)
|
|
|
|
positive_2 = paddle.tensor.fill_constant([1], "int32", 12)
|
|
expand_shape = paddle.static.data(
|
|
name="expand_shape",
|
|
shape=[2],
|
|
dtype="int32",
|
|
)
|
|
|
|
out_1 = paddle.broadcast_to(x, shape=[12, 14])
|
|
out_2 = paddle.broadcast_to(x, shape=[positive_2, 14])
|
|
out_3 = paddle.broadcast_to(x, shape=expand_shape)
|
|
out_4 = paddle.broadcast_to(x_zero, shape=[0, 14])
|
|
|
|
g0 = base.backward.gradients(out_2, x)
|
|
|
|
exe = base.Executor(place=base.CPUPlace())
|
|
res_1, res_2, res_3, res_4 = exe.run(
|
|
feed={
|
|
"x": input,
|
|
"x_zero": zero_size_input,
|
|
"expand_shape": np.array([12, 14]).astype("int32"),
|
|
},
|
|
fetch_list=[out_1, out_2, out_3, out_4],
|
|
)
|
|
np.testing.assert_array_equal(res_1, np.tile(input, (1, 1)))
|
|
np.testing.assert_array_equal(res_2, np.tile(input, (1, 1)))
|
|
np.testing.assert_array_equal(res_3, np.tile(input, (1, 1)))
|
|
np.testing.assert_array_equal(res_4, zero_size_input)
|
|
|
|
def test_api_fp16_gpu(self):
|
|
if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
|
|
place = get_device_place()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
input = np.random.random([12, 14]).astype("float16")
|
|
x = paddle.static.data(
|
|
name="x", shape=[12, 14], dtype="float16"
|
|
)
|
|
|
|
zero_size_input = np.random.random([0, 14]).astype("float32")
|
|
x_zero = paddle.static.data(
|
|
name='x_zero', shape=[0, 14], dtype="float32"
|
|
)
|
|
|
|
positive_2 = paddle.tensor.fill_constant([1], "int32", 12)
|
|
expand_shape = paddle.static.data(
|
|
name="expand_shape",
|
|
shape=[2],
|
|
dtype="int32",
|
|
)
|
|
|
|
out_1 = paddle.broadcast_to(x, shape=[12, 14])
|
|
out_2 = paddle.broadcast_to(x, shape=[positive_2, 14])
|
|
out_3 = paddle.broadcast_to(x, shape=expand_shape)
|
|
out_4 = paddle.broadcast_to(x_zero, shape=[0, 14])
|
|
|
|
exe = paddle.static.Executor(place)
|
|
res_1, res_2, res_3, res_4 = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={
|
|
"x": input,
|
|
"x_zero": zero_size_input,
|
|
"expand_shape": np.array([12, 14]).astype("int32"),
|
|
},
|
|
fetch_list=[out_1, out_2, out_3, out_4],
|
|
)
|
|
np.testing.assert_array_equal(res_1, np.tile(input, (1, 1)))
|
|
np.testing.assert_array_equal(res_2, np.tile(input, (1, 1)))
|
|
np.testing.assert_array_equal(res_3, np.tile(input, (1, 1)))
|
|
np.testing.assert_array_equal(res_4, zero_size_input)
|
|
|
|
def test_expand_zero_size_tensor_valid(self):
|
|
# 测试 0-size Tensor expand 到合法形状的情况
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
zero_input = np.random.random([0, 14]).astype("float32")
|
|
x = paddle.static.data(name="x", shape=[0, 14], dtype="float32")
|
|
|
|
# 合法 expand: 形状不变
|
|
out_1 = paddle.broadcast_to(x, shape=[0, 14])
|
|
|
|
exe = paddle.static.Executor(base.CPUPlace())
|
|
res_1 = exe.run(
|
|
feed={"x": zero_input},
|
|
fetch_list=[out_1],
|
|
)
|
|
|
|
# 验证结果
|
|
np.testing.assert_array_equal(res_1[0], zero_input)
|
|
self.assertEqual(res_1[0].shape, (0, 14))
|
|
|
|
def test_expand_non_zero_size_tensor(self):
|
|
# 测试非 0-size Tensor expand 到合法形状
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
input_data = np.random.random([2, 1]).astype("float32")
|
|
x = paddle.static.data(name="x", shape=[2, 1], dtype="float32")
|
|
|
|
# expand 到 [2, 3]
|
|
out_1 = paddle.broadcast_to(x, shape=[2, 3])
|
|
# expand 到 [-1, 3] (-1 表示保持输入对应维度)
|
|
out_2 = paddle.broadcast_to(x, shape=[-1, 3])
|
|
|
|
exe = paddle.static.Executor(base.CPUPlace())
|
|
res_1, res_2 = exe.run(
|
|
feed={"x": input_data},
|
|
fetch_list=[out_1, out_2],
|
|
)
|
|
|
|
expected_output = np.tile(input_data, (1, 3))
|
|
np.testing.assert_array_equal(res_1, expected_output)
|
|
np.testing.assert_array_equal(res_2, expected_output)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|