wehub-resource-sync
265 lines
8.5 KiB
Python
265 lines
8.5 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.
|
|
|
|
import unittest
|
|
|
|
import numpy as np
|
|
from get_test_cover_info import (
|
|
XPUOpTestWrapper,
|
|
create_test_class,
|
|
get_xpu_op_support_types,
|
|
)
|
|
from op_test_xpu import XPUOpTest
|
|
|
|
import paddle
|
|
from paddle import base
|
|
|
|
paddle.enable_static()
|
|
np.random.seed(10)
|
|
|
|
|
|
# Situation 1: repeat_times is a list (without tensor)
|
|
class XPUTestTileOpRank1(XPUOpTestWrapper):
|
|
def __init__(self):
|
|
self.op_name = 'tile'
|
|
self.use_dynamic_create_class = False
|
|
|
|
class TestTileOpRank1(XPUOpTest):
|
|
def setUp(self):
|
|
self.dtype = self.in_type
|
|
self.__class__.no_need_check_grad = True
|
|
self.place = paddle.XPUPlace(0)
|
|
self.op_type = "tile"
|
|
self.init_data()
|
|
self.inputs = {
|
|
'X': np.random.random(self.ori_shape).astype(self.dtype)
|
|
}
|
|
self.attrs = {'repeat_times': self.repeat_times}
|
|
output = np.tile(self.inputs['X'], self.repeat_times)
|
|
self.outputs = {'Out': output}
|
|
|
|
def init_data(self):
|
|
self.ori_shape = [100]
|
|
self.repeat_times = [2]
|
|
|
|
def test_check_output(self):
|
|
self.check_output_with_place(self.place)
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad_with_place(self.place, ['X'], 'Out')
|
|
|
|
# with dimension expanding
|
|
class TestTileOpRank2Expanding(TestTileOpRank1):
|
|
def init_data(self):
|
|
self.ori_shape = [120]
|
|
self.repeat_times = [2, 2]
|
|
|
|
class TestTileOpRank2(TestTileOpRank1):
|
|
def init_data(self):
|
|
self.ori_shape = [12, 14]
|
|
self.repeat_times = [2, 3]
|
|
|
|
class TestTileOpRank3_Corner(TestTileOpRank1):
|
|
def init_data(self):
|
|
self.ori_shape = (2, 10, 5)
|
|
self.repeat_times = (1, 1, 1)
|
|
|
|
class TestTileOpRank3_Corner2(TestTileOpRank1):
|
|
def init_data(self):
|
|
self.ori_shape = (2, 10, 5)
|
|
self.repeat_times = (2, 2)
|
|
|
|
class TestTileOpRank3(TestTileOpRank1):
|
|
def init_data(self):
|
|
self.ori_shape = (2, 4, 15)
|
|
self.repeat_times = (2, 1, 4)
|
|
|
|
class TestTileOpRank4(TestTileOpRank1):
|
|
def init_data(self):
|
|
self.ori_shape = (2, 4, 5, 7)
|
|
self.repeat_times = (3, 2, 1, 2)
|
|
|
|
class TestTileOpRank_ZeroDim1(TestTileOpRank1):
|
|
def init_data(self):
|
|
self.ori_shape = []
|
|
self.repeat_times = []
|
|
|
|
class TestTileOpRank_ZeroDim2(TestTileOpRank1):
|
|
def init_data(self):
|
|
self.ori_shape = []
|
|
self.repeat_times = [2]
|
|
|
|
class TestTileOpRank_ZeroDim3(TestTileOpRank1):
|
|
def init_data(self):
|
|
self.ori_shape = []
|
|
self.repeat_times = [2, 3]
|
|
|
|
class TestTileOpRank_ZeroElement(TestTileOpRank1):
|
|
def init_data(self):
|
|
self.ori_shape = [0]
|
|
self.repeat_times = [8]
|
|
|
|
|
|
# Situation 2: repeat_times is a list (with tensor)
|
|
class XPUTestTileOpRank1_tensor_attr(XPUOpTestWrapper):
|
|
def __init__(self):
|
|
self.op_name = 'tile'
|
|
self.use_dynamic_create_class = False
|
|
|
|
class TestTileOpRank1_tensor_attr(XPUOpTest):
|
|
def setUp(self):
|
|
self.dtype = self.in_type
|
|
self.__class__.no_need_check_grad = True
|
|
self.place = paddle.XPUPlace(0)
|
|
self.op_type = "tile"
|
|
self.init_data()
|
|
repeat_times_tensor = []
|
|
for index, ele in enumerate(self.repeat_times):
|
|
repeat_times_tensor.append(
|
|
("x" + str(index), np.ones(1).astype('int32') * ele)
|
|
)
|
|
|
|
self.inputs = {
|
|
'X': np.random.random(self.ori_shape).astype(self.dtype),
|
|
'repeat_times_tensor': repeat_times_tensor,
|
|
}
|
|
self.attrs = {"repeat_times": self.infer_repeat_times}
|
|
output = np.tile(self.inputs['X'], self.repeat_times)
|
|
self.outputs = {'Out': output}
|
|
|
|
def init_data(self):
|
|
self.ori_shape = [100]
|
|
self.repeat_times = [2]
|
|
self.infer_repeat_times = [-1]
|
|
|
|
def test_check_output(self):
|
|
self.check_output_with_place(self.place)
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad_with_place(self.place, ['X'], 'Out')
|
|
|
|
class TestTileOpRank2_Corner_tensor_attr(TestTileOpRank1_tensor_attr):
|
|
def init_data(self):
|
|
self.ori_shape = [12, 14]
|
|
self.repeat_times = [1, 1]
|
|
self.infer_repeat_times = [1, -1]
|
|
|
|
class TestTileOpRank2_attr_tensor(TestTileOpRank1_tensor_attr):
|
|
def init_data(self):
|
|
self.ori_shape = [12, 14]
|
|
self.repeat_times = [2, 3]
|
|
self.infer_repeat_times = [-1, 3]
|
|
|
|
|
|
# Situation 3: repeat_times is a tensor
|
|
class XPUTestTileOpRank1_tensor(XPUOpTestWrapper):
|
|
def __init__(self):
|
|
self.op_name = 'tile'
|
|
self.use_dynamic_create_class = False
|
|
|
|
class TestTileOpRank1_tensor(XPUOpTest):
|
|
def setUp(self):
|
|
self.dtype = self.in_type
|
|
self.__class__.no_need_check_grad = True
|
|
self.place = paddle.XPUPlace(0)
|
|
self.op_type = "tile"
|
|
self.init_data()
|
|
|
|
self.inputs = {
|
|
'X': np.random.random(self.ori_shape).astype(self.dtype),
|
|
'RepeatTimes': np.array(self.repeat_times).astype("int32"),
|
|
}
|
|
self.attrs = {}
|
|
output = np.tile(self.inputs['X'], self.repeat_times)
|
|
self.outputs = {'Out': output}
|
|
|
|
def init_data(self):
|
|
self.ori_shape = [100]
|
|
self.repeat_times = [2]
|
|
|
|
def test_check_output(self):
|
|
self.check_output_with_place(self.place)
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad_with_place(self.place, ['X'], 'Out')
|
|
|
|
class TestTileOpRank2_tensor(TestTileOpRank1_tensor):
|
|
def init_data(self):
|
|
self.ori_shape = [12, 14]
|
|
self.repeat_times = [2, 3]
|
|
|
|
|
|
support_types = get_xpu_op_support_types('tile')
|
|
for stype in support_types:
|
|
create_test_class(globals(), XPUTestTileOpRank1, stype)
|
|
create_test_class(globals(), XPUTestTileOpRank1_tensor_attr, stype)
|
|
create_test_class(globals(), XPUTestTileOpRank1_tensor, stype)
|
|
|
|
|
|
# Test python API
|
|
class TestTileAPI(unittest.TestCase):
|
|
def test_api(self):
|
|
with base.dygraph.guard(paddle.XPUPlace(0)):
|
|
np_x = np.random.random([12, 14]).astype("float32")
|
|
x = paddle.to_tensor(np_x)
|
|
|
|
positive_2 = np.array([2]).astype("int32")
|
|
positive_2 = paddle.to_tensor(positive_2)
|
|
|
|
repeat_times = np.array([2, 3]).astype("int32")
|
|
repeat_times = paddle.to_tensor(repeat_times)
|
|
|
|
out_1 = paddle.tile(x, repeat_times=[2, 3])
|
|
out_2 = paddle.tile(x, repeat_times=[positive_2, 3])
|
|
out_3 = paddle.tile(x, repeat_times=repeat_times)
|
|
|
|
np.testing.assert_array_equal(out_1.numpy(), np.tile(np_x, (2, 3)))
|
|
np.testing.assert_array_equal(out_2.numpy(), np.tile(np_x, (2, 3)))
|
|
np.testing.assert_array_equal(out_3.numpy(), np.tile(np_x, (2, 3)))
|
|
|
|
|
|
class TestTileAPI_ZeroDim(unittest.TestCase):
|
|
def test_dygraph(self):
|
|
paddle.disable_static()
|
|
|
|
x = paddle.rand([])
|
|
x.stop_gradient = False
|
|
|
|
out = paddle.tile(x, [])
|
|
out.retain_grads()
|
|
out.backward()
|
|
self.assertEqual(out.shape, [])
|
|
self.assertEqual(x.grad.shape, [])
|
|
self.assertEqual(out.grad.shape, [])
|
|
|
|
out = paddle.tile(x, [3])
|
|
out.retain_grads()
|
|
out.backward()
|
|
self.assertEqual(out.shape, [3])
|
|
self.assertEqual(x.grad.shape, [])
|
|
self.assertEqual(out.grad.shape, [3])
|
|
|
|
out = paddle.tile(x, [2, 3])
|
|
out.retain_grads()
|
|
out.backward()
|
|
self.assertEqual(out.shape, [2, 3])
|
|
self.assertEqual(x.grad.shape, [])
|
|
self.assertEqual(out.grad.shape, [2, 3])
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|