xpu support for fill_constant Op (#27675)
wangchaochaohu
4 years ago
committed by
GitHub
parent
a820871669
commit
c5fcc96d5b
@ -0,0 +1,23 @@
|
||||
/* 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. */
|
||||
#include "paddle/fluid/operators/fill_constant_op.h"
|
||||
|
||||
namespace ops = paddle::operators;
|
||||
#ifdef PADDLE_WITH_XPU
|
||||
REGISTER_OP_XPU_KERNEL(fill_constant, ops::FillConstantKernel<float>,
|
||||
ops::FillConstantKernel<int64_t>,
|
||||
ops::FillConstantKernel<double>,
|
||||
ops::FillConstantKernel<bool>,
|
||||
ops::FillConstantKernel<int>);
|
||||
#endif
|
||||
@ -0,0 +1,241 @@
|
||||
# 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.
|
||||
|
||||
from __future__ import print_function
|
||||
|
||||
import sys
|
||||
sys.path.append("..")
|
||||
import unittest
|
||||
from op_test import OpTest
|
||||
|
||||
import paddle
|
||||
import numpy as np
|
||||
|
||||
|
||||
# Situation 1: Attr(shape) is a list(without tensor)
|
||||
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
|
||||
"core is not compiled with XPU")
|
||||
class TestFillConstantOp1(OpTest):
|
||||
def setUp(self):
|
||||
'''Test fill_constant op with specified value'''
|
||||
self.op_type = "fill_constant"
|
||||
|
||||
self.inputs = {}
|
||||
self.attrs = {'shape': [123, 92], 'dtype': 5, 'value': 3.8}
|
||||
self.outputs = {'Out': np.full((123, 92), 3.8)}
|
||||
|
||||
def test_check_output(self):
|
||||
place = paddle.XPUPlace(0)
|
||||
self.check_output_with_place(place)
|
||||
|
||||
|
||||
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
|
||||
"core is not compiled with XPU")
|
||||
class TestFillConstantOp2(OpTest):
|
||||
def setUp(self):
|
||||
'''Test fill_constant op with default value'''
|
||||
self.op_type = "fill_constant"
|
||||
|
||||
self.inputs = {}
|
||||
self.attrs = {'shape': [123, 92], 'dtype': 5}
|
||||
self.outputs = {'Out': np.full((123, 92), 0.0)}
|
||||
|
||||
def test_check_output(self):
|
||||
place = paddle.XPUPlace(0)
|
||||
self.check_output_with_place(place)
|
||||
|
||||
|
||||
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
|
||||
"core is not compiled with XPU")
|
||||
class TestFillConstantOp3(OpTest):
|
||||
def setUp(self):
|
||||
'''Test fill_constant op with specified int64 value'''
|
||||
self.op_type = "fill_constant"
|
||||
|
||||
self.inputs = {}
|
||||
self.attrs = {'shape': [123, 92], 'dtype': 3, 'value': 10000000000}
|
||||
self.outputs = {'Out': np.full((123, 92), 10000000000)}
|
||||
|
||||
def test_check_output(self):
|
||||
place = paddle.XPUPlace(0)
|
||||
self.check_output_with_place(place)
|
||||
|
||||
|
||||
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
|
||||
"core is not compiled with XPU")
|
||||
class TestFillConstantOp4(OpTest):
|
||||
def setUp(self):
|
||||
'''Test fill_constant op with specified int value'''
|
||||
self.op_type = "fill_constant"
|
||||
|
||||
self.inputs = {}
|
||||
self.attrs = {'shape': [123, 92], 'dtype': 2, 'value': 3}
|
||||
self.outputs = {'Out': np.full((123, 92), 3)}
|
||||
|
||||
def test_check_output(self):
|
||||
place = paddle.XPUPlace(0)
|
||||
self.check_output_with_place(place)
|
||||
|
||||
|
||||
# Situation 2: Attr(shape) is a list(with tensor)
|
||||
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
|
||||
"core is not compiled with XPU")
|
||||
class TestFillConstantOp1_ShapeTensorList(OpTest):
|
||||
def setUp(self):
|
||||
'''Test fill_constant op with specified value'''
|
||||
self.op_type = "fill_constant"
|
||||
self.init_data()
|
||||
shape_tensor_list = []
|
||||
for index, ele in enumerate(self.shape):
|
||||
shape_tensor_list.append(("x" + str(index), np.ones(
|
||||
(1)).astype('int32') * ele))
|
||||
|
||||
self.inputs = {"ShapeTensorList": shape_tensor_list}
|
||||
self.attrs = {
|
||||
'shape': self.infer_shape,
|
||||
'dtype': 5,
|
||||
'value': self.value
|
||||
}
|
||||
self.outputs = {'Out': np.full(self.shape, self.value)}
|
||||
|
||||
def init_data(self):
|
||||
self.shape = [123, 92]
|
||||
self.infer_shape = [-1, 92]
|
||||
self.value = 3.8
|
||||
|
||||
def test_check_output(self):
|
||||
place = paddle.XPUPlace(0)
|
||||
self.check_output_with_place(place)
|
||||
|
||||
|
||||
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
|
||||
"core is not compiled with XPU")
|
||||
class TestFillConstantOp2_ShapeTensorList(OpTest):
|
||||
def setUp(self):
|
||||
'''Test fill_constant op with default value'''
|
||||
self.op_type = "fill_constant"
|
||||
self.init_data()
|
||||
shape_tensor_list = []
|
||||
for index, ele in enumerate(self.shape):
|
||||
shape_tensor_list.append(("x" + str(index), np.ones(
|
||||
(1)).astype('int32') * ele))
|
||||
|
||||
self.inputs = {"ShapeTensorList": shape_tensor_list}
|
||||
self.attrs = {'shape': self.infer_shape, 'dtype': 5}
|
||||
self.outputs = {'Out': np.full(self.shape, 0.0)}
|
||||
|
||||
def init_data(self):
|
||||
self.shape = [123, 92]
|
||||
self.infer_shape = [-1, -1]
|
||||
|
||||
def test_check_output(self):
|
||||
place = paddle.XPUPlace(0)
|
||||
self.check_output_with_place(place)
|
||||
|
||||
|
||||
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
|
||||
"core is not compiled with XPU")
|
||||
class TestFillConstantOp3_ShapeTensorList(TestFillConstantOp1_ShapeTensorList):
|
||||
def init_data(self):
|
||||
self.shape = [123, 92]
|
||||
self.infer_shape = [123, -1]
|
||||
self.value = 10000000000
|
||||
|
||||
|
||||
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
|
||||
"core is not compiled with XPU")
|
||||
class TestFillConstantOp4_ShapeTensorList(TestFillConstantOp1_ShapeTensorList):
|
||||
def init_data(self):
|
||||
self.shape = [123, 92]
|
||||
self.infer_shape = [123, -1]
|
||||
self.value = 3
|
||||
|
||||
|
||||
# Situation 3: shape is a tensor
|
||||
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
|
||||
"core is not compiled with XPU")
|
||||
class TestFillConstantOp1_ShapeTensor(OpTest):
|
||||
def setUp(self):
|
||||
'''Test fill_constant op with specified value'''
|
||||
self.op_type = "fill_constant"
|
||||
self.init_data()
|
||||
|
||||
self.inputs = {"ShapeTensor": np.array(self.shape).astype("int32")}
|
||||
self.attrs = {'value': self.value, 'dtype': 5}
|
||||
self.outputs = {'Out': np.full(self.shape, self.value)}
|
||||
|
||||
def init_data(self):
|
||||
self.shape = [123, 92]
|
||||
self.value = 3.8
|
||||
|
||||
def test_check_output(self):
|
||||
place = paddle.XPUPlace(0)
|
||||
self.check_output_with_place(place)
|
||||
|
||||
|
||||
# Situation 4: value is a tensor
|
||||
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
|
||||
"core is not compiled with XPU")
|
||||
class TestFillConstantOp1_ValueTensor(OpTest):
|
||||
def setUp(self):
|
||||
'''Test fill_constant op with specified value'''
|
||||
self.op_type = "fill_constant"
|
||||
self.init_data()
|
||||
|
||||
self.inputs = {
|
||||
"ShapeTensor": np.array(self.shape).astype("int32"),
|
||||
'ValueTensor': np.array([self.value]).astype("float32")
|
||||
}
|
||||
self.attrs = {'value': self.value + 1.0, 'dtype': 5}
|
||||
self.outputs = {'Out': np.full(self.shape, self.value)}
|
||||
|
||||
def init_data(self):
|
||||
self.shape = [123, 92]
|
||||
self.value = 3.8
|
||||
self.dtype = np.float32
|
||||
|
||||
def test_check_output(self):
|
||||
place = paddle.XPUPlace(0)
|
||||
self.check_output_with_place(place)
|
||||
|
||||
|
||||
# Situation 5: value is a tensor
|
||||
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
|
||||
"core is not compiled with XPU")
|
||||
class TestFillConstantOp2_ValueTensor(OpTest):
|
||||
def setUp(self):
|
||||
'''Test fill_constant op with specified value'''
|
||||
self.op_type = "fill_constant"
|
||||
self.init_data()
|
||||
|
||||
self.inputs = {
|
||||
"ShapeTensor": np.array(self.shape).astype("int32"),
|
||||
'ValueTensor': np.array([self.value]).astype("int32")
|
||||
}
|
||||
self.attrs = {'value': self.value, 'dtype': 2}
|
||||
self.outputs = {'Out': np.full(self.shape, self.value)}
|
||||
|
||||
def init_data(self):
|
||||
self.shape = [123, 92]
|
||||
self.value = 3
|
||||
self.dtype = np.int32
|
||||
|
||||
def test_check_output(self):
|
||||
place = paddle.XPUPlace(0)
|
||||
self.check_output_with_place(place)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
paddle.enable_static()
|
||||
unittest.main()
|
||||
Loading…
Reference in new issue