未验证 提交 cd0b03cd 编写于 作者: z8hanghuan's avatar z8hanghuan 提交者: GitHub

add phi empty kernel for xpu,*test=kunlun (#44745)

* add phi empty,*test=kunlun

* support empty op in xpu, *test=kunlun

* support empty op in xpu, *test=kunlun
上级 6d5744b4
......@@ -151,6 +151,16 @@ XPUOpMap& get_kl2_ops() {
pOpKernelType(vartype::FP16, XPUPlace()),
pOpKernelType(vartype::INT64, XPUPlace()),
pOpKernelType(vartype::INT32, XPUPlace())})},
{"empty",
XPUKernelSet({pOpKernelType(vartype::INT64, XPUPlace()),
pOpKernelType(vartype::INT32, XPUPlace()),
pOpKernelType(vartype::INT16, XPUPlace()),
pOpKernelType(vartype::INT8, XPUPlace()),
pOpKernelType(vartype::UINT8, XPUPlace()),
pOpKernelType(vartype::BOOL, XPUPlace()),
pOpKernelType(vartype::FP16, XPUPlace()),
pOpKernelType(vartype::FP32, XPUPlace()),
pOpKernelType(vartype::FP64, XPUPlace())})},
{"equal",
XPUKernelSet({pOpKernelType(vartype::INT64, XPUPlace()),
pOpKernelType(vartype::INT32, XPUPlace()),
......
......@@ -110,3 +110,19 @@ PD_REGISTER_KERNEL(empty_like,
kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND);
}
#endif
#ifdef PADDLE_WITH_XPU
PD_REGISTER_KERNEL(empty,
XPU,
ALL_LAYOUT,
phi::EmptyKernel,
float,
double,
int8_t,
uint8_t,
int16_t,
int,
int64_t,
bool,
phi::dtype::float16) {}
#endif
#Copyright (c) 2022 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
import numpy as np
import paddle
import paddle.fluid as fluid
from op_test_xpu import XPUOpTest
from paddle.fluid import Program, program_guard
from paddle.fluid.framework import convert_np_dtype_to_dtype_
from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper
paddle.enable_static()
class XPUTestEmptyOp(XPUOpTestWrapper):
def __init__(self):
self.op_name = 'empty'
self.use_dynamic_create_class = False
# Situation 1: Attr(shape) is a list(without tensor)
class TestEmptyOp(XPUOpTest):
def setUp(self):
self.op_type = "empty"
self.init_dtype()
self.set_xpu()
self.place = paddle.XPUPlace(0)
self.set_shape()
self.set_inputs()
self.init_config()
def test_check_output(self):
self.check_output_customized(self.verify_output)
def verify_output(self, outs):
data_type = outs[0].dtype
if data_type in [
'float32', 'float64', 'int32', 'int64', 'int8', 'uint8',
'float16', 'int16'
]:
max_value = np.nanmax(outs[0])
min_value = np.nanmin(outs[0])
always_full_zero = max_value == 0.0 and min_value == 0.0
always_non_full_zero = max_value >= min_value
self.assertTrue(always_full_zero or always_non_full_zero,
'always_full_zero or always_non_full_zero.')
elif data_type in ['bool']:
total_num = outs[0].size
true_num = np.sum(outs[0] == True)
false_num = np.sum(outs[0] == False)
self.assertTrue(total_num == true_num + false_num,
'The value should always be True or False.')
else:
#pass
self.assertTrue(False, 'invalid data type')
def set_shape(self):
self.shape = [500, 3]
def set_inputs(self):
self.inputs = {}
def init_config(self):
dtype_inner = convert_np_dtype_to_dtype_(self.dtype)
self.attrs = {'shape': self.shape, 'dtype': dtype_inner}
self.outputs = {'Out': np.zeros(self.shape).astype(self.dtype)}
def init_dtype(self):
self.dtype = self.in_type
def set_xpu(self):
self.__class__.use_xpu = True
self.__class__.no_need_check_grad = True
self.__class__.op_type = self.op_type
class TestEmptyOpCase1(TestEmptyOp):
def set_shape(self):
self.shape = [50]
class TestEmptyOpCase2(TestEmptyOp):
def set_shape(self):
self.shape = [1, 50, 3, 4]
class TestEmptyOpCase3(TestEmptyOp):
def set_shape(self):
self.shape = [5, 5, 5]
# Situation 2: shape is a tensor
class TestEmptyOp_ShapeTensor(TestEmptyOp):
def set_inputs(self):
self.inputs = {"ShapeTensor": np.array(self.shape).astype("int32")}
# Situation 3: Attr(shape) is a list(with tensor)
class TestEmptyOp_ShapeTensorList(TestEmptyOp):
def set_inputs(self):
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}
support_types = get_xpu_op_support_types('empty')
for stype in support_types:
create_test_class(globals(), XPUTestEmptyOp, stype)
if __name__ == '__main__':
unittest.main()
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