/
elementwise_kernel.h
180 lines (156 loc) · 6.16 KB
/
elementwise_kernel.h
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
// 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.
#pragma once
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/infermeta/binary.h"
namespace phi {
template <typename T, typename Context>
void FMaxKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
int axis,
DenseTensor* out);
template <typename T, typename Context>
void FMinKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
int axis,
DenseTensor* out);
template <typename T, typename Context>
void MaximumRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
int axis,
DenseTensor* out);
template <typename T, typename Context>
void MaximumKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out);
template <typename T, typename Context>
void MinimumRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
int axis,
DenseTensor* out);
template <typename T, typename Context>
void MinimumKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out);
template <typename T, typename Context>
void ModuloRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
int axis,
DenseTensor* out);
template <typename T, typename Context>
void ModuloKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out);
template <typename T, typename Context>
void FloorDivideRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
int axis,
DenseTensor* out);
template <typename T, typename Context>
void FloorDivideKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out);
template <typename T, typename Context>
void ElementwisePowRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
int axis,
DenseTensor* out);
template <typename T, typename Context>
void ElementwisePowKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out);
template <typename T, typename Context>
void ElementwiseHeavisideRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
int axis,
DenseTensor* out);
template <typename T, typename Context>
void ElementwiseHeavisideKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out);
template <typename T, typename Context>
DenseTensor Maximum(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y) {
DenseTensor dense_out;
MetaTensor meta_out(&dense_out);
ElementwiseInferMeta(x, y, &meta_out);
MaximumKernel<T, Context>(dev_ctx, x, y, &dense_out);
return dense_out;
}
template <typename T, typename Context>
DenseTensor Minimum(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y) {
DenseTensor dense_out;
MetaTensor meta_out(&dense_out);
ElementwiseInferMeta(x, y, &meta_out);
MinimumKernel<T, Context>(dev_ctx, x, y, &dense_out);
return dense_out;
}
template <typename T, typename Context>
DenseTensor Modulo(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y) {
DenseTensor dense_out;
MetaTensor meta_out(&dense_out);
ElementwiseInferMeta(x, y, &meta_out);
ModuloKernel<T, Context>(dev_ctx, x, y, &dense_out);
return dense_out;
}
template <typename T, typename Context>
DenseTensor FloorDivide(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y) {
DenseTensor dense_out;
MetaTensor meta_out(&dense_out);
ElementwiseInferMeta(x, y, &meta_out);
FloorDivideKernel<T, Context>(dev_ctx, x, y, &dense_out);
return dense_out;
}
template <typename T, typename Context>
DenseTensor ElementwiseHeaviside(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y) {
DenseTensor dense_out;
MetaTensor meta_out(&dense_out);
ElementwiseInferMeta(x, y, &meta_out);
ElementwiseHeavisideKernel<T, Context>(dev_ctx, x, y, &dense_out);
return dense_out;
}
template <typename T, typename Context>
DenseTensor ElementwisePow(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y) {
DenseTensor dense_out;
MetaTensor meta_out(&dense_out);
ElementwiseInferMeta(x, y, &meta_out);
ElementwisePowKernel<T, Context>(dev_ctx, x, y, &dense_out);
return dense_out;
}
} // namespace phi