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
// 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.
#include "paddle/phi/kernels/sparse/unary_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/sparse_coo_tensor.h"
#include "paddle/phi/core/sparse_csr_tensor.h"
#include "paddle/phi/core/tensor_utils.h"
#include "paddle/phi/kernels/activation_kernel.h"
#include "paddle/phi/kernels/empty_kernel.h"
#define DEFINE_SPARSE_UNARY_KERNEL(DenseKernelFunc) \
namespace phi { \
namespace sparse { \
\
template <typename T, typename Context> \
void SparseCoo##DenseKernelFunc(const Context& dev_ctx, \
const SparseCooTensor& x, \
SparseCooTensor* out) { \
DenseTensor non_zero_indices = \
phi::EmptyLike<T, Context>(dev_ctx, x.non_zero_indices()); \
DenseTensor non_zero_elements = \
phi::EmptyLike<T, Context>(dev_ctx, x.non_zero_elements()); \
phi::Copy(dev_ctx, \
x.non_zero_indices(), \
dev_ctx.GetPlace(), \
false, \
&non_zero_indices); \
phi::DenseKernelFunc<T, Context>( \
dev_ctx, x.non_zero_elements(), &non_zero_elements); \
out->SetMember(non_zero_indices, non_zero_elements, x.dims(), true); \
} \
\
template <typename T, typename Context> \
void SparseCsr##DenseKernelFunc(const Context& dev_ctx, \
const SparseCsrTensor& x, \
SparseCsrTensor* out) { \
DenseTensor non_zero_crows = \
phi::EmptyLike<T, Context>(dev_ctx, x.non_zero_crows()); \
DenseTensor non_zero_cols = \
phi::EmptyLike<T, Context>(dev_ctx, x.non_zero_cols()); \
DenseTensor non_zero_elements = \
phi::EmptyLike<T, Context>(dev_ctx, x.non_zero_elements()); \
phi::Copy(dev_ctx, \
x.non_zero_crows(), \
dev_ctx.GetPlace(), \
false, \
&non_zero_crows); \
phi::Copy(dev_ctx, \
x.non_zero_cols(), \
dev_ctx.GetPlace(), \
false, \
&non_zero_cols); \
phi::DenseKernelFunc<T, Context>( \
dev_ctx, x.non_zero_elements(), &non_zero_elements); \
out->SetMember( \
non_zero_crows, non_zero_cols, non_zero_elements, x.dims()); \
} \
} \
}
#define REGISTER_CPU_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc) \
PD_REGISTER_KERNEL(sparse_coo_##kernel_name, \
CPU, \
ALL_LAYOUT, \
phi::sparse::SparseCoo##DenseKernelFunc, \
float, \
double) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO); \
} \
PD_REGISTER_KERNEL(sparse_csr_##kernel_name, \
CPU, \
ALL_LAYOUT, \
phi::sparse::SparseCsr##DenseKernelFunc, \
float, \
double) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR); \
}
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
#define REGISTER_GPU_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc) \
PD_REGISTER_KERNEL(sparse_coo_##kernel_name, \
GPU, \
ALL_LAYOUT, \
phi::sparse::SparseCoo##DenseKernelFunc, \
float, \
double, \
phi::dtype::float16) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO); \
} \
\
PD_REGISTER_KERNEL(sparse_csr_##kernel_name, \
GPU, \
ALL_LAYOUT, \
phi::sparse::SparseCsr##DenseKernelFunc, \
float, \
double, \
phi::dtype::float16) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR); \
}
#else
// This macro definition is empty when GPU is disabled
#define REGISTER_GPU_SPARSE_UNARY_KERNEL(sparse_kernel_name, DenseKernelFunc)
#endif
#define REGISTER_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc) \
REGISTER_CPU_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc) \
REGISTER_GPU_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc)
#define DEFINE_AND_REGISTER_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc) \
DEFINE_SPARSE_UNARY_KERNEL(DenseKernelFunc) \
REGISTER_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc)
// NOTE: the following code is to bypass the restriction of Paddle
// kernel registration mechanism. Do NOT refactor them unless you
// know what you are doing.
// If you want to implement any new kernel, please follow `sin`,
// `tanh` etc, do NOT follow `sqrt`.
DEFINE_SPARSE_UNARY_KERNEL(SqrtKernel)
PD_REGISTER_KERNEL(sparse_coo_sqrt,
CPU,
ALL_LAYOUT,
phi::sparse::SparseCooSqrtKernel,
float,
double) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
PD_REGISTER_KERNEL(sparse_csr_sqrt,
CPU,
ALL_LAYOUT,
phi::sparse::SparseCsrSqrtKernel,
float,
double) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR);
}
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
PD_REGISTER_KERNEL(sparse_coo_sqrt,
GPU,
ALL_LAYOUT,
phi::sparse::SparseCooSqrtKernel,
float,
double,
phi::dtype::float16) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
PD_REGISTER_KERNEL(sparse_csr_sqrt,
GPU,
ALL_LAYOUT,
phi::sparse::SparseCsrSqrtKernel,
float,
double,
phi::dtype::float16) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR);
}
#endif
DEFINE_AND_REGISTER_SPARSE_UNARY_KERNEL(sin, SinKernel)
DEFINE_AND_REGISTER_SPARSE_UNARY_KERNEL(tanh, TanhKernel)
DEFINE_AND_REGISTER_SPARSE_UNARY_KERNEL(relu, ReluKernel)