-
Notifications
You must be signed in to change notification settings - Fork 166
/
swish.cu
157 lines (142 loc) · 6.88 KB
/
swish.cu
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
#include <stdio.h>
#include <stdlib.h>
#include <float.h>
#include <vector>
#include <algorithm>
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <torch/types.h>
#include <torch/extension.h>
#define WARP_SIZE 32
#define INT4(value) (reinterpret_cast<int4*>(&(value))[0])
#define FLOAT4(value) (reinterpret_cast<float4*>(&(value))[0])
#define HALF2(value) (reinterpret_cast<half2*>(&(value))[0])
#define BFLOAT2(value) (reinterpret_cast<__nv_bfloat162*>(&(value))[0])
#define LDST128BITS(value) (reinterpret_cast<float4*>(&(value))[0])
// -------------------------------------- FP32 --------------------------------------
// Swish x: N, y: N y=x*sigmoid(x)
__device__ __forceinline__ float swish(float x) {
return x / (1.0f + expf(-x));
}
__global__ void swish_f32_kernel(float* x, float* y, int N) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N) y[idx] = swish(x[idx]);
}
__global__ void swish_f32x4_kernel(float* x, float* y, int N) {
int idx = (blockIdx.x * blockDim.x + threadIdx.x) * 4;
if (idx < N) {
float4 reg_x = FLOAT4(x[idx]);
float4 reg_y;
reg_y.x = swish(reg_x.x);
reg_y.y = swish(reg_x.y);
reg_y.z = swish(reg_x.z);
reg_y.w = swish(reg_x.w);
FLOAT4(y[idx]) = reg_y;
}
}
// -------------------------------------- FP16 --------------------------------------
__device__ __forceinline__ half swish_half(half x) {
return __hmul(x, __hdiv(
__float2half(1.0f), __hadd(__float2half(1.0f), hexp(__hneg(x)))));
}
__global__ void swish_f16_kernel(half* x, half* y, int N) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N) y[idx] = swish_half(x[idx]);
}
__global__ void swish_f16x2_kernel(half* x, half* y, int N) {
int idx = 2 * (blockIdx.x * blockDim.x + threadIdx.x);
if (idx < N) {
half2 reg_x = HALF2(x[idx]);
half2 reg_y;
reg_y.x = swish_half(reg_x.x);
reg_y.y = swish_half(reg_x.y);
HALF2(y[idx]) = reg_y;
}
}
__global__ void swish_f16x8_kernel(half* x, half* y, int N) {
int idx = 8 * (blockIdx.x * blockDim.x + threadIdx.x);
half2 reg_x_0 = HALF2(x[idx + 0]);
half2 reg_x_1 = HALF2(x[idx + 2]);
half2 reg_x_2 = HALF2(x[idx + 4]);
half2 reg_x_3 = HALF2(x[idx + 6]);
half2 reg_y_0, reg_y_1, reg_y_2, reg_y_3;
reg_y_0.x = swish_half(reg_x_0.x);
reg_y_0.y = swish_half(reg_x_0.y);
reg_y_1.x = swish_half(reg_x_1.x);
reg_y_1.y = swish_half(reg_x_1.y);
reg_y_2.x = swish_half(reg_x_2.x);
reg_y_2.y = swish_half(reg_x_2.y);
reg_y_3.x = swish_half(reg_x_3.x);
reg_y_3.y = swish_half(reg_x_3.y);
if ((idx + 0) < N) { HALF2(y[idx + 0]) = reg_y_0; }
if ((idx + 2) < N) { HALF2(y[idx + 2]) = reg_y_1; }
if ((idx + 4) < N) { HALF2(y[idx + 4]) = reg_y_2; }
if ((idx + 6) < N) { HALF2(y[idx + 6]) = reg_y_3; }
}
__global__ void swish_f16x8_pack_kernel(half* x, half* y, int N) {
int idx = 8 * (blockIdx.x * blockDim.x + threadIdx.x);
half pack_x[8], pack_y[8];
LDST128BITS(pack_x[0]) = LDST128BITS(x[idx]);
#pragma unroll
for (int i = 0; i < 8; i++) {
pack_y[i] = swish_half(pack_x[i]);
}
if ((idx + 7) < N) { LDST128BITS(y[idx]) = LDST128BITS(pack_y[0]); }
}
// --------------------- PyTorch bindings for custom kernel -----------------------
#define STRINGFY(str) #str
#define TORCH_BINDING_COMMON_EXTENSION(func) \
m.def(STRINGFY(func), &func, STRINGFY(func));
#define CHECK_TORCH_TENSOR_DTYPE(T, th_type) \
if(((T).options().dtype() != (th_type))) { \
std::cout << "Tensor Info:" << (T).options() << std::endl; \
throw std::runtime_error("values must be "#th_type); \
}
#define TORCH_BINDING_SWISH(packed_type, th_type, element_type, n_elements) \
void swish_##packed_type(torch::Tensor x, torch::Tensor y) { \
CHECK_TORCH_TENSOR_DTYPE(x, (th_type)) \
CHECK_TORCH_TENSOR_DTYPE(y, (th_type)) \
const int ndim = x.dim(); \
if (ndim != 2) { \
int N = 1; \
for (int i = 0; i < ndim; ++i) { N *= x.size(i); } \
dim3 block(256 / (n_elements)); \
dim3 grid((N + 256 - 1) / 256); \
swish_##packed_type##_kernel<<<grid, block>>>( \
reinterpret_cast<element_type*>(x.data_ptr()), \
reinterpret_cast<element_type*>(y.data_ptr()), N); \
} else { \
const int S = x.size(0); \
const int K = x.size(1); \
const int N = S * K; \
if ((K/(n_elements)) <= 1024) { \
dim3 block(K/(n_elements)); \
dim3 grid(S); \
swish_##packed_type##_kernel<<<grid, block>>>( \
reinterpret_cast<element_type*>(x.data_ptr()), \
reinterpret_cast<element_type*>(y.data_ptr()), N); \
} else { \
int N = 1; \
for (int i = 0; i < ndim; ++i) { N *= x.size(i); } \
dim3 block(256 / (n_elements)); \
dim3 grid((N + 256 - 1) / 256); \
swish_##packed_type##_kernel<<<grid, block>>>( \
reinterpret_cast<element_type*>(x.data_ptr()), \
reinterpret_cast<element_type*>(y.data_ptr()), N); \
} \
} \
}
TORCH_BINDING_SWISH(f32, torch::kFloat32, float, 1)
TORCH_BINDING_SWISH(f32x4, torch::kFloat32, float, 4)
TORCH_BINDING_SWISH(f16, torch::kHalf, half, 1)
TORCH_BINDING_SWISH(f16x2, torch::kHalf, half, 2)
TORCH_BINDING_SWISH(f16x8, torch::kHalf, half, 8)
TORCH_BINDING_SWISH(f16x8_pack, torch::kHalf, half, 8)
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
TORCH_BINDING_COMMON_EXTENSION(swish_f32)
TORCH_BINDING_COMMON_EXTENSION(swish_f32x4)
TORCH_BINDING_COMMON_EXTENSION(swish_f16)
TORCH_BINDING_COMMON_EXTENSION(swish_f16x2)
TORCH_BINDING_COMMON_EXTENSION(swish_f16x8)
TORCH_BINDING_COMMON_EXTENSION(swish_f16x8_pack)
}