-
Notifications
You must be signed in to change notification settings - Fork 166
/
rope.cu
120 lines (110 loc) · 4.2 KB
/
rope.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
#include <stdio.h>
#include <stdlib.h>
#include <float.h>
#include <vector>
#include <algorithm>
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cuda_bf16.h>
#include <cuda_fp8.h>
#include <torch/types.h>
#include <torch/extension.h>
#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 BLOCK_SIZE 256
#define theta 10000.0f
__global__ void rope_f32_kernel(float* x, float* out, int seq_len, int N){
int idx = blockIdx.x * blockDim.x + threadIdx.x;
float x1 = x[idx * 2];
float x2 = x[idx * 2 + 1];
int token_pos = idx / N;
int token_idx = idx % N;
float exp_v = 1.0f / powf(theta, token_idx / (N * 2));
float sin_v = sinf(token_pos / exp_v);
float cos_v = cosf(token_pos / exp_v);
float out1 = x1 * cos_v - x2 * sin_v;
float out2 = x1 * sin_v + x2 * cos_v;
out[idx * 2] = out1;
out[idx * 2 + 1] = out2;
}
// another index method of rope.
__global__ void rope_f32_v2_kernel(float* x, float* out, int seq_len, int N){
int token_pos = blockIdx.x;
int tid = threadIdx.x;
float x1 = x[token_pos * N * 2 + tid * 2];
float x2 = x[token_pos * N * 2 + tid * 2 + 1];
float exp_v = 1.0f / powf(theta, (int)(tid / 2) / (N * 2));
float sin_v = sinf(token_pos / exp_v);
float cos_v = cosf(token_pos / exp_v);
float out1 = x1 * cos_v - x2 * sin_v;
float out2 = x1 * sin_v + x2 * cos_v;
out[token_pos * N * 2 + tid * 2] = out1;
out[token_pos * N * 2 + tid * 2 + 1] = out2;
}
__global__ void rope_f32x4_pack_kernel(float* x, float* out, int seq_len, int N){
int idx = blockIdx.x * blockDim.x + threadIdx.x;
float4 x_v = FLOAT4(x[idx * 4]);
int token_pos = idx / N;
int token_idx = idx % N;
float exp_f_v = 1.0f / powf(theta, token_idx * 2 / (N * 4));
float exp_s_v = 1.0f / powf(theta, ((token_idx * 2) + 1) / (N * 4));
float sin_f_v = sinf(token_pos / exp_f_v);
float cos_f_v = cosf(token_pos / exp_f_v);
float sin_s_v = sinf(token_pos / exp_s_v);
float cos_s_v = cosf(token_pos / exp_s_v);
float4 out_v;
out_v.x = x_v.x * cos_f_v - x_v.y * sin_f_v;
out_v.y = x_v.x * sin_f_v + x_v.y * cos_f_v;
out_v.z = x_v.z * cos_s_v - x_v.w * sin_s_v;
out_v.w = x_v.z * sin_s_v + x_v.w * cos_s_v;
FLOAT4(out[idx * 4]) = out_v;
}
// --------------------- 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); \
}
void rope_f32(torch::Tensor x, torch::Tensor out) {
CHECK_TORCH_TENSOR_DTYPE(x, torch::kFloat32)
CHECK_TORCH_TENSOR_DTYPE(out, torch::kFloat32)
int seq_len = x.size(0);
int hidden_size = x.size(1);
int N = (int)(hidden_size/2);
dim3 grid((seq_len * N + BLOCK_SIZE - 1) / BLOCK_SIZE);
dim3 block(BLOCK_SIZE);
rope_f32_kernel<<<grid, block>>>(
x.data_ptr<float>(), out.data_ptr<float>(), seq_len, N);
}
void rope_f32_v2(torch::Tensor x, torch::Tensor out) {
CHECK_TORCH_TENSOR_DTYPE(x, torch::kFloat32)
CHECK_TORCH_TENSOR_DTYPE(out, torch::kFloat32)
int seq_len = x.size(0);
int hidden_size = x.size(1);
int N = (int)(hidden_size/2);
dim3 grid(seq_len);
dim3 block(N);
rope_f32_v2_kernel<<<grid, block>>>(
x.data_ptr<float>(), out.data_ptr<float>(), seq_len, N);
}
void rope_f32x4_pack(torch::Tensor x, torch::Tensor out) {
CHECK_TORCH_TENSOR_DTYPE(x, torch::kFloat32)
CHECK_TORCH_TENSOR_DTYPE(out, torch::kFloat32)
int seq_len = x.size(0);
int hidden_size = x.size(1);
int N = (int)(hidden_size/4);
dim3 grid((seq_len * N + BLOCK_SIZE - 1) / BLOCK_SIZE);
dim3 block(BLOCK_SIZE);
rope_f32x4_pack_kernel<<<grid, block>>>(
x.data_ptr<float>(), out.data_ptr<float>(), seq_len, N);
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
TORCH_BINDING_COMMON_EXTENSION(rope_f32)
TORCH_BINDING_COMMON_EXTENSION(rope_f32_v2)
TORCH_BINDING_COMMON_EXTENSION(rope_f32x4_pack)
}