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
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
|
#include "THCTensorMath.h"
#include "THCGeneral.h"
#include "THCBlas.h"
#include "THCTensorCopy.h"
#include "THCApply.cuh"
#include "THCReduce.cuh"
#include <thrust/functional.h>
/* Perform an inclusive scan along an outer dimension of a tensor.
*
* - num_orows is the size of the flattened outer dimensions;
* - num_irows is the size of the flattened inner dimensions;
* - row_size is the size of the dimension along which to compute the variance;
*
* The dimensions to the outside and inside of the specified dimension are considered as flattened.
* Thread blocks with the same blockIdx.y process an "outer row" (i.e. an element of the flattened
* outer dimensions, which contains several "inner rows").
* Each thread processes a single inner row at a time.
*/
template<class BinaryOp>
__global__ void THCudaTensor_kernel_scanOuterDim(float *tgt_, float *src_,
unsigned num_orows, unsigned num_irows, unsigned row_size,
float init, BinaryOp binary_op)
{
for (unsigned orow = blockIdx.x; orow < num_orows; orow += gridDim.x) {
for (unsigned irow = blockIdx.y * blockDim.x + threadIdx.x; irow < num_irows; irow += gridDim.y * blockDim.x) {
float *src = src_ + orow * row_size * num_irows + irow;
float *tgt = tgt_ + orow * row_size * num_irows + irow;
float acc = init;
for (unsigned col = 0; col < row_size; ++col) {
acc = binary_op(acc, *src);
*tgt = acc;
src += num_irows;
tgt += num_irows;
}
}
}
}
template<class BinaryOp>
__host__ void THCudaTensor_scanOuterDim(THCState *state, THCudaTensor *tgt, THCudaTensor *src, long dimension,
float init, BinaryOp binary_op)
{
unsigned ndim = THCudaTensor_nDimension(state, src);
// Treat all outer dimensions (i.e. dim < dimension) as one.
unsigned num_orows = 1;
for (long dim = 0; dim < dimension; dim++) {
num_orows *= THCudaTensor_size(state, src, dim);
}
unsigned row_size = THCudaTensor_size(state, src, dimension);
// Treat all inner dimensions (i.e. dim > dimension) as one.
unsigned num_irows = 1;
for (unsigned dim = dimension + 1; dim < ndim; dim++) {
num_irows *= THCudaTensor_size(state, src, dim);
}
dim3 threads(min(512, num_irows));
unsigned maxGridDim = 1024;
dim3 grid(min(maxGridDim, num_orows), min(maxGridDim, THCCeilDiv(num_irows, threads.x)));
THCudaTensor_kernel_scanOuterDim<<<grid, threads, 0, THCState_getCurrentStream(state)>>>(
THCudaTensor_data(state, tgt), THCudaTensor_data(state, src), num_orows, num_irows, row_size, init, binary_op);
cudaError errcode = cudaGetLastError();
if (errcode != cudaSuccess) {
THError(cudaGetErrorString(errcode));
}
}
/* Perform an inclusive scan along the innermost dimension of a tensor.
*
* - num_rows is the size of the flattened outer dimensions;
* - row_size is the size of the innermost dimension;
*
* The outer dimensions of the tensor are considered as a single dimension, i.e. the tensor is
* considered as having 'num_rows' rows of size 'row_size'.
* Each thread block processes one or more sets of contiguous rows (processing multiple rows
* per thread block is quicker than processing a single row, especially for short rows).
*/
template<int num_threads_x, int num_threads_y, class BinaryFunction>
__global__ void THCudaTensor_kernel_scanInnermostDim(float *tgt_, float *src_,
unsigned num_rows, unsigned row_size,
float init, BinaryFunction binary_op)
{
__shared__ float sbuf[num_threads_y][2 * num_threads_x];
float* row_buf = sbuf[threadIdx.y];
for (unsigned block_row = blockIdx.x * blockDim.y;
block_row < num_rows;
block_row += blockDim.y * gridDim.x) {
unsigned row = block_row + threadIdx.y;
float block_total = init;
float *row_src = src_ + row * row_size;
float *row_tgt = tgt_ + row * row_size;
// Perform scan on one block at a time, keeping track of the total value of
// all blocks processed so far.
for (unsigned block_col = 0; block_col < row_size; block_col += 2 * num_threads_x) {
// Load data into shared memory (two values per thread).
unsigned col1 = block_col + threadIdx.x;
unsigned col2 = block_col + num_threads_x + threadIdx.x;
if (row < num_rows) {
if (col1 < row_size) {
row_buf[threadIdx.x] = row_src[col1];
} else {
row_buf[threadIdx.x] = init;
}
if (col2 < row_size) {
row_buf[num_threads_x + threadIdx.x] = row_src[col2];
} else {
row_buf[num_threads_x + threadIdx.x] = init;
}
// Add the total value of all previous blocks to the first value of this block.
if (threadIdx.x == 0) {
row_buf[0] = binary_op(row_buf[0], block_total);
}
}
__syncthreads();
// Parallel reduction (up-sweep).
for (unsigned s = num_threads_x, d = 1; s >= 1; s >>= 1, d <<= 1) {
if (row < num_rows && threadIdx.x < s) {
unsigned offset = (2 * threadIdx.x + 1) * d - 1;
row_buf[offset + d] = binary_op(row_buf[offset], row_buf[offset + d]);
}
__syncthreads();
}
// Down-sweep.
for (unsigned s = 2, d = num_threads_x / 2; d >= 1; s <<= 1, d >>= 1) {
if (row < num_rows && threadIdx.x < s - 1) {
unsigned offset = 2 * (threadIdx.x + 1) * d - 1;
row_buf[offset + d] = binary_op(row_buf[offset], row_buf[offset + d]);
}
__syncthreads();
}
// Write back to output.
if (row < num_rows) {
if (col1 < row_size) row_tgt[col1] = row_buf[threadIdx.x];
if (col2 < row_size) row_tgt[col2] = row_buf[num_threads_x + threadIdx.x];
}
block_total = row_buf[2 * num_threads_x - 1];
__syncthreads();
}
}
}
template<class BinaryFunction>
__host__ void THCudaTensor_scanInnermostDim(THCState *state, THCudaTensor *tgt, THCudaTensor *src, float init, BinaryFunction binary_op)
{
unsigned ndim = THCudaTensor_nDimension(state, src);
// Treat all outer dimensions as a single dimension.
unsigned num_rows = 1;
for (unsigned dim = 0; dim < ndim - 1; dim++) {
num_rows *= THCudaTensor_size(state, src, dim);
}
unsigned row_size = THCudaTensor_size(state, src, ndim - 1);
dim3 threads(16, 32);
dim3 grid(min(1024, THCCeilDiv(num_rows, threads.y)));
THCudaTensor_kernel_scanInnermostDim<16, 32><<<grid, threads, 0, THCState_getCurrentStream(state)>>>(
THCudaTensor_data(state, tgt), THCudaTensor_data(state, src), num_rows, row_size, init, binary_op);
cudaError errcode = cudaGetLastError();
if (errcode != cudaSuccess) {
THError(cudaGetErrorString(errcode));
}
}
template<class BinaryFunction>
void THCudaTensor_scanDim(THCState *state, THCudaTensor *self_, THCudaTensor *src, long dimension, float init, BinaryFunction binary_op)
{
THCudaTensor_resizeAs(state, self_, src);
THCudaTensor *self = THCudaTensor_newContiguous(state, self_);
src = THCudaTensor_newContiguous(state, src);
if (dimension == THCudaTensor_nDimension(state, src) - 1) {
THCudaTensor_scanInnermostDim(state, self, src, init, binary_op);
} else {
THCudaTensor_scanOuterDim(state, self, src, dimension, init, binary_op);
}
THCudaTensor_free(state, src);
THCudaTensor_freeCopyTo(state, self, self_);
}
void THCudaTensor_cumsum(THCState *state, THCudaTensor *self, THCudaTensor *src, long dimension)
{
THAssert(THCudaTensor_checkGPU(state, 2, self, src));
return THCudaTensor_scanDim(state, self, src, dimension, 0.0f, thrust::plus<float>());
}
void THCudaTensor_cumprod(THCState *state, THCudaTensor *self, THCudaTensor *src, long dimension)
{
THAssert(THCudaTensor_checkGPU(state, 2, self, src));
return THCudaTensor_scanDim(state, self, src, dimension, 1.0f, thrust::multiplies<float>());
}
|
