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BroadcastSum.cu
1 /**
2 * Copyright (c) 2015-present, Facebook, Inc.
3 * All rights reserved.
4 *
5 * This source code is licensed under the BSD+Patents license found in the
6 * LICENSE file in the root directory of this source tree.
7 */
8
9 // Copyright 2004-present Facebook. All Rights Reserved.
10
11 #include <algorithm>
12 #include "../../FaissAssert.h"
13
14 #include "../utils/DeviceUtils.h"
15 #include "../utils/MathOperators.cuh"
16 #include "../utils/Tensor.cuh"
17 #include "../utils/StaticUtils.h"
18
19 namespace faiss { namespace gpu {
20
21 template <typename T, int kRowsPerBlock, int kRowUnroll, int kColLoad>
22 __global__ void sumAlongColumns(Tensor<T, 1, true> input,
23 Tensor<T, 2, true> output) {
24 static_assert(kRowsPerBlock % kRowUnroll == 0, "must fit rows");
25
26 // blockIdx.x: which chunk of rows we are responsible for updating
27 // blockIdx.y: which chunk of columns we are responsible for
28 // updating
29 int rowStart = blockIdx.x * kRowsPerBlock;
30 int rowEnd = rowStart + kRowsPerBlock;
31 int colStart = blockIdx.y * blockDim.x * kColLoad;
32
33 // FIXME: if we have exact multiples, don't need this
34 bool endRow = (blockIdx.x == gridDim.x - 1);
35 bool endCol = (blockIdx.y == gridDim.y - 1);
36
37 if (endRow) {
38 if (output.getSize(0) % kRowsPerBlock == 0) {
39 endRow = false;
40 }
41 }
42
43 if (endCol) {
44 for (int col = colStart + threadIdx.x;
45 col < input.getSize(0); col += blockDim.x) {
46 T val = input[col];
47
48 if (endRow) {
49 for (int row = rowStart; row < output.getSize(0); ++row) {
50 T out = output[row][col].ldg();
51 out = Math<T>::add(out, val);
52 output[row][col] = out;
53 }
54 } else {
55 T rows[kRowUnroll];
56
57 for (int row = rowStart; row < rowEnd; row += kRowUnroll) {
58 #pragma unroll
59 for (int i = 0; i < kRowUnroll; ++i) {
60 rows[i] = output[row + i][col].ldg();
61 }
62
63 #pragma unroll
64 for (int i = 0; i < kRowUnroll; ++i) {
65 rows[i] = Math<T>::add(rows[i], val);
66 }
67
68 #pragma unroll
69 for (int i = 0; i < kRowUnroll; ++i) {
70 output[row + i][col] = rows[i];
71 }
72 }
73 }
74 }
75 } else {
76 int col = colStart + threadIdx.x;
77
78 T val[kColLoad];
79
80 #pragma unroll
81 for (int i = 0; i < kColLoad; ++i) {
82 val[i] = input[col + i * blockDim.x];
83 }
84
85 if (endRow) {
86 for (int row = rowStart; row < output.getSize(0); ++row) {
87 #pragma unroll
88 for (int i = 0; i < kColLoad; ++i) {
89 T out = output[row][col + i * blockDim.x].ldg();
90 out = Math<T>::add(out, val[i]);
91 output[row][col + i * blockDim.x] = out;
92 }
93 }
94 } else {
95 T rows[kRowUnroll * kColLoad];
96
97 for (int row = rowStart; row < rowEnd; row += kRowUnroll) {
98 #pragma unroll
99 for (int i = 0; i < kRowUnroll; ++i) {
100 #pragma unroll
101 for (int j = 0; j < kColLoad; ++j) {
102 rows[i * kColLoad + j] =
103 output[row + i][col + j * blockDim.x].ldg();
104 }
105 }
106
107 #pragma unroll
108 for (int i = 0; i < kRowUnroll; ++i) {
109 #pragma unroll
110 for (int j = 0; j < kColLoad; ++j) {
111 rows[i * kColLoad + j] =
112 Math<T>::add(rows[i * kColLoad + j], val[j]);
113 }
114 }
115
116 #pragma unroll
117 for (int i = 0; i < kRowUnroll; ++i) {
118 #pragma unroll
119 for (int j = 0; j < kColLoad; ++j) {
120 output[row + i][col + j * blockDim.x] =
121 rows[i * kColLoad + j];
122 }
123 }
124 }
125 }
126 }
127 }
128
129 template <typename T, int kRowsPerBlock, int kRowUnroll, int kColLoad>
130 __global__ void assignAlongColumns(Tensor<T, 1, true> input,
131 Tensor<T, 2, true> output) {
132 static_assert(kRowsPerBlock % kRowUnroll == 0, "must fit rows");
133
134 // blockIdx.x: which chunk of rows we are responsible for updating
135 // blockIdx.y: which chunk of columns we are responsible for
136 // updating
137 int rowStart = blockIdx.x * kRowsPerBlock;
138 int rowEnd = rowStart + kRowsPerBlock;
139 int colStart = blockIdx.y * blockDim.x * kColLoad;
140
141 // FIXME: if we have exact multiples, don't need this
142 bool endRow = (blockIdx.x == gridDim.x - 1);
143 bool endCol = (blockIdx.y == gridDim.y - 1);
144
145 if (endRow) {
146 if (output.getSize(0) % kRowsPerBlock == 0) {
147 endRow = false;
148 }
149 }
150
151 if (endCol) {
152 for (int col = colStart + threadIdx.x;
153 col < input.getSize(0); col += blockDim.x) {
154 T val = input[col];
155
156 if (endRow) {
157 for (int row = rowStart; row < output.getSize(0); ++row) {
158 output[row][col] = val;
159 }
160 } else {
161 for (int row = rowStart; row < rowEnd; row += kRowUnroll) {
162 #pragma unroll
163 for (int i = 0; i < kRowUnroll; ++i) {
164 output[row + i][col] = val;
165 }
166 }
167 }
168 }
169 } else {
170 int col = colStart + threadIdx.x;
171
172 T val[kColLoad];
173
174 #pragma unroll
175 for (int i = 0; i < kColLoad; ++i) {
176 val[i] = input[col + i * blockDim.x];
177 }
178
179 if (endRow) {
180 for (int row = rowStart; row < output.getSize(0); ++row) {
181 #pragma unroll
182 for (int i = 0; i < kColLoad; ++i) {
183 output[row][col + i * blockDim.x] = val[i];
184 }
185 }
186 } else {
187 for (int row = rowStart; row < rowEnd; row += kRowUnroll) {
188 #pragma unroll
189 for (int i = 0; i < kRowUnroll; ++i) {
190 #pragma unroll
191 for (int j = 0; j < kColLoad; ++j) {
192 output[row + i][col + j * blockDim.x] = val[j];
193 }
194 }
195 }
196 }
197 }
198 }
199
200 template <typename T, typename TVec>
201 __global__ void sumAlongRows(Tensor<T, 1, true> input,
202 Tensor<TVec, 2, true> output) {
203 __shared__ T sval;
204
205 int row = blockIdx.x;
206
207 if (threadIdx.x == 0) {
208 sval = input[row];
209 }
210
211 __syncthreads();
212
213 T val = sval;
214
215 // FIXME: speed up
216 for (int i = threadIdx.x; i < output.getSize(1); i += blockDim.x) {
217 TVec out = output[row][i];
218 out = Math<TVec>::add(out, val);
219 output[row][i] = out;
220 }
221 }
222
223 template <typename T, typename TVec>
224 void runSumAlongColumns(Tensor<T, 1, true>& input,
225 Tensor<T, 2, true>& output,
226 cudaStream_t stream) {
227 FAISS_ASSERT(input.getSize(0) == output.getSize(1));
228
229 int threadsPerBlock = 256;
230 constexpr int kRowUnroll = 4;
231 constexpr int kRowsPerBlock = kRowUnroll * 4;
232 constexpr int kColLoad = 4;
233
234 auto block = dim3(threadsPerBlock);
235
236 if (input.template canCastResize<TVec>() &&
237 output.template canCastResize<TVec>()) {
238 auto inputV = input.template castResize<TVec>();
239 auto outputV = output.template castResize<TVec>();
240
241 auto grid =
242 dim3(utils::divUp(outputV.getSize(0), kRowsPerBlock),
243 utils::divUp(outputV.getSize(1), threadsPerBlock * kColLoad));
244
245 sumAlongColumns<TVec, kRowsPerBlock, kRowUnroll, kColLoad>
246 <<<grid, block, 0, stream>>>(inputV, outputV);
247 } else {
248 auto grid =
249 dim3(utils::divUp(output.getSize(0), kRowsPerBlock),
250 utils::divUp(output.getSize(1), threadsPerBlock * kColLoad));
251
252 sumAlongColumns<T, kRowsPerBlock, kRowUnroll, kColLoad>
253 <<<grid, block, 0, stream>>>(input, output);
254 }
255
256 CUDA_TEST_ERROR();
257 }
258
259 void runSumAlongColumns(Tensor<float, 1, true>& input,
260 Tensor<float, 2, true>& output,
261 cudaStream_t stream) {
262 runSumAlongColumns<float, float4>(input, output, stream);
263 }
264
265 #ifdef FAISS_USE_FLOAT16
266 void runSumAlongColumns(Tensor<half, 1, true>& input,
267 Tensor<half, 2, true>& output,
268 cudaStream_t stream) {
269 runSumAlongColumns<half, half2>(input, output, stream);
270 }
271 #endif
272
273 template <typename T, typename TVec>
274 void runAssignAlongColumns(Tensor<T, 1, true>& input,
275 Tensor<T, 2, true>& output,
276 cudaStream_t stream) {
277 FAISS_ASSERT(input.getSize(0) == output.getSize(1));
278
279 int threadsPerBlock = 256;
280 constexpr int kRowUnroll = 4;
281 constexpr int kRowsPerBlock = kRowUnroll * 4;
282 constexpr int kColLoad = 4;
283
284 auto block = dim3(threadsPerBlock);
285
286 if (input.template canCastResize<TVec>() &&
287 output.template canCastResize<TVec>()) {
288 auto inputV = input.template castResize<TVec>();
289 auto outputV = output.template castResize<TVec>();
290
291 auto grid =
292 dim3(utils::divUp(outputV.getSize(0), kRowsPerBlock),
293 utils::divUp(outputV.getSize(1), threadsPerBlock * kColLoad));
294
295 assignAlongColumns<TVec, kRowsPerBlock, kRowUnroll, kColLoad>
296 <<<grid, block, 0, stream>>>(inputV, outputV);
297 } else {
298 auto grid =
299 dim3(utils::divUp(output.getSize(0), kRowsPerBlock),
300 utils::divUp(output.getSize(1), threadsPerBlock * kColLoad));
301
302 assignAlongColumns<T, kRowsPerBlock, kRowUnroll, kColLoad>
303 <<<grid, block, 0, stream>>>(input, output);
304 }
305
306 CUDA_TEST_ERROR();
307 }
308
309 void runAssignAlongColumns(Tensor<float, 1, true>& input,
310 Tensor<float, 2, true>& output,
311 cudaStream_t stream) {
312 runAssignAlongColumns<float, float4>(input, output, stream);
313 }
314
315 #ifdef FAISS_USE_FLOAT16
316 void runAssignAlongColumns(Tensor<half, 1, true>& input,
317 Tensor<half, 2, true>& output,
318 cudaStream_t stream) {
319 runAssignAlongColumns<half, half2>(input, output, stream);
320 }
321 #endif
322
323 template <typename T, typename TVec>
324 void runSumAlongRows(Tensor<T, 1, true>& input,
325 Tensor<T, 2, true>& output,
326 cudaStream_t stream) {
327 FAISS_ASSERT(input.getSize(0) == output.getSize(0));
328
329 if (output.template canCastResize<TVec>()) {
330 auto outputV = output.template castResize<TVec>();
331
332 int threadsPerBlock =
333 std::min(outputV.getSize(1), getMaxThreadsCurrentDevice());
334 auto grid = dim3(outputV.getSize(0));
335 auto block = dim3(threadsPerBlock);
336
337 sumAlongRows<T, TVec><<<grid, block, 0, stream>>>(input, outputV);
338 } else {
339 int threadsPerBlock =
340 std::min(output.getSize(1), getMaxThreadsCurrentDevice());
341 auto grid = dim3(output.getSize(0));
342 auto block = dim3(threadsPerBlock);
343
344 sumAlongRows<T, T><<<grid, block, 0, stream>>>(input, output);
345 }
346
347 CUDA_TEST_ERROR();
348 }
349
350 void runSumAlongRows(Tensor<float, 1, true>& input,
351 Tensor<float, 2, true>& output,
352 cudaStream_t stream) {
353 runSumAlongRows<float, float4>(input, output, stream);
354 }
355
356 #ifdef FAISS_USE_FLOAT16
357 void runSumAlongRows(Tensor<half, 1, true>& input,
358 Tensor<half, 2, true>& output,
359 cudaStream_t stream) {
360 runSumAlongRows<half, half2>(input, output, stream);
361 }
362 #endif
363
364 } } // namespace