1 // This file is part of Eigen, a lightweight C++ template library
2 // for linear algebra.
3 //
4 // Copyright (C) 2014-2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
5 // Copyright (C) 2015 Navdeep Jaitly <ndjaitly@google.com>
6 // Copyright (C) 2014 Eric Martin <eric@ericmart.in>
7 //
8 // This Source Code Form is subject to the terms of the Mozilla
9 // Public License v. 2.0. If a copy of the MPL was not distributed
10 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
11
12 #ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_CUDA_H
13 #define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_CUDA_H
14
15 #if defined(EIGEN_USE_GPU) && defined(__CUDACC__)
16
17 namespace Eigen {
18
19 template<typename Scalar, typename Index, typename LhsMapper,
20 typename RhsMapper, typename OutputMapper, bool needs_edge_check>
21 __device__ EIGEN_STRONG_INLINE void
EigenContractionKernelInternal(const LhsMapper lhs,const RhsMapper rhs,const OutputMapper output,Scalar * lhs_shmem,Scalar * rhs_shmem,const Index m_size,const Index n_size,const Index k_size)22 EigenContractionKernelInternal(const LhsMapper lhs, const RhsMapper rhs,
23 const OutputMapper output, Scalar* lhs_shmem, Scalar* rhs_shmem,
24 const Index m_size, const Index n_size, const Index k_size) {
25
26 const Index m_block_idx = blockIdx.x;
27 const Index n_block_idx = blockIdx.y;
28
29 const Index base_m = 64 * m_block_idx;
30 const Index base_n = 64 * n_block_idx;
31
32 // declare and initialize 64 registers for output 8x8 block
33
34 // prefetch registers
35 Scalar lhs_pf0;
36 Scalar lhs_pf1;
37 Scalar lhs_pf2;
38 Scalar lhs_pf3;
39 Scalar lhs_pf4;
40 Scalar lhs_pf5;
41 Scalar lhs_pf6;
42 Scalar lhs_pf7;
43
44 Scalar rhs_pf0;
45 Scalar rhs_pf1;
46 Scalar rhs_pf2;
47 Scalar rhs_pf3;
48 Scalar rhs_pf4;
49 Scalar rhs_pf5;
50 Scalar rhs_pf6;
51 Scalar rhs_pf7;
52
53 // shared memory is formatted
54 // (contract idx in block, nocontract idx in block, block idx)
55 // where block idx is column major. This transposition limits the number of
56 // bank conflicts when reading the LHS. The core idea is that since the contracting
57 // index is shared by both sides, then the contracting index should be in threadIdx.x.
58
59 // On the LHS, we pad each row inside of each block with an extra element. This makes
60 // each block 8 rows of 9 elements, which is 72 elements. This gives no bank conflicts
61 // on writes and very few 2-way conflicts on reads. There is an 8x8 grid of these blocks.
62
63 // On the RHS we just add 8 padding elements to the end of each block. This gives no bank
64 // conflicts on writes and also none on reads.
65
66 // storage indices
67 const Index lhs_store_idx_base = threadIdx.y * 72 + threadIdx.x * 9 + threadIdx.z;
68 const Index rhs_store_idx_base = threadIdx.y * 72 + threadIdx.z * 8 + threadIdx.x;
69
70 const Index lhs_store_idx_0 = lhs_store_idx_base + 576 * 0;
71 const Index lhs_store_idx_1 = lhs_store_idx_base + 576 * 1;
72 const Index lhs_store_idx_2 = lhs_store_idx_base + 576 * 2;
73 const Index lhs_store_idx_3 = lhs_store_idx_base + 576 * 3;
74 const Index lhs_store_idx_4 = lhs_store_idx_base + 576 * 4;
75 const Index lhs_store_idx_5 = lhs_store_idx_base + 576 * 5;
76 const Index lhs_store_idx_6 = lhs_store_idx_base + 576 * 6;
77 const Index lhs_store_idx_7 = lhs_store_idx_base + 576 * 7;
78
79 const Index rhs_store_idx_0 = rhs_store_idx_base + 576 * 0;
80 const Index rhs_store_idx_1 = rhs_store_idx_base + 576 * 1;
81 const Index rhs_store_idx_2 = rhs_store_idx_base + 576 * 2;
82 const Index rhs_store_idx_3 = rhs_store_idx_base + 576 * 3;
83 const Index rhs_store_idx_4 = rhs_store_idx_base + 576 * 4;
84 const Index rhs_store_idx_5 = rhs_store_idx_base + 576 * 5;
85 const Index rhs_store_idx_6 = rhs_store_idx_base + 576 * 6;
86 const Index rhs_store_idx_7 = rhs_store_idx_base + 576 * 7;
87
88 // in the loading code, the following variables are important:
89 // threadIdx.x: the vertical position in an 8x8 block
90 // threadIdx.y: the vertical index of the 8x8 block in the grid
91 // threadIdx.z: the horizontal position in an 8x8 block
92 // k: the horizontal index of the 8x8 block in the grid
93 //
94 // The k parameter is implicit (it was the loop counter for a loop that went
95 // from 0 to <8, but now that loop is unrolled in the below code.
96
97 const Index load_idx_vert = threadIdx.x + 8 * threadIdx.y;
98 const Index lhs_vert = base_m + load_idx_vert;
99
100 #define prefetchIntoRegisters(base_k) \
101 { \
102 lhs_pf0 = conv(0); \
103 lhs_pf1 = conv(0); \
104 lhs_pf2 = conv(0); \
105 lhs_pf3 = conv(0); \
106 lhs_pf4 = conv(0); \
107 lhs_pf5 = conv(0); \
108 lhs_pf6 = conv(0); \
109 lhs_pf7 = conv(0); \
110 \
111 rhs_pf0 = conv(0); \
112 rhs_pf1 = conv(0); \
113 rhs_pf2 = conv(0); \
114 rhs_pf3 = conv(0); \
115 rhs_pf4 = conv(0); \
116 rhs_pf5 = conv(0); \
117 rhs_pf6 = conv(0); \
118 rhs_pf7 = conv(0); \
119 \
120 if (!needs_edge_check || lhs_vert < m_size) { \
121 const Index lhs_horiz_0 = base_k + threadIdx.z + 0 * 8; \
122 const Index lhs_horiz_1 = base_k + threadIdx.z + 1 * 8; \
123 const Index lhs_horiz_2 = base_k + threadIdx.z + 2 * 8; \
124 const Index lhs_horiz_3 = base_k + threadIdx.z + 3 * 8; \
125 const Index lhs_horiz_4 = base_k + threadIdx.z + 4 * 8; \
126 const Index lhs_horiz_5 = base_k + threadIdx.z + 5 * 8; \
127 const Index lhs_horiz_6 = base_k + threadIdx.z + 6 * 8; \
128 const Index lhs_horiz_7 = base_k + threadIdx.z + 7 * 8; \
129 \
130 if (!needs_edge_check || lhs_horiz_7 < k_size) { \
131 lhs_pf0 = lhs(lhs_vert, lhs_horiz_0); \
132 lhs_pf1 = lhs(lhs_vert, lhs_horiz_1); \
133 lhs_pf2 = lhs(lhs_vert, lhs_horiz_2); \
134 lhs_pf3 = lhs(lhs_vert, lhs_horiz_3); \
135 lhs_pf4 = lhs(lhs_vert, lhs_horiz_4); \
136 lhs_pf5 = lhs(lhs_vert, lhs_horiz_5); \
137 lhs_pf6 = lhs(lhs_vert, lhs_horiz_6); \
138 lhs_pf7 = lhs(lhs_vert, lhs_horiz_7); \
139 } else if (lhs_horiz_6 < k_size) { \
140 lhs_pf0 = lhs(lhs_vert, lhs_horiz_0); \
141 lhs_pf1 = lhs(lhs_vert, lhs_horiz_1); \
142 lhs_pf2 = lhs(lhs_vert, lhs_horiz_2); \
143 lhs_pf3 = lhs(lhs_vert, lhs_horiz_3); \
144 lhs_pf4 = lhs(lhs_vert, lhs_horiz_4); \
145 lhs_pf5 = lhs(lhs_vert, lhs_horiz_5); \
146 lhs_pf6 = lhs(lhs_vert, lhs_horiz_6); \
147 } else if (lhs_horiz_5 < k_size) { \
148 lhs_pf0 = lhs(lhs_vert, lhs_horiz_0); \
149 lhs_pf1 = lhs(lhs_vert, lhs_horiz_1); \
150 lhs_pf2 = lhs(lhs_vert, lhs_horiz_2); \
151 lhs_pf3 = lhs(lhs_vert, lhs_horiz_3); \
152 lhs_pf4 = lhs(lhs_vert, lhs_horiz_4); \
153 lhs_pf5 = lhs(lhs_vert, lhs_horiz_5); \
154 } else if (lhs_horiz_4 < k_size) { \
155 lhs_pf0 = lhs(lhs_vert, lhs_horiz_0); \
156 lhs_pf1 = lhs(lhs_vert, lhs_horiz_1); \
157 lhs_pf2 = lhs(lhs_vert, lhs_horiz_2); \
158 lhs_pf3 = lhs(lhs_vert, lhs_horiz_3); \
159 lhs_pf4 = lhs(lhs_vert, lhs_horiz_4); \
160 } else if (lhs_horiz_3 < k_size) { \
161 lhs_pf0 = lhs(lhs_vert, lhs_horiz_0); \
162 lhs_pf1 = lhs(lhs_vert, lhs_horiz_1); \
163 lhs_pf2 = lhs(lhs_vert, lhs_horiz_2); \
164 lhs_pf3 = lhs(lhs_vert, lhs_horiz_3); \
165 } else if (lhs_horiz_2 < k_size) { \
166 lhs_pf0 = lhs(lhs_vert, lhs_horiz_0); \
167 lhs_pf1 = lhs(lhs_vert, lhs_horiz_1); \
168 lhs_pf2 = lhs(lhs_vert, lhs_horiz_2); \
169 } else if (lhs_horiz_1 < k_size) { \
170 lhs_pf0 = lhs(lhs_vert, lhs_horiz_0); \
171 lhs_pf1 = lhs(lhs_vert, lhs_horiz_1); \
172 } else if (lhs_horiz_0 < k_size) { \
173 lhs_pf0 = lhs(lhs_vert, lhs_horiz_0); \
174 } \
175 } \
176 \
177 const Index rhs_vert = base_k + load_idx_vert; \
178 if (!needs_edge_check || rhs_vert < k_size) { \
179 const Index rhs_horiz_0 = base_n + threadIdx.z + 0 * 8; \
180 const Index rhs_horiz_1 = base_n + threadIdx.z + 1 * 8; \
181 const Index rhs_horiz_2 = base_n + threadIdx.z + 2 * 8; \
182 const Index rhs_horiz_3 = base_n + threadIdx.z + 3 * 8; \
183 const Index rhs_horiz_4 = base_n + threadIdx.z + 4 * 8; \
184 const Index rhs_horiz_5 = base_n + threadIdx.z + 5 * 8; \
185 const Index rhs_horiz_6 = base_n + threadIdx.z + 6 * 8; \
186 const Index rhs_horiz_7 = base_n + threadIdx.z + 7 * 8; \
187 \
188 if (rhs_horiz_7 < n_size) { \
189 rhs_pf0 = rhs(rhs_vert, rhs_horiz_0); \
190 rhs_pf1 = rhs(rhs_vert, rhs_horiz_1); \
191 rhs_pf2 = rhs(rhs_vert, rhs_horiz_2); \
192 rhs_pf3 = rhs(rhs_vert, rhs_horiz_3); \
193 rhs_pf4 = rhs(rhs_vert, rhs_horiz_4); \
194 rhs_pf5 = rhs(rhs_vert, rhs_horiz_5); \
195 rhs_pf6 = rhs(rhs_vert, rhs_horiz_6); \
196 rhs_pf7 = rhs(rhs_vert, rhs_horiz_7); \
197 } else if (rhs_horiz_6 < n_size) { \
198 rhs_pf0 = rhs(rhs_vert, rhs_horiz_0); \
199 rhs_pf1 = rhs(rhs_vert, rhs_horiz_1); \
200 rhs_pf2 = rhs(rhs_vert, rhs_horiz_2); \
201 rhs_pf3 = rhs(rhs_vert, rhs_horiz_3); \
202 rhs_pf4 = rhs(rhs_vert, rhs_horiz_4); \
203 rhs_pf5 = rhs(rhs_vert, rhs_horiz_5); \
204 rhs_pf6 = rhs(rhs_vert, rhs_horiz_6); \
205 } else if (rhs_horiz_5 < n_size) { \
206 rhs_pf0 = rhs(rhs_vert, rhs_horiz_0); \
207 rhs_pf1 = rhs(rhs_vert, rhs_horiz_1); \
208 rhs_pf2 = rhs(rhs_vert, rhs_horiz_2); \
209 rhs_pf3 = rhs(rhs_vert, rhs_horiz_3); \
210 rhs_pf4 = rhs(rhs_vert, rhs_horiz_4); \
211 rhs_pf5 = rhs(rhs_vert, rhs_horiz_5); \
212 } else if (rhs_horiz_4 < n_size) { \
213 rhs_pf0 = rhs(rhs_vert, rhs_horiz_0); \
214 rhs_pf1 = rhs(rhs_vert, rhs_horiz_1); \
215 rhs_pf2 = rhs(rhs_vert, rhs_horiz_2); \
216 rhs_pf3 = rhs(rhs_vert, rhs_horiz_3); \
217 rhs_pf4 = rhs(rhs_vert, rhs_horiz_4); \
218 } else if (rhs_horiz_3 < n_size) { \
219 rhs_pf0 = rhs(rhs_vert, rhs_horiz_0); \
220 rhs_pf1 = rhs(rhs_vert, rhs_horiz_1); \
221 rhs_pf2 = rhs(rhs_vert, rhs_horiz_2); \
222 rhs_pf3 = rhs(rhs_vert, rhs_horiz_3); \
223 } else if (rhs_horiz_2 < n_size) { \
224 rhs_pf0 = rhs(rhs_vert, rhs_horiz_0); \
225 rhs_pf1 = rhs(rhs_vert, rhs_horiz_1); \
226 rhs_pf2 = rhs(rhs_vert, rhs_horiz_2); \
227 } else if (rhs_horiz_1 < n_size) { \
228 rhs_pf0 = rhs(rhs_vert, rhs_horiz_0); \
229 rhs_pf1 = rhs(rhs_vert, rhs_horiz_1); \
230 } else if (rhs_horiz_0 < n_size) { \
231 rhs_pf0 = rhs(rhs_vert, rhs_horiz_0); \
232 } \
233 } \
234 } \
235
236 #define writeRegToShmem(_) \
237 lhs_shmem[lhs_store_idx_0] = lhs_pf0; \
238 rhs_shmem[rhs_store_idx_0] = rhs_pf0; \
239 \
240 lhs_shmem[lhs_store_idx_1] = lhs_pf1; \
241 rhs_shmem[rhs_store_idx_1] = rhs_pf1; \
242 \
243 lhs_shmem[lhs_store_idx_2] = lhs_pf2; \
244 rhs_shmem[rhs_store_idx_2] = rhs_pf2; \
245 \
246 lhs_shmem[lhs_store_idx_3] = lhs_pf3; \
247 rhs_shmem[rhs_store_idx_3] = rhs_pf3; \
248 \
249 lhs_shmem[lhs_store_idx_4] = lhs_pf4; \
250 rhs_shmem[rhs_store_idx_4] = rhs_pf4; \
251 \
252 lhs_shmem[lhs_store_idx_5] = lhs_pf5; \
253 rhs_shmem[rhs_store_idx_5] = rhs_pf5; \
254 \
255 lhs_shmem[lhs_store_idx_6] = lhs_pf6; \
256 rhs_shmem[rhs_store_idx_6] = rhs_pf6; \
257 \
258 lhs_shmem[lhs_store_idx_7] = lhs_pf7; \
259 rhs_shmem[rhs_store_idx_7] = rhs_pf7; \
260
261 // declare and initialize result array
262 #define res(i, j) _res_##i##j
263 #define initResultRow(i) \
264 Scalar res(i, 0) = conv(0); \
265 Scalar res(i, 1) = conv(0); \
266 Scalar res(i, 2) = conv(0); \
267 Scalar res(i, 3) = conv(0); \
268 Scalar res(i, 4) = conv(0); \
269 Scalar res(i, 5) = conv(0); \
270 Scalar res(i, 6) = conv(0); \
271 Scalar res(i, 7) = conv(0); \
272
273 internal::scalar_cast_op<int, Scalar> conv;
274 initResultRow(0);
275 initResultRow(1);
276 initResultRow(2);
277 initResultRow(3);
278 initResultRow(4);
279 initResultRow(5);
280 initResultRow(6);
281 initResultRow(7);
282 #undef initResultRow
283
284 for (Index base_k = 0; base_k < k_size; base_k += 64) {
285 // wait for previous iteration to finish with shmem. Despite common sense,
286 // the code is a bit faster with this here then at bottom of loop
287 __syncthreads();
288
289 prefetchIntoRegisters(base_k);
290 writeRegToShmem();
291
292 #undef prefetchIntoRegisters
293 #undef writeRegToShmem
294
295 // wait for shared mem packing to be done before starting computation
296 __syncthreads();
297
298 // compute 8x8 matrix product by outer product. This involves packing one column
299 // of LHS and one row of RHS into registers (takes 16 registers).
300
301 #define lcol(i) _lcol##i
302 Scalar lcol(0);
303 Scalar lcol(1);
304 Scalar lcol(2);
305 Scalar lcol(3);
306 Scalar lcol(4);
307 Scalar lcol(5);
308 Scalar lcol(6);
309 Scalar lcol(7);
310
311 #define rrow(j) _rrow##j
312 Scalar rrow(0);
313 Scalar rrow(1);
314 Scalar rrow(2);
315 Scalar rrow(3);
316 Scalar rrow(4);
317 Scalar rrow(5);
318 Scalar rrow(6);
319 Scalar rrow(7);
320
321 // Now x corresponds to k, y to m, and z to n
322 const Scalar* lhs_block = &lhs_shmem[threadIdx.x + 9 * threadIdx.y];
323 const Scalar* rhs_block = &rhs_shmem[threadIdx.x + 8 * threadIdx.z];
324
325 #define lhs_element(i, j) lhs_block[72 * ((i) + 8 * (j))]
326 #define rhs_element(i, j) rhs_block[72 * ((i) + 8 * (j))]
327
328 #define loadData(i, j) \
329 lcol(0) = lhs_element(0, j); \
330 rrow(0) = rhs_element(i, 0); \
331 lcol(1) = lhs_element(1, j); \
332 rrow(1) = rhs_element(i, 1); \
333 lcol(2) = lhs_element(2, j); \
334 rrow(2) = rhs_element(i, 2); \
335 lcol(3) = lhs_element(3, j); \
336 rrow(3) = rhs_element(i, 3); \
337 lcol(4) = lhs_element(4, j); \
338 rrow(4) = rhs_element(i, 4); \
339 lcol(5) = lhs_element(5, j); \
340 rrow(5) = rhs_element(i, 5); \
341 lcol(6) = lhs_element(6, j); \
342 rrow(6) = rhs_element(i, 6); \
343 lcol(7) = lhs_element(7, j); \
344 rrow(7) = rhs_element(i, 7); \
345
346 #define computeCol(j) \
347 res(0, j) += lcol(0) * rrow(j); \
348 res(1, j) += lcol(1) * rrow(j); \
349 res(2, j) += lcol(2) * rrow(j); \
350 res(3, j) += lcol(3) * rrow(j); \
351 res(4, j) += lcol(4) * rrow(j); \
352 res(5, j) += lcol(5) * rrow(j); \
353 res(6, j) += lcol(6) * rrow(j); \
354 res(7, j) += lcol(7) * rrow(j); \
355
356 #define computePass(i) \
357 loadData(i, i); \
358 \
359 computeCol(0); \
360 computeCol(1); \
361 computeCol(2); \
362 computeCol(3); \
363 computeCol(4); \
364 computeCol(5); \
365 computeCol(6); \
366 computeCol(7); \
367
368 computePass(0);
369 computePass(1);
370 computePass(2);
371 computePass(3);
372 computePass(4);
373 computePass(5);
374 computePass(6);
375 computePass(7);
376
377 #undef lcol
378 #undef rrow
379 #undef lhs_element
380 #undef rhs_element
381 #undef loadData
382 #undef computeCol
383 #undef computePass
384 } // end loop over k
385
386 // we've now iterated over all of the large (ie width 64) k blocks and
387 // accumulated results in registers. At this point thread (x, y, z) contains
388 // the sum across all big k blocks of the product of little k block of index (x, y)
389 // with block of index (y, z). To compute the final output, we need to reduce
390 // the 8 threads over y by summation.
391 #define shuffleInc(i, j, mask) res(i, j) += __shfl_xor(res(i, j), mask)
392
393 #define reduceRow(i, mask) \
394 shuffleInc(i, 0, mask); \
395 shuffleInc(i, 1, mask); \
396 shuffleInc(i, 2, mask); \
397 shuffleInc(i, 3, mask); \
398 shuffleInc(i, 4, mask); \
399 shuffleInc(i, 5, mask); \
400 shuffleInc(i, 6, mask); \
401 shuffleInc(i, 7, mask); \
402
403 #define reduceMatrix(mask) \
404 reduceRow(0, mask); \
405 reduceRow(1, mask); \
406 reduceRow(2, mask); \
407 reduceRow(3, mask); \
408 reduceRow(4, mask); \
409 reduceRow(5, mask); \
410 reduceRow(6, mask); \
411 reduceRow(7, mask); \
412
413 // actually perform the reduction, now each thread of index (_, y, z)
414 // contains the correct values in its registers that belong in the output
415 // block
416 reduceMatrix(1);
417 reduceMatrix(2);
418 reduceMatrix(4);
419
420 #undef shuffleInc
421 #undef reduceRow
422 #undef reduceMatrix
423
424 // now we need to copy the 64 values into main memory. We can't split work
425 // among threads because all variables are in registers. There's 2 ways
426 // to do this:
427 // (1) have 1 thread do 64 writes from registers into global memory
428 // (2) have 1 thread do 64 writes into shared memory, and then 8 threads
429 // each do 8 writes into global memory. We can just overwrite the shared
430 // memory from the problem we just solved.
431 // (2) is slightly faster than (1) due to less branching and more ILP
432
433 // TODO: won't yield much gain, but could just use currently unused shared mem
434 // and then we won't have to sync
435 // wait for shared mem to be out of use
436 __syncthreads();
437
438 #define writeResultShmem(i, j) \
439 lhs_shmem[i + 8 * threadIdx.y + 64 * threadIdx.z + 512 * j] = res(i, j); \
440
441 #define writeRow(i) \
442 writeResultShmem(i, 0); \
443 writeResultShmem(i, 1); \
444 writeResultShmem(i, 2); \
445 writeResultShmem(i, 3); \
446 writeResultShmem(i, 4); \
447 writeResultShmem(i, 5); \
448 writeResultShmem(i, 6); \
449 writeResultShmem(i, 7); \
450
451 if (threadIdx.x == 0) {
452 writeRow(0);
453 writeRow(1);
454 writeRow(2);
455 writeRow(3);
456 writeRow(4);
457 writeRow(5);
458 writeRow(6);
459 writeRow(7);
460 }
461 #undef writeResultShmem
462 #undef writeRow
463
464 const int max_i_write = numext::mini((int)((m_size - base_m - threadIdx.y + 7) / 8), 8);
465 const int max_j_write = numext::mini((int)((n_size - base_n - threadIdx.z + 7) / 8), 8);
466
467 if (threadIdx.x < max_i_write) {
468 if (max_j_write == 8) {
469 // TODO: can i trade bank conflicts for coalesced writes?
470 Scalar val0 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 0];
471 Scalar val1 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 1];
472 Scalar val2 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 2];
473 Scalar val3 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 3];
474 Scalar val4 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 4];
475 Scalar val5 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 5];
476 Scalar val6 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 6];
477 Scalar val7 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 7];
478
479 output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 0) = val0;
480 output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 1) = val1;
481 output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 2) = val2;
482 output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 3) = val3;
483 output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 4) = val4;
484 output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 5) = val5;
485 output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 6) = val6;
486 output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 7) = val7;
487 } else {
488 #pragma unroll 7
489 for (int j = 0; j < max_j_write; j++) {
490 Scalar val = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * j];
491 output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * j) = val;
492 }
493 }
494 }
495 #undef res
496 }
497
498
499 template<typename Scalar, typename Index, typename LhsMapper,
500 typename RhsMapper, typename OutputMapper>
501 __global__ void
502 __launch_bounds__(512)
EigenContractionKernel(const LhsMapper lhs,const RhsMapper rhs,const OutputMapper output,const Index m_size,const Index n_size,const Index k_size)503 EigenContractionKernel(const LhsMapper lhs, const RhsMapper rhs,
504 const OutputMapper output,
505 const Index m_size, const Index n_size, const Index k_size) {
506 __shared__ Scalar lhs_shmem[72 * 64];
507 __shared__ Scalar rhs_shmem[72 * 64];
508
509 const Index m_block_idx = blockIdx.x;
510 const Index n_block_idx = blockIdx.y;
511
512 const Index base_m = 64 * m_block_idx;
513 const Index base_n = 64 * n_block_idx;
514
515 if (base_m + 63 < m_size && base_n + 63 < n_size) {
516 EigenContractionKernelInternal<Scalar, Index, LhsMapper, RhsMapper, OutputMapper, false>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size);
517 } else {
518 EigenContractionKernelInternal<Scalar, Index, LhsMapper, RhsMapper, OutputMapper, true>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size);
519 }
520 }
521
522
523 template<typename Index, typename LhsMapper,
524 typename RhsMapper, typename OutputMapper, bool CHECK_LHS_BOUNDARY,
525 bool CHECK_RHS_BOUNDARY>
526 __device__ EIGEN_STRONG_INLINE void
EigenFloatContractionKernelInternal16x16(const LhsMapper lhs,const RhsMapper rhs,const OutputMapper output,float2 lhs_shmem2[][16],float2 rhs_shmem2[][8],const Index m_size,const Index n_size,const Index k_size,const Index base_m,const Index base_n)527 EigenFloatContractionKernelInternal16x16(const LhsMapper lhs, const RhsMapper rhs,
528 const OutputMapper output, float2 lhs_shmem2[][16],
529 float2 rhs_shmem2[][8], const Index m_size,
530 const Index n_size, const Index k_size,
531 const Index base_m, const Index base_n) {
532 typedef float Scalar;
533
534 // prefetch registers
535 float4 lhs_pf0, rhs_pf0;
536
537 float4 results[4];
538 for (int i=0; i < 4; i++) {
539 results[i].x = results[i].y = results[i].z = results[i].w = 0;
540 }
541
542
543 #define prefetch_lhs(reg, row, col) \
544 if (!CHECK_LHS_BOUNDARY) { \
545 if (col < k_size) { \
546 reg =lhs.loadPacket<Unaligned>(row, col); \
547 } \
548 } else { \
549 if (col < k_size) { \
550 if (row + 3 < m_size) { \
551 reg =lhs.loadPacket<Unaligned>(row, col); \
552 } else if (row + 2 < m_size) { \
553 reg.x =lhs(row + 0, col); \
554 reg.y =lhs(row + 1, col); \
555 reg.z =lhs(row + 2, col); \
556 } else if (row + 1 < m_size) { \
557 reg.x =lhs(row + 0, col); \
558 reg.y =lhs(row + 1, col); \
559 } else if (row < m_size) { \
560 reg.x =lhs(row + 0, col); \
561 } \
562 } \
563 } \
564
565
566 Index lhs_vert = base_m+threadIdx.x*4;
567
568 for (Index k = 0; k < k_size; k += 16) {
569 lhs_pf0 = internal::pset1<float4>(0);
570 rhs_pf0 = internal::pset1<float4>(0);
571
572 Index lhs_horiz = threadIdx.y+k;
573 prefetch_lhs(lhs_pf0, lhs_vert, lhs_horiz)
574
575 Index rhs_vert = k+(threadIdx.x%4)*4;
576 Index rhs_horiz0 = (threadIdx.x>>2)+threadIdx.y*4+base_n;
577
578 if (!CHECK_RHS_BOUNDARY) {
579 if ((rhs_vert + 3) < k_size) {
580 // just CHECK_RHS_BOUNDARY
581 rhs_pf0 = rhs.loadPacket<Unaligned>(rhs_vert, rhs_horiz0);
582 } else if (rhs_vert + 2 < k_size) {
583 // just CHECK_RHS_BOUNDARY
584 rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
585 rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
586 rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
587 } else if (rhs_vert + 1 < k_size) {
588 rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
589 rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
590 } else if (rhs_vert < k_size) {
591 rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
592 }
593 } else {
594 if (rhs_horiz0 < n_size) {
595 if ((rhs_vert + 3) < k_size) {
596 rhs_pf0 = rhs.loadPacket<Unaligned>(rhs_vert, rhs_horiz0);
597 } else if ((rhs_vert + 2) < k_size) {
598 rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
599 rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
600 rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
601 } else if ((rhs_vert + 1) < k_size) {
602 rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
603 rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
604 } else if (rhs_vert < k_size) {
605 rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
606 }
607 }
608 }
609 float x1, x2 ;
610 // the following can be a bitwise operation..... some day.
611 if((threadIdx.x%8) < 4) {
612 x1 = rhs_pf0.y;
613 x2 = rhs_pf0.w;
614 } else {
615 x1 = rhs_pf0.x;
616 x2 = rhs_pf0.z;
617 }
618 x1 = __shfl_xor(x1, 4);
619 x2 = __shfl_xor(x2, 4);
620 if((threadIdx.x%8) < 4) {
621 rhs_pf0.y = x1;
622 rhs_pf0.w = x2;
623 } else {
624 rhs_pf0.x = x1;
625 rhs_pf0.z = x2;
626 }
627
628 // We have 64 features.
629 // Row 0 -> times (0, 4, 8, 12, 1, 5, 9, 13) for features 0, 1.
630 // Row 1 -> times (0, 4, 8, 12, 1, 5, 9, 13) for features 2, 3.
631 // ...
632 // Row 31 -> times (0, 4, 8, 12, 1, 5, 9, 13) for features 62, 63
633 // Row 32 -> times (2, 6, 10, 14, 3, 7, 11, 15) for features 0, 1
634 // ...
635 rhs_shmem2[(threadIdx.x>>3)+ threadIdx.y*2][threadIdx.x%8] = make_float2(rhs_pf0.x, rhs_pf0.y);
636 rhs_shmem2[(threadIdx.x>>3)+ threadIdx.y*2+32][threadIdx.x%8] = make_float2(rhs_pf0.z, rhs_pf0.w);
637
638 // Row 0 (time 0) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), .. (60, 61)
639 // Row 1 (time 1) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), .. (60, 61)
640 // ...
641 // Row 15 (time 15) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), .. (60, 61)
642 // Row 16 (time 0) -> features (2, 3), (6, 7), .. (30, 31), (34, 35), .. (62, 63)
643 // ...
644
645 lhs_shmem2[threadIdx.y][threadIdx.x] = make_float2(lhs_pf0.x, lhs_pf0.y);
646 lhs_shmem2[threadIdx.y+16][threadIdx.x] = make_float2(lhs_pf0.z, lhs_pf0.w);
647
648
649 #define add_vals(fl1, fl2, fr1, fr2)\
650 results[0].x += fl1.x * fr1.x;\
651 results[0].y += fl1.y * fr1.x;\
652 results[0].z += fl2.x * fr1.x;\
653 results[0].w += fl2.y * fr1.x;\
654 \
655 results[1].x += fl1.x * fr1.y;\
656 results[1].y += fl1.y * fr1.y;\
657 results[1].z += fl2.x * fr1.y;\
658 results[1].w += fl2.y * fr1.y;\
659 \
660 results[2].x += fl1.x * fr2.x;\
661 results[2].y += fl1.y * fr2.x;\
662 results[2].z += fl2.x * fr2.x;\
663 results[2].w += fl2.y * fr2.x;\
664 \
665 results[3].x += fl1.x * fr2.y;\
666 results[3].y += fl1.y * fr2.y;\
667 results[3].z += fl2.x * fr2.y;\
668 results[3].w += fl2.y * fr2.y;\
669
670 __syncthreads();
671
672 // Do the multiplies.
673 #pragma unroll
674 for (int koff = 0; koff < 16; koff ++) {
675 // 32 x threads.
676 float2 fl1 = lhs_shmem2[koff][threadIdx.x];
677 float2 fl2 = lhs_shmem2[koff + 16][threadIdx.x];
678
679 int start_feature = threadIdx.y * 4;
680 float2 fr1 = rhs_shmem2[(start_feature>>1) + 32*((koff%4)/2)][koff/4 + (koff%2)*4];
681 float2 fr2 = rhs_shmem2[(start_feature>>1) + 1 + 32*((koff%4)/2)][koff/4 + (koff%2)*4];
682
683 add_vals(fl1, fl2, fr1, fr2)
684 }
685 __syncthreads();
686 }
687
688 #undef prefetch_lhs
689 #undef add_vals
690
691 Index horiz_base = threadIdx.y*4+base_n;
692 if (!CHECK_LHS_BOUNDARY && !CHECK_RHS_BOUNDARY) {
693 for (int i = 0; i < 4; i++) {
694 output(lhs_vert, horiz_base + i) = results[i].x;
695 output(lhs_vert + 1, horiz_base + i) = results[i].y;
696 output(lhs_vert + 2, horiz_base + i) = results[i].z;
697 output(lhs_vert + 3, horiz_base + i) = results[i].w;
698 }
699 } else if (!CHECK_RHS_BOUNDARY) {
700 // CHECK LHS
701 if (lhs_vert + 3 < m_size) {
702 for (int i = 0; i < 4; i++) {
703 output(lhs_vert, horiz_base + i) = results[i].x;
704 output(lhs_vert + 1, horiz_base + i) = results[i].y;
705 output(lhs_vert + 2, horiz_base + i) = results[i].z;
706 output(lhs_vert + 3, horiz_base + i) = results[i].w;
707 }
708 } else if (lhs_vert + 2 < m_size) {
709 for (int i = 0; i < 4; i++) {
710 output(lhs_vert, horiz_base + i) = results[i].x;
711 output(lhs_vert + 1, horiz_base + i) = results[i].y;
712 output(lhs_vert + 2, horiz_base + i) = results[i].z;
713 }
714 } else if (lhs_vert + 1 < m_size) {
715 for (int i = 0; i < 4; i++) {
716 output(lhs_vert, horiz_base + i) = results[i].x;
717 output(lhs_vert + 1, horiz_base + i) = results[i].y;
718 }
719 } else if (lhs_vert < m_size) {
720 for (int i = 0; i < 4; i++) {
721 output(lhs_vert, horiz_base + i) = results[i].x;
722 }
723 }
724 } else if (!CHECK_LHS_BOUNDARY) {
725 // CHECK RHS
726 /*
727 int ncols_rem = fminf(n_size- horiz_base, 4);
728 for (int i = 0; i < ncols_rem; i++) {
729 output(lhs_vert, horiz_base + i) = results[i].x;
730 output(lhs_vert + 1, horiz_base + i) = results[i].y;
731 output(lhs_vert + 2, horiz_base + i) = results[i].z;
732 output(lhs_vert + 3, horiz_base + i) = results[i].w;
733 }*/
734 for (int i = 0; i < 4; i++) {
735 if (horiz_base+i < n_size) {
736 output(lhs_vert, horiz_base + i) = results[i].x;
737 output(lhs_vert + 1, horiz_base + i) = results[i].y;
738 output(lhs_vert + 2, horiz_base + i) = results[i].z;
739 output(lhs_vert + 3, horiz_base + i) = results[i].w;
740 }
741 }
742 } else {
743 // CHECK both boundaries.
744 for (int i = 0; i < 4; i++) {
745 if (horiz_base+i < n_size) {
746 if (lhs_vert < m_size)
747 output(lhs_vert, horiz_base + i) = results[i].x;
748 if (lhs_vert + 1 < m_size)
749 output(lhs_vert + 1, horiz_base + i) = results[i].y;
750 if (lhs_vert + 2 < m_size)
751 output(lhs_vert + 2, horiz_base + i) = results[i].z;
752 if (lhs_vert + 3 < m_size)
753 output(lhs_vert + 3, horiz_base + i) = results[i].w;
754 }
755 }
756 }
757 }
758
759
760 template<typename Index, typename LhsMapper,
761 typename RhsMapper, typename OutputMapper, bool CHECK_LHS_BOUNDARY,
762 bool CHECK_RHS_BOUNDARY>
763 __device__ EIGEN_STRONG_INLINE void
EigenFloatContractionKernelInternal(const LhsMapper lhs,const RhsMapper rhs,const OutputMapper output,float2 lhs_shmem2[][32],float2 rhs_shmem2[][8],const Index m_size,const Index n_size,const Index k_size,const Index base_m,const Index base_n)764 EigenFloatContractionKernelInternal(const LhsMapper lhs, const RhsMapper rhs,
765 const OutputMapper output, float2 lhs_shmem2[][32],
766 float2 rhs_shmem2[][8], const Index m_size,
767 const Index n_size, const Index k_size,
768 const Index base_m, const Index base_n) {
769 typedef float Scalar;
770
771 // prefetch registers
772 float4 lhs_pf0, lhs_pf1, lhs_pf2, lhs_pf3;
773 float4 rhs_pf0, rhs_pf1;
774
775 float4 results[8];
776 for (int i=0; i < 8; i++) {
777 results[i].x = results[i].y = results[i].z = results[i].w = 0;
778 }
779
780
781 Index lhs_vert = base_m+threadIdx.x*4+(threadIdx.y%4)*32;
782 for (Index k = 0; k < k_size; k += 32) {
783 lhs_pf0 = internal::pset1<float4>(0);
784 lhs_pf1 = internal::pset1<float4>(0);
785 lhs_pf2 = internal::pset1<float4>(0);
786 lhs_pf3 = internal::pset1<float4>(0);
787
788 rhs_pf0 = internal::pset1<float4>(0);
789 rhs_pf1 = internal::pset1<float4>(0);
790
791 if (!CHECK_LHS_BOUNDARY) {
792 if ((threadIdx.y/4+k+24) < k_size) {
793 lhs_pf0 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k));
794 lhs_pf1 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+8));
795 lhs_pf2 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+16));
796 lhs_pf3 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+24));
797 } else if ((threadIdx.y/4+k+16) < k_size) {
798 lhs_pf0 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k));
799 lhs_pf1 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+8));
800 lhs_pf2 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+16));
801 } else if ((threadIdx.y/4+k+8) < k_size) {
802 lhs_pf0 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k));
803 lhs_pf1 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+8));
804 } else if ((threadIdx.y/4+k) < k_size) {
805 lhs_pf0 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k));
806 }
807 } else {
808 // just CHECK_LHS_BOUNDARY
809 if (lhs_vert + 3 < m_size) {
810 if ((threadIdx.y/4+k+24) < k_size) {
811 lhs_pf0 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k));
812 lhs_pf1 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+8));
813 lhs_pf2 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+16));
814 lhs_pf3 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+24));
815 } else if ((threadIdx.y/4+k+16) < k_size) {
816 lhs_pf0 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k));
817 lhs_pf1 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+8));
818 lhs_pf2 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+16));
819 } else if ((threadIdx.y/4+k+8) < k_size) {
820 lhs_pf0 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k));
821 lhs_pf1 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k+8));
822 } else if ((threadIdx.y/4+k) < k_size) {
823 lhs_pf0 =lhs.loadPacket<Unaligned>(lhs_vert, (threadIdx.y/4+k));
824 }
825 } else if (lhs_vert + 2 < m_size) {
826 if ((threadIdx.y/4+k+24) < k_size) {
827 lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
828 lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
829 lhs_pf0.z =lhs(lhs_vert + 2, (threadIdx.y/4+k));
830 lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
831 lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8));
832 lhs_pf1.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+8));
833 lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16));
834 lhs_pf2.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+16));
835 lhs_pf2.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+16));
836 lhs_pf3.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+24));
837 lhs_pf3.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+24));
838 lhs_pf3.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+24));
839 } else if ((threadIdx.y/4+k+16) < k_size) {
840 lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
841 lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
842 lhs_pf0.z =lhs(lhs_vert + 2, (threadIdx.y/4+k));
843 lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
844 lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8));
845 lhs_pf1.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+8));
846 lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16));
847 lhs_pf2.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+16));
848 lhs_pf2.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+16));
849 } else if ((threadIdx.y/4+k+8) < k_size) {
850 lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
851 lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
852 lhs_pf0.z =lhs(lhs_vert + 2, (threadIdx.y/4+k));
853 lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
854 lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8));
855 lhs_pf1.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+8));
856 } else if ((threadIdx.y/4+k) < k_size) {
857 lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
858 lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
859 lhs_pf0.z =lhs(lhs_vert + 2, (threadIdx.y/4+k));
860 }
861 } else if (lhs_vert + 1 < m_size) {
862 if ((threadIdx.y/4+k+24) < k_size) {
863 lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
864 lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
865 lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
866 lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8));
867 lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16));
868 lhs_pf2.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+16));
869 lhs_pf3.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+24));
870 lhs_pf3.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+24));
871 } else if ((threadIdx.y/4+k+16) < k_size) {
872 lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
873 lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
874 lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
875 lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8));
876 lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16));
877 lhs_pf2.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+16));
878 } else if ((threadIdx.y/4+k+8) < k_size) {
879 lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
880 lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
881 lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
882 lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8));
883 } else if ((threadIdx.y/4+k) < k_size) {
884 lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
885 lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
886 }
887 } else if (lhs_vert < m_size) {
888 if ((threadIdx.y/4+k+24) < k_size) {
889 lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
890 lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
891 lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16));
892 lhs_pf3.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+24));
893 } else if ((threadIdx.y/4+k+16) < k_size) {
894 lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
895 lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
896 lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16));
897 } else if ((threadIdx.y/4+k+8) < k_size) {
898 lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
899 lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
900 } else if ((threadIdx.y/4+k) < k_size) {
901 lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
902 }
903 }
904 }
905 __syncthreads();
906 Index rhs_vert = k+threadIdx.x*4;
907 Index rhs_horiz0 = threadIdx.y*2+base_n;
908 Index rhs_horiz1 = threadIdx.y*2+1+base_n;
909 if (!CHECK_RHS_BOUNDARY) {
910 if ((rhs_vert + 3) < k_size) {
911 // just CHECK_RHS_BOUNDARY
912 rhs_pf0 = rhs.loadPacket<Unaligned>(rhs_vert, rhs_horiz0);
913 rhs_pf1 = rhs.loadPacket<Unaligned>(rhs_vert, rhs_horiz1);
914 } else if (rhs_vert + 2 < k_size) {
915 // just CHECK_RHS_BOUNDARY
916 rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
917 rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
918 rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
919 rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
920 rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1);
921 rhs_pf1.z = rhs(rhs_vert + 2, rhs_horiz1);
922 } else if (rhs_vert + 1 < k_size) {
923 rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
924 rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
925 rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
926 rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1);
927 } else if (rhs_vert < k_size) {
928 rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
929 rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
930 }
931 } else {
932 if (rhs_horiz1 < n_size) {
933 if ((rhs_vert + 3) < k_size) {
934 // just CHECK_RHS_BOUNDARY
935 rhs_pf0 = rhs.loadPacket<Unaligned>(rhs_vert, rhs_horiz0);
936 rhs_pf1 = rhs.loadPacket<Unaligned>(rhs_vert, rhs_horiz1);
937 } else if (rhs_vert + 2 < k_size) {
938 // just CHECK_RHS_BOUNDARY
939 rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
940 rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
941 rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
942 rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
943 rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1);
944 rhs_pf1.z = rhs(rhs_vert + 2, rhs_horiz1);
945 } else if (k+threadIdx.x*4 + 1 < k_size) {
946 rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
947 rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
948 rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
949 rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1);
950 } else if (k+threadIdx.x*4 < k_size) {
951 rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
952 rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
953 }
954 } else if (rhs_horiz0 < n_size) {
955 if ((rhs_vert + 3) < k_size) {
956 // just CHECK_RHS_BOUNDARY
957 rhs_pf0 = rhs.loadPacket<Unaligned>(rhs_vert, rhs_horiz0);
958 } else if ((rhs_vert + 2) < k_size) {
959 // just CHECK_RHS_BOUNDARY
960 rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
961 rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
962 rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
963 } else if ((rhs_vert + 1) < k_size) {
964 rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
965 rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
966 } else if (rhs_vert < k_size) {
967 rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
968 }
969 }
970 }
971 __syncthreads();
972 // Loaded. Do computation
973 // Row 0 -> times (0, 4, 8, .. 28) for features 0, 1.
974 // Row 1 -> times (0, 4, 8, .. 28) for features 2, 3.
975 // ..
976 // Row 31 -> times (0, 4, 8, .. 28) for features 62, 63
977 rhs_shmem2[threadIdx.y][threadIdx.x] = make_float2(rhs_pf0.x, rhs_pf1.x);
978 // Row 32 -> times (1, 5, 9, .. 29) for features 0, 1.
979 // Row 33 -> times (1, 5, 9, .. 29) for features 2, 3.
980 // ..
981 rhs_shmem2[threadIdx.y+32][threadIdx.x] = make_float2(rhs_pf0.y, rhs_pf1.y);
982 // Row 64 -> times (2, 6, 10, .. 30) for features 0, 1.
983 // Row 65 -> times (2, 6, 10, .. 30) for features 2, 3.
984 rhs_shmem2[threadIdx.y+64][threadIdx.x] = make_float2(rhs_pf0.z, rhs_pf1.z);
985 // Row 96 -> times (3, 7, 11, .. 31) for features 0, 1.
986 // Row 97 -> times (3, 7, 11, .. 31) for features 2, 3.
987 rhs_shmem2[threadIdx.y+96][threadIdx.x] = make_float2(rhs_pf0.w, rhs_pf1.w);
988
989 // LHS.
990 // Row 0 (time 0) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), .. (60, 61) .. (124, 125)
991 // Row 1 (time 1) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), .. (60, 61) .. (124, 125)
992 // ...
993 // Row 8 (time 0) -> features (2, 3), (6, 7), .. (30, 31), (34, 35), .. (62, 63) .. (126, 127)
994 // Row 15 (time 7) -> features (2, 3), (6, 7), .. (30, 31), (34, 35), .. (62, 63) .. (126, 127)
995
996
997 #define add_vals(a_feat1, a_feat2, f1, f2, f3, f4)\
998 results[0].x += a_feat1.x * f1.x;\
999 results[1].x += a_feat1.x * f1.y;\
1000 results[2].x += a_feat1.x * f2.x;\
1001 results[3].x += a_feat1.x * f2.y;\
1002 results[4].x += a_feat1.x * f3.x;\
1003 results[5].x += a_feat1.x * f3.y;\
1004 results[6].x += a_feat1.x * f4.x;\
1005 results[7].x += a_feat1.x * f4.y;\
1006 \
1007 results[0].y += a_feat1.y * f1.x;\
1008 results[1].y += a_feat1.y * f1.y;\
1009 results[2].y += a_feat1.y * f2.x;\
1010 results[3].y += a_feat1.y * f2.y;\
1011 results[4].y += a_feat1.y * f3.x;\
1012 results[5].y += a_feat1.y * f3.y;\
1013 results[6].y += a_feat1.y * f4.x;\
1014 results[7].y += a_feat1.y * f4.y;\
1015 \
1016 results[0].z += a_feat2.x * f1.x;\
1017 results[1].z += a_feat2.x * f1.y;\
1018 results[2].z += a_feat2.x * f2.x;\
1019 results[3].z += a_feat2.x * f2.y;\
1020 results[4].z += a_feat2.x * f3.x;\
1021 results[5].z += a_feat2.x * f3.y;\
1022 results[6].z += a_feat2.x * f4.x;\
1023 results[7].z += a_feat2.x * f4.y;\
1024 \
1025 results[0].w += a_feat2.y * f1.x;\
1026 results[1].w += a_feat2.y * f1.y;\
1027 results[2].w += a_feat2.y * f2.x;\
1028 results[3].w += a_feat2.y * f2.y;\
1029 results[4].w += a_feat2.y * f3.x;\
1030 results[5].w += a_feat2.y * f3.y;\
1031 results[6].w += a_feat2.y * f4.x;\
1032 results[7].w += a_feat2.y * f4.y;\
1033
1034 lhs_shmem2[threadIdx.y/4][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf0.x, lhs_pf0.y);
1035 lhs_shmem2[threadIdx.y/4+8][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf1.x, lhs_pf1.y);
1036 lhs_shmem2[threadIdx.y/4+16][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf2.x, lhs_pf2.y);
1037 lhs_shmem2[threadIdx.y/4+24][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf3.x, lhs_pf3.y);
1038
1039 lhs_shmem2[threadIdx.y/4 + 32][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf0.z, lhs_pf0.w);
1040 lhs_shmem2[threadIdx.y/4 + 40][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf1.z, lhs_pf1.w);
1041 lhs_shmem2[threadIdx.y/4 + 48][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf2.z, lhs_pf2.w);
1042 lhs_shmem2[threadIdx.y/4 + 56][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf3.z, lhs_pf3.w);
1043
1044 __syncthreads();
1045
1046 // Do the multiplies.
1047 #pragma unroll
1048 for (int koff = 0; koff < 32; koff ++) {
1049 float2 a3 = lhs_shmem2[koff][threadIdx.x + (threadIdx.y % 4) * 8];
1050 float2 a4 = lhs_shmem2[koff + 32][threadIdx.x + (threadIdx.y % 4) * 8];
1051
1052 // first feature is at (threadIdx.y/4) * 8 last is at start + 8.
1053 int start_feature = (threadIdx.y / 4) * 8;
1054
1055 float2 br1 = rhs_shmem2[start_feature/2 + (koff % 4) * 32][koff/4];
1056 float2 br2 = rhs_shmem2[start_feature/2 + 1 + (koff % 4) * 32][koff/4];
1057 float2 br3 = rhs_shmem2[start_feature/2 + 2 + (koff % 4) * 32][koff/4];
1058 float2 br4 = rhs_shmem2[start_feature/2 + 3 + (koff % 4) * 32][koff/4];
1059
1060 add_vals(a3, a4, br1, br2, br3, br4)
1061 }
1062 __syncthreads();
1063 } // end loop over k
1064
1065
1066 __syncthreads();
1067 Index horiz_base = (threadIdx.y/4)*8+base_n;
1068 if (!CHECK_LHS_BOUNDARY && !CHECK_RHS_BOUNDARY) {
1069 for (int i = 0; i < 8; i++) {
1070 output(lhs_vert, horiz_base + i) = results[i].x;
1071 output(lhs_vert + 1, horiz_base + i) = results[i].y;
1072 output(lhs_vert + 2, horiz_base + i) = results[i].z;
1073 output(lhs_vert + 3, horiz_base + i) = results[i].w;
1074 }
1075 } else if (!CHECK_RHS_BOUNDARY) {
1076 if (lhs_vert + 3 < m_size) {
1077 for (int i = 0; i < 8; i++) {
1078 output(lhs_vert, horiz_base + i) = results[i].x;
1079 output(lhs_vert + 1, horiz_base + i) = results[i].y;
1080 output(lhs_vert + 2, horiz_base + i) = results[i].z;
1081 output(lhs_vert + 3, horiz_base + i) = results[i].w;
1082 }
1083 } else if (lhs_vert + 2 < m_size) {
1084 for (int i = 0; i < 8; i++) {
1085 output(lhs_vert, horiz_base + i) = results[i].x;
1086 output(lhs_vert + 1, horiz_base + i) = results[i].y;
1087 output(lhs_vert + 2, horiz_base + i) = results[i].z;
1088 }
1089 } else if (lhs_vert + 1 < m_size) {
1090 for (int i = 0; i < 8; i++) {
1091 output(lhs_vert, horiz_base + i) = results[i].x;
1092 output(lhs_vert + 1, horiz_base + i) = results[i].y;
1093 }
1094 } else if (lhs_vert < m_size) {
1095 for (int i = 0; i < 8; i++) {
1096 output(lhs_vert, horiz_base + i) = results[i].x;
1097 }
1098 }
1099 } else if (!CHECK_LHS_BOUNDARY) {
1100 // CHECK BOUNDARY_B
1101 for (int i = 0; i < 8; i++) {
1102 if (horiz_base + i < n_size) {
1103 output(lhs_vert, horiz_base + i) = results[i].x;
1104 output(lhs_vert + 1, horiz_base + i) = results[i].y;
1105 output(lhs_vert + 2, horiz_base + i) = results[i].z;
1106 output(lhs_vert + 3, horiz_base + i) = results[i].w;
1107 }
1108 }
1109 } else {
1110 // CHECK both boundaries.
1111 for (int i = 0; i < 8; i++) {
1112 if (horiz_base + i < n_size) {
1113 if (lhs_vert < m_size)
1114 output(lhs_vert, horiz_base + i) = results[i].x;
1115 if (lhs_vert + 1 < m_size)
1116 output(lhs_vert + 1, horiz_base + i) = results[i].y;
1117 if (lhs_vert + 2 < m_size)
1118 output(lhs_vert + 2, horiz_base + i) = results[i].z;
1119 if (lhs_vert + 3 < m_size)
1120 output(lhs_vert + 3, horiz_base + i) = results[i].w;
1121 }
1122 }
1123 }
1124 }
1125
1126
1127 template<typename Index, typename LhsMapper,
1128 typename RhsMapper, typename OutputMapper>
1129 __global__ void
1130 __launch_bounds__(256)
EigenFloatContractionKernel(const LhsMapper lhs,const RhsMapper rhs,const OutputMapper output,const Index m_size,const Index n_size,const Index k_size)1131 EigenFloatContractionKernel(const LhsMapper lhs, const RhsMapper rhs,
1132 const OutputMapper output,
1133 const Index m_size, const Index n_size, const Index k_size) {
1134 __shared__ float2 lhs_shmem[64*32];
1135 __shared__ float2 rhs_shmem[128*8];
1136
1137 typedef float2 LHS_MEM[64][32];
1138 typedef float2 RHS_MEM[128][8];
1139
1140 typedef float2 LHS_MEM16x16[32][16];
1141 typedef float2 RHS_MEM16x16[64][8];
1142
1143 const Index m_block_idx = blockIdx.x;
1144 const Index n_block_idx = blockIdx.y;
1145
1146 const Index base_m = 128 * m_block_idx;
1147 const Index base_n = 64 * n_block_idx;
1148
1149 bool check_rhs = (base_n + 63) >= n_size;
1150 bool check_lhs128 = (base_m + 127) >= m_size;
1151
1152 if (!check_rhs) {
1153 if (!check_lhs128) {
1154 // >= 128 rows left
1155 EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, false, false>(
1156 lhs, rhs, output, *((LHS_MEM *) lhs_shmem), *((RHS_MEM *) rhs_shmem), m_size, n_size, k_size, base_m, base_n);
1157 } else {
1158 EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, true, false>(
1159 lhs, rhs, output, *((LHS_MEM *) lhs_shmem), *((RHS_MEM *) rhs_shmem), m_size, n_size, k_size, base_m, base_n);
1160 }
1161 } else {
1162 if (!check_lhs128) {
1163 // >= 128 rows left
1164 EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, false, true>(
1165 lhs, rhs, output, *((LHS_MEM *) lhs_shmem), *((RHS_MEM *) rhs_shmem), m_size, n_size, k_size, base_m, base_n);
1166 } else {
1167 EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, true, true>(
1168 lhs, rhs, output, *((LHS_MEM *) lhs_shmem), *((RHS_MEM *) rhs_shmem), m_size, n_size, k_size, base_m, base_n);
1169 }
1170 }
1171 }
1172
1173 template<typename Index, typename LhsMapper,
1174 typename RhsMapper, typename OutputMapper>
1175 __global__ void
1176 __launch_bounds__(256)
EigenFloatContractionKernel16x16(const LhsMapper lhs,const RhsMapper rhs,const OutputMapper output,const Index m_size,const Index n_size,const Index k_size)1177 EigenFloatContractionKernel16x16(const LhsMapper lhs, const RhsMapper rhs,
1178 const OutputMapper output,
1179 const Index m_size, const Index n_size, const Index k_size) {
1180 __shared__ float2 lhs_shmem[32][16];
1181 __shared__ float2 rhs_shmem[64][8];
1182
1183 const Index m_block_idx = blockIdx.x;
1184 const Index n_block_idx = blockIdx.y;
1185
1186 const Index base_m = 64 * m_block_idx;
1187 const Index base_n = 64 * n_block_idx;
1188
1189 if (base_m + 63 < m_size) {
1190 if (base_n + 63 < n_size) {
1191 EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, false, false>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n);
1192 } else {
1193 EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, false, true>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n);
1194 }
1195 } else {
1196 if (base_n + 63 < n_size) {
1197 EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, true, false>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n);
1198 } else {
1199 EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, true, true>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n);
1200 }
1201 }
1202 }
1203
1204
1205 template<typename Indices, typename LeftArgType, typename RightArgType>
1206 struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, GpuDevice> :
1207 public TensorContractionEvaluatorBase<TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, GpuDevice> > {
1208
1209 typedef GpuDevice Device;
1210
1211 typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, Device> Self;
1212 typedef TensorContractionEvaluatorBase<Self> Base;
1213
1214 typedef TensorContractionOp<Indices, LeftArgType, RightArgType> XprType;
1215 typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
1216 typedef typename XprType::Index Index;
1217 typedef typename XprType::CoeffReturnType CoeffReturnType;
1218 typedef typename PacketType<CoeffReturnType, GpuDevice>::type PacketReturnType;
1219
1220 enum {
1221 Layout = TensorEvaluator<LeftArgType, Device>::Layout,
1222 };
1223
1224 // Most of the code is assuming that both input tensors are ColMajor. If the
1225 // inputs are RowMajor, we will "cheat" by swapping the LHS and RHS:
1226 // If we want to compute A * B = C, where A is LHS and B is RHS, the code
1227 // will pretend B is LHS and A is RHS.
1228 typedef typename internal::conditional<
1229 static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType;
1230 typedef typename internal::conditional<
1231 static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType;
1232
1233 static const int LDims =
1234 internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value;
1235 static const int RDims =
1236 internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value;
1237 static const int ContractDims = internal::array_size<Indices>::value;
1238
1239 typedef array<Index, LDims> left_dim_mapper_t;
1240 typedef array<Index, RDims> right_dim_mapper_t;
1241
1242 typedef array<Index, ContractDims> contract_t;
1243 typedef array<Index, LDims - ContractDims> left_nocontract_t;
1244 typedef array<Index, RDims - ContractDims> right_nocontract_t;
1245
1246 static const int NumDims = LDims + RDims - 2 * ContractDims;
1247
1248 typedef DSizes<Index, NumDims> Dimensions;
1249
1250 // typedefs needed in evalTo
1251 typedef typename internal::remove_const<typename EvalLeftArgType::Scalar>::type LhsScalar;
1252 typedef typename internal::remove_const<typename EvalRightArgType::Scalar>::type RhsScalar;
1253
1254 typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
1255 typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
1256
1257 typedef typename LeftEvaluator::Dimensions LeftDimensions;
1258 typedef typename RightEvaluator::Dimensions RightDimensions;
1259
1260 EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device) :
1261 Base(op, device) {}
1262
1263 // We need to redefine this method to make nvcc happy
1264 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) {
1265 this->m_leftImpl.evalSubExprsIfNeeded(NULL);
1266 this->m_rightImpl.evalSubExprsIfNeeded(NULL);
1267 if (data) {
1268 evalTo(data);
1269 return false;
1270 } else {
1271 this->m_result = static_cast<Scalar *>(this->m_device.allocate(this->dimensions().TotalSize() * sizeof(Scalar)));
1272 evalTo(this->m_result);
1273 return true;
1274 }
1275 }
1276
1277 void evalTo(Scalar* buffer) const {
1278 if (this->m_lhs_inner_dim_contiguous) {
1279 if (this->m_rhs_inner_dim_contiguous) {
1280 if (this->m_rhs_inner_dim_reordered) {
1281 evalTyped<true, true, true, Unaligned>(buffer);
1282 }
1283 else {
1284 evalTyped<true, true, false, Unaligned>(buffer);
1285 }
1286 }
1287 else {
1288 if (this->m_rhs_inner_dim_reordered) {
1289 evalTyped<true, false, true, Unaligned>(buffer);
1290 }
1291 else {
1292 evalTyped<true, false, false, Unaligned>(buffer);
1293 }
1294 }
1295 }
1296 else {
1297 if (this->m_rhs_inner_dim_contiguous) {
1298 if (this->m_rhs_inner_dim_reordered) {
1299 evalTyped<false, true, true, Unaligned>(buffer);
1300 }
1301 else {
1302 evalTyped<false, true, false, Unaligned>(buffer);
1303 }
1304 }
1305 else {
1306 if (this->m_rhs_inner_dim_reordered) {
1307 evalTyped<false, false, true, Unaligned>(buffer);
1308 }
1309 else {
1310 evalTyped<false, false, false, Unaligned>(buffer);
1311 }
1312 }
1313 }
1314 }
1315
1316 template <typename LhsScalar, typename RhsScalar, typename Index, typename LhsMapper, typename RhsMapper, typename OutputMapper> struct LaunchKernels {
1317 static void Run(const LhsMapper& lhs, const RhsMapper& rhs, const OutputMapper& output, Index m, Index n, Index k, const GpuDevice& device) {
1318 const Index m_blocks = (m + 63) / 64;
1319 const Index n_blocks = (n + 63) / 64;
1320 const dim3 num_blocks(m_blocks, n_blocks, 1);
1321 const dim3 block_size(8, 8, 8);
1322 LAUNCH_CUDA_KERNEL((EigenContractionKernel<Scalar, Index, LhsMapper, RhsMapper, OutputMapper>), num_blocks, block_size, 0, device, lhs, rhs, output, m, n, k);
1323 }
1324 };
1325
1326 template <typename Index, typename LhsMapper, typename RhsMapper, typename OutputMapper> struct LaunchKernels<float, float, Index, LhsMapper, RhsMapper, OutputMapper> {
1327 static void Run(const LhsMapper& lhs, const RhsMapper& rhs, const OutputMapper& output, Index m, Index n, Index k, const GpuDevice& device) {
1328 if (m < 768 || n < 768) {
1329 const Index m_blocks = (m + 63) / 64;
1330 const Index n_blocks = (n + 63) / 64;
1331 const dim3 num_blocks(m_blocks, n_blocks, 1);
1332 const dim3 block_size(16, 16, 1);
1333 LAUNCH_CUDA_KERNEL((EigenFloatContractionKernel16x16<Index, LhsMapper, RhsMapper, OutputMapper>), num_blocks, block_size, 0, device, lhs, rhs, output, m, n, k);
1334 } else {
1335 const Index m_blocks = (m + 127) / 128;
1336 const Index n_blocks = (n + 63) / 64;
1337 const dim3 num_blocks(m_blocks, n_blocks, 1);
1338 const dim3 block_size(8, 32, 1);
1339 LAUNCH_CUDA_KERNEL((EigenFloatContractionKernel<Index, LhsMapper, RhsMapper, OutputMapper>), num_blocks, block_size, 0, device, lhs, rhs, output, m, n, k);
1340 }
1341 }
1342 };
1343
1344 template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
1345 void evalTyped(Scalar* buffer) const {
1346 // columns in left side, rows in right side
1347 const Index k = this->m_k_size;
1348 EIGEN_UNUSED_VARIABLE(k)
1349
1350 // rows in left side
1351 const Index m = this->m_i_size;
1352
1353 // columns in right side
1354 const Index n = this->m_j_size;
1355
1356 // zero out the result buffer (which must be of size at least m * n * sizeof(Scalar)
1357 this->m_device.memset(buffer, 0, m * n * sizeof(Scalar));
1358
1359 typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs,
1360 LeftEvaluator, left_nocontract_t,
1361 contract_t, 4,
1362 lhs_inner_dim_contiguous,
1363 false, Unaligned> LhsMapper;
1364
1365 typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs,
1366 RightEvaluator, right_nocontract_t,
1367 contract_t, 4,
1368 rhs_inner_dim_contiguous,
1369 rhs_inner_dim_reordered, Unaligned> RhsMapper;
1370
1371 typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
1372
1373
1374 // initialize data mappers
1375 LhsMapper lhs(this->m_leftImpl, this->m_left_nocontract_strides, this->m_i_strides,
1376 this->m_left_contracting_strides, this->m_k_strides);
1377
1378 RhsMapper rhs(this->m_rightImpl, this->m_right_nocontract_strides, this->m_j_strides,
1379 this->m_right_contracting_strides, this->m_k_strides);
1380
1381 OutputMapper output(buffer, m);
1382
1383 setCudaSharedMemConfig(cudaSharedMemBankSizeEightByte);
1384 LaunchKernels<LhsScalar, RhsScalar, Index, LhsMapper, RhsMapper, OutputMapper>::Run(lhs, rhs, output, m, n, k, this->m_device);
1385 }
1386 };
1387
1388 } // end namespace Eigen
1389
1390 #endif // EIGEN_USE_GPU and __CUDACC__
1391 #endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_CUDA_H
1392