TensorContractionCuda.h
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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
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)
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
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
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)
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)
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
Eigen::internal::remove_const< typename XprType::Scalar >::type
typename XprType::Scalar type
Definition: Meta.h:73
EIGEN_STRONG_INLINE
#define EIGEN_STRONG_INLINE
Definition: Macros.h:493
Eigen::TensorEvaluator::data
EIGEN_DEVICE_FUNC internal::traits< Derived >::template MakePointer< Scalar >::Type data() const
Definition: TensorEvaluator.h:111
Eigen::ColMajor
Definition: Constants.h:320
Eigen::TensorEvaluator::dimensions
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions & dimensions() const
Definition: TensorEvaluator.h:54
Eigen::TensorEvaluator::CoeffReturnType
Derived::Scalar CoeffReturnType
Definition: TensorEvaluator.h:32
Eigen::TensorEvaluator::TensorEvaluator
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const Derived &m, const Device &device)
Definition: TensorEvaluator.h:48
Eigen
Definition: LDLT.h:16
Eigen::Unaligned
Definition: Constants.h:228
Eigen::Index
EIGEN_DEFAULT_DENSE_INDEX_TYPE Index
The Index type as used for the API.
Definition: Meta.h:33
Eigen::TensorEvaluator::PacketReturnType
PacketType< CoeffReturnType, Device >::type PacketReturnType
Definition: TensorEvaluator.h:33
Eigen::TensorEvaluator::Dimensions
Derived::Dimensions Dimensions
Definition: TensorEvaluator.h:34
Eigen::TensorEvaluator::device
const Device & device() const
required by sycl in order to construct sycl buffer from raw pointer
Definition: TensorEvaluator.h:114
Eigen::TensorEvaluator::evalSubExprsIfNeeded
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType *dest)
Definition: TensorEvaluator.h:56
Eigen::TensorEvaluator::Scalar
Derived::Scalar Scalar
Definition: TensorEvaluator.h:31
EIGEN_UNUSED_VARIABLE
#define EIGEN_UNUSED_VARIABLE(var)
Definition: Macros.h:616
Eigen::PacketType::type
internal::packet_traits< Scalar >::type type
Definition: TensorMeta.h:51
Eigen::y
const T & y
Definition: AutoDiffScalar.h:548

hebiros
Author(s): Xavier Artache , Matthew Tesch
autogenerated on Thu Sep 3 2020 04:09:15