Alberto Cabrera Pérez commited on
Commit
31edd77
·
1 Parent(s): 3020711

sycl: refactor quantization to q8_1 (llama/14815)

Browse files

* sycl: quantization to q8_1 refactor

* Refactored src1 copy logic in op_mul_mat

ggml/src/ggml-sycl/backend.hpp CHANGED
@@ -28,6 +28,7 @@
28
  #include "mmvq.hpp"
29
  #include "norm.hpp"
30
  #include "outprod.hpp"
 
31
  #include "quants.hpp"
32
  #include "rope.hpp"
33
  #include "set_rows.hpp"
 
28
  #include "mmvq.hpp"
29
  #include "norm.hpp"
30
  #include "outprod.hpp"
31
+ #include "quantize.hpp"
32
  #include "quants.hpp"
33
  #include "rope.hpp"
34
  #include "set_rows.hpp"
ggml/src/ggml-sycl/ggml-sycl.cpp CHANGED
@@ -44,6 +44,7 @@
44
  #include "ggml-sycl/set_rows.hpp"
45
  #include "ggml-sycl/sycl_hw.hpp"
46
  #include "ggml-sycl/getrows.hpp"
 
47
  #include "ggml.h"
48
 
49
  static bool g_sycl_loaded = false;
@@ -1373,120 +1374,6 @@ typedef void (*ggml_sycl_op_mul_mat_t)(
1373
 
1374
 
1375
 
1376
- template<int QUANT_BLOCK_TILE>
1377
- static void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded,
1378
- const sycl::nd_item<3> &item_ct1) {
1379
- const int ix = (item_ct1.get_local_range(2) * item_ct1.get_group(2) +
1380
- item_ct1.get_local_id(2)) * QUANT_BLOCK_TILE;
1381
-
1382
- if (ix >= kx_padded) {
1383
- return;
1384
- }
1385
-
1386
- const int iy = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
1387
- item_ct1.get_local_id(1);
1388
-
1389
- const int i_padded = iy*kx_padded + ix;
1390
-
1391
- block_q8_1 * y = (block_q8_1 *) vy;
1392
-
1393
- const int ib = i_padded / QK8_1; // block index
1394
- const int iqs = i_padded % QK8_1; // quant index
1395
- typedef sycl::vec<float, QUANT_BLOCK_TILE> TC;
1396
- typedef sycl::vec<int8_t, QUANT_BLOCK_TILE> TQ;
1397
- TC zeros;
1398
- TQ qzeros;
1399
- #pragma unroll
1400
- for (int i = 0; i < QUANT_BLOCK_TILE; i++)
1401
- {
1402
- zeros[i] = 0.f;
1403
- qzeros[i] = 0;
1404
- }
1405
- const TC xi = ix < kx ? *(const TC *)&x[iy * kx + ix] : zeros;
1406
- float sum = xi[0];
1407
- float amax = sycl::fabs(xi[0]);
1408
- #pragma unroll
1409
- for (int i = 1; i < QUANT_BLOCK_TILE; i++)
1410
- {
1411
- sum += xi[i];
1412
- amax = sycl::fmax(sycl::fabs(xi[i]), amax);
1413
- }
1414
- sum = warp_reduce_sum(sum, item_ct1);
1415
- amax = warp_reduce_max(amax, item_ct1);
1416
-
1417
- const float d = amax / 127;
1418
- TQ q = qzeros;
1419
- if (amax != 0.0f)
1420
- {
1421
- #pragma unroll
1422
- for (int i = 0; i < QUANT_BLOCK_TILE; i++) {
1423
- q[i] = sycl::round(xi[i] / d);
1424
- }
1425
- }
1426
-
1427
- *(TQ *)&y[ib].qs[iqs] = q;
1428
-
1429
- if (iqs > 0) {
1430
- return;
1431
- }
1432
-
1433
- reinterpret_cast<sycl::half &>(y[ib].ds.x()) = d;
1434
- reinterpret_cast<sycl::half &>(y[ib].ds.y()) = sum;
1435
- }
1436
-
1437
- template <int ElementsPerWI>
1438
- static __dpct_inline__ void quantize_and_reorder_q8_1(const float * __restrict__ x, void * reordered_q8_tensor,
1439
- const int kx, const int kx_padded, const sycl::nd_item<1> & it) {
1440
- /*
1441
- Quantizes and reorders the resultant q8 tensor in a per row fashion
1442
- Each sub-group calculates one quant block. i.e. QK8_1 quant values and the d and sum values
1443
- */
1444
-
1445
- auto subgroup_id = it.get_group(0);
1446
- auto wi_id = it.get_local_id(0);
1447
-
1448
- const int num_blocks_per_row = kx / QK8_1;
1449
- auto row = subgroup_id / num_blocks_per_row;
1450
- auto col = subgroup_id % num_blocks_per_row;
1451
-
1452
- auto row_offset = row * (kx_padded / QK8_1) * sizeof(block_q8_1);
1453
- auto col_offset = QK8_1 * col + wi_id * ElementsPerWI;
1454
-
1455
- auto quant_ptr = (int8_t *) ((char *) reordered_q8_tensor + row_offset + col_offset);
1456
- auto ds_ptr = (sycl::half2 *) ((char *) reordered_q8_tensor + row_offset + kx + col * sizeof(sycl::half2));
1457
-
1458
- sycl::vec<float, ElementsPerWI> wi_f32_vals;
1459
- sycl::vec<int8_t, ElementsPerWI> quantized_values;
1460
-
1461
- auto float_ptr_offset = subgroup_id * QK8_1 + ElementsPerWI * wi_id;
1462
- wi_f32_vals = *reinterpret_cast<const sycl::vec<float, ElementsPerWI> *>(x + float_ptr_offset);
1463
-
1464
- float sum = 0.0f;
1465
- float amax = 0.0f;
1466
-
1467
- #pragma unroll(ElementsPerWI)
1468
- for (int i = 0; i < ElementsPerWI; i++) {
1469
- sum += wi_f32_vals[i];
1470
- amax = sycl::fmax(amax, sycl::fabs(wi_f32_vals[i]));
1471
- quantized_values[i] = 0;
1472
- }
1473
- sum = sycl::reduce_over_group(it.get_group(), sum, sycl::plus<float>());
1474
- amax = sycl::reduce_over_group(it.get_group(), amax, sycl::maximum<float>());
1475
- float d = amax == 0 ? 1 : amax / 127;
1476
-
1477
- #pragma unroll(ElementsPerWI)
1478
- for (int i = 0; i < ElementsPerWI; i++) {
1479
- quantized_values[i] = sycl::round(wi_f32_vals[i] / d);
1480
- }
1481
-
1482
- d = amax == 0 ? 0 : d;
1483
-
1484
- *reinterpret_cast<sycl::vec<int8_t, ElementsPerWI> *>(quant_ptr) = quantized_values;
1485
- if (wi_id == 0) {
1486
- *ds_ptr = sycl::half2(sycl::half(d), sycl::half(sum));
1487
- }
1488
- }
1489
-
1490
  static void mul_mat_p021_f16_f32(
1491
  const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst,
1492
  const int ncols_x, const int nrows_x, const int nchannels_x, const int nchannels_y,
@@ -1770,32 +1657,6 @@ static void pool2d_nchw_kernel(
1770
  o_ptr[cur_oh * ow + cur_ow] = res;
1771
  }
1772
 
1773
- static void quantize_row_q8_1_sycl(const float * x, void * vy, const int kx, const int ky, const int kx_padded,
1774
- bool reorder_q8_tensor, queue_ptr stream) {
1775
- if (reorder_q8_tensor) {
1776
- auto local_range = std::size_t(WARP_SIZE);
1777
- auto num_quant_blocks = ky * (kx / QK8_1);
1778
- auto global_range = num_quant_blocks * local_range;
1779
- stream->parallel_for(sycl::nd_range<1>({ global_range }, { local_range }),
1780
- [=](sycl::nd_item<1> it) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
1781
- quantize_and_reorder_q8_1<QK8_1 / WARP_SIZE>(x, vy, kx, kx_padded, it);
1782
- });
1783
- } else {
1784
- const int block_num_x = (kx_padded + SYCL_QUANTIZE_BLOCK_SIZE - 1) / SYCL_QUANTIZE_BLOCK_SIZE;
1785
- const sycl::range<3> num_blocks(1, ky, block_num_x);
1786
- int constexpr QUANT_BLOCK_TILE = QK8_1 / WARP_SIZE;
1787
- static_assert(QK8_1 % WARP_SIZE == 0);
1788
- const sycl::range<3> block_size(1, 1, SYCL_QUANTIZE_BLOCK_SIZE / QUANT_BLOCK_TILE);
1789
- {
1790
- dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
1791
-
1792
- stream->parallel_for(sycl::nd_range<3>(num_blocks * block_size, block_size),
1793
- [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
1794
- quantize_q8_1<QUANT_BLOCK_TILE>(x, vy, kx, kx_padded, item_ct1);
1795
- });
1796
- }
1797
- }
1798
- }
1799
 
1800
  static void ggml_mul_mat_p021_f16_f32_sycl(const void *vx, const float *y,
1801
  float *dst, const int ncols_x,
@@ -2372,10 +2233,10 @@ static void ggml_sycl_set_peer_access(const int n_tokens, int main_device) {
2372
  peer_access_enabled = enable_peer_access;
2373
  }
2374
 
 
2375
  static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
2376
  const ggml_tensor *src1, ggml_tensor *dst,
2377
- ggml_sycl_op_mul_mat_t op,
2378
- const bool convert_src1_to_q8_1) try {
2379
 
2380
  GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne);
2381
 
@@ -2470,6 +2331,8 @@ static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_ten
2470
  }
2471
  }
2472
 
 
 
2473
  for (int i = 0; i < ggml_sycl_info().device_count; ++i) {
2474
  if ((!split && i != ctx.device) || dev[i].row_low == dev[i].row_high) {
2475
  continue;
@@ -2495,20 +2358,19 @@ static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_ten
2495
  dev[i].src1_ddf = dev[i].src1_ddf_alloc.alloc(ctx.pool(i), ggml_nelements(src1));
2496
  }
2497
 
2498
- if (convert_src1_to_q8_1) {
2499
  dev[i].src1_ddq = dev[i].src1_ddq_alloc.alloc(ctx.pool(i), nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs);
2500
 
2501
  if (src1_on_device && src1_is_contiguous) {
2502
- bool reorder_q8_tensor = src0->extra && ((ggml_tensor_extra_gpu *)src0->extra)->optimized_feature.reorder;
2503
  scope_op_debug_print scope_dbg_print(__func__, "/quantize_row_q8_1_sycl", dst,
2504
  /*num_src=*/2, " : converting src1 to Q8_1");
2505
- quantize_row_q8_1_sycl(dev[i].src1_ddf, dev[i].src1_ddq, ne10, nrows1, src1_padded_col_size, reorder_q8_tensor, stream);
2506
- /*
2507
- DPCT1010:90: SYCL uses exceptions to report errors and does not
2508
- use the error codes. The call was replaced with 0. You need to
2509
- rewrite this code.
2510
- */
2511
- SYCL_CHECK(0);
2512
  }
2513
  }
2514
 
@@ -2524,11 +2386,6 @@ static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_ten
2524
  // here an event is recorded that signals that the main device has finished calculating the input data
2525
  if (split && used_devices > 1) {
2526
  ggml_sycl_set_device(ctx.device);
2527
- /*
2528
- DPCT1024:91: The original code returned the error code that was further
2529
- consumed by the program logic. This original code was replaced with 0.
2530
- You may need to rewrite the program logic consuming the error code.
2531
- */
2532
  SYCL_CHECK(CHECK_TRY_ERROR(
2533
  *src0_extra->events[ctx.device][0] =
2534
  ctx.stream()->ext_oneapi_submit_barrier()));
@@ -2552,11 +2409,6 @@ static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_ten
2552
 
2553
  // wait for main GPU data if necessary
2554
  if (split && (i != ctx.device || is != 0)) {
2555
- /*
2556
- DPCT1009:163: SYCL uses exceptions to report errors and does not
2557
- use the error codes. The original code was commented out and a
2558
- warning string was inserted. You need to rewrite this code.
2559
- */
2560
  SYCL_CHECK(CHECK_TRY_ERROR(stream->ext_oneapi_submit_barrier(
2561
  {*src0_extra->events[ctx.device][0]})));
2562
  }
@@ -2582,39 +2434,42 @@ static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_ten
2582
  // copy src0, src1 to device if necessary
2583
  if (src1_is_contiguous) {
2584
  if (i != ctx.device) {
2585
- if (convert_src1_to_q8_1) {
2586
  char * src1_ddq_i_source = dev[ctx.device].src1_ddq + src1_ddq_i_offset;
2587
- SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(
2588
- src1_ddq_i, src1_ddq_i_source,
2589
- src1_ncols * src1_padded_col_size * q8_1_ts /
2590
- q8_1_bs).wait()));
 
2591
  } else {
2592
-
2593
  float * src1_ddf_i_source = (float *) src1_extra->data_device[ctx.device];
2594
- src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10;
2595
 
2596
- SYCL_CHECK(CHECK_TRY_ERROR(dev2dev_memcpy(*stream, *main_stream,
2597
- src1_ddf_i, src1_ddf_i_source,
2598
- src1_ncols * ne10 * sizeof(float))));
2599
  }
2600
  }
2601
- } else if (src1_on_device && !src1_is_contiguous) {
2602
- SYCL_CHECK(ggml_sycl_cpy_tensor_2d(
2603
- src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream));
2604
  } else {
2605
- GGML_ABORT("fatal error");
2606
- }
 
 
 
 
2607
 
2608
- if (convert_src1_to_q8_1 && !src1_is_contiguous) {
2609
- scope_op_debug_print scope_dbg_print(__func__, "/quantize_row_q8_1_sycl", dst,
2610
- /*num_src=*/2, " : converting src1 to Q8_1");
2611
- quantize_row_q8_1_sycl(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, false, stream);
2612
- /*
2613
- DPCT1010:92: SYCL uses exceptions to report errors and does
2614
- not use the error codes. The call was replaced with 0. You
2615
- need to rewrite this code.
2616
- */
2617
- SYCL_CHECK(0);
 
 
2618
  }
2619
 
2620
  if (src1_col_0 == 0 && !src0_is_contiguous && i02 % i02_divisor == 0) {
@@ -2626,12 +2481,6 @@ static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_ten
2626
  // do the computation
2627
  SYCL_CHECK(CHECK_TRY_ERROR(op(ctx, src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i,
2628
  dev[i].row_low, dev[i].row_high, src1_ncols, src1_padded_col_size, stream)));
2629
- /*
2630
- DPCT1010:93: SYCL uses exceptions to report errors and does not
2631
- use the error codes. The call was replaced with 0. You need to
2632
- rewrite this code.
2633
- */
2634
- SYCL_CHECK(0);
2635
 
2636
  // copy dst to host or other device if necessary
2637
  if (!dst_on_device) {
@@ -2662,12 +2511,6 @@ static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_ten
2662
 
2663
  // add event for the main device to wait on until other device is done
2664
  if (split && (i != ctx.device || is != 0)) {
2665
- /*
2666
- DPCT1024:94: The original code returned the error code that
2667
- was further consumed by the program logic. This original
2668
- code was replaced with 0. You may need to rewrite the
2669
- program logic consuming the error code.
2670
- */
2671
  SYCL_CHECK(CHECK_TRY_ERROR(
2672
  *src0_extra->events[i][is] =
2673
  stream->ext_oneapi_submit_barrier()));
@@ -3351,19 +3194,20 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor
3351
  // KQ + KQV multi-batch
3352
  ggml_sycl_mul_mat_batched_sycl(ctx, src0, src1, dst);
3353
  } else if (use_dequantize_mul_mat_vec) {
3354
- constexpr bool convert_src1_to_q8_1 = false;
3355
  opt_for_reorder(&ctx, src0, src1, dst, mul_mat_algo::DMMV);
3356
- ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_dequantize_mul_mat_vec, convert_src1_to_q8_1);
3357
  } else if (use_mul_mat_vec_q) {
3358
- constexpr bool convert_src1_to_q8_1 = true;
3359
  opt_for_reorder(&ctx, src0, src1, dst, mul_mat_algo::MMVQ);
3360
- ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_vec_q, convert_src1_to_q8_1);
 
 
 
 
 
3361
  } else if (use_mul_mat_q) {
3362
- constexpr bool convert_src1_to_q8_1 = true;
3363
- ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_q, convert_src1_to_q8_1);
3364
  } else {
3365
- constexpr bool convert_src1_to_q8_1 = false;
3366
- ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_sycl, convert_src1_to_q8_1);
3367
  }
3368
  }
3369
 
 
44
  #include "ggml-sycl/set_rows.hpp"
45
  #include "ggml-sycl/sycl_hw.hpp"
46
  #include "ggml-sycl/getrows.hpp"
47
+ #include "ggml-sycl/quantize.hpp"
48
  #include "ggml.h"
49
 
50
  static bool g_sycl_loaded = false;
 
1374
 
1375
 
1376
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1377
  static void mul_mat_p021_f16_f32(
1378
  const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst,
1379
  const int ncols_x, const int nrows_x, const int nchannels_x, const int nchannels_y,
 
1657
  o_ptr[cur_oh * ow + cur_ow] = res;
1658
  }
1659
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1660
 
1661
  static void ggml_mul_mat_p021_f16_f32_sycl(const void *vx, const float *y,
1662
  float *dst, const int ncols_x,
 
2233
  peer_access_enabled = enable_peer_access;
2234
  }
2235
 
2236
+ template <template <int> typename quantize_f>
2237
  static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
2238
  const ggml_tensor *src1, ggml_tensor *dst,
2239
+ ggml_sycl_op_mul_mat_t op) try {
 
2240
 
2241
  GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne);
2242
 
 
2331
  }
2332
  }
2333
 
2334
+ constexpr bool quantize_enabled = !std::is_same_v<quantize_f<QK8_1 / WARP_SIZE>,
2335
+ no_quantize_q8_1<QK8_1 / WARP_SIZE>>;
2336
  for (int i = 0; i < ggml_sycl_info().device_count; ++i) {
2337
  if ((!split && i != ctx.device) || dev[i].row_low == dev[i].row_high) {
2338
  continue;
 
2358
  dev[i].src1_ddf = dev[i].src1_ddf_alloc.alloc(ctx.pool(i), ggml_nelements(src1));
2359
  }
2360
 
2361
+ if constexpr(quantize_enabled) {
2362
  dev[i].src1_ddq = dev[i].src1_ddq_alloc.alloc(ctx.pool(i), nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs);
2363
 
2364
  if (src1_on_device && src1_is_contiguous) {
 
2365
  scope_op_debug_print scope_dbg_print(__func__, "/quantize_row_q8_1_sycl", dst,
2366
  /*num_src=*/2, " : converting src1 to Q8_1");
2367
+ try {
2368
+ quantize_row_q8_1_sycl<quantize_f>(dev[i].src1_ddf, dev[i].src1_ddq, ne10, nrows1, src1_padded_col_size, stream);
2369
+ } catch (sycl::exception const &exc) {
2370
+ std::cerr << "Quantize_row_q8_1_sycl error" << exc.what() << "Exception caught at file:" << __FILE__
2371
+ << ", line:" << __LINE__ << std::endl;
2372
+ std::exit(1);
2373
+ }
2374
  }
2375
  }
2376
 
 
2386
  // here an event is recorded that signals that the main device has finished calculating the input data
2387
  if (split && used_devices > 1) {
2388
  ggml_sycl_set_device(ctx.device);
 
 
 
 
 
2389
  SYCL_CHECK(CHECK_TRY_ERROR(
2390
  *src0_extra->events[ctx.device][0] =
2391
  ctx.stream()->ext_oneapi_submit_barrier()));
 
2409
 
2410
  // wait for main GPU data if necessary
2411
  if (split && (i != ctx.device || is != 0)) {
 
 
 
 
 
2412
  SYCL_CHECK(CHECK_TRY_ERROR(stream->ext_oneapi_submit_barrier(
2413
  {*src0_extra->events[ctx.device][0]})));
2414
  }
 
2434
  // copy src0, src1 to device if necessary
2435
  if (src1_is_contiguous) {
2436
  if (i != ctx.device) {
2437
+ if constexpr (quantize_enabled) {
2438
  char * src1_ddq_i_source = dev[ctx.device].src1_ddq + src1_ddq_i_offset;
2439
+ SYCL_CHECK(
2440
+ CHECK_TRY_ERROR(stream
2441
+ ->memcpy(src1_ddq_i, src1_ddq_i_source,
2442
+ src1_ncols * src1_padded_col_size * q8_1_ts / q8_1_bs)
2443
+ .wait()));
2444
  } else {
 
2445
  float * src1_ddf_i_source = (float *) src1_extra->data_device[ctx.device];
2446
+ src1_ddf_i_source += (i0 * ne11 + src1_col_0) * ne10;
2447
 
2448
+ SYCL_CHECK(
2449
+ CHECK_TRY_ERROR(dev2dev_memcpy(*stream, *main_stream, src1_ddf_i, src1_ddf_i_source,
2450
+ src1_ncols * ne10 * sizeof(float))));
2451
  }
2452
  }
 
 
 
2453
  } else {
2454
+ if (src1_on_device) {
2455
+ SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src1_ddf_i, src1, i03, i02, src1_col_0,
2456
+ src1_col_0 + src1_ncols, stream));
2457
+ } else {
2458
+ GGML_ABORT("src1 is non-contiguous and not on device");
2459
+ }
2460
 
2461
+ if constexpr (quantize_enabled) {
2462
+ scope_op_debug_print scope_dbg_print(__func__, "/quantize_row_q8_1_sycl", dst,
2463
+ /*num_src=*/2, " : converting src1 to Q8_1");
2464
+ try {
2465
+ quantize_row_q8_1_sycl<quantize_q8_1>(src1_ddf_i, src1_ddq_i, ne10, src1_ncols,
2466
+ src1_padded_col_size, stream);
2467
+ } catch (const sycl::exception & exc) {
2468
+ std::cerr << "Quantize_row_q8_1_sycl error" << exc.what()
2469
+ << "Exception caught at file:" << __FILE__ << ", line:" << __LINE__ << std::endl;
2470
+ std::exit(1);
2471
+ }
2472
+ }
2473
  }
2474
 
2475
  if (src1_col_0 == 0 && !src0_is_contiguous && i02 % i02_divisor == 0) {
 
2481
  // do the computation
2482
  SYCL_CHECK(CHECK_TRY_ERROR(op(ctx, src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i,
2483
  dev[i].row_low, dev[i].row_high, src1_ncols, src1_padded_col_size, stream)));
 
 
 
 
 
 
2484
 
2485
  // copy dst to host or other device if necessary
2486
  if (!dst_on_device) {
 
2511
 
2512
  // add event for the main device to wait on until other device is done
2513
  if (split && (i != ctx.device || is != 0)) {
 
 
 
 
 
 
2514
  SYCL_CHECK(CHECK_TRY_ERROR(
2515
  *src0_extra->events[i][is] =
2516
  stream->ext_oneapi_submit_barrier()));
 
3194
  // KQ + KQV multi-batch
3195
  ggml_sycl_mul_mat_batched_sycl(ctx, src0, src1, dst);
3196
  } else if (use_dequantize_mul_mat_vec) {
 
3197
  opt_for_reorder(&ctx, src0, src1, dst, mul_mat_algo::DMMV);
3198
+ ggml_sycl_op_mul_mat<no_quantize_q8_1>(ctx, src0, src1, dst, ggml_sycl_op_dequantize_mul_mat_vec);
3199
  } else if (use_mul_mat_vec_q) {
 
3200
  opt_for_reorder(&ctx, src0, src1, dst, mul_mat_algo::MMVQ);
3201
+ ggml_tensor_extra_gpu * extra = static_cast<ggml_tensor_extra_gpu *>(src0->extra);
3202
+ if (extra && extra->optimized_feature.reorder) {
3203
+ ggml_sycl_op_mul_mat<quantize_and_reorder_q8_1_soa>(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_vec_q);
3204
+ } else {
3205
+ ggml_sycl_op_mul_mat<quantize_q8_1>(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_vec_q);
3206
+ }
3207
  } else if (use_mul_mat_q) {
3208
+ ggml_sycl_op_mul_mat<quantize_q8_1>(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_q);
 
3209
  } else {
3210
+ ggml_sycl_op_mul_mat<no_quantize_q8_1>(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_sycl);
 
3211
  }
3212
  }
3213
 
ggml/src/ggml-sycl/quantize.hpp ADDED
@@ -0,0 +1,133 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ /***************************************************************************
2
+ *
3
+ * Copyright (C) 2025 Codeplay Software Ltd.
4
+ * Copyright (C) 2025 Intel Corporation
5
+ *
6
+ * MIT License
7
+ *
8
+ * Unless required by applicable law or agreed to in writing, software
9
+ * distributed under the License is distributed on an "AS IS" BASIS,
10
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
11
+ * See the License for the specific language governing permissions and
12
+ * limitations under the License.
13
+ *
14
+ * quantize.hpp
15
+ *
16
+ * Description:
17
+ * Sycl backend specific quantization functions
18
+ **************************************************************************/
19
+
20
+ #pragma once
21
+
22
+ #include <sycl/nd_item.hpp>
23
+
24
+ #include "ggml-sycl/dpct/helper.hpp"
25
+
26
+ template <int ElementsPerWI>
27
+ __dpct_inline__ static void quantize_q8_1_impl(const float * __restrict__ x,
28
+ sycl::vec<int8_t, ElementsPerWI> & quantized_values, float & d,
29
+ float & sum, const sycl::nd_item<1> & it) {
30
+ auto subgroup_id = it.get_group(0);
31
+ auto wi_id = it.get_local_id(0);
32
+
33
+ sycl::vec<float, ElementsPerWI> wi_f32_vals;
34
+
35
+ auto float_ptr_offset = subgroup_id * QK8_1 + ElementsPerWI * wi_id;
36
+ wi_f32_vals = *reinterpret_cast<const sycl::vec<float, ElementsPerWI> *>(x + float_ptr_offset);
37
+
38
+ float amax = 0.0f;
39
+
40
+ #pragma unroll(ElementsPerWI)
41
+ for (int i = 0; i < ElementsPerWI; i++) {
42
+ sum += wi_f32_vals[i];
43
+ amax = sycl::fmax(amax, sycl::fabs(wi_f32_vals[i]));
44
+ quantized_values[i] = 0;
45
+ }
46
+ sum = sycl::reduce_over_group(it.get_sub_group(), sum, sycl::plus<float>());
47
+ amax = sycl::reduce_over_group(it.get_sub_group(), amax, sycl::maximum<float>());
48
+ d = amax == 0 ? 1 : amax / 127;
49
+
50
+ #pragma unroll(ElementsPerWI)
51
+ for (int i = 0; i < ElementsPerWI; i++) {
52
+ quantized_values[i] = sycl::round(wi_f32_vals[i] / d);
53
+ }
54
+
55
+ d = amax == 0 ? 0 : d;
56
+ }
57
+
58
+ // No op to control codepath in ggml_sycl_op_mul_mat
59
+ template <int ElementsPerWI> struct no_quantize_q8_1 {
60
+ void operator()(const float *, void *, int, int, const sycl::nd_item<1> &) const {}
61
+ };
62
+
63
+ template <int ElementsPerWI> struct quantize_and_reorder_q8_1_soa {
64
+ __dpct_inline__ void operator()(const float * __restrict__ x, void * reordered_q8_tensor, const int kx,
65
+ const int kx_padded, const sycl::nd_item<1> & it) const {
66
+ /*
67
+ Quantizes and reorders the resultant q8 tensor in a per row fashion
68
+ Each sub-group calculates one quant block. i.e. QK8_1 quant values and the d and sum values
69
+ */
70
+ auto subgroup_id = it.get_group(0);
71
+ auto wi_id = it.get_local_id(0);
72
+
73
+ sycl::vec<int8_t, ElementsPerWI> quantized_values;
74
+ float d = 0.0f;
75
+ float sum = 0.0f;
76
+ quantize_q8_1_impl<ElementsPerWI>(x, quantized_values, d, sum, it);
77
+
78
+ const int num_blocks_per_row = kx / QK8_1;
79
+ auto row = subgroup_id / num_blocks_per_row;
80
+ auto col = subgroup_id % num_blocks_per_row;
81
+ auto row_offset = row * (kx_padded / QK8_1) * sizeof(block_q8_1);
82
+ auto col_offset = QK8_1 * col + wi_id * ElementsPerWI;
83
+
84
+ auto quant_ptr = (int8_t *) ((char *) reordered_q8_tensor + row_offset + col_offset);
85
+ *reinterpret_cast<sycl::vec<int8_t, ElementsPerWI> *>(quant_ptr) = quantized_values;
86
+
87
+ auto ds_ptr = (sycl::half2 *) ((char *) reordered_q8_tensor + row_offset + kx + col * sizeof(sycl::half2));
88
+ if (wi_id == 0) {
89
+ *ds_ptr = sycl::half2(sycl::half(d), sycl::half(sum));
90
+ }
91
+ }
92
+ };
93
+
94
+ template <int ElementsPerWI> struct quantize_q8_1 {
95
+ __dpct_inline__ void operator()(const float * __restrict__ x, void * q8_tensor, const int kx, const int kx_padded,
96
+ const sycl::nd_item<1> & it) const {
97
+ auto subgroup_id = it.get_group(0);
98
+ auto wi_id = it.get_local_id(0);
99
+
100
+ const int num_blocks_per_row = kx / QK8_1;
101
+ auto row = subgroup_id / num_blocks_per_row;
102
+ const int pitch = kx_padded / QK8_1;
103
+
104
+ sycl::vec<int8_t, ElementsPerWI> quantized_values;
105
+ float d = 0.0f;
106
+ float sum = 0.0f;
107
+ quantize_q8_1_impl<ElementsPerWI>(x, quantized_values, d, sum, it);
108
+
109
+ block_q8_1 * quant_ptr = (block_q8_1 *) q8_tensor;
110
+ auto block_id = subgroup_id % num_blocks_per_row + row * pitch;
111
+
112
+ int8_t * qs = &(quant_ptr[block_id].qs[wi_id * ElementsPerWI]);
113
+ *reinterpret_cast<sycl::vec<int8_t, ElementsPerWI> *>(qs) = quantized_values;
114
+ if (wi_id == 0) {
115
+ quant_ptr[block_id].ds = sycl::half2(sycl::half(d), sycl::half(sum));
116
+ }
117
+ }
118
+ };
119
+
120
+ template <template <int> typename quantize_f>
121
+ void quantize_row_q8_1_sycl(const float * x, void * vy, const int kx, const int ky, const int kx_padded,
122
+ dpct::queue_ptr stream) {
123
+ static_assert(QK8_1 % WARP_SIZE == 0);
124
+ auto local_range = std::size_t(WARP_SIZE);
125
+ auto num_quant_blocks = ky * (kx / QK8_1);
126
+ auto global_range = num_quant_blocks * local_range;
127
+ dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
128
+
129
+ stream->parallel_for(sycl::nd_range<1>({ global_range }, { local_range }),
130
+ [=](sycl::nd_item<1> it) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
131
+ quantize_f<QK8_1 / WARP_SIZE>()(x, vy, kx, kx_padded, it);
132
+ });
133
+ }