lhez commited on
Commit
5e203ec
·
1 Parent(s): e62ef85

opencl: add `set_rows` for `f16` and `f32` (llama/14547)

Browse files

* opencl: add `set_rows` for `f16` and `f32`

* opencl: better choose workgroup size for `set_rows`

ggml/src/ggml-opencl/CMakeLists.txt CHANGED
@@ -88,6 +88,7 @@ set(GGML_OPENCL_KERNELS
88
  rms_norm
89
  rope
90
  scale
 
91
  sigmoid
92
  silu
93
  softmax_4_f32
 
88
  rms_norm
89
  rope
90
  scale
91
+ set_rows
92
  sigmoid
93
  silu
94
  softmax_4_f32
ggml/src/ggml-opencl/ggml-opencl.cpp CHANGED
@@ -351,6 +351,7 @@ struct ggml_backend_opencl_context {
351
  cl_program program_gemv_noshuffle_general;
352
  cl_program program_gemv_noshuffle;
353
  cl_program program_get_rows;
 
354
  cl_program program_glu;
355
  cl_program program_im2col_f16;
356
  cl_program program_im2col_f32;
@@ -412,6 +413,7 @@ struct ggml_backend_opencl_context {
412
  cl_kernel kernel_soft_max, kernel_soft_max_4;
413
  cl_kernel kernel_soft_max_f16, kernel_soft_max_4_f16;
414
  cl_kernel kernel_get_rows_f32, kernel_get_rows_f16, kernel_get_rows_q4_0;
 
415
  cl_kernel kernel_rope_norm_f32, kernel_rope_norm_f16, kernel_rope_neox_f32, kernel_rope_neox_f16;
416
  cl_kernel kernel_rope_multi_f32, kernel_rope_multi_f16, kernel_rope_vision_f32, kernel_rope_vision_f16;
417
  cl_kernel kernel_cpy_f16_f16, kernel_cpy_f16_f32, kernel_cpy_f32_f16, kernel_cpy_f32_f32;
@@ -529,6 +531,16 @@ struct ggml_backend_opencl_context {
529
  fclose(ftrace);
530
  }
531
 
 
 
 
 
 
 
 
 
 
 
532
  void enqueue_ndrange_kernel(cl_kernel kernel, cl_uint work_dim, size_t *global_work_size, size_t *local_work_size, const ggml_tensor * tensor) {
533
  #ifdef GGML_OPENCL_PROFILING
534
  cl_event evt;
@@ -1431,6 +1443,23 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
1431
  }
1432
  }
1433
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1434
  // mul_mv_id_q4_0_f32_8x_flat
1435
  {
1436
  #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -2233,8 +2262,17 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
2233
  {
2234
  // TODO: add support
2235
  // ref: https://github.com/ggml-org/llama.cpp/pull/14274
2236
- return false;
2237
- } break;
 
 
 
 
 
 
 
 
 
2238
  case GGML_OP_CPY:
2239
  case GGML_OP_DUP:
2240
  case GGML_OP_CONT:
@@ -3374,6 +3412,111 @@ static void ggml_cl_get_rows(ggml_backend_t backend, const ggml_tensor * src0, c
3374
  backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
3375
  }
3376
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3377
  static void ggml_cl_add(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
3378
  GGML_ASSERT(src0);
3379
  GGML_ASSERT(src0->extra);
@@ -6388,6 +6531,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
6388
  }
6389
  func = ggml_cl_get_rows;
6390
  break;
 
 
 
 
 
 
6391
  case GGML_OP_CPY:
6392
  if (!any_on_device) {
6393
  return false;
 
351
  cl_program program_gemv_noshuffle_general;
352
  cl_program program_gemv_noshuffle;
353
  cl_program program_get_rows;
354
+ cl_program program_set_rows;
355
  cl_program program_glu;
356
  cl_program program_im2col_f16;
357
  cl_program program_im2col_f32;
 
413
  cl_kernel kernel_soft_max, kernel_soft_max_4;
414
  cl_kernel kernel_soft_max_f16, kernel_soft_max_4_f16;
415
  cl_kernel kernel_get_rows_f32, kernel_get_rows_f16, kernel_get_rows_q4_0;
416
+ cl_kernel kernel_set_rows_f32, kernel_set_rows_f16;
417
  cl_kernel kernel_rope_norm_f32, kernel_rope_norm_f16, kernel_rope_neox_f32, kernel_rope_neox_f16;
418
  cl_kernel kernel_rope_multi_f32, kernel_rope_multi_f16, kernel_rope_vision_f32, kernel_rope_vision_f16;
419
  cl_kernel kernel_cpy_f16_f16, kernel_cpy_f16_f32, kernel_cpy_f32_f16, kernel_cpy_f32_f32;
 
531
  fclose(ftrace);
532
  }
533
 
534
+ size_t get_kernel_workgroup_size(cl_kernel kernel) const {
535
+ size_t workgroup_size = 0;
536
+ size_t ret_size = 0;
537
+ CL_CHECK(
538
+ clGetKernelWorkGroupInfo(kernel, device, CL_KERNEL_WORK_GROUP_SIZE,
539
+ sizeof(size_t), &workgroup_size, &ret_size));
540
+ GGML_ASSERT(sizeof(size_t) == ret_size);
541
+ return workgroup_size;
542
+ }
543
+
544
  void enqueue_ndrange_kernel(cl_kernel kernel, cl_uint work_dim, size_t *global_work_size, size_t *local_work_size, const ggml_tensor * tensor) {
545
  #ifdef GGML_OPENCL_PROFILING
546
  cl_event evt;
 
1443
  }
1444
  }
1445
 
1446
+ // set_rows
1447
+ {
1448
+ #ifdef GGML_OPENCL_EMBED_KERNELS
1449
+ const std::string kernel_src {
1450
+ #include "set_rows.cl.h"
1451
+ };
1452
+ #else
1453
+ const std::string kernel_src = read_file("set_rows.cl");
1454
+ #endif
1455
+ backend_ctx->program_set_rows =
1456
+ build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
1457
+
1458
+ CL_CHECK((backend_ctx->kernel_set_rows_f32 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_f32", &err), err));
1459
+ CL_CHECK((backend_ctx->kernel_set_rows_f16 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_f16", &err), err));
1460
+ GGML_LOG_CONT(".");
1461
+ }
1462
+
1463
  // mul_mv_id_q4_0_f32_8x_flat
1464
  {
1465
  #ifdef GGML_OPENCL_EMBED_KERNELS
 
2262
  {
2263
  // TODO: add support
2264
  // ref: https://github.com/ggml-org/llama.cpp/pull/14274
2265
+ if (op->src[0]->type != GGML_TYPE_F32) {
2266
+ return false;
2267
+ }
2268
+ switch (op->type) {
2269
+ case GGML_TYPE_F16:
2270
+ case GGML_TYPE_F32:
2271
+ return true;
2272
+ default:
2273
+ return false;
2274
+ }
2275
+ }
2276
  case GGML_OP_CPY:
2277
  case GGML_OP_DUP:
2278
  case GGML_OP_CONT:
 
3412
  backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
3413
  }
3414
 
3415
+ static void ggml_cl_set_rows(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
3416
+ GGML_ASSERT(src0);
3417
+ GGML_ASSERT(src0->extra);
3418
+ GGML_ASSERT(src1);
3419
+ GGML_ASSERT(src1->extra);
3420
+ GGML_ASSERT(dst);
3421
+ GGML_ASSERT(dst->extra);
3422
+
3423
+ // ne0 = ne00
3424
+ // ne2 = ne02
3425
+ // ne3 = ne03
3426
+
3427
+ const int ne01 = src0->ne[1];
3428
+ const int ne02 = src0->ne[2];
3429
+ const int ne03 = src0->ne[3];
3430
+
3431
+ const cl_ulong nb01 = src0->nb[1];
3432
+ const cl_ulong nb02 = src0->nb[2];
3433
+ const cl_ulong nb03 = src0->nb[3];
3434
+
3435
+ const int ne11 = src1->ne[1];
3436
+ const int ne12 = src1->ne[2];
3437
+
3438
+ const cl_ulong nb10 = src1->nb[0];
3439
+ const cl_ulong nb11 = src1->nb[1];
3440
+ const cl_ulong nb12 = src1->nb[2];
3441
+
3442
+ const int ne0 = dst->ne[0];
3443
+
3444
+ const cl_ulong nb1 = dst->nb[1];
3445
+ const cl_ulong nb2 = dst->nb[2];
3446
+ const cl_ulong nb3 = dst->nb[3];
3447
+
3448
+ const int nblk0 = ne0/ggml_blck_size(dst->type);
3449
+
3450
+ ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
3451
+
3452
+ ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
3453
+ ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
3454
+ ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
3455
+
3456
+ cl_ulong offset0 = extra0->offset + src0->view_offs;
3457
+ cl_ulong offset1 = extra1->offset + src1->view_offs;
3458
+ cl_ulong offsetd = extrad->offset + dst->view_offs;
3459
+
3460
+ cl_kernel kernel;
3461
+
3462
+ switch (dst->type) {
3463
+ case GGML_TYPE_F32:
3464
+ kernel = backend_ctx->kernel_set_rows_f32;
3465
+ break;
3466
+ case GGML_TYPE_F16:
3467
+ kernel = backend_ctx->kernel_set_rows_f16;
3468
+ break;
3469
+ default:
3470
+ GGML_ABORT("not implemented");
3471
+ }
3472
+
3473
+ CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device));
3474
+ CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
3475
+ CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1->data_device));
3476
+ CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
3477
+ CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad->data_device));
3478
+ CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
3479
+ CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne01));
3480
+ CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong), &nb01));
3481
+ CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb02));
3482
+ CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb03));
3483
+ CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &ne11));
3484
+ CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne12));
3485
+ CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb10));
3486
+ CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb11));
3487
+ CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb12));
3488
+ CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &nblk0));
3489
+ CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb1));
3490
+ CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb2));
3491
+ CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb3));
3492
+
3493
+ int nth0 = 64;
3494
+ if (backend_ctx->gpu_family == INTEL) {
3495
+ nth0 = 32;
3496
+ } else if (backend_ctx->gpu_family == ADRENO) {
3497
+ nth0 = 64;
3498
+ }
3499
+
3500
+ int max_workgroup_size = backend_ctx->get_kernel_workgroup_size(kernel);
3501
+ while (nth0 < nblk0 && nth0 < max_workgroup_size) {
3502
+ nth0 *= 2;
3503
+ }
3504
+
3505
+ int rows_per_workgroup = 1;
3506
+ if (nth0 > nblk0) {
3507
+ rows_per_workgroup = nth0 / nblk0;
3508
+ nth0 = nblk0;
3509
+ }
3510
+
3511
+ size_t global_work_size[] = {
3512
+ (size_t)(ne01 + rows_per_workgroup - 1)/rows_per_workgroup*nth0,
3513
+ (size_t)ne02*rows_per_workgroup,
3514
+ (size_t)ne03};
3515
+ size_t local_work_size[] = {(size_t)nth0, (size_t)rows_per_workgroup, 1};
3516
+
3517
+ backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
3518
+ }
3519
+
3520
  static void ggml_cl_add(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
3521
  GGML_ASSERT(src0);
3522
  GGML_ASSERT(src0->extra);
 
6531
  }
6532
  func = ggml_cl_get_rows;
6533
  break;
6534
+ case GGML_OP_SET_ROWS:
6535
+ if (!any_on_device) {
6536
+ return false;
6537
+ }
6538
+ func = ggml_cl_set_rows;
6539
+ break;
6540
  case GGML_OP_CPY:
6541
  if (!any_on_device) {
6542
  return false;
ggml/src/ggml-opencl/kernels/set_rows.cl ADDED
@@ -0,0 +1,95 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #pragma OPENCL EXTENSION cl_khr_fp16 : enable
2
+
3
+ kernel void kernel_set_rows_f32(
4
+ global char * src0,
5
+ ulong offset0,
6
+ global char * src1,
7
+ ulong offset1,
8
+ global char * dst,
9
+ ulong offsetd,
10
+ int ne01,
11
+ ulong nb01,
12
+ ulong nb02,
13
+ ulong nb03,
14
+ int ne11,
15
+ int ne12,
16
+ ulong nb10,
17
+ ulong nb11,
18
+ ulong nb12,
19
+ int nblk0,
20
+ ulong nb1,
21
+ ulong nb2,
22
+ ulong nb3
23
+ ) {
24
+ src0 = src0 + offset0;
25
+ src1 = src1 + offset1;
26
+ dst = dst + offsetd;
27
+
28
+ int i03 = get_group_id(2);
29
+ int i02 = get_group_id(1);
30
+ int i01 = get_group_id(0)*get_local_size(1) + get_local_id(1);
31
+
32
+ if (i01 >= ne01) {
33
+ return;
34
+ }
35
+
36
+ int i12 = i03%ne12;
37
+ int i11 = i02%ne11;
38
+
39
+ int i10 = i01;
40
+ long i1 = ((global long *)(src1 + i10*nb10 + i11*nb11 + i12*nb12))[0];
41
+
42
+ global float * dst_row = (global float *) (dst + i1*nb1 + i02*nb2 + i03*nb3);
43
+ global float * src_row = (global float *) (src0 + i01*nb01 + i02*nb02 + i03*nb03);
44
+
45
+ for (int ind = get_local_id(0); ind < nblk0; ind += get_local_size(0)) {
46
+ dst_row[ind] = (float)src_row[ind];
47
+ }
48
+ }
49
+
50
+ kernel void kernel_set_rows_f16(
51
+ global char * src0,
52
+ ulong offset0,
53
+ global char * src1,
54
+ ulong offset1,
55
+ global char * dst,
56
+ ulong offsetd,
57
+ int ne01,
58
+ ulong nb01,
59
+ ulong nb02,
60
+ ulong nb03,
61
+ int ne11,
62
+ int ne12,
63
+ ulong nb10,
64
+ ulong nb11,
65
+ ulong nb12,
66
+ int nblk0,
67
+ ulong nb1,
68
+ ulong nb2,
69
+ ulong nb3
70
+ ) {
71
+ src0 = src0 + offset0;
72
+ src1 = src1 + offset1;
73
+ dst = dst + offsetd;
74
+
75
+ int i03 = get_group_id(2);
76
+ int i02 = get_group_id(1);
77
+ int i01 = get_group_id(0)*get_local_size(1) + get_local_id(1);
78
+
79
+ if (i01 >= ne01) {
80
+ return;
81
+ }
82
+
83
+ int i12 = i03%ne12;
84
+ int i11 = i02%ne11;
85
+
86
+ int i10 = i01;
87
+ long i1 = ((global long *)(src1 + i10*nb10 + i11*nb11 + i12*nb12))[0];
88
+
89
+ global half * dst_row = (global half *) (dst + i1*nb1 + i02*nb2 + i03*nb3);
90
+ global float * src_row = (global float *) (src0 + i01*nb01 + i02*nb02 + i03*nb03);
91
+
92
+ for (int ind = get_local_id(0); ind < nblk0; ind += get_local_size(0)) {
93
+ dst_row[ind] = src_row[ind];
94
+ }
95
+ }