ExternalVendorCode/whisper.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/whisper.cpp.git synced 2025-05-28 21:14:11 +00:00
Code Issues Actions 4 Packages Projects Releases Wiki Activity

sycl: addressing non-contiguous src1 mul_mats (nc and batched) (llama/13343)

Browse Source 
* sycl: fixed non-contiguous src1 mul_mats (nc and batched)

* Fixed wrong static_cast inside kernel
This commit is contained in:
Alberto Cabrera Pérez 2025-05-08 10:08:01 +01:00 committed by Georgi Gerganov
parent fbad8058c4
commit e8a7f1b7bb
4 changed files with 138 additions and 125 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

17
ggml/src/ggml-sycl/common.hpp
View File

@ -114,17 +114,12 @@ static void crash() {
GGML_ABORT("SYCL error");
}
#define SYCL_CHECK(err) \
do { \
auto err_ = (err); \
if (err_ != 0) \
ggml_sycl_error( \
#err, \
__func__, \
__FILE__, \
__LINE__, \
"Meet error in this line code!"); \
} while (0)
#define SYCL_CHECK(err) \
do { \
auto err_ = (err); \
if (err_ != 0) \
ggml_sycl_error(#err, __func__, __FILE__, __LINE__, "Exception caught in this line of code."); \
} while (0)
#if DPCT_COMPAT_RT_VERSION >= 11100
#define GGML_SYCL_ASSUME(x) __builtin_assume(x)

66
ggml/src/ggml-sycl/convert.cpp
View File

@ -437,41 +437,52 @@ static void dequantize_row_iq4_nl_sycl(const void *vx, dst_t *y, const int64_t k
}
template <typename src_t, typename dst_t>
static void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k,
const sycl::nd_item<3> &item_ct1) {
static void convert_unary_nc(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t ne00, const int64_t ne01,
const int64_t ne02, const int64_t s01, const int64_t s02, const int64_t s03,
const sycl::nd_item<3> & item_ct1) {
const int64_t work_group_size = item_ct1.get_local_range(2);
const int64_t global_id = item_ct1.get_local_id(2) + work_group_size * item_ct1.get_group(2);
const int64_t global_id = item_ct1.get_local_id(2) + work_group_size * item_ct1.get_group(2);
const int64_t i01 = item_ct1.get_group(1);
const int64_t i02 = item_ct1.get_group(0) % ne02;
const int64_t i03 = item_ct1.get_group(0) / ne02;
// make each work-item deal with more elements since sycl global range can not exceed max int
const src_t * x = (const src_t *) vx;
for (int64_t i = global_id; i < k; i += work_group_size * item_ct1.get_group_range(2)) {
y[i] = x[i];
const src_t * x = static_cast<const src_t *>(vx);
const int64_t ix = i03 * s03 + i02 * s02 + i01 * s01;
const int64_t iy = ((i03 * ne02 + i02) * ne01 + i01) * ne00;
#pragma unroll
for (int64_t i00 = global_id; i00 < ne00; i00 += work_group_size * item_ct1.get_group_range(2)) {
y[iy + i00] = static_cast<dst_t>(x[ix + i00]);
}
}
template <typename src_t, typename dst_t>
static void convert_unary_sycl(const void *__restrict__ vx,
dst_t *__restrict__ y, const int64_t k,
dpct::queue_ptr stream) {
const int64_t num_blocks = (k + SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / SYCL_DEQUANTIZE_BLOCK_SIZE;
static void convert_unary_nc_sycl(const void * __restrict__ vx, dst_t * __restrict__ y,
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
const int64_t s01, const int64_t s02, const int64_t s03, dpct::queue_ptr queue) {
dpct::has_capability_or_fail(queue->get_device(), { sycl::aspect::fp16 });
sycl::range<3> global_size(ne02 * ne03, ne01, ceil_div(ne00, SYCL_DEQUANTIZE_BLOCK_SIZE));
// decrease global range when it exceeds the max int
int64_t local_size = downsample_sycl_global_range(num_blocks, SYCL_DEQUANTIZE_BLOCK_SIZE);
sycl::range<3> block_nums(1, 1, num_blocks);
sycl::range<3> local_range(1, 1, local_size);
{
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
// TODO: Downsample logic is separated from the kernel, a rewrite is desirable
int64_t downsized_workgroup = downsample_sycl_global_range(global_size[0], SYCL_DEQUANTIZE_BLOCK_SIZE);
sycl::range<3> workgroup_size(1, 1, downsized_workgroup);
stream->parallel_for(
sycl::nd_range<3>(block_nums * local_range, local_range),
[=](sycl::nd_item<3> item_ct1) {
convert_unary<src_t>(vx, y, k, item_ct1);
});
}
queue->parallel_for(sycl::nd_range<3>(global_size * workgroup_size, workgroup_size), [=](sycl::nd_item<3> item_ct1) {
convert_unary_nc<src_t>(vx, y, ne00, ne01, ne02, s01, s02, s03, item_ct1);
});
}
to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor *dst) {
template <typename src_t, typename dst_t>
static void convert_unary_sycl(const void * vx, dst_t * y, const int64_t k, dpct::queue_ptr queue) {
convert_unary_nc_sycl<src_t>(vx, y, k, 1, 1, 1, k, k, k, queue);
}
to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) {
switch (type) {
case GGML_TYPE_Q4_0:
if (dst->src[0]->extra &&
@ -574,3 +585,12 @@ to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor *dst) {
return nullptr;
}
}
to_fp16_nc_sycl_t get_to_fp16_nc_sycl(ggml_type type) {
switch (type) {
case GGML_TYPE_F32:
return convert_unary_nc_sycl<float>;
default:
return nullptr;
}
}

21
ggml/src/ggml-sycl/convert.hpp
View File

@ -1,6 +1,6 @@
//
// MIT license
// Copyright (C) 2024 Intel Corporation
// Copyright (C) 2025 Intel Corporation
// SPDX-License-Identifier: MIT
//
@ -16,12 +16,19 @@
#include "common.hpp"
template <typename T>
using to_t_sycl_t = void (*)(const void *__restrict__ x, T *__restrict__ y,
int64_t k, dpct::queue_ptr stream);
typedef to_t_sycl_t<float> to_fp32_sycl_t;
using to_t_sycl_t = void (*)(const void * __restrict__ x, T * __restrict__ y, int64_t k, dpct::queue_ptr stream);
typedef to_t_sycl_t<float> to_fp32_sycl_t;
typedef to_t_sycl_t<sycl::half> to_fp16_sycl_t;
to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor *dst);
to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor *dst);
to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst);
to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor * dst);
#endif // GGML_SYCL_CONVERT_HPP
// Nc = Non-contiguous
template <typename T>
using to_t_nc_sycl_t = void (*)(const void * x, T * y, int64_t ne00, int64_t ne01, int64_t ne02, int64_t ne03,
int64_t s01, int64_t s02, int64_t s03, dpct::queue_ptr queue);
typedef to_t_nc_sycl_t<sycl::half> to_fp16_nc_sycl_t;
to_fp16_nc_sycl_t get_to_fp16_nc_sycl(ggml_type type);
#endif // GGML_SYCL_CONVERT_HPP

159
ggml/src/ggml-sycl/ggml-sycl.cpp
View File

@ -2694,35 +2694,31 @@ catch (sycl::exception const &exc) {
std::exit(1);
}
static void k_compute_batched_ptrs(const sycl::half *src0_as_f16,
const sycl::half *src1_as_f16, char *dst,
const void **ptrs_src, void **ptrs_dst,
int64_t ne12, int64_t ne13, int64_t ne23,
size_t nb02, size_t nb03, size_t nb12,
size_t nb13, size_t nbd2, size_t nbd3,
int64_t r2, int64_t r3,
const sycl::nd_item<3> &item_ct1) {
int64_t i13 = item_ct1.get_group(2) * item_ct1.get_local_range(2) +
item_ct1.get_local_id(2);
int64_t i12 = item_ct1.get_group(1) * item_ct1.get_local_range(1) +
item_ct1.get_local_id(1);
static void k_compute_batched_ptrs(const sycl::half * src0_as_f16, const sycl::half * src1_as_f16, char * dst,
const void ** ptrs_src, void ** ptrs_dst, int64_t ne12, int64_t ne13, int64_t ne23,
size_t nb02, size_t nb03, size_t nb12, size_t nb13, size_t nbd2, size_t nbd3,
int64_t r2, int64_t r3, const sycl::nd_item<3> & item_ct1) {
const int64_t i13 = item_ct1.get_group(2) * item_ct1.get_local_range(2) + item_ct1.get_local_id(2);
const int64_t i12 = item_ct1.get_group(1) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1);
if (i13 >= ne13 || i12 >= ne12) {
return;
}
int64_t i03 = i13 / r3;
int64_t i02 = i12 / r2;
const int64_t i03 = i13 / r3;
const int64_t i02 = i12 / r2;
ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03;
ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12 + i13*nb13;
ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3;
const uint8_t * src0_bytes = reinterpret_cast<const uint8_t *>(src0_as_f16);
const uint8_t * src1_bytes = reinterpret_cast<const uint8_t *>(src1_as_f16);
uint8_t * dst_bytes = reinterpret_cast<uint8_t *>(dst);
ptrs_src[0 * ne23 + i12 + i13 * ne12] = src0_bytes + i02 * nb02 + i03 * nb03;
ptrs_src[1 * ne23 + i12 + i13 * ne12] = src1_bytes + i12 * nb12 + i13 * nb13;
ptrs_dst[0 * ne23 + i12 + i13 * ne12] = dst_bytes + i12 * nbd2 + i13 * nbd3;
}
static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx,
const ggml_tensor *src0,
const ggml_tensor *src1,
ggml_tensor *dst) try {
static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, const ggml_tensor * src0,
const ggml_tensor * src1, ggml_tensor * dst) try {
GGML_ASSERT(!ggml_is_transposed(src0));
GGML_ASSERT(!ggml_is_transposed(src1));
GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer));
@ -2730,102 +2726,100 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx,
GGML_TENSOR_BINARY_OP_LOCALS
// TODO: see https://github.com/ggml-org/llama.cpp/pull/13155
// Batched mul_mat requires a rewrite to support both oneDNN and non-contiguous dst
GGML_ASSERT(ggml_is_contiguous(dst));
SYCL_CHECK(ggml_sycl_set_device(ctx.device));
queue_ptr main_stream = ctx.stream();;
queue_ptr queue = ctx.stream();
void * src0_ddq = src0->data;
sycl::half *src0_as_f16 = (sycl::half *)src0_ddq;
float * src1_ddf = (float *) src1->data;
float * dst_ddf = (float *) dst->data;
dpct::has_capability_or_fail(queue->get_device(), { sycl::aspect::fp16 });
const sycl::half * src0_f16 = static_cast<const sycl::half *>(src0->data);
float * dst_ddf = static_cast<float *>(dst->data);
const sycl::half * src1_f16 = static_cast<const sycl::half *>(src1->data);
const size_t type_size_src1 = ggml_type_size(src1->type);
GGML_ASSERT(nb10 == type_size_src1);
// SRC1 strides
int64_t s11 = nb11 / type_size_src1;
int64_t s12 = nb12 / type_size_src1;
int64_t s13 = nb13 / type_size_src1;
ggml_sycl_pool_alloc<sycl::half> src1_f16_alloc(ctx.pool());
// convert src1 to fp16
ggml_sycl_pool_alloc<sycl::half> src1_f16_alloc(ctx.pool());
if (src1->type != GGML_TYPE_F16) {
const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type, dst);
const to_fp16_nc_sycl_t to_fp16_nc_sycl = get_to_fp16_nc_sycl(src1->type);
GGML_ASSERT(to_fp16_nc_sycl != nullptr);
const int64_t ne_src1 = ggml_nelements(src1);
src1_f16_alloc.alloc(ne_src1);
GGML_ASSERT(to_fp16_sycl != nullptr);
to_fp16_sycl(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream);
to_fp16_nc_sycl(src1_f16, src1_f16_alloc.get(), ne10, ne11, ne12, ne13, s11, s12, s13, queue);
src1_f16 = src1_f16_alloc.get();
s11 = ne10;
s12 = ne11 * s11;
s13 = ne12 * s12;
}
sycl::half *src1_f16 = src1->type == GGML_TYPE_F16 ? (sycl::half *)src1_ddf
: src1_f16_alloc.get();
char * dst_t;
ggml_sycl_pool_alloc<sycl::half> dst_f16(ctx.pool());
char * dst_t = reinterpret_cast<char *>(dst_ddf);
dpct::library_data_t cu_compute_type = dpct::library_data_t::real_float;
dpct::library_data_t cu_data_type = dpct::library_data_t::real_float;
dpct::library_data_t mkl_compute_type = dpct::library_data_t::real_float;
dpct::library_data_t mkl_data_type = dpct::library_data_t::real_float;
// dst strides
size_t nbd2 = dst->nb[2];
size_t nbd3 = dst->nb[3];
const float alpha_f32 = 1.0f;
const float beta_f32 = 0.0f;
const float beta_f32 = 0.0f;
const void * alpha = &alpha_f32;
const void * beta = &beta_f32;
dst_t = (char *) dst_ddf;
GGML_ASSERT(ne12 % ne02 == 0);
GGML_ASSERT(ne13 % ne03 == 0);
// broadcast factors
const int64_t r2 = ne12/ne02;
const int64_t r3 = ne13/ne03;
const int64_t r2 = ne12 / ne02;
const int64_t r3 = ne13 / ne03;
if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) {
// there is no broadcast and src0, src1 are contiguous across dims 2, 3
SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(
*main_stream, oneapi::math::transpose::trans, oneapi::math::transpose::nontrans, ne01, ne11, ne10, alpha,
(const char *) src0_as_f16, dpct::library_data_t::real_half, nb01 / nb00, nb02 / nb00,
(const char *) src1_f16, dpct::library_data_t::real_half, nb11 / nb10, nb12 / nb10, beta, (char *) dst_t,
cu_data_type, ne01, nb2 / nb0, ne12 * ne13, cu_compute_type)));
SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(*queue, oneapi::math::transpose::trans,
oneapi::math::transpose::nontrans, ne01, ne11, ne10, alpha,
src0_f16, dpct::library_data_t::real_half, nb01 / nb00, nb02 / nb00,
src1_f16, dpct::library_data_t::real_half, s11, s12, beta, dst_t,
mkl_data_type, ne0, ne1 * ne0, ne12 * ne13, mkl_compute_type)));
} else {
const int ne23 = ne12*ne13;
const int ne23 = ne12 * ne13;
ggml_sycl_pool_alloc<const void *> ptrs_src(ctx.pool(), 2*ne23);
ggml_sycl_pool_alloc< void *> ptrs_dst(ctx.pool(), 1*ne23);
ggml_sycl_pool_alloc<const void *> ptrs_src(ctx.pool(), 2 * ne23);
ggml_sycl_pool_alloc<void *> ptrs_dst(ctx.pool(), 1 * ne23);
ggml_sycl_pool_alloc<matrix_info_t<float>> matrix_info(ctx.host_pool(), 1);
sycl::range<3> block_dims(1, ne12, ne13);
/*
DPCT1049:47: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
{
dpct::has_capability_or_fail(main_stream->get_device(),
{sycl::aspect::fp16});
main_stream->submit([&](sycl::handler &cgh) {
const void **ptrs_src_get = ptrs_src.get();
void **ptrs_dst_get = ptrs_dst.get();
size_t nb12_scaled = src1->type == GGML_TYPE_F16 ? nb12 : nb12 / 2;
size_t nb13_scaled = src1->type == GGML_TYPE_F16 ? nb13 : nb13 / 2;
cgh.parallel_for(sycl::nd_range<3>(block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
k_compute_batched_ptrs(
src0_as_f16, src1_f16,
dst_t, ptrs_src_get,
ptrs_dst_get, ne12, ne13, ne23,
nb02, nb03, nb12_scaled, nb13_scaled,
nbd2, nbd3, r2, r3, item_ct1);
});
queue->submit([&](sycl::handler & cgh) {
const void ** ptrs_src_get = ptrs_src.get();
void ** ptrs_dst_get = ptrs_dst.get();
size_t nb12_scaled = src1->type == GGML_TYPE_F16 ? nb12 : s12 * sizeof(sycl::half);
size_t nb13_scaled = src1->type == GGML_TYPE_F16 ? nb13 : s13 * sizeof(sycl::half);
cgh.parallel_for(sycl::nd_range<3>(block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
k_compute_batched_ptrs(src0_f16, src1_f16, dst_t, ptrs_src_get, ptrs_dst_get, ne12, ne13, ne23, nb02,
nb03, nb12_scaled, nb13_scaled, nbd2, nbd3, r2, r3, item_ct1);
});
}
});
SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(
*main_stream, oneapi::math::transpose::trans, oneapi::math::transpose::nontrans, ne01, ne11, ne10, alpha,
*queue, oneapi::math::transpose::trans, oneapi::math::transpose::nontrans, ne01, ne11, ne10, alpha,
(const void **) (ptrs_src.get() + 0 * ne23), dpct::library_data_t::real_half, nb01 / nb00,
(const void **) (ptrs_src.get() + 1 * ne23), dpct::library_data_t::real_half, nb11 / nb10, beta,
(void **) (ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23, cu_compute_type, matrix_info.get())));
(const void **) (ptrs_src.get() + 1 * ne23), dpct::library_data_t::real_half, s11, beta,
(void **) (ptrs_dst.get() + 0 * ne23), mkl_data_type, ne0, ne23, mkl_compute_type, matrix_info.get())));
}
}
catch (sycl::exception const &exc) {
std::cerr << exc.what() << "Exception caught at file:" << __FILE__
<< ", line:" << __LINE__ << std::endl;
std::exit(1);
} catch (const sycl::exception & exc) {
std::cerr << exc.what() << "Exception caught at file:" << __FILE__ << ", line:" << __LINE__ << std::endl;
std::exit(1);
}
inline bool ggml_sycl_supports_mmq(enum ggml_type type) {
@ -2966,7 +2960,7 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor
// The kernel from the if path is faster for that specific case, but does not support all mul mats.
ggml_sycl_mul_mat_batched_sycl(ctx, src0, src1, dst);
}
} else if (!split && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
} else if (!split && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
// KQV single-batch
ggml_sycl_mul_mat_vec_nc(ctx, src0, src1, dst);
} else if (!split && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
@ -3873,9 +3867,6 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
if (a->ne[3] != b->ne[3]) {
return false;
}
if (!ggml_is_contiguous(b)) {
return false;
}
ggml_type a_type = a->type;
if (a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ4_XS ||
a_type == GGML_TYPE_IQ3_XXS || a_type == GGML_TYPE_IQ3_S ||