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[SYCL] Use batched mul_mat pathway (#5591)

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* Use batched mul_mat pathway

* rm extra line

* Explicitly state scaled data type

---------

Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>
This commit is contained in:
AidanBeltonS 2024-03-01 07:36:47 +00:00 committed by GitHub
parent 052051d8ae
commit 38d1521608
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1 changed files with 44 additions and 63 deletions
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89
ggml-sycl.cpp
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@ -12726,6 +12726,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1));
GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0);
@ -13270,11 +13271,11 @@ static void k_compute_batched_ptrs(const sycl::half *src0_as_f16,
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*nb12/2 + i13*nb13/2;
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;
}
static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0,
static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0,
const ggml_tensor *src1,
ggml_tensor *dst) try {
GGML_ASSERT(!ggml_is_transposed(src0));
@ -13282,18 +13283,10 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0,
GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
GGML_ASSERT(src0->type == GGML_TYPE_F16);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne);
GGML_TENSOR_BINARY_OP_LOCALS
GGML_TENSOR_LOCALS(int64_t, nb0, src0, nb);
GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);
GGML_TENSOR_LOCALS(int64_t, nb1, src1, nb);
const int64_t ne1 = ggml_nelements(src1);
const int64_t ne = ggml_nelements(dst);
const int64_t ne_dst = ggml_nelements(dst);
SYCL_CHECK(ggml_sycl_set_device(g_main_device));
dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0];
@ -13312,11 +13305,16 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0,
float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index];
// convert src1 to fp16
sycl_pool_alloc<sycl::half> src1_f16_alloc;
if (src1->type != GGML_TYPE_F16) {
const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type);
const int64_t ne_src1 = ggml_nelements(src1);
src1_f16_alloc.alloc(ne_src1);
GGML_ASSERT(to_fp16_sycl != nullptr);
sycl_pool_alloc<sycl::half> src1_as_f16(ne1);
to_fp16_sycl(src1_ddf, src1_as_f16.get(), ne1, main_stream);
to_fp16_sycl(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream);
}
sycl::half *src1_f16 = src1->type == GGML_TYPE_F16 ? (sycl::half *)src1_ddf
: src1_f16_alloc.get();
sycl_pool_alloc<sycl::half> dst_f16;
char * dst_t;
@ -13337,20 +13335,12 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0,
const void * alpha = &alpha_f16;
const void * beta = &beta_f16;
if (dst->op_params[0] == GGML_PREC_DEFAULT) {
dst_t = (char *) dst_f16.alloc(ne);
// TODO: Renable (dst->op_params[0] =! GGML_PREC_DEFAULT) pathway
// once oneMKL open source supports half, half, float, float: datatypes
dst_t = (char *) dst_f16.alloc(ne_dst);
nbd2 /= sizeof(float) / sizeof(sycl::half);
nbd3 /= sizeof(float) / sizeof(sycl::half);
} else {
dst_t = (char *) dst_ddf;
cu_compute_type = dpct::library_data_t::real_float;
cu_data_type = dpct::library_data_t::real_float;
alpha = &alpha_f32;
beta = &beta_f32;
}
GGML_ASSERT(ne12 % ne02 == 0);
GGML_ASSERT(ne13 % ne03 == 0);
@ -13386,10 +13376,10 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0,
*g_sycl_handles[g_main_device_index], oneapi::mkl::transpose::trans,
oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha,
(const char *)src0_as_f16, dpct::library_data_t::real_half,
nb01 / sizeof(sycl::half), src0->nb[2] / sizeof(sycl::half),
(const char *)src1_as_f16.get(), dpct::library_data_t::real_half,
nb11 / sizeof(float), src1->nb[2] / sizeof(float), beta,
(char *)dst_t, cu_data_type, ne01, dst->nb[2] / sizeof(float),
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)));
} else {
// use syclGemmBatchedEx
@ -13409,44 +13399,35 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0,
{sycl::aspect::fp16});
main_stream->submit([&](sycl::handler &cgh) {
const sycl::half *src1_as_f16_get_ct1 = src1_as_f16.get();
const void **ptrs_src_get_ct3 = ptrs_src.get();
void **ptrs_dst_get_ct4 = ptrs_dst.get();
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_as_f16_get_ct1,
dst_t, ptrs_src_get_ct3,
ptrs_dst_get_ct4, ne12, ne13, ne23,
nb02, nb03, nb12, nb13, nbd2, nbd3, r2,
r3, item_ct1);
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);
});
});
}
/*
DPCT1010:95: SYCL uses exceptions to report errors and does not use the
error codes. The call was replaced with 0. You need to rewrite this
code.
*/
SYCL_CHECK(0);
SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(
*g_sycl_handles[g_main_device_index], oneapi::mkl::transpose::trans,
oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha,
(const void **)(ptrs_src.get() + 0 * ne23),
dpct::library_data_t::real_half, nb01 / sizeof(sycl::half),
dpct::library_data_t::real_half, nb01 / nb00,
(const void **)(ptrs_src.get() + 1 * ne23),
dpct::library_data_t::real_half, nb11 / sizeof(float), beta,
dpct::library_data_t::real_half, nb11 / nb10, beta,
(void **)(ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23,
cu_compute_type)));
}
#endif
if (dst->op_params[0] == GGML_PREC_DEFAULT) {
const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
to_fp32_sycl(dst_f16.get(), dst_ddf, ne, main_stream);
}
to_fp32_sycl(dst_f16.get(), dst_ddf, ne_dst, main_stream);
}
catch (sycl::exception const &exc) {
std::cerr << exc.what() << "Exception caught at file:" << __FILE__
@ -13491,10 +13472,10 @@ static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
// KQV single-batch
// GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_nc\n");
ggml_sycl_mul_mat_vec_nc(src0, src1, dst);
} else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
} else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
// KQ + KQV multi-batch
// GGML_SYCL_DEBUG("ggml_sycl_mul_mat_mat_batched_sycl\n");
ggml_sycl_mul_mat_mat_batched_sycl(src0, src1, dst);
// GGML_SYCL_DEBUG("ggml_sycl_mul_mat_batched_sycl\n");
ggml_sycl_mul_mat_batched_sycl(src0, src1, dst);
} else if (src0->type == GGML_TYPE_F32) {
// GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat\n");
ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false);