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[SYCL] Align GEMM dispatch (#7566)

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* align GEMM dispatch
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Meng, Hengyu 2024-05-29 07:00:24 +08:00 committed by GitHub
parent 02c1ecad07
commit b864b50ce5
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GPG Key ID: B5690EEEBB952194
3 changed files with 61 additions and 68 deletions
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4
CMakeLists.txt
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@ -628,6 +628,10 @@ if (LLAMA_SYCL)
add_compile_definitions(GGML_SYCL_F16) add_compile_definitions(GGML_SYCL_F16)
endif() endif()
if (LLAMA_CUDA_FORCE_MMQ)
add_compile_definitions(GGML_SYCL_FORCE_MMQ)
endif()
add_compile_options(-I./) #include DPCT add_compile_options(-I./) #include DPCT
add_compile_options(-I/${SYCL_INCLUDE_DIR}) add_compile_options(-I/${SYCL_INCLUDE_DIR})

3
README.md
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@ -477,7 +477,8 @@ Building the program with BLAS support may lead to some performance improvements
|--------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |--------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. | | LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. |
| LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | | LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
| LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. | | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. |
| LLAMA_CUDA_FORCE_MMQ | Boolean | false | Force the use of dequantization + matrix multiplication kernels instead of leveraging Math libraries. | |
| LLAMA_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. | | LLAMA_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. |
| LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
| LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. | | LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. |

122
ggml-sycl.cpp
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@ -3022,20 +3022,19 @@ static int g_work_group_size = 0;
// typedef sycl::half ggml_fp16_t; // typedef sycl::half ggml_fp16_t;
#define __SYCL_ARCH__ DPCT_COMPATIBILITY_TEMP #define __SYCL_ARCH__ DPCT_COMPATIBILITY_TEMP
#define VER_4VEC 610 //todo for hardward optimize. #define VER_4VEC 130 //todo for hardward optimize.
#define VER_GEN9 700 //todo for hardward optimize. #define VER_GEN9 700 //todo for hardward optimize.
#define VER_GEN12 1000000 //todo for hardward optimize. #define VER_GEN12 1000000 //todo for hardward optimize.
#define VER_GEN13 (VER_GEN12 + 1030) //todo for hardward optimize. #define VER_GEN13 (VER_GEN12 + 1030) //todo for hardward optimize.
#define GGML_SYCL_MAX_NODES 8192 //TODO: adapt to hardwares #define GGML_SYCL_MAX_NODES 8192 //TODO: adapt to hardwares
#if !defined(GGML_SYCL_FORCE_MMQ)
//define for XMX in Intel GPU #define SYCL_USE_XMX
//TODO: currently, it's not used for XMX really. #endif
#define SYCL_USE_XMX
// max batch size to use MMQ kernels when tensor cores are available // max batch size to use MMQ kernels when tensor cores are available
#define XMX_MAX_BATCH_SIZE 32 #define MMQ_MAX_BATCH_SIZE 32
#if defined(_MSC_VER) #if defined(_MSC_VER)
@ -15249,6 +15248,29 @@ catch (sycl::exception const &exc) {
std::exit(1); std::exit(1);
} }
inline bool ggml_sycl_supports_mmq(enum ggml_type type) {
// TODO: accuracy issues in MMQ
return false;
}
bool ggml_sycl_supports_dmmv(enum ggml_type type) {
switch (type) {
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
case GGML_TYPE_Q4_K:
case GGML_TYPE_Q5_K:
case GGML_TYPE_Q6_K:
case GGML_TYPE_F16:
return true;
default:
return false;
}
}
static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const bool all_on_device = const bool all_on_device =
@ -15265,76 +15287,42 @@ static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
} }
} }
// check data types and tensor shapes for custom matrix multiplication kernels:
bool use_dequantize_mul_mat_vec = ggml_sycl_supports_dmmv(src0->type)
&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
&& src0->ne[0] % GGML_SYCL_DMMV_X == 0 && src1->ne[1] == 1;
bool use_mul_mat_vec_q = ggml_is_quantized(src0->type)
&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
&& src1->ne[1] <= MMVQ_MAX_BATCH_SIZE;
bool use_mul_mat_q = ggml_sycl_supports_mmq(src0->type)
&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
// mmvq and mmq need the __dp4a instruction which is available for gen12+
// Workaround in https://github.com/ggerganov/llama.cpp/commit/95f84d5ce8b449a9b16009434aca800df504a02e
use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS);
#ifdef SYCL_USE_XMX #ifdef SYCL_USE_XMX
const bool use_xmx = true; use_mul_mat_q = use_mul_mat_q && (src1->ne[1] <= MMQ_MAX_BATCH_SIZE);
#else #endif // SYCL_USE_XMX
const bool use_xmx = false;
#endif
// debug helpers if (!split && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
//printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]);
//printf(" %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]);
//printf("src1: %8d %8d %8d %8d\n", src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]);
//printf(" %8d %8d %8d %8d\n", src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3]);
//printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
//printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
if (!split && all_on_device && !use_xmx && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
// KQ single-batch // KQ single-batch
// GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_p021\n");
ggml_sycl_mul_mat_vec_p021(src0, src1, dst); ggml_sycl_mul_mat_vec_p021(src0, src1, dst);
} else if (!split && all_on_device && !use_xmx && 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_transposed(src1) && src1->ne[1] == 1) {
// KQV single-batch // KQV single-batch
// GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_nc\n");
ggml_sycl_mul_mat_vec_nc(src0, src1, dst); ggml_sycl_mul_mat_vec_nc(src0, src1, dst);
} else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { } else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16) && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
// KQ + KQV multi-batch // KQ + KQV multi-batch
// GGML_SYCL_DEBUG("ggml_sycl_mul_mat_batched_sycl\n");
ggml_sycl_mul_mat_batched_sycl(src0, src1, dst); ggml_sycl_mul_mat_batched_sycl(src0, src1, dst);
} else if (src0->type == GGML_TYPE_F32) { } else if (use_dequantize_mul_mat_vec) {
// GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat\n"); ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_dequantize_mul_mat_vec, false);
ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false); } else if (use_mul_mat_vec_q) {
} else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) { ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_vec_q, true);
// GGML_SYCL_DEBUG("ggml_is_quantized or GGML_TYPE_F16\n"); } else if (use_mul_mat_q) {
if (src1->ne[1] == 1 && src0->ne[0] % GGML_SYCL_DMMV_X == 0) { ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_q, true);
#ifdef GGML_SYCL_FORCE_DMMV
const bool use_mul_mat_vec_q = false;
#else
bool use_mul_mat_vec_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type);
use_mul_mat_vec_q = use_mul_mat_vec_q ||
(src0->type == GGML_TYPE_IQ2_XXS) || (src0->type == GGML_TYPE_IQ2_XS) || (src0->type == GGML_TYPE_IQ2_S) ||
(src0->type == GGML_TYPE_IQ3_XXS) || (src0->type == GGML_TYPE_IQ3_S) ||
(src0->type == GGML_TYPE_IQ4_NL) || (src0->type == GGML_TYPE_IQ4_XS) ||
(src0->type == GGML_TYPE_IQ1_S) || (src0->type == GGML_TYPE_IQ1_M);
#endif // GGML_SYCL_FORCE_DMMV
if (use_mul_mat_vec_q) {
// GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_vec_q path\n");
ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_vec_q, true);
} else {
// GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_dequantize_mul_mat_vec path\n");
ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_dequantize_mul_mat_vec, false);
}
} else {
bool use_mul_mat_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type);
use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS);
if (use_xmx && min_compute_capability >= VER_GEN9 && src1->ne[1] > XMX_MAX_BATCH_SIZE) {
use_mul_mat_q = false;
}
if (use_mul_mat_q) {
// GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_q path\n");
ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_q, true);
} else {
// GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_sycl path\n");
ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false);
}
}
} else { } else {
GGML_ASSERT(false); ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false);
} }
} }