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https://github.com/ggerganov/whisper.cpp.git
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Align GEMM dispatch (llama/7566)
* align GEMM dispatch
This commit is contained in:
Meng, Hengyu
Georgi Gerganov
parent
046834198d
commit
3563473d2c
122
ggml-sycl.cpp
122
ggml-sycl.cpp
@ -3022,20 +3022,19 @@ static int g_work_group_size = 0;
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// typedef sycl::half ggml_fp16_t;
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#define __SYCL_ARCH__ DPCT_COMPATIBILITY_TEMP
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#define VER_4VEC 610 //todo for hardward optimize.
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#define VER_4VEC 130 //todo for hardward optimize.
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#define VER_GEN9 700 //todo for hardward optimize.
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#define VER_GEN12 1000000 //todo for hardward optimize.
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#define VER_GEN13 (VER_GEN12 + 1030) //todo for hardward optimize.
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#define GGML_SYCL_MAX_NODES 8192 //TODO: adapt to hardwares
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//define for XMX in Intel GPU
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//TODO: currently, it's not used for XMX really.
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#define SYCL_USE_XMX
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#if !defined(GGML_SYCL_FORCE_MMQ)
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#define SYCL_USE_XMX
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#endif
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// max batch size to use MMQ kernels when tensor cores are available
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#define XMX_MAX_BATCH_SIZE 32
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#define MMQ_MAX_BATCH_SIZE 32
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#if defined(_MSC_VER)
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@ -15249,6 +15248,29 @@ catch (sycl::exception const &exc) {
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std::exit(1);
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}
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inline bool ggml_sycl_supports_mmq(enum ggml_type type) {
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// TODO: accuracy issues in MMQ
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return false;
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}
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bool ggml_sycl_supports_dmmv(enum ggml_type type) {
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switch (type) {
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case GGML_TYPE_Q4_0:
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case GGML_TYPE_Q4_1:
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case GGML_TYPE_Q5_0:
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case GGML_TYPE_Q5_1:
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case GGML_TYPE_Q8_0:
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case GGML_TYPE_Q2_K:
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case GGML_TYPE_Q3_K:
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case GGML_TYPE_Q4_K:
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case GGML_TYPE_Q5_K:
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case GGML_TYPE_Q6_K:
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case GGML_TYPE_F16:
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return true;
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default:
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return false;
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}
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}
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static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
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const bool all_on_device =
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@ -15265,76 +15287,42 @@ static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
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}
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}
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// check data types and tensor shapes for custom matrix multiplication kernels:
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bool use_dequantize_mul_mat_vec = ggml_sycl_supports_dmmv(src0->type)
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&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
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&& src0->ne[0] % GGML_SYCL_DMMV_X == 0 && src1->ne[1] == 1;
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bool use_mul_mat_vec_q = ggml_is_quantized(src0->type)
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&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
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&& src1->ne[1] <= MMVQ_MAX_BATCH_SIZE;
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bool use_mul_mat_q = ggml_sycl_supports_mmq(src0->type)
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&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
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// mmvq and mmq need the __dp4a instruction which is available for gen12+
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// Workaround in https://github.com/ggerganov/llama.cpp/commit/95f84d5ce8b449a9b16009434aca800df504a02e
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use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS);
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#ifdef SYCL_USE_XMX
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const bool use_xmx = true;
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#else
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const bool use_xmx = false;
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#endif
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use_mul_mat_q = use_mul_mat_q && (src1->ne[1] <= MMQ_MAX_BATCH_SIZE);
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#endif // SYCL_USE_XMX
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// debug helpers
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//printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]);
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//printf(" %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]);
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//printf("src1: %8d %8d %8d %8d\n", src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]);
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//printf(" %8d %8d %8d %8d\n", src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3]);
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//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);
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//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);
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if (!split && all_on_device && !use_xmx && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
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if (!split && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
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// KQ single-batch
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// GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_p021\n");
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ggml_sycl_mul_mat_vec_p021(src0, src1, dst);
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} 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) {
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} else if (!split && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
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// KQV single-batch
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// GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_nc\n");
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ggml_sycl_mul_mat_vec_nc(src0, src1, dst);
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} else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
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} 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) {
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// KQ + KQV multi-batch
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// GGML_SYCL_DEBUG("ggml_sycl_mul_mat_batched_sycl\n");
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ggml_sycl_mul_mat_batched_sycl(src0, src1, dst);
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} else if (src0->type == GGML_TYPE_F32) {
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// GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat\n");
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ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false);
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} else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) {
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// GGML_SYCL_DEBUG("ggml_is_quantized or GGML_TYPE_F16\n");
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if (src1->ne[1] == 1 && src0->ne[0] % GGML_SYCL_DMMV_X == 0) {
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#ifdef GGML_SYCL_FORCE_DMMV
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const bool use_mul_mat_vec_q = false;
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#else
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bool use_mul_mat_vec_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type);
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use_mul_mat_vec_q = use_mul_mat_vec_q ||
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(src0->type == GGML_TYPE_IQ2_XXS) || (src0->type == GGML_TYPE_IQ2_XS) || (src0->type == GGML_TYPE_IQ2_S) ||
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(src0->type == GGML_TYPE_IQ3_XXS) || (src0->type == GGML_TYPE_IQ3_S) ||
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(src0->type == GGML_TYPE_IQ4_NL) || (src0->type == GGML_TYPE_IQ4_XS) ||
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(src0->type == GGML_TYPE_IQ1_S) || (src0->type == GGML_TYPE_IQ1_M);
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#endif // GGML_SYCL_FORCE_DMMV
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if (use_mul_mat_vec_q) {
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// GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_vec_q path\n");
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ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_vec_q, true);
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} else {
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// GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_dequantize_mul_mat_vec path\n");
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ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_dequantize_mul_mat_vec, false);
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}
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} else {
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bool use_mul_mat_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type);
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use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS);
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if (use_xmx && min_compute_capability >= VER_GEN9 && src1->ne[1] > XMX_MAX_BATCH_SIZE) {
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use_mul_mat_q = false;
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}
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if (use_mul_mat_q) {
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// GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_q path\n");
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ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_q, true);
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} else {
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// GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_sycl path\n");
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ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false);
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}
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}
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} else if (use_dequantize_mul_mat_vec) {
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ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_dequantize_mul_mat_vec, false);
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} else if (use_mul_mat_vec_q) {
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ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_vec_q, true);
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} else if (use_mul_mat_q) {
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ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_q, true);
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} else {
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GGML_ASSERT(false);
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ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false);
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}
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}
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