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https://github.com/ggml-org/llama.cpp.git
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SYCL: Initial set_rows kernel implementation (#14562)
* SYCL: Initial set_rows kernel implementation * Revert max_threads to 256 * Refactor set_rows and address review comments * Deduplicate conversion function * Remove guard before kernel launch and refactor * Fix and add back SFINAE
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@ -30,6 +30,7 @@
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#include "outprod.hpp"
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#include "quants.hpp"
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#include "rope.hpp"
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#include "set_rows.hpp"
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#include "softmax.hpp"
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#include "tsembd.hpp"
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#include "wkv.hpp"
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@ -41,6 +41,7 @@
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#include "ggml-sycl/element_wise.hpp"
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#include "ggml-sycl/presets.hpp"
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#include "ggml-sycl/gemm.hpp"
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#include "ggml-sycl/set_rows.hpp"
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#include "ggml-sycl/sycl_hw.hpp"
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#include "ggml-sycl/getrows.hpp"
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#include "ggml.h"
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@ -3605,6 +3606,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg
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case GGML_OP_GET_ROWS:
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ggml_sycl_get_rows(ctx, dst);
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break;
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case GGML_OP_SET_ROWS:
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ggml_sycl_op_set_rows(ctx, dst);
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break;
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case GGML_OP_DUP:
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ggml_sycl_dup(ctx, dst);
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break;
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@ -4299,7 +4303,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
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{
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// TODO: add support
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// ref: https://github.com/ggml-org/llama.cpp/pull/14274
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return false;
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return (op->type == GGML_TYPE_F32 || (op->type == GGML_TYPE_F16 && op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_I64));
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} break;
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case GGML_OP_CPY:
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{
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131
ggml/src/ggml-sycl/set_rows.cpp
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131
ggml/src/ggml-sycl/set_rows.cpp
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@ -0,0 +1,131 @@
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#include "set_rows.hpp"
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namespace utils {
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template<typename T>
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static constexpr bool is_arithmetic_v() {
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return std::is_arithmetic_v<T> || std::is_same_v<T, sycl::half> || std::is_same_v<T, sycl::ext::oneapi::bfloat16>;
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}
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}
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template<typename TIn, typename TOut>
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static inline std::enable_if_t<utils::is_arithmetic_v<TIn>() && utils::is_arithmetic_v<TOut>(), void>
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convert (const char* src, char* dst) {
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auto src_val = *reinterpret_cast<const TIn*>(src);
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auto dst_val = sycl::vec<TIn, 1>(src_val).template convert<TOut, sycl::rounding_mode::automatic>()[0];
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*reinterpret_cast<TOut*>(dst) = dst_val;;
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}
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template<typename TIn, typename TOut>
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static void k_set_rows(
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const char * __restrict__ src0, const int64_t * __restrict__ src1, char * __restrict__ dst,
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const int64_t ne00, const int64_t ne01, const int64_t ne11, const int64_t ne12,
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const size_t nb01, const size_t nb02, const size_t nb03,
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const size_t nb10, const size_t nb11, const size_t nb12,
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const size_t nb1, const size_t nb2, const size_t nb3,
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const size_t src_type_size, const size_t dst_type_size,
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const sycl::nd_item<3> & item_ct1) {
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const int i03 = item_ct1.get_group(0);
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const int i02 = item_ct1.get_group(1);
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const int i01 = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); // Row index
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if (i01 >= ne01) {
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return;
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}
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const int i12 = i03 % ne12;
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const int i11 = i02 % ne11;
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const int i10 = i01;
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const int64_t dst_row = *(const int64_t *)((const char *)src1 + calculate_offset<3>({nb10, nb11, nb12}, {i10, i11, i12}));
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const char * src0_row = src0 + calculate_offset<3>({nb01, nb02, nb03}, {i01, i02, i03});
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char * dst_row_ptr = dst + dst_row*nb1 + i02*nb2 + i03*nb3;
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for (int col = item_ct1.get_local_id(0); col < ne00; col += item_ct1.get_local_range(0)) {
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const char * src_elem = src0_row + col * src_type_size;
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char * dst_elem = dst_row_ptr + col * dst_type_size;
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convert<TIn, TOut>(src_elem, dst_elem);
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}
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}
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template<typename TIn, typename TOut>
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static void set_rows_sycl(
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const char * src0_d, const int64_t * src1_d, char * dst_d,
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const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
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const int64_t ne11, const int64_t ne12, const size_t nb01, const size_t nb02, const size_t nb03,
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const size_t nb10, const size_t nb11, const size_t nb12,
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const size_t nb1, const size_t nb2, const size_t nb3,
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const size_t src_type_size, const size_t dst_type_size,
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queue_ptr stream) {
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constexpr int max_threads_per_row = 64; // KEEPING 64 for now
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const int threads_per_row = std::min((int)ne00, max_threads_per_row);
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constexpr int max_threads_per_block = 64;
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const int rows_per_block = std::max(1, max_threads_per_block / threads_per_row);
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const sycl::range<3> block_size(1, rows_per_block, threads_per_row);
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const sycl::range<3> grid_size(ne03, ne02, (ne01 + rows_per_block - 1) / rows_per_block);
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sycl_parallel_for(
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stream,
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sycl::nd_range<3>(grid_size * block_size, block_size),
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[=](sycl::nd_item<3> item_ct1) {
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k_set_rows<TIn, TOut>(
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src0_d, src1_d, dst_d,
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ne00, ne01, ne11, ne12,
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nb01, nb02, nb03,
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nb10, nb11, nb12,
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nb1, nb2, nb3,
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src_type_size, dst_type_size,
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item_ct1
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);
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}
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);
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}
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void ggml_sycl_op_set_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
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scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
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const ggml_tensor * src0 = dst->src[0];
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const ggml_tensor * src1 = dst->src[1];
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GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
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GGML_ASSERT(dst->src[1]->type == GGML_TYPE_I64);
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GGML_TENSOR_BINARY_OP_LOCALS
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const int64_t * src1_dd = static_cast<const int64_t *>(src1->data);
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dpct::queue_ptr stream = ctx.stream();
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switch (dst->type) {
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case GGML_TYPE_F32:
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set_rows_sycl<float, float>(
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(const char *)src0->data, src1_dd, (char *)dst->data,
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ne00, ne01, ne02, ne03,
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ne11, ne12,
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nb01, nb02, nb03,
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nb10, nb11, nb12,
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nb1, nb2, nb3,
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sizeof(float), sizeof(float),
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stream
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);
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break;
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case GGML_TYPE_F16:
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dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
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set_rows_sycl<float, sycl::half>(
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(const char *)src0->data, src1_dd, (char *)dst->data,
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ne00, ne01, ne02, ne03,
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ne11, ne12,
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nb01, nb02, nb03,
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nb10, nb11, nb12,
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nb1, nb2, nb3,
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sizeof(float), sizeof(sycl::half),
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stream
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);
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break;
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default:
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GGML_ABORT("Unsupported tensor type!");
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break;
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}
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}
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8
ggml/src/ggml-sycl/set_rows.hpp
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8
ggml/src/ggml-sycl/set_rows.hpp
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@ -0,0 +1,8 @@
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#ifndef GGML_SYCL_SET_ROWS_HPP
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#define GGML_SYCL_SET_ROWS_HPP
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#include "common.hpp"
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void ggml_sycl_op_set_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
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#endif // GGML_SYCL_SET_ROWS_HPP
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