mirror of
https://github.com/ggml-org/llama.cpp.git
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103 lines
3.9 KiB
C++
103 lines
3.9 KiB
C++
//
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// MIT license
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// Copyright (C) 2024 Intel Corporation
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// SPDX-License-Identifier: MIT
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//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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#ifndef GGML_SYCL_GEMM_HPP
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#define GGML_SYCL_GEMM_HPP
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#include "ggml-sycl.h"
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#if GGML_SYCL_DNNL
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#include "dnnl.hpp"
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#include "dnnl_sycl.hpp"
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class DnnlGemmWrapper {
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public:
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using dt = dnnl::memory::data_type;
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using tag = dnnl::memory::format_tag;
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template<typename T>
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static constexpr dt to_dt() {
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if constexpr (std::is_same_v<T, float>) return dt::f32;
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else if constexpr (std::is_same_v<T, sycl::half>) return dt::f16;
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else static_assert(0);
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}
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// matrix A has m rows, k columns
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// matrix B has k rows, n columns
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// nra - number of elements to skip when moving into next row in A
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// nrb - number of elements to skip when moving into next row in B
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// nca - number of elements to skip when moving into next column in A
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// ncb - number of elements to skip when moving into next column in B
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// stride_a - number of elements to skip when moving to next A matrix
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// stride_b - number of elements to skip when moving to next B matrix
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// batches_a - number of A matrices
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// batches_b - number of B matrices
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static void gemm(ggml_backend_sycl_context & ctx, int m, int n, int k,
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const void * a, dt at, dnnl_dim_t nra, dnnl_dim_t nca, dnnl_dim_t stride_a,
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const void * b, dt bt, dnnl_dim_t nrb, dnnl_dim_t ncb, dnnl_dim_t stride_b,
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void * c, dt ct, const queue_ptr & q, dnnl_dim_t batches_a, dnnl_dim_t batches_b) {
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auto stream = ctx.stream_dnnl(q);
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auto eng = ctx.engine_dnnl(q);
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// { # strides, # rows, # columns }
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dnnl::memory::dims a_dims = { batches_a, m, k };
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dnnl::memory::dims b_dims = { batches_b, k, n };
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dnnl::memory::dims c_dims = { std::max(batches_a, batches_b), m, n };
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// { # elements to skip to next stride, # elements to skip to next row, # elements to skip to next column }
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dnnl::memory::dims a_strides = { stride_a, nra, nca };
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dnnl::memory::dims b_strides = { stride_b, nrb, ncb };
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const auto a_in_md = dnnl::memory::desc(a_dims, at, a_strides);
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const auto b_in_md = dnnl::memory::desc(b_dims, bt, b_strides);
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const auto c_md = dnnl::memory::desc(c_dims, ct, tag::abc);
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dnnl::primitive_attr primitive_attr;
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primitive_attr.set_scratchpad_mode(dnnl::scratchpad_mode::user);
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#ifdef GGML_SYCL_F16
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primitive_attr.set_fpmath_mode(dnnl::fpmath_mode::f16);
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#endif
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auto a_mem = dnnl::memory(a_in_md, eng, const_cast<void*>(a));
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auto b_mem = dnnl::memory(b_in_md, eng, const_cast<void*>(b));
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auto matmul_pd = dnnl::matmul::primitive_desc(eng, a_in_md, b_in_md, c_md, primitive_attr);
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auto c_mem = dnnl::memory(matmul_pd.dst_desc(), eng, c);
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auto scratchpad_md = matmul_pd.scratchpad_desc();
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auto scratchpad_mem = ctx.get_scratchpad_mem(scratchpad_md, eng, q);
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auto matmul_prim = dnnl::matmul(matmul_pd);
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std::unordered_map<int, dnnl::memory> matmul_args;
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matmul_args.insert({ DNNL_ARG_SRC, a_mem });
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matmul_args.insert({ DNNL_ARG_WEIGHTS, b_mem });
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matmul_args.insert({ DNNL_ARG_DST, c_mem });
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matmul_args.insert({ DNNL_ARG_SCRATCHPAD, scratchpad_mem });
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matmul_prim.execute(stream, matmul_args);
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}
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// matrices A and B are column major, both having k rows
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// matrix A has m column, matrix B has n columns
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// output: column major matrix C = A transposed * B
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static void row_gemm(ggml_backend_sycl_context & ctx, int m, int n, int k,
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const void * a, dt at, const void * b, dt bt, void * c, dt ct, const queue_ptr & q) {
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gemm(ctx, m, n, k, a, at, k, 1, k * m, b, bt, 1, k, n * k, c, ct, q, 1, 1);
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}
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};
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#endif
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#endif // GGML_SYCL_GEMM_HPP
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