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https://github.com/ROCm/composable_kernel.git
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f6c4d614e3
* [CK_TILE] Move hipmalloc/memcpy calls out of gpu reference gemm * [CK_TILE] Move hipmalloc/memcpy calls out of gpu reference gemm - review changes * [CK_TILE] Move hipmalloc/memcpy calls out of gpu reference gemm - review fix
159 lines
5.8 KiB
C++
159 lines
5.8 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <cstdlib>
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#include <thread>
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#include "ck_tile/core.hpp"
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#include "ck_tile/host/host_tensor.hpp"
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namespace ck_tile {
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template <typename ADataType,
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typename BDataType,
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typename AccDataType,
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typename CDataType,
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typename AElementOp = ck_tile::identity,
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typename BElementOp = ck_tile::identity,
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typename ACCElementOp = ck_tile::identity>
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CK_TILE_HOST void reference_gemm(const HostTensor<ADataType>& a_m_k,
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const HostTensor<BDataType>& b_k_n,
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HostTensor<CDataType>& c_m_n,
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const AElementOp& a_element_op = {},
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const BElementOp& b_element_op = {},
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const ACCElementOp& acc_element_op = {})
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{
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const std::size_t M = a_m_k.get_length(0);
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const std::size_t N = b_k_n.get_length(1);
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const std::size_t K = a_m_k.get_length(1);
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auto f_mn = [&](auto m, auto n) {
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AccDataType v_acc = 0;
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for(std::size_t k = 0; k < K; ++k)
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{
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ADataType v_a = a_element_op(a_m_k(m, k));
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BDataType v_b = b_element_op(b_k_n(k, n));
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v_acc +=
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ck_tile::type_convert<AccDataType>(v_a) * ck_tile::type_convert<AccDataType>(v_b);
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}
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c_m_n(m, n) = ck_tile::type_convert<CDataType>(acc_element_op(v_acc));
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};
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make_ParallelTensorFunctor(f_mn, M, N)(std::thread::hardware_concurrency());
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}
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template <typename ADataType,
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typename BDataType,
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typename AccDataType,
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typename CDataType,
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typename LayoutA,
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typename LayoutB,
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typename LayoutC>
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__global__ void naive_gemm_kernel(ADataType* A,
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BDataType* B,
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CDataType* C,
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ck_tile::index_t M,
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ck_tile::index_t N,
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ck_tile::index_t K,
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ck_tile::index_t strideA,
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ck_tile::index_t strideB,
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ck_tile::index_t strideC)
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{
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int idx = blockIdx.x * blockDim.x + threadIdx.x;
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int row = idx / N; // Compute row index
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int col = idx % N; // Compute column index
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if(row < M && col < N)
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{
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AccDataType acc = 0.0;
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for(int k = 0; k < K; ++k)
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{
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// Adjust indexing based on matrix layout
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int a_index = (std::is_same_v<LayoutA, tensor_layout::gemm::RowMajor>)
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? row * strideA + k
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: k * strideA + row;
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int b_index = (std::is_same_v<LayoutB, tensor_layout::gemm::ColumnMajor>)
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? col * strideB + k
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: k * strideB + col;
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acc += static_cast<AccDataType>(A[a_index]) * static_cast<AccDataType>(B[b_index]);
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}
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int c_index = (std::is_same_v<LayoutC, tensor_layout::gemm::RowMajor>)
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? row * strideC + col
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: col * strideC + row;
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C[c_index] = acc;
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}
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}
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template <typename ADataType,
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typename BDataType,
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typename AccDataType,
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typename CDataType,
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typename LayoutA,
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typename LayoutB,
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typename LayoutC>
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void reference_gemm_gpu(ADataType* a_ptr,
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BDataType* b_ptr,
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CDataType* c_ptr,
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index_t M,
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index_t N,
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index_t K,
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index_t stride_a,
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index_t stride_b,
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index_t stride_c)
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{
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int totalElements = M * N;
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int numThreadsPerBlock = 256; // Common choice for threads per block
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int numBlocks = (totalElements + numThreadsPerBlock - 1) / numThreadsPerBlock;
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naive_gemm_kernel<ADataType, BDataType, AccDataType, CDataType, LayoutA, LayoutB, LayoutC>
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<<<numBlocks, numThreadsPerBlock>>>(
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a_ptr, b_ptr, c_ptr, M, N, K, stride_a, stride_b, stride_c);
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return;
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}
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template <typename ADataType,
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typename BDataType,
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typename AccDataType,
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typename CDataType,
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typename LayoutA,
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typename LayoutB,
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typename LayoutC>
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void reference_batched_gemm_gpu(ADataType* a_ptr,
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BDataType* b_ptr,
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CDataType* c_ptr,
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index_t M,
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index_t N,
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index_t K,
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index_t stride_a,
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index_t stride_b,
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index_t stride_c,
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index_t batch_stride_A,
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index_t batch_stride_B,
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index_t batch_stride_C,
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index_t batch_count)
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{
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int totalElements = M * N;
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int numThreadsPerBlock = 256; // Common choice for threads per block
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int numBlocks = (totalElements + numThreadsPerBlock - 1) / numThreadsPerBlock;
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for(index_t batch_id = 0; batch_id < batch_count; ++batch_id)
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{
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ADataType* d_ATemp = a_ptr + batch_id * batch_stride_A;
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BDataType* d_BTemp = b_ptr + batch_id * batch_stride_B;
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CDataType* d_CTemp = c_ptr + batch_id * batch_stride_C;
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naive_gemm_kernel<ADataType, BDataType, AccDataType, CDataType, LayoutA, LayoutB, LayoutC>
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<<<numBlocks, numThreadsPerBlock>>>(
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d_ATemp, d_BTemp, d_CTemp, M, N, K, stride_a, stride_b, stride_c);
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}
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return;
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}
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} // namespace ck_tile
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