mirror of
https://github.com/ROCm/composable_kernel.git
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Wavelet (inter-wave consumer-producer) GEMM (#310)
* wavelet gemm programming model support for CK * GEMM pipeline update for wavelet progrmmaing model * Updated wavelet programming pipeline * fixes for global-write for math-wave * fixed bug in global writes * Updated comments for better readability * fixed clang format errors * added block_lds without barrier sync * clean * clean * clean * clean * refactor * prototype 4 layouts fix default stride all problem sizes tidy move file update build script restore old file fix build * refactor standalone test to use gemm test harness * simplify gemm test * update build script * remove redundant * early return when cmd arg doesn't match * tidy * report failure when result not validated * tidy * Add comment depicting B2C mapping pattern. * Formatting & comments. * Comparison with custom B2C mapping pattern. * Example for wavelet gemm. * Add wavelet to Gemm standalone test. * Remove debug code. * Remove dangling #endif directive. Co-authored-by: root <Raman Jana> Co-authored-by: Chao Liu <chao.liu2@amd.com> Co-authored-by: Adam Osewski <aosewski@amd.com> Co-authored-by: Anthony Chang <ac.chang@outlook.com> Co-authored-by: Adam Osewski <19374865+aosewski@users.noreply.github.com>
This commit is contained in:
Raman R jana
@@ -154,6 +154,50 @@ struct BlockToCTileMap_M00_N0_M01Adapt
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index_t idx_M01 = idx_M0 % M01_;
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index_t idx_N0_M01_local = idx_N0 + idx_M01 * N0;
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/**
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* idxN0
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*
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* |< mtx N >|
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*
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* NPerBlock NPerBlock NPerBlock NPerBlock
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* N_0 N_1 N_2 N_3
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* - |-----------|-----------|-----------|-----|-----|-
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* ^ | - - 0 |/----> 2 | | | |
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* | | | / | | | | | M_0 MPerBlock
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* | M | /| | | | | |
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* |-0---|---/-|-----|-----|-----------|-----|-----|-
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* | 1 | / | | | blockid | | |
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* idxM0 | | | / | V | 5 | | | M_1 MPerBlock
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* | - V 1 | - 3 | | | |
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* |-----------|-----------|-----------|-----|-----|-
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* mtx M | | | | | |
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* | | | | | | M_2 MPerBlock
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* | | | | | |
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* |-----------|-----------|-----------|-----|-----|-
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* | | | | | |
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* | | | | | | M_3 MPerBlock
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* | | | | | |
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* |-----------|-----------|-----------|-----|-----|-
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* V | | | | | |
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* - |-----------|-----------|-----------|-----|-----|- M_4 MPerBlock
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* | | | | | |
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* |-----------|-----------|-----------|-----|-----|-
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* Example:
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* assume:
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* M0 = 5
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* N0 = 4
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* block_1d_id = 5
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* M01 = 2
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*
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* idx_N0 = 1
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* idx_M0 = 1
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* M01_adapt = 2
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* idx_M00 = 0
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* idx_M01 = 1
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* idx_N0_M01_local = 5
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* output {1, 2}
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*/
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return make_tuple(idx_N0_M01_local % M01_adapt + idx_M00 * M01_,
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idx_N0_M01_local / M01_adapt);
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}
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@@ -0,0 +1,157 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "ck/utility/common_header.hpp"
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namespace ck {
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template <typename TileLoadThreadGroup, index_t NumGemmKPrefetchStage>
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struct GridwiseGemmLoadWave;
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// 1-stage prefetch
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template <typename TileLoadThreadGroup>
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struct GridwiseGemmLoadWave<TileLoadThreadGroup, 1>
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{
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__host__ __device__ static constexpr bool IsSupported(index_t /* num_loop */)
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{
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// TODO: improve applicability
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return true;
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}
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__host__ __device__ static constexpr bool CalculateHasMainLoop(index_t num_loop)
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{
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return num_loop > 1;
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}
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template <bool HasMainLoop,
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typename AGridDesc,
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typename ABlockDesc,
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typename ABlockTransfer,
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typename AGridBuffer,
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typename ABlockBuffer,
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typename ABlockTransferStep,
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typename BGridDesc,
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typename BBlockDesc,
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typename BBlockTransfer,
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typename BGridBuffer,
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typename BBlockBuffer,
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typename BBlockTransferStep>
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static __device__ void RunLoadWavePipeline(const AGridDesc& a_grid_desc,
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const ABlockDesc& a_block_desc,
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ABlockTransfer& a_blockwise_copy,
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const AGridBuffer& a_grid_buf,
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ABlockBuffer& a_block_buf,
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const ABlockTransferStep& a_block_copy_step,
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const BGridDesc& b_grid_desc,
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const BBlockDesc& b_block_desc,
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BBlockTransfer& b_blockwise_copy,
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const BGridBuffer& b_grid_buf,
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BBlockBuffer& b_block_buf,
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const BBlockTransferStep& b_block_copy_step,
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index_t num_loop)
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{
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// global read 0
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a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
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b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf);
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// move to 1
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a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
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b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
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// LDS write 0
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a_blockwise_copy.RunWrite(a_block_desc, a_block_buf);
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b_blockwise_copy.RunWrite(b_block_desc, b_block_buf);
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if constexpr(HasMainLoop)
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{
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index_t i = 0;
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do
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{
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// sync for Load threads()
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block_sync_lds();
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// global read i + 1
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a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
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b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf);
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// move to i + 2
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a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
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b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
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// sync with math threads()
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block_sync_lds();
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// LDS write i+1
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a_blockwise_copy.RunWrite(a_block_desc, a_block_buf);
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b_blockwise_copy.RunWrite(b_block_desc, b_block_buf);
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++i;
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} while(i < (num_loop - 1));
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}
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// tail
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{
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block_sync_lds();
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// GEMM num_loop - 1
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}
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}
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};
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template <typename TileMathThreadGroup, index_t NumGemmKPrefetchStage>
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struct GridwiseGemmMathWave;
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// 1- stage prefetch
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template <typename TileMathThreadGroup>
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struct GridwiseGemmMathWave<TileMathThreadGroup, 1>
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{
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__host__ __device__ static constexpr bool IsSupported(index_t /* num_loop */) { return true; }
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__host__ __device__ static constexpr bool CalculateHasMainLoop(index_t num_loop)
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{
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return num_loop > 1;
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}
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template <bool HasMainLoop,
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typename ABlockBuffer,
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typename BBlockBuffer,
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typename BlockwiseGemm,
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typename CThreadBuffer>
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static __device__ void RunMathWavePipeline(ABlockBuffer& a_block_buf,
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BBlockBuffer& b_block_buf,
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const BlockwiseGemm& block_gemm,
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CThreadBuffer& c_thread_buf,
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index_t num_loop)
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{
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// Initialize C
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c_thread_buf.Clear();
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// main body
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if constexpr(HasMainLoop)
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{
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index_t i = 0;
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do
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{
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block_sync_lds();
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// GEMM i
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block_gemm.Run(a_block_buf, b_block_buf, c_thread_buf);
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block_sync_lds();
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++i;
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} while(i < (num_loop - 1));
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}
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// tail
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{
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block_sync_lds();
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// GEMM num_loop - 1
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block_gemm.Run(a_block_buf, b_block_buf, c_thread_buf);
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}
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}
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};
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} // namespace ck
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@@ -0,0 +1,744 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "ck/utility/common_header.hpp"
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#include "ck/tensor_description/multi_index_transform_helper.hpp"
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#include "ck/tensor_description/tensor_descriptor.hpp"
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#include "ck/tensor_description/tensor_descriptor_helper.hpp"
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#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_gemm_waveletmodel.hpp"
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#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
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#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
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#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r1.hpp"
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#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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namespace ck {
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template <typename ABDataType,
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typename FloatGemmAcc,
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typename EDataTypeShuffle,
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typename EDataType,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename EElementwiseOperation,
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InMemoryDataOperationEnum CGlobalMemoryDataOperation,
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typename AGridDesc_M_K,
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typename BGridDesc_N_K,
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typename EGridDesc_M_N,
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index_t NumGemmKPrefetchStage,
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index_t TileLoadThreadGroupSize,
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index_t TileMathThreadGroupSize,
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index_t MPerBlock,
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index_t NPerBlock,
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index_t KPerBlock,
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index_t AK1Value,
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index_t BK1Value,
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index_t MPerXdl,
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index_t NPerXdl,
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index_t MXdlPerWave,
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index_t NXdlPerWave,
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typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
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typename ABlockTransferThreadClusterArrangeOrder,
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typename ABlockTransferSrcAccessOrder,
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index_t ABlockTransferSrcVectorDim,
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index_t ABlockTransferSrcScalarPerVector,
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index_t ABlockTransferDstScalarPerVector_AK1,
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bool AThreadTransferSrcResetCoordinateAfterRun,
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index_t ABlockLdsExtraM,
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typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
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typename BBlockTransferThreadClusterArrangeOrder,
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typename BBlockTransferSrcAccessOrder,
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index_t BBlockTransferSrcVectorDim,
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index_t BBlockTransferSrcScalarPerVector,
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index_t BBlockTransferDstScalarPerVector_BK1,
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bool BThreadTransferSrcResetCoordinateAfterRun,
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index_t BBlockLdsExtraN,
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index_t CShuffleMXdlPerWavePerShuffle,
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index_t CShuffleNXdlPerWavePerShuffle,
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typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
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index_t CShuffleBlockTransferScalarPerVector_NPerBlock>
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struct GridwiseGemm_k0mk1_k0nk1_mn_xdl_waveletmodel_cshuffle
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{
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static constexpr auto I0 = Number<0>{};
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static constexpr auto I1 = Number<1>{};
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static constexpr auto I2 = Number<2>{};
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static constexpr auto I3 = Number<3>{};
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static constexpr auto I4 = Number<4>{};
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static constexpr auto I5 = Number<5>{};
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static constexpr auto I6 = Number<6>{};
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static constexpr auto I7 = Number<7>{};
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// K1 should be Number<...>
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static constexpr auto AK1 = Number<AK1Value>{};
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static constexpr auto BK1 = Number<BK1Value>{};
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static constexpr auto AK0PerBlock = Number<KPerBlock / AK1Value>{};
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static constexpr auto BK0PerBlock = Number<KPerBlock / BK1Value>{};
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struct TileLoadThreadGroup
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{
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__device__ static constexpr index_t GetNumOfThread() { return TileLoadThreadGroupSize; }
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__device__ static constexpr bool IsBelong()
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{
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return (get_thread_local_1d_id() >= TileLoadThreadGroupSize);
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}
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__device__ static index_t GetThreadId()
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{
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return get_thread_local_1d_id() - TileMathThreadGroupSize;
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}
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};
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struct TileMathThreadGroup
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{
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__device__ static constexpr index_t GetNumOfThread() { return TileMathThreadGroupSize; }
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__device__ static constexpr bool IsBelong()
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{
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return get_thread_local_1d_id() < TileMathThreadGroupSize;
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}
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__device__ static index_t GetThreadId() { return get_thread_local_1d_id(); }
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};
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using CShuffleBlockTransferThreadGroup = ThisThreadBlock<TileMathThreadGroupSize>;
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// load and math+store Wave pipelines.
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// TODO: build pipelines blocks scheduling parallel tasks
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using GridwiseGemmLoad = GridwiseGemmLoadWave<TileLoadThreadGroup, NumGemmKPrefetchStage>;
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using GridwiseGemmMath = GridwiseGemmMathWave<TileMathThreadGroup, NumGemmKPrefetchStage>;
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__host__ __device__ static constexpr auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()
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{
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// A matrix in LDS memory, dst of blockwise copy
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return make_naive_tensor_descriptor(
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make_tuple(AK0PerBlock, Number<MPerBlock>{}, AK1),
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make_tuple(Number<MPerBlock + ABlockLdsExtraM>{} * AK1, AK1, I1));
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}
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__host__ __device__ static constexpr auto GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()
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{
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// B matrix in LDS memory, dst of blockwise copy
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return make_naive_tensor_descriptor(
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make_tuple(BK0PerBlock, Number<NPerBlock>{}, BK1),
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make_tuple(Number<NPerBlock + BBlockLdsExtraN>{} * BK1, BK1, I1));
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}
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__host__ __device__ static constexpr auto
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GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()
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{
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constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
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constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);
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constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
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make_naive_tensor_descriptor_packed(
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make_tuple(I1,
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Number<CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl>{},
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I1,
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Number<CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>{}));
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return c_shuffle_block_desc_mblock_mperblock_nblock_nperblock;
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}
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__host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
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{
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// LDS allocation for A and B: be careful of alignment
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constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
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constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
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// lds max alignment
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constexpr auto max_lds_align = math::lcm(AK1, BK1);
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constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
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a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
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constexpr auto b_block_space_size_aligned = math::integer_least_multiple(
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b_block_desc_bk0_n_bk1.GetElementSpaceSize(), max_lds_align);
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// LDS allocation for C shuffle in LDS
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constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
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GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
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constexpr auto c_block_size =
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c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize();
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return math::max((a_block_space_size_aligned + b_block_space_size_aligned) *
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sizeof(ABDataType),
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c_block_size * sizeof(EDataTypeShuffle));
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}
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// block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
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template <typename Block2ETileMap>
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__host__ __device__ static constexpr bool
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CheckValidity(const AGridDesc_M_K& a_grid_desc_m_k,
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const BGridDesc_N_K& b_grid_desc_n_k,
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const EGridDesc_M_N& e_grid_desc_m_n,
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const Block2ETileMap& /*block_2_etile_map*/)
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{
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static_assert((MPerBlock % (MPerXdl * MXdlPerWave) == 0) &&
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(NPerBlock % (NXdlPerWave * NPerXdl)) == 0,
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"Invalid tuning param!");
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const auto M = a_grid_desc_m_k.GetLength(I0);
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const auto N = b_grid_desc_n_k.GetLength(I0);
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const auto K = a_grid_desc_m_k.GetLength(I1);
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// check consistency of desc
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if(!(M == e_grid_desc_m_n.GetLength(I0) && N == e_grid_desc_m_n.GetLength(I1) &&
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K == b_grid_desc_n_k.GetLength(I1)))
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{
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return false;
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}
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// check tile size
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if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K % KPerBlock == 0))
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{
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return false;
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}
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// check gridwise gemm pipeline
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const auto num_k_loop = K / KPerBlock;
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if(!GridwiseGemmMath::IsSupported(num_k_loop))
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{
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return false;
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}
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// TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
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// check tensor size: cannot be larger than 2GB each
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constexpr long_index_t TwoGB = (long_index_t{1} << 31);
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if(!(a_grid_desc_m_k.GetElementSpaceSize() * sizeof(ABDataType) <= TwoGB &&
|
||||
b_grid_desc_n_k.GetElementSpaceSize() * sizeof(ABDataType) <= TwoGB &&
|
||||
e_grid_desc_m_n.GetElementSpaceSize() * sizeof(EDataType) <= TwoGB))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr bool CalculateHasMainKBlockLoop(index_t K)
|
||||
{
|
||||
const index_t num_loop = K / KPerBlock;
|
||||
|
||||
return GridwiseGemmMath::CalculateHasMainLoop(num_loop);
|
||||
}
|
||||
|
||||
// return block_id to E matrix tile idx (m0, n0) mapping
|
||||
__host__ __device__ static constexpr auto
|
||||
MakeDefaultBlock2ETileMap(const EGridDesc_M_N& e_grid_desc_m_n)
|
||||
{
|
||||
const auto M = e_grid_desc_m_n.GetLength(I0);
|
||||
const auto N = e_grid_desc_m_n.GetLength(I1);
|
||||
|
||||
constexpr auto M1 = Number<MPerBlock>{};
|
||||
constexpr auto N1 = Number<NPerBlock>{};
|
||||
|
||||
const auto M0 = M / M1;
|
||||
const auto N0 = N / N1;
|
||||
|
||||
constexpr auto M01 = I1;
|
||||
constexpr auto N01 = I1;
|
||||
|
||||
const auto m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor =
|
||||
make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_unmerge_transform(make_tuple(M0, M01)),
|
||||
make_unmerge_transform(make_tuple(N0, N01))),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1, 3>{}));
|
||||
|
||||
const auto cblockid_to_m00_m01_n00_n01_block_cluster_adaptor =
|
||||
make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_merge_transform(make_tuple(M0, N0, M01, N01))),
|
||||
make_tuple(Sequence<0, 1, 2, 3>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
|
||||
const auto cblockid_to_m0_n0_block_cluster_adaptor =
|
||||
chain_tensor_adaptors(m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor,
|
||||
cblockid_to_m00_m01_n00_n01_block_cluster_adaptor);
|
||||
|
||||
return cblockid_to_m0_n0_block_cluster_adaptor;
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr index_t
|
||||
CalculateGridSize(const EGridDesc_M_N& e_grid_desc_m_n)
|
||||
{
|
||||
const auto M = e_grid_desc_m_n.GetLength(I0);
|
||||
const auto N = e_grid_desc_m_n.GetLength(I1);
|
||||
|
||||
const index_t grid_size = (M / MPerBlock) * (N / NPerBlock);
|
||||
|
||||
return grid_size;
|
||||
}
|
||||
|
||||
// A desc for source in blockwise copy
|
||||
__host__ __device__ static constexpr auto
|
||||
MakeDefaultAGridDescriptor_AK0_M_AK1(const AGridDesc_M_K& a_grid_desc_m_k)
|
||||
{
|
||||
const auto M = a_grid_desc_m_k.GetLength(I0);
|
||||
const auto K = a_grid_desc_m_k.GetLength(I1);
|
||||
|
||||
const auto AK0 = K / AK1;
|
||||
|
||||
return transform_tensor_descriptor(a_grid_desc_m_k,
|
||||
make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
|
||||
make_pass_through_transform(M)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
}
|
||||
|
||||
// B desc for source in blockwise copy
|
||||
__host__ __device__ static constexpr auto
|
||||
MakeDefaultBGridDescriptor_BK0_N_BK1(const BGridDesc_N_K& b_grid_desc_n_k)
|
||||
{
|
||||
const auto N = b_grid_desc_n_k.GetLength(I0);
|
||||
const auto K = b_grid_desc_n_k.GetLength(I1);
|
||||
|
||||
const auto BK0 = K / BK1;
|
||||
|
||||
return transform_tensor_descriptor(b_grid_desc_n_k,
|
||||
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
|
||||
make_pass_through_transform(N)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
}
|
||||
|
||||
// E desc for destination in blockwise copy
|
||||
template <typename EGridDescriptor_M_N>
|
||||
__host__ __device__ static constexpr auto MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
|
||||
const EGridDescriptor_M_N& e_grid_desc_m_n)
|
||||
{
|
||||
const auto M = e_grid_desc_m_n.GetLength(I0);
|
||||
const auto N = e_grid_desc_m_n.GetLength(I1);
|
||||
|
||||
const auto MBlock = M / MPerBlock;
|
||||
const auto NBlock = N / NPerBlock;
|
||||
|
||||
const auto e_grid_desc_mblock_mperblock_nblock_nperblock = transform_tensor_descriptor(
|
||||
e_grid_desc_m_n,
|
||||
make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{})),
|
||||
make_unmerge_transform(make_tuple(NBlock, Number<NPerBlock>{}))),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
|
||||
|
||||
return e_grid_desc_mblock_mperblock_nblock_nperblock;
|
||||
}
|
||||
|
||||
using EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock = remove_cvref_t<decltype(
|
||||
MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(EGridDesc_M_N{}))>;
|
||||
|
||||
using DefaultBlock2ETileMap =
|
||||
remove_cvref_t<decltype(MakeDefaultBlock2ETileMap(EGridDesc_M_N{}))>;
|
||||
|
||||
template <bool HasMainKBlockLoop,
|
||||
typename AGridDesc_AK0_M_AK1,
|
||||
typename BGridDesc_BK0_N_BK1,
|
||||
typename Block2ETileMap>
|
||||
__device__ static void Run(const ABDataType* __restrict__ p_a_grid,
|
||||
const ABDataType* __restrict__ p_b_grid,
|
||||
EDataType* __restrict__ p_e_grid,
|
||||
void* __restrict__ p_shared,
|
||||
const AElementwiseOperation& a_element_op,
|
||||
const BElementwiseOperation& b_element_op,
|
||||
const EElementwiseOperation& e_element_op,
|
||||
const AGridDesc_AK0_M_AK1& a_grid_desc_ak0_m_ak1,
|
||||
const BGridDesc_BK0_N_BK1& b_grid_desc_bk0_n_bk1,
|
||||
const EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock&
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
const Block2ETileMap& block_2_etile_map)
|
||||
{
|
||||
// build loadWave and MathWave pipelines
|
||||
// loadWave and MathWave synchronized through LDS
|
||||
|
||||
// A matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
|
||||
|
||||
// B matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
|
||||
|
||||
// lds max alignment
|
||||
constexpr auto max_lds_align = math::lcm(AK1, BK1);
|
||||
|
||||
// LDS allocation for A and B: be careful of alignment
|
||||
constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
|
||||
a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
|
||||
|
||||
auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
|
||||
static_cast<ABDataType*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
|
||||
|
||||
auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
|
||||
static_cast<ABDataType*>(p_shared) + a_block_space_size_aligned,
|
||||
b_block_desc_bk0_n_bk1.GetElementSpaceSize());
|
||||
|
||||
constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1, 0, 0);
|
||||
constexpr auto b_block_slice_copy_step = make_multi_index(KPerBlock / BK1, 0, 0);
|
||||
|
||||
const index_t num_k_block_main_loop = __builtin_amdgcn_readfirstlane(
|
||||
(a_grid_desc_ak0_m_ak1.GetLength(I0) * a_grid_desc_ak0_m_ak1.GetLength(I2)) /
|
||||
KPerBlock);
|
||||
|
||||
// divide block work by [M, N]
|
||||
const auto block_work_idx =
|
||||
block_2_etile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
|
||||
|
||||
// HACK: this force m/n_block_data_idx_on_grid into SGPR
|
||||
const index_t m_block_data_idx_on_grid =
|
||||
__builtin_amdgcn_readfirstlane(block_work_idx[I0] * MPerBlock);
|
||||
|
||||
const index_t n_block_data_idx_on_grid =
|
||||
__builtin_amdgcn_readfirstlane(block_work_idx[I1] * NPerBlock);
|
||||
|
||||
if(TileLoadThreadGroup::IsBelong())
|
||||
{
|
||||
|
||||
// LoadWave
|
||||
const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_a_grid, a_grid_desc_ak0_m_ak1.GetElementSpaceSize());
|
||||
const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_b_grid, b_grid_desc_bk0_n_bk1.GetElementSpaceSize());
|
||||
|
||||
// A matrix blockwise copy
|
||||
auto a_blockwise_copy =
|
||||
ThreadGroupTensorSliceTransfer_v4r1<TileLoadThreadGroup,
|
||||
AElementwiseOperation,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
Sequence<AK0PerBlock, MPerBlock, AK1>,
|
||||
ABlockTransferThreadClusterLengths_AK0_M_AK1,
|
||||
ABlockTransferThreadClusterArrangeOrder,
|
||||
ABDataType,
|
||||
ABDataType,
|
||||
decltype(a_grid_desc_ak0_m_ak1),
|
||||
decltype(a_block_desc_ak0_m_ak1),
|
||||
ABlockTransferSrcAccessOrder,
|
||||
Sequence<1, 0, 2>,
|
||||
ABlockTransferSrcVectorDim,
|
||||
2,
|
||||
ABlockTransferSrcScalarPerVector,
|
||||
ABlockTransferDstScalarPerVector_AK1,
|
||||
1,
|
||||
1,
|
||||
AThreadTransferSrcResetCoordinateAfterRun,
|
||||
true,
|
||||
NumGemmKPrefetchStage>(
|
||||
a_grid_desc_ak0_m_ak1,
|
||||
make_multi_index(0, m_block_data_idx_on_grid, 0),
|
||||
a_element_op,
|
||||
a_block_desc_ak0_m_ak1,
|
||||
make_multi_index(0, 0, 0),
|
||||
ck::tensor_operation::element_wise::PassThrough{});
|
||||
|
||||
// B matrix blockwise copy
|
||||
auto b_blockwise_copy =
|
||||
ThreadGroupTensorSliceTransfer_v4r1<TileLoadThreadGroup,
|
||||
BElementwiseOperation,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
Sequence<BK0PerBlock, NPerBlock, BK1>,
|
||||
BBlockTransferThreadClusterLengths_BK0_N_BK1,
|
||||
BBlockTransferThreadClusterArrangeOrder,
|
||||
ABDataType,
|
||||
ABDataType,
|
||||
decltype(b_grid_desc_bk0_n_bk1),
|
||||
decltype(b_block_desc_bk0_n_bk1),
|
||||
BBlockTransferSrcAccessOrder,
|
||||
Sequence<1, 0, 2>,
|
||||
BBlockTransferSrcVectorDim,
|
||||
2,
|
||||
BBlockTransferSrcScalarPerVector,
|
||||
BBlockTransferDstScalarPerVector_BK1,
|
||||
1,
|
||||
1,
|
||||
BThreadTransferSrcResetCoordinateAfterRun,
|
||||
true,
|
||||
NumGemmKPrefetchStage>(
|
||||
b_grid_desc_bk0_n_bk1,
|
||||
make_multi_index(0, n_block_data_idx_on_grid, 0),
|
||||
b_element_op,
|
||||
b_block_desc_bk0_n_bk1,
|
||||
make_multi_index(0, 0, 0),
|
||||
ck::tensor_operation::element_wise::PassThrough{});
|
||||
|
||||
GridwiseGemmLoad::template RunLoadWavePipeline<HasMainKBlockLoop>(
|
||||
a_grid_desc_ak0_m_ak1,
|
||||
a_block_desc_ak0_m_ak1,
|
||||
a_blockwise_copy,
|
||||
a_grid_buf,
|
||||
a_block_buf,
|
||||
a_block_slice_copy_step,
|
||||
b_grid_desc_bk0_n_bk1,
|
||||
b_block_desc_bk0_n_bk1,
|
||||
b_blockwise_copy,
|
||||
b_grid_buf,
|
||||
b_block_buf,
|
||||
b_block_slice_copy_step,
|
||||
num_k_block_main_loop);
|
||||
|
||||
block_sync_lds();
|
||||
block_sync_lds();
|
||||
}
|
||||
else if(TileMathThreadGroup::IsBelong())
|
||||
{
|
||||
// branch early for math wave
|
||||
constexpr index_t KPack =
|
||||
math::max(math::lcm(AK1, BK1),
|
||||
MfmaSelector<ABDataType, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
|
||||
|
||||
auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<
|
||||
TileMathThreadGroupSize,
|
||||
ABDataType,
|
||||
FloatGemmAcc,
|
||||
decltype(a_block_desc_ak0_m_ak1),
|
||||
decltype(b_block_desc_bk0_n_bk1),
|
||||
MPerXdl,
|
||||
NPerXdl,
|
||||
MXdlPerWave,
|
||||
NXdlPerWave,
|
||||
KPack>{};
|
||||
|
||||
auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
|
||||
auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_e_grid, e_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
|
||||
|
||||
// TODO re-architect LDS+math stages
|
||||
// Writing data to GMEM: only math wave is doing the work in cshuffle
|
||||
GridwiseGemmMath::template RunMathWavePipeline<HasMainKBlockLoop>(
|
||||
a_block_buf, b_block_buf, blockwise_gemm, c_thread_buf, num_k_block_main_loop);
|
||||
|
||||
// GEMM definition
|
||||
// c_mtx += transpose(a_mtx) * b_mtx
|
||||
// a_mtx[K0PerBlock, MPerBlock] is in LDS
|
||||
// b_mtx[K0PerBlock, NPerBlock] is in LDS
|
||||
// c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
|
||||
// register
|
||||
// sanity check
|
||||
|
||||
// shuffle C and write out
|
||||
{
|
||||
static_assert(MXdlPerWave % CShuffleMXdlPerWavePerShuffle == 0 &&
|
||||
NXdlPerWave % CShuffleNXdlPerWavePerShuffle == 0,
|
||||
"wrong!");
|
||||
|
||||
constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
|
||||
constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);
|
||||
|
||||
// TODO: hacky, fix it!
|
||||
constexpr auto c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
|
||||
blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
|
||||
|
||||
// TODO: hacky, fix it!
|
||||
// c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp is only used to get lengths
|
||||
constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp =
|
||||
blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
|
||||
|
||||
constexpr auto M0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I0);
|
||||
constexpr auto N0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I1);
|
||||
constexpr auto M1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I2);
|
||||
constexpr auto N1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I3);
|
||||
constexpr auto M2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I4);
|
||||
constexpr auto M3 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I5);
|
||||
constexpr auto M4 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I6);
|
||||
constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I7);
|
||||
|
||||
constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
|
||||
GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
|
||||
|
||||
auto c_shuffle_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
|
||||
static_cast<EDataTypeShuffle*>(p_shared),
|
||||
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
|
||||
|
||||
constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 = transform_tensor_descriptor(
|
||||
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
|
||||
make_tuple(
|
||||
make_freeze_transform(I0),
|
||||
make_unmerge_transform(make_tuple(
|
||||
Number<CShuffleMXdlPerWavePerShuffle>{}, // M0 (MXdlPerWave) per shuffle
|
||||
M1, // M1 = MWave
|
||||
M2, // M2 * M3 * M4 = MPerXdl
|
||||
M3,
|
||||
M4)),
|
||||
make_freeze_transform(I0),
|
||||
make_unmerge_transform(make_tuple(
|
||||
Number<CShuffleNXdlPerWavePerShuffle>{}, // N0 (NXdlPerWave) per shuffle
|
||||
N1, // N1 = NWave
|
||||
N2))), // N2 = NPerXdl
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
|
||||
make_tuple(Sequence<>{},
|
||||
Sequence<0, 2, 4, 5, 6>{},
|
||||
Sequence<>{},
|
||||
Sequence<1, 3, 7>{}));
|
||||
|
||||
// calculate origin of thread output tensor on global memory
|
||||
// blockwise GEMM c matrix starting index
|
||||
const auto c_thread_mtx_on_block =
|
||||
blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);
|
||||
|
||||
const index_t m_thread_data_on_block = c_thread_mtx_on_block[I0];
|
||||
const index_t n_thread_data_on_block = c_thread_mtx_on_block[I1];
|
||||
|
||||
const auto m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor =
|
||||
make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),
|
||||
make_tuple(Sequence<0, 1, 2, 3, 4>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
|
||||
const auto m_thread_data_on_block_idx =
|
||||
m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
|
||||
make_multi_index(m_thread_data_on_block));
|
||||
|
||||
const auto n_thread_data_on_block_to_n0_n1_n2_adaptor =
|
||||
make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),
|
||||
make_tuple(Sequence<0, 1, 2>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
|
||||
const auto n_thread_data_on_block_idx =
|
||||
n_thread_data_on_block_to_n0_n1_n2_adaptor.CalculateBottomIndex(
|
||||
make_multi_index(n_thread_data_on_block));
|
||||
|
||||
// shuffle: threadwise copy C from VGPR to LDS
|
||||
auto c_thread_copy_vgpr_to_lds = ThreadwiseTensorSliceTransfer_v1r3<
|
||||
FloatGemmAcc,
|
||||
EDataTypeShuffle,
|
||||
decltype(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),
|
||||
decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
Sequence<CShuffleMXdlPerWavePerShuffle,
|
||||
CShuffleNXdlPerWavePerShuffle,
|
||||
I1,
|
||||
I1,
|
||||
M2,
|
||||
I1,
|
||||
M4,
|
||||
I1>,
|
||||
Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
|
||||
7,
|
||||
1,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
1,
|
||||
true>{c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
make_multi_index(0,
|
||||
0,
|
||||
m_thread_data_on_block_idx[I1],
|
||||
n_thread_data_on_block_idx[I1],
|
||||
m_thread_data_on_block_idx[I2],
|
||||
m_thread_data_on_block_idx[I3],
|
||||
m_thread_data_on_block_idx[I4],
|
||||
n_thread_data_on_block_idx[I2]),
|
||||
ck::tensor_operation::element_wise::PassThrough{}};
|
||||
|
||||
// shuffle: blockwise copy C from LDS to global
|
||||
auto c_shuffle_block_copy_lds_to_global = ThreadGroupTensorSliceTransfer_v6r1<
|
||||
CShuffleBlockTransferThreadGroup, // ThreadGroup
|
||||
EElementwiseOperation, // ElementwiseOperation,
|
||||
CGlobalMemoryDataOperation, // DstInMemOp,
|
||||
Sequence<1,
|
||||
CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
|
||||
1,
|
||||
CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>, // BlockSliceLengths,
|
||||
CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,
|
||||
EDataTypeShuffle, // typename SrcData,
|
||||
EDataType, // typename DstData,
|
||||
decltype(c_shuffle_block_desc_mblock_mperblock_nblock_nperblock),
|
||||
decltype(e_grid_desc_mblock_mperblock_nblock_nperblock),
|
||||
Sequence<0, 1, 2, 3>, // typename DimAccessOrder,
|
||||
3, // index_t VectorDim,
|
||||
CShuffleBlockTransferScalarPerVector_NPerBlock, // index_t ScalarPerVector,
|
||||
true, // bool ThreadTransferSrcResetCoordinateAfterRun,
|
||||
false> // bool ThreadTransferDstResetCoordinateAfterRun>
|
||||
{c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
|
||||
make_multi_index(0, 0, 0, 0),
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
make_multi_index(block_work_idx[I0], 0, block_work_idx[I1], 0),
|
||||
e_element_op};
|
||||
|
||||
// space filling curve for threadwise C in VGPR
|
||||
constexpr auto sfc_c_vgpr =
|
||||
SpaceFillingCurve<Sequence<MXdlPerWave, NXdlPerWave, 1, 1, M2, 1, M4, 1>,
|
||||
Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
|
||||
Sequence<CShuffleMXdlPerWavePerShuffle,
|
||||
CShuffleNXdlPerWavePerShuffle,
|
||||
1,
|
||||
1,
|
||||
M2,
|
||||
1,
|
||||
M4,
|
||||
1>>{};
|
||||
|
||||
// space filling curve for shuffled blockwise C in global mem
|
||||
constexpr auto sfc_c_global =
|
||||
SpaceFillingCurve<Sequence<1, MPerBlock, 1, NPerBlock>,
|
||||
Sequence<0, 2, 1, 3>,
|
||||
Sequence<1,
|
||||
CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
|
||||
1,
|
||||
CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>>{};
|
||||
|
||||
constexpr index_t num_access = sfc_c_vgpr.GetNumOfAccess();
|
||||
|
||||
static_assert(num_access == sfc_c_global.GetNumOfAccess(), "wrong!");
|
||||
|
||||
// Different way of getting coalesced writes:
|
||||
// We can get rid of doing cshuffle. Instead of reading A rows in contiguous manner
|
||||
// do it interleaved, then mfma can have nice c-mat layout as below:
|
||||
//
|
||||
// TODO
|
||||
// We do not need to do LDS swizzle to align global writes writing cache lines:
|
||||
// v_mfma cmat, amat, bmat, cmat - c-mat register layout are 1xN
|
||||
// elments (N is vertical or strided
|
||||
// dimension)
|
||||
// v_mfma cmat, bmat, amat, cmat - c-mat register layout are Mx1
|
||||
// elments (M is coalescing
|
||||
// dimension) by enumerating M index in
|
||||
// amat, bmat you can align cmat
|
||||
// register(s) to contiguous M elements
|
||||
// for example
|
||||
// 1st mfma instruction output space : 0 4 8 12 16 ....
|
||||
// 2nd mfma instruction output space : 1 5 9 13 17 ....
|
||||
// 3rd mfma instruction output space : 2 6 10 14 18 ....
|
||||
// 4th mfma instruction output space : 3 7 11 15 19 ....
|
||||
// you can pack 4 registers output space into 2WORD and do global write
|
||||
// (no LDS swizzling required)
|
||||
|
||||
static_for<0, num_access, 1>{}([&](auto access_id) {
|
||||
// make sure it's safe to write to LDS
|
||||
block_sync_lds();
|
||||
|
||||
// each thread write its data from VGPR to LDS
|
||||
c_thread_copy_vgpr_to_lds.Run(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
sfc_c_vgpr.GetIndexTupleOfNumber(access_id),
|
||||
c_thread_buf,
|
||||
c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
c_shuffle_block_buf);
|
||||
// make sure it's safe to read from LDS
|
||||
block_sync_lds();
|
||||
|
||||
// each block copy its data from LDS to global
|
||||
c_shuffle_block_copy_lds_to_global.Run(
|
||||
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
|
||||
c_shuffle_block_buf,
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
c_grid_buf);
|
||||
|
||||
if constexpr(access_id < num_access - 1)
|
||||
{
|
||||
constexpr auto c_global_step = sfc_c_global.GetForwardStep(access_id);
|
||||
|
||||
// move on C
|
||||
c_shuffle_block_copy_lds_to_global.MoveDstSliceWindow(
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock, c_global_step);
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user