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https://github.com/ROCm/composable_kernel.git
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Grouped GEMM Multiple D tile loop. (#1247)
* Overload output stream operator for LoopScheduler and PiplineVersion * Add Run overload accepting grid descriptors MK. * Add __device__ keyword for CalculateGridSize * Create device op GroupedGemmMultipleD * Add GroupedGemm MultipleD Tile Loop implementation. * Add an example for GroupedGemm MultipleD tile loop. * Device Op GroupedGEMMTileLoop. * Bunch of small changes in exmaple. * CkProfiler * Remove unused tparam. * Fix include statement. * Fix output stream overloads. * Do not make descriptors and check validity untill we find group. * Fix gemm desc initialization. * Revert device op * Fix compilation for DTYPES=FP16 * Validate tensor transfers paramters. * Validate on host only NK dims if M is not known. * Fix bug. * A convenient debug func for selecting threads. * Fix has main k block loop bug. * Make sure that b2c has up to date tile offset. * Output stream operator for Sequence type. * Cmake file formatting.
This commit is contained in:
Adam Osewski
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <iostream>
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#include <sstream>
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#include <tuple>
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#include "ck/host_utility/device_prop.hpp"
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#include "ck/host_utility/kernel_launch.hpp"
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#include "ck/host_utility/hip_check_error.hpp"
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#include "ck/host_utility/stream_utility.hpp"
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#include "ck/utility/common_header.hpp"
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#include "ck/utility/loop_scheduler.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/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_grouped_gemm_tile_loop.hpp"
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#include "ck/tensor_operation/gpu/device/gemm_specialization.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_multiple_d_xdl_cshuffle.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_selector.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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///
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/// @brief Entry point kernel for device-wide Grouped GEMM operation.
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///
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/// @param[in] gemm_descs_const The pointer to the array of GEMM descriptor structures.
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/// @param[in] group_count The number of together processed GEMMs.
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///
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/// @tparam GridwiseGemm The specific GridwiseGEMM algorithm implementation.
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/// @tparam GemmDesc The structure holding all necessary descriptors and
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/// other data needed for grouped gemm calculation and work
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/// distribution.
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/// @tparam LocalBlock2ETileMap The structure providing mapping between workgroup ids,
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/// the data tiles to process and the output tiles.
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///
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template <typename GridwiseGemm,
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typename GemmDesc,
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GemmSpecialization GemmSpec,
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typename DsDataType,
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typename ALayout,
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typename BLayout,
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typename DsLayout,
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typename ELayout,
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typename OffsettedBlockToCTileMap,
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typename LocalBlock2ETileMap,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CDEElementwiseOperation>
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__global__ void
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#if CK_USE_LAUNCH_BOUNDS
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__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
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#endif
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kernel_grouped_gemm_multiple_d_xdl(const void CK_CONSTANT_ADDRESS_SPACE* gemm_descs_const,
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const index_t group_count,
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const AElementwiseOperation a_element_op,
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const BElementwiseOperation b_element_op,
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const CDEElementwiseOperation cde_element_op)
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{
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#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
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defined(__gfx94__))
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constexpr index_t shared_size = GridwiseGemm::GetSharedMemoryNumberOfByte();
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__shared__ uint8_t p_shared[shared_size];
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const auto gemm_desc_ptr =
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reinterpret_cast<const GemmDesc*>(cast_pointer_to_generic_address_space(gemm_descs_const));
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constexpr auto NumDTensor = DsDataType::Size();
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index_t tile_id = get_block_1d_id();
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index_t tile_offset = 0;
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index_t group_id = -1;
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index_t group_offset = 0;
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index_t grid_size_grp = 0;
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index_t gemm_tile_id_start = 0;
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index_t gemm_tile_id_end = 0;
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using AGridDescMK =
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remove_cvref_t<decltype(GridwiseGemm::template MakeAGridDescriptor_M_K<ALayout, GemmSpec>(
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1, 1, 1))>;
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using BGridDescNK =
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remove_cvref_t<decltype(GridwiseGemm::template MakeBGridDescriptor_N_K<BLayout, GemmSpec>(
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1, 1, 1))>;
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using EGridDescMN =
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remove_cvref_t<decltype(GridwiseGemm::template MakeEGridDescriptor_M_N<ELayout, GemmSpec>(
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1, 1, 1))>;
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using DsGridDescMN =
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remove_cvref_t<decltype(GridwiseGemm::template MakeDsGridDescriptor_M_N<DsLayout, GemmSpec>(
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{}, {}, {}))>;
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index_t M = 0, N = 0, K = 0;
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index_t StrideA, StrideB, StrideE;
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std::array<index_t, NumDTensor> StrideDs;
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AGridDescMK a_grid_desc_mk;
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BGridDescNK b_grid_desc_nk;
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EGridDescMN e_grid_desc_mn;
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DsGridDescMN ds_grid_desc_mn;
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auto b2c_tile_map = OffsettedBlockToCTileMap(LocalBlock2ETileMap(1, 1), 1, 1);
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do
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{
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// Find corresponding GEMM group for our tile
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while(!(tile_id >= gemm_tile_id_start && tile_id < gemm_tile_id_end) &&
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group_id < group_count)
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{
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group_offset += grid_size_grp;
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group_id++;
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if(group_id >= group_count)
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return;
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M = gemm_desc_ptr[group_id].M;
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N = gemm_desc_ptr[group_id].N;
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K = gemm_desc_ptr[group_id].K;
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if(M * N * K == 0)
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{
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grid_size_grp = 0;
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continue;
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}
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b2c_tile_map =
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OffsettedBlockToCTileMap(LocalBlock2ETileMap(M, N), group_offset, tile_offset);
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grid_size_grp = b2c_tile_map.CalculateGridSize(M, N);
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gemm_tile_id_start = group_offset;
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gemm_tile_id_end = group_offset + grid_size_grp;
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}
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StrideA = gemm_desc_ptr[group_id].StrideA;
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StrideB = gemm_desc_ptr[group_id].StrideB;
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StrideDs = gemm_desc_ptr[group_id].StrideDs;
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StrideE = gemm_desc_ptr[group_id].StrideE;
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a_grid_desc_mk =
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GridwiseGemm::template MakeAGridDescriptor_M_K<ALayout, GemmSpec>(M, K, StrideA);
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b_grid_desc_nk =
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GridwiseGemm::template MakeBGridDescriptor_N_K<BLayout, GemmSpec>(K, N, StrideB);
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e_grid_desc_mn =
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GridwiseGemm::template MakeEGridDescriptor_M_N<ELayout, GemmSpec>(M, N, StrideE);
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static_for<0, NumDTensor, 1>{}([&](auto j) {
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using DLayout = remove_cvref_t<tuple_element_t<j.value, DsLayout>>;
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ds_grid_desc_mn(j) = GridwiseGemm::template MakeEGridDescriptor_M_N<DLayout, GemmSpec>(
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M, N, StrideDs[j]);
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});
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using DsGridPointer = decltype(GridwiseGemm::MakeDsGridPointer());
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DsGridPointer p_ds_grid;
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static_for<0, NumDTensor, 1>{}([&](auto i) {
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using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
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p_ds_grid(i) = static_cast<const DDataType*>(gemm_desc_ptr[group_id].p_ds_grid[i]);
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});
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bool has_main_kblock_loop =
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GridwiseGemm::CalculateHasMainKBlockLoop(a_grid_desc_mk.GetLength(Number<1>{}));
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// Update tile offset if we have moved within group
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b2c_tile_map.UpdateTileOffset(tile_offset);
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if(has_main_kblock_loop)
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{
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GridwiseGemm::template Run<true>(gemm_desc_ptr[group_id].p_a_grid,
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gemm_desc_ptr[group_id].p_b_grid,
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p_ds_grid,
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gemm_desc_ptr[group_id].p_e_grid,
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static_cast<void*>(p_shared),
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a_element_op,
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b_element_op,
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cde_element_op,
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a_grid_desc_mk,
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b_grid_desc_nk,
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ds_grid_desc_mn,
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e_grid_desc_mn,
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b2c_tile_map);
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}
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else
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{
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GridwiseGemm::template Run<false>(gemm_desc_ptr[group_id].p_a_grid,
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gemm_desc_ptr[group_id].p_b_grid,
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p_ds_grid,
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gemm_desc_ptr[group_id].p_e_grid,
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static_cast<void*>(p_shared),
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a_element_op,
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b_element_op,
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cde_element_op,
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a_grid_desc_mk,
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b_grid_desc_nk,
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ds_grid_desc_mn,
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e_grid_desc_mn,
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b2c_tile_map);
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}
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tile_id += get_grid_size();
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tile_offset += get_grid_size();
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} while(group_id < group_count);
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#else
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ignore = gemm_descs_const;
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ignore = group_count;
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ignore = a_element_op;
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ignore = b_element_op;
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ignore = cde_element_op;
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#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
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}
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template <typename ALayout,
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typename BLayout,
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typename DsLayout,
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typename ELayout,
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typename ADataType,
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typename BDataType,
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typename AccDataType,
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typename CShuffleDataType,
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typename DsDataType,
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typename EDataType,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CDEElementwiseOperation,
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GemmSpecialization GemmSpec,
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ck::index_t NumGemmKPrefetchStage,
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ck::index_t BlockSize,
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ck::index_t MPerBlock,
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ck::index_t NPerBlock,
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ck::index_t KPerBlock,
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ck::index_t AK1,
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ck::index_t BK1,
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ck::index_t MPerXDL,
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ck::index_t NPerXDL,
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ck::index_t MXdlPerWave,
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ck::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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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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index_t BBlockLdsExtraN,
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index_t CShuffleMXdlPerWavePerShuffle,
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index_t CShuffleNXdlPerWavePerShuffle,
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typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
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index_t CDEShuffleBlockTransferScalarPerVector_NPerBlock,
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PipelineVersion PipelineVer = PipelineVersion::v1,
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LoopScheduler LoopSched = make_default_loop_scheduler(),
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typename ComputeDataType = EDataType>
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struct DeviceGroupedGemmMultipleDXdlCShuffleTileLoop
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: public DeviceGroupedGemmTileLoop<ALayout,
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BLayout,
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DsLayout,
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ELayout,
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ADataType,
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BDataType,
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DsDataType,
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EDataType,
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AElementwiseOperation,
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BElementwiseOperation,
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CDEElementwiseOperation>
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{
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using DeviceOp = DeviceGroupedGemmMultipleDXdlCShuffleTileLoop;
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static constexpr index_t NumDTensor = DsDataType::Size();
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using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle<
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ADataType,
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BDataType,
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ComputeDataType,
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AccDataType,
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CShuffleDataType,
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DsDataType,
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EDataType,
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AElementwiseOperation,
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BElementwiseOperation,
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CDEElementwiseOperation,
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InMemoryDataOperationEnum::Set,
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NumGemmKPrefetchStage,
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BlockSize,
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MPerBlock,
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NPerBlock,
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KPerBlock,
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AK1,
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BK1,
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MPerXDL,
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NPerXDL,
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MXdlPerWave,
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NXdlPerWave,
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ABlockTransferThreadClusterLengths_AK0_M_AK1,
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ABlockTransferThreadClusterArrangeOrder,
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ABlockTransferSrcAccessOrder,
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ABlockTransferSrcVectorDim,
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ABlockTransferSrcScalarPerVector,
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ABlockTransferDstScalarPerVector_AK1,
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false, // AThreadTransferSrcResetCoordinateAfterRun,
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ABlockLdsExtraM,
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BBlockTransferThreadClusterLengths_BK0_N_BK1,
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BBlockTransferThreadClusterArrangeOrder,
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BBlockTransferSrcAccessOrder,
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BBlockTransferSrcVectorDim,
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BBlockTransferSrcScalarPerVector,
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BBlockTransferDstScalarPerVector_BK1,
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false, // BThreadTransferSrcResetCoordinateAfterRun,
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BBlockLdsExtraN,
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CShuffleMXdlPerWavePerShuffle,
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CShuffleNXdlPerWavePerShuffle,
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CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
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CDEShuffleBlockTransferScalarPerVector_NPerBlock,
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LoopSched,
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PipelineVer>;
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template <typename UnderlyingBlockToCTileMap>
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struct OffsettedBlockToCTileMap
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{
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using underlying_type = UnderlyingBlockToCTileMap;
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__host__ __device__ OffsettedBlockToCTileMap(UnderlyingBlockToCTileMap block_to_ctile_map,
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index_t group_offset,
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index_t tile_offset)
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: block_to_ctile_map_{block_to_ctile_map},
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group_offset_{group_offset},
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tile_offset_{tile_offset}
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{
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}
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template <typename TopIdx>
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__host__ __device__ constexpr auto CalculateBottomIndex(const TopIdx& idx_top) const
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{
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return block_to_ctile_map_.CalculateBottomIndex(
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make_multi_index(idx_top[Number<0>{}] + tile_offset_ - group_offset_));
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}
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template <typename CTileIdx, typename CTileDim>
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__host__ __device__ bool ValidCTileIndex(const CTileIdx& c_tile_idx,
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const CTileDim& c_tile_dim) const
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{
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return block_to_ctile_map_.ValidCTileIndex(c_tile_idx, c_tile_dim);
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}
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template <typename CGridDesc_M_N>
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__host__ constexpr bool CheckValidity(const CGridDesc_M_N& c_grid_desc_m_n) const
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{
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return block_to_ctile_map_.CheckValidity(c_grid_desc_m_n);
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}
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__host__ __device__ constexpr index_t CalculateGridSize(index_t M, index_t N) const
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{
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return block_to_ctile_map_.CalculateGridSize(M, N);
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}
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__device__ void UpdateTileOffset(index_t offset) { tile_offset_ = offset; }
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UnderlyingBlockToCTileMap block_to_ctile_map_;
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index_t group_offset_;
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index_t tile_offset_;
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};
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using KernelArguments = GroupedGemmTileLoopKernelArguments<NumDTensor>;
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using Block2ETileMap = BlockToCTileMap_N00_M0_N01Adapt<MPerBlock, NPerBlock>;
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using OffsetedLocalBlock2ETileMap = OffsettedBlockToCTileMap<Block2ETileMap>;
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// Argument
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struct Argument : public BaseArgument
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{
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Argument(std::vector<const void*>& /* p_As */,
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std::vector<const void*>& /* p_Bs */,
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std::vector<std::array<const void*, NumDTensor>>& /* p_Ds */,
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std::vector<void*>& /* p_Es */,
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std::vector<GemmDesc>& gemm_descs,
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AElementwiseOperation a_element_op,
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BElementwiseOperation b_element_op,
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CDEElementwiseOperation cde_element_op,
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int occupancy_num_blocks,
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int gpu_cu_count)
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: group_count_{static_cast<index_t>(gemm_descs.size())},
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occupancy_num_blocks_{occupancy_num_blocks},
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gpu_cu_count_{gpu_cu_count},
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gemm_descs_{gemm_descs},
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a_element_op_{a_element_op},
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b_element_op_{b_element_op},
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cde_element_op_{cde_element_op},
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tile_count_{0}
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{
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for(const auto& desc : gemm_descs)
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{
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const auto M = desc.M_;
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const auto N = desc.N_;
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const auto b2c_tile_map = Block2ETileMap(M, N);
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tile_count_ += b2c_tile_map.CalculateGridSize(M, N);
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}
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}
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index_t group_count_;
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const void* p_dev_gemm_args_;
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int occupancy_num_blocks_;
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int gpu_cu_count_;
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const std::vector<GemmDesc>& gemm_descs_;
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AElementwiseOperation a_element_op_;
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BElementwiseOperation b_element_op_;
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CDEElementwiseOperation cde_element_op_;
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index_t tile_count_;
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};
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struct KernelConfig
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{
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// The oversubscription factor for the number of blocks that can simultaneously reside on
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// GPU.
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static constexpr int BLOCK_SUBSCRIPTION_FACTOR = 1;
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static constexpr int BLOCK_WAVES = BlockSize / get_warp_size();
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static constexpr int CU_SIMDS = 4;
|
||||
// Assume we want to have at most 2 waves per SIMD
|
||||
static constexpr int CU_BLOCKS = math::integer_divide_floor(2 * CU_SIMDS, BLOCK_WAVES);
|
||||
};
|
||||
|
||||
// Invoker
|
||||
struct Invoker : public BaseInvoker
|
||||
{
|
||||
///
|
||||
/// @brief Launch Grouped Gemm kernel.
|
||||
///
|
||||
/// @note This function overload is using user provided device buffer for kernel
|
||||
/// arguments.
|
||||
///
|
||||
/// @param[in] arg The structure containing kernel arguments (in host
|
||||
/// memory).
|
||||
/// @param[in] dev_gemm_args The pointer to device memory with kernel arguments.
|
||||
/// @param[in] stream_config The device stream configuration.
|
||||
///
|
||||
/// @return The average kernel execution time (if time measurement is enabled.)
|
||||
///
|
||||
float Run(const Argument& arg,
|
||||
const void* dev_gemm_args,
|
||||
const StreamConfig& stream_config = StreamConfig{})
|
||||
{
|
||||
if(dev_gemm_args == nullptr)
|
||||
{
|
||||
std::ostringstream err;
|
||||
err << "The gemm arguments device buffer is not allocated!"
|
||||
<< " In " << __FILE__ << ":" << __LINE__ << ", in function: " << __func__;
|
||||
throw std::runtime_error(err.str());
|
||||
}
|
||||
|
||||
float ave_time = 0;
|
||||
ave_time = DispatchKernel(arg, dev_gemm_args, stream_config);
|
||||
|
||||
return ave_time;
|
||||
}
|
||||
|
||||
///
|
||||
/// @brief Launch Grouped Gemm kernel.
|
||||
///
|
||||
/// @note This function overload is using device buffers (for kernel arguments and
|
||||
/// for kernel auxiliary workspace) provided with an argument. The user should
|
||||
/// call @see GetDeviceKernelArgSize, and @see SetDeviceKernelArgs, on arg
|
||||
/// parameter to properly allocate those buffers.
|
||||
///
|
||||
/// @param[in] arg The structure containing kernel arguments (in host memory).
|
||||
/// @param[in] stream_config The device stream configuration.
|
||||
///
|
||||
/// @return The average kernel execution time (if time measurement is enabled.)
|
||||
///
|
||||
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
|
||||
{
|
||||
if(arg.p_dev_gemm_args_ == nullptr)
|
||||
{
|
||||
std::ostringstream err;
|
||||
err << "The gemm arguments device buffer is not allocated!"
|
||||
<< " In " << __FILE__ << ":" << __LINE__ << ", in function: " << __func__;
|
||||
throw std::runtime_error(err.str());
|
||||
}
|
||||
|
||||
return Run(arg, arg.p_dev_gemm_args_, stream_config);
|
||||
}
|
||||
|
||||
float Run(const BaseArgument* p_arg,
|
||||
const StreamConfig& stream_config = StreamConfig{}) override
|
||||
{
|
||||
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
|
||||
}
|
||||
|
||||
private:
|
||||
float DispatchKernel(const Argument& arg,
|
||||
const void* dev_gemm_args,
|
||||
const StreamConfig& stream_config) const
|
||||
{
|
||||
const auto kernel = kernel_grouped_gemm_multiple_d_xdl<GridwiseGemm,
|
||||
KernelArguments,
|
||||
GemmSpec,
|
||||
DsDataType,
|
||||
ALayout,
|
||||
BLayout,
|
||||
DsLayout,
|
||||
ELayout,
|
||||
OffsetedLocalBlock2ETileMap,
|
||||
Block2ETileMap,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CDEElementwiseOperation>;
|
||||
return LaunchKernel(kernel, arg, dev_gemm_args, stream_config);
|
||||
}
|
||||
|
||||
template <typename KernelFunction>
|
||||
int CalculateMaxOccupancyGridSize(const KernelFunction& kernel,
|
||||
const StreamConfig& stream_config) const
|
||||
{
|
||||
// Calculate max number of workgroups that can simultaneously reside on the CU.
|
||||
int occ_num_blocks = 0;
|
||||
size_t dyn_shared_mem_per_blk = 0;
|
||||
hip_check_error(hipOccupancyMaxActiveBlocksPerMultiprocessor(
|
||||
&occ_num_blocks, kernel, BlockSize, dyn_shared_mem_per_blk));
|
||||
|
||||
int cu_count = getAvailableComputeUnitCount(stream_config);
|
||||
|
||||
if(stream_config.log_level_ > 0)
|
||||
{
|
||||
std::cout << "MaxActiveBlocksPerCU: " << occ_num_blocks
|
||||
<< ", available CUs count: " << cu_count << ", occup. grid size: "
|
||||
<< ck::math::min(occ_num_blocks, KernelConfig::CU_BLOCKS) * cu_count
|
||||
<< std::endl;
|
||||
}
|
||||
|
||||
return cu_count * ck::math::min(occ_num_blocks, KernelConfig::CU_BLOCKS);
|
||||
}
|
||||
|
||||
template <typename KernelFunction>
|
||||
float LaunchKernel(const KernelFunction& kernel,
|
||||
const Argument& arg,
|
||||
const void* dev_gemm_args,
|
||||
const StreamConfig& stream_config) const
|
||||
{
|
||||
int grid_size = CalculateMaxOccupancyGridSize(kernel, stream_config);
|
||||
|
||||
if(stream_config.log_level_ > 0)
|
||||
{
|
||||
std::cout << "grid_size: " << grid_size << " tile_count: " << arg.tile_count_
|
||||
<< std::endl;
|
||||
}
|
||||
|
||||
return launch_and_time_kernel(stream_config,
|
||||
kernel,
|
||||
dim3(grid_size),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
cast_pointer_to_constant_address_space(dev_gemm_args),
|
||||
arg.group_count_,
|
||||
arg.a_element_op_,
|
||||
arg.b_element_op_,
|
||||
arg.cde_element_op_);
|
||||
}
|
||||
};
|
||||
|
||||
static constexpr bool IsValidCompilationParameter()
|
||||
{
|
||||
// TODO: properly implement this check
|
||||
return true;
|
||||
}
|
||||
|
||||
static bool IsSupportedArgument(const Argument& arg)
|
||||
{
|
||||
if(!ck::is_xdl_supported())
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
using DsGridDescMN = remove_cvref_t<
|
||||
decltype(GridwiseGemm::template MakeDsGridDescriptor_M_N<DsLayout, GemmSpec>(
|
||||
{}, {}, {}))>;
|
||||
|
||||
bool supported = true;
|
||||
|
||||
for(const auto& gdesc : arg.gemm_descs_)
|
||||
{
|
||||
const auto M = gdesc.M_;
|
||||
const auto N = gdesc.N_;
|
||||
const auto K = gdesc.K_;
|
||||
|
||||
const auto StrideA = gdesc.stride_A_;
|
||||
const auto StrideB = gdesc.stride_B_;
|
||||
const auto StrideE = gdesc.stride_C_;
|
||||
const auto& StrideDs = gdesc.stride_Ds_;
|
||||
|
||||
// If M dimension is unknown at launch time then validate just NK.
|
||||
// If N or K dim is zero (or unknown) then the vector loads responsibility lies on
|
||||
// the user.
|
||||
if(N * K == 0)
|
||||
continue;
|
||||
|
||||
const auto a_grid_desc_mk =
|
||||
GridwiseGemm::template MakeAGridDescriptor_M_K<ALayout, GemmSpec>(M, K, StrideA);
|
||||
const auto b_grid_desc_nk =
|
||||
GridwiseGemm::template MakeBGridDescriptor_N_K<BLayout, GemmSpec>(K, N, StrideB);
|
||||
const auto e_grid_desc_mn =
|
||||
GridwiseGemm::template MakeEGridDescriptor_M_N<ELayout, GemmSpec>(M, N, StrideE);
|
||||
|
||||
DsGridDescMN ds_grid_desc_mn;
|
||||
static_for<0, NumDTensor, 1>{}([&](auto j) {
|
||||
using DLayout = remove_cvref_t<tuple_element_t<j.value, DsLayout>>;
|
||||
ds_grid_desc_mn(j) =
|
||||
GridwiseGemm::template MakeEGridDescriptor_M_N<DLayout, GemmSpec>(
|
||||
M, N, StrideDs[j]);
|
||||
});
|
||||
|
||||
const auto b2c_tile_map = Block2ETileMap(M, N);
|
||||
|
||||
if(!(GridwiseGemm::template CheckValidity(a_grid_desc_mk,
|
||||
b_grid_desc_nk,
|
||||
ds_grid_desc_mn,
|
||||
e_grid_desc_mn,
|
||||
b2c_tile_map) &&
|
||||
GridwiseGemm::template CheckTensorTransfersValidity<ALayout, BLayout, ELayout>(
|
||||
M, N, K)))
|
||||
{
|
||||
#if DEBUG_LOG
|
||||
std::cout << "The provided GEMM problem size (M,N,K) [" << M << "," << N << "," << K
|
||||
<< "] are not supported by current template parameters!"
|
||||
<< " In " << __FILE__ << ":" << __LINE__ << ", in function: " << __func__;
|
||||
#endif
|
||||
supported = false;
|
||||
}
|
||||
}
|
||||
|
||||
return supported;
|
||||
}
|
||||
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
|
||||
static auto MakeArgument(std::vector<const void*>& p_As,
|
||||
std::vector<const void*>& p_Bs,
|
||||
std::vector<std::array<const void*, NumDTensor>>& p_Ds,
|
||||
std::vector<void*>& p_Es,
|
||||
std::vector<GemmDesc> gemm_descs,
|
||||
AElementwiseOperation a_elementwise_op,
|
||||
BElementwiseOperation b_elementwise_op,
|
||||
CDEElementwiseOperation cde_elementwise_op)
|
||||
{
|
||||
const auto kernel = kernel_grouped_gemm_multiple_d_xdl<GridwiseGemm,
|
||||
KernelArguments,
|
||||
GemmSpec,
|
||||
DsDataType,
|
||||
ALayout,
|
||||
BLayout,
|
||||
DsLayout,
|
||||
ELayout,
|
||||
OffsetedLocalBlock2ETileMap,
|
||||
Block2ETileMap,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CDEElementwiseOperation>;
|
||||
int occupancy, num_cu;
|
||||
hip_check_error(
|
||||
hipOccupancyMaxActiveBlocksPerMultiprocessor(&occupancy, kernel, BlockSize, 0));
|
||||
|
||||
hipDeviceProp_t dev_prop;
|
||||
hipDevice_t dev;
|
||||
hip_check_error(hipGetDevice(&dev));
|
||||
hip_check_error(hipGetDeviceProperties(&dev_prop, dev));
|
||||
num_cu = dev_prop.multiProcessorCount;
|
||||
|
||||
return Argument{p_As,
|
||||
p_Bs,
|
||||
p_Ds,
|
||||
p_Es,
|
||||
gemm_descs,
|
||||
a_elementwise_op,
|
||||
b_elementwise_op,
|
||||
cde_elementwise_op,
|
||||
occupancy,
|
||||
num_cu};
|
||||
}
|
||||
|
||||
std::unique_ptr<BaseArgument>
|
||||
MakeArgumentPointer(std::vector<const void*>& p_As,
|
||||
std::vector<const void*>& p_Bs,
|
||||
std::vector<std::array<const void*, NumDTensor>>& p_Ds,
|
||||
std::vector<void*>& p_Es,
|
||||
std::vector<GemmDesc>& gemm_descs,
|
||||
AElementwiseOperation a_elementwise_op,
|
||||
BElementwiseOperation b_elementwise_op,
|
||||
CDEElementwiseOperation cde_elementwise_op) override
|
||||
{
|
||||
const auto kernel = kernel_grouped_gemm_multiple_d_xdl<GridwiseGemm,
|
||||
KernelArguments,
|
||||
GemmSpec,
|
||||
DsDataType,
|
||||
ALayout,
|
||||
BLayout,
|
||||
DsLayout,
|
||||
ELayout,
|
||||
OffsetedLocalBlock2ETileMap,
|
||||
Block2ETileMap,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CDEElementwiseOperation>;
|
||||
int occupancy, num_cu;
|
||||
hip_check_error(
|
||||
hipOccupancyMaxActiveBlocksPerMultiprocessor(&occupancy, kernel, BlockSize, 0));
|
||||
|
||||
hipDeviceProp_t dev_prop;
|
||||
hipDevice_t dev;
|
||||
hip_check_error(hipGetDevice(&dev));
|
||||
hip_check_error(hipGetDeviceProperties(&dev_prop, dev));
|
||||
num_cu = dev_prop.multiProcessorCount;
|
||||
|
||||
return std::make_unique<Argument>(p_As,
|
||||
p_Bs,
|
||||
p_Ds,
|
||||
p_Es,
|
||||
gemm_descs,
|
||||
a_elementwise_op,
|
||||
b_elementwise_op,
|
||||
cde_elementwise_op,
|
||||
occupancy,
|
||||
num_cu);
|
||||
}
|
||||
|
||||
static auto MakeInvoker() { return Invoker{}; }
|
||||
|
||||
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
||||
{
|
||||
return std::make_unique<Invoker>(Invoker{});
|
||||
}
|
||||
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::ostringstream();
|
||||
|
||||
// clang-format off
|
||||
str << "DeviceGroupedGemmMultipleDXdlCShuffleTileLoop"
|
||||
<< "<"
|
||||
<< std::string(ALayout::name)[0] << ","
|
||||
<< std::string(BLayout::name)[0] << ","
|
||||
<< std::string(ELayout::name)[0] << ","
|
||||
<< BlockSize << ", "
|
||||
<< MPerBlock << ", "
|
||||
<< NPerBlock << ", "
|
||||
<< KPerBlock << ", "
|
||||
<< AK1 << ", "
|
||||
<< BK1 << ", "
|
||||
<< MPerXDL << ", "
|
||||
<< NPerXDL << ", "
|
||||
<< MXdlPerWave << ", "
|
||||
<< NXdlPerWave << ", "
|
||||
<< ABlockTransferSrcScalarPerVector << ", "
|
||||
<< BBlockTransferSrcScalarPerVector << ", "
|
||||
<< CShuffleMXdlPerWavePerShuffle << ", "
|
||||
<< CShuffleNXdlPerWavePerShuffle << ", "
|
||||
<< getGemmSpecializationString(GemmSpec) << ", "
|
||||
<< PipelineVer << ", "
|
||||
<< LoopSched
|
||||
<< ">";
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
|
||||
void SetDeviceKernelArgs(Argument& arg, void* p_dev_kernel_args) const
|
||||
{
|
||||
arg.p_dev_gemm_args_ = p_dev_kernel_args;
|
||||
}
|
||||
|
||||
void SetDeviceKernelArgs(BaseArgument* p_arg, void* p_dev_kernel_args) const override
|
||||
{
|
||||
return SetDeviceKernelArgs(*dynamic_cast<Argument*>(p_arg), p_dev_kernel_args);
|
||||
}
|
||||
|
||||
size_t GetDeviceKernelArgSize(const BaseArgument* p_arg) const override
|
||||
{
|
||||
return dynamic_cast<const Argument*>(p_arg)->group_count_ * sizeof(KernelArguments);
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user