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DeviceGemm_Wmma_CShuffleV3 with BlockGemmPipelineVersion::v3 (#2096)
* Prepare files for DeviceGemm_Wmma_CShuffleV3
* Implement main part of CShuffleV3 with block pipeline v3 for WMMA
* Remove unused functions and template params for A/B descriptors
* Support both gfx11 and gfx12
* Enable SplitK for gfx12 and disable for gfx11
* Added RowColRow layout for DeviceGemmV2 fp16
* Added more instances for Row, Col, Row data layout
* Added instances for DeviceGemm_Wmma_CShuffleV3, Col, Row, Row data layout
* Added instances for DeviceGemm_Wmma_CShuffleV3, Col, Col, Row data layout
* Added more instances for DeviceGemm_Wmma_CShuffleV3, Row, Row, Row data layout
* Fix formatting
* Add documentation
Based on e5ad48a784
* Enable gemm_universal profiling for gfx11/12
* Add WMMA intrinsics for F8/BF8
* Support F8/BF8 DeviceGemm_Wmma_CShuffleV3, add basic instances
* Add BF16 instances and tests
* Fix test_gemm_universal_wmma_fp8 by adding CK_USE_WMMA_FP8
---------
Co-authored-by: Anca Hamuraru <anca@streamhpc.com>
This commit is contained in:
Anton Gorenko
@@ -0,0 +1,542 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2025, 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 "ck/utility/common_header.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_gemm_v2.hpp"
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#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_gemm_wmma_cshuffle_v3.hpp"
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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/flush_cache.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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/// @brief \"Universal\" GEMM operation with SplitK support.
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///
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/// @par Overview
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/// This GEMM operation implements the following mathematical equation:
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/// C{M,N} = C_op(A_op(A{M,K}) * B_op(B{K,N}))
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/// Where A, B are input tensors and C is the output tensor. The A/B/C_op are
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/// elementwise operations applied to the A, B, and C tensors, respectively.
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/// The \"universal\" gemm comes with multiple pipelines optimized for different usage
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/// scenarios. That's why it's called \"universal\". It's universal through it's design
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/// and versatilty.
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///
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/// @note This Kernel implementation supports SplitK algorithm. It can be configured
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/// to split the dot product accumulated over the K dimension into multiple working groups.
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/// The partial products of different workgroups are then reduced using the AtomicAdd
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/// operation.
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///
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/// @tparam ALayout A tensor data layout.
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/// @tparam BLayout B tensor data layout.
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/// @tparam CLayout C tensor data layout.
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/// @tparam ADataType A tensor data type.
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/// @tparam BDataType B tensor data type.
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/// @tparam CDataType C tensor data type.
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/// @tparam AccDataType The accumulation data type related to the hardware
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/// matrix-multiplication instruction.
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/// @tparam CShuffleDataType The data type used to store matrix-multiplication results into
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/// LDS memory during \"CShuffle\" data layout optimization.
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/// @tparam AElementwiseOperation Elementwise operation applied to the A input tensor elements.
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/// @tparam BElementwiseOperation Elementwise operation applied to the B input tensor elements.
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/// @tparam CElementwiseOperation Elementwise operation applied to the C output tensor
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/// (after GEMM).
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/// @tparam GemmSpec Determines used "padding" version.
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/// @tparam BlockSize The number of threads within workgroup.
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/// @tparam MPerBlock The input/output data tile size in the M dimension.
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/// @tparam NPerBlock The input/output data tile size in the N dimension.
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/// @tparam KPerBlock The input data tile size in the K dimension.
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/// @tparam AK1 The vector load size from global memory for A tensor.
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/// @tparam BK1 The vector load size from global memory for B tensor.
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/// @tparam MPerWmma M size of Wave Matrix Multiply Accumulate (WMMA) instruction.
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/// @tparam NPerWmma N size of Wave Matrix Multiply Accumulate (WMMA) instruction.
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/// @tparam MRepeat The number of iterations in the M dimension over output tile per wavefront.
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/// @tparam NRepeat The number of iterations in the N dimension over output tile per wavefront.
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/// @tparam ABlockTransferThreadClusterLengths_AK0_M_AK1 Spatial thread distribution over the input
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/// data. Can be interpreted as the answer
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/// to the question, "How many threads can be
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/// arranged on each input data axis?"
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/// @tparam ABlockTransferThreadClusterArrangeOrder The order of thread spatial distribution over
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/// the input tensor dimension. Can be interpreted
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/// as the answer to the question: "In which
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/// order to spread threads through tensor axes?".
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/// @tparam ABlockTransferSrcAccessOrder The order of accessing input tensor axes. Can be
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/// interpreted as the answer to the question "Which dimension
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/// to read first? And which next?" etc.
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/// @tparam ABlockTransferSrcVectorDim The index of axis on which we could do vectorized memory
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/// access - the one with contiguous memory.
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/// @tparam ABlockTransferSrcScalarPerVector The size of vector access instruction - the number of
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/// elements accessed per thread per instruction.
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/// @tparam ABlockTransferDstScalarPerVector_AK1 The size of vectorized store into LDS memory.
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/// @tparam ABlockLdsExtraM Whether to use padding for LDS or not. With
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/// universal GEMM there's no need for padding.
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/// @tparam BBlockTransferThreadClusterLengths_BK0_N_BK1 Spatial thread distribution over the input
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/// data. Can be interpreted as the answer
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/// to the question: "How many threads to
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/// arrange on each input data axis?"
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/// @tparam BBlockTransferThreadClusterArrangeOrder The order of thread spatial distribution over
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/// the input tensor dimension. Can be interpreted
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/// as the answer to the question: "In which
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/// order to spread threads through tensor axes?".
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/// @tparam BBlockTransferSrcAccessOrder he order of accessing input tensor axes. Can be
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/// interpreted as the answer to the question "Which dimension
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/// to read first? And which next?" etc.
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/// @tparam BBlockTransferSrcVectorDim The index of axis on which we could do vectorized memory
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/// access - the one with contiguous memory.
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/// @tparam BBlockTransferSrcScalarPerVector The size of vector access instruction - the number of
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/// elements accessed per thread per instruction.
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/// @tparam BBlockTransferDstScalarPerVector_BK1 The size of vectorized store into LDS memory.
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/// @tparam BBlockLdsExtraN Whether to use padding for LDS or not. With
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/// universal GEMM there's no need for padding.
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/// @tparam CShuffleMRepeatPerShuffle The number of matrix-multiplication instructions
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/// results to process per wave per iteration of CShuffle
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/// in M dimension.
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/// @tparam CShuffleNRepeatPerShuffle The number of matrix-multiplication instructions
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/// results to process per wave per iteration of CShuffle
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/// in N dimension.
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/// @tparam CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock The spatial
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/// thread distribution used for storing data into output
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/// tensor across output data layout dimensions.
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/// @tparam CShuffleBlockTransferScalarPerVector_NPerBlock The size of vectorized memory access.
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/// Used when storing data to output tensor.
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/// @tparam BlkGemmPipeSched The version of blockwise-gemm pipeline scheduler (interwave or
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/// intrawave).
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/// @tparam BlkGemmPipelineVer The version of blockwise-gemm pipeline.
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/// @tparam ComputeTypeA Data type used for A input of hardware matrix-multiplication
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/// instructions.
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/// @tparam ComputeTypeB Data type used for B input of hardware matrix-multiplication
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/// instructions.
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/// @tparam PermuteA Whether the A input tensor has gridwise-gemm friendly data layout
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/// in global memory. Currently not supported!
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/// @tparam PermuteB Whether the B input tensor has gridwise-gemm friendly data layout
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/// in global memory (pre-shuffled).
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template <typename ALayout,
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typename BLayout,
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typename CLayout,
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typename ADataType,
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typename BDataType,
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typename CDataType,
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typename AccDataType,
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typename CShuffleDataType,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CElementwiseOperation,
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GemmSpecialization GemmSpec,
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index_t BlockSize,
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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 AK1,
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index_t BK1,
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index_t MPerWmma,
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index_t NPerWmma,
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index_t MRepeat,
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index_t NRepeat,
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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 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 BBlockLdsExtraN,
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index_t CShuffleMRepeatPerShuffle,
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index_t CShuffleNRepeatPerShuffle,
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typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
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index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
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BlockGemmPipelineScheduler BlkGemmPipeSched = BlockGemmPipelineScheduler::Intrawave,
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BlockGemmPipelineVersion BlkGemmPipelineVer = BlockGemmPipelineVersion::v1,
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typename ComputeTypeA = CDataType,
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typename ComputeTypeB = ComputeTypeA,
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bool PermuteA = false,
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bool PermuteB = false>
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struct DeviceGemm_Wmma_CShuffleV3 : public DeviceGemmV2<ALayout,
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BLayout,
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CLayout,
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ADataType,
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BDataType,
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CDataType,
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AElementwiseOperation,
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BElementwiseOperation,
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CElementwiseOperation>
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{
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// GridwiseGemm
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using GridwiseGemm = GridwiseGemm_wmma_cshuffle_v3<
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ALayout,
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BLayout,
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CLayout,
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ADataType,
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BDataType,
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AccDataType,
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CShuffleDataType,
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CDataType,
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AElementwiseOperation,
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BElementwiseOperation,
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CElementwiseOperation,
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GemmSpec,
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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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MPerWmma,
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NPerWmma,
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MRepeat,
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NRepeat,
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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,
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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,
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BBlockLdsExtraN,
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CShuffleMRepeatPerShuffle,
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CShuffleNRepeatPerShuffle,
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CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
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CShuffleBlockTransferScalarPerVector_NPerBlock,
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BlkGemmPipeSched,
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BlkGemmPipelineVer,
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ComputeTypeA,
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ComputeTypeB,
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PermuteA,
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PermuteB>;
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using Argument = typename GridwiseGemm::Argument;
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/// @brief Helper structure responsible for kernel invocation.
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///
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/// @paragraph The `Invoker` class is responsible for preparation and invocation of actual GPU
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/// kernel function. It usually determines the launched grid size prepares kernel
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/// arguments as well as perform specific kernel configuration selection based on
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/// runtime arguments.
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///
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/// @note If appropriately configured it may measure kernel execution time.
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///
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struct Invoker : public BaseInvoker
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{
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/// @brief This function issues GPU kernel execution.
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/// @param arg The GPU kernel arguments.
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/// @param stream_config The HIP stream configuration helper structure.
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/// @return The kernel's average execution time (if time measurement is
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/// enabled).
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float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
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{
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if(stream_config.log_level_ > 0)
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{
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arg.Print();
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GridwiseGemm::BlockwiseGemmPipe::HotLoopInstList::Print();
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}
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if(!GridwiseGemm::CheckValidity(arg))
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{
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throw std::runtime_error("wrong! GridwiseGemm has invalid setting");
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}
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index_t gdx, gdy, gdz;
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std::tie(gdx, gdy, gdz) = GridwiseGemm::CalculateGridSize(arg.M, arg.N, arg.KBatch);
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float ave_time = 0;
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index_t k_grain = arg.KBatch * KPerBlock;
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index_t K_split = (arg.K + k_grain - 1) / k_grain * KPerBlock;
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const bool has_main_k_block_loop = GridwiseGemm::CalculateHasMainKBlockLoop(K_split);
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const auto Run = [&](const auto& kernel) {
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if(stream_config.flush_cache)
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{
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Argument arg_ = arg;
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const auto a_grid_desc_ak0_m_ak1 = GridwiseGemm::MakeAGridDescriptor_AK0_M_AK1(
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arg_.M, arg_.MPadded, arg_.K, arg_.KPadded, arg_.StrideA, arg_.AK0);
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const auto b_grid_desc_bk0_n_bk1 = GridwiseGemm::MakeBGridDescriptor_BK0_N_BK1(
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arg_.K, arg_.KPadded, arg_.N, arg_.NPadded, arg_.StrideB, arg_.BK0);
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auto size_a_buffer =
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a_grid_desc_ak0_m_ak1.GetElementSpaceSize() * sizeof(ADataType);
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auto size_b_buffer =
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b_grid_desc_bk0_n_bk1.GetElementSpaceSize() * sizeof(BDataType);
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ck::utility::RotatingMemWrapper<Argument> rotating_mem(
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arg_, stream_config.rotating_count, size_a_buffer, size_b_buffer);
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rotating_mem.Print();
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auto run_flush_cache = [&]() {
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// flush icache
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ck::utility::flush_icache();
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// rotating mem
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rotating_mem.Next();
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// clear c mem
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if(arg_.KBatch > 1)
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HIP_CHECK_ERROR(hipMemsetAsync(arg_.p_c_grid,
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0,
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arg_.M * arg_.N * sizeof(CDataType),
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stream_config.stream_id_));
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};
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ave_time = ck::utility::launch_and_time_kernel_with_preprocess<false>(
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stream_config,
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run_flush_cache,
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kernel,
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dim3(gdx, gdy, gdz),
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dim3(BlockSize),
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0,
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arg_);
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}
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else
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{
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if(arg.KBatch > 1)
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HIP_CHECK_ERROR(hipMemsetAsync(arg.p_c_grid,
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0,
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arg.M * arg.N * sizeof(CDataType),
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stream_config.stream_id_));
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ave_time = launch_and_time_kernel(
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stream_config, kernel, dim3(gdx, gdy, gdz), dim3(BlockSize), 0, arg);
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}
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};
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constexpr index_t minimum_occupancy = []() {
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if constexpr(BlkGemmPipeSched == BlockGemmPipelineScheduler::Interwave)
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{
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return 2;
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}
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else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v3)
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{
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return (MPerBlock * NPerBlock / BlockSize <= 128) ? 2 : 1;
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}
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else
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{
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return 1;
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}
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}();
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if(has_main_k_block_loop)
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{
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// Tail number always full
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if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v3)
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{
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if(arg.KBatch > 1)
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{
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const auto kernel =
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kernel_gemm_wmma_cshuffle_v3<GridwiseGemm,
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true,
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InMemoryDataOperationEnum::AtomicAdd,
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minimum_occupancy>;
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Run(kernel);
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}
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else
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{
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const auto kernel =
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kernel_gemm_wmma_cshuffle_v3<GridwiseGemm,
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true,
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InMemoryDataOperationEnum::Set,
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minimum_occupancy>;
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Run(kernel);
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}
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}
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else
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{
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// TODO: Implement
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}
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}
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else
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{
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// TODO: Implement
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}
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return ave_time;
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}
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// polymorphic
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float Run(const BaseArgument* p_arg,
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const StreamConfig& stream_config = StreamConfig{}) override
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{
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return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
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}
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};
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static constexpr bool IsValidCompilationParameter()
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{
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// TODO: properly implement this check
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return true;
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}
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static bool IsSupportedArgument(const Argument& arg)
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{
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if(!ck::is_gfx11_supported() && !ck::is_gfx12_supported())
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{
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return false;
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}
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if constexpr(std::is_same_v<CDataType, ck::half_t> ||
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std::is_same_v<CDataType, ck::bhalf_t>)
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{
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if(arg.KBatch > 1 && ck::is_gfx11_supported())
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{
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// gfx11 does not support *_atomic_pk_add_f16/bf16 instructions
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return false;
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||||
}
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||||
}
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||||
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if constexpr(std::is_same_v<ADataType, f8_t> || std::is_same_v<ADataType, bf8_t> ||
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std::is_same_v<BDataType, f8_t> || std::is_same_v<BDataType, bf8_t>)
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||||
{
|
||||
if(ck::is_gfx11_supported())
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||||
{
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return false;
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}
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}
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if((arg.K % AK1 != 0 || arg.K % BK1 != 0) && !(GemmSpec == GemmSpecialization::MKPadding ||
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GemmSpec == GemmSpecialization::NKPadding ||
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||||
GemmSpec == GemmSpecialization::MNKPadding ||
|
||||
GemmSpec == GemmSpecialization::KPadding))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
return GridwiseGemm::CheckValidity(arg);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
|
||||
index_t GetKPerBlock() override { return KPerBlock; }
|
||||
|
||||
bool GetPermuteA() override { return PermuteA; }
|
||||
bool GetPermuteB() override { return PermuteB; }
|
||||
|
||||
static auto MakeArgument(const ADataType* p_a,
|
||||
const BDataType* p_b,
|
||||
CDataType* p_c,
|
||||
index_t M,
|
||||
index_t N,
|
||||
index_t K,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
index_t StrideC,
|
||||
index_t KBatch,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation)
|
||||
{
|
||||
return Argument{p_a, p_b, p_c, M, N, K, StrideA, StrideB, StrideC, KBatch};
|
||||
}
|
||||
|
||||
static auto MakeInvoker() { return Invoker{}; }
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a,
|
||||
const void* p_b,
|
||||
void* p_c,
|
||||
index_t M,
|
||||
index_t N,
|
||||
index_t K,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
index_t StrideC,
|
||||
index_t KBatch,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation) override
|
||||
{
|
||||
return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
|
||||
static_cast<const BDataType*>(p_b),
|
||||
static_cast<CDataType*>(p_c),
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideC,
|
||||
KBatch);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
||||
{
|
||||
return std::make_unique<Invoker>(Invoker{});
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
std::map<BlockGemmPipelineScheduler, std::string> BlkGemmPipelineSchedulerToString{
|
||||
{BlockGemmPipelineScheduler::Intrawave, "Intrawave"},
|
||||
{BlockGemmPipelineScheduler::Interwave, "Interwave"}};
|
||||
|
||||
std::map<BlockGemmPipelineVersion, std::string> BlkGemmPipelineVersionToString{
|
||||
{BlockGemmPipelineVersion::v1, "v1"},
|
||||
{BlockGemmPipelineVersion::v2, "v2"},
|
||||
{BlockGemmPipelineVersion::v3, "v3"},
|
||||
{BlockGemmPipelineVersion::v4, "v4"},
|
||||
{BlockGemmPipelineVersion::v5, "v5"}};
|
||||
|
||||
// clang-format off
|
||||
str << "DeviceGemm_Wmma_CShuffleV3"
|
||||
<< "<"
|
||||
<< getGemmSpecializationString(GemmSpec) << ", "
|
||||
<< std::string(ALayout::name)[0]
|
||||
<< std::string(BLayout::name)[0]
|
||||
<< std::string(CLayout::name)[0]
|
||||
<< ">"
|
||||
<< " BlkSize: "
|
||||
<< BlockSize << ", "
|
||||
<< "BlkTile: "
|
||||
<< MPerBlock << "x" << NPerBlock << "x" << KPerBlock << ", "
|
||||
<< "WaveTile: "
|
||||
<< MPerWmma << "x"<<NPerWmma << ", "
|
||||
<< "WaveMap: "
|
||||
<< MRepeat << "x" << NRepeat << ", "
|
||||
<< "VmemReadVec: "
|
||||
<< ABlockTransferSrcScalarPerVector << "x" << BBlockTransferSrcScalarPerVector << ", "
|
||||
<< "BlkGemmPipelineScheduler: "
|
||||
<< BlkGemmPipelineSchedulerToString[BlkGemmPipeSched] << ", "
|
||||
<< "BlkGemmPipelineVersion: "
|
||||
<< BlkGemmPipelineVersionToString[BlkGemmPipelineVer] << ", "
|
||||
<< "BlkGemmPipelinePrefetchStages: "
|
||||
<< GridwiseGemm::BlockwiseGemmPipe::PrefetchStages << ", "
|
||||
<< "KPack: "
|
||||
<< GridwiseGemm::KPack;
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
REGISTER_EXTRA_PRINTING_METHODS
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user