mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-14 02:02:46 +00:00
Skip lds of b matrix (#326)
* start
* read for gridwise gemm
* add MakeBGridDescriptor_K0_N0_N1_N2_N3_K1
* add thread copy desc and register buffer
* add K0PerBlock dim
* add read global data
* finish gridwise gemm
* finish blockwise gemm
* add print data
* add smallest config
* add compare code for gridwis gemm
* fix NXdlPerWave
* fix k0perthread and gridewis gemm main loop
* remove b matrix lds alloc
* fix name
* add test code
* create b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3 from parameter
* add double register
* modify b_thread_desc_
* add float
* fp16 tag
* add tail for pipeline
* finish main loop
* optimize main loop
* start clear gridwise gemm
* clear code
* clear redundant code
* change file name
* change file name
* fix bug after merge develop
* fix input parameters
* using MultiK0 control b load data loop
* fix some config
* 4 buffer
* fix bug
* one can use
* change read order
* change buffer array to tuple
* change to 8 buffer
* interleave buffer load
* change to 16
* read 8 buffer
* add data buffer to template
* fix after merge develop(head file)
* format
* change to 4 buffer
* remove unnecessary lambda fun
[ROCm/composable_kernel commit: 10b3278b05]
This commit is contained in:
ltqin
@@ -4,5 +4,6 @@ add_example_executable(example_gemm_dl_int8 gemm_dl_int8.cpp)
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add_example_executable(example_gemm_xdl_fp16 gemm_xdl_fp16.cpp)
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add_example_executable(example_gemm_xdl_bf16 gemm_xdl_bf16.cpp)
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add_example_executable(example_gemm_xdl_int8 gemm_xdl_int8.cpp)
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add_example_executable(example_gemm_xdl_skip_b_lds_fp16 gemm_xdl_skip_b_lds_fp16.cpp)
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# FIXME: re-enable this exampe as test when SWDEV-335738 is fixed
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add_example_executable_no_testing(example_gemm_xdl_fp64 gemm_xdl_fp64.cpp)
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260
example/01_gemm/gemm_xdl_skip_b_lds_fp16.cpp
Normal file
260
example/01_gemm/gemm_xdl_skip_b_lds_fp16.cpp
Normal file
@@ -0,0 +1,260 @@
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#include <iostream>
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#include <numeric>
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#include <initializer_list>
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#include <cstdlib>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
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#include "ck/tensor_operation/gpu/device/device_gemm_xdl.hpp"
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#include "ck/tensor_operation/gpu/device/device_gemm_xdl_skip_b_lds.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/host_tensor/device_memory.hpp"
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#include "ck/library/host_tensor/host_tensor.hpp"
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#include "ck/library/host_tensor/host_tensor_generator.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
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template <ck::index_t... Is>
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using S = ck::Sequence<Is...>;
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using F16 = ck::half_t;
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using F32 = float;
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using Row = ck::tensor_layout::gemm::RowMajor;
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using Col = ck::tensor_layout::gemm::ColumnMajor;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using ALayout = ck::tensor_layout::gemm::RowMajor;
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using BLayout = ck::tensor_layout::gemm::ColumnMajor;
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using CLayout = ck::tensor_layout::gemm::RowMajor;
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using AElementOp = ck::tensor_operation::element_wise::PassThrough;
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using BElementOp = ck::tensor_operation::element_wise::PassThrough;
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using CElementOp = ck::tensor_operation::element_wise::PassThrough;
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static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
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#define USING_SKIP_LDS 1
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// clang-format off
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#if USING_SKIP_LDS
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using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdlSkipBLds
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//###########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BThreadTransfer| BBlock| CThreadTransfer| CThreadTransfer|
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//###########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Spacialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| SrcScalar| buffer| SrcDstVectorDim| DstScalar|
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//###########| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerVector| size | | PerVector|
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//###########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
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#if 0
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< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 16, 64, 4, 8, 16, 16, 1, 1, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 8, 8, 7, 1>;
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using ADataType = ck::half_t;
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using BDataType = ck::half_t;
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using CDataType = ck::half_t;
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using AccDataType = float;
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#else
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< F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 16, 64, 4, 4, 16, 16, 1, 1, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 4, 4, 7, 1>;
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using ADataType = float;
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using BDataType = float;
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using CDataType = float;
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using AccDataType = float;
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#endif
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#else
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using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl
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//###########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
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//###########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Spacialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
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//###########| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
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//###########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
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< F32, F32, F32, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 16, 64, 4, 4, 16, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, 7, 1, 2>;
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using ADataType = float;
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using BDataType = float;
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using CDataType = float;
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using AccDataType = float;
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#endif
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// clang-format on
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using ReferenceGemmInstance = ck::tensor_operation::host::
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ReferenceGemm<ADataType, BDataType, CDataType, float, AElementOp, BElementOp, CElementOp>;
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template <typename DataType>
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std::ostream& show_2d_matrix(std::ostream& os, Tensor<DataType>& matrix)
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{
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os << "[" << std::endl;
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for(size_t x = 0; x < matrix.mDesc.GetLengths()[0]; x++)
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{
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os << "[";
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for(size_t y = 0; y < matrix.mDesc.GetLengths()[1]; y++)
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{
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os << std::setw(5) << static_cast<float>(matrix(x, y));
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}
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os << "]" << std::endl;
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}
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os << "]";
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return os;
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}
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int main(int argc, char* argv[])
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{
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bool do_verification = 0;
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int init_method = 0;
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bool time_kernel = false;
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// GEMM shape
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#if 1
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ck::index_t M = 16;
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ck::index_t N = 64 * 120;
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ck::index_t K = 4096;
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ck::index_t StrideA = K;
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ck::index_t StrideB = K;
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ck::index_t StrideC = N;
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#else
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ck::index_t M = 16;
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ck::index_t N = 16;
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ck::index_t K = 32;
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ck::index_t StrideA = 8;
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ck::index_t StrideB = 8;
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ck::index_t StrideC = 16;
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#endif
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if(argc == 4)
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{
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do_verification = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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time_kernel = std::stoi(argv[3]);
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}
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else if(argc == 10)
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{
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do_verification = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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time_kernel = std::stoi(argv[3]);
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M = std::stoi(argv[4]);
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N = std::stoi(argv[5]);
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K = std::stoi(argv[6]);
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StrideA = std::stoi(argv[7]);
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StrideB = std::stoi(argv[8]);
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StrideC = std::stoi(argv[9]);
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}
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else
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{
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printf("arg1: verification (0=no, 1=yes)\n");
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printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
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printf("arg3: time kernel (0=n0, 1=yes)\n");
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printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
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exit(0);
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}
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auto f_host_tensor_descriptor =
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[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
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if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
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{
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return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
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std::vector<std::size_t>({stride, 1}));
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}
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else
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{
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return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
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std::vector<std::size_t>({1, stride}));
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}
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};
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Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
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Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
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Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
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std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
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std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
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switch(init_method)
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{
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case 0: break;
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case 1:
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a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
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b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
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break;
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case 2:
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a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
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b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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break;
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default:
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// a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
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a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
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b_k_n.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
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}
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DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
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DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
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DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace());
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a_m_k_device_buf.ToDevice(a_m_k.mData.data());
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b_k_n_device_buf.ToDevice(b_k_n.mData.data());
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auto a_element_op = AElementOp{};
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auto b_element_op = BElementOp{};
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auto c_element_op = CElementOp{};
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// do GEMM
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auto gemm = DeviceGemmInstance{};
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auto invoker = gemm.MakeInvoker();
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auto argument = gemm.MakeArgument(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
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M,
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N,
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K,
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StrideA,
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StrideB,
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StrideC,
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a_element_op,
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b_element_op,
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c_element_op);
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if(!gemm.IsSupportedArgument(argument))
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{
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throw std::runtime_error(
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"wrong! device_gemm with the specified compilation parameters does "
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"not support this GEMM problem");
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}
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float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
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std::size_t flop = std::size_t(2) * M * N * K;
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std::size_t num_btype =
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sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * M * N;
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gb_per_sec = num_btype / 1.E6 / ave_time;
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std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
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<< gemm.GetTypeString() << std::endl;
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c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
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if(do_verification)
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{
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auto ref_gemm = ReferenceGemmInstance{};
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auto ref_invoker = ref_gemm.MakeInvoker();
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auto ref_argument = ref_gemm.MakeArgument(
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a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
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ref_invoker.Run(ref_argument);
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#if 0
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{
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show_2d_matrix(std::cout << "a : ", a_m_k) << std::endl;
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show_2d_matrix(std::cout << "b: ", b_k_n) << std::endl;
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show_2d_matrix(std::cout << "c_device: ", c_m_n_device_result) << std::endl;
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show_2d_matrix(std::cout << "c_host :", c_m_n_host_result) << std::endl;
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}
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#endif
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ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData);
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}
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return 0;
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}
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@@ -0,0 +1,320 @@
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#ifndef CK_BLOCKWISE_GEMM_XDLOPS_B_REGISTER_HPP
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#define CK_BLOCKWISE_GEMM_XDLOPS_B_REGISTER_HPP
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#include "ck/utility/common_header.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/warp/xdlops_gemm.hpp"
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#include "ck/tensor_description/tensor_adaptor.hpp"
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namespace ck {
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template <index_t BlockSize,
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typename FloatAB,
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typename FloatAcc,
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typename AK0MK1BlockDesc,
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typename BK0K0BN0N1N2N3K1BlockDesc,
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index_t MPerBlock,
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index_t NPerBlock,
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index_t K0PerBlock,
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index_t MPerXDL,
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index_t NPerXDL,
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index_t MRepeat,
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index_t NRepeat,
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index_t KPack>
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struct BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1r1
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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 index_t WaveSize = 64;
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static constexpr index_t KPerBlock = K0PerBlock * KPack;
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static constexpr index_t A_K0 = AK0MK1BlockDesc{}.GetLength(I0);
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static constexpr index_t A_K1 = AK0MK1BlockDesc{}.GetLength(I2);
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static constexpr auto xdlops_gemm = XdlopsGemm<FloatAB, MPerXDL, NPerXDL, KPack>{};
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static constexpr index_t KPerThread = KPerBlock / xdlops_gemm.K0PerXdlops;
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static constexpr index_t K0PerThread = K0PerBlock / xdlops_gemm.K0PerXdlops;
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static constexpr index_t MWaves = MPerBlock / (MRepeat * MPerXDL);
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static constexpr index_t NWaves = NPerBlock / (NRepeat * NPerXDL);
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StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr,
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FloatAcc,
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MRepeat * NRepeat,
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xdlops_gemm.GetRegSizePerXdlops(),
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true>
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c_thread_buf_;
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__host__ __device__ constexpr auto& GetCThreadBuffer() { return c_thread_buf_; }
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__device__ static auto GetWaveIdx()
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{
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const index_t thread_id = get_thread_local_1d_id();
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constexpr auto threadid_to_wave_idx_adaptor = make_single_stage_tensor_adaptor(
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make_tuple(make_merge_transform(make_tuple(MWaves, NWaves, WaveSize))),
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make_tuple(Sequence<0, 1, 2>{}),
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make_tuple(Sequence<0>{}));
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return threadid_to_wave_idx_adaptor.CalculateBottomIndex(make_multi_index(thread_id));
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}
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__device__ static auto CalculateAThreadOriginDataIndex()
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{
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const auto wave_idx = GetWaveIdx();
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const auto waveId_m = wave_idx[I0];
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const auto xdlops_a_idx = xdlops_gemm.CalculateAThreadOriginDataIndex();
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|
||||
return make_tuple(0, waveId_m, xdlops_a_idx[I1], KPerThread * xdlops_a_idx[I0]);
|
||||
}
|
||||
|
||||
__device__ static auto CalculateBThreadOriginDataIndex()
|
||||
{
|
||||
const auto wave_idx = GetWaveIdx();
|
||||
|
||||
const auto waveId_n = wave_idx[I1];
|
||||
|
||||
const auto xdlops_b_idx = xdlops_gemm.CalculateBThreadOriginDataIndex();
|
||||
|
||||
return make_tuple(0, waveId_n, xdlops_b_idx[I1], KPerThread * xdlops_b_idx[I0]);
|
||||
}
|
||||
|
||||
template <index_t m0, index_t n0, index_t xdlops_i, index_t blk_i>
|
||||
__device__ static auto
|
||||
CalculateCThreadOriginDataIndex(Number<m0>, Number<n0>, Number<xdlops_i>, Number<blk_i>)
|
||||
{
|
||||
const auto wave_idx = GetWaveIdx();
|
||||
|
||||
const auto waveId_m = wave_idx[I0];
|
||||
const auto waveId_n = wave_idx[I1];
|
||||
|
||||
const auto blk_idx = xdlops_gemm.GetBeginOfThreadBlk(xdlops_i, blk_i);
|
||||
|
||||
constexpr auto mrepeat_mwave_mperxdl_to_m_adaptor = make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_unmerge_transform(make_tuple(MRepeat, MWaves, MPerXDL))),
|
||||
make_tuple(Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 1, 2>{}));
|
||||
|
||||
constexpr auto nrepeat_nwave_nperxdl_to_n_adaptor = make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_unmerge_transform(make_tuple(NRepeat, NWaves, NPerXDL))),
|
||||
make_tuple(Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 1, 2>{}));
|
||||
|
||||
const index_t c_thread_m = mrepeat_mwave_mperxdl_to_m_adaptor.CalculateBottomIndex(
|
||||
make_tuple(m0, waveId_m, blk_idx[I0]))[I0];
|
||||
const index_t c_thread_n = nrepeat_nwave_nperxdl_to_n_adaptor.CalculateBottomIndex(
|
||||
make_tuple(n0, waveId_n, blk_idx[I1]))[I0];
|
||||
|
||||
return make_tuple(c_thread_m, c_thread_n);
|
||||
}
|
||||
|
||||
__host__ __device__ BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1r1()
|
||||
{
|
||||
static_assert(AK0MK1BlockDesc::IsKnownAtCompileTime() &&
|
||||
BK0K0BN0N1N2N3K1BlockDesc::IsKnownAtCompileTime(),
|
||||
"wrong! Desc should be known at compile-time");
|
||||
|
||||
static_assert(BlockSize == MWaves * NWaves * WaveSize,
|
||||
"BlockSize != MWaves * NWaves * WaveSize\n");
|
||||
|
||||
static_assert(MPerBlock % (MPerXDL * MRepeat) == 0 && NPerBlock % (NPerXDL * NRepeat) == 0,
|
||||
"wrong!");
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2()
|
||||
{
|
||||
constexpr auto c_m0_m1_m2_n_tblk_lens = xdlops_gemm.GetCM0M1M2NThreadBlkLengths();
|
||||
|
||||
constexpr auto M0 = c_m0_m1_m2_n_tblk_lens[I0];
|
||||
constexpr auto M1 = c_m0_m1_m2_n_tblk_lens[I1];
|
||||
constexpr auto M2 = c_m0_m1_m2_n_tblk_lens[I2];
|
||||
constexpr auto N = c_m0_m1_m2_n_tblk_lens[I3];
|
||||
|
||||
return make_naive_tensor_descriptor_packed(
|
||||
make_tuple(Number<MRepeat>{}, Number<NRepeat>{}, I1, I1, M0, M1, M2, N));
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto GetCThreadDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2()
|
||||
{
|
||||
constexpr auto c_m0_m1_m2_n_tblk_lens = xdlops_gemm.GetCM0M1M2NThreadBlkLengths();
|
||||
|
||||
constexpr auto M0 = c_m0_m1_m2_n_tblk_lens[I0];
|
||||
constexpr auto M1 = c_m0_m1_m2_n_tblk_lens[I1];
|
||||
constexpr auto M2 = c_m0_m1_m2_n_tblk_lens[I2];
|
||||
constexpr auto N = c_m0_m1_m2_n_tblk_lens[I3];
|
||||
|
||||
return make_naive_tensor_descriptor_packed(
|
||||
make_tuple(I1, Number<MRepeat>{}, Number<NRepeat>{}, I1, I1, M0, M1, M2, N));
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2()
|
||||
{
|
||||
constexpr auto c_block_desc_m0_n0_m1_n1_m2_n2 =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{},
|
||||
Number<NRepeat>{},
|
||||
Number<MWaves>{},
|
||||
Number<NWaves>{},
|
||||
Number<MPerXDL>{},
|
||||
Number<NPerXDL>{}));
|
||||
|
||||
return xdlops_gemm.MakeCDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_block_desc_m0_n0_m1_n1_m2_n2);
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto GetCBlockDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2()
|
||||
{
|
||||
constexpr auto c_block_desc_g_m0_n0_m1_n1_m2_n2 =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(I1,
|
||||
Number<MRepeat>{},
|
||||
Number<NRepeat>{},
|
||||
Number<MWaves>{},
|
||||
Number<NWaves>{},
|
||||
Number<MPerXDL>{},
|
||||
Number<NPerXDL>{}));
|
||||
|
||||
return xdlops_gemm.MakeCDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(
|
||||
c_block_desc_g_m0_n0_m1_n1_m2_n2);
|
||||
}
|
||||
|
||||
template <typename CGridDesc_M_N>
|
||||
__host__ __device__ static constexpr auto
|
||||
MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(const CGridDesc_M_N& c_grid_desc_m_n)
|
||||
{
|
||||
const auto M = c_grid_desc_m_n.GetLength(I0);
|
||||
const auto N = c_grid_desc_m_n.GetLength(I1);
|
||||
|
||||
const auto c_grid_desc_m0_n0_m1_n1_m2_n2 = transform_tensor_descriptor(
|
||||
c_grid_desc_m_n,
|
||||
make_tuple(make_unmerge_transform(make_tuple(M / (MWaves * MPerXDL), MWaves, MPerXDL)),
|
||||
make_unmerge_transform(make_tuple(N / (NWaves * NPerXDL), NWaves, NPerXDL))),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0, 2, 4>{}, Sequence<1, 3, 5>{}));
|
||||
|
||||
return xdlops_gemm.MakeCDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_grid_desc_m0_n0_m1_n1_m2_n2);
|
||||
}
|
||||
|
||||
template <typename CGridDesc_G_M_N>
|
||||
__host__ __device__ static constexpr auto
|
||||
MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(const CGridDesc_G_M_N& c_grid_desc_g_m_n)
|
||||
{
|
||||
const auto G = c_grid_desc_g_m_n.GetLength(I0);
|
||||
const auto M = c_grid_desc_g_m_n.GetLength(I1);
|
||||
const auto N = c_grid_desc_g_m_n.GetLength(I2);
|
||||
|
||||
const auto c_grid_desc_g_m0_n0_m1_n1_m2_n2 = transform_tensor_descriptor(
|
||||
c_grid_desc_g_m_n,
|
||||
make_tuple(make_pass_through_transform(G),
|
||||
make_unmerge_transform(make_tuple(M / (MWaves * MPerXDL), MWaves, MPerXDL)),
|
||||
make_unmerge_transform(make_tuple(N / (NWaves * NPerXDL), NWaves, NPerXDL))),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1, 3, 5>{}, Sequence<2, 4, 6>{}));
|
||||
|
||||
return xdlops_gemm.MakeCDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(
|
||||
c_grid_desc_g_m0_n0_m1_n1_m2_n2);
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto MakeABlockDescriptor_M0_M1_M2_K()
|
||||
{
|
||||
return transform_tensor_descriptor(
|
||||
AK0MK1BlockDesc{},
|
||||
make_tuple(
|
||||
make_merge_transform_v3_division_mod(make_tuple(Number<A_K0>{}, Number<A_K1>{})),
|
||||
make_unmerge_transform(
|
||||
make_tuple(Number<MRepeat>{}, Number<MWaves>{}, Number<MPerXDL>{}))),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<3>{}, Sequence<0, 1, 2>{}));
|
||||
}
|
||||
|
||||
__device__ void MoveABlockSliceWindow()
|
||||
{
|
||||
a_thread_copy_.MoveSrcSliceWindow(a_block_desc_m0_m1_m2_k,
|
||||
make_multi_index(0, 0, 0, K0PerBlock * KPack));
|
||||
}
|
||||
__device__ void ResetABlockStartWindow()
|
||||
{
|
||||
a_thread_copy_.SetSrcCoord(CalculateAThreadOriginDataIndex());
|
||||
}
|
||||
|
||||
static constexpr auto a_block_desc_m0_m1_m2_k = MakeABlockDescriptor_M0_M1_M2_K();
|
||||
|
||||
template <typename ABlockBuffer, typename BBlockBuffer, typename CThreadBuffer>
|
||||
__device__ void Run(const ABlockBuffer& a_block_buf,
|
||||
const BBlockBuffer& b_thread_buf,
|
||||
CThreadBuffer& c_thread_buf) const
|
||||
{
|
||||
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatAB>(
|
||||
a_thread_desc_.GetElementSpaceSize());
|
||||
|
||||
static_for<0, MRepeat, 1>{}([&](auto m0) {
|
||||
// read A
|
||||
a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
|
||||
make_tuple(m0, I0, I0, I0),
|
||||
a_block_buf,
|
||||
a_thread_desc_,
|
||||
make_tuple(I0, I0, I0, I0),
|
||||
a_thread_buf);
|
||||
|
||||
static_for<0, NRepeat, 1>{}([&](auto n0) {
|
||||
// read B
|
||||
static_for<0, KPerThread, KPack>{}([&](auto k) {
|
||||
vector_type<FloatAB, KPack> a_thread_vec;
|
||||
vector_type<FloatAB, KPack> b_thread_vec;
|
||||
constexpr index_t k0 = k / KPack;
|
||||
static_for<0, KPack, 1>{}([&](auto i) {
|
||||
a_thread_vec.template AsType<FloatAB>()(i) = a_thread_buf
|
||||
[Number<a_thread_desc_.CalculateOffset(make_tuple(0, 0, 0, k + i))>{}];
|
||||
b_thread_vec.template AsType<FloatAB>()(i) = b_thread_buf
|
||||
[Number<b_thread_desc_.CalculateOffset(make_tuple(k0, n0, i))>{}];
|
||||
});
|
||||
|
||||
using mfma_input_type =
|
||||
typename vector_type<FloatAB, xdlops_gemm.K1PerXdlops>::type;
|
||||
|
||||
constexpr index_t c_offset =
|
||||
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
|
||||
|
||||
xdlops_gemm.template Run(
|
||||
a_thread_vec.template AsType<mfma_input_type>(),
|
||||
b_thread_vec.template AsType<mfma_input_type>(),
|
||||
c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
|
||||
});
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
private:
|
||||
// A[M0, M1, M2, KPerThread]
|
||||
static constexpr auto a_thread_desc_ =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(I1, I1, I1, Number<KPerThread>{}));
|
||||
|
||||
// B[N0, N1, N2, KPerThread]
|
||||
static constexpr auto b_thread_desc_ =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(Number<K0PerThread>{}, // KPerThread
|
||||
Number<NRepeat>{}, // repeat
|
||||
Number<KPack>{}));
|
||||
|
||||
// C[M, N, NumRegXdlops]
|
||||
static constexpr auto c_thread_desc_ = make_naive_tensor_descriptor_packed(
|
||||
make_tuple(Number<MRepeat>{}, Number<NRepeat>{}, xdlops_gemm.GetRegSizePerXdlops()));
|
||||
|
||||
using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<FloatAB,
|
||||
FloatAB,
|
||||
decltype(a_block_desc_m0_m1_m2_k),
|
||||
decltype(a_thread_desc_),
|
||||
Sequence<1, 1, 1, KPerThread>,
|
||||
Sequence<0, 1, 2, 3>,
|
||||
3,
|
||||
A_K1,
|
||||
A_K1>;
|
||||
|
||||
AThreadCopy a_thread_copy_{CalculateAThreadOriginDataIndex()};
|
||||
};
|
||||
|
||||
} // namespace ck
|
||||
#endif
|
||||
@@ -0,0 +1,521 @@
|
||||
#ifndef DEVICE_GEMM_XDL_SKIP_B_LDS_HPP
|
||||
#define DEVICE_GEMM_XDL_SKIP_B_LDS_HPP
|
||||
|
||||
#include <iostream>
|
||||
#include <sstream>
|
||||
|
||||
#include "ck/utility/common_header.hpp"
|
||||
#include "ck/tensor_description/tensor_descriptor.hpp"
|
||||
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_skip_b_lds_v1.hpp"
|
||||
#include "ck/device_utility/device_prop.hpp"
|
||||
#include "ck/device_utility/kernel_launch.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
|
||||
template <typename ADataType,
|
||||
typename BDataType,
|
||||
typename CDataType,
|
||||
typename AccDataType,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename CLayout,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CElementwiseOperation,
|
||||
GemmSpecialization GemmSpec,
|
||||
ck::index_t BlockSize,
|
||||
ck::index_t MPerBlock,
|
||||
ck::index_t NPerBlock,
|
||||
ck::index_t K0PerBlock,
|
||||
ck::index_t K1,
|
||||
ck::index_t MPerXDL,
|
||||
ck::index_t NPerXDL,
|
||||
ck::index_t MXdlPerWave,
|
||||
ck::index_t NXdlPerWave,
|
||||
typename ABlockTransferThreadClusterLengths_K0_M_K1,
|
||||
typename ABlockTransferThreadClusterArrangeOrder,
|
||||
typename ABlockTransferSrcAccessOrder,
|
||||
ck::index_t ABlockTransferSrcVectorDim,
|
||||
ck::index_t ABlockTransferSrcScalarPerVector,
|
||||
ck::index_t ABlockTransferDstScalarPerVector_K1,
|
||||
bool ABlockLdsAddExtraM,
|
||||
ck::index_t BBlockTransferSrcScalarPerVector,
|
||||
ck::index_t BBlockBufferSize,
|
||||
ck::index_t CThreadTransferSrcDstVectorDim,
|
||||
ck::index_t CThreadTransferDstScalarPerVector>
|
||||
struct DeviceGemmXdlSkipBLds : public DeviceGemm<ALayout,
|
||||
BLayout,
|
||||
CLayout,
|
||||
ADataType,
|
||||
BDataType,
|
||||
CDataType,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation>
|
||||
{
|
||||
static constexpr auto I0 = Number<0>{};
|
||||
static constexpr auto I1 = Number<1>{};
|
||||
static constexpr auto I2 = Number<2>{};
|
||||
|
||||
static constexpr auto K1Number = Number<K1>{};
|
||||
static_assert(BBlockBufferSize >= 2);
|
||||
|
||||
static auto MakeAGridDescriptor_K0_M_K1(index_t M, index_t K, index_t StrideA)
|
||||
{
|
||||
assert(K % K1 == 0);
|
||||
|
||||
const index_t K0 = K / K1;
|
||||
|
||||
const auto a_grid_desc_m_k = [&]() {
|
||||
if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(StrideA, I1));
|
||||
}
|
||||
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ALayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(I1, StrideA));
|
||||
}
|
||||
}();
|
||||
|
||||
if constexpr(GemmSpec == GemmSpecialization::MNPadding)
|
||||
{
|
||||
const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
|
||||
|
||||
return transform_tensor_descriptor(
|
||||
a_grid_desc_m_k,
|
||||
make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
|
||||
make_right_pad_transform(M, PadM)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
}
|
||||
else
|
||||
{
|
||||
return transform_tensor_descriptor(
|
||||
a_grid_desc_m_k,
|
||||
make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
|
||||
make_pass_through_transform(M)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
}
|
||||
}
|
||||
|
||||
static auto MakeBGridDescriptor_K0_N_K1(index_t K, index_t N, index_t StrideB)
|
||||
{
|
||||
assert(K % K1 == 0);
|
||||
|
||||
const index_t K0 = K / K1;
|
||||
|
||||
const auto b_grid_desc_k_n = [&]() {
|
||||
if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(StrideB, I1));
|
||||
}
|
||||
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(I1, StrideB));
|
||||
}
|
||||
}();
|
||||
|
||||
if constexpr(GemmSpec == GemmSpecialization::MNPadding)
|
||||
{
|
||||
const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
|
||||
|
||||
return transform_tensor_descriptor(
|
||||
b_grid_desc_k_n,
|
||||
make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
|
||||
make_right_pad_transform(N, PadN)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
}
|
||||
else
|
||||
{
|
||||
return transform_tensor_descriptor(
|
||||
b_grid_desc_k_n,
|
||||
make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
|
||||
make_pass_through_transform(N)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
}
|
||||
}
|
||||
|
||||
static auto MakeCGridDescriptor_M_N(index_t M, index_t N, index_t StrideC)
|
||||
{
|
||||
const auto c_grid_desc_m_n = [&]() {
|
||||
if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(StrideC, I1));
|
||||
}
|
||||
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, CLayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(I1, StrideC));
|
||||
}
|
||||
}();
|
||||
|
||||
if constexpr(GemmSpec == GemmSpecialization::MNPadding)
|
||||
{
|
||||
const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
|
||||
const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
|
||||
|
||||
return transform_tensor_descriptor(
|
||||
c_grid_desc_m_n,
|
||||
make_tuple(make_right_pad_transform(M, PadM), make_right_pad_transform(N, PadN)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
}
|
||||
else
|
||||
{
|
||||
|
||||
return transform_tensor_descriptor(
|
||||
c_grid_desc_m_n,
|
||||
make_tuple(make_pass_through_transform(M), make_pass_through_transform(N)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
}
|
||||
}
|
||||
|
||||
using AGridDesc_K0_M_K1 = decltype(MakeAGridDescriptor_K0_M_K1(1, 1, 1));
|
||||
using BGridDesc_K0_N_K1 = decltype(MakeBGridDescriptor_K0_N_K1(1, 1, 1));
|
||||
using CGridDesc_M_N = decltype(MakeCGridDescriptor_M_N(1, 1, 1));
|
||||
|
||||
// GridwiseGemm
|
||||
using GridwiseGemm = GridwiseGemm_k0mk1_k0nk1_mn_xdlops_skip_b_lds_v1<
|
||||
BlockSize,
|
||||
ADataType, // TODO: distinguish A/B datatype
|
||||
AccDataType,
|
||||
CDataType,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
AGridDesc_K0_M_K1,
|
||||
BGridDesc_K0_N_K1,
|
||||
CGridDesc_M_N,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation,
|
||||
MPerBlock,
|
||||
NPerBlock,
|
||||
K0PerBlock,
|
||||
MPerXDL,
|
||||
NPerXDL,
|
||||
K1,
|
||||
MXdlPerWave,
|
||||
NXdlPerWave,
|
||||
ABlockTransferThreadClusterLengths_K0_M_K1,
|
||||
ABlockTransferThreadClusterArrangeOrder,
|
||||
ABlockTransferSrcAccessOrder,
|
||||
ABlockTransferSrcVectorDim,
|
||||
ABlockTransferSrcScalarPerVector,
|
||||
ABlockTransferDstScalarPerVector_K1,
|
||||
false, // AThreadTransferSrcResetCoordinateAfterRun,
|
||||
ABlockLdsAddExtraM,
|
||||
BBlockTransferSrcScalarPerVector,
|
||||
false, // BThreadTransferSrcResetCoordinateAfterRun,
|
||||
BBlockBufferSize,
|
||||
Sequence<0, 2, 4, 5, 6, 1, 3, 7>, // CThreadTransferSrcDstAccessOrder,
|
||||
CThreadTransferSrcDstVectorDim,
|
||||
CThreadTransferDstScalarPerVector>;
|
||||
|
||||
// Argument
|
||||
struct Argument : public BaseArgument
|
||||
{
|
||||
Argument(const ADataType* p_a_grid,
|
||||
const BDataType* p_b_grid,
|
||||
CDataType* p_c_grid,
|
||||
index_t M,
|
||||
index_t N,
|
||||
index_t K,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
index_t StrideC,
|
||||
index_t M01,
|
||||
index_t N01,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CElementwiseOperation c_element_op)
|
||||
: p_a_grid_{p_a_grid},
|
||||
p_b_grid_{p_b_grid},
|
||||
p_c_grid_{p_c_grid},
|
||||
a_grid_desc_k0_m_k1_{},
|
||||
b_grid_desc_k0_n_k1_{},
|
||||
c_grid_desc_m_n_{},
|
||||
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_{},
|
||||
block_2_ctile_map_{},
|
||||
M01_{M01},
|
||||
N01_{N01},
|
||||
a_element_op_{a_element_op},
|
||||
b_element_op_{b_element_op},
|
||||
c_element_op_{c_element_op}
|
||||
{
|
||||
a_grid_desc_k0_m_k1_ =
|
||||
DeviceGemmXdlSkipBLds::MakeAGridDescriptor_K0_M_K1(M, K, StrideA);
|
||||
b_grid_desc_k0_n_k1_ =
|
||||
DeviceGemmXdlSkipBLds::MakeBGridDescriptor_K0_N_K1(K, N, StrideB);
|
||||
c_grid_desc_m_n_ = DeviceGemmXdlSkipBLds::MakeCGridDescriptor_M_N(M, N, StrideC);
|
||||
|
||||
if(GridwiseGemm::CheckValidity(
|
||||
a_grid_desc_k0_m_k1_, b_grid_desc_k0_n_k1_, c_grid_desc_m_n_, M01_, N01_))
|
||||
{
|
||||
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_ =
|
||||
GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_grid_desc_m_n_);
|
||||
|
||||
block_2_ctile_map_ =
|
||||
GridwiseGemm::MakeDefaultBlock2CTileMap(c_grid_desc_m_n_, M01, N01);
|
||||
|
||||
b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3_ =
|
||||
GridwiseGemm::MakeBGridDescriptor_K0_K1_K2_N0_N1_N2_N3_K3(b_grid_desc_k0_n_k1_);
|
||||
}
|
||||
}
|
||||
|
||||
// private:
|
||||
const ADataType* p_a_grid_;
|
||||
const BDataType* p_b_grid_;
|
||||
CDataType* p_c_grid_;
|
||||
AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1_;
|
||||
BGridDesc_K0_N_K1 b_grid_desc_k0_n_k1_;
|
||||
CGridDesc_M_N c_grid_desc_m_n_;
|
||||
typename GridwiseGemm::BGridDesc_K0_K1_K2_N0_N1_N2_N3_K3
|
||||
b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3_;
|
||||
typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2
|
||||
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_;
|
||||
typename GridwiseGemm::DefaultBlock2CTileMap block_2_ctile_map_;
|
||||
index_t M01_;
|
||||
index_t N01_;
|
||||
AElementwiseOperation a_element_op_;
|
||||
BElementwiseOperation b_element_op_;
|
||||
CElementwiseOperation c_element_op_;
|
||||
};
|
||||
|
||||
// Invoker
|
||||
struct Invoker : public BaseInvoker
|
||||
{
|
||||
using Argument = DeviceGemmXdlSkipBLds::Argument;
|
||||
|
||||
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
|
||||
{
|
||||
{
|
||||
std::cout << "arg.a_grid_desc_k0_m_k1_{" << arg.a_grid_desc_k0_m_k1_.GetLength(I0)
|
||||
<< ", " << arg.a_grid_desc_k0_m_k1_.GetLength(I1) << ", "
|
||||
<< arg.a_grid_desc_k0_m_k1_.GetLength(I2) << "}" << std::endl;
|
||||
|
||||
std::cout << "arg.b_grid_desc_k0_n_k1_{" << arg.b_grid_desc_k0_n_k1_.GetLength(I0)
|
||||
<< ", " << arg.b_grid_desc_k0_n_k1_.GetLength(I1) << ", "
|
||||
<< arg.b_grid_desc_k0_n_k1_.GetLength(I2) << "}" << std::endl;
|
||||
|
||||
std::cout << "arg.c_grid_desc_m_n_{ " << arg.c_grid_desc_m_n_.GetLength(I0) << ", "
|
||||
<< arg.c_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
|
||||
}
|
||||
|
||||
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
|
||||
arg.b_grid_desc_k0_n_k1_,
|
||||
arg.c_grid_desc_m_n_,
|
||||
arg.M01_,
|
||||
arg.N01_))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3 has invalid setting");
|
||||
}
|
||||
|
||||
const index_t grid_size = GridwiseGemm::CalculateGridSize(arg.c_grid_desc_m_n_);
|
||||
|
||||
const auto K0 = arg.a_grid_desc_k0_m_k1_.GetLength(I0);
|
||||
|
||||
const bool has_main_k0_block_loop = GridwiseGemm::CalculateHasMainK0BlockLoop(K0);
|
||||
|
||||
float ave_time = 0;
|
||||
|
||||
if(has_main_k0_block_loop)
|
||||
{
|
||||
const auto kernel = kernel_gemm_xdlops_skip_b_lds_v1<
|
||||
GridwiseGemm,
|
||||
ADataType, // TODO: distiguish A/B datatype
|
||||
CDataType,
|
||||
remove_reference_t<DeviceGemmXdlSkipBLds::AGridDesc_K0_M_K1>,
|
||||
remove_reference_t<DeviceGemmXdlSkipBLds::BGridDesc_K0_N_K1>,
|
||||
remove_reference_t<typename GridwiseGemm::BGridDesc_K0_K1_K2_N0_N1_N2_N3_K3>,
|
||||
remove_reference_t<typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation,
|
||||
remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
|
||||
true>;
|
||||
|
||||
ave_time = launch_and_time_kernel(stream_config,
|
||||
kernel,
|
||||
dim3(grid_size),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
arg.p_a_grid_,
|
||||
arg.p_b_grid_,
|
||||
arg.p_c_grid_,
|
||||
arg.a_grid_desc_k0_m_k1_,
|
||||
arg.b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3_,
|
||||
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_,
|
||||
arg.a_element_op_,
|
||||
arg.b_element_op_,
|
||||
arg.c_element_op_,
|
||||
arg.block_2_ctile_map_);
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto kernel = kernel_gemm_xdlops_skip_b_lds_v1<
|
||||
GridwiseGemm,
|
||||
ADataType, // TODO: distiguish A/B datatype
|
||||
CDataType,
|
||||
remove_reference_t<DeviceGemmXdlSkipBLds::AGridDesc_K0_M_K1>,
|
||||
remove_reference_t<DeviceGemmXdlSkipBLds::BGridDesc_K0_N_K1>,
|
||||
remove_reference_t<typename GridwiseGemm::BGridDesc_K0_K1_K2_N0_N1_N2_N3_K3>,
|
||||
remove_reference_t<typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation,
|
||||
remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
|
||||
false>;
|
||||
|
||||
ave_time = launch_and_time_kernel(stream_config,
|
||||
kernel,
|
||||
dim3(grid_size),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
arg.p_a_grid_,
|
||||
arg.p_b_grid_,
|
||||
arg.p_c_grid_,
|
||||
arg.a_grid_desc_k0_m_k1_,
|
||||
arg.b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3_,
|
||||
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_,
|
||||
arg.a_element_op_,
|
||||
arg.b_element_op_,
|
||||
arg.c_element_op_,
|
||||
arg.block_2_ctile_map_);
|
||||
}
|
||||
|
||||
return ave_time;
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
float Run(const BaseArgument* p_arg,
|
||||
const StreamConfig& stream_config = StreamConfig{}) override
|
||||
{
|
||||
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
|
||||
}
|
||||
};
|
||||
|
||||
static constexpr bool IsValidCompilationParameter()
|
||||
{
|
||||
// TODO: properly implement this check
|
||||
return true;
|
||||
}
|
||||
|
||||
static bool IsSupportedArgument(const Argument& arg)
|
||||
{
|
||||
return GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
|
||||
arg.b_grid_desc_k0_n_k1_,
|
||||
arg.c_grid_desc_m_n_,
|
||||
arg.M01_,
|
||||
arg.N01_);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
|
||||
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,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CElementwiseOperation c_element_op)
|
||||
{
|
||||
return Argument{p_a,
|
||||
p_b,
|
||||
p_c,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideC,
|
||||
1,
|
||||
1,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op};
|
||||
}
|
||||
|
||||
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,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CElementwiseOperation c_element_op) 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,
|
||||
1,
|
||||
1,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
||||
{
|
||||
return std::make_unique<Invoker>(Invoker{});
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
// clang-format off
|
||||
str << "DeviceGemmXdlSkipBLds"
|
||||
<< "<"
|
||||
<< BlockSize << ", "
|
||||
<< MPerBlock << ", "
|
||||
<< NPerBlock << ", "
|
||||
<< K0PerBlock << ", "
|
||||
<< K1 << ", "
|
||||
<< MPerXDL << ", "
|
||||
<< NPerXDL << ", "
|
||||
<< MXdlPerWave << ", "
|
||||
<< NXdlPerWave
|
||||
<< ">";
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
#endif
|
||||
@@ -0,0 +1,677 @@
|
||||
#ifndef CK_GRIDWISE_GEMM_XDLOPS_SKIP_B_LDS_V1_HPP
|
||||
#define CK_GRIDWISE_GEMM_XDLOPS_SKIP_B_LDS_V1_HPP
|
||||
|
||||
#include "ck/utility/common_header.hpp"
|
||||
#include "ck/tensor_description/multi_index_transform_helper.hpp"
|
||||
#include "ck/tensor_description/tensor_descriptor.hpp"
|
||||
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp"
|
||||
#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops_skip_b_lds.hpp"
|
||||
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
|
||||
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
namespace ck {
|
||||
|
||||
template <typename GridwiseGemm,
|
||||
typename FloatAB,
|
||||
typename FloatC,
|
||||
typename AGridDesc_K0_M_K1,
|
||||
typename BGridDesc_K0_N_K1,
|
||||
typename BGridDesc_K0_K1_K2_N0_N1_N2_N3_K3,
|
||||
typename CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CElementwiseOperation,
|
||||
typename Block2CTileMap,
|
||||
bool HasMainK0BlockLoop>
|
||||
__global__ void
|
||||
#if CK_USE_LAUNCH_BOUNDS
|
||||
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
|
||||
#endif
|
||||
kernel_gemm_xdlops_skip_b_lds_v1(
|
||||
const FloatAB* __restrict__ p_a_grid,
|
||||
const FloatAB* __restrict__ p_b_grid,
|
||||
FloatC* __restrict__ p_c_grid,
|
||||
const AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1,
|
||||
const BGridDesc_K0_K1_K2_N0_N1_N2_N3_K3 b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
|
||||
const CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2 c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
const AElementwiseOperation a_element_op,
|
||||
const BElementwiseOperation b_element_op,
|
||||
const CElementwiseOperation c_element_op,
|
||||
const Block2CTileMap block_2_ctile_map)
|
||||
{
|
||||
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
|
||||
__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
|
||||
|
||||
GridwiseGemm::template Run<HasMainK0BlockLoop>(p_a_grid,
|
||||
p_b_grid,
|
||||
p_c_grid,
|
||||
p_shared,
|
||||
a_grid_desc_k0_m_k1,
|
||||
b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
|
||||
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op,
|
||||
block_2_ctile_map);
|
||||
#else
|
||||
ignore = p_a_grid;
|
||||
ignore = p_b_grid;
|
||||
ignore = p_c_grid;
|
||||
ignore = a_grid_desc_k0_m_k1;
|
||||
ignore = b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3;
|
||||
ignore = c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2;
|
||||
ignore = a_element_op;
|
||||
ignore = b_element_op;
|
||||
ignore = c_element_op;
|
||||
ignore = block_2_ctile_map;
|
||||
#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
|
||||
}
|
||||
|
||||
template <index_t BlockSize,
|
||||
typename FloatAB,
|
||||
typename FloatAcc,
|
||||
typename FloatC,
|
||||
InMemoryDataOperationEnum CGlobalMemoryDataOperation,
|
||||
typename AGridDesc_K0_M_K1,
|
||||
typename BGridDesc_K0_N_K1,
|
||||
typename CGridDesc_M_N,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CElementwiseOperation,
|
||||
index_t MPerBlock,
|
||||
index_t NPerBlock,
|
||||
index_t K0PerBlock,
|
||||
index_t MPerXDL,
|
||||
index_t NPerXDL,
|
||||
index_t K1Value,
|
||||
index_t MXdlPerWave,
|
||||
index_t NXdlPerWave,
|
||||
typename ABlockTransferThreadClusterLengths_K0_M_K1,
|
||||
typename ABlockTransferThreadClusterArrangeOrder,
|
||||
typename ABlockTransferSrcAccessOrder,
|
||||
index_t ABlockTransferSrcVectorDim,
|
||||
index_t ABlockTransferSrcScalarPerVector,
|
||||
index_t ABlockTransferDstScalarPerVector_K1,
|
||||
bool AThreadTransferSrcResetCoordinateAfterRun,
|
||||
bool ABlockLdsExtraM,
|
||||
index_t BBlockTransferSrcScalarPerVector,
|
||||
bool BThreadTransferSrcResetCoordinateAfterRun,
|
||||
index_t BBlockBufferSize,
|
||||
typename CThreadTransferSrcDstAccessOrder,
|
||||
index_t CThreadTransferSrcDstVectorDim,
|
||||
index_t CThreadTransferDstScalarPerVector>
|
||||
struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_skip_b_lds_v1
|
||||
{
|
||||
static constexpr auto I0 = Number<0>{};
|
||||
static constexpr auto I1 = Number<1>{};
|
||||
static constexpr auto I2 = Number<2>{};
|
||||
static constexpr auto I3 = Number<3>{};
|
||||
static constexpr auto I4 = Number<4>{};
|
||||
static constexpr auto I5 = Number<5>{};
|
||||
static constexpr auto I6 = Number<6>{};
|
||||
static constexpr auto I7 = Number<7>{};
|
||||
|
||||
// K1 should be Number<...>
|
||||
static constexpr auto K1 = Number<K1Value>{};
|
||||
|
||||
static constexpr index_t WaveSize = 64;
|
||||
static constexpr index_t MWaves = MPerBlock / (MXdlPerWave * MPerXDL);
|
||||
static constexpr index_t NWaves = NPerBlock / (NXdlPerWave * NPerXDL);
|
||||
|
||||
static constexpr auto xdlops_gemm = XdlopsGemm<FloatAB, MPerXDL, NPerXDL, K1>{};
|
||||
static constexpr index_t K0PerThread = K0PerBlock / xdlops_gemm.K0PerXdlops;
|
||||
|
||||
using ThisThreadBlock = ThisThreadBlock<BlockSize>;
|
||||
|
||||
__host__ __device__ static constexpr auto GetABlockDescriptor_K0PerBlock_MPerBlock_K1()
|
||||
{
|
||||
constexpr auto max_lds_align = K1;
|
||||
|
||||
// A matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto a_block_desc_k0_m_k1 = [&]() {
|
||||
if constexpr(ABlockLdsExtraM)
|
||||
{
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(Number<K0PerBlock * BBlockBufferSize>{}, Number<MPerBlock>{}, K1),
|
||||
make_tuple(Number<MPerBlock + 1>{} * K1, K1, I1));
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_descriptor_aligned(
|
||||
make_tuple(Number<K0PerBlock * BBlockBufferSize>{}, Number<MPerBlock>{}, K1),
|
||||
max_lds_align);
|
||||
}
|
||||
}();
|
||||
|
||||
return a_block_desc_k0_m_k1;
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
|
||||
{
|
||||
// LDS allocation for A and B: be careful of alignment
|
||||
constexpr auto a_block_desc_k0_m_k1 = GetABlockDescriptor_K0PerBlock_MPerBlock_K1();
|
||||
|
||||
constexpr auto max_lds_align = K1;
|
||||
|
||||
constexpr auto a_block_space_size_aligned =
|
||||
math::integer_least_multiple(a_block_desc_k0_m_k1.GetElementSpaceSize(), max_lds_align);
|
||||
|
||||
return (a_block_space_size_aligned) * sizeof(FloatAB);
|
||||
}
|
||||
|
||||
// block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
|
||||
__host__ __device__ static constexpr bool
|
||||
CheckValidity(const AGridDesc_K0_M_K1& a_grid_desc_k0_m_k1,
|
||||
const BGridDesc_K0_N_K1& b_grid_desc_k0_n_k1,
|
||||
const CGridDesc_M_N& c_grid_desc_m_n,
|
||||
index_t M01,
|
||||
index_t N01)
|
||||
{
|
||||
static_assert(is_known_at_compile_time<remove_cv_t<decltype(K1)>>::value,
|
||||
"wrong! K1 need to be known at compile-time");
|
||||
|
||||
static_assert((MPerBlock % (MPerXDL * MXdlPerWave) == 0) &&
|
||||
(NPerBlock % (NXdlPerWave * NPerXDL)) == 0,
|
||||
"Invalid tuning param!");
|
||||
|
||||
const auto M = a_grid_desc_k0_m_k1.GetLength(I1);
|
||||
const auto N = b_grid_desc_k0_n_k1.GetLength(I1);
|
||||
const auto K0 = a_grid_desc_k0_m_k1.GetLength(I0);
|
||||
|
||||
if(!(M == c_grid_desc_m_n.GetLength(I0) && N == c_grid_desc_m_n.GetLength(I1) &&
|
||||
K0 == b_grid_desc_k0_n_k1.GetLength(I0) && K1 == a_grid_desc_k0_m_k1.GetLength(I2) &&
|
||||
K1 == b_grid_desc_k0_n_k1.GetLength(I2)))
|
||||
return false;
|
||||
|
||||
if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K0 % K0PerBlock == 0))
|
||||
return false;
|
||||
|
||||
// 2-stage prefetch currently only support even number of K0 loop
|
||||
// TODO: add support for odd number of K0 loop
|
||||
if(!((K0 / K0PerBlock) % BBlockBufferSize == 0))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
// check M01, N01
|
||||
constexpr auto M1 = Number<MPerBlock>{};
|
||||
constexpr auto N1 = Number<NPerBlock>{};
|
||||
|
||||
const auto M0 = M / M1;
|
||||
const auto N0 = N / N1;
|
||||
|
||||
if(!(M0 % M01 == 0 && N0 % N01 == 0))
|
||||
return false;
|
||||
|
||||
// TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
|
||||
return true;
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr index_t
|
||||
CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n)
|
||||
{
|
||||
const auto M = c_grid_desc_m_n.GetLength(I0);
|
||||
const auto N = c_grid_desc_m_n.GetLength(I1);
|
||||
|
||||
const index_t grid_size = (M / MPerBlock) * (N / NPerBlock);
|
||||
|
||||
return grid_size;
|
||||
}
|
||||
|
||||
// TODO move this function into GEMM-pipeline class
|
||||
__host__ __device__ static constexpr bool CalculateHasMainK0BlockLoop(index_t K0)
|
||||
{
|
||||
const bool has_main_k0_block_loop = (K0 / (BBlockBufferSize * K0PerBlock)) > 1;
|
||||
|
||||
return has_main_k0_block_loop;
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto
|
||||
MakeBGridDescriptor_K0_K1_K2_N0_N1_N2_N3_K3(const BGridDesc_K0_N_K1& b_grid_desc_k0_n_k1)
|
||||
{
|
||||
const auto K0 = b_grid_desc_k0_n_k1.GetLength(I0);
|
||||
const auto N = b_grid_desc_k0_n_k1.GetLength(I1);
|
||||
|
||||
const auto b_griddesc_k0_nblockid_nrepeat_waves_nperxdlops_k1 = transform_tensor_descriptor(
|
||||
b_grid_desc_k0_n_k1,
|
||||
make_tuple(make_unmerge_transform(
|
||||
make_tuple(K0 / K0PerBlock, xdlops_gemm.K0PerXdlops, K0PerThread)),
|
||||
make_unmerge_transform(make_tuple(
|
||||
N / (NXdlPerWave * NWaves * NPerXDL), NXdlPerWave, NWaves, NPerXDL)),
|
||||
make_pass_through_transform(K1)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
|
||||
make_tuple(Sequence<0, 1, 2>{}, Sequence<3, 4, 5, 6>{}, Sequence<7>{}));
|
||||
return b_griddesc_k0_nblockid_nrepeat_waves_nperxdlops_k1;
|
||||
}
|
||||
|
||||
__device__ static auto GetWaveIdx()
|
||||
{
|
||||
const index_t thread_id = get_thread_local_1d_id();
|
||||
|
||||
constexpr auto threadid_to_wave_idx_adaptor = make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_merge_transform(make_tuple(MWaves, NWaves, WaveSize))),
|
||||
make_tuple(Sequence<0, 1, 2>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
|
||||
return threadid_to_wave_idx_adaptor.CalculateBottomIndex(make_multi_index(thread_id));
|
||||
}
|
||||
|
||||
__device__ static auto GetWaveKNIdx(const index_t thread_id)
|
||||
{
|
||||
constexpr auto wave_threadid_to_nk_idx_adaptor = make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_merge_transform(make_tuple(xdlops_gemm.K0PerXdlops, NPerXDL))),
|
||||
make_tuple(Sequence<0, 1>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
|
||||
return wave_threadid_to_nk_idx_adaptor.CalculateBottomIndex(make_multi_index(thread_id));
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto
|
||||
MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(const CGridDesc_M_N& c_grid_desc_m_n)
|
||||
{
|
||||
constexpr auto max_lds_align = K1;
|
||||
|
||||
// A matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto a_block_desc_k0_m_k1 = [&]() {
|
||||
if constexpr(ABlockLdsExtraM)
|
||||
{
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
|
||||
make_tuple(Number<MPerBlock + 1>{} * K1, K1, I1));
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_descriptor_aligned(
|
||||
make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
|
||||
}
|
||||
}();
|
||||
|
||||
// B matrix threadwise copy
|
||||
constexpr auto b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3 =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(I1,
|
||||
I1,
|
||||
Number<K0PerThread>{}, // K0PerThread
|
||||
I1, // NBlockId
|
||||
Number<NXdlPerWave>{}, // repeat
|
||||
I1, // waves
|
||||
I1, // NPerXdlops
|
||||
Number<K1>{}));
|
||||
|
||||
using BlockwiseGemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1r1<
|
||||
BlockSize,
|
||||
FloatAB,
|
||||
FloatAcc,
|
||||
decltype(a_block_desc_k0_m_k1),
|
||||
decltype(b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3),
|
||||
MPerBlock,
|
||||
NPerBlock,
|
||||
K0PerBlock,
|
||||
MPerXDL,
|
||||
NPerXDL,
|
||||
MXdlPerWave,
|
||||
NXdlPerWave,
|
||||
K1>;
|
||||
|
||||
return BlockwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_grid_desc_m_n);
|
||||
}
|
||||
|
||||
// return block_id to C matrix tile idx (m0, n0) mapping
|
||||
__host__ __device__ static constexpr auto
|
||||
MakeDefaultBlock2CTileMap(const CGridDesc_M_N& c_grid_desc_m_n, index_t M01, index_t N01)
|
||||
{
|
||||
const auto M = c_grid_desc_m_n.GetLength(I0);
|
||||
const auto N = c_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;
|
||||
|
||||
const auto M00 = M0 / M01;
|
||||
const auto N00 = N0 / N01;
|
||||
|
||||
const auto m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor =
|
||||
make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_unmerge_transform(make_tuple(M00, M01)),
|
||||
make_unmerge_transform(make_tuple(N00, 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(M00, N00, 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;
|
||||
}
|
||||
|
||||
using CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2 =
|
||||
decltype(MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(CGridDesc_M_N{}));
|
||||
using DefaultBlock2CTileMap = decltype(MakeDefaultBlock2CTileMap(CGridDesc_M_N{}, 1, 1));
|
||||
using BGridDesc_K0_K1_K2_N0_N1_N2_N3_K3 =
|
||||
decltype(MakeBGridDescriptor_K0_K1_K2_N0_N1_N2_N3_K3(BGridDesc_K0_N_K1{}));
|
||||
|
||||
template <bool HasMainK0BlockLoop, typename Block2CTileMap = DefaultBlock2CTileMap>
|
||||
__device__ static void
|
||||
Run(const FloatAB* __restrict__ p_a_grid,
|
||||
const FloatAB* __restrict__ p_b_grid,
|
||||
FloatC* __restrict__ p_c_grid,
|
||||
void* __restrict__ p_shared,
|
||||
const AGridDesc_K0_M_K1& a_grid_desc_k0_m_k1,
|
||||
const BGridDesc_K0_K1_K2_N0_N1_N2_N3_K3 b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
|
||||
const CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2& c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
const AElementwiseOperation& a_element_op,
|
||||
const BElementwiseOperation& b_element_op,
|
||||
const CElementwiseOperation& c_element_op,
|
||||
const Block2CTileMap& block_2_ctile_map)
|
||||
{
|
||||
const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_a_grid, a_grid_desc_k0_m_k1.GetElementSpaceSize());
|
||||
const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_b_grid, b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3.GetElementSpaceSize());
|
||||
auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_c_grid, c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetElementSpaceSize());
|
||||
|
||||
const auto K0 = a_grid_desc_k0_m_k1.GetLength(I0);
|
||||
|
||||
// divide block work by [M, N]
|
||||
const auto block_work_idx =
|
||||
block_2_ctile_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);
|
||||
|
||||
// A matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto a_block_desc_k0_m_k1 = GetABlockDescriptor_K0PerBlock_MPerBlock_K1();
|
||||
|
||||
// A matrix blockwise copy
|
||||
auto a_blockwise_copy = ThreadGroupTensorSliceTransfer_v4r1<
|
||||
ThisThreadBlock,
|
||||
AElementwiseOperation,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
Sequence<K0PerBlock * BBlockBufferSize, MPerBlock, K1>,
|
||||
ABlockTransferThreadClusterLengths_K0_M_K1,
|
||||
ABlockTransferThreadClusterArrangeOrder,
|
||||
FloatAB,
|
||||
FloatAB,
|
||||
decltype(a_grid_desc_k0_m_k1),
|
||||
decltype(a_block_desc_k0_m_k1),
|
||||
ABlockTransferSrcAccessOrder,
|
||||
Sequence<1, 0, 2>,
|
||||
ABlockTransferSrcVectorDim,
|
||||
2,
|
||||
ABlockTransferSrcScalarPerVector,
|
||||
ABlockTransferDstScalarPerVector_K1,
|
||||
1,
|
||||
1,
|
||||
AThreadTransferSrcResetCoordinateAfterRun,
|
||||
true,
|
||||
1>(a_grid_desc_k0_m_k1,
|
||||
make_multi_index(0, m_block_data_idx_on_grid, 0),
|
||||
a_element_op,
|
||||
a_block_desc_k0_m_k1,
|
||||
make_multi_index(0, 0, 0),
|
||||
ck::tensor_operation::element_wise::PassThrough{});
|
||||
|
||||
ignore = b_element_op;
|
||||
// B matrix threadwise copy
|
||||
constexpr auto b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3 =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(I1,
|
||||
I1,
|
||||
Number<K0PerThread>{}, // K0PerThread
|
||||
I1, // NBlockId
|
||||
Number<NXdlPerWave>{}, // repeat
|
||||
I1, // waves
|
||||
I1, // NPerXdlops
|
||||
Number<K1>{}));
|
||||
|
||||
auto b_thread_buf = generate_tuple(
|
||||
[&](auto i) {
|
||||
ignore = i;
|
||||
return StaticBuffer<AddressSpaceEnum::Vgpr,
|
||||
FloatAB,
|
||||
b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3.GetElementSpaceSize(),
|
||||
true>{};
|
||||
},
|
||||
Number<BBlockBufferSize>{});
|
||||
|
||||
const auto wave_id = GetWaveIdx();
|
||||
const auto wave_k_n_id = GetWaveKNIdx(wave_id[I2]);
|
||||
|
||||
#if 0
|
||||
const index_t block_id = get_block_1d_id();
|
||||
const index_t thread_id = get_thread_local_1d_id();
|
||||
printf("block id: %d m blockid: %d n block id: %d ,thread id: %d, wave id :{%d %d %d} "
|
||||
"kn id: {%d %d}\n",
|
||||
block_id,
|
||||
block_work_idx[I0],
|
||||
block_work_idx[I1],
|
||||
thread_id,
|
||||
wave_id[I0],
|
||||
wave_id[I1],
|
||||
wave_id[I2],
|
||||
wave_k_n_id[I0],
|
||||
wave_k_n_id[I1]);
|
||||
printf("mfma thread k per xdlops: %d K0PerThread: %d HasMainK0BlockLoop: %d K0: %d \t",
|
||||
xdlops_gemm.K0PerXdlops, K0PerThread, HasMainK0BlockLoop, b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3.GetLength(I0));
|
||||
#endif
|
||||
|
||||
auto b_threadwise_copy =
|
||||
ThreadwiseTensorSliceTransfer_v2<FloatAB,
|
||||
FloatAB,
|
||||
decltype(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3),
|
||||
decltype(b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3),
|
||||
Sequence<I1,
|
||||
I1,
|
||||
Number<K0PerThread>{},
|
||||
I1,
|
||||
Number<NXdlPerWave>{},
|
||||
I1,
|
||||
I1,
|
||||
Number<K1>{}>,
|
||||
Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
|
||||
7,
|
||||
BBlockTransferSrcScalarPerVector,
|
||||
BThreadTransferSrcResetCoordinateAfterRun,
|
||||
true>(
|
||||
b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
|
||||
make_multi_index(
|
||||
0, wave_k_n_id[I0], 0, block_work_idx[I1], 0, wave_id[I1], wave_k_n_id[I1], 0));
|
||||
|
||||
// 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
|
||||
auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1r1<
|
||||
BlockSize,
|
||||
FloatAB,
|
||||
FloatAcc,
|
||||
decltype(a_block_desc_k0_m_k1),
|
||||
decltype(b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3),
|
||||
MPerBlock,
|
||||
NPerBlock,
|
||||
K0PerBlock,
|
||||
MPerXDL,
|
||||
NPerXDL,
|
||||
MXdlPerWave,
|
||||
NXdlPerWave,
|
||||
K1>{};
|
||||
|
||||
auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
|
||||
|
||||
// LDS allocation for A
|
||||
auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
|
||||
static_cast<FloatAB*>(p_shared), a_block_desc_k0_m_k1.GetElementSpaceSize());
|
||||
|
||||
// gridwise GEMM pipeline
|
||||
constexpr auto a_block_slice_copy_step =
|
||||
make_multi_index(K0PerBlock * BBlockBufferSize, 0, 0);
|
||||
constexpr auto b_thread_slice_copy_step = make_multi_index(1, 0, 0, 0, 0, 0, 0, 0);
|
||||
// preload data to regiester and LDS
|
||||
{
|
||||
// Read
|
||||
a_blockwise_copy.RunRead(a_grid_desc_k0_m_k1, a_grid_buf);
|
||||
a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc_k0_m_k1, a_block_slice_copy_step);
|
||||
|
||||
static_for<0, BBlockBufferSize, 1>{}([&](auto ii) {
|
||||
b_threadwise_copy.Run(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
|
||||
b_grid_buf,
|
||||
b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3,
|
||||
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
|
||||
b_thread_buf(Number<ii>{}));
|
||||
b_threadwise_copy.MoveSrcSliceWindow(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
|
||||
b_thread_slice_copy_step);
|
||||
});
|
||||
|
||||
// Initialize C
|
||||
c_thread_buf.Clear();
|
||||
// a data write to lds
|
||||
a_blockwise_copy.RunWrite(a_block_desc_k0_m_k1, a_block_buf);
|
||||
// main body
|
||||
if constexpr(HasMainK0BlockLoop)
|
||||
{
|
||||
index_t K0BlockMainLoop =
|
||||
__builtin_amdgcn_readfirstlane(K0 / (BBlockBufferSize * K0PerBlock));
|
||||
index_t i = 0;
|
||||
do
|
||||
{
|
||||
a_blockwise_copy.RunRead(a_grid_desc_k0_m_k1, a_grid_buf);
|
||||
blockwise_gemm.ResetABlockStartWindow();
|
||||
block_sync_lds();
|
||||
|
||||
static_for<0, BBlockBufferSize, 1>{}([&](auto ii) {
|
||||
blockwise_gemm.Run(a_block_buf, b_thread_buf(Number<ii>{}), c_thread_buf);
|
||||
blockwise_gemm.MoveABlockSliceWindow();
|
||||
s_nop();
|
||||
|
||||
b_threadwise_copy.Run(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
|
||||
b_grid_buf,
|
||||
b_thread_desc_k0_k1_k2_n0_n1_n2_n3_k3,
|
||||
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
|
||||
b_thread_buf(Number<ii>{}));
|
||||
b_threadwise_copy.MoveSrcSliceWindow(b_grid_desc_k0_k1_k2_n0_n1_n2_n3_k3,
|
||||
b_thread_slice_copy_step);
|
||||
});
|
||||
|
||||
block_sync_lds();
|
||||
a_blockwise_copy.RunWrite(a_block_desc_k0_m_k1, a_block_buf);
|
||||
// move a and b window
|
||||
a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc_k0_m_k1,
|
||||
a_block_slice_copy_step);
|
||||
|
||||
i += 1;
|
||||
} while(i < (K0BlockMainLoop - 1));
|
||||
}
|
||||
|
||||
// tail
|
||||
{
|
||||
block_sync_lds();
|
||||
|
||||
blockwise_gemm.ResetABlockStartWindow();
|
||||
|
||||
static_for<0, BBlockBufferSize, 1>{}([&](auto ii) {
|
||||
blockwise_gemm.Run(a_block_buf, b_thread_buf(Number<ii>{}), c_thread_buf);
|
||||
blockwise_gemm.MoveABlockSliceWindow();
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// output: register to global memory
|
||||
{
|
||||
constexpr auto c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
|
||||
blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
|
||||
|
||||
constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
|
||||
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.GetLength(I0);
|
||||
constexpr auto N0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I1);
|
||||
constexpr auto M1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I2);
|
||||
constexpr auto N1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I3);
|
||||
constexpr auto M2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I4);
|
||||
constexpr auto M3 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I5);
|
||||
constexpr auto M4 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I6);
|
||||
constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I7);
|
||||
|
||||
// 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_grid =
|
||||
m_block_data_idx_on_grid + c_thread_mtx_on_block[I0];
|
||||
|
||||
const index_t n_thread_data_on_grid =
|
||||
n_block_data_idx_on_grid + c_thread_mtx_on_block[I1];
|
||||
|
||||
const auto m_thread_data_on_grid_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_grid_idx =
|
||||
m_thread_data_on_grid_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
|
||||
make_multi_index(m_thread_data_on_grid));
|
||||
|
||||
const auto n_thread_data_on_grid_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_grid_idx =
|
||||
n_thread_data_on_grid_to_n0_n1_n2_adaptor.CalculateBottomIndex(
|
||||
make_multi_index(n_thread_data_on_grid));
|
||||
|
||||
auto c_thread_copy =
|
||||
ThreadwiseTensorSliceTransfer_v1r3<FloatAcc,
|
||||
FloatC,
|
||||
decltype(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),
|
||||
decltype(c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2),
|
||||
CElementwiseOperation,
|
||||
Sequence<M0, N0, I1, I1, M2, I1, M4, I1>,
|
||||
CThreadTransferSrcDstAccessOrder,
|
||||
CThreadTransferSrcDstVectorDim,
|
||||
CThreadTransferDstScalarPerVector,
|
||||
CGlobalMemoryDataOperation,
|
||||
1,
|
||||
true>{
|
||||
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
make_multi_index(m_thread_data_on_grid_idx[I0],
|
||||
n_thread_data_on_grid_idx[I0],
|
||||
m_thread_data_on_grid_idx[I1],
|
||||
n_thread_data_on_grid_idx[I1],
|
||||
m_thread_data_on_grid_idx[I2],
|
||||
m_thread_data_on_grid_idx[I3],
|
||||
m_thread_data_on_grid_idx[I4],
|
||||
n_thread_data_on_grid_idx[I2]),
|
||||
c_element_op};
|
||||
|
||||
c_thread_copy.Run(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
|
||||
c_thread_buf,
|
||||
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
c_grid_buf);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace ck
|
||||
#endif
|
||||
@@ -1192,6 +1192,10 @@ struct ThreadwiseTensorSliceTransfer_v4
|
||||
|
||||
move_tensor_coordinate(SrcDesc{}, src_ref_coord_, src_slice_move_step_iter);
|
||||
}
|
||||
__device__ void SetSrcCoord(const Index& src_ref_idx)
|
||||
{
|
||||
src_ref_coord_ = make_tensor_coordinate(SrcDesc{}, src_ref_idx);
|
||||
}
|
||||
|
||||
private:
|
||||
SrcCoord src_ref_coord_;
|
||||
|
||||
@@ -18,5 +18,15 @@ __device__ void block_sync_lds()
|
||||
__syncthreads();
|
||||
#endif
|
||||
}
|
||||
__device__ void s_nop()
|
||||
{
|
||||
#if 1
|
||||
asm volatile("\
|
||||
s_nop 0 \n \
|
||||
" ::);
|
||||
#else
|
||||
__builtin_amdgcn_sched_barrier(0);
|
||||
#endif
|
||||
}
|
||||
|
||||
} // namespace ck
|
||||
|
||||
Reference in New Issue
Block a user