mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-18 20:09:25 +00:00
[Navi3x] Add Device Operations (#567)
* wmma_op + unit test
* add arch limitation to wmma test
* change arch limitation
* Refactor + Add all type unit test(int4 compile failed)
* Add f32_16x16x16_bf16 unit test
* tempsave
* tempsave
* tempsave
* runtime bug, cannot find symbol
* workaround for incorrect HIP warpSize return value
* debugging
* tempsave
* Correctness OK, waiting for optimization
* Tidy up + format
* temp save
* temp save, reproduce the v_bfi_b32 issue
* add inline asm for wmmaop test
* tidy up
* clean some debug purpose code
* discard some codes
* clang format
* clang format
* compiler issue fixed + increase tile size
* navi3x_multipleD+example
* temp save
* workable
* batchedgemm[OK], groupconv[debug]
* groupconv: Sanity check[OK], Performance[Bad]
* navi3x_groupconv_need_optimization
* format
* Add arch limitation to all wmma examples
* fix bug: example30 input conv args
[ROCm/composable_kernel commit: 0cfda84d05]
This commit is contained in:
Haocong WANG
@@ -38,7 +38,9 @@ add_example_executable_no_testing(example_gemm_xdl_fp64 gemm_xdl_fp64.cpp)
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add_dependencies(example_gemm_xdl example_gemm_xdl_skip_b_lds_fp16)
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add_dependencies(example_gemm_xdl example_gemm_xdl_fp64)
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add_custom_target(example_gemm_wmma)
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add_example_executable(example_gemm_wmma_fp16 gemm_wmma_fp16.cpp)
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add_dependencies(example_gemm_wmma example_gemm_wmma_fp16)
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if(GPU_TARGETS MATCHES "gfx1100")
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add_custom_target(example_gemm_wmma)
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add_example_executable(example_gemm_wmma_fp16 gemm_wmma_fp16.cpp)
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add_dependencies(example_gemm_wmma example_gemm_wmma_fp16)
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endif()
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@@ -1 +1,4 @@
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add_example_executable(example_gemm_bilinear_xdl_fp16 gemm_bilinear_xdl_fp16.cpp)
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if(GPU_TARGETS MATCHES "gfx1100")
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add_example_executable(example_gemm_bilinear_wmma_fp16 gemm_bilinear_wmma_fp16.cpp)
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endif()
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304
example/02_gemm_bilinear/gemm_bilinear_wmma_fp16.cpp
Normal file
304
example/02_gemm_bilinear/gemm_bilinear_wmma_fp16.cpp
Normal file
@@ -0,0 +1,304 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#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/impl/device_gemm_multiple_d_wmma_cshuffle.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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#include "ck/library/utility/literals.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
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#include "ck/library/utility/check_err.hpp"
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struct AlphaBetaAdd
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{
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AlphaBetaAdd(float alpha, float beta) : alpha_(alpha), beta_(beta){};
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template <typename E, typename C, typename D>
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__host__ __device__ constexpr void operator()(E& e, const C& c, const D& d) const;
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template <>
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__host__ __device__ constexpr void operator()<ck::half_t, float, ck::half_t>(
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ck::half_t& e, const float& c, const ck::half_t& d) const
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{
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e = ck::type_convert<ck::half_t>(alpha_ * c + beta_ * ck::type_convert<float>(d));
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};
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float alpha_;
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float beta_;
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};
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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 ADataType = F16;
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using BDataType = F16;
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using AccDataType = F32;
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using CShuffleDataType = F32;
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using DDataType = F16;
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using EDataType = F16;
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using ALayout = Row;
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using BLayout = Col;
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using DLayout = Row;
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using ELayout = Row;
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using AElementOp = PassThrough;
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using BElementOp = PassThrough;
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using CDEElementOp = AlphaBetaAdd;
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static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
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using DeviceOpInstance =
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ck::tensor_operation::device::DeviceGemmMultipleD_Wmma_CShuffle<ALayout,
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BLayout,
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ck::Tuple<DLayout>,
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ELayout,
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ADataType,
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BDataType,
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ck::Tuple<DDataType>,
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EDataType,
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AccDataType,
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CShuffleDataType,
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AElementOp,
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BElementOp,
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CDEElementOp,
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GemmSpec,
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256,
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128,
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256,
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8,
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8,
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16,
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16,
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4,
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4,
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S<4, 64, 1>,
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S<1, 0, 2>,
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S<1, 0, 2>,
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2,
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8,
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8,
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true,
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S<4, 64, 1>,
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S<1, 0, 2>,
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S<1, 0, 2>,
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2,
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8,
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8,
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true,
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1,
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1,
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S<1, 32, 1, 8>,
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8>;
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int main(int argc, char* argv[])
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{
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bool do_verification = true;
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int init_method = 1;
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bool time_kernel = true;
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// GEMM shape
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ck::index_t M = 3840;
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ck::index_t N = 4096;
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ck::index_t K = 4096;
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ck::index_t StrideA = 4096;
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ck::index_t StrideB = 4096;
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ck::index_t StrideD = 4096;
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ck::index_t StrideE = 4096;
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float alpha = 1.0f;
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float beta = 1.0f;
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if(argc == 1)
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{
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// use default case
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}
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else 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 == 6)
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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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alpha = std::stof(argv[4]);
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beta = std::stof(argv[5]);
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}
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else if(argc == 13)
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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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StrideD = std::stoi(argv[9]);
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StrideE = std::stoi(argv[10]);
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alpha = std::stof(argv[11]);
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beta = std::stof(argv[12]);
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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=no, 1=yes)\n");
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printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD, StrideE, alpha, "
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"beta\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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using namespace ck::literals;
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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({row, col}, {stride, 1_uz});
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}
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else
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{
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return HostTensorDescriptor({row, col}, {1_uz, 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<DDataType> d_m_n(f_host_tensor_descriptor(M, N, StrideD, DLayout{}));
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Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
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Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
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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 << "d_m_n: " << d_m_n.mDesc << std::endl;
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std::cout << "e_m_n: " << e_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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d_m_n.GenerateTensorValue(GeneratorTensor_2<DDataType>{-5, 5});
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break;
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default:
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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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d_m_n.GenerateTensorValue(GeneratorTensor_3<DDataType>{-0.5, 0.5});
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}
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DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
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DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
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DeviceMem d_device_buf(sizeof(DDataType) * d_m_n.mDesc.GetElementSpaceSize());
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DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
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a_device_buf.ToDevice(a_m_k.mData.data());
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b_device_buf.ToDevice(b_k_n.mData.data());
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d_device_buf.ToDevice(d_m_n.mData.data());
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e_device_buf.ToDevice(e_m_n_device_result.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 cde_element_op = CDEElementOp{alpha, beta};
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// do GEMM
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auto device_op = DeviceOpInstance{};
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auto invoker = device_op.MakeInvoker();
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auto argument =
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device_op.MakeArgument(a_device_buf.GetDeviceBuffer(),
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b_device_buf.GetDeviceBuffer(),
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std::array<const void*, 1>{d_device_buf.GetDeviceBuffer()},
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e_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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std::array<ck::index_t, 1>{StrideD},
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StrideE,
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a_element_op,
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b_element_op,
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cde_element_op);
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if(!device_op.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(EDataType) * 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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<< std::endl;
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e_device_buf.FromDevice(e_m_n_device_result.mData.data());
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if(do_verification)
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{
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Tensor<CShuffleDataType> c_m_n({M, N});
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using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
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BDataType,
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CShuffleDataType,
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AccDataType,
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AElementOp,
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BElementOp,
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PassThrough>;
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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 =
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ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, PassThrough{});
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ref_invoker.Run(ref_argument);
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for(int m = 0; m < M; ++m)
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{
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for(int n = 0; n < N; ++n)
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{
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cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), d_m_n(m, n));
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}
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}
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e_device_buf.FromDevice(e_m_n_device_result.mData.data());
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return ck::utils::check_err(e_m_n_device_result, e_m_n_host_result) ? 0 : 1;
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}
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return 0;
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}
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@@ -1 +1,5 @@
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add_example_executable(example_batched_gemm_bias_e_permute_xdl_fp16 batched_gemm_bias_e_permute_xdl_fp16.cpp)
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if(GPU_TARGETS MATCHES "gfx1100")
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add_example_executable(example_batched_gemm_bias_e_permute_wmma_fp16 batched_gemm_bias_e_permute_wmma_fp16.cpp)
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endif()
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@@ -0,0 +1,431 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
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|
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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/impl/device_batched_contraction_multiple_d_wmma_cshuffle.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/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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#include "ck/library/utility/numeric.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 PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using Add = ck::tensor_operation::element_wise::Add;
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using ADataType = F16;
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using BDataType = F16;
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using AccDataType = F32;
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using CShuffleDataType = F16;
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using DDataType = F16;
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using DsDataType = ck::Tuple<DDataType>;
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using EDataType = F16;
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static constexpr ck::index_t NumDimG = 2;
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static constexpr ck::index_t NumDimM = 2;
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static constexpr ck::index_t NumDimN = 2;
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static constexpr ck::index_t NumDimK = 1;
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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 CDEElementOp = ck::tensor_operation::element_wise::Add;
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static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
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static constexpr auto ABSpec = ck::tensor_operation::device::TensorSpecialization::Packed;
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static constexpr auto DESpec = ck::tensor_operation::device::TensorSpecialization::Default;
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using DeviceOpInstanceKKNN =
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ck::tensor_operation::device::DeviceBatchedContractionMultipleD_Wmma_CShuffle<NumDimG,
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NumDimM,
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NumDimN,
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||||
NumDimK,
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||||
ADataType,
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BDataType,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
AccDataType,
|
||||
CShuffleDataType,
|
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AElementOp,
|
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BElementOp,
|
||||
CDEElementOp,
|
||||
GemmSpec,
|
||||
ABSpec,
|
||||
ABSpec,
|
||||
DESpec,
|
||||
256,
|
||||
128,
|
||||
256,
|
||||
8,
|
||||
8,
|
||||
16,
|
||||
16,
|
||||
4,
|
||||
4,
|
||||
S<4, 64, 1>,
|
||||
S<1, 0, 2>,
|
||||
S<1, 0, 2>,
|
||||
2,
|
||||
8,
|
||||
8,
|
||||
true,
|
||||
S<4, 64, 1>,
|
||||
S<1, 0, 2>,
|
||||
S<1, 0, 2>,
|
||||
2,
|
||||
8,
|
||||
8,
|
||||
true,
|
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1,
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1,
|
||||
S<1, 32, 1, 8>,
|
||||
8>;
|
||||
|
||||
using DeviceOpInstance = DeviceOpInstanceKKNN;
|
||||
|
||||
// hardcoded for NumDimM == NumDimN == NumDimK == 2
|
||||
template <ck::index_t NumDimG,
|
||||
ck::index_t NumDimM,
|
||||
ck::index_t NumDimN,
|
||||
ck::index_t NumDimK,
|
||||
typename ADataType,
|
||||
typename BDataType,
|
||||
typename EDataType,
|
||||
typename AccDataType,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CDEElementwiseOperation,
|
||||
ck::enable_if_t<NumDimG == 2 && NumDimM == 2 && NumDimN == 2 && NumDimK == 1, bool> =
|
||||
false>
|
||||
struct ReferenceContraction_G2_M2_N2_K1 : public ck::tensor_operation::device::BaseOperator
|
||||
{
|
||||
// Argument
|
||||
struct Argument : public ck::tensor_operation::device::BaseArgument
|
||||
{
|
||||
Argument(const Tensor<ADataType>& a_gs_ms_ks,
|
||||
const Tensor<BDataType>& b_gs_ns_ks,
|
||||
Tensor<EDataType>& e_gs_ms_ns,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op)
|
||||
: a_gs_ms_ks_{a_gs_ms_ks},
|
||||
b_gs_ns_ks_{b_gs_ns_ks},
|
||||
e_gs_ms_ns_{e_gs_ms_ns},
|
||||
a_element_op_{a_element_op},
|
||||
b_element_op_{b_element_op},
|
||||
cde_element_op_{cde_element_op}
|
||||
{
|
||||
}
|
||||
|
||||
const Tensor<ADataType>& a_gs_ms_ks_;
|
||||
const Tensor<BDataType>& b_gs_ns_ks_;
|
||||
Tensor<EDataType>& e_gs_ms_ns_;
|
||||
|
||||
AElementwiseOperation a_element_op_;
|
||||
BElementwiseOperation b_element_op_;
|
||||
CDEElementwiseOperation cde_element_op_;
|
||||
};
|
||||
|
||||
// Invoker
|
||||
struct Invoker : public ck::tensor_operation::device::BaseInvoker
|
||||
{
|
||||
using Argument = ReferenceContraction_G2_M2_N2_K1::Argument;
|
||||
|
||||
float Run(const Argument& arg)
|
||||
{
|
||||
auto f_ms_ns = [&](auto g0, auto g1, auto m0, auto m1, auto n0, auto n1) {
|
||||
const int K0 = arg.a_gs_ms_ks_.mDesc.GetLengths()[4];
|
||||
|
||||
AccDataType v_acc = 0;
|
||||
|
||||
for(int k0 = 0; k0 < K0; ++k0)
|
||||
{
|
||||
AccDataType v_a;
|
||||
AccDataType v_b;
|
||||
|
||||
arg.a_element_op_(
|
||||
v_a,
|
||||
ck::type_convert<const AccDataType>(arg.a_gs_ms_ks_(g0, g1, m0, m1, k0)));
|
||||
arg.b_element_op_(
|
||||
v_b,
|
||||
ck::type_convert<const AccDataType>(arg.b_gs_ns_ks_(g0, g1, n0, n1, k0)));
|
||||
|
||||
v_acc += v_a * v_b;
|
||||
}
|
||||
|
||||
AccDataType v_c;
|
||||
|
||||
arg.cde_element_op_(v_c, v_acc);
|
||||
|
||||
arg.e_gs_ms_ns_(g0, g1, m0, m1, n0, n1) = v_c;
|
||||
};
|
||||
|
||||
make_ParallelTensorFunctor(f_ms_ns,
|
||||
arg.e_gs_ms_ns_.mDesc.GetLengths()[0],
|
||||
arg.e_gs_ms_ns_.mDesc.GetLengths()[1],
|
||||
arg.e_gs_ms_ns_.mDesc.GetLengths()[2],
|
||||
arg.e_gs_ms_ns_.mDesc.GetLengths()[3],
|
||||
arg.e_gs_ms_ns_.mDesc.GetLengths()[4],
|
||||
arg.e_gs_ms_ns_.mDesc.GetLengths()[5])(
|
||||
std::thread::hardware_concurrency());
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
float Run(const ck::tensor_operation::device::BaseArgument* p_arg,
|
||||
const StreamConfig& /* stream_config */ = StreamConfig{}) override
|
||||
{
|
||||
return Run(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
};
|
||||
|
||||
static constexpr bool IsValidCompilationParameter()
|
||||
{
|
||||
// TODO: properly implement this check
|
||||
return true;
|
||||
}
|
||||
|
||||
bool IsSupportedArgument(const ck::tensor_operation::device::BaseArgument*) override
|
||||
{
|
||||
return true;
|
||||
}
|
||||
|
||||
static auto MakeArgument(const Tensor<ADataType>& a_gs_ms_ks,
|
||||
const Tensor<BDataType>& b_gs_ns_ks,
|
||||
Tensor<EDataType>& e_gs_ms_ns,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op)
|
||||
{
|
||||
return Argument{
|
||||
a_gs_ms_ks, b_gs_ns_ks, e_gs_ms_ns, a_element_op, b_element_op, cde_element_op};
|
||||
}
|
||||
|
||||
static auto MakeInvoker() { return Invoker{}; }
|
||||
|
||||
virtual std::unique_ptr<ck::tensor_operation::device::BaseInvoker> MakeInvokerPointer()
|
||||
{
|
||||
return std::make_unique<Invoker>(Invoker{});
|
||||
}
|
||||
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
// clang-format off
|
||||
str << "ReferenceContraction_G2_M2_N2_K1"
|
||||
<< std::endl;
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
};
|
||||
|
||||
int main(int argc, char* argv[])
|
||||
{
|
||||
bool do_verification = true;
|
||||
int init_method = 1;
|
||||
bool time_kernel = true;
|
||||
|
||||
ck::index_t G0 = 1;
|
||||
ck::index_t G1 = 2;
|
||||
|
||||
ck::index_t M0 = 4;
|
||||
ck::index_t M1 = 128;
|
||||
|
||||
ck::index_t N0 = 16;
|
||||
ck::index_t N1 = 256;
|
||||
|
||||
ck::index_t K0 = 2048;
|
||||
|
||||
// A[G0, G1, M0, M1, K0]
|
||||
std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, G1, M0, M1, K0};
|
||||
std::vector<ck::index_t> a_gs_ms_ks_strides{G1 * M0 * M1 * K0, M0 * M1 * K0, M1 * K0, K0, 1};
|
||||
// B[G0, G1, N0, N1, K0]
|
||||
std::vector<ck::index_t> b_gs_ns_ks_lengths{G0, G1, N0, N1, K0};
|
||||
std::vector<ck::index_t> b_gs_ns_ks_strides{G1 * N0 * N1 * K0, N0 * N1 * K0, N1 * K0, K0, 1};
|
||||
|
||||
// D[G0, G1, M0, N0, M1, N1]
|
||||
std::vector<ck::index_t> d_gs_ms_ns_lengths{G0, G1, M0, M1, N0, N1};
|
||||
std::vector<ck::index_t> d_gs_ms_ns_strides{G1 * N0 * N1, N0 * N1, 0, 0, N1, 1};
|
||||
// E[G0, G1, M0, N0, M1, N1]
|
||||
std::vector<ck::index_t> e_gs_ms_ns_lengths{G0, G1, M0, M1, N0, N1};
|
||||
std::vector<ck::index_t> e_gs_ms_ns_strides{
|
||||
G1 * M0 * N0 * M1 * N1, M0 * N0 * M1 * N1, N0 * M1 * N1, N1, M1 * N1, 1};
|
||||
|
||||
if(argc == 1)
|
||||
{
|
||||
// use default case
|
||||
}
|
||||
else if(argc == 4)
|
||||
{
|
||||
do_verification = std::stoi(argv[1]);
|
||||
init_method = std::stoi(argv[2]);
|
||||
time_kernel = std::stoi(argv[3]);
|
||||
}
|
||||
else
|
||||
{
|
||||
printf("arg1: verification (0=no, 1=yes)\n");
|
||||
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
|
||||
printf("arg3: time kernel (0=no, 1=yes)\n");
|
||||
exit(0);
|
||||
}
|
||||
Tensor<ADataType> a_gs_ms_ks(a_gs_ms_ks_lengths, a_gs_ms_ks_strides);
|
||||
Tensor<BDataType> b_gs_ns_ks(b_gs_ns_ks_lengths, b_gs_ns_ks_strides);
|
||||
Tensor<DDataType> d_gs_ms_ns(d_gs_ms_ns_lengths, d_gs_ms_ns_strides);
|
||||
Tensor<EDataType> e_gs_ms_ns_host_result(e_gs_ms_ns_lengths, e_gs_ms_ns_strides);
|
||||
Tensor<EDataType> e_gs_ms_ns_device_result(e_gs_ms_ns_lengths, e_gs_ms_ns_strides);
|
||||
std::cout << "a_gs_ms_ks: " << a_gs_ms_ks.mDesc << std::endl;
|
||||
std::cout << "b_gs_ns_ks: " << b_gs_ns_ks.mDesc << std::endl;
|
||||
std::cout << "d_gs_ms_ns: " << d_gs_ms_ns.mDesc << std::endl;
|
||||
std::cout << "e_gs_ms_ns: " << e_gs_ms_ns_host_result.mDesc << std::endl;
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
|
||||
b_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_2<DDataType>{-5, 5});
|
||||
break;
|
||||
default:
|
||||
a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
|
||||
b_gs_ns_ks.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_3<DDataType>{-0.5, 0.5});
|
||||
break;
|
||||
}
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a_gs_ms_ks.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_gs_ns_ks.mDesc.GetElementSpaceSize());
|
||||
DeviceMem d_device_buf(sizeof(DDataType) * d_gs_ms_ns.mDesc.GetElementSpaceSize());
|
||||
DeviceMem e_device_buf(sizeof(EDataType) *
|
||||
e_gs_ms_ns_device_result.mDesc.GetElementSpaceSize());
|
||||
|
||||
a_device_buf.ToDevice(a_gs_ms_ks.mData.data());
|
||||
b_device_buf.ToDevice(b_gs_ns_ks.mData.data());
|
||||
d_device_buf.ToDevice(d_gs_ms_ns.mData.data());
|
||||
|
||||
// set zero
|
||||
e_device_buf.SetZero();
|
||||
|
||||
auto a_element_op = AElementOp{};
|
||||
auto b_element_op = BElementOp{};
|
||||
auto cde_element_op = CDEElementOp{};
|
||||
|
||||
// device operation
|
||||
auto op = DeviceOpInstance{};
|
||||
auto invoker = op.MakeInvoker();
|
||||
auto argument = op.MakeArgument(a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
std::array<const void*, 1>{d_device_buf.GetDeviceBuffer()},
|
||||
e_device_buf.GetDeviceBuffer(),
|
||||
a_gs_ms_ks_lengths,
|
||||
a_gs_ms_ks_strides,
|
||||
b_gs_ns_ks_lengths,
|
||||
b_gs_ns_ks_strides,
|
||||
std::array<std::vector<ck::index_t>, 1>{d_gs_ms_ns_lengths},
|
||||
std::array<std::vector<ck::index_t>, 1>{d_gs_ms_ns_strides},
|
||||
e_gs_ms_ns_lengths,
|
||||
e_gs_ms_ns_strides,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op);
|
||||
|
||||
if(!op.IsSupportedArgument(argument))
|
||||
{
|
||||
std::cout << op.GetTypeString() << " does not support this problem" << std::endl;
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
|
||||
|
||||
ck::index_t G =
|
||||
ck::accumulate_n<ck::index_t>(e_gs_ms_ns_lengths.begin(), NumDimG, 1, std::multiplies<>{});
|
||||
|
||||
ck::index_t M = ck::accumulate_n<ck::index_t>(
|
||||
e_gs_ms_ns_lengths.begin() + NumDimG, NumDimM, 1, std::multiplies<>{});
|
||||
|
||||
ck::index_t N = ck::accumulate_n<ck::index_t>(
|
||||
e_gs_ms_ns_lengths.begin() + NumDimG + NumDimM, NumDimN, 1, std::multiplies<>{});
|
||||
|
||||
ck::index_t K = ck::accumulate_n<ck::index_t>(
|
||||
a_gs_ms_ks_lengths.begin() + NumDimG + NumDimM, NumDimK, 1, std::multiplies<>{});
|
||||
std::cout << "GMNK=" << G << ", " << M << ", " << N << ", " << K << std::endl;
|
||||
std::size_t flop = std::size_t(2) * G * M * N * K;
|
||||
std::size_t num_btype = sizeof(ADataType) * G * M * K + sizeof(BDataType) * G * K * N +
|
||||
sizeof(DDataType) * G * M * N + sizeof(EDataType) * G * M * N;
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
||||
|
||||
float gb_per_sec = num_btype / 1.E6 / ave_time;
|
||||
|
||||
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
|
||||
<< op.GetTypeString() << std::endl;
|
||||
|
||||
e_device_buf.FromDevice(e_gs_ms_ns_device_result.mData.data());
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
Tensor<CShuffleDataType> c_ms_ns_host_result(e_gs_ms_ns_lengths, e_gs_ms_ns_strides);
|
||||
|
||||
using ReferenceOpInstance = ReferenceContraction_G2_M2_N2_K1<NumDimG,
|
||||
NumDimM,
|
||||
NumDimN,
|
||||
NumDimK,
|
||||
ADataType,
|
||||
BDataType,
|
||||
CShuffleDataType,
|
||||
AccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
PassThrough>;
|
||||
|
||||
auto ref_gemm = ReferenceOpInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument = ref_gemm.MakeArgument(
|
||||
a_gs_ms_ks, b_gs_ns_ks, c_ms_ns_host_result, a_element_op, b_element_op, PassThrough{});
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
for(size_t g0 = 0; g0 < e_gs_ms_ns_host_result.mDesc.GetLengths()[0]; ++g0)
|
||||
{
|
||||
for(size_t g1 = 0; g1 < e_gs_ms_ns_host_result.mDesc.GetLengths()[1]; ++g1)
|
||||
{
|
||||
for(size_t m0 = 0; m0 < e_gs_ms_ns_host_result.mDesc.GetLengths()[2]; ++m0)
|
||||
{
|
||||
for(size_t m1 = 0; m1 < e_gs_ms_ns_host_result.mDesc.GetLengths()[3]; ++m1)
|
||||
{
|
||||
for(size_t n0 = 0; n0 < e_gs_ms_ns_host_result.mDesc.GetLengths()[4]; ++n0)
|
||||
{
|
||||
for(size_t n1 = 0; n1 < e_gs_ms_ns_host_result.mDesc.GetLengths()[5];
|
||||
++n1)
|
||||
{
|
||||
cde_element_op(e_gs_ms_ns_host_result(g0, g1, m0, m1, n0, n1),
|
||||
c_ms_ns_host_result(g0, g1, m0, m1, n0, n1),
|
||||
d_gs_ms_ns(g0, g1, m0, m1, n0, n1));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return ck::utils::check_err(e_gs_ms_ns_device_result, e_gs_ms_ns_host_result) ? 0 : 1;
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
@@ -16,6 +16,9 @@ if(USE_BITINT_EXTENSION_INT4)
|
||||
add_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_bias_relu_add_xdl_int4)
|
||||
endif() # USE_BITINT_EXTENSION_INT4
|
||||
|
||||
if(GPU_TARGETS MATCHES "gfx1100")
|
||||
add_example_executable(example_grouped_conv_fwd_bias_relu_add_wmma_fp16 grouped_conv_fwd_bias_relu_add_wmma_fp16.cpp)
|
||||
endif()
|
||||
|
||||
add_example_executable(example_grouped_conv_fwd_xdl_fp16 grouped_conv_fwd_xdl_fp16.cpp)
|
||||
|
||||
|
||||
@@ -137,7 +137,7 @@ inline bool parse_cmd_args(int argc,
|
||||
|
||||
const ck::index_t num_dim_spatial = std::stoi(argv[4]);
|
||||
conv_param = ck::utils::conv::parse_conv_param(
|
||||
num_dim_spatial, threshold_to_catch_partial_args, argv);
|
||||
num_dim_spatial, threshold_to_catch_partial_args + 1, argv);
|
||||
}
|
||||
else
|
||||
{
|
||||
|
||||
355
example/30_grouped_conv_fwd_multiple_d/common_wmma.hpp
Normal file
355
example/30_grouped_conv_fwd_multiple_d/common_wmma.hpp
Normal file
@@ -0,0 +1,355 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <algorithm>
|
||||
#include <array>
|
||||
#include <iostream>
|
||||
#include <string>
|
||||
#include <type_traits>
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/convolution_forward_specialization.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_wmma_cshuffle.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
#include "ck/library/utility/algorithm.hpp"
|
||||
#include "ck/library/utility/check_err.hpp"
|
||||
#include "ck/library/utility/device_memory.hpp"
|
||||
#include "ck/library/utility/host_tensor.hpp"
|
||||
#include "ck/library/utility/host_tensor_generator.hpp"
|
||||
#include "ck/library/utility/convolution_parameter.hpp"
|
||||
#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
|
||||
|
||||
using BF16 = ck::bhalf_t;
|
||||
using FP16 = ck::half_t;
|
||||
using FP32 = float;
|
||||
#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
|
||||
using I4 = ck::int4_t;
|
||||
#endif
|
||||
using I8 = std::int8_t;
|
||||
using I32 = std::int32_t;
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
static constexpr auto ConvSpec =
|
||||
ck::tensor_operation::device::ConvolutionForwardSpecialization::Default;
|
||||
|
||||
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
|
||||
|
||||
template <typename InputLay, typename WeightLay, typename OutputLay>
|
||||
struct CommonLayoutSetting
|
||||
{
|
||||
using InputLayout = InputLay;
|
||||
using WeightLayout = WeightLay;
|
||||
using OutputLayout = OutputLay;
|
||||
};
|
||||
|
||||
template <ck::index_t NDimSpatial>
|
||||
struct CommonLayoutSettingSelector;
|
||||
|
||||
namespace ctl = ck::tensor_layout::convolution;
|
||||
|
||||
template <>
|
||||
struct CommonLayoutSettingSelector<1> final
|
||||
: CommonLayoutSetting<ctl::G_NW_C, ctl::G_K_X_C, ctl::G_NW_K>
|
||||
{
|
||||
};
|
||||
|
||||
template <>
|
||||
struct CommonLayoutSettingSelector<2> final
|
||||
: CommonLayoutSetting<ctl::G_NHW_C, ctl::G_K_YX_C, ctl::G_NHW_K>
|
||||
{
|
||||
};
|
||||
|
||||
template <>
|
||||
struct CommonLayoutSettingSelector<3> final
|
||||
: CommonLayoutSetting<ctl::G_NDHW_C, ctl::G_K_ZYX_C, ctl::G_NDHW_K>
|
||||
{
|
||||
};
|
||||
|
||||
template <ck::index_t NDimSpatial>
|
||||
using InputLayout = typename CommonLayoutSettingSelector<NDimSpatial>::InputLayout;
|
||||
|
||||
template <ck::index_t NDimSpatial>
|
||||
using WeightLayout = typename CommonLayoutSettingSelector<NDimSpatial>::WeightLayout;
|
||||
|
||||
template <ck::index_t NDimSpatial>
|
||||
using OutputLayout = typename CommonLayoutSettingSelector<NDimSpatial>::OutputLayout;
|
||||
|
||||
struct ExecutionConfig final
|
||||
{
|
||||
bool do_verification = true;
|
||||
int init_method = 1;
|
||||
bool time_kernel = true;
|
||||
};
|
||||
|
||||
#define DefaultConvParam \
|
||||
ck::utils::conv::ConvParam \
|
||||
{ \
|
||||
2, 32, 2, 256, 192, {3, 3}, {71, 71}, {2, 2}, {1, 1}, {1, 1}, { 1, 1 } \
|
||||
}
|
||||
|
||||
inline void print_help_msg()
|
||||
{
|
||||
std::cerr << "arg1: verification (0=no, 1=yes)\n"
|
||||
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
|
||||
<< "arg3: time kernel (0=no, 1=yes)\n"
|
||||
<< ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
|
||||
}
|
||||
|
||||
inline bool parse_cmd_args(int argc,
|
||||
char* argv[],
|
||||
ExecutionConfig& config,
|
||||
ck::utils::conv::ConvParam& conv_param)
|
||||
{
|
||||
constexpr int num_execution_config_args =
|
||||
3; // arguments for do_verification, init_method, time_kernel
|
||||
constexpr int num_conv_param_leading_args = 5; // arguments for num_dim_spatial_, G_, N_, K_, C_
|
||||
|
||||
constexpr int threshold_to_catch_partial_args = 1 + num_execution_config_args;
|
||||
constexpr int threshold_to_catch_all_args =
|
||||
threshold_to_catch_partial_args + num_conv_param_leading_args;
|
||||
|
||||
if(argc == 1)
|
||||
{
|
||||
// use default
|
||||
}
|
||||
// catch only ExecutionConfig arguments
|
||||
else if(argc == threshold_to_catch_partial_args)
|
||||
{
|
||||
config.do_verification = std::stoi(argv[1]);
|
||||
config.init_method = std::stoi(argv[2]);
|
||||
config.time_kernel = std::stoi(argv[3]);
|
||||
}
|
||||
// catch both ExecutionConfig & ConvParam arguments
|
||||
else if(threshold_to_catch_all_args < argc && ((argc - threshold_to_catch_all_args) % 3 == 0))
|
||||
{
|
||||
config.do_verification = std::stoi(argv[1]);
|
||||
config.init_method = std::stoi(argv[2]);
|
||||
config.time_kernel = std::stoi(argv[3]);
|
||||
|
||||
const ck::index_t num_dim_spatial = std::stoi(argv[4]);
|
||||
conv_param = ck::utils::conv::parse_conv_param(
|
||||
num_dim_spatial, threshold_to_catch_partial_args + 1, argv);
|
||||
}
|
||||
else
|
||||
{
|
||||
print_help_msg();
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
inline HostTensorDescriptor make_input_descriptor(const ck::utils::conv::ConvParam& conv_param)
|
||||
{
|
||||
switch(conv_param.num_dim_spatial_)
|
||||
{
|
||||
case 1:
|
||||
return HostTensorDescriptor(
|
||||
{conv_param.G_, conv_param.N_, conv_param.C_, conv_param.input_spatial_lengths_[0]},
|
||||
{
|
||||
conv_param.C_, // g
|
||||
conv_param.input_spatial_lengths_[0] * conv_param.G_ * conv_param.C_, // n
|
||||
1, // c
|
||||
conv_param.G_ * conv_param.C_ // wi
|
||||
});
|
||||
|
||||
case 2:
|
||||
return HostTensorDescriptor(
|
||||
{conv_param.G_,
|
||||
conv_param.N_,
|
||||
conv_param.C_,
|
||||
conv_param.input_spatial_lengths_[0],
|
||||
conv_param.input_spatial_lengths_[1]},
|
||||
{
|
||||
conv_param.C_, // g
|
||||
conv_param.input_spatial_lengths_[0] * conv_param.input_spatial_lengths_[1] *
|
||||
conv_param.G_ * conv_param.C_, // n
|
||||
1, // c
|
||||
conv_param.input_spatial_lengths_[1] * conv_param.G_ * conv_param.C_, // hi
|
||||
conv_param.G_ * conv_param.C_ // wi
|
||||
});
|
||||
|
||||
case 3:
|
||||
return HostTensorDescriptor(
|
||||
{conv_param.G_,
|
||||
conv_param.N_,
|
||||
conv_param.C_,
|
||||
conv_param.input_spatial_lengths_[0],
|
||||
conv_param.input_spatial_lengths_[1],
|
||||
conv_param.input_spatial_lengths_[2]},
|
||||
{
|
||||
conv_param.C_, // g
|
||||
conv_param.input_spatial_lengths_[0] * conv_param.input_spatial_lengths_[1] *
|
||||
conv_param.input_spatial_lengths_[2] * conv_param.G_ * conv_param.C_, // n
|
||||
1, // c
|
||||
conv_param.input_spatial_lengths_[1] * conv_param.input_spatial_lengths_[2] *
|
||||
conv_param.G_ * conv_param.C_, // di
|
||||
conv_param.input_spatial_lengths_[2] * conv_param.G_ * conv_param.C_, // hi
|
||||
conv_param.G_ * conv_param.C_ // wi
|
||||
});
|
||||
}
|
||||
|
||||
throw std::runtime_error("unsuppored # dim spatial");
|
||||
}
|
||||
|
||||
inline HostTensorDescriptor make_weight_descriptor(const ck::utils::conv::ConvParam& conv_param)
|
||||
{
|
||||
switch(conv_param.num_dim_spatial_)
|
||||
{
|
||||
case 1:
|
||||
return HostTensorDescriptor(
|
||||
{conv_param.G_, conv_param.K_, conv_param.C_, conv_param.filter_spatial_lengths_[0]},
|
||||
{
|
||||
conv_param.K_ * conv_param.filter_spatial_lengths_[0] * conv_param.C_, // g
|
||||
conv_param.filter_spatial_lengths_[0] * conv_param.C_, // k
|
||||
1, // c
|
||||
conv_param.C_ // x
|
||||
});
|
||||
case 2:
|
||||
return HostTensorDescriptor(
|
||||
{conv_param.G_,
|
||||
conv_param.K_,
|
||||
conv_param.C_,
|
||||
conv_param.filter_spatial_lengths_[0],
|
||||
conv_param.filter_spatial_lengths_[1]},
|
||||
{
|
||||
conv_param.K_ * conv_param.filter_spatial_lengths_[0] *
|
||||
conv_param.filter_spatial_lengths_[1] * conv_param.C_, // g
|
||||
conv_param.filter_spatial_lengths_[0] * conv_param.filter_spatial_lengths_[1] *
|
||||
conv_param.C_, // k
|
||||
1, // c
|
||||
conv_param.filter_spatial_lengths_[1] * conv_param.C_, // y
|
||||
conv_param.C_ // x
|
||||
});
|
||||
case 3:
|
||||
return HostTensorDescriptor(
|
||||
{conv_param.G_,
|
||||
conv_param.K_,
|
||||
conv_param.C_,
|
||||
conv_param.filter_spatial_lengths_[0],
|
||||
conv_param.filter_spatial_lengths_[1],
|
||||
conv_param.filter_spatial_lengths_[2]},
|
||||
{
|
||||
conv_param.K_ * conv_param.filter_spatial_lengths_[0] *
|
||||
conv_param.filter_spatial_lengths_[1] * conv_param.filter_spatial_lengths_[2] *
|
||||
conv_param.C_, // g
|
||||
conv_param.filter_spatial_lengths_[0] * conv_param.filter_spatial_lengths_[1] *
|
||||
conv_param.filter_spatial_lengths_[2] * conv_param.C_, // k
|
||||
1, // c
|
||||
conv_param.filter_spatial_lengths_[1] * conv_param.filter_spatial_lengths_[2] *
|
||||
conv_param.C_, // z
|
||||
conv_param.filter_spatial_lengths_[2] * conv_param.C_, // y
|
||||
conv_param.C_ // x
|
||||
});
|
||||
}
|
||||
|
||||
throw std::runtime_error("unsuppored # dim spatial");
|
||||
}
|
||||
|
||||
inline HostTensorDescriptor make_bias_descriptor(const ck::utils::conv::ConvParam& conv_param)
|
||||
{
|
||||
switch(conv_param.num_dim_spatial_)
|
||||
{
|
||||
case 1:
|
||||
return HostTensorDescriptor(
|
||||
{conv_param.G_, conv_param.N_, conv_param.K_, conv_param.output_spatial_lengths_[0]},
|
||||
{
|
||||
conv_param.K_, // g
|
||||
0, // k
|
||||
1, // c
|
||||
0 // x
|
||||
});
|
||||
case 2:
|
||||
return HostTensorDescriptor({conv_param.G_,
|
||||
conv_param.N_,
|
||||
conv_param.K_,
|
||||
conv_param.output_spatial_lengths_[0],
|
||||
conv_param.output_spatial_lengths_[1]},
|
||||
{
|
||||
conv_param.K_, // g
|
||||
0, // n
|
||||
1, // k
|
||||
0, // ho
|
||||
0 // wo
|
||||
});
|
||||
case 3:
|
||||
return HostTensorDescriptor({conv_param.G_,
|
||||
conv_param.N_,
|
||||
conv_param.K_,
|
||||
conv_param.output_spatial_lengths_[0],
|
||||
conv_param.output_spatial_lengths_[1],
|
||||
conv_param.output_spatial_lengths_[2]},
|
||||
{
|
||||
conv_param.K_, // g
|
||||
0, // n
|
||||
1, // k
|
||||
0, // z
|
||||
0, // y
|
||||
0 // x
|
||||
});
|
||||
}
|
||||
|
||||
throw std::runtime_error("unsuppored # dim spatial");
|
||||
}
|
||||
|
||||
inline HostTensorDescriptor make_output_descriptor(const ck::utils::conv::ConvParam& conv_param)
|
||||
{
|
||||
|
||||
switch(conv_param.num_dim_spatial_)
|
||||
{
|
||||
case 1:
|
||||
return HostTensorDescriptor(
|
||||
{conv_param.G_, conv_param.N_, conv_param.K_, conv_param.output_spatial_lengths_[0]},
|
||||
{
|
||||
conv_param.K_, // g
|
||||
conv_param.output_spatial_lengths_[0] * conv_param.G_ * conv_param.K_, // n
|
||||
1, // k
|
||||
conv_param.G_ * conv_param.K_ // wo
|
||||
});
|
||||
case 2:
|
||||
return HostTensorDescriptor(
|
||||
{conv_param.G_,
|
||||
conv_param.N_,
|
||||
conv_param.K_,
|
||||
conv_param.output_spatial_lengths_[0],
|
||||
conv_param.output_spatial_lengths_[1]},
|
||||
{
|
||||
conv_param.K_, // g
|
||||
conv_param.output_spatial_lengths_[0] * conv_param.output_spatial_lengths_[1] *
|
||||
conv_param.G_ * conv_param.K_, // n
|
||||
1, // k
|
||||
conv_param.output_spatial_lengths_[1] * conv_param.G_ * conv_param.K_, // ho
|
||||
conv_param.G_ * conv_param.K_ // wo
|
||||
});
|
||||
|
||||
case 3:
|
||||
return HostTensorDescriptor(
|
||||
{conv_param.G_,
|
||||
conv_param.N_,
|
||||
conv_param.K_,
|
||||
conv_param.output_spatial_lengths_[0],
|
||||
conv_param.output_spatial_lengths_[1],
|
||||
conv_param.output_spatial_lengths_[2]},
|
||||
{
|
||||
conv_param.K_, // g
|
||||
conv_param.output_spatial_lengths_[0] * conv_param.output_spatial_lengths_[1] *
|
||||
conv_param.output_spatial_lengths_[2] * conv_param.G_ * conv_param.K_, // n
|
||||
1, // k
|
||||
conv_param.output_spatial_lengths_[1] * conv_param.output_spatial_lengths_[2] *
|
||||
conv_param.G_ * conv_param.K_, // do
|
||||
conv_param.output_spatial_lengths_[2] * conv_param.G_ * conv_param.K_, // ho
|
||||
conv_param.G_ * conv_param.K_ // wo
|
||||
});
|
||||
}
|
||||
|
||||
throw std::runtime_error("unsuppored # dim spatial");
|
||||
}
|
||||
@@ -0,0 +1,26 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include "common_wmma.hpp"
|
||||
|
||||
// kernel data types
|
||||
using InKernelDataType = FP16;
|
||||
using WeiKernelDataType = FP16;
|
||||
using AccDataType = FP32;
|
||||
using CShuffleDataType = FP16;
|
||||
using BiasKernelDataType = FP16;
|
||||
using ResidualKernelDataType = FP16;
|
||||
using OutKernelDataType = FP16;
|
||||
|
||||
// tensor data types
|
||||
using InUserDataType = InKernelDataType;
|
||||
using WeiUserDataType = WeiKernelDataType;
|
||||
using OutUserDataType = OutKernelDataType;
|
||||
|
||||
using InElementOp = PassThrough;
|
||||
using WeiElementOp = PassThrough;
|
||||
using OutElementOp = ck::tensor_operation::element_wise::AddReluAdd;
|
||||
|
||||
#include "run_grouped_conv_fwd_bias_relu_add_wmma_example.inc"
|
||||
|
||||
int main(int argc, char* argv[]) { return !run_grouped_conv_fwd_bias_relu_add_example(argc, argv); }
|
||||
@@ -0,0 +1,286 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
template <typename BiasLay, typename ResidualLay>
|
||||
struct LayoutSetting
|
||||
{
|
||||
using BiasLayout = BiasLay;
|
||||
using ResidualLayout = ResidualLay;
|
||||
};
|
||||
|
||||
template <ck::index_t NDimSpatial>
|
||||
struct LayoutSettingSelector;
|
||||
|
||||
template <>
|
||||
struct LayoutSettingSelector<1> final : LayoutSetting<ctl::G_K, ctl::G_NW_K>
|
||||
{
|
||||
};
|
||||
|
||||
template <>
|
||||
struct LayoutSettingSelector<2> final : LayoutSetting<ctl::G_K, ctl::G_NHW_K>
|
||||
{
|
||||
};
|
||||
|
||||
template <>
|
||||
struct LayoutSettingSelector<3> final : LayoutSetting<ctl::G_K, ctl::G_NDHW_K>
|
||||
{
|
||||
};
|
||||
|
||||
template <ck::index_t NDimSpatial>
|
||||
using BiasLayout = typename LayoutSettingSelector<NDimSpatial>::BiasLayout;
|
||||
|
||||
template <ck::index_t NDimSpatial>
|
||||
using ResidualLayout = typename LayoutSettingSelector<NDimSpatial>::ResidualLayout;
|
||||
|
||||
template <ck::index_t NDimSpatial>
|
||||
using DeviceConvFwdInstance =
|
||||
ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD_Wmma_CShuffle<
|
||||
NDimSpatial,
|
||||
InputLayout<NDimSpatial>,
|
||||
WeightLayout<NDimSpatial>,
|
||||
ck::Tuple<BiasLayout<NDimSpatial>, ResidualLayout<NDimSpatial>>,
|
||||
OutputLayout<NDimSpatial>,
|
||||
InKernelDataType,
|
||||
WeiKernelDataType,
|
||||
ck::Tuple<BiasKernelDataType, ResidualKernelDataType>,
|
||||
OutKernelDataType,
|
||||
AccDataType,
|
||||
CShuffleDataType,
|
||||
InElementOp,
|
||||
WeiElementOp,
|
||||
OutElementOp,
|
||||
ConvSpec, // ConvForwardSpecialization
|
||||
GemmSpec, // GemmSpecialization
|
||||
256, // BlockSize
|
||||
128, // MPerBlock
|
||||
128, // NPerBlock
|
||||
4, // K0PerBlock
|
||||
8, // K1
|
||||
16, // MPerWMMA
|
||||
16, // NPerWMMA
|
||||
4, // MRepeat
|
||||
2, // NRepeat
|
||||
S<4, 64, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1
|
||||
S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
|
||||
S<1, 0, 2>, // ABlockTransferSrcAccessOrder
|
||||
2, // ABlockTransferSrcVectorDim
|
||||
8, // ABlockTransferSrcScalarPerVector
|
||||
8, // ABlockTransferDstScalarPerVector_AK1
|
||||
true, // ABlockLdsExtraM
|
||||
S<4, 64, 1>, // BBlockTransferThreadClusterLengths_BK0_N_BK1
|
||||
S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
|
||||
S<1, 0, 2>, // BBlockTransferSrcAccessOrder
|
||||
2, // BBlockTransferSrcVectorDim
|
||||
8, // BBlockTransferSrcScalarPerVector
|
||||
8, // BBlockTransferDstScalarPerVector_BK1
|
||||
true, // BBlockLdsExtraN
|
||||
1,
|
||||
1,
|
||||
S<1, 32, 1, 8>,
|
||||
8>;
|
||||
|
||||
template <ck::index_t NDimSpatial>
|
||||
using HostConvFwdInstance = ck::tensor_operation::host::ReferenceConvFwd<NDimSpatial,
|
||||
InUserDataType,
|
||||
WeiUserDataType,
|
||||
CShuffleDataType,
|
||||
InElementOp,
|
||||
WeiElementOp,
|
||||
PassThrough>;
|
||||
|
||||
template <ck::index_t NDimSpatial>
|
||||
bool run_grouped_conv_fwd_bias_relu_add(const ExecutionConfig& config,
|
||||
const ck::utils::conv::ConvParam& conv_param)
|
||||
{
|
||||
static_assert(1 <= NDimSpatial && NDimSpatial <= 3, "Unsupported NDimSpatial");
|
||||
|
||||
const auto in_g_n_c_wis_desc = make_input_descriptor(conv_param);
|
||||
const auto wei_g_k_c_xs_desc = make_weight_descriptor(conv_param);
|
||||
const auto bias_g_n_k_wos_desc = make_bias_descriptor(conv_param);
|
||||
const auto out_g_n_k_wos_desc = make_output_descriptor(conv_param);
|
||||
|
||||
Tensor<InUserDataType> in(in_g_n_c_wis_desc);
|
||||
Tensor<WeiUserDataType> wei(wei_g_k_c_xs_desc);
|
||||
Tensor<OutUserDataType> bias(bias_g_n_k_wos_desc);
|
||||
Tensor<OutUserDataType> residual(bias_g_n_k_wos_desc);
|
||||
Tensor<OutUserDataType> out_host(out_g_n_k_wos_desc);
|
||||
Tensor<OutKernelDataType> out_device(out_g_n_k_wos_desc);
|
||||
|
||||
std::cout << "in: " << in.mDesc << std::endl;
|
||||
std::cout << "wei: " << wei.mDesc << std::endl;
|
||||
std::cout << "bias: " << bias.mDesc << std::endl;
|
||||
std::cout << "residual: " << residual.mDesc << std::endl;
|
||||
std::cout << "out: " << out_host.mDesc << std::endl;
|
||||
|
||||
switch(config.init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
in.GenerateTensorValue(GeneratorTensor_2<InUserDataType>{-5, 5});
|
||||
wei.GenerateTensorValue(GeneratorTensor_2<WeiUserDataType>{-5, 5});
|
||||
bias.GenerateTensorValue(GeneratorTensor_2<OutUserDataType>{-5, 5});
|
||||
break;
|
||||
default:
|
||||
in.GenerateTensorValue(GeneratorTensor_3<InUserDataType>{0.0, 1.0});
|
||||
wei.GenerateTensorValue(GeneratorTensor_3<WeiUserDataType>{-0.5, 0.5});
|
||||
bias.GenerateTensorValue(GeneratorTensor_3<OutUserDataType>{-0.5, 0.5});
|
||||
}
|
||||
|
||||
DeviceMem in_device_buf(sizeof(InKernelDataType) * in.mDesc.GetElementSpaceSize());
|
||||
DeviceMem wei_device_buf(sizeof(WeiKernelDataType) * wei.mDesc.GetElementSpaceSize());
|
||||
DeviceMem bias_device_buf(sizeof(OutKernelDataType) * bias.mDesc.GetElementSpaceSize());
|
||||
DeviceMem residual_device_buf(sizeof(OutKernelDataType) * residual.mDesc.GetElementSpaceSize());
|
||||
DeviceMem out_device_buf(sizeof(OutKernelDataType) * out_device.mDesc.GetElementSpaceSize());
|
||||
|
||||
#ifdef BUILD_INT4_EXAMPLE
|
||||
const Tensor<InKernelDataType> in_converted(in);
|
||||
const Tensor<WeiKernelDataType> wei_converted(wei);
|
||||
const Tensor<OutKernelDataType> bias_converted(bias);
|
||||
const Tensor<OutKernelDataType> residual_converted(residual);
|
||||
|
||||
in_device_buf.ToDevice(in_converted.mData.data());
|
||||
wei_device_buf.ToDevice(wei_converted.mData.data());
|
||||
bias_device_buf.ToDevice(bias_converted.mData.data());
|
||||
residual_device_buf.ToDevice(residual_converted.mData.data());
|
||||
#else
|
||||
in_device_buf.ToDevice(in.mData.data());
|
||||
wei_device_buf.ToDevice(wei.mData.data());
|
||||
bias_device_buf.ToDevice(bias.mData.data());
|
||||
residual_device_buf.ToDevice(residual.mData.data());
|
||||
#endif
|
||||
|
||||
std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_lengths{};
|
||||
std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_strides{};
|
||||
std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_lengths{};
|
||||
std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_strides{};
|
||||
std::array<ck::index_t, NDimSpatial + 3> d0_g_n_k_wos_lengths{};
|
||||
std::array<ck::index_t, NDimSpatial + 3> d0_g_n_k_wos_strides{};
|
||||
std::array<ck::index_t, NDimSpatial + 3> d1_g_n_k_wos_lengths{};
|
||||
std::array<ck::index_t, NDimSpatial + 3> d1_g_n_k_wos_strides{};
|
||||
std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_lengths{};
|
||||
std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_strides{};
|
||||
std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
|
||||
std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
|
||||
std::array<ck::index_t, NDimSpatial> input_left_pads{};
|
||||
std::array<ck::index_t, NDimSpatial> input_right_pads{};
|
||||
|
||||
auto copy = [](const auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
|
||||
|
||||
copy(in_g_n_c_wis_desc.GetLengths(), a_g_n_c_wis_lengths);
|
||||
copy(in_g_n_c_wis_desc.GetStrides(), a_g_n_c_wis_strides);
|
||||
copy(wei_g_k_c_xs_desc.GetLengths(), b_g_k_c_xs_lengths);
|
||||
copy(wei_g_k_c_xs_desc.GetStrides(), b_g_k_c_xs_strides);
|
||||
copy(bias_g_n_k_wos_desc.GetLengths(), d0_g_n_k_wos_lengths);
|
||||
copy(bias_g_n_k_wos_desc.GetStrides(), d0_g_n_k_wos_strides);
|
||||
copy(bias_g_n_k_wos_desc.GetLengths(), d1_g_n_k_wos_lengths);
|
||||
copy(bias_g_n_k_wos_desc.GetStrides(), d1_g_n_k_wos_strides);
|
||||
copy(out_g_n_k_wos_desc.GetLengths(), e_g_n_k_wos_lengths);
|
||||
copy(out_g_n_k_wos_desc.GetStrides(), e_g_n_k_wos_strides);
|
||||
copy(conv_param.conv_filter_strides_, conv_filter_strides);
|
||||
copy(conv_param.conv_filter_dilations_, conv_filter_dilations);
|
||||
copy(conv_param.input_left_pads_, input_left_pads);
|
||||
copy(conv_param.input_right_pads_, input_right_pads);
|
||||
|
||||
// do Conv
|
||||
auto conv = DeviceConvFwdInstance<NDimSpatial>{};
|
||||
auto invoker = conv.MakeInvoker();
|
||||
auto argument =
|
||||
conv.MakeArgument(in_device_buf.GetDeviceBuffer(),
|
||||
wei_device_buf.GetDeviceBuffer(),
|
||||
std::array<const void*, 2>{bias_device_buf.GetDeviceBuffer(),
|
||||
residual_device_buf.GetDeviceBuffer()},
|
||||
out_device_buf.GetDeviceBuffer(),
|
||||
a_g_n_c_wis_lengths,
|
||||
a_g_n_c_wis_strides,
|
||||
b_g_k_c_xs_lengths,
|
||||
b_g_k_c_xs_strides,
|
||||
std::array<std::array<ck::index_t, NDimSpatial + 3>, 2>{
|
||||
{d0_g_n_k_wos_lengths, d1_g_n_k_wos_lengths}},
|
||||
std::array<std::array<ck::index_t, NDimSpatial + 3>, 2>{
|
||||
{d0_g_n_k_wos_strides, d1_g_n_k_wos_strides}},
|
||||
e_g_n_k_wos_lengths,
|
||||
e_g_n_k_wos_strides,
|
||||
conv_filter_strides,
|
||||
conv_filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
InElementOp{},
|
||||
WeiElementOp{},
|
||||
OutElementOp{});
|
||||
|
||||
if(!conv.IsSupportedArgument(argument))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! device_conv with the specified compilation parameters does "
|
||||
"not support this Conv problem");
|
||||
}
|
||||
|
||||
float avg_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
|
||||
|
||||
std::size_t flop = conv_param.GetFlops();
|
||||
std::size_t num_btype = conv_param.GetByte<InUserDataType, WeiUserDataType, OutUserDataType>();
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
|
||||
float gb_per_sec = num_btype / 1.E6 / avg_time;
|
||||
std::cout << "Perf: " << avg_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
|
||||
<< conv.GetTypeString() << std::endl;
|
||||
|
||||
if(config.do_verification)
|
||||
{
|
||||
Tensor<CShuffleDataType> c_host(out_g_n_k_wos_desc);
|
||||
|
||||
auto ref_conv = HostConvFwdInstance<NDimSpatial>{};
|
||||
auto ref_invoker = ref_conv.MakeInvoker();
|
||||
auto ref_argument = ref_conv.MakeArgument(in,
|
||||
wei,
|
||||
c_host,
|
||||
conv_param.conv_filter_strides_,
|
||||
conv_param.conv_filter_dilations_,
|
||||
conv_param.input_left_pads_,
|
||||
conv_param.input_right_pads_,
|
||||
InElementOp{},
|
||||
WeiElementOp{},
|
||||
PassThrough{});
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
// TODO: implement elementwise operation for host
|
||||
out_host.ForEach([&](auto&, auto idx) {
|
||||
OutElementOp{}(out_host(idx), c_host(idx), bias(idx), residual(idx));
|
||||
});
|
||||
|
||||
out_device_buf.FromDevice(out_device.mData.data());
|
||||
|
||||
#ifdef BUILD_INT4_EXAMPLE
|
||||
const Tensor<OutUserDataType> out_device_converted(out_device);
|
||||
|
||||
return ck::utils::check_err(
|
||||
out_device_converted, out_host, "Error: incorrect results!", 1e-5f, 1e-4f);
|
||||
#else
|
||||
return ck::utils::check_err(
|
||||
out_device, out_host, "Error: incorrect results!", 1e-5f, 1e-4f);
|
||||
#endif
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool run_grouped_conv_fwd_bias_relu_add_example(int argc, char* argv[])
|
||||
{
|
||||
ExecutionConfig config;
|
||||
ck::utils::conv::ConvParam conv_param = DefaultConvParam;
|
||||
|
||||
if(!parse_cmd_args(argc, argv, config, conv_param))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
switch(conv_param.num_dim_spatial_)
|
||||
{
|
||||
case 1: return run_grouped_conv_fwd_bias_relu_add<1>(config, conv_param);
|
||||
case 2: return run_grouped_conv_fwd_bias_relu_add<2>(config, conv_param);
|
||||
case 3: return run_grouped_conv_fwd_bias_relu_add<3>(config, conv_param);
|
||||
}
|
||||
|
||||
return false;
|
||||
}
|
||||
Reference in New Issue
Block a user