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* add bmm api * add bf16 multi_d * add ckProfiler for bf16 * add ckProfiler files * add more instance; fixed 64bit index issue * fixed naming * enabled batched Ds * use long_index for ds offsets * clean * add bmm fp8 ckProfiler * Update example/24_batched_gemm/batched_gemm_xdl_bf16_v3.cpp Co-authored-by: Bartłomiej Kocot <bartlomiejkocot98@gmail.com> * Update example/24_batched_gemm/batched_gemm_xdl_fp8_rowwise_v3.cpp Co-authored-by: Bartłomiej Kocot <bartlomiejkocot98@gmail.com> * Update example/24_batched_gemm/run_batched_gemm_example_rowwise.inc Co-authored-by: Bartłomiej Kocot <bartlomiejkocot98@gmail.com> * Update library/src/tensor_operation_instance/gpu/gemm_universal_batched/device_batched_gemm_xdl_universal_bf16_bf16_bf16/device_batched_gemm_xdl_universal_bf16_bf16_bf16_mk_nk_mn.hpp Co-authored-by: Bartłomiej Kocot <bartlomiejkocot98@gmail.com> * Update library/src/tensor_operation_instance/gpu/gemm_universal_batched/device_batched_gemm_xdl_universal_bf16_bf16_bf16/device_batched_gemm_xdl_universal_bf16_bf16_bf16_mk_nk_mn_mem_v1_default_instance.cpp Co-authored-by: Bartłomiej Kocot <bartlomiejkocot98@gmail.com> * Update library/src/tensor_operation_instance/gpu/gemm_universal_batched/device_batched_gemm_xdl_universal_bf16_bf16_bf16/device_batched_gemm_xdl_universal_bf16_bf16_bf16_mk_nk_mn_mem_v2_default_instance.cpp Co-authored-by: Bartłomiej Kocot <bartlomiejkocot98@gmail.com> * Update profiler/src/profile_gemm_universal_batched.cpp Co-authored-by: Bartłomiej Kocot <bartlomiejkocot98@gmail.com> * Update profiler/include/profiler/profile_gemm_universal_batched_impl.hpp Co-authored-by: Bartłomiej Kocot <bartlomiejkocot98@gmail.com> * clean * Update include/ck/tensor_operation/gpu/device/impl/device_batched_gemm_multiple_d_xdl_cshuffle_v3.hpp * Update include/ck/tensor_operation/gpu/device/impl/device_batched_gemm_multiple_d_xdl_cshuffle_v3.hpp * Update library/src/tensor_operation_instance/gpu/gemm_universal_batched/device_batched_gemm_xdl_universal_bf16_bf16_bf16/device_batched_gemm_xdl_universal_bf16_bf16_bf16_mk_nk_mn_comp_default_instance.cpp * Update include/ck/tensor_operation/gpu/device/impl/device_batched_gemm_multiple_d_xdl_cshuffle_v3.hpp * Update include/ck/tensor_operation/gpu/device/impl/device_batched_gemm_multiple_d_xdl_cshuffle_v3.hpp * Update include/ck/tensor_operation/gpu/device/impl/device_batched_gemm_multiple_d_xdl_cshuffle_v3.hpp * refactor batch offset func * add splitk suppport into bmm_v3 * clean * clean * format * fixed * fix --------- Co-authored-by: Jing Zhang <jizhan@fb.com> Co-authored-by: zjing14 <zhangjing14@gmail.com>
190 lines
9.0 KiB
C++
190 lines
9.0 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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#include <cstdint>
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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 "profiler/profile_gemm_universal_batched_impl.hpp"
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#include "profiler_operation_registry.hpp"
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#include "ck/library/tensor_operation_instance/gpu/gemm_universal_batched.hpp"
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enum struct GemmMatrixLayout
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{
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MK_KN_MN, // 0
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MK_NK_MN, // 1
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KM_KN_MN, // 2
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KM_NK_MN, // 3
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};
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enum struct GemmDataType
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{
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BF16_BF16_BF16, // 0
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F8_F8_BF16, // 1
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};
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#define OP_NAME "gemm_universal_batched"
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#define OP_DESC "Batched GEMM Universal"
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int profile_batched_gemm_universal(int argc, char* argv[])
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{
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if(argc != 19 && argc != 22)
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{
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// clang-format off
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printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
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printf("arg2: data type (0: bf16, 1: fp8->bf16)\n");
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printf("arg3: matrix layout (0: A[g, m, k] * B[g, k, n] = C[g, m, n];\n");
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printf(" 1: A[g, m, k] * B[g, n, k] = C[g, m, n];\n");
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printf(" 2: A[g, k, m] * B[g, k, n] = C[g, m, n];\n");
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printf(" 3: A[g, k, m] * B[g, n, k] = C[g, m, n])\n");
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printf("arg4: verification (0: no; 1: yes)\n");
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printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n");
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printf("arg6: print tensor value (0: no; 1: yes)\n");
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printf("arg7: time kernel (0=n0, 1=yes)\n");
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printf("arg8 to 18: M, N, K, StrideA, StrideB, StrideC, BatchStrideA, BatchStrideB, BatchStrideC, BatchCount, KBatch\n");
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printf("optional:\n");
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printf("arg19: number of warm-up cycles (default 1)\n");
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printf("arg20: number of iterations (default 10)\n");
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printf("arg21: memory for rotating buffer (default 0, size in MB)\n");
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// clang-format on
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exit(1);
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}
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int n_warmup = 1;
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int n_iter = 10;
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uint64_t rotating = 0;
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if(argc == 22)
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{
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n_warmup = std::stoi(argv[19]);
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n_iter = std::stoi(argv[20]);
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rotating = std::stoull(argv[21]) * 1024 * 1024;
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}
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const auto data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
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const auto layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
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const bool do_verification = std::stoi(argv[4]);
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const int init_method = std::stoi(argv[5]);
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const bool do_log = std::stoi(argv[6]);
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const bool time_kernel = std::stoi(argv[7]);
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const int M = std::stoi(argv[8]);
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const int N = std::stoi(argv[9]);
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const int K = std::stoi(argv[10]);
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const int StrideA = std::stoi(argv[11]);
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const int StrideB = std::stoi(argv[12]);
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const int StrideC = std::stoi(argv[13]);
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const int BatchStrideA = std::stoi(argv[14]);
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const int BatchStrideB = std::stoi(argv[15]);
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const int BatchStrideC = std::stoi(argv[16]);
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const int BatchCount = std::stoi(argv[17]);
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const int KBatch = std::stoi(argv[18]);
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#if defined(CK_USE_FP8_ON_UNSUPPORTED_ARCH) || defined(CK_USE_GFX94)
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using F8 = ck::f8_t;
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#endif
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using BF16 = ck::bhalf_t;
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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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auto profile =
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[&](auto a_type, auto b_type, auto c_type, auto a_layout, auto b_layout, auto c_layout) {
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using ADataType = decltype(a_type);
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using BDataType = decltype(b_type);
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using DsDataType = ck::Tuple<>;
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using CDataType = decltype(c_type);
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using ALayout = decltype(a_layout);
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using BLayout = decltype(b_layout);
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using DsLayout = ck::Tuple<>;
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using CLayout = decltype(c_layout);
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const int DefaultStrideA = ck::is_same_v<ALayout, Row> ? K : M;
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const int DefaultStrideB = ck::is_same_v<BLayout, Row> ? N : K;
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const int DefaultStrideC = ck::is_same_v<CLayout, Row> ? N : M;
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const int StrideA_ = (StrideA < 0) ? DefaultStrideA : StrideA;
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const int StrideB_ = (StrideB < 0) ? DefaultStrideB : StrideB;
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const int StrideC_ = (StrideC < 0) ? DefaultStrideC : StrideC;
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const int DefaultBatchStrideA = (ck::is_same_v<ALayout, Row> ? M : K) * StrideA_;
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const int DefaultBatchStrideB = (ck::is_same_v<BLayout, Row> ? K : N) * StrideB_;
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const int DefaultBatchStrideC = (ck::is_same_v<CLayout, Row> ? M : N) * StrideC_;
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const int BatchStrideA_ = (BatchStrideA < 0) ? DefaultBatchStrideA : BatchStrideA;
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const int BatchStrideB_ = (BatchStrideB < 0) ? DefaultBatchStrideB : BatchStrideB;
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const int BatchStrideC_ = (BatchStrideC < 0) ? DefaultBatchStrideC : BatchStrideC;
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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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using DeviceOp = ck::tensor_operation::device::DeviceBatchedGemmV2MultiD<ALayout,
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BLayout,
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DsLayout,
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CLayout,
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ADataType,
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BDataType,
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DsDataType,
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CDataType,
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AElementOp,
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BElementOp,
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CElementOp>;
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bool pass = ck::profiler::profile_gemm_universal_batched_impl<ADataType,
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BDataType,
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CDataType,
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ALayout,
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BLayout,
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CLayout,
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AElementOp,
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BElementOp,
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CElementOp,
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DeviceOp>(do_verification,
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init_method,
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do_log,
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time_kernel,
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M,
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N,
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K,
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BatchStrideA_,
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BatchStrideB_,
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BatchStrideC_,
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StrideA_,
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StrideB_,
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StrideC_,
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BatchCount,
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KBatch,
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n_warmup,
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n_iter,
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rotating);
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return pass ? 0 : 1;
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};
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if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::MK_NK_MN)
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{
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return profile(BF16{}, BF16{}, BF16{}, Row{}, Col{}, Row{});
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}
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#if defined(CK_USE_FP8_ON_UNSUPPORTED_ARCH) || defined(CK_USE_GFX94)
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else if(data_type == GemmDataType::F8_F8_BF16 && layout == GemmMatrixLayout::MK_NK_MN)
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{
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return profile(F8{}, F8{}, BF16{}, Row{}, Col{}, Row{});
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}
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#endif
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else
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{
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std::cout << "this data_type & layout is not implemented" << std::endl;
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return 1;
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}
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}
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REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_batched_gemm_universal);
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