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d85f065b15
* chore(copyright): update copyright header for codegen directory * chore(copyright): update copyright header for example directory
262 lines
12 KiB
C++
262 lines
12 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#pragma once
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template <typename PrecType,
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typename FlatmmConfig,
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int ScaleGranularityM = -1,
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int ScaleGranularityN = -1,
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bool UsePersistentKernel = false,
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typename ALayout,
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typename BLayout,
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typename CLayout>
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int run_flatmm_example_with_layouts(int argc,
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char* argv[],
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const ALayout a_layout = ALayout{},
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const BLayout b_layout = BLayout{},
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[[maybe_unused]] const CLayout c_layout = CLayout{})
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{
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auto [result, arg_parser] = create_args(argc, argv);
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if(!result)
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return -1;
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using ADataType = typename GemmBasicTypeConfig<PrecType>::ADataType;
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using BDataType = typename GemmBasicTypeConfig<PrecType>::BDataType;
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using CDataType = typename GemmBasicTypeConfig<PrecType>::CDataType;
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using AccDataType = typename GemmBasicTypeConfig<PrecType>::AccDataType;
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ck_tile::index_t M = arg_parser.get_int("m");
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ck_tile::index_t N = arg_parser.get_int("n");
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ck_tile::index_t K = arg_parser.get_int("k");
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ck_tile::index_t stride_A = arg_parser.get_int("stride_a");
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ck_tile::index_t stride_B = arg_parser.get_int("stride_b");
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ck_tile::index_t stride_C = arg_parser.get_int("stride_c");
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ck_tile::index_t kbatch = arg_parser.get_int("split_k");
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int n_warmup = arg_parser.get_int("warmup");
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int n_repeat = arg_parser.get_int("repeat");
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ck_tile::index_t init_method = arg_parser.get_int("init");
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// persistent not added
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stride_A = ck_tile::get_default_stride(M, K, stride_A, is_row_major(a_layout));
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stride_B = ck_tile::get_default_stride(K, N, stride_B, is_row_major(b_layout));
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stride_C = ck_tile::get_default_stride(M, N, stride_C, is_row_major(CLayout{}));
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ck_tile::HostTensor<ADataType> a_host(
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ck_tile::host_tensor_descriptor(M, K, stride_A, is_row_major(a_layout)));
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ck_tile::HostTensor<BDataType> b_origin_host(
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ck_tile::host_tensor_descriptor(K, N, stride_B, is_row_major(b_layout)));
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ck_tile::HostTensor<CDataType> c_rslt_host(
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ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
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ck_tile::HostTensor<AccDataType> per_token_scale(ck_tile::HostTensorDescriptor({M}, {1}));
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ck_tile::HostTensor<AccDataType> per_channel_scale(ck_tile::HostTensorDescriptor({N}, {1}));
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// TODO: add different init types
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if(init_method == 0)
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{
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// ck_tile::FillUniformDistribution<ADataType>{-.5f, .5f}(a_host);
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// ck_tile::FillUniformDistribution<BDataType>{-.5f, .5f}(b_origin_host);
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ck_tile::FillUniformDistribution<ADataType>{0.0f, 1.0f}(a_host);
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ck_tile::FillUniformDistribution<BDataType>{-.5f, .5f}(b_origin_host);
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ck_tile::FillUniformDistribution<AccDataType>{-1.f, 1.f}(per_token_scale);
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ck_tile::FillUniformDistribution<AccDataType>{-1.f, 1.f}(per_channel_scale);
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}
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else if(init_method == 1)
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{
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ck_tile::FillMonotonicSeq<ADataType>{}(a_host);
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ck_tile::FillMonotonicSeq<BDataType>{}(b_origin_host);
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ck_tile::FillUniformDistribution<AccDataType>{1.f, 1.f}(per_token_scale);
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ck_tile::FillUniformDistribution<AccDataType>{1.f, 1.f}(per_channel_scale);
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}
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else if(init_method == 2)
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{
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ck_tile::FillUniformDistribution<ADataType>{1.f, 1.f}(a_host);
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ck_tile::FillUniformDistribution<BDataType>{1.f, 1.f}(b_origin_host);
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ck_tile::FillUniformDistribution<AccDataType>{1.f, 1.f}(per_token_scale);
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ck_tile::FillUniformDistribution<AccDataType>{1.f, 1.f}(per_channel_scale);
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}
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else
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{
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a_host.SetZero();
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b_origin_host.SetZero();
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}
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ck_tile::DeviceMem a_dev_buf(a_host.get_element_space_size_in_bytes());
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ck_tile::DeviceMem c_dev_buf(c_rslt_host.get_element_space_size_in_bytes());
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ck_tile::DeviceMem per_token_scale_dev_buf(per_token_scale.get_element_space_size_in_bytes());
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ck_tile::DeviceMem per_channel_scale_dev_buf(
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per_channel_scale.get_element_space_size_in_bytes());
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a_dev_buf.ToDevice(a_host.data());
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c_rslt_host.SetZero();
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per_token_scale_dev_buf.ToDevice(per_token_scale.data());
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per_channel_scale_dev_buf.ToDevice(per_channel_scale.data());
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// do pre-shuffle
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ck_tile::HostTensor<BDataType> b_shuffle_host = [&]() {
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if constexpr(FlatmmConfig::TiledMMAPermuteN)
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{
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return shuffle_b_v1<FlatmmConfig>(b_origin_host);
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}
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else
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{
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return shuffle_b_v0<FlatmmConfig>(b_origin_host);
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}
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}();
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ck_tile::DeviceMem b_shuffle_dev_buf(b_shuffle_host.get_element_space_size_in_bytes());
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b_shuffle_dev_buf.ToDevice(b_shuffle_host.data());
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auto per_token_scale_dev_ptr = ck_tile::FlatmmScalePointer<ScaleGranularityM>{
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static_cast<float*>(per_token_scale_dev_buf.GetDeviceBuffer())};
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auto per_channel_scale_dev_ptr = ck_tile::FlatmmScalePointer<ScaleGranularityN>{
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static_cast<float*>(per_channel_scale_dev_buf.GetDeviceBuffer())};
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invoke_flatmm<FlatmmConfig,
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ADataType,
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BDataType,
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ck_tile::tuple<>,
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AccDataType,
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CDataType,
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ALayout,
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BLayout,
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ck_tile::tuple<>,
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CLayout,
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decltype(per_token_scale_dev_ptr),
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decltype(per_channel_scale_dev_ptr),
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UsePersistentKernel>(a_dev_buf,
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b_shuffle_dev_buf,
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c_dev_buf,
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M,
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N,
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K,
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stride_A,
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stride_B,
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stride_C,
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kbatch,
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per_token_scale_dev_ptr,
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per_channel_scale_dev_ptr,
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n_warmup,
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n_repeat);
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c_dev_buf.FromDevice(c_rslt_host.data());
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bool pass = true;
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if(arg_parser.get_int("v") == 1)
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{
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if(ScaleGranularityM != -1 || ScaleGranularityN != -1)
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throw std::runtime_error("ScaleAB is not supported for CPU verification!\n");
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ck_tile::HostTensor<CDataType> c_ref_host(
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ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
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c_ref_host.SetZero();
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ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
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a_host, b_origin_host, c_ref_host);
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const float max_accumulated_value =
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*std::max_element(c_ref_host.mData.begin(), c_ref_host.mData.end());
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const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
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K, kbatch, max_accumulated_value);
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pass = ck_tile::check_err(c_rslt_host,
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c_ref_host,
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"Error: Incorrect results!",
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rtol_atol.at(ck_tile::number<0>{}),
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rtol_atol.at(ck_tile::number<1>{}));
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std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
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<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
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<< std::endl;
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std::cout << "The CPU veification result is:" << (pass ? "correct" : "fail") << std::endl;
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}
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else if(arg_parser.get_int("v") == 2)
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{
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ck_tile::DeviceMem b_origin_dev_buf(b_origin_host.get_element_space_size_in_bytes());
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b_origin_dev_buf.ToDevice(b_origin_host.data());
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ck_tile::HostTensor<CDataType> c_gpu_ref_host(
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ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
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ck_tile::DeviceMem c_gpu_ref_dev_buf(c_gpu_ref_host.get_element_space_size_in_bytes());
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c_gpu_ref_host.SetZero();
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c_gpu_ref_dev_buf.SetZero();
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ADataType* d_A;
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BDataType* d_B;
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CDataType* d_C;
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ck_tile::hip_check_error(hipMalloc(&d_A, M * K * sizeof(ADataType)));
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ck_tile::hip_check_error(hipMalloc(&d_B, N * K * sizeof(BDataType)));
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ck_tile::hip_check_error(hipMalloc(&d_C, M * N * sizeof(CDataType)));
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ck_tile::hip_check_error(hipMemcpy(
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d_A, a_dev_buf.GetDeviceBuffer(), M * K * sizeof(ADataType), hipMemcpyHostToDevice));
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ck_tile::hip_check_error(hipMemcpy(d_B,
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b_origin_dev_buf.GetDeviceBuffer(),
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N * K * sizeof(BDataType),
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hipMemcpyHostToDevice));
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if constexpr(ScaleGranularityM == -1 && ScaleGranularityN == -1)
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{
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ck_tile::reference_gemm_gpu<ADataType,
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BDataType,
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AccDataType,
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CDataType,
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ALayout,
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BLayout,
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CLayout>(
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d_A, d_B, d_C, M, N, K, stride_A, stride_B, stride_C);
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}
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else
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{
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ck_tile::reference_blockwise_gemm_gpu<ADataType,
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BDataType,
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AccDataType,
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CDataType,
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ALayout,
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BLayout,
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CLayout>(
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d_A,
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d_B,
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d_C,
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M,
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N,
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K,
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stride_A,
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stride_B,
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stride_C,
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ScaleGranularityM,
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ScaleGranularityN,
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K,
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static_cast<float*>(per_token_scale_dev_buf.GetDeviceBuffer()),
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static_cast<float*>(per_channel_scale_dev_buf.GetDeviceBuffer()));
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}
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ck_tile::hip_check_error(hipMemcpy(c_gpu_ref_dev_buf.GetDeviceBuffer(),
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d_C,
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M * N * sizeof(CDataType),
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hipMemcpyDeviceToHost));
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ck_tile::hip_check_error(hipFree(d_A));
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ck_tile::hip_check_error(hipFree(d_B));
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ck_tile::hip_check_error(hipFree(d_C));
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c_gpu_ref_dev_buf.FromDevice(c_gpu_ref_host.data());
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const float max_accumulated_value =
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*std::max_element(c_gpu_ref_host.mData.begin(), c_gpu_ref_host.mData.end());
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const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
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K, kbatch, max_accumulated_value);
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pass = ck_tile::check_err(c_rslt_host,
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c_gpu_ref_host,
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"Error: Incorrect results!",
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rtol_atol.at(ck_tile::number<0>{}),
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rtol_atol.at(ck_tile::number<1>{}));
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std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
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<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
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<< std::endl;
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std::cout << "The GPU veification result is: " << (pass ? "correct" : "fail") << std::endl;
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}
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return pass;
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}
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