mirror of
https://github.com/ROCm/composable_kernel.git
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500fa99512
* convnd_fwd fp16 example * update example * update example * update instance * updating refernce conv * update reference conv * update conv fwd profiler * update conv 1d and 3d instance * update include path * clean * update profiler for conv bwd data and weight * update conv bwd weight * clean * update conv example * update profiler for conv bwd weight * update ckprofiler for conv bwd data * fix reference conv bwd data bug; update conv bwd data test * update examples * fix initialization issue * update test for conv fwd * clean * clean * remove test case too sensitive to error threshhold * fix test * clean * fix build * adding conv multiple d * adding conv multiple D * add matrix padder * add gemm padding to convnd * adding group conv * update gemm multi-d * refactor * refactor * refactor * clean * clean * refactor * refactor * reorg * add ds * add bias * clean * add G * adding group * adding group * adding group * update Tensor * clean * update example * update DeviceGemmMultipleD_Xdl_CShuffle * update conv bwd-data and bwd-weight * upate contraction example * update gemm and batch gemm with e permute * fix example build * instance for grouped conv1d * update example * adding group conv instance * update gemm bilinear instance * update gemm+add+add+fastgelu instance * update profiler * update profiler * update test * update test and client example * clean * add grouped conv into profiler * update profiler * clean * add test grouped conv, update all conv test to gtest * update test
357 lines
16 KiB
C++
357 lines
16 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "ck/ck.hpp"
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#include "ck/utility/reduction_operator.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_gemm_reduce.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/convolution_parameter.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/reference_tensor_operation/cpu/reference_gemm.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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namespace instance {
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using F32 = float;
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using F16 = ck::half_t;
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using ReducePtrsGlobal = ck::Tuple<F32*, F32*>;
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using Div = ck::tensor_operation::element_wise::UnaryDivide;
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using Identity = ck::tensor_operation::element_wise::PassThrough;
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using Square = ck::tensor_operation::element_wise::UnarySquare;
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using ReduceInElementOps = ck::Tuple<Identity, Square>;
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using ReduceOutElementOps = ck::Tuple<Div, Div>;
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using DeviceGemmReduceNoOpPtr =
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ck::tensor_operation::device::DeviceGemmReducePtr<0, ReducePtrsGlobal::Size()>;
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void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instances(
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std::vector<DeviceGemmReduceNoOpPtr>&);
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void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_nk_mn_instances(
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std::vector<DeviceGemmReduceNoOpPtr>&);
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void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_kn_mn_instances(
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std::vector<DeviceGemmReduceNoOpPtr>&);
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void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_nk_mn_instances(
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std::vector<DeviceGemmReduceNoOpPtr>&);
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} // namespace instance
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} // namespace device
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} // namespace tensor_operation
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} // namespace ck
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namespace ck {
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namespace profiler {
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template <typename ADataType,
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typename BDataType,
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typename CDataType,
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typename ReduceDataType,
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typename ALayout,
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typename BLayout,
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typename CLayout>
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bool profile_gemm_reduce_impl(int do_verification,
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int init_method,
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bool do_log,
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bool time_kernel,
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int M,
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int N,
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int K,
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int StrideA,
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int StrideB,
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int StrideC)
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{
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bool pass = true;
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auto f_host_tensor_descriptor =
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[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
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if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
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{
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return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
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std::vector<std::size_t>({stride, 1}));
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}
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else
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{
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return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
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std::vector<std::size_t>({1, stride}));
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}
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};
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Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
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Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
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Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<ReduceDataType> reduce0_m_host_result(
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HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
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Tensor<ReduceDataType> reduce1_m_host_result(
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HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
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Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<ReduceDataType> reduce0_m_device_result(
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HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
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Tensor<ReduceDataType> reduce1_m_device_result(
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HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(M)})));
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std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
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std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
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std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
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std::cout << "reduce0_m: " << reduce0_m_host_result.mDesc << std::endl;
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std::cout << "reduce1_m: " << reduce1_m_host_result.mDesc << std::endl;
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std::size_t num_thread = 1;
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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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std::srand(0);
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a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5}, num_thread);
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b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5}, num_thread);
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break;
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default:
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std::srand(0);
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a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0}, num_thread);
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b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread);
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}
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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 ReduceOp0 = ck::reduce::Add;
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using ReduceOp1 = ck::reduce::Add;
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using UnaryIdenticElementOp = ck::tensor_operation::element_wise::PassThrough;
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using UnarySquareElementOp = ck::tensor_operation::element_wise::UnarySquare;
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using UnaryDivElementOp = ck::tensor_operation::element_wise::UnaryDivide;
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auto a_element_op = AElementOp{};
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auto b_element_op = BElementOp{};
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auto c_element_op = CElementOp{};
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std::array<void*, 3> gemm_element_ops = {&a_element_op, &b_element_op, &c_element_op};
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const auto reduce0_op = ReduceOp0{};
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const auto reduce1_op = ReduceOp1{};
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auto passthrough = UnaryIdenticElementOp{};
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auto square = UnarySquareElementOp{};
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auto div = UnaryDivElementOp{N};
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std::array<void*, 2> reduce_in_element_ops = {&passthrough, &square};
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std::array<void*, 2> reduce_out_element_ops = {&div, &div};
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if(do_verification)
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{
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using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
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BDataType,
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CDataType,
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ReduceDataType,
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AElementOp,
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BElementOp,
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CElementOp>;
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using ReduceAccDataType = ReduceDataType;
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auto ref_gemm = ReferenceGemmInstance{};
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auto ref_invoker = ref_gemm.MakeInvoker();
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auto ref_argument = ref_gemm.MakeArgument(
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a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
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ref_invoker.Run(ref_argument);
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for(int m = 0; m < M; ++m)
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{
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auto reduce0_acc = reduce0_op.GetIdentityValue<ReduceAccDataType>();
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auto reduce1_acc = reduce1_op.GetIdentityValue<ReduceAccDataType>();
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for(int n = 0; n < N; ++n)
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{
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ReduceAccDataType d0_val =
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ck::type_convert<ReduceAccDataType>(c_m_n_host_result(m, n));
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ReduceAccDataType d1_val;
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square(d1_val, d0_val);
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reduce0_op(reduce0_acc, d0_val);
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reduce1_op(reduce1_acc, d1_val);
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}
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div(reduce0_acc, reduce0_acc);
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div(reduce1_acc, reduce1_acc);
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reduce0_m_host_result(m) = ck::type_convert<ReduceDataType>(reduce0_acc);
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reduce1_m_host_result(m) = ck::type_convert<ReduceDataType>(reduce1_acc);
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}
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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 c_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
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DeviceMem reduce0_device_buf(sizeof(ReduceDataType) *
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reduce0_m_device_result.mDesc.GetElementSpaceSize());
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DeviceMem reduce1_device_buf(sizeof(ReduceDataType) *
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reduce1_m_device_result.mDesc.GetElementSpaceSize());
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std::array<void*, 2> p_reduces = {reduce0_device_buf.GetDeviceBuffer(),
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reduce1_device_buf.GetDeviceBuffer()};
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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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// add device GEMM instances
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std::vector<ck::tensor_operation::device::instance::DeviceGemmReduceNoOpPtr> gemm_ptrs;
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if constexpr(is_same<ADataType, half_t>::value && is_same<BDataType, half_t>::value &&
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is_same<CDataType, half_t>::value)
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{
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if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
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is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
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is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
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{
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ck::tensor_operation::device::instance::
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add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instances(
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gemm_ptrs);
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}
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else if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
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is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
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is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
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{
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ck::tensor_operation::device::instance::
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add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_nk_mn_instances(
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gemm_ptrs);
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}
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else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
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is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
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is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
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{
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ck::tensor_operation::device::instance::
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add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_kn_mn_instances(
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gemm_ptrs);
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}
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else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
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is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
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is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
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{
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ck::tensor_operation::device::instance::
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add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_nk_mn_instances(
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gemm_ptrs);
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}
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}
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if(gemm_ptrs.size() <= 0)
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{
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throw std::runtime_error("wrong! no device GEMM instance found");
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}
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std::string best_gemm_name;
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float best_ave_time = 0;
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float best_tflops = 0;
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float best_gb_per_sec = 0;
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// profile device GEMM instances
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for(auto& gemm_ptr : gemm_ptrs)
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{
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auto argument_ptr = gemm_ptr->MakeArgumentPointer(a_device_buf.GetDeviceBuffer(),
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b_device_buf.GetDeviceBuffer(),
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nullptr,
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{},
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c_device_buf.GetDeviceBuffer(),
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p_reduces,
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M,
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N,
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K,
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StrideA,
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StrideB,
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StrideC,
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{},
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gemm_element_ops,
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{},
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reduce_in_element_ops,
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reduce_out_element_ops);
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auto invoker_ptr = gemm_ptr->MakeInvokerPointer();
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if(gemm_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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// init DO, D1 to 0
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reduce0_device_buf.SetZero();
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reduce1_device_buf.SetZero();
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float ave_time =
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
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std::string gemm_name = gemm_ptr->GetTypeString();
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std::size_t flop = std::size_t(2) * M * N * K;
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std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
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sizeof(CDataType) * M * N + sizeof(CDataType) * 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
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<< " GB/s, " << gemm_name << std::endl;
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if(tflops > best_tflops)
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{
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best_gemm_name = gemm_name;
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best_tflops = tflops;
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best_ave_time = ave_time;
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best_gb_per_sec = gb_per_sec;
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}
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if(do_verification)
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{
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c_device_buf.FromDevice(c_m_n_device_result.mData.data());
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reduce0_device_buf.FromDevice(reduce0_m_device_result.mData.data());
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reduce1_device_buf.FromDevice(reduce1_m_device_result.mData.data());
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ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData);
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ck::utils::check_err(reduce0_m_device_result.mData, reduce0_m_host_result.mData);
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ck::utils::check_err(reduce1_m_device_result.mData, reduce1_m_host_result.mData);
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if(do_log)
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{
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LogRangeAsType<float>(std::cout << "a : ", a_m_k.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "b: ", b_k_n.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "c_host: ", c_m_n_host_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(std::cout << "c_device: ", c_m_n_device_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "d0_host: ", reduce0_m_host_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "d0_device: ", reduce0_m_device_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "d1_host: ", reduce1_m_host_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "d1_device: ", reduce1_m_device_result.mData, ",")
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<< std::endl;
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}
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}
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}
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else
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{
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std::cout << "does not support this GEMM problem" << std::endl;
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}
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}
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std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
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<< best_gb_per_sec << " GB/s, " << best_gemm_name << std::endl;
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return pass;
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}
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} // namespace profiler
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} // namespace ck
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