mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-16 10:59:55 +00:00
Gemm+Bilinear (#316)
* refactor
* update example
* update example
* gemm bilinear
* clean
* update
[ROCm/composable_kernel commit: 9e4429f9c3]
This commit is contained in:
Chao Liu
@@ -159,10 +159,10 @@ bool profile_batched_gemm_impl(int do_verification,
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BatchStrideA,
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BatchStrideB,
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BatchStrideC,
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BatchCount,
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ck::tensor_operation::element_wise::PassThrough{},
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ck::tensor_operation::element_wise::PassThrough{},
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ck::tensor_operation::element_wise::PassThrough{},
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BatchCount);
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ck::tensor_operation::element_wise::PassThrough{});
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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@@ -1,315 +0,0 @@
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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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#pragma once
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#include "ck/ck.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_bias.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/host_tensor/device_memory.hpp"
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#include "ck/library/host_tensor/host_tensor.hpp"
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#include "ck/library/host_tensor/host_tensor_generator.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_gemm_bias_2d.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 DeviceGemmAlphaBetaPtr = ck::tensor_operation::device::DeviceGemmBiasPtr<
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ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::AlphaBetaAdd>;
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void add_device_gemm_xdl_c_shuffle_bias_2d_f32_f32_f32_km_kn_mn_instances(
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std::vector<DeviceGemmAlphaBetaPtr>&);
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void add_device_gemm_xdl_c_shuffle_bias_2d_f32_f32_f32_km_nk_mn_instances(
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std::vector<DeviceGemmAlphaBetaPtr>&);
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void add_device_gemm_xdl_c_shuffle_bias_2d_f32_f32_f32_mk_kn_mn_instances(
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std::vector<DeviceGemmAlphaBetaPtr>&);
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void add_device_gemm_xdl_c_shuffle_bias_2d_f32_f32_f32_mk_nk_mn_instances(
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std::vector<DeviceGemmAlphaBetaPtr>&);
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void add_device_gemm_xdl_c_shuffle_bias_2d_f16_f16_f16_km_kn_mn_instances(
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std::vector<DeviceGemmAlphaBetaPtr>&);
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void add_device_gemm_xdl_c_shuffle_bias_2d_f16_f16_f16_km_nk_mn_instances(
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std::vector<DeviceGemmAlphaBetaPtr>&);
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void add_device_gemm_xdl_c_shuffle_bias_2d_f16_f16_f16_mk_kn_mn_instances(
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std::vector<DeviceGemmAlphaBetaPtr>&);
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void add_device_gemm_xdl_c_shuffle_bias_2d_f16_f16_f16_mk_nk_mn_instances(
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std::vector<DeviceGemmAlphaBetaPtr>&);
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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 C0DataType,
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typename CDataType,
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typename AccDataType,
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typename ALayout,
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typename BLayout,
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typename CLayout>
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void profile_gemm_bias_2d_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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float alpha,
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float beta)
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{
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auto f_host_tensor_descriptor =
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[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
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if(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<C0DataType> c0_m_n(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
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std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
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std::cout << "c0_m_n: " << c0_m_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::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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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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c0_m_n.GenerateTensorValue(GeneratorTensor_2<C0DataType>{-5, 5}, num_thread);
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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}, num_thread);
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b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread);
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c0_m_n.GenerateTensorValue(GeneratorTensor_3<C0DataType>{-0.5, 0.5}, num_thread);
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}
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// set zero to c_device_buf
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c_m_n_device_result.GenerateTensorValue(GeneratorTensor_0<CDataType>{}, num_thread);
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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::AlphaBetaAdd;
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const auto a_element_op = AElementOp{};
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const auto b_element_op = BElementOp{};
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const auto c_element_op = CElementOp{alpha, beta};
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if(do_verification)
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{
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using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemmBias2D<ADataType,
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BDataType,
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C0DataType,
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CDataType,
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AccDataType,
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AElementOp,
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BElementOp,
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CElementOp>;
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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, c0_m_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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}
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DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
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DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
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DeviceMem c0_device_buf(sizeof(C0DataType) * c0_m_n.mDesc.GetElementSpace());
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DeviceMem c_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace());
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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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c0_device_buf.ToDevice(c0_m_n.mData.data());
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c_device_buf.ToDevice(c_m_n_device_result.mData.data());
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// add device GEMM instances
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std::vector<ck::tensor_operation::device::instance::DeviceGemmAlphaBetaPtr> 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_xdl_c_shuffle_bias_2d_f16_f16_f16_mk_kn_mn_instances(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_xdl_c_shuffle_bias_2d_f16_f16_f16_mk_nk_mn_instances(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_xdl_c_shuffle_bias_2d_f16_f16_f16_km_kn_mn_instances(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_xdl_c_shuffle_bias_2d_f16_f16_f16_km_nk_mn_instances(gemm_ptrs);
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}
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}
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else if constexpr(is_same<ADataType, float>::value && is_same<BDataType, float>::value &&
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is_same<CDataType, float>::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_xdl_c_shuffle_bias_2d_f32_f32_f32_mk_kn_mn_instances(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_xdl_c_shuffle_bias_2d_f32_f32_f32_mk_nk_mn_instances(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_xdl_c_shuffle_bias_2d_f32_f32_f32_km_kn_mn_instances(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_xdl_c_shuffle_bias_2d_f32_f32_f32_km_nk_mn_instances(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 =
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gemm_ptr->MakeArgumentPointer(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
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static_cast<C0DataType*>(c0_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
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M,
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N,
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K,
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StrideA,
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StrideB,
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StrideC,
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a_element_op,
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b_element_op,
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c_element_op);
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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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std::string gemm_name = gemm_ptr->GetTypeString();
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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::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 * M + sizeof(CDataType) * M * N;
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gb_per_sec = num_btype / 1.E6 / ave_time;
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std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
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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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ck::utils::check_err(c_m_n_device_result.mData, c_m_n_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 << "c0 : ", c0_m_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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}
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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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}
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} // namespace profiler
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} // namespace ck
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@@ -1,291 +0,0 @@
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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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#pragma once
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#include "ck/ck.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_bias_activation_add.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/conv_util.hpp"
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#include "ck/library/host_tensor/device_memory.hpp"
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#include "ck/library/host_tensor/host_tensor.hpp"
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#include "ck/library/host_tensor/host_tensor_generator.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_gemm_bias_activation_add.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 DeviceGemmBiasReluAddPtr = ck::tensor_operation::device::DeviceGemmBiasActivationAddPtr<
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ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::AddReluAdd>;
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void add_device_gemm_xdl_c_shuffle_bias_relu_add_f16_f16_f16_mk_kn_mn_instances(
|
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std::vector<DeviceGemmBiasReluAddPtr>&);
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void add_device_gemm_xdl_c_shuffle_bias_relu_add_f16_f16_f16_mk_nk_mn_instances(
|
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std::vector<DeviceGemmBiasReluAddPtr>&);
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void add_device_gemm_xdl_c_shuffle_bias_relu_add_f16_f16_f16_km_kn_mn_instances(
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std::vector<DeviceGemmBiasReluAddPtr>&);
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void add_device_gemm_xdl_c_shuffle_bias_relu_add_f16_f16_f16_km_nk_mn_instances(
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std::vector<DeviceGemmBiasReluAddPtr>&);
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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 {
|
||||
namespace profiler {
|
||||
|
||||
template <typename ADataType,
|
||||
typename BDataType,
|
||||
typename CDataType,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename CLayout>
|
||||
void profile_gemm_bias_relu_add_impl(int do_verification,
|
||||
int init_method,
|
||||
bool do_log,
|
||||
bool time_kernel,
|
||||
int M,
|
||||
int N,
|
||||
int K,
|
||||
int StrideA,
|
||||
int StrideB,
|
||||
int StrideC,
|
||||
int StrideC1,
|
||||
int KBatch = 1)
|
||||
{
|
||||
auto f_host_tensor_descriptor =
|
||||
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
|
||||
if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
|
||||
std::vector<std::size_t>({stride, 1}));
|
||||
}
|
||||
else
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
|
||||
std::vector<std::size_t>({1, stride}));
|
||||
}
|
||||
};
|
||||
|
||||
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
|
||||
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
|
||||
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
|
||||
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
|
||||
|
||||
// c0_n[n]
|
||||
Tensor<CDataType> c0_n(HostTensorDescriptor(
|
||||
std::vector<std::size_t>({static_cast<std::size_t>(N)}), std::vector<std::size_t>({1})));
|
||||
|
||||
// c1_m_n[m ,n]
|
||||
Tensor<BDataType> c1_m_n(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
|
||||
|
||||
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
|
||||
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
|
||||
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
|
||||
std::cout << "c0_n: " << c0_n.mDesc << std::endl;
|
||||
std::cout << "c1_m_n: " << c1_m_n.mDesc << std::endl;
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
|
||||
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
c0_n.GenerateTensorValue(GeneratorTensor_2<CDataType>{-5, 5});
|
||||
c1_m_n.GenerateTensorValue(GeneratorTensor_2<CDataType>{-5, 5});
|
||||
break;
|
||||
default:
|
||||
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
|
||||
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
c0_n.GenerateTensorValue(GeneratorTensor_3<CDataType>{0.0, 1.0});
|
||||
c1_m_n.GenerateTensorValue(GeneratorTensor_3<CDataType>{0.0, 1.0});
|
||||
}
|
||||
|
||||
// set zero to c_device_buf
|
||||
c_m_n_device_result.GenerateTensorValue(GeneratorTensor_0<CDataType>{});
|
||||
|
||||
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using CElementOp = ck::tensor_operation::element_wise::AddReluAdd;
|
||||
|
||||
const auto a_element_op = AElementOp{};
|
||||
const auto b_element_op = BElementOp{};
|
||||
const auto c_element_op = CElementOp{};
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
using ReferenceGemmInstance =
|
||||
ck::tensor_operation::host::ReferenceGemmBiasActivationAdd<ADataType,
|
||||
BDataType,
|
||||
CDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
CElementOp>;
|
||||
|
||||
auto ref_gemm = ReferenceGemmInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument = ref_gemm.MakeArgument(a_m_k,
|
||||
b_k_n,
|
||||
c_m_n_host_result,
|
||||
c0_n,
|
||||
c1_m_n,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op);
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
}
|
||||
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
|
||||
DeviceMem c_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace());
|
||||
DeviceMem c0_n_device_buf(sizeof(CDataType) * c0_n.mDesc.GetElementSpace());
|
||||
DeviceMem c1_m_n_device_buf(sizeof(CDataType) * c1_m_n.mDesc.GetElementSpace());
|
||||
|
||||
a_device_buf.ToDevice(a_m_k.mData.data());
|
||||
b_device_buf.ToDevice(b_k_n.mData.data());
|
||||
c_device_buf.ToDevice(c_m_n_device_result.mData.data());
|
||||
c0_n_device_buf.ToDevice(c0_n.mData.data());
|
||||
c1_m_n_device_buf.ToDevice(c1_m_n.mData.data());
|
||||
|
||||
// add device GEMM instances
|
||||
std::vector<ck::tensor_operation::device::instance::DeviceGemmBiasReluAddPtr> gemm_ptrs;
|
||||
|
||||
if constexpr(is_same<ADataType, half_t>::value && is_same<BDataType, half_t>::value &&
|
||||
is_same<CDataType, half_t>::value)
|
||||
{
|
||||
if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::instance::
|
||||
add_device_gemm_xdl_c_shuffle_bias_relu_add_f16_f16_f16_mk_kn_mn_instances(
|
||||
gemm_ptrs);
|
||||
}
|
||||
else if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::instance::
|
||||
add_device_gemm_xdl_c_shuffle_bias_relu_add_f16_f16_f16_mk_nk_mn_instances(
|
||||
gemm_ptrs);
|
||||
}
|
||||
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::instance::
|
||||
add_device_gemm_xdl_c_shuffle_bias_relu_add_f16_f16_f16_km_kn_mn_instances(
|
||||
gemm_ptrs);
|
||||
}
|
||||
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::instance::
|
||||
add_device_gemm_xdl_c_shuffle_bias_relu_add_f16_f16_f16_km_nk_mn_instances(
|
||||
gemm_ptrs);
|
||||
}
|
||||
}
|
||||
|
||||
if(gemm_ptrs.size() <= 0)
|
||||
{
|
||||
throw std::runtime_error("wrong! no device GEMM instance found");
|
||||
}
|
||||
|
||||
std::string best_gemm_name;
|
||||
float best_ave_time = 0;
|
||||
float best_tflops = 0;
|
||||
float best_gb_per_sec = 0;
|
||||
|
||||
// profile device GEMM instances
|
||||
for(auto& gemm_ptr : gemm_ptrs)
|
||||
{
|
||||
auto argument_ptr = gemm_ptr->MakeArgumentPointer(
|
||||
static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
|
||||
static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
|
||||
static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
|
||||
static_cast<CDataType*>(c0_n_device_buf.GetDeviceBuffer()),
|
||||
static_cast<CDataType*>(c1_m_n_device_buf.GetDeviceBuffer()),
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideC,
|
||||
StrideC1,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op,
|
||||
KBatch);
|
||||
|
||||
auto invoker_ptr = gemm_ptr->MakeInvokerPointer();
|
||||
|
||||
if(gemm_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
std::string gemm_name = gemm_ptr->GetTypeString();
|
||||
|
||||
float ave_time =
|
||||
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
|
||||
|
||||
std::size_t flop = std::size_t(2) * M * N * K;
|
||||
|
||||
std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * M +
|
||||
sizeof(CDataType) * M * N + sizeof(CDataType) * N +
|
||||
sizeof(CDataType) * 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, " << gemm_name << std::endl;
|
||||
|
||||
if(tflops > best_tflops)
|
||||
{
|
||||
best_gemm_name = gemm_name;
|
||||
best_tflops = tflops;
|
||||
best_ave_time = ave_time;
|
||||
best_gb_per_sec = gb_per_sec;
|
||||
}
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
c_device_buf.FromDevice(c_m_n_device_result.mData.data());
|
||||
|
||||
ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData);
|
||||
|
||||
if(do_log)
|
||||
{
|
||||
LogRangeAsType<float>(std::cout << "a: ", a_m_k.mData, ",") << std::endl;
|
||||
LogRangeAsType<float>(std::cout << "b: ", b_k_n.mData, ",") << std::endl;
|
||||
LogRangeAsType<float>(std::cout << "c0: ", c0_n.mData, ",") << std::endl;
|
||||
LogRangeAsType<float>(std::cout << "c1: ", c1_m_n.mData, ",") << std::endl;
|
||||
LogRangeAsType<float>(std::cout << "c_host: ", c_m_n_host_result.mData, ",")
|
||||
<< std::endl;
|
||||
LogRangeAsType<float>(std::cout << "c_device: ", c_m_n_device_result.mData, ",")
|
||||
<< std::endl;
|
||||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cout << "does not support this GEMM problem" << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
|
||||
<< best_gb_per_sec << " GB/s, " << best_gemm_name << std::endl;
|
||||
}
|
||||
|
||||
} // namespace profiler
|
||||
} // namespace ck
|
||||
@@ -1,269 +0,0 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_gemm_bias_activation.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
#include "ck/library/utility/check_err.hpp"
|
||||
#include "ck/library/utility/conv_util.hpp"
|
||||
#include "ck/library/host_tensor/device_memory.hpp"
|
||||
#include "ck/library/host_tensor/host_tensor.hpp"
|
||||
#include "ck/library/host_tensor/host_tensor_generator.hpp"
|
||||
#include "ck/library/reference_tensor_operation/cpu/reference_gemm_bias_activation.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace instance {
|
||||
|
||||
using DeviceGemmBiasReluPtr = ck::tensor_operation::device::DeviceGemmBiasActivationPtr<
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
ck::tensor_operation::element_wise::AddRelu>;
|
||||
|
||||
void add_device_gemm_xdl_c_shuffle_bias_relu_f16_f16_f16_mk_kn_mn_instances(
|
||||
std::vector<DeviceGemmBiasReluPtr>&);
|
||||
void add_device_gemm_xdl_c_shuffle_bias_relu_f16_f16_f16_mk_nk_mn_instances(
|
||||
std::vector<DeviceGemmBiasReluPtr>&);
|
||||
void add_device_gemm_xdl_c_shuffle_bias_relu_f16_f16_f16_km_kn_mn_instances(
|
||||
std::vector<DeviceGemmBiasReluPtr>&);
|
||||
void add_device_gemm_xdl_c_shuffle_bias_relu_f16_f16_f16_km_nk_mn_instances(
|
||||
std::vector<DeviceGemmBiasReluPtr>&);
|
||||
|
||||
} // namespace instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
|
||||
namespace ck {
|
||||
namespace profiler {
|
||||
|
||||
template <typename ADataType,
|
||||
typename BDataType,
|
||||
typename CDataType,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename CLayout>
|
||||
void profile_gemm_bias_relu_impl(int do_verification,
|
||||
int init_method,
|
||||
bool do_log,
|
||||
bool time_kernel,
|
||||
int M,
|
||||
int N,
|
||||
int K,
|
||||
int StrideA,
|
||||
int StrideB,
|
||||
int StrideC,
|
||||
int KBatch = 1)
|
||||
{
|
||||
auto f_host_tensor_descriptor =
|
||||
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
|
||||
if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
|
||||
std::vector<std::size_t>({stride, 1}));
|
||||
}
|
||||
else
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
|
||||
std::vector<std::size_t>({1, stride}));
|
||||
}
|
||||
};
|
||||
|
||||
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
|
||||
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
|
||||
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
|
||||
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
|
||||
|
||||
// c0_n[n]
|
||||
Tensor<CDataType> c0_n(HostTensorDescriptor(
|
||||
std::vector<std::size_t>({static_cast<std::size_t>(N)}), std::vector<std::size_t>({1})));
|
||||
|
||||
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
|
||||
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
|
||||
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
|
||||
std::cout << "c0_n: " << c0_n.mDesc << std::endl;
|
||||
|
||||
std::size_t num_thread = 1;
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5}, num_thread);
|
||||
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5}, num_thread);
|
||||
c0_n.GenerateTensorValue(GeneratorTensor_2<CDataType>{-5, 5});
|
||||
break;
|
||||
default:
|
||||
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0}, num_thread);
|
||||
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread);
|
||||
c0_n.GenerateTensorValue(GeneratorTensor_3<CDataType>{0.0, 1.0});
|
||||
}
|
||||
|
||||
// set zero to c_device_buf
|
||||
c_m_n_device_result.GenerateTensorValue(GeneratorTensor_0<CDataType>{}, num_thread);
|
||||
|
||||
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using CElementOp = ck::tensor_operation::element_wise::AddRelu;
|
||||
|
||||
const auto a_element_op = AElementOp{};
|
||||
const auto b_element_op = BElementOp{};
|
||||
const auto c_element_op = CElementOp{};
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
using ReferenceGemmInstance =
|
||||
ck::tensor_operation::host::ReferenceGemmBiasActivation<ADataType,
|
||||
BDataType,
|
||||
CDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
CElementOp>;
|
||||
|
||||
auto ref_gemm = ReferenceGemmInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument = ref_gemm.MakeArgument(
|
||||
a_m_k, b_k_n, c_m_n_host_result, c0_n, a_element_op, b_element_op, c_element_op);
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
}
|
||||
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
|
||||
DeviceMem c_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace());
|
||||
DeviceMem c0_n_device_buf(sizeof(CDataType) * c0_n.mDesc.GetElementSpace());
|
||||
|
||||
a_device_buf.ToDevice(a_m_k.mData.data());
|
||||
b_device_buf.ToDevice(b_k_n.mData.data());
|
||||
c_device_buf.ToDevice(c_m_n_device_result.mData.data());
|
||||
c0_n_device_buf.ToDevice(c0_n.mData.data());
|
||||
|
||||
// add device GEMM instances
|
||||
std::vector<ck::tensor_operation::device::instance::DeviceGemmBiasReluPtr> gemm_ptrs;
|
||||
|
||||
if constexpr(is_same<ADataType, half_t>::value && is_same<BDataType, half_t>::value &&
|
||||
is_same<CDataType, half_t>::value)
|
||||
{
|
||||
if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::instance::
|
||||
add_device_gemm_xdl_c_shuffle_bias_relu_f16_f16_f16_mk_kn_mn_instances(gemm_ptrs);
|
||||
}
|
||||
else if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::instance::
|
||||
add_device_gemm_xdl_c_shuffle_bias_relu_f16_f16_f16_mk_nk_mn_instances(gemm_ptrs);
|
||||
}
|
||||
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::instance::
|
||||
add_device_gemm_xdl_c_shuffle_bias_relu_f16_f16_f16_km_kn_mn_instances(gemm_ptrs);
|
||||
}
|
||||
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::instance::
|
||||
add_device_gemm_xdl_c_shuffle_bias_relu_f16_f16_f16_km_nk_mn_instances(gemm_ptrs);
|
||||
}
|
||||
}
|
||||
|
||||
if(gemm_ptrs.size() <= 0)
|
||||
{
|
||||
throw std::runtime_error("wrong! no device GEMM instance found");
|
||||
}
|
||||
|
||||
std::string best_gemm_name;
|
||||
float best_ave_time = 0;
|
||||
float best_tflops = 0;
|
||||
float best_gb_per_sec = 0;
|
||||
|
||||
// profile device GEMM instances
|
||||
for(auto& gemm_ptr : gemm_ptrs)
|
||||
{
|
||||
auto argument_ptr = gemm_ptr->MakeArgumentPointer(
|
||||
static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
|
||||
static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
|
||||
static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
|
||||
static_cast<CDataType*>(c0_n_device_buf.GetDeviceBuffer()),
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideC,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op,
|
||||
KBatch);
|
||||
|
||||
auto invoker_ptr = gemm_ptr->MakeInvokerPointer();
|
||||
|
||||
if(gemm_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
std::string gemm_name = gemm_ptr->GetTypeString();
|
||||
|
||||
float ave_time =
|
||||
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
|
||||
|
||||
std::size_t flop = std::size_t(2) * M * N * K;
|
||||
|
||||
std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * M +
|
||||
sizeof(CDataType) * M * N + sizeof(CDataType) * 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, " << gemm_name << std::endl;
|
||||
|
||||
if(tflops > best_tflops)
|
||||
{
|
||||
best_gemm_name = gemm_name;
|
||||
best_tflops = tflops;
|
||||
best_ave_time = ave_time;
|
||||
best_gb_per_sec = gb_per_sec;
|
||||
}
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
c_device_buf.FromDevice(c_m_n_device_result.mData.data());
|
||||
|
||||
ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData);
|
||||
|
||||
if(do_log)
|
||||
{
|
||||
LogRangeAsType<float>(std::cout << "a : ", a_m_k.mData, ",") << std::endl;
|
||||
LogRangeAsType<float>(std::cout << "b: ", b_k_n.mData, ",") << std::endl;
|
||||
LogRangeAsType<float>(std::cout << "c0 : ", c0_n.mData, ",") << std::endl;
|
||||
LogRangeAsType<float>(std::cout << "c_host : ", c_m_n_host_result.mData, ",")
|
||||
<< std::endl;
|
||||
LogRangeAsType<float>(std::cout << "c_device: ", c_m_n_device_result.mData, ",")
|
||||
<< std::endl;
|
||||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cout << "does not support this GEMM problem" << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
|
||||
<< best_gb_per_sec << " GB/s, " << best_gemm_name << std::endl;
|
||||
}
|
||||
|
||||
} // namespace profiler
|
||||
} // namespace ck
|
||||
233
profiler/include/profile_gemm_bilinear_impl.hpp
Normal file
233
profiler/include/profile_gemm_bilinear_impl.hpp
Normal file
@@ -0,0 +1,233 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <iomanip>
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_gemm_multiple_d.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
#include "ck/library/tensor_operation_instance/gpu/gemm_bilinear.hpp"
|
||||
|
||||
#include "ck/library/utility/check_err.hpp"
|
||||
#include "ck/library/host_tensor/device_memory.hpp"
|
||||
#include "ck/library/host_tensor/host_tensor.hpp"
|
||||
#include "ck/library/host_tensor/host_tensor_generator.hpp"
|
||||
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace profiler {
|
||||
|
||||
template <typename ADataType,
|
||||
typename BDataType,
|
||||
typename AccDataType,
|
||||
typename DDataType,
|
||||
typename EDataType,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename DELayout> // assume Ds and E have same layout
|
||||
bool profile_gemm_bilinear_impl(int do_verification,
|
||||
int init_method,
|
||||
bool /*do_log*/,
|
||||
bool time_kernel,
|
||||
int M,
|
||||
int N,
|
||||
int K,
|
||||
int StrideA,
|
||||
int StrideB,
|
||||
int StrideD,
|
||||
int StrideE,
|
||||
float alpha,
|
||||
float beta)
|
||||
{
|
||||
auto f_host_tensor_descriptor =
|
||||
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
|
||||
if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
|
||||
std::vector<std::size_t>({stride, 1}));
|
||||
}
|
||||
else
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
|
||||
std::vector<std::size_t>({1, stride}));
|
||||
}
|
||||
};
|
||||
|
||||
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
|
||||
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
|
||||
Tensor<DDataType> d_m_n(f_host_tensor_descriptor(M, N, StrideD, DELayout{}));
|
||||
Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, DELayout{}));
|
||||
Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, DELayout{}));
|
||||
|
||||
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
|
||||
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
|
||||
std::cout << "d_m_n: " << d_m_n.mDesc << std::endl;
|
||||
std::cout << "e_m_n: " << e_m_n_device_result.mDesc << std::endl;
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
|
||||
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
d_m_n.GenerateTensorValue(GeneratorTensor_2<DDataType>{-5, 5});
|
||||
break;
|
||||
default:
|
||||
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
|
||||
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
d_m_n.GenerateTensorValue(GeneratorTensor_3<DDataType>{0.0, 1.0});
|
||||
}
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
using Bilinear = ck::tensor_operation::element_wise::Bilinear;
|
||||
|
||||
using AElementOp = PassThrough;
|
||||
using BElementOp = PassThrough;
|
||||
using CDEElementOp = Bilinear;
|
||||
|
||||
const auto a_element_op = AElementOp{};
|
||||
const auto b_element_op = BElementOp{};
|
||||
const auto cde_element_op = CDEElementOp{alpha, beta};
|
||||
|
||||
using DeviceOp = ck::tensor_operation::device::DeviceGemmMultipleD<
|
||||
ALayout,
|
||||
BLayout,
|
||||
DELayout,
|
||||
ADataType,
|
||||
BDataType,
|
||||
ck::Tuple<DDataType>,
|
||||
EDataType,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
ck::tensor_operation::element_wise::Bilinear>;
|
||||
|
||||
// get device op instances
|
||||
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
|
||||
DeviceOp>::GetInstances();
|
||||
|
||||
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
|
||||
|
||||
// run reference
|
||||
if(do_verification)
|
||||
{
|
||||
Tensor<AccDataType> c_m_n(HostTensorDescriptor(
|
||||
std::vector<std::size_t>{static_cast<std::size_t>(M), static_cast<std::size_t>(N)}));
|
||||
|
||||
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
|
||||
BDataType,
|
||||
AccDataType,
|
||||
AccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
PassThrough>;
|
||||
|
||||
auto ref_gemm = ReferenceGemmInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument =
|
||||
ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, PassThrough{});
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
for(int m = 0; m < M; ++m)
|
||||
{
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), d_m_n(m, n));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
|
||||
DeviceMem d_m_n_device_buf(sizeof(DDataType) * d_m_n.mDesc.GetElementSpace());
|
||||
DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpace());
|
||||
|
||||
a_device_buf.ToDevice(a_m_k.mData.data());
|
||||
b_device_buf.ToDevice(b_k_n.mData.data());
|
||||
d_m_n_device_buf.ToDevice(d_m_n.mData.data());
|
||||
|
||||
std::string best_op_name;
|
||||
float best_ave_time = 0;
|
||||
float best_tflops = 0;
|
||||
float best_gb_per_sec = 0;
|
||||
|
||||
bool pass = true;
|
||||
|
||||
// profile device operation instances
|
||||
for(auto& op_ptr : op_ptrs)
|
||||
{
|
||||
auto argument_ptr = op_ptr->MakeArgumentPointer(
|
||||
a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
std::array<const void*, 1>{d_m_n_device_buf.GetDeviceBuffer()},
|
||||
e_device_buf.GetDeviceBuffer(),
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
std::array<ck::index_t, 1>{StrideD},
|
||||
StrideE,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op);
|
||||
|
||||
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
||||
|
||||
std::string op_name = op_ptr->GetTypeString();
|
||||
|
||||
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
// re-init E to zero before profiling a kernel
|
||||
e_device_buf.SetZero();
|
||||
|
||||
float ave_time =
|
||||
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
|
||||
|
||||
std::size_t flop = std::size_t(2) * M * N * K;
|
||||
|
||||
std::size_t num_btype =
|
||||
sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(EDataType) * 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: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
|
||||
<< gb_per_sec << " GB/s, " << op_name << std::endl;
|
||||
|
||||
if(tflops > best_tflops)
|
||||
{
|
||||
best_op_name = op_name;
|
||||
best_tflops = tflops;
|
||||
best_ave_time = ave_time;
|
||||
best_gb_per_sec = gb_per_sec;
|
||||
}
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
|
||||
|
||||
pass = pass &&
|
||||
ck::utils::check_err(e_m_n_device_result.mData, e_m_n_host_result.mData);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cout << op_name << " does not support this problem" << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
|
||||
<< best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
|
||||
|
||||
return pass;
|
||||
}
|
||||
|
||||
} // namespace profiler
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user