mirror of
https://github.com/ROCm/composable_kernel.git
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4a2a56c22f
* Rangify STL algorithms This commit adapts rangified std::copy(), std::fill() & std::transform() * Rangify check_err() By rangifying check_err(), we can not only compare values between std::vector<>s, but also compare any ranges which have same value type. * Allow constructing Tensor<> like a HostTensorDescriptor * Simplify Tensor<> object construction logics * Remove more unnecessary 'HostTensorDescriptor' objects * Re-format example code * Re-write more HostTensorDescriptor ctor call
234 lines
9.5 KiB
C++
234 lines
9.5 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 <memory>
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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_batched_gemm.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/library/tensor_operation_instance/gpu/batched_gemm.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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#include "ck/library/utility/literals.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
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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 ALayout,
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typename BLayout,
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typename CLayout>
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bool profile_batched_gemm_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 BatchStrideA,
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int BatchStrideB,
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int BatchStrideC,
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int StrideA,
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int StrideB,
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int StrideC,
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int BatchCount)
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{
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bool pass = true;
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auto f_host_tensor_descriptor = [](std::size_t batch_count,
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std::size_t row,
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std::size_t col,
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std::size_t stride,
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std::size_t batch_stride,
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auto layout) {
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using namespace ck::literals;
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if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
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{
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return HostTensorDescriptor({batch_count, row, col}, {batch_stride, stride, 1_uz});
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}
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else
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{
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return HostTensorDescriptor({batch_count, row, col}, {batch_stride, 1_uz, stride});
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}
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};
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Tensor<ADataType> a_g_m_k(
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f_host_tensor_descriptor(BatchCount, M, K, StrideA, BatchStrideA, ALayout{}));
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Tensor<BDataType> b_g_k_n(
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f_host_tensor_descriptor(BatchCount, K, N, StrideB, BatchStrideB, BLayout{}));
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Tensor<CDataType> c_g_m_n_host_result(
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f_host_tensor_descriptor(BatchCount, M, N, StrideC, BatchStrideC, CLayout{}));
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Tensor<CDataType> c_g_m_n_device_result(
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f_host_tensor_descriptor(BatchCount, M, N, StrideC, BatchStrideC, CLayout{}));
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std::cout << "a_g_m_k: " << a_g_m_k.mDesc << std::endl;
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std::cout << "b_g_k_n: " << b_g_k_n.mDesc << std::endl;
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std::cout << "c_g_m_n: " << c_g_m_n_host_result.mDesc << std::endl;
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switch(init_method)
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{
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case 0: break;
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case 1:
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a_g_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
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b_g_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
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break;
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default:
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a_g_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
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b_g_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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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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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{};
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if(do_verification)
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{
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using ReferenceBatchedGemmInstance =
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ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
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BDataType,
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CDataType,
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float,
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AElementOp,
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BElementOp,
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CElementOp>;
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auto ref_batched_gemm = ReferenceBatchedGemmInstance{};
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auto ref_invoker = ref_batched_gemm.MakeInvoker();
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auto ref_argument = ref_batched_gemm.MakeArgument(
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a_g_m_k, b_g_k_n, c_g_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_g_m_k.mDesc.GetElementSpaceSize());
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DeviceMem b_device_buf(sizeof(BDataType) * b_g_k_n.mDesc.GetElementSpaceSize());
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DeviceMem c_device_buf(sizeof(CDataType) * c_g_m_n_device_result.mDesc.GetElementSpaceSize());
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a_device_buf.ToDevice(a_g_m_k.mData.data());
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b_device_buf.ToDevice(b_g_k_n.mData.data());
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c_device_buf.ToDevice(c_g_m_n_device_result.mData.data());
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using DeviceOp = ck::tensor_operation::device::DeviceBatchedGemm<ALayout,
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BLayout,
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CLayout,
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ADataType,
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BDataType,
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CDataType,
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AElementOp,
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BElementOp,
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CElementOp>;
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// get device op instances
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const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
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std::string best_op_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 op instances
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for(auto& op_ptr : op_ptrs)
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{
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auto argument_ptr =
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op_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<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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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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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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// re-init C to zero before profiling next kernel
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c_device_buf.SetZero();
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std::string op_name = op_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) * BatchCount * 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) *
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BatchCount;
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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, " << op_name << std::endl;
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if(tflops > best_tflops)
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{
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best_op_name = op_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_g_m_n_device_result.mData.data());
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pass = pass & ck::utils::check_err(c_g_m_n_device_result, c_g_m_n_host_result);
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if(do_log)
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{
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LogRangeAsType<float>(std::cout << "a : ", a_g_m_k.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "b: ", b_g_k_n.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "c_host: ", c_g_m_n_host_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "c_device: ", c_g_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 << op_ptr->GetTypeString() << " does not support this 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_op_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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