mirror of
https://github.com/ROCm/composable_kernel.git
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169 lines
6.2 KiB
C++
169 lines
6.2 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <iomanip>
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#include <random>
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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_elementwise.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_elementwise_dynamic_vector_dims_impl.hpp"
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#include "ck/library/tensor_operation_instance/gpu/permute_scale.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_elementwise.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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namespace ck {
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namespace profiler {
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template <typename ADataType, typename BDataType, index_t NumDim>
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bool profile_permute_scale_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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std::vector<index_t> lengths_vector,
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std::vector<index_t> input_strides_vector,
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std::vector<index_t> output_strides_vector)
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{
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bool pass = true;
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bool instance_found = false;
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using ElementOp = ck::tensor_operation::element_wise::Scale;
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float scale = 2.f;
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std::array<Tensor<ADataType>, 1> as = {Tensor<ADataType>(lengths_vector, input_strides_vector)};
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Tensor<ADataType>& a = as[0];
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Tensor<BDataType> b(lengths_vector, output_strides_vector);
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Tensor<BDataType> host_b(lengths_vector, output_strides_vector);
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std::cout << "A: " << a.mDesc << std::endl;
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std::cout << "B: " << b.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: a.GenerateTensorValue(GeneratorTensor_2<ADataType>{-1, 2}); break;
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default: a.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0}); break;
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}
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DeviceMem a_device_buf(sizeof(ADataType) * a.mDesc.GetElementSpaceSize());
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DeviceMem b_device_buf(sizeof(BDataType) * b.mDesc.GetElementSpaceSize());
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a_device_buf.ToDevice(a.mData.data());
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std::array<const void*, 1> input = {a_device_buf.GetDeviceBuffer()};
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std::array<void*, 1> output = {b_device_buf.GetDeviceBuffer()};
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using DeviceOp = ck::tensor_operation::device::
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DeviceElementwise<ck::Tuple<ADataType>, ck::Tuple<BDataType>, ElementOp, NumDim>;
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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_instance_name;
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float best_ave_time = std::numeric_limits<float>::max();
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float best_gb_per_sec = 0;
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float best_tflops = 0;
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if(do_verification)
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{
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using ReferenceElementwiseInstance =
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ck::tensor_operation::host::ReferenceElementwise<1, ADataType, BDataType, ElementOp>;
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auto ref_elementwise = ReferenceElementwiseInstance{};
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auto ref_invoker = ref_elementwise.MakeInvoker();
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auto ref_argument = ref_elementwise.MakeArgument(as, host_b, ElementOp{scale});
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ref_invoker.Run(ref_argument);
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}
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auto copy = [](const auto& x, auto& y) { std::copy(x.begin(), x.end(), y.begin()); };
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std::array<ck::index_t, NumDim> lengths{};
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std::array<ck::index_t, NumDim> input_strides{};
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std::array<ck::index_t, NumDim> output_strides{};
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copy(lengths_vector, lengths);
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copy(input_strides_vector, input_strides);
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copy(output_strides_vector, output_strides);
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for(auto& op_ptr : op_ptrs)
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{
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auto argument_ptr = op_ptr->MakeArgumentPointer(
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lengths, {input_strides}, {output_strides}, input, output, ElementOp{scale});
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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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instance_found = true;
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b_device_buf.SetZero();
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
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if(do_verification)
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{
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b_device_buf.FromDevice(b.mData.data());
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pass &= ck::utils::check_err(
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b.mData, host_b.mData, "Error: Incorrect results b", 1e-3, 1e-3);
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if(do_log)
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{
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LogRangeAsType<float>(std::cout << "a : ", a.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "host_b: ", host_b.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "b: ", b.mData, ",") << std::endl;
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}
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}
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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) * a.mDesc.GetElementSpaceSize() / sizeof(ADataType);
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std::size_t num_btype = sizeof(ADataType) * a.mDesc.GetElementSpaceSize() +
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sizeof(BDataType) * b.mDesc.GetElementSpaceSize();
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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: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
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<< gb_per_sec << " GB/s, " << op_name << std::endl;
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if(tflops > best_tflops)
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{
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best_instance_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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}
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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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if(time_kernel)
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{
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std::cout << "Best perf = " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, "
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<< best_instance_name << std::endl;
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}
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return pass && instance_found;
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}
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} // namespace profiler
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} // namespace ck
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