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https://github.com/ROCm/composable_kernel.git
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e78a897ec0
* [CK] Add command option instance_index and param_mask to run partial ck test Many CK test are instance test. it will loop all instance in the instance library. It causes test often out-of-time if we run test on simulator/emulator. This PR add option instance_index and param_mask to reduce the workload of instance test instance_index: only run test 1 available instance with specified index. param_mask: filter the embedded parameter with specified mask * fix CI error * fix clang format --------- Co-authored-by: illsilin_amdeng <Illia.Silin@amd.com>
289 lines
11 KiB
C++
289 lines
11 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <iomanip>
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#include "ck/ck.hpp"
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#include "ck/library/tensor_operation_instance/gpu/elementwise_normalization.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_layernorm.hpp"
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namespace ck {
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namespace profiler {
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template <typename HostTensorA, typename HostTensorB, typename HostTensorC, typename Functor>
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void host_elementwise2D(HostTensorC& C,
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const HostTensorA& A,
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const HostTensorB& B,
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const std::vector<std::size_t>& shape,
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Functor functor)
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{
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using ctype = ck::remove_reference_t<decltype(C(0, 0))>;
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for(std::size_t m = 0; m < shape[0]; ++m)
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for(std::size_t n = 0; n < shape[1]; ++n)
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{
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auto a_val = A(m, n);
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auto b_val = B(m, n);
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ctype c_val = 0;
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functor(c_val, a_val, b_val);
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C(m, n) = c_val;
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}
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}
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template <typename ADataType,
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typename BDataType,
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typename GammaDataType,
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typename BetaDataType,
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typename AccDataType,
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typename YDataType>
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bool profile_elementwise_layernorm_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> length,
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index_t instance_index = -1)
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{
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using Add = ck::tensor_operation::element_wise::Add;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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if(length.size() != 2)
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return false;
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index_t M = length[0];
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index_t N = length[1];
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index_t Stride = N;
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constexpr int Rank = 2;
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constexpr int NumReduceDim = 1;
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std::vector<index_t> reduce_dim = {1};
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std::vector<index_t> gammaBetaLength = {N};
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std::vector<index_t> gammaBetaStride = {0, 1};
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auto f_host_tensor_descriptor2d = [](std::size_t row, std::size_t col, std::size_t stride) {
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using namespace ck::literals;
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return HostTensorDescriptor({row, col}, {stride, 1_uz});
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};
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Tensor<ADataType> a(length);
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Tensor<BDataType> b(length);
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Tensor<GammaDataType> gamma(gammaBetaLength);
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Tensor<BetaDataType> beta(gammaBetaLength);
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Tensor<YDataType> y(length);
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Tensor<YDataType> host_y(length);
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Tensor<AccDataType> host_save_mean({M});
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Tensor<AccDataType> host_save_inv_std({M});
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switch(init_method)
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{
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case 0:
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a.GenerateTensorValue(GeneratorTensor_1<ADataType>{});
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b.GenerateTensorValue(GeneratorTensor_1<BDataType>{});
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gamma.GenerateTensorValue(GeneratorTensor_1<GammaDataType>{});
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beta.GenerateTensorValue(GeneratorTensor_1<BetaDataType>{});
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break;
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case 1:
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a.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
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b.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
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gamma.GenerateTensorValue(GeneratorTensor_2<GammaDataType>{-5, 5});
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beta.GenerateTensorValue(GeneratorTensor_2<BetaDataType>{-5, 5});
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break;
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default:
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a.GenerateTensorValue(GeneratorTensor_3<ADataType>{0, 1});
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b.GenerateTensorValue(GeneratorTensor_3<BDataType>{0, 1});
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gamma.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{-0.5, 0.5});
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beta.GenerateTensorValue(GeneratorTensor_3<BetaDataType>{-0.5, 0.5});
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}
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DeviceMem a_dev(sizeof(ADataType) * a.mDesc.GetElementSpaceSize());
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DeviceMem b_dev(sizeof(ADataType) * b.mDesc.GetElementSpaceSize());
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DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize());
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DeviceMem beta_dev(sizeof(BetaDataType) * beta.mDesc.GetElementSpaceSize());
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DeviceMem y_dev(sizeof(YDataType) * y.mDesc.GetElementSpaceSize());
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a_dev.ToDevice(a.mData.data());
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b_dev.ToDevice(b.mData.data());
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gamma_dev.ToDevice(gamma.mData.data());
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beta_dev.ToDevice(beta.mData.data());
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std::array<const void*, 2> input = {a_dev.GetDeviceBuffer(), b_dev.GetDeviceBuffer()};
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// add device normalization instances
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using DeviceOp = ck::tensor_operation::device::DeviceElementwiseNormalization<
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ck::Tuple<ADataType, BDataType>,
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GammaDataType,
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BetaDataType,
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AccDataType,
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YDataType,
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Add,
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PassThrough,
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2,
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1>;
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// get device op instances
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const auto instance_ptrs =
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ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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std::cout << "found " << instance_ptrs.size() << " instances" << std::endl;
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std::string best_instance_name;
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float best_avg_time = std::numeric_limits<float>::max();
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float best_gb_per_sec = 0;
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if(do_verification)
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{
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using XDataType = ADataType;
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std::vector<std::size_t> mn = {static_cast<unsigned long>(M),
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static_cast<unsigned long>(N)};
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Tensor<XDataType> x(f_host_tensor_descriptor2d(M, N, Stride));
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host_elementwise2D<Tensor<ADataType>, Tensor<BDataType>, Tensor<XDataType>, Add>(
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x, a, b, mn, Add{});
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using ReferenceInstance = ck::tensor_operation::host::ReferenceLayernorm<XDataType,
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GammaDataType,
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BetaDataType,
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YDataType,
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AccDataType,
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AccDataType,
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PassThrough,
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Rank,
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NumReduceDim>;
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ReferenceInstance ref;
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auto ref_argument = ref.MakeArgument(x,
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gamma,
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beta,
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host_y,
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host_save_mean,
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host_save_inv_std,
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PassThrough{},
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{M, N},
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{1},
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1e-4);
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auto ref_invoker = ref.MakeInvoker();
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ref_invoker.Run(ref_argument);
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}
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int num_kernel = 0;
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for(auto& inst_ptr : instance_ptrs)
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{
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auto argument_ptr = inst_ptr->MakeArgumentPointer(
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length,
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{
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std::vector<ck::index_t>{a.mDesc.GetStrides().begin(), a.mDesc.GetStrides().end()},
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std::vector<ck::index_t>{b.mDesc.GetStrides().begin(), b.mDesc.GetStrides().end()},
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},
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gammaBetaStride,
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gammaBetaStride,
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std::vector<ck::index_t>{y.mDesc.GetStrides().begin(), y.mDesc.GetStrides().end()},
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reduce_dim,
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1e-4,
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input,
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gamma_dev.GetDeviceBuffer(),
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beta_dev.GetDeviceBuffer(),
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y_dev.GetDeviceBuffer(),
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Add{},
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PassThrough{});
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if(inst_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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++num_kernel;
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if((instance_index != -1) && (instance_index + 1 != num_kernel))
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{
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// skip test if instance_index is specified
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continue;
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}
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}
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else
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{
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continue;
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}
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auto invoker_ptr = inst_ptr->MakeInvokerPointer();
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float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
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std::size_t num_bytes = a.mDesc.GetElementSize() * sizeof(ADataType) +
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b.mDesc.GetElementSize() * sizeof(BDataType) +
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gamma.mDesc.GetElementSize() * sizeof(GammaDataType) +
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beta.mDesc.GetElementSize() * sizeof(BetaDataType) +
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y.mDesc.GetElementSize() * sizeof(YDataType);
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float gb_per_sec = num_bytes / 1.E6 / avg_time;
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if(time_kernel)
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std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << gb_per_sec << " GB/s, "
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<< inst_ptr->GetTypeString() << std::endl;
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if(avg_time < best_avg_time)
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{
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best_instance_name = inst_ptr->GetTypeString();
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best_avg_time = avg_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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y_dev.FromDevice(y.mData.data());
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bool pass =
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ck::utils::check_err(y.mData, host_y.mData, "Error: Incorrect results", 5e-3, 5e-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 << "b : ", b.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "host_y : ", host_y.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "y : ", y.mData, ",") << std::endl;
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}
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if(!pass)
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{
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std::cout << inst_ptr->GetTypeString() << " failed verification: ";
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LogRange(std::cout << "lengths = [", length, ", ") << "]." << std::endl;
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return false;
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}
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else
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{
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if(time_kernel)
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std::cout << "pass" << std::endl;
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}
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}
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}
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if(time_kernel)
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{
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LogRange(std::cout << "length = ", length, ",") << ", ";
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std::cout << "num_kernel = " << num_kernel << ", best perf = " << best_avg_time << " ms, "
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<< best_gb_per_sec << " GB/s, " << best_instance_name << std::endl;
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}
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if(num_kernel == 0)
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{
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std::cout << "Error: No kernel is tested" << std::endl;
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return false;
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}
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if(instance_index != -1)
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{
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std::cout << "elementwise_layernorm_instance (" << instance_index << "/" << num_kernel
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<< "): Passed" << std::endl;
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}
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return true;
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}
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} // namespace profiler
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} // namespace ck
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