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https://github.com/ROCm/composable_kernel.git
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20333fd850
* [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>
[ROCm/composable_kernel commit: e78a897ec0]
348 lines
14 KiB
C++
348 lines
14 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <iomanip>
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#include "ck/ck.hpp"
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#include "ck/library/tensor_operation_instance/gpu/pool3d_fwd.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_pool_fwd.hpp"
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namespace ck {
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namespace profiler {
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struct PoolFwdInputParams
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{
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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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bool return_index;
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int reduce_op;
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};
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struct PoolFwdKernelParams
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{
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std::vector<index_t> in_length; // NCDHW
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std::vector<index_t> window_spatial_lengths;
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std::vector<index_t> window_strides;
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std::vector<index_t> window_dilations;
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std::vector<index_t> input_left_pads;
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std::vector<index_t> input_right_pads;
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};
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template <typename InDataType,
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typename OutDataType,
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typename ComputeDataType,
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typename IndexDataType,
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typename InLayout,
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typename OutLayout,
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ck::ReduceTensorOp ReduceOpId,
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bool PropagateNan,
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bool OutputIndex>
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bool profile_pool3d_fwd_impl(PoolFwdInputParams& in_params,
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PoolFwdKernelParams& kernel_params,
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index_t instance_index = -1)
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{
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constexpr index_t InOutRank = 5;
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constexpr index_t WindowRank = 3;
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if(kernel_params.in_length.size() != InOutRank ||
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kernel_params.window_spatial_lengths.size() != WindowRank ||
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kernel_params.window_strides.size() != WindowRank ||
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kernel_params.window_dilations.size() != WindowRank ||
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kernel_params.input_left_pads.size() != WindowRank ||
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kernel_params.input_right_pads.size() != WindowRank)
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return false;
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std::vector<index_t> out_length(InOutRank);
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int N = kernel_params.in_length[0];
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int C = kernel_params.in_length[1];
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out_length[0] = N;
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out_length[1] = C;
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// Calculate Do, Ho, Wo
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for(int i = 2; i < InOutRank; ++i)
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{
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auto pad1 = kernel_params.input_left_pads[i - 2];
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auto pad2 = kernel_params.input_right_pads[i - 2];
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auto windows_size = kernel_params.window_spatial_lengths[i - 2];
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auto windows_stride = kernel_params.window_strides[i - 2];
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auto windows_dilation = kernel_params.window_dilations[i - 2];
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auto eff = (windows_size - 1) * windows_dilation + 1;
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out_length[i] = (kernel_params.in_length[i] + pad1 + pad2 - eff) / windows_stride + 1;
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}
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int Di = kernel_params.in_length[2];
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int Hi = kernel_params.in_length[3];
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int Wi = kernel_params.in_length[4];
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int Do = out_length[2];
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int Ho = out_length[3];
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int Wo = out_length[4];
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auto f_host_tensor_descriptor =
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[](std::size_t N_, std::size_t C_, std::size_t D, std::size_t H, std::size_t W) {
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using namespace ck::literals;
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return HostTensorDescriptor({N_, C_, D, H, W},
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{D * C_ * H * W, 1_uz, C_ * H * W, W * C_, C_},
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ck::tensor_layout::convolution::NDHWC{});
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};
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Tensor<InDataType> in_n_c_di_hi_wi(f_host_tensor_descriptor(N, C, Di, Hi, Wi));
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Tensor<OutDataType> out_n_c_do_ho_wo_host(f_host_tensor_descriptor(N, C, Do, Ho, Wo));
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Tensor<IndexDataType> out_indices_n_c_do_ho_wo_host(f_host_tensor_descriptor(N, C, Do, Ho, Wo));
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Tensor<OutDataType> out_n_c_do_ho_wo_device(f_host_tensor_descriptor(N, C, Do, Ho, Wo));
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Tensor<IndexDataType> out_indices_n_c_do_ho_wo_device(
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f_host_tensor_descriptor(N, C, Do, Ho, Wo));
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constexpr int inDataRangeTensor1{1};
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constexpr int inDataRangeTensor2{5};
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constexpr double inDataRangeTensor3{0.5};
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switch(in_params.init_method)
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{
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case 0:
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in_n_c_di_hi_wi.GenerateTensorValue(GeneratorTensor_1<InDataType>{inDataRangeTensor1});
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break;
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case 1:
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in_n_c_di_hi_wi.GenerateTensorValue(
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GeneratorTensor_2<InDataType>{-inDataRangeTensor2, inDataRangeTensor2});
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break;
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default:
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in_n_c_di_hi_wi.GenerateTensorValue(
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GeneratorTensor_3<InDataType>{-inDataRangeTensor3, inDataRangeTensor3});
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}
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DeviceMem in_device_buf(sizeof(InDataType) * in_n_c_di_hi_wi.mDesc.GetElementSpaceSize());
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DeviceMem out_device_buf(sizeof(OutDataType) *
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out_n_c_do_ho_wo_device.mDesc.GetElementSpaceSize());
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DeviceMem out_indices_device_buf(sizeof(IndexDataType) *
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out_indices_n_c_do_ho_wo_device.mDesc.GetElementSpaceSize());
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in_device_buf.ToDevice(in_n_c_di_hi_wi.mData.data());
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// add device normalization instances
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using DeviceOp = ck::tensor_operation::device::DevicePoolFwd<InOutRank,
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WindowRank,
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InDataType,
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OutDataType,
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IndexDataType,
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InLayout,
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OutLayout,
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ReduceOpId,
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OutputIndex>;
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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(in_params.do_verification)
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{
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using ReferenceInstance = ck::tensor_operation::host::ReferencePoolingFwd<InOutRank,
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WindowRank,
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InDataType,
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OutDataType,
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ComputeDataType,
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IndexDataType,
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ReduceOpId,
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PropagateNan,
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OutputIndex>;
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ReferenceInstance ref;
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auto ref_argument = ref.MakeArgument(in_n_c_di_hi_wi,
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out_n_c_do_ho_wo_host,
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out_indices_n_c_do_ho_wo_host,
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kernel_params.window_spatial_lengths,
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kernel_params.window_strides,
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kernel_params.window_dilations,
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kernel_params.input_left_pads,
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kernel_params.input_right_pads);
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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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static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
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static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
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static_cast<IndexDataType*>(out_indices_device_buf.GetDeviceBuffer()),
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kernel_params.in_length,
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kernel_params.window_spatial_lengths,
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out_length,
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{Di * C * Hi * Wi, 1, C * Hi * Wi, Wi * C, C},
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{Do * C * Ho * Wo, 1, C * Ho * Wo, Wo * C, C},
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{Do * C * Ho * Wo, 1, C * Ho * Wo, Wo * C, C},
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kernel_params.window_strides,
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kernel_params.window_dilations,
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kernel_params.input_left_pads,
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kernel_params.input_right_pads,
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{2, 3, 4});
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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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if(in_params.time_kernel)
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{
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std::cout << inst_ptr->GetTypeString() << " skipped due to unsupported argument: ";
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LogRange(std::cout << "input lengths = ", kernel_params.in_length, ", ")
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<< std::endl;
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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 =
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, in_params.time_kernel});
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std::size_t num_bytes = in_n_c_di_hi_wi.mDesc.GetElementSize() * sizeof(InDataType) +
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out_n_c_do_ho_wo_host.mDesc.GetElementSize() * sizeof(OutDataType);
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if constexpr(OutputIndex)
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num_bytes +=
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out_indices_n_c_do_ho_wo_host.mDesc.GetElementSize() * sizeof(IndexDataType);
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float gb_per_sec = num_bytes / 1.E6 / avg_time;
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if(in_params.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(in_params.do_verification)
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{
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out_device_buf.FromDevice(out_n_c_do_ho_wo_device.mData.data());
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auto number_of_accumulations = 1;
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static_assert(
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ReduceOpId == ck::ReduceTensorOp::AVG || ReduceOpId == ck::ReduceTensorOp::MAX,
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"Warning: Unhandled ReduceOpId for setting up the number of accumulations!");
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if constexpr(ReduceOpId == ck::ReduceTensorOp::AVG)
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{
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for(size_t i = 0; i < kernel_params.window_spatial_lengths.size(); ++i)
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{
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number_of_accumulations *= kernel_params.window_spatial_lengths.at(i);
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}
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}
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auto absolute_error_threshold = 1.0;
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switch(in_params.init_method)
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{
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case 0: absolute_error_threshold = static_cast<double>(inDataRangeTensor1); break;
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case 1: absolute_error_threshold = static_cast<double>(inDataRangeTensor2); break;
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default: absolute_error_threshold = inDataRangeTensor3;
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}
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absolute_error_threshold =
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ck::utils::get_absolute_threshold<ComputeDataType, OutDataType>(
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absolute_error_threshold, number_of_accumulations);
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auto relative_error_threshold =
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ck::utils::get_relative_threshold<ComputeDataType, OutDataType>(
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number_of_accumulations);
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bool pass = ck::utils::check_err(out_n_c_do_ho_wo_device.mData,
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out_n_c_do_ho_wo_host.mData,
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"Error: Incorrect results",
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relative_error_threshold,
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absolute_error_threshold);
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if constexpr(OutputIndex)
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{
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out_indices_device_buf.FromDevice(out_indices_n_c_do_ho_wo_device.mData.data());
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pass = pass && ck::utils::check_err(out_indices_n_c_do_ho_wo_device,
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out_indices_n_c_do_ho_wo_host);
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}
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if(in_params.do_log)
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{
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LogRangeAsType<float>(
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std::cout << "in_n_c_di_hi_wi : ", in_n_c_di_hi_wi.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "out_n_c_do_ho_wo_host : ", out_n_c_do_ho_wo_host.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "out_n_c_do_ho_wo_device : ", out_n_c_do_ho_wo_device.mData, ",")
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<< std::endl;
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if constexpr(OutputIndex)
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LogRangeAsType<float>(std::cout << "out_indices_n_c_do_ho_wo_device : ",
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out_indices_n_c_do_ho_wo_device.mData,
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",")
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<< 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 = [", kernel_params.in_length, ", ")
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<< "]." << 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(in_params.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(in_params.time_kernel)
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{
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LogRange(std::cout << "length = ", kernel_params.in_length, ",") << std::endl;
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std::cout << "best perf = " << best_avg_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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if(num_kernel == 0)
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{
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std::cout << "Error: No kernel is applicable" << 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 << "max_pool3d_fwd_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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