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c8563f2101
* chore(copyright): update copyright header for test directory * chore(copyright): update copyright header for test directory * chore(copyright): update copyright header for client_example directory * chore(copyright): update copyright header for test directory
249 lines
9.5 KiB
C++
249 lines
9.5 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#include <iomanip>
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#include <vector>
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#include <iostream>
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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_pool_fwd.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/pool3d_fwd.hpp"
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using InDataType = ck::half_t;
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using OutDataType = ck::half_t;
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using IndexDataType = int32_t;
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// We use pool3d to implement pool2d in this example
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using InLayout = ck::tensor_layout::convolution::NDHWC;
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using OutLayout = ck::tensor_layout::convolution::NDHWC;
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constexpr ck::index_t InOutRank = 5;
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constexpr ck::index_t WindowRank = 3;
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#if 1
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constexpr auto ReduceOpId = ck::ReduceTensorOp::MAX;
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constexpr bool OutputIndex = true;
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#else
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constexpr auto ReduceOpId = ck::ReduceTensorOp::AVG;
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constexpr bool OutputIndex = false;
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#endif
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struct SimpleDeviceMem
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{
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SimpleDeviceMem() = delete;
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SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
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{
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(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
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}
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void* GetDeviceBuffer() { return p_mem_; }
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~SimpleDeviceMem() { (void)hipFree(p_mem_); }
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void* p_mem_;
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};
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void TransformPool2dparamToPool3d(std::vector<ck::index_t>& input_lengths,
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std::vector<ck::index_t>& window_lengths,
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std::vector<ck::index_t>& output_lengths,
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std::vector<ck::index_t>& input_stride,
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std::vector<ck::index_t>& output_stride,
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std::vector<ck::index_t>& indices_stride,
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std::vector<ck::index_t>& window_strides,
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std::vector<ck::index_t>& window_dilations,
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std::vector<ck::index_t>& input_left_pads,
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std::vector<ck::index_t>& input_right_pads,
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std::vector<ck::index_t>& pooling_dims)
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{
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// NCHW to NCDHW
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input_lengths.insert(input_lengths.begin() + 2, 1);
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output_lengths.insert(output_lengths.begin() + 2, 1);
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input_stride.insert(input_stride.begin() + 2, 0);
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output_stride.insert(output_stride.begin() + 2, 0);
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indices_stride.insert(indices_stride.begin() + 2, 0);
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// YX to ZYX
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window_lengths.insert(window_lengths.begin(), 1);
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window_strides.insert(window_strides.begin(), 0);
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window_dilations.insert(window_dilations.begin(), 0);
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input_left_pads.insert(input_left_pads.begin(), 0);
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input_right_pads.insert(input_right_pads.begin(), 0);
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pooling_dims = {2, 3, 4};
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}
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int main(int argc, char* argv[])
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{
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ck::index_t N = 2;
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ck::index_t C = 32;
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ck::index_t Y = 2;
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ck::index_t X = 2;
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ck::index_t Hi = 30;
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ck::index_t Wi = 30;
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ck::index_t window_stride_h = 2;
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ck::index_t window_stride_w = 2;
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ck::index_t window_dilation_h = 1;
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ck::index_t window_dilation_w = 1;
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ck::index_t in_left_pad_h = 1;
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ck::index_t in_left_pad_w = 1;
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ck::index_t in_right_pad_h = 1;
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ck::index_t in_right_pad_w = 1;
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const ck::index_t Ys = (Y - 1) * window_dilation_h + 1;
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const ck::index_t Xs = (X - 1) * window_dilation_w + 1;
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ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - Ys) / window_stride_h + 1;
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ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - Xs) / window_stride_w + 1;
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// Pool API only support the order of NCHW
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std::vector<ck::index_t> in_length = {N, C, Hi, Wi};
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std::vector<ck::index_t> out_length = {N, C, Ho, Wo};
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std::vector<ck::index_t> window_spatial_lengths = {Y, X};
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std::vector<ck::index_t> window_strides = {window_stride_h, window_stride_w};
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std::vector<ck::index_t> window_dilations = {window_dilation_h, window_dilation_w};
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std::vector<ck::index_t> input_left_pads = {in_left_pad_h, in_left_pad_w};
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std::vector<ck::index_t> input_right_pads = {in_right_pad_h, in_right_pad_w};
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std::vector<ck::index_t> pooling_dims = {2, 3};
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std::size_t in_tensor_size = N * C * Hi * Wi;
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std::size_t out_tensor_size = N * C * Ho * Wo;
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// tensor layout = NHWC
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std::vector<ck::index_t> in_tensor_stride = {C * Hi * Wi, 1, Wi * C, C};
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std::vector<ck::index_t> out_tensor_stride = {C * Ho * Wo, 1, Wo * C, C};
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TransformPool2dparamToPool3d(in_length,
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window_spatial_lengths,
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out_length,
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in_tensor_stride,
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out_tensor_stride,
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out_tensor_stride,
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window_strides,
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window_dilations,
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input_left_pads,
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input_right_pads,
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pooling_dims);
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SimpleDeviceMem in_device_buf(sizeof(InDataType) * in_tensor_size);
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SimpleDeviceMem out_device_buf(sizeof(OutDataType) * out_tensor_size);
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SimpleDeviceMem out_indices_device_buf(sizeof(IndexDataType) * out_tensor_size);
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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 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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bool found = false;
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int best_op_id = -1;
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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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// profile device operation instances
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std::cout << "Run all instances and do timing" << std::endl;
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for(int i = 0; i < op_ptrs.size(); ++i)
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{
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auto& op_ptr = op_ptrs[i];
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auto argument_ptr = op_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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in_length,
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window_spatial_lengths,
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out_length,
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in_tensor_stride,
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out_tensor_stride,
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out_tensor_stride,
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window_strides,
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window_dilations,
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input_left_pads,
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input_right_pads,
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pooling_dims);
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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std::string op_name = op_ptr->GetTypeString();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
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std::size_t num_bytes =
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in_tensor_size * sizeof(InDataType) + out_tensor_size * sizeof(OutDataType);
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if constexpr(OutputIndex)
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num_bytes += out_tensor_size * sizeof(IndexDataType);
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float gb_per_sec = num_bytes / 1.E6 / ave_time;
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std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << gb_per_sec << " GB/s, "
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<< op_name << std::endl;
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if(ave_time < best_ave_time)
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{
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found = true;
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best_op_id = i;
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best_op_name = op_name;
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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_name << " does not support this problem" << std::endl;
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}
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}
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// run the best intance
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if(found)
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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_op_name << std::endl;
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auto& op_ptr = op_ptrs[best_op_id];
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std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
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<< std::endl;
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auto argument_ptr = op_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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in_length,
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window_spatial_lengths,
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out_length,
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in_tensor_stride,
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out_tensor_stride,
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out_tensor_stride,
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window_strides,
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window_dilations,
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input_left_pads,
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input_right_pads,
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pooling_dims);
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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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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
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}
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std::cout << "Done" << std::endl;
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}
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return 0;
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}
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