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d85f065b15
* chore(copyright): update copyright header for codegen directory * chore(copyright): update copyright header for example directory
208 lines
9.3 KiB
C++
208 lines
9.3 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#pragma once
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#include <iostream>
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#include "ck/ck.hpp"
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#include "ck/utility/reduction_enums.hpp"
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#include "ck/utility/reduction_functions_accumulate.hpp"
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#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_pool3d_fwd_ndhwc_ndhwc.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.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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using ::ck::DeviceMem;
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using ::ck::hip_check_error;
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using ::ck::HostTensorDescriptor;
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using ::ck::Tensor;
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template <typename TensorLayout>
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std::vector<ck::index_t> f_tensor_strides_ncdhw(ck::index_t N_,
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ck::index_t C_,
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ck::index_t D,
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ck::index_t H,
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ck::index_t W,
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TensorLayout layout)
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{
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using namespace ck::literals;
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(void)N_;
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if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCDHW>::value)
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return {C_ * D * H * W, D * H * W, H * W, W, 1_uz};
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else if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NDHWC>::value)
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return {D * C_ * H * W, 1_uz, C_ * H * W, W * C_, C_};
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throw std::runtime_error("Pool3d_fwd: problem with layout. ");
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return {0, 0, 0, 0, 0};
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};
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template <typename TensorLayout>
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HostTensorDescriptor f_host_tensor_descriptor(std::size_t N_,
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std::size_t C_,
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std::size_t D,
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std::size_t H,
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std::size_t W,
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TensorLayout layout)
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{
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using namespace ck::literals;
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if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCDHW>::value)
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{
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return HostTensorDescriptor(
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{N_, C_, D, H, W}, {C_ * D * H * W, D * H * W, H * W, W, 1_uz}, layout);
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}
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else if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NDHWC>::value)
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{
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return HostTensorDescriptor(
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{N_, C_, D, H, W}, {D * C_ * H * W, 1_uz, C_ * H * W, W * C_, C_}, layout);
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}
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throw std::runtime_error("Pool3d_fwd: problem with layout. ");
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return HostTensorDescriptor({0, 0, 0, 0, 0}, {0, 0, 0, 0, 0}, layout);
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};
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template <typename DevicePoolFwdInstance,
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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 pool3d_test(bool do_verification,
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bool time_kernel,
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ck::index_t N,
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ck::index_t C,
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ck::index_t Z,
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ck::index_t Y,
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ck::index_t X,
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ck::index_t Di,
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ck::index_t Hi,
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ck::index_t Wi,
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ck::index_t window_stride_d,
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ck::index_t window_stride_h,
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ck::index_t window_stride_w,
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ck::index_t window_dilation_d,
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ck::index_t window_dilation_h,
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ck::index_t window_dilation_w,
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ck::index_t in_left_pad_d,
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ck::index_t in_left_pad_h,
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ck::index_t in_left_pad_w,
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ck::index_t in_right_pad_d,
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ck::index_t in_right_pad_h,
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ck::index_t in_right_pad_w)
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{
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const ck::index_t Zs = (Z - 1) * window_dilation_d + 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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const ck::index_t Do = (Di + in_left_pad_d + in_right_pad_d - Zs) / window_stride_d + 1;
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const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - Ys) / window_stride_h + 1;
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const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - Xs) / window_stride_w + 1;
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const std::vector<ck::index_t> window_spatial_lengths{Z, Y, X};
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const std::vector<ck::index_t> window_strides{
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window_stride_d, window_stride_h, window_stride_w};
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const std::vector<ck::index_t> window_dilations{
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window_dilation_d, window_dilation_h, window_dilation_w};
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const std::vector<ck::index_t> input_left_pads{in_left_pad_d, in_left_pad_h, in_left_pad_w};
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const std::vector<ck::index_t> input_right_pads{in_right_pad_d, in_right_pad_h, in_right_pad_w};
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Tensor<InDataType> in_n_c_di_hi_wi(f_host_tensor_descriptor(N, C, Di, Hi, Wi, InLayout{}));
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Tensor<OutDataType> out_n_c_do_ho_wo_host(
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f_host_tensor_descriptor(N, C, Do, Ho, Wo, OutLayout{}));
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Tensor<IndexDataType> out_indices_n_c_do_ho_wo_host(
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f_host_tensor_descriptor(N, C, Do, Ho, Wo, OutLayout{}));
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Tensor<OutDataType> out_n_c_do_ho_wo_device(
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f_host_tensor_descriptor(N, C, Do, Ho, Wo, OutLayout{}));
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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, OutLayout{}));
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std::cout << "in_n_c_di_hi_wi: " << in_n_c_di_hi_wi.mDesc << std::endl;
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std::cout << "out_n_c_do_ho_wo: " << out_n_c_do_ho_wo_host.mDesc << std::endl;
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in_n_c_di_hi_wi.GenerateTensorValue(GeneratorTensor_3<InDataType>{-1.0, 1.0});
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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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auto pool = DevicePoolFwdInstance{};
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auto invoker_ptr = pool.MakeInvokerPointer();
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auto argument_ptr = pool.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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{N, C, Di, Hi, Wi},
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{Z, Y, X},
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{N, C, Do, Ho, Wo},
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f_tensor_strides_ncdhw(N, C, Di, Hi, Wi, InLayout{}),
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f_tensor_strides_ncdhw(N, C, Do, Ho, Wo, OutLayout{}),
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f_tensor_strides_ncdhw(N, C, Do, Ho, Wo, OutLayout{}),
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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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{2, 3, 4});
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if(!pool.IsSupportedArgument(argument_ptr.get()))
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{
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throw std::runtime_error("wrong! device_op with the specified compilation parameters does "
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"not support this problem");
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}
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float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
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std::cout << "Perf: " << ave_time << std::endl;
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bool pass = true;
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if(do_verification)
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{
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using ReferencePoolingFwdInstance =
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ck::tensor_operation::host::ReferencePoolingFwd<5,
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3,
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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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auto ref_pooling = ReferencePoolingFwdInstance{};
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auto ref_pooling_invoker = ref_pooling.MakeInvoker();
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auto ref_pooling_argument = ref_pooling.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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window_spatial_lengths,
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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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ref_pooling_invoker.Run(ref_pooling_argument);
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out_device_buf.FromDevice(out_n_c_do_ho_wo_device.mData.data());
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pass = pass && ck::utils::check_err(out_n_c_do_ho_wo_device, out_n_c_do_ho_wo_host);
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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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}
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return (pass);
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};
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