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https://github.com/ROCm/composable_kernel.git
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b5e2f26808
* Wrap ck host utitlies in CK namespace.
The CK and CK-Tile source code bases are incompatible because CK is not properly using namespaces everywhere. In particular, we need to put hip_check_error in the ck namespace.
Move all functions in include/ck_/host_utility that were in global namespace into the ck namespace.
There may be additional namespace problems like this, and it's possible we'll have namespace clashes. But it is good design to properly guard our to code bases (CK and CKTile) so that they can both coexist. Moreover, estabilishing this compatiblity is essential if we are going to allow the builder to instantiate kernels from either template library.
* Add using declarations to test code.
After moving some of the untils into the ck namespace, most examples and a few tests had to be updated to recognize the new namespace declarations. We add using declarations to individual compute units for functions that were previously in the global namespace.
* Add using declarations to client examples.
[ROCm/composable_kernel commit: ad57f6ef0b]
202 lines
8.9 KiB
C++
202 lines
8.9 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 <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_pool2d_fwd_nhwc_nhwc.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::HostTensorDescriptor;
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using ::ck::Tensor;
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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 pool_test(bool do_verification,
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int init_method,
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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 Y,
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ck::index_t X,
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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_h,
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ck::index_t window_stride_w,
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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_h,
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ck::index_t in_left_pad_w,
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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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using DevicePoolFwdInstance =
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ck::tensor_operation::device::DevicePool2dFwd_NHWC_NHWC<InDataType,
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OutDataType,
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IndexDataType,
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ComputeDataType,
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ReduceOpId,
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OutputIndex,
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64, // BlockSize
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64, // ReduceMThreadClusterSize
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1, // ReduceKThreadClusterSize
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4, // ReduceMThreadSliceSize
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1, // ReduceKThreadSliceSize
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1>; // InSrcOutDstVectorSize
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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 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{Y, X};
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const std::vector<ck::index_t> window_strides{window_stride_h, window_stride_w};
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const std::vector<ck::index_t> window_dilations{window_dilation_h, window_dilation_w};
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const std::vector<ck::index_t> input_left_pads{in_left_pad_h, in_left_pad_w};
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const std::vector<ck::index_t> input_right_pads{in_right_pad_h, in_right_pad_w};
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// tensor layout
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auto f_host_tensor_descriptor =
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[](std::size_t N_, std::size_t C_, std::size_t H, std::size_t W, auto layout) {
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using namespace ck::literals;
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if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCHW>::value)
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{
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return HostTensorDescriptor({N_, C_, H, W}, {C_ * H * W, H * W, W, 1_uz}, layout);
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}
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else if constexpr(ck::is_same<decltype(layout),
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ck::tensor_layout::convolution::NHWC>::value)
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{
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return HostTensorDescriptor({N_, C_, H, W}, {C_ * H * W, 1_uz, W * C_, C_}, layout);
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}
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};
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Tensor<InDataType> in_n_c_hi_wi(f_host_tensor_descriptor(N, C, Hi, Wi, InLayout{}));
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Tensor<OutDataType> out_n_c_ho_wo_host(f_host_tensor_descriptor(N, C, Ho, Wo, OutLayout{}));
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Tensor<IndexDataType> out_indices_n_c_ho_wo_host(
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f_host_tensor_descriptor(N, C, Ho, Wo, OutLayout{}));
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Tensor<OutDataType> out_n_c_ho_wo_device(f_host_tensor_descriptor(N, C, Ho, Wo, OutLayout{}));
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Tensor<IndexDataType> out_indices_n_c_ho_wo_device(
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f_host_tensor_descriptor(N, C, Ho, Wo, OutLayout{}));
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std::cout << "in_n_c_hi_wi: " << in_n_c_hi_wi.mDesc << std::endl;
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std::cout << "out_n_c_ho_wo: " << out_n_c_ho_wo_host.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: in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_1<InDataType>{1}); break;
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case 2: in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5}); break;
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default: in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_3<InDataType>{-5.0, 5.0});
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}
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DeviceMem in_device_buf(sizeof(InDataType) * in_n_c_hi_wi.mDesc.GetElementSpaceSize());
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DeviceMem out_device_buf(sizeof(OutDataType) *
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out_n_c_ho_wo_device.mDesc.GetElementSpaceSize());
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DeviceMem out_indices_device_buf(sizeof(IndexDataType) *
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out_indices_n_c_ho_wo_device.mDesc.GetElementSpaceSize());
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in_device_buf.ToDevice(in_n_c_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, Hi, Wi},
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{Y, X},
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{N, C, Ho, Wo},
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{C * Hi * Wi, 1, Wi * C, C},
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{C * Ho * Wo, 1, Wo * C, C},
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{C * Ho * Wo, 1, Wo * C, C},
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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});
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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::size_t flop = std::size_t(2) * N * C * Ho * Wo * Y * X;
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std::size_t num_btype =
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sizeof(InDataType) * (N * C * Hi * Wi) + sizeof(OutDataType) * (N * C * Ho * Wo);
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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: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
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<< " GB / s " << 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<4,
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2,
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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_hi_wi,
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out_n_c_ho_wo_host,
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out_indices_n_c_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_ho_wo_device.mData.data());
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pass = pass && ck::utils::check_err(out_n_c_ho_wo_device, out_n_c_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_ho_wo_device.mData.data());
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pass = pass &&
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ck::utils::check_err(out_indices_n_c_ho_wo_device, out_indices_n_c_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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