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https://github.com/ROCm/composable_kernel.git
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ad57f6ef0b
* 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.
236 lines
12 KiB
C++
236 lines
12 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/tensor_operation/gpu/device/impl/device_pool2d_fwd_nhwc_nhwc.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_max_pool_bwd_impl.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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#include "ck/library/reference_tensor_operation/cpu/reference_maxpool_bwd.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 IndexDataType,
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typename ComputeDataType,
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typename DInDataType,
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typename DOutDataType,
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bool PropagateNan>
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bool maxpool_bwd_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 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 PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using DevicePoolFwdInstance =
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ck::tensor_operation::device::DevicePool2dFwd_NHWC_NHWC<InDataType, // InDataType
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OutDataType, // OutDataType
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IndexDataType, // IndexDataType
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ComputeDataType, // ComputeDataType
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ck::ReduceTensorOp::MAX,
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true,
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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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using DeviceMaxPoolBwdInstance = ck::tensor_operation::device::
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DeviceMaxPoolBwdImpl<DOutDataType, IndexDataType, DInDataType, 4>;
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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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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) {
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using namespace ck::literals;
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// reference need Tensor with NCHW order
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return HostTensorDescriptor({N_, C_, H, W},
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{C_ * H * W, 1_uz, W * C_, C_},
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ck::tensor_layout::convolution::NCHW{});
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};
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// in
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Tensor<InDataType> in_n_c_hi_wi(f_host_tensor_descriptor(N, C, Hi, Wi));
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// out
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Tensor<OutDataType> out_n_c_ho_wo_host(f_host_tensor_descriptor(N, C, Ho, Wo));
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Tensor<OutDataType> out_n_c_ho_wo_device(f_host_tensor_descriptor(N, C, Ho, Wo));
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// indices
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Tensor<IndexDataType> indices_n_c_ho_wo_device(f_host_tensor_descriptor(N, C, Ho, Wo));
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Tensor<IndexDataType> indices_n_c_ho_wo_host(f_host_tensor_descriptor(N, C, Ho, Wo));
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// dout
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Tensor<DOutDataType> dout_n_c_ho_wo(f_host_tensor_descriptor(N, C, Ho, Wo));
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// din
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Tensor<DInDataType> din_n_c_hi_wi_host(f_host_tensor_descriptor(N, C, Hi, Wi));
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Tensor<DInDataType> din_n_c_hi_wi_device(f_host_tensor_descriptor(N, C, Hi, Wi));
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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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std::cout << "indices_n_c_ho_wo: " << indices_n_c_ho_wo_host.mDesc << std::endl;
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std::cout << "dout_n_c_ho_wo: " << dout_n_c_ho_wo.mDesc << std::endl;
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std::cout << "din_n_c_hi_wi: " << din_n_c_hi_wi_host.mDesc << std::endl;
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in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_3<InDataType>{-1.0, 1.0});
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dout_n_c_ho_wo.GenerateTensorValue(GeneratorTensor_3<DOutDataType>{-1.0, 1.0});
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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 indices_device_buf(sizeof(IndexDataType) *
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indices_n_c_ho_wo_device.mDesc.GetElementSpaceSize());
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DeviceMem dout_device_buf(sizeof(DOutDataType) * dout_n_c_ho_wo.mDesc.GetElementSpaceSize());
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DeviceMem din_device_buf(sizeof(DInDataType) *
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din_n_c_hi_wi_device.mDesc.GetElementSpaceSize());
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in_device_buf.ToDevice(in_n_c_hi_wi.mData.data());
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dout_device_buf.ToDevice(dout_n_c_ho_wo.mData.data());
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auto pool_fwd = DevicePoolFwdInstance{};
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auto pool_fwd_invoker_ptr = pool_fwd.MakeInvokerPointer();
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auto pool_fwd_argument_ptr = pool_fwd.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*>(indices_device_buf.GetDeviceBuffer()),
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{N, C, Hi, Wi},
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window_spatial_lengths,
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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_fwd.IsSupportedArgument(pool_fwd_argument_ptr.get()))
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{
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throw std::runtime_error("wrong! pool_fwd 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_fwd =
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pool_fwd_invoker_ptr->Run(pool_fwd_argument_ptr.get(), StreamConfig{nullptr, time_kernel});
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auto pool_bwd = DeviceMaxPoolBwdInstance{};
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auto pool_bwd_invoker_ptr = pool_bwd.MakeInvokerPointer();
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auto pool_bwd_argument_ptr = pool_bwd.MakeArgumentPointer(
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static_cast<DOutDataType*>(dout_device_buf.GetDeviceBuffer()),
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static_cast<IndexDataType*>(indices_device_buf.GetDeviceBuffer()),
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static_cast<DInDataType*>(din_device_buf.GetDeviceBuffer()),
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dout_n_c_ho_wo.mDesc.GetElementSpaceSize(),
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din_n_c_hi_wi_device.mDesc.GetElementSpaceSize(),
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window_spatial_lengths,
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window_strides,
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window_dilations);
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if(!pool_bwd.IsSupportedArgument(pool_bwd_argument_ptr.get()))
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{
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throw std::runtime_error("wrong! pool_bwd with the specified compilation parameters does "
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"not support this problem");
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}
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size_t pool_bwd_workspace_sz = pool_bwd.GetWorkSpaceSize(pool_bwd_argument_ptr.get());
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DeviceMem pool_bwd_workspace_device_buf(pool_bwd_workspace_sz);
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pool_bwd.SetWorkSpacePointer(pool_bwd_argument_ptr.get(),
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pool_bwd_workspace_device_buf.GetDeviceBuffer());
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float ave_time_bwd =
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pool_bwd_invoker_ptr->Run(pool_bwd_argument_ptr.get(), StreamConfig{nullptr, time_kernel});
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std::cout << "Pool fwd perf: " << ave_time_fwd << " ms" << std::endl;
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std::cout << "Pool bwd perf: " << ave_time_bwd << " ms" << 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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ck::ReduceTensorOp::MAX,
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PropagateNan,
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true>;
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auto ref_pooling_fwd = ReferencePoolingFwdInstance{};
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auto ref_pooling_fwd_invoker = ref_pooling_fwd.MakeInvoker();
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auto ref_pooling_fwd_argument = ref_pooling_fwd.MakeArgument(in_n_c_hi_wi,
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out_n_c_ho_wo_host,
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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_fwd_invoker.Run(ref_pooling_fwd_argument);
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using ReferencePoolingBwdInstance =
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ck::tensor_operation::host::ReferenceMaxPoolBwd<DOutDataType,
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IndexDataType,
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ComputeDataType,
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DInDataType,
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PassThrough>;
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auto ref_pooling_bwd = ReferencePoolingBwdInstance{};
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auto ref_pooling_bwd_invoker = ref_pooling_bwd.MakeInvoker();
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auto ref_pooling_bwd_argument = ref_pooling_bwd.MakeArgument(
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dout_n_c_ho_wo, indices_n_c_ho_wo_host, din_n_c_hi_wi_host, PassThrough{});
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ref_pooling_bwd_invoker.Run(ref_pooling_bwd_argument);
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out_device_buf.FromDevice(out_n_c_ho_wo_device.mData.data());
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indices_device_buf.FromDevice(indices_n_c_ho_wo_device.mData.data());
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din_device_buf.FromDevice(din_n_c_hi_wi_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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pass = pass && ck::utils::check_err(indices_n_c_ho_wo_device, indices_n_c_ho_wo_host);
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pass = pass && ck::utils::check_err(din_n_c_hi_wi_device, din_n_c_hi_wi_host);
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}
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return (pass);
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};
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