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https://github.com/ROCm/composable_kernel.git
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282 lines
12 KiB
C++
282 lines
12 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, 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/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/host_tensor/device_memory.hpp"
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#include "ck/library/host_tensor/host_tensor.hpp"
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#include "ck/library/host_tensor/host_tensor_generator.hpp"
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template <typename InDataType,
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typename OutDataType,
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typename AccDataType,
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typename IndexDataType,
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ck::ReduceTensorOp ReduceOpId,
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bool PropagateNan,
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bool OutputIndex>
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static void pool_host_verify(const Tensor<InDataType>& in,
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Tensor<OutDataType>& out,
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Tensor<IndexDataType>& out_indices,
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const std::array<ck::index_t, 2>& window_spatial_lengths,
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const std::array<ck::index_t, 2>& window_strides,
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const std::array<ck::index_t, 2>& in_left_pads,
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const std::array<ck::index_t, 2>& /*in_right_pads*/)
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{
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const int32_t reduceLength = window_spatial_lengths[0] * window_spatial_lengths[1];
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using ReduceOperation = typename ck::reduce_binary_operator<ReduceOpId>::opType;
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auto elementwise_ops =
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ck::reduce_unary_operator<ReduceOpId, true, true>::GetElementwiseOperator(reduceLength);
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auto in_elementwise_op = std::get<0>(elementwise_ops);
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auto acc_elementwise_op = std::get<1>(elementwise_ops);
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if constexpr(!OutputIndex)
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{
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using Accumulation =
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ck::detail::AccumulateWithNanCheck<PropagateNan, ReduceOperation, AccDataType>;
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auto f_nchw = [&](auto n, auto c, auto ho, auto wo) {
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auto accuVal = ReduceOperation::template GetIdentityValue<AccDataType>();
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for(ck::index_t y = 0; y < window_spatial_lengths[0]; ++y)
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{
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ck::index_t hi = ho * window_strides[0] + y - in_left_pads[0];
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for(ck::index_t x = 0; x < window_spatial_lengths[1]; ++x)
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{
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ck::index_t wi = wo * window_strides[1] + x - in_left_pads[1];
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if(hi >= 0 && hi < static_cast<ck::index_t>(in.mDesc.GetLengths()[2]) &&
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wi >= 0 && wi < static_cast<ck::index_t>(in.mDesc.GetLengths()[3]))
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{
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AccDataType currVal = static_cast<AccDataType>(in(n, c, hi, wi));
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in_elementwise_op(currVal, currVal);
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Accumulation::Calculate(accuVal, currVal);
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}
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}
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}
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acc_elementwise_op(accuVal, accuVal);
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out(n, c, ho, wo) = accuVal;
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};
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make_ParallelTensorFunctor(f_nchw,
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out.mDesc.GetLengths()[0],
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out.mDesc.GetLengths()[1],
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out.mDesc.GetLengths()[2],
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out.mDesc.GetLengths()[3])(std::thread::hardware_concurrency());
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}
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else
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{
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using Accumulation = ck::detail::AccumulateWithIndexAndNanCheck<PropagateNan,
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ReduceOperation,
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AccDataType,
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IndexDataType>;
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auto f_nchw = [&](auto n, auto c, auto ho, auto wo) {
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auto accuVal = ReduceOperation::template GetIdentityValue<AccDataType>();
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IndexDataType accuIndex = 0;
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for(ck::index_t y = 0; y < window_spatial_lengths[0]; ++y)
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{
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ck::index_t hi = ho * window_strides[0] + y - in_left_pads[0];
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for(ck::index_t x = 0; x < window_spatial_lengths[1]; ++x)
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{
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ck::index_t wi = wo * window_strides[1] + x - in_left_pads[1];
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if(hi >= 0 && hi < in.mDesc.GetLengths()[2] && wi >= 0 &&
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wi < in.mDesc.GetLengths()[3])
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{
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AccDataType currVal = static_cast<AccDataType>(in(n, c, hi, wi));
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IndexDataType currIndex = y * window_spatial_lengths[1] + x;
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in_elementwise_op(currVal, currVal);
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Accumulation::Calculate(accuVal, currVal, accuIndex, currIndex);
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}
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}
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}
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acc_elementwise_op(accuVal, accuVal);
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out(n, c, ho, wo) = accuVal;
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out_indices(n, c, ho, wo) = accuIndex;
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};
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make_ParallelTensorFunctor(f_nchw,
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out.mDesc.GetLengths()[0],
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out.mDesc.GetLengths()[1],
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out.mDesc.GetLengths()[2],
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out.mDesc.GetLengths()[3])(std::thread::hardware_concurrency());
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};
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}
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template <typename InDataType,
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typename OutDataType,
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typename AccDataType,
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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 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_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C<
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InDataType, // InDataType
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OutDataType, // OutDataType
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AccDataType, // AccDataType
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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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4>; // InSrcOutDstVectorSize
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const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - Y) / window_stride_h + 1;
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const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - X) / window_stride_w + 1;
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const std::array<ck::index_t, 2> window_spatial_lengths{{Y, X}};
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const std::array<ck::index_t, 2> window_strides{{window_stride_h, window_stride_w}};
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const std::array<ck::index_t, 2> input_left_pads{{in_left_pad_h, in_left_pad_w}};
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const std::array<ck::index_t, 2> 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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if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCHW>::value)
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{
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return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
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std::vector<std::size_t>({C_ * H * W, H * W, W, 1}));
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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(std::vector<std::size_t>({N_, C_, H, W}),
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std::vector<std::size_t>({C_ * H * W, 1, W * C_, C_}));
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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.GetElementSpace());
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DeviceMem out_device_buf(sizeof(OutDataType) * out_n_c_ho_wo_device.mDesc.GetElementSpace());
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DeviceMem out_indices_device_buf(sizeof(IndexDataType) *
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out_indices_n_c_ho_wo_device.mDesc.GetElementSpace());
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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,
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C,
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std::array<ck::index_t, 2>{{Hi, Wi}},
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std::array<ck::index_t, 2>{{Y, X}},
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std::array<ck::index_t, 2>{{Ho, Wo}},
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window_strides,
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input_left_pads,
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input_right_pads);
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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 << " GB/s"
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<< std::endl;
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bool pass = true;
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if(do_verification)
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{
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pool_host_verify<InDataType,
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OutDataType,
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AccDataType,
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IndexDataType,
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ReduceOpId,
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PropagateNan,
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OutputIndex>(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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input_left_pads,
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input_right_pads);
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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.mData, out_n_c_ho_wo_host.mData);
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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 && ck::utils::check_err(out_indices_n_c_ho_wo_device.mData,
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out_indices_n_c_ho_wo_host.mData);
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};
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}
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return (pass);
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};
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