mirror of
https://github.com/ROCm/composable_kernel.git
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Add bilinear conv fwd and bwd data instances (#1164)
This commit is contained in:
Bartłomiej Kocot
13
example/62_convnd_activ/binary/CMakeLists.txt
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13
example/62_convnd_activ/binary/CMakeLists.txt
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@@ -0,0 +1,13 @@
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list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942)
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set(target 0)
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foreach(gpu IN LISTS GPU_TARGETS)
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if(gpu IN_LIST gpu_list AND target EQUAL 0)
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add_custom_target(example_convnd_activ_binary_xdl)
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# Bilinear residual
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add_example_executable(example_convnd_fwd_xdl_bilinear_residual_fp16 convnd_fwd_xdl_bilinear_residual_fp16.cpp)
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add_example_dependencies(example_convnd_activ_binary_xdl example_convnd_fwd_xdl_bilinear_residual_fp16)
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add_example_executable(example_convnd_bwd_data_xdl_bilinear_residual_fp16 convnd_bwd_data_xdl_bilinear_residual_fp16.cpp)
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add_example_dependencies(example_convnd_activ_binary_xdl example_convnd_bwd_data_xdl_bilinear_residual_fp16)
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set(target 1)
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endif()
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endforeach()
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@@ -0,0 +1,266 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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#include <cstdlib>
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#include <iostream>
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#include <numeric>
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#include <type_traits>
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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/element/element_wise_operation.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_data_multiple_d_xdl_cshuffle_v1.hpp"
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#include "ck/tensor_operation/gpu/device/convolution_backward_data_specialization.hpp"
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#include "ck/library/utility/algorithm.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/convolution_parameter.hpp"
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#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_conv_bwd_data.hpp"
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#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
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constexpr ck::index_t NDimSpatial = 3;
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using InDataType = ck::half_t;
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using WeiDataType = ck::half_t;
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using AccDataType = float;
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using CShuffleDataType = ck::half_t;
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using OutDataType = ck::half_t;
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template <ck::index_t... Is>
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using S = ck::Sequence<Is...>;
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using InLayout = ck::tensor_layout::convolution::GNDHWC;
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using WeiLayout = ck::tensor_layout::convolution::GKZYXC;
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using OutLayout = ck::tensor_layout::convolution::GNDHWK;
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using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
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using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
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using InElementOp = ck::tensor_operation::element_wise::Bilinear;
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static constexpr auto ConvSpec =
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ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::Default;
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template <typename OutElementOp>
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using DeviceGroupedConvNDBwdDataInstance =
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ck::tensor_operation::device::DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<
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NDimSpatial,
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OutLayout,
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WeiLayout,
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ck::Tuple<InLayout>,
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InLayout,
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OutDataType,
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WeiDataType,
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AccDataType,
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CShuffleDataType,
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ck::Tuple<InDataType>,
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InDataType,
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OutElementOp,
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WeiElementOp,
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InElementOp,
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ConvSpec, // ConvForwardSpecialization
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true,
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true,
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1, //
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256, // BlockSize
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128, // MPerBlock
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256, // NPerBlock
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32, // KPerBlock
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8, // AK1
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2, // BK1
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32, // MPerXdl
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32, // NPerXdl
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2, // MXdlPerWave
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4, // NXdlPerWave
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S<4, 64, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1
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S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
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S<1, 0, 2>, // ABlockTransferSrcAccessOrder
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2, // ABlockTransferSrcVectorDim
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8, // ABlockTransferSrcScalarPerVector
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8, // ABlockTransferDstScalarPerVector_AK1
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1, // ABlockLdsExtraM
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S<4, 64, 1>, // BBlockTransferThreadClusterLengths_BK0_N_BK1
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S<0, 2, 1>, // BBlockTransferThreadClusterArrangeOrder
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S<0, 2, 1>, // BBlockTransferSrcAccessOrder
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1, // BBlockTransferSrcVectorDim
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4, // BBlockTransferSrcScalarPerVector
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2, // BBlockTransferDstScalarPerVector_BK1
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0, // BBlockLdsExtraN
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1,
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1,
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S<1, 32, 1, 8>,
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8>;
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using DeviceGroupedConvNDActivInstance = DeviceGroupedConvNDBwdDataInstance<OutElementOp>;
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namespace {
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// Use custom implementation to pass two more tensors for post op
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template <ck::index_t NDimSpatial,
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typename InDataType,
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typename WeiDataType,
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typename OutDataType,
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typename InElementOp,
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typename WeiElementOp,
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typename OutElementOp,
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typename DeviceConvNDInstance>
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bool run_grouped_conv(bool do_verification,
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int init_method,
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bool time_kernel,
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const ck::utils::conv::ConvParam& conv_param,
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const HostTensorDescriptor& in_g_n_c_wis_desc,
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const HostTensorDescriptor& wei_g_k_c_xs_desc,
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const HostTensorDescriptor& out_g_n_k_wos_desc,
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const InElementOp& in_element_op,
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const WeiElementOp& wei_element_op,
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const OutElementOp& out_element_op)
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{
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constexpr ck::index_t NumDs = 1;
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Tensor<OutDataType> out(out_g_n_k_wos_desc);
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Tensor<WeiDataType> wei(wei_g_k_c_xs_desc);
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Tensor<InDataType> in_host(in_g_n_c_wis_desc);
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std::cout << "out: " << out.mDesc << std::endl;
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std::cout << "wei: " << wei.mDesc << std::endl;
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std::cout << "in: " << in_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:
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out.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5});
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wei.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
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in_host.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
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break;
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default:
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out.GenerateTensorValue(GeneratorTensor_3<OutDataType>{0.0, 1.0});
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wei.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
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in_host.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0});
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}
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// Initialize based on out_host
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Tensor<InDataType> in_device(in_host);
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DeviceMem out_device_buf(sizeof(OutDataType) * out.mDesc.GetElementSpaceSize());
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DeviceMem wei_device_buf(sizeof(WeiDataType) * wei.mDesc.GetElementSpaceSize());
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DeviceMem in_device_buf(sizeof(InDataType) * in_device.mDesc.GetElementSpaceSize());
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out_device_buf.ToDevice(out.mData.data());
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wei_device_buf.ToDevice(wei.mData.data());
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in_device_buf.ToDevice(in_device.mData.data());
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std::array<ck::index_t, NDimSpatial + 3> a_g_n_k_wos_lengths{};
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std::array<ck::index_t, NDimSpatial + 3> a_g_n_k_wos_strides{};
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std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_lengths{};
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std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_strides{};
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std::array<ck::index_t, NDimSpatial + 3> e_g_n_c_wis_lengths{};
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std::array<ck::index_t, NDimSpatial + 3> e_g_n_c_wis_strides{};
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std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
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std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
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std::array<ck::index_t, NDimSpatial> input_left_pads{};
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std::array<ck::index_t, NDimSpatial> input_right_pads{};
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auto copy = [](auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
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copy(out_g_n_k_wos_desc.GetLengths(), a_g_n_k_wos_lengths);
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copy(out_g_n_k_wos_desc.GetStrides(), a_g_n_k_wos_strides);
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copy(wei_g_k_c_xs_desc.GetLengths(), b_g_k_c_xs_lengths);
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copy(wei_g_k_c_xs_desc.GetStrides(), b_g_k_c_xs_strides);
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copy(in_g_n_c_wis_desc.GetLengths(), e_g_n_c_wis_lengths);
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copy(in_g_n_c_wis_desc.GetStrides(), e_g_n_c_wis_strides);
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copy(conv_param.conv_filter_strides_, conv_filter_strides);
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copy(conv_param.conv_filter_dilations_, conv_filter_dilations);
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copy(conv_param.input_left_pads_, input_left_pads);
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copy(conv_param.input_right_pads_, input_right_pads);
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// Use output as D
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const std::array<const void*, NumDs> ds = {in_device_buf.GetDeviceBuffer()};
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auto conv = DeviceConvNDInstance{};
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auto invoker = conv.MakeInvoker();
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auto argument = conv.MakeArgument(
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out_device_buf.GetDeviceBuffer(),
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wei_device_buf.GetDeviceBuffer(),
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ds,
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in_device_buf.GetDeviceBuffer(),
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a_g_n_k_wos_lengths,
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a_g_n_k_wos_strides,
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b_g_k_c_xs_lengths,
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b_g_k_c_xs_strides,
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std::array<std::array<ck::index_t, NDimSpatial + 3>, NumDs>{e_g_n_c_wis_lengths},
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std::array<std::array<ck::index_t, NDimSpatial + 3>, NumDs>{e_g_n_c_wis_strides},
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e_g_n_c_wis_lengths,
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e_g_n_c_wis_strides,
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conv_filter_strides,
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conv_filter_dilations,
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input_left_pads,
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input_right_pads,
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out_element_op,
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wei_element_op,
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in_element_op);
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if(!conv.IsSupportedArgument(argument))
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{
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throw std::runtime_error("The device op with the specified compilation parameters does "
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"not support this convolution problem.");
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}
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float avg_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
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std::size_t flop =
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conv_param.GetFlops() + 3 * conv_param.GetInputByte<InDataType>() / sizeof(InDataType);
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std::size_t num_btype = conv_param.GetByte<InDataType, WeiDataType, OutDataType>() +
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conv_param.GetOutputByte<InDataType>();
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float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
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float gb_per_sec = num_btype / 1.E6 / avg_time;
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std::cout << "Perf: " << avg_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
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<< conv.GetTypeString() << std::endl;
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if(do_verification)
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{
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std::array<Tensor<OutDataType>, NumDs> d_tensors = {in_host};
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auto ref_conv =
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ck::tensor_operation::host::ReferenceConvBwdData<NDimSpatial,
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InDataType,
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WeiDataType,
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OutDataType,
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InElementOp,
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WeiElementOp,
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OutElementOp,
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0, /*Num A Elementwise Tensors*/
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0, /*Num B Elementwise Tensors*/
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NumDs>();
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auto ref_invoker = ref_conv.MakeInvoker();
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auto ref_argument = ref_conv.MakeArgument(in_host,
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wei,
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out,
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conv_param.conv_filter_strides_,
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conv_param.conv_filter_dilations_,
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conv_param.input_left_pads_,
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conv_param.input_right_pads_,
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in_element_op,
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wei_element_op,
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out_element_op,
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{},
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{},
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d_tensors);
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ref_invoker.Run(ref_argument);
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in_device_buf.FromDevice(in_device.mData.data());
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return ck::utils::check_err(in_device.mData, in_host.mData);
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}
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return true;
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}
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} // namespace
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#include "../run_convnd_activ_example.inc"
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int main(int argc, char* argv[]) { return !run_convnd_example(argc, argv); }
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@@ -0,0 +1,266 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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#include <cstdlib>
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#include <iostream>
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#include <numeric>
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#include <type_traits>
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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/element/element_wise_operation.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_abd_xdl_cshuffle.hpp"
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#include "ck/library/utility/algorithm.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/convolution_parameter.hpp"
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#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
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#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
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constexpr ck::index_t NDimSpatial = 3;
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using InDataType = ck::half_t;
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using WeiDataType = ck::half_t;
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using AccDataType = float;
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using CShuffleDataType = ck::half_t;
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using OutDataType = ck::half_t;
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template <ck::index_t... Is>
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using S = ck::Sequence<Is...>;
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using InLayout = ck::tensor_layout::convolution::GNDHWC;
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using WeiLayout = ck::tensor_layout::convolution::GKZYXC;
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using OutLayout = ck::tensor_layout::convolution::GNDHWK;
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using InElementOp = ck::tensor_operation::element_wise::PassThrough;
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using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
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using OutElementOp = ck::tensor_operation::element_wise::Bilinear;
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static constexpr auto ConvSpec =
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ck::tensor_operation::device::ConvolutionForwardSpecialization::Default;
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static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
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template <typename OutElementOp>
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using DeviceGroupedConvNDFwdInstance =
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ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<
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NDimSpatial,
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InLayout,
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WeiLayout,
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ck::Tuple<OutLayout>,
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OutLayout,
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InDataType,
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WeiDataType,
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AccDataType,
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CShuffleDataType,
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ck::Tuple<OutDataType>,
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OutDataType,
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InElementOp,
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WeiElementOp,
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OutElementOp,
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ConvSpec, // ConvForwardSpecialization
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GemmSpec, // GemmSpecialization
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1, //
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256, // BlockSize
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128, // MPerBlock
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256, // NPerBlock
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32, // KPerBlock
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8, // AK1
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8, // BK1
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32, // MPerXdl
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32, // NPerXdl
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2, // MXdlPerWave
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4, // NXdlPerWave
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S<4, 64, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1
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S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
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S<1, 0, 2>, // ABlockTransferSrcAccessOrder
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2, // ABlockTransferSrcVectorDim
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8, // ABlockTransferSrcScalarPerVector
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8, // ABlockTransferDstScalarPerVector_AK1
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1, // ABlockLdsExtraM
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S<4, 64, 1>, // BBlockTransferThreadClusterLengths_BK0_N_BK1
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S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
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S<1, 0, 2>, // BBlockTransferSrcAccessOrder
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2, // BBlockTransferSrcVectorDim
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8, // BBlockTransferSrcScalarPerVector
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8, // BBlockTransferDstScalarPerVector_BK1
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1, // BBlockLdsExtraN
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1,
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1,
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S<1, 32, 1, 8>,
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8>;
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using DeviceGroupedConvNDActivInstance = DeviceGroupedConvNDFwdInstance<OutElementOp>;
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namespace {
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// Use custom implementation to pass two more tensors for post op
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template <ck::index_t NDimSpatial,
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typename InDataType,
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typename WeiDataType,
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typename OutDataType,
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typename InElementOp,
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typename WeiElementOp,
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typename OutElementOp,
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typename DeviceConvNDFwdInstance>
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bool run_grouped_conv(bool do_verification,
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int init_method,
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bool time_kernel,
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const ck::utils::conv::ConvParam& conv_param,
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const HostTensorDescriptor& in_g_n_c_wis_desc,
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const HostTensorDescriptor& wei_g_k_c_xs_desc,
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const HostTensorDescriptor& out_g_n_k_wos_desc,
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const InElementOp& in_element_op,
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const WeiElementOp& wei_element_op,
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const OutElementOp& out_element_op)
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{
|
||||
constexpr ck::index_t NumDs = 1;
|
||||
Tensor<InDataType> in(in_g_n_c_wis_desc);
|
||||
Tensor<WeiDataType> wei(wei_g_k_c_xs_desc);
|
||||
Tensor<OutDataType> out_host(out_g_n_k_wos_desc);
|
||||
|
||||
std::cout << "in: " << in.mDesc << std::endl;
|
||||
std::cout << "wei: " << wei.mDesc << std::endl;
|
||||
std::cout << "out: " << out_host.mDesc << std::endl;
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
in.GenerateTensorValue(GeneratorTensor_2<InDataType>{-2, 2});
|
||||
wei.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-2, 2});
|
||||
out_host.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-2, 2});
|
||||
break;
|
||||
default:
|
||||
in.GenerateTensorValue(GeneratorTensor_3<InDataType>{-1.0, 1.0});
|
||||
wei.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-2, 2});
|
||||
out_host.GenerateTensorValue(GeneratorTensor_3<OutDataType>{-0.05, 0.05});
|
||||
}
|
||||
|
||||
// Initialize based on out_host
|
||||
Tensor<OutDataType> out_device(out_host);
|
||||
|
||||
DeviceMem in_device_buf(sizeof(InDataType) * in.mDesc.GetElementSpaceSize());
|
||||
DeviceMem wei_device_buf(sizeof(WeiDataType) * wei.mDesc.GetElementSpaceSize());
|
||||
DeviceMem out_device_buf(sizeof(OutDataType) * out_device.mDesc.GetElementSpaceSize());
|
||||
|
||||
in_device_buf.ToDevice(in.mData.data());
|
||||
wei_device_buf.ToDevice(wei.mData.data());
|
||||
out_device_buf.ToDevice(out_device.mData.data());
|
||||
|
||||
std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_lengths{};
|
||||
std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_strides{};
|
||||
std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_lengths{};
|
||||
std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_strides{};
|
||||
std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_lengths{};
|
||||
std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_strides{};
|
||||
std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
|
||||
std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
|
||||
std::array<ck::index_t, NDimSpatial> input_left_pads{};
|
||||
std::array<ck::index_t, NDimSpatial> input_right_pads{};
|
||||
|
||||
auto copy = [](const auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
|
||||
|
||||
copy(in_g_n_c_wis_desc.GetLengths(), a_g_n_c_wis_lengths);
|
||||
copy(in_g_n_c_wis_desc.GetStrides(), a_g_n_c_wis_strides);
|
||||
copy(wei_g_k_c_xs_desc.GetLengths(), b_g_k_c_xs_lengths);
|
||||
copy(wei_g_k_c_xs_desc.GetStrides(), b_g_k_c_xs_strides);
|
||||
copy(out_g_n_k_wos_desc.GetLengths(), e_g_n_k_wos_lengths);
|
||||
copy(out_g_n_k_wos_desc.GetStrides(), e_g_n_k_wos_strides);
|
||||
copy(conv_param.conv_filter_strides_, conv_filter_strides);
|
||||
copy(conv_param.conv_filter_dilations_, conv_filter_dilations);
|
||||
copy(conv_param.input_left_pads_, input_left_pads);
|
||||
copy(conv_param.input_right_pads_, input_right_pads);
|
||||
|
||||
// Use output as D
|
||||
const std::array<const void*, NumDs> ds = {out_device_buf.GetDeviceBuffer()};
|
||||
|
||||
auto conv = DeviceConvNDFwdInstance{};
|
||||
auto invoker = conv.MakeInvoker();
|
||||
auto argument = conv.MakeArgument(
|
||||
in_device_buf.GetDeviceBuffer(),
|
||||
wei_device_buf.GetDeviceBuffer(),
|
||||
ds,
|
||||
out_device_buf.GetDeviceBuffer(),
|
||||
a_g_n_c_wis_lengths,
|
||||
a_g_n_c_wis_strides,
|
||||
b_g_k_c_xs_lengths,
|
||||
b_g_k_c_xs_strides,
|
||||
std::array<std::array<ck::index_t, NDimSpatial + 3>, NumDs>{e_g_n_k_wos_lengths},
|
||||
std::array<std::array<ck::index_t, NDimSpatial + 3>, NumDs>{e_g_n_k_wos_strides},
|
||||
e_g_n_k_wos_lengths,
|
||||
e_g_n_k_wos_strides,
|
||||
conv_filter_strides,
|
||||
conv_filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
in_element_op,
|
||||
wei_element_op,
|
||||
out_element_op);
|
||||
|
||||
if(!conv.IsSupportedArgument(argument))
|
||||
{
|
||||
throw std::runtime_error("The device op with the specified compilation parameters does "
|
||||
"not support this convolution problem.");
|
||||
}
|
||||
|
||||
float avg_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
|
||||
|
||||
std::size_t flop =
|
||||
conv_param.GetFlops() + 3 * conv_param.GetOutputByte<OutDataType>() / sizeof(OutDataType);
|
||||
std::size_t num_btype = conv_param.GetByte<InDataType, WeiDataType, OutDataType>() +
|
||||
conv_param.GetOutputByte<OutDataType>();
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
|
||||
float gb_per_sec = num_btype / 1.E6 / avg_time;
|
||||
std::cout << "Perf: " << avg_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
|
||||
<< conv.GetTypeString() << std::endl;
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
std::array<Tensor<OutDataType>, NumDs> d_tensors = {out_host};
|
||||
auto ref_conv =
|
||||
ck::tensor_operation::host::ReferenceConvFwd<NDimSpatial,
|
||||
InDataType,
|
||||
WeiDataType,
|
||||
OutDataType,
|
||||
InElementOp,
|
||||
WeiElementOp,
|
||||
OutElementOp,
|
||||
0, /*Num A Elementwise Tensors*/
|
||||
0, /*Num B Elementwise Tensors*/
|
||||
NumDs>();
|
||||
|
||||
auto ref_invoker = ref_conv.MakeInvoker();
|
||||
auto ref_argument = ref_conv.MakeArgument(in,
|
||||
wei,
|
||||
out_host,
|
||||
conv_param.conv_filter_strides_,
|
||||
conv_param.conv_filter_dilations_,
|
||||
conv_param.input_left_pads_,
|
||||
conv_param.input_right_pads_,
|
||||
in_element_op,
|
||||
wei_element_op,
|
||||
out_element_op,
|
||||
{},
|
||||
{},
|
||||
d_tensors);
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
out_device_buf.FromDevice(out_device.mData.data());
|
||||
|
||||
return ck::utils::check_err(out_device, out_host, "Error: incorrect results!");
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
} // namespace
|
||||
|
||||
#include "../run_convnd_activ_example.inc"
|
||||
|
||||
int main(int argc, char* argv[]) { return !run_convnd_example(argc, argv); }
|
||||
Reference in New Issue
Block a user