mirror of
https://github.com/ROCm/composable_kernel.git
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BatchNorm backward instance/external API/profiler/tests (#519)
* Refine the device batchnorm-backward base API templates and data type assignments
* Remove duplicated kernel file
* Add batchnorm backward instances and external API
* Add batchnorm-backward profiler and tests
* Add client example which uses batchnorm backward external API
* Merge test/batchnorm_fwd and test/batchnorm_bwd into one directory
* Loose the threshold for batchnorm-backward check_err()
[ROCm/composable_kernel commit: 63af525c06]
This commit is contained in:
Qianfeng
@@ -1,2 +1,4 @@
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add_executable(client_batchnorm_fwd_nhwc batchnorm_fwd_nhwc.cpp)
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add_executable(client_batchnorm_bwd_nhwc batchnorm_bwd_nhwc.cpp)
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target_link_libraries(client_batchnorm_fwd_nhwc PRIVATE composable_kernel::device_operations)
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target_link_libraries(client_batchnorm_bwd_nhwc PRIVATE composable_kernel::device_operations)
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201
client_example/13_batchnorm/batchnorm_bwd_nhwc.cpp
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201
client_example/13_batchnorm/batchnorm_bwd_nhwc.cpp
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@@ -0,0 +1,201 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#include <functional>
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#include <numeric>
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#include <iomanip>
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#include <iostream>
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#include <vector>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/device_reduce.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/library/tensor_operation_instance/gpu/batchnorm_backward.hpp"
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using XDataType = ck::half_t;
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using DxDataType = float;
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using DyDataType = float;
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using AccDataType = float;
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using ScaleDataType = ck::half_t;
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using DscaleDbiasDataType = float;
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using MeanVarDataType = float;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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constexpr int Rank = 4;
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constexpr int NumBatchNormReduceDim = 3;
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const double epsilon = std::numeric_limits<float>::epsilon();
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struct SimpleDeviceMem
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{
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SimpleDeviceMem() = delete;
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SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
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{
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(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
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}
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void* GetDeviceBuffer() { return p_mem_; }
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~SimpleDeviceMem() { (void)hipFree(p_mem_); }
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void* p_mem_;
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};
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int main(int argc, char* argv[])
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{
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std::array<ck::index_t, Rank> xyLengths{16, 8, 128, 256};
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std::array<ck::index_t, Rank> xyStrides{8 * 128 * 256, 128 * 256, 256, 1};
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std::array<ck::index_t, Rank - NumBatchNormReduceDim> scaleBiasMeanVarLengths{256};
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std::array<ck::index_t, Rank - NumBatchNormReduceDim> scaleBiasMeanVarStrides{1};
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std::array<int, NumBatchNormReduceDim> reduceDims{0, 1, 2};
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ck::index_t numXYElement =
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std::accumulate(xyLengths.begin(), xyLengths.end(), 1, std::multiplies<ck::index_t>());
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ck::index_t numScaleBiasMeanVarElement = std::accumulate(scaleBiasMeanVarLengths.begin(),
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scaleBiasMeanVarLengths.end(),
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1,
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std::multiplies<ck::index_t>());
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SimpleDeviceMem x(sizeof(XDataType) * numXYElement);
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SimpleDeviceMem dy(sizeof(DyDataType) * numXYElement);
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SimpleDeviceMem scale(sizeof(ScaleDataType) * numScaleBiasMeanVarElement);
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SimpleDeviceMem mean(sizeof(MeanVarDataType) * numScaleBiasMeanVarElement);
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SimpleDeviceMem invVariance(sizeof(MeanVarDataType) * numScaleBiasMeanVarElement);
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SimpleDeviceMem dx(sizeof(DxDataType) * numXYElement);
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SimpleDeviceMem dscale(sizeof(DscaleDbiasDataType) * numScaleBiasMeanVarElement);
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SimpleDeviceMem dbias(sizeof(DscaleDbiasDataType) * numScaleBiasMeanVarElement);
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using DeviceOp = ck::tensor_operation::device::DeviceBatchNormBwd<XDataType,
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DxDataType,
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DyDataType,
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AccDataType,
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ScaleDataType,
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DscaleDbiasDataType,
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MeanVarDataType,
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PassThrough,
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Rank,
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NumBatchNormReduceDim>;
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const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
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std::string best_op_name;
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bool found = false;
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int best_op_id = -1;
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float best_ave_time = std::numeric_limits<float>::max();
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float best_gb_per_sec = 0;
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// profile device operation instances
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std::cout << "Run all instances and do timing" << std::endl;
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for(int i = 0; i < op_ptrs.size(); ++i)
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{
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auto& op_ptr = op_ptrs[i];
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auto argument_ptr = op_ptr->MakeArgumentPointer(xyLengths,
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xyStrides,
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xyStrides,
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xyStrides,
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reduceDims,
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scaleBiasMeanVarLengths,
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scaleBiasMeanVarStrides,
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scaleBiasMeanVarStrides,
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scaleBiasMeanVarStrides,
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x.GetDeviceBuffer(),
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dy.GetDeviceBuffer(),
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scale.GetDeviceBuffer(),
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mean.GetDeviceBuffer(),
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invVariance.GetDeviceBuffer(),
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epsilon,
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PassThrough{},
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dx.GetDeviceBuffer(),
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dscale.GetDeviceBuffer(),
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dbias.GetDeviceBuffer());
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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std::string op_name = op_ptr->GetTypeString();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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size_t workspace_sz = op_ptr->GetWorkSpaceSize(argument_ptr.get());
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SimpleDeviceMem workspace(workspace_sz);
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op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace.GetDeviceBuffer());
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float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
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std::size_t num_bytes =
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numXYElement * (sizeof(XDataType) + sizeof(DyDataType) + sizeof(DxDataType)) +
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numScaleBiasMeanVarElement *
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(sizeof(ScaleDataType) + sizeof(DscaleDbiasDataType) * 2 +
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sizeof(MeanVarDataType) * 2);
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float gb_per_sec = num_bytes / 1.E6 / ave_time;
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std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << gb_per_sec << " GB/s, "
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<< op_name << std::endl;
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if(ave_time < best_ave_time)
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{
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found = true;
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best_op_id = i;
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best_op_name = op_name;
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best_ave_time = ave_time;
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best_gb_per_sec = gb_per_sec;
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}
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}
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else
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{
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std::cout << op_name << " does not support this problem" << std::endl;
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}
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}
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if(found)
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{
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std::cout << "Best Perf: " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, "
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<< best_op_name << std::endl;
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// run the best intance
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auto& op_ptr = op_ptrs[best_op_id];
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std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
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<< std::endl;
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auto argument_ptr = op_ptr->MakeArgumentPointer(xyLengths,
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xyStrides,
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xyStrides,
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xyStrides,
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reduceDims,
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scaleBiasMeanVarLengths,
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scaleBiasMeanVarStrides,
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scaleBiasMeanVarStrides,
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scaleBiasMeanVarStrides,
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x.GetDeviceBuffer(),
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dy.GetDeviceBuffer(),
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scale.GetDeviceBuffer(),
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mean.GetDeviceBuffer(),
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invVariance.GetDeviceBuffer(),
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epsilon,
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PassThrough{},
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dx.GetDeviceBuffer(),
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dscale.GetDeviceBuffer(),
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dbias.GetDeviceBuffer());
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
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}
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std::cout << "Done" << std::endl;
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}
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return 0;
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}
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