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https://github.com/ROCm/composable_kernel.git
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500fa99512
* convnd_fwd fp16 example * update example * update example * update instance * updating refernce conv * update reference conv * update conv fwd profiler * update conv 1d and 3d instance * update include path * clean * update profiler for conv bwd data and weight * update conv bwd weight * clean * update conv example * update profiler for conv bwd weight * update ckprofiler for conv bwd data * fix reference conv bwd data bug; update conv bwd data test * update examples * fix initialization issue * update test for conv fwd * clean * clean * remove test case too sensitive to error threshhold * fix test * clean * fix build * adding conv multiple d * adding conv multiple D * add matrix padder * add gemm padding to convnd * adding group conv * update gemm multi-d * refactor * refactor * refactor * clean * clean * refactor * refactor * reorg * add ds * add bias * clean * add G * adding group * adding group * adding group * update Tensor * clean * update example * update DeviceGemmMultipleD_Xdl_CShuffle * update conv bwd-data and bwd-weight * upate contraction example * update gemm and batch gemm with e permute * fix example build * instance for grouped conv1d * update example * adding group conv instance * update gemm bilinear instance * update gemm+add+add+fastgelu instance * update profiler * update profiler * update test * update test and client example * clean * add grouped conv into profiler * update profiler * clean * add test grouped conv, update all conv test to gtest * update test
250 lines
10 KiB
C++
250 lines
10 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 "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/device/device_conv_bwd_data.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/convolution_backward_data.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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namespace ck {
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namespace profiler {
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template <typename DataType>
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void show_data_nhwc_layout(Tensor<DataType>& nhwc)
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{
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std::cout << "[";
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for(int n = 0; n < ck::type_convert<int>(nhwc.mDesc.GetLengths()[0]); n++)
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{
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std::cout << "[";
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for(int hi = 0; hi < ck::type_convert<int>(nhwc.mDesc.GetLengths()[2]); hi++)
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{
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std::cout << "[";
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for(int wi = 0; wi < ck::type_convert<int>(nhwc.mDesc.GetLengths()[3]); wi++)
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{
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std::cout << "[";
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for(int c = 0; c < ck::type_convert<int>(nhwc.mDesc.GetLengths()[1]); c++)
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{
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std::cout << static_cast<float>(nhwc(n, c, hi, wi)) << " ";
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}
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std::cout << "]";
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}
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std::cout << "]";
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}
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std::cout << "]";
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}
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std::cout << "]";
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}
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template <ck::index_t NDimSpatial,
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typename InLayout,
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typename WeiLayout,
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typename OutLayout,
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typename InDataType,
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typename WeiDataType,
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typename OutDataType>
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bool profile_conv_bwd_data_impl(int do_verification,
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int init_method,
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bool do_log,
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bool time_kernel,
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const ck::utils::conv::ConvParam& conv_param)
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{
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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::PassThrough;
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const auto in_element_op = InElementOp{};
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const auto wei_element_op = WeiElementOp{};
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const auto out_element_op = OutElementOp{};
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const auto in_g_n_c_wis_desc =
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ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(conv_param);
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const auto wei_g_k_c_xs_desc =
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ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(conv_param);
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const auto out_g_n_k_wos_desc =
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ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(conv_param);
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Tensor<InDataType> input_host_result(in_g_n_c_wis_desc);
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Tensor<InDataType> input_device_result(in_g_n_c_wis_desc);
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Tensor<WeiDataType> weight(wei_g_k_c_xs_desc);
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Tensor<OutDataType> output(out_g_n_k_wos_desc);
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std::cout << "input: " << input_host_result.mDesc << std::endl;
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std::cout << "weight: " << weight.mDesc << std::endl;
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std::cout << "output: " << output.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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output.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5});
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weight.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
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break;
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default:
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output.GenerateTensorValue(GeneratorTensor_3<OutDataType>{0.0, 1.0});
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weight.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
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}
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DeviceMem in_device_buf(sizeof(InDataType) * input_device_result.mDesc.GetElementSpaceSize());
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DeviceMem wei_device_buf(sizeof(WeiDataType) * weight.mDesc.GetElementSpaceSize());
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DeviceMem out_device_buf(sizeof(OutDataType) * output.mDesc.GetElementSpaceSize());
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out_device_buf.ToDevice(output.mData.data());
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wei_device_buf.ToDevice(weight.mData.data());
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if(do_verification)
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{
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auto ref_conv = 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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auto ref_invoker = ref_conv.MakeInvoker();
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auto ref_argument = ref_conv.MakeArgument(input_host_result,
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weight,
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output,
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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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InElementOp{},
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WeiElementOp{},
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OutElementOp{});
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ref_invoker.Run(ref_argument);
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}
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using DeviceOp = ck::tensor_operation::device::DeviceConvBwdData<NDimSpatial,
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InLayout,
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WeiLayout,
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OutLayout,
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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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// get device op instances
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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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float best_avg_time = 0;
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float best_tflops = 0;
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float best_gb_per_sec = 0;
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// profile device Conv instances
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bool pass = true;
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for(auto& op_ptr : op_ptrs)
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{
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auto argument_ptr =
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op_ptr->MakeArgumentPointer(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
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static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
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static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
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conv_param.N_,
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conv_param.K_,
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conv_param.C_,
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conv_param.input_spatial_lengths_,
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conv_param.filter_spatial_lengths_,
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conv_param.output_spatial_lengths_,
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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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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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// for conv bwd data, some input tensor element are zero, but not written by kernel,
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// need to set zero
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in_device_buf.SetZero();
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std::string op_name = op_ptr->GetTypeString();
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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float avg_time =
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
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std::size_t flop = conv_param.GetFlops();
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std::size_t num_btype = conv_param.GetByte<InDataType, WeiDataType, OutDataType>();
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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
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<< " GB/s" << std::endl;
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if(tflops > best_tflops)
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{
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best_op_name = op_name;
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best_tflops = tflops;
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best_avg_time = avg_time;
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best_gb_per_sec = gb_per_sec;
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}
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if(do_verification)
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{
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in_device_buf.FromDevice(input_device_result.mData.data());
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pass =
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pass & ck::utils::check_err(input_device_result.mData, input_host_result.mData);
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if(do_log)
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{
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std::cout << "in : ";
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show_data_nhwc_layout(output);
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std::cout << std::endl;
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std::cout << "wei: ";
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show_data_nhwc_layout(weight);
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std::cout << std::endl;
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std::cout << "out_host : ";
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show_data_nhwc_layout(input_host_result);
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std::cout << std::endl;
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std::cout << "out_device: ";
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show_data_nhwc_layout(input_device_result);
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std::cout << std::endl;
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}
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}
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}
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else
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{
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std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
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}
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}
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std::cout << "Best configuration parameters:"
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<< "\nname: " << best_op_name << "\navg_time: " << best_avg_time
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<< "\ntflops: " << best_tflops << "\nGB/s: " << best_gb_per_sec << std::endl;
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return pass;
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}
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} // namespace profiler
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} // namespace ck
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