mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-11 08:50:17 +00:00
d8415a96b3
* add verify flag and update scripts * replace old check_error function with the new check_err * fix syntax * remove blank spaces * remove empty line * add check_err for tensors * fix syntax * replace tensors with vectors in check_err calls * fix syntax * remove blank spaces * fix syntax * add new line at end of file * disable conv2d_bwd_weight test, add gpu check * set check_gpu using export * check GPU using runShell * add definition of runShell * fix script syntax * reduce the number of threads, add full qa option * run processing scripts in bash * fix the branch and host names in performance scripts, add chronos * replace parameterizedCron with cron * archive the perf log files * try to fix git call * pass branch and host names as arguments into scripts * fix script arguments * fix script arguments * process results on master * fix pipeline * add definition of gpu_arch * run processing scripts in docker * fix the brackets * add agent master for the processing stage * get rid of show_node_info call on master * try using mici label instead of master, disable MI100 tests for now * fix syntax * simplify container for results processing * remove node(master) from the process_results stage * put all stages in original order * change the agent label from master to mici for gfx908
286 lines
12 KiB
C++
286 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 "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_backward_weight.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/utility/conv_util.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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#include "ck/library/reference_tensor_operation/cpu/reference_conv_backward_weight.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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namespace instance {
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using DeviceConvBwdWeightNoOpPtr =
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DeviceConvBwdWeightPtr<ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::PassThrough>;
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void add_device_conv2d_bwd_weight_xdl_nhwc_kyxc_nhwk_f16_instances(
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std::vector<DeviceConvBwdWeightNoOpPtr>&);
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void add_device_conv2d_bwd_weight_xdl_nhwc_kyxc_nhwk_f32_instances(
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std::vector<DeviceConvBwdWeightNoOpPtr>&);
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} // namespace instance
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} // namespace device
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} // namespace tensor_operation
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} // namespace ck
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namespace ck {
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namespace profiler {
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template <int NDimSpatial,
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typename InDataType,
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typename WeiDataType,
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typename OutDataType,
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typename InLayout,
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typename WeiLayout,
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typename OutLayout>
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bool profile_conv_bwd_weight_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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ck::index_t N,
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ck::index_t K,
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ck::index_t C,
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std::vector<ck::index_t> input_spatial_lengths,
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std::vector<ck::index_t> filter_spatial_lengths,
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std::vector<ck::index_t> output_spatial_lengths,
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std::vector<ck::index_t> conv_filter_strides,
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std::vector<ck::index_t> conv_filter_dilations,
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std::vector<ck::index_t> input_left_pads,
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std::vector<ck::index_t> input_right_pads,
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ck::index_t split_k)
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{
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const ck::index_t Y = filter_spatial_lengths[0];
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const ck::index_t X = filter_spatial_lengths[1];
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const ck::index_t Hi = input_spatial_lengths[0];
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const ck::index_t Wi = input_spatial_lengths[1];
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const ck::index_t Ho = output_spatial_lengths[0];
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const ck::index_t Wo = output_spatial_lengths[1];
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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(is_same<decltype(layout), ck::tensor_layout::convolution::NCHW>::value ||
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is_same<decltype(layout), ck::tensor_layout::convolution::KCYX>::value ||
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is_same<decltype(layout), ck::tensor_layout::convolution::NKHW>::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(is_same<decltype(layout), tensor_layout::convolution::NHWC>::value ||
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is_same<decltype(layout), tensor_layout::convolution::KYXC>::value ||
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is_same<decltype(layout), tensor_layout::convolution::NHWK>::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<WeiDataType> wei_k_c_y_x_host_result(f_host_tensor_descriptor(K, C, Y, X, WeiLayout{}));
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Tensor<WeiDataType> wei_k_c_y_x_device_result(
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f_host_tensor_descriptor(K, C, Y, X, WeiLayout{}));
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Tensor<OutDataType> out_n_k_ho_wo(f_host_tensor_descriptor(N, K, 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 << "wei_k_c_y_x: " << wei_k_c_y_x_host_result.mDesc << std::endl;
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std::cout << "out_n_k_ho_wo: " << out_n_k_ho_wo.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_n_k_ho_wo.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5});
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in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
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break;
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default:
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out_n_k_ho_wo.GenerateTensorValue(GeneratorTensor_1<OutDataType>{1});
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in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_1<InDataType>{1});
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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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if(do_verification)
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{
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using ReferenceConvBwdWeightInstance =
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ck::tensor_operation::host::ReferenceConvBwdWeight<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_conv = ReferenceConvBwdWeightInstance{};
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auto ref_invoker = ref_conv.MakeInvoker();
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auto ref_argument = ref_conv.MakeArgument(in_n_c_hi_wi,
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wei_k_c_y_x_host_result,
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out_n_k_ho_wo,
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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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in_element_op,
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wei_element_op,
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out_element_op);
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ref_invoker.Run(ref_argument);
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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 wei_device_buf(sizeof(WeiDataType) *
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wei_k_c_y_x_device_result.mDesc.GetElementSpace());
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DeviceMem out_device_buf(sizeof(OutDataType) * out_n_k_ho_wo.mDesc.GetElementSpace());
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out_device_buf.ToDevice(out_n_k_ho_wo.mData.data());
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in_device_buf.ToDevice(in_n_c_hi_wi.mData.data());
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using DeviceConvBwdWeightNoOpPtr =
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ck::tensor_operation::device::DeviceConvBwdWeightPtr<PassThrough, PassThrough, PassThrough>;
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// add device Conv instances
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std::vector<DeviceConvBwdWeightNoOpPtr> conv_ptrs;
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if constexpr(ck::is_same_v<ck::remove_cv_t<InDataType>, float> &&
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ck::is_same_v<ck::remove_cv_t<WeiDataType>, float> &&
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ck::is_same_v<ck::remove_cv_t<OutDataType>, float>)
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{
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ck::tensor_operation::device::instance::
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add_device_conv2d_bwd_weight_xdl_nhwc_kyxc_nhwk_f32_instances(conv_ptrs);
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}
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else if constexpr(ck::is_same_v<ck::remove_cv_t<InDataType>, ck::half_t> &&
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ck::is_same_v<ck::remove_cv_t<WeiDataType>, ck::half_t> &&
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ck::is_same_v<ck::remove_cv_t<OutDataType>, ck::half_t>)
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{
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ck::tensor_operation::device::instance::
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add_device_conv2d_bwd_weight_xdl_nhwc_kyxc_nhwk_f16_instances(conv_ptrs);
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}
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if(conv_ptrs.size() <= 0)
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{
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throw std::runtime_error("wrong! no device Conv instance found");
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}
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std::string best_conv_name;
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float best_ave_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& conv_ptr : conv_ptrs)
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{
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// using atomic, so need to reset input
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if(split_k > 1)
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{
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wei_device_buf.SetZero();
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}
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auto argument_ptr = conv_ptr->MakeArgumentPointer(
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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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N,
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K,
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C,
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input_spatial_lengths,
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filter_spatial_lengths,
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output_spatial_lengths,
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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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in_element_op,
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wei_element_op,
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out_element_op,
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split_k);
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auto invoker_ptr = conv_ptr->MakeInvokerPointer();
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if(conv_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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std::string conv_name = conv_ptr->GetTypeString();
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float ave_time =
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
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std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X;
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std::size_t num_btype = sizeof(InDataType) * (N * C * Hi * Wi) +
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sizeof(WeiDataType) * (K * C * Y * X) +
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sizeof(OutDataType) * (N * K * 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
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<< " GB/s, " << conv_name << std::endl;
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if(tflops > best_tflops)
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{
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best_conv_name = conv_name;
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best_tflops = tflops;
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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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if(do_verification)
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{
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wei_device_buf.FromDevice(wei_k_c_y_x_device_result.mData.data());
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pass = ck::utils::check_err(wei_k_c_y_x_host_result.mData,
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wei_k_c_y_x_device_result.mData);
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if(pass == false)
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{
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std::cout << "Fail info:" << conv_ptr->GetTypeString() << std::endl;
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}
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if(do_log)
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{
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LogRangeAsType<float>(std::cout << "out: ", out_n_k_ho_wo.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(std::cout << "in : ", in_n_c_hi_wi.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "wei_host : ", wei_k_c_y_x_host_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "wei_device: ", wei_k_c_y_x_device_result.mData, ",")
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<< std::endl;
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}
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}
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}
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}
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std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
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<< best_gb_per_sec << " GB/s, " << best_conv_name << 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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