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https://github.com/ROCm/composable_kernel.git
synced 2026-05-01 12:11:19 +00:00
[CK] Integrate GPU reference into ckProfiler for convolutions (#3379)
Refactor and integrate CK GPU references into ckProfiler. - All convolution layouts and groupings supported for all three directions - Unit tests verifying GPU and CPU reference is the same - Support added to profiler (do_verification = 2 enables GPU reference) - One profiler-based test per direction changed to GPU reference to demonstrate usag Closes AICK-427
This commit is contained in:
Johannes Graner
385
test/gpu_reference/gpu_reference_utils.hpp
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385
test/gpu_reference/gpu_reference_utils.hpp
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// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#pragma once
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#include "ck/ck.hpp"
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#include "ck/host_utility/hip_check_error.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/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_host_tensor_descriptor_helper.hpp"
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// CPU references
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#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_conv_bwd_data.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_conv_bwd_weight.hpp"
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// GPU references
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#include "ck/library/reference_tensor_operation/gpu/naive_conv_fwd_gpu.hpp"
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#include "ck/library/reference_tensor_operation/gpu/naive_conv_bwd_data_gpu.hpp"
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#include "ck/library/reference_tensor_operation/gpu/naive_conv_bwd_weight_gpu.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "common_test_params.hpp"
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namespace ck {
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namespace test {
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enum class ConvKernelType
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{
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Forward,
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BackwardData,
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BackwardWeight
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};
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// Helper function to initialize and copy a tensor to device
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template <typename DataType>
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void initialize_and_copy_tensor(Tensor<DataType>& host_tensor, DeviceMem& device_mem)
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{
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host_tensor.GenerateTensorValue(GeneratorTensor_2<DataType>{-5, 5});
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device_mem.ToDevice(host_tensor.mData.data());
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}
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// Helper to get default layout types based on NDimSpatial
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template <index_t NDimSpatial>
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struct DefaultConvLayouts
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{
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using InLayout = std::conditional_t<NDimSpatial == 3,
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tensor_layout::convolution::GNCDHW,
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std::conditional_t<NDimSpatial == 2,
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tensor_layout::convolution::GNCHW,
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tensor_layout::convolution::GNCW>>;
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using WeiLayout = std::conditional_t<NDimSpatial == 3,
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tensor_layout::convolution::GKCZYX,
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std::conditional_t<NDimSpatial == 2,
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tensor_layout::convolution::GKCYX,
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tensor_layout::convolution::GKCX>>;
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using OutLayout = std::conditional_t<NDimSpatial == 3,
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tensor_layout::convolution::GNKDHW,
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std::conditional_t<NDimSpatial == 2,
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tensor_layout::convolution::GNKHW,
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tensor_layout::convolution::GNKW>>;
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};
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// Forward convolution implementation
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template <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 InLayout,
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typename WeiLayout,
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typename OutLayout>
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bool test_conv_fwd_impl(const ck::utils::conv::ConvParam& params,
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const Tensor<InDataType>& input_cpu,
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const Tensor<WeiDataType>& weight_cpu,
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DeviceMem& input_dev,
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DeviceMem& weight_dev,
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DeviceMem& output_dev)
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{
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using InElementOp = tensor_operation::element_wise::PassThrough;
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using WeiElementOp = tensor_operation::element_wise::PassThrough;
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using OutElementOp = tensor_operation::element_wise::PassThrough;
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// Call GPU reference with ConvParam directly
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ref::naive_conv_fwd<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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reinterpret_cast<const InDataType*>(input_dev.GetDeviceBuffer()),
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reinterpret_cast<const WeiDataType*>(weight_dev.GetDeviceBuffer()),
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reinterpret_cast<OutDataType*>(output_dev.GetDeviceBuffer()),
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params);
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HIP_CHECK_ERROR(hipDeviceSynchronize());
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// Run CPU reference
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std::vector<long_index_t> strides_long(params.conv_filter_strides_.begin(),
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params.conv_filter_strides_.end());
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std::vector<long_index_t> dilations_long(params.conv_filter_dilations_.begin(),
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params.conv_filter_dilations_.end());
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std::vector<long_index_t> pads_long(params.input_left_pads_.begin(),
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params.input_left_pads_.end());
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Tensor<InDataType> input_ref = input_cpu;
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Tensor<WeiDataType> weight_ref = weight_cpu;
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Tensor<OutDataType> output_ref(
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ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(params));
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auto ref_conv = tensor_operation::host::ReferenceConvFwd<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_arg = ref_conv.MakeArgument(input_ref,
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weight_ref,
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output_ref,
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strides_long,
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dilations_long,
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pads_long,
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pads_long,
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InElementOp{},
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WeiElementOp{},
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OutElementOp{});
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ref_invoker.Run(ref_arg);
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// Copy result from device and compare
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Tensor<OutDataType> output_gpu(output_ref.mDesc);
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output_dev.FromDevice(output_gpu.mData.data());
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HIP_CHECK_ERROR(hipDeviceSynchronize());
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// Compare results
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return ck::utils::check_err(output_gpu, output_ref);
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}
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// Backward data convolution implementation
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template <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 InLayout,
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typename WeiLayout,
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typename OutLayout>
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bool test_conv_bwd_data_impl(const ck::utils::conv::ConvParam& params,
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const Tensor<WeiDataType>& weight_cpu,
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const Tensor<OutDataType>& output_cpu,
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DeviceMem& weight_dev,
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DeviceMem& output_dev,
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DeviceMem& input_dev)
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{
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using InElementOp = tensor_operation::element_wise::PassThrough;
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using WeiElementOp = tensor_operation::element_wise::PassThrough;
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using OutElementOp = tensor_operation::element_wise::PassThrough;
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// Call GPU reference with ConvParam directly
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ref::naive_conv_bwd_data<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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reinterpret_cast<InDataType*>(input_dev.GetDeviceBuffer()),
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reinterpret_cast<const WeiDataType*>(weight_dev.GetDeviceBuffer()),
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reinterpret_cast<const OutDataType*>(output_dev.GetDeviceBuffer()),
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params);
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HIP_CHECK_ERROR(hipDeviceSynchronize());
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// Run CPU reference
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std::vector<long_index_t> strides_long(params.conv_filter_strides_.begin(),
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params.conv_filter_strides_.end());
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std::vector<long_index_t> dilations_long(params.conv_filter_dilations_.begin(),
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params.conv_filter_dilations_.end());
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std::vector<long_index_t> pads_long(params.input_left_pads_.begin(),
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params.input_left_pads_.end());
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Tensor<InDataType> input_ref(
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ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(params));
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Tensor<WeiDataType> weight_ref = weight_cpu;
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Tensor<OutDataType> output_ref = output_cpu;
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auto ref_conv = 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_arg = ref_conv.MakeArgument(input_ref,
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weight_ref,
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output_ref,
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strides_long,
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dilations_long,
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pads_long,
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pads_long,
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InElementOp{},
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WeiElementOp{},
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OutElementOp{});
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ref_invoker.Run(ref_arg);
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// Copy result from device and compare
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Tensor<InDataType> input_gpu(input_ref.mDesc);
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input_dev.FromDevice(input_gpu.mData.data());
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HIP_CHECK_ERROR(hipDeviceSynchronize());
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// Compare results
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return ck::utils::check_err(input_gpu, input_ref);
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}
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// Backward weight convolution implementation
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template <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 InLayout,
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typename WeiLayout,
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typename OutLayout>
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bool test_conv_bwd_weight_impl(const ck::utils::conv::ConvParam& params,
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const Tensor<InDataType>& input_cpu,
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const Tensor<OutDataType>& output_cpu,
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DeviceMem& input_dev,
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DeviceMem& output_dev,
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DeviceMem& weight_dev)
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{
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using InElementOp = tensor_operation::element_wise::PassThrough;
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using WeiElementOp = tensor_operation::element_wise::PassThrough;
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using OutElementOp = tensor_operation::element_wise::PassThrough;
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// Call GPU reference with ConvParam directly
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ref::naive_conv_bwd_weight<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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reinterpret_cast<const InDataType*>(input_dev.GetDeviceBuffer()),
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reinterpret_cast<WeiDataType*>(weight_dev.GetDeviceBuffer()),
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reinterpret_cast<const OutDataType*>(output_dev.GetDeviceBuffer()),
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params);
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HIP_CHECK_ERROR(hipDeviceSynchronize());
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// Run CPU reference
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std::vector<long_index_t> strides_long(params.conv_filter_strides_.begin(),
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params.conv_filter_strides_.end());
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std::vector<long_index_t> dilations_long(params.conv_filter_dilations_.begin(),
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params.conv_filter_dilations_.end());
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std::vector<long_index_t> pads_long(params.input_left_pads_.begin(),
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params.input_left_pads_.end());
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Tensor<InDataType> input_ref = input_cpu;
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Tensor<WeiDataType> weight_ref(
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ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(params));
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Tensor<OutDataType> output_ref = output_cpu;
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auto ref_conv = tensor_operation::host::ReferenceConvBwdWeight<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_arg = ref_conv.MakeArgument(input_ref,
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weight_ref,
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output_ref,
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strides_long,
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dilations_long,
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pads_long,
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pads_long,
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InElementOp{},
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WeiElementOp{},
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OutElementOp{});
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ref_invoker.Run(ref_arg);
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// Copy result from device and compare
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Tensor<WeiDataType> weight_gpu(weight_ref.mDesc);
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weight_dev.FromDevice(weight_gpu.mData.data());
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HIP_CHECK_ERROR(hipDeviceSynchronize());
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// Compare results
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return ck::utils::check_err(weight_gpu, weight_ref);
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}
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// Main test function - dispatches to specific implementations
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template <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 InLayout = typename DefaultConvLayouts<NDimSpatial>::InLayout,
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typename WeiLayout = typename DefaultConvLayouts<NDimSpatial>::WeiLayout,
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typename OutLayout = typename DefaultConvLayouts<NDimSpatial>::OutLayout>
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bool test_conv_gpu_ref(const ck::utils::conv::ConvParam& params, ConvKernelType kernel_type)
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{
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// Create tensor descriptors using the specified layouts
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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>(params);
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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>(params);
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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>(params);
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// Create tensors using tensor descriptors (supports multiple layouts)
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Tensor<InDataType> input(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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// Allocate device memory
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DeviceMem input_dev(input.mData.size() * sizeof(InDataType));
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DeviceMem weight_dev(weight.mData.size() * sizeof(WeiDataType));
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DeviceMem output_dev(output.mData.size() * sizeof(OutDataType));
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// Initialize and copy tensors based on kernel type
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if(kernel_type == ConvKernelType::Forward)
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{
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initialize_and_copy_tensor(input, input_dev);
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initialize_and_copy_tensor(weight, weight_dev);
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}
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else if(kernel_type == ConvKernelType::BackwardData)
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{
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initialize_and_copy_tensor(weight, weight_dev);
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initialize_and_copy_tensor(output, output_dev);
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}
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else // BackwardWeight
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{
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initialize_and_copy_tensor(input, input_dev);
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initialize_and_copy_tensor(output, output_dev);
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}
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// Dispatch to appropriate implementation with layout types
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if(kernel_type == ConvKernelType::Forward)
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{
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return test_conv_fwd_impl<NDimSpatial,
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InDataType,
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WeiDataType,
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OutDataType,
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InLayout,
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WeiLayout,
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OutLayout>(
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params, input, weight, input_dev, weight_dev, output_dev);
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}
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else if(kernel_type == ConvKernelType::BackwardData)
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{
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return test_conv_bwd_data_impl<NDimSpatial,
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InDataType,
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WeiDataType,
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OutDataType,
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InLayout,
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WeiLayout,
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OutLayout>(
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params, weight, output, weight_dev, output_dev, input_dev);
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}
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else // BackwardWeight
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{
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return test_conv_bwd_weight_impl<NDimSpatial,
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InDataType,
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WeiDataType,
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OutDataType,
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InLayout,
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WeiLayout,
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OutLayout>(
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params, input, output, input_dev, output_dev, weight_dev);
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}
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}
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} // namespace test
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} // namespace ck
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