mirror of
https://github.com/ROCm/composable_kernel.git
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c190d8d61f
* test_convnd_fwd
* test_convnd_bwd_data
* test_conv_bwd_data_scale
* test_grouped_convnd_fwd_clamp
* test_grouped_convnd_fwd_scale
* multiple A/B tensors and D tensor for fwd GPU ref
* test_grouped_convnd_fwd_scaleadd_ab
* test_grouped_convnd_fwd_bias_clamp
* test_grouped_convnd_fwd_bilinear
* test_grouped_convnd_fwd_gk_bias_clamp
* Extend GPU reference to enable batchnorm epilogue
* test_grouped_convnd_fwd{,_gk}_bias_bnorm_clamp
* test_grouped_conv_bwd_data_bilinear
* test_grouped_convnd_bwd_weight_bilinear
* Add missing template instantiation
* Perform operations in float in reference
* Slightly increase tolerance for batchnorm profiler
* Revert "Slightly increase tolerance for batchnorm profiler"
This reverts commit a3b2475229.
* Revert "test_grouped_convnd_fwd{,_gk}_bias_bnorm_clamp"
This reverts commit 6da4576060.
* Revert "Extend GPU reference to enable batchnorm epilogue"
This reverts commit e2f75fa10e.
* Clarify variable names
* Refactor elementwise ops into helper functions
* Make helpers C++17-compatible
611 lines
28 KiB
C++
611 lines
28 KiB
C++
// 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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// Forward convolution with D tensor support
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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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typename OutElementOp>
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bool test_conv_fwd_with_d_tensor_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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const Tensor<OutDataType>& d_cpu,
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DeviceMem& input_dev,
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DeviceMem& weight_dev,
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DeviceMem& d_dev,
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DeviceMem& output_dev,
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OutElementOp out_element_op)
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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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// Create D tensor lengths and strides for GPU reference
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std::vector<index_t> d_lengths_vec(NDimSpatial + 3);
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d_lengths_vec[0] = params.G_;
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d_lengths_vec[1] = params.N_;
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d_lengths_vec[2] = params.K_;
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for(index_t i = 0; i < NDimSpatial; ++i)
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{
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d_lengths_vec[3 + i] = static_cast<index_t>(params.output_spatial_lengths_[i]);
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}
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std::vector<index_t> d_strides_vec =
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ref::compute_conv_tensor_strides<OutLayout>(d_lengths_vec, params.num_dim_spatial_);
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std::array<const OutDataType*, 1> d_ptrs = {
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reinterpret_cast<const OutDataType*>(d_dev.GetDeviceBuffer())};
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std::array<std::vector<index_t>, 1> d_lengths = {d_lengths_vec};
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std::array<std::vector<index_t>, 1> d_strides = {d_strides_vec};
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// Call GPU reference with D tensor
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std::array<const InDataType*, 1> in_ptrs = {
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reinterpret_cast<const InDataType*>(input_dev.GetDeviceBuffer())};
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std::array<const WeiDataType*, 1> wei_ptrs = {
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reinterpret_cast<const WeiDataType*>(weight_dev.GetDeviceBuffer())};
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ref::naive_conv_fwd_multi_abd<0,
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0,
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1,
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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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OutDataType>( // Explicitly specify TD = OutDataType
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in_ptrs,
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wei_ptrs,
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d_ptrs,
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reinterpret_cast<OutDataType*>(output_dev.GetDeviceBuffer()),
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params,
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d_lengths,
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d_strides,
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InElementOp{},
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WeiElementOp{},
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out_element_op);
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HIP_CHECK_ERROR(hipDeviceSynchronize());
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// Run CPU reference
|
|
std::vector<long_index_t> strides_long(params.conv_filter_strides_.begin(),
|
|
params.conv_filter_strides_.end());
|
|
std::vector<long_index_t> dilations_long(params.conv_filter_dilations_.begin(),
|
|
params.conv_filter_dilations_.end());
|
|
std::vector<long_index_t> pads_long(params.input_left_pads_.begin(),
|
|
params.input_left_pads_.end());
|
|
|
|
Tensor<InDataType> input_ref = input_cpu;
|
|
Tensor<WeiDataType> weight_ref = weight_cpu;
|
|
Tensor<OutDataType> output_ref(
|
|
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(params));
|
|
|
|
std::array<Tensor<OutDataType>, 1> d_tensors_ref = {d_cpu};
|
|
|
|
auto ref_conv = tensor_operation::host::ReferenceConvFwd<NDimSpatial,
|
|
InDataType,
|
|
WeiDataType,
|
|
OutDataType,
|
|
InElementOp,
|
|
WeiElementOp,
|
|
OutElementOp,
|
|
0, // NumA
|
|
0, // NumB
|
|
1 // NumD
|
|
>();
|
|
auto ref_invoker = ref_conv.MakeInvoker();
|
|
auto ref_arg = ref_conv.MakeArgument(input_ref,
|
|
weight_ref,
|
|
output_ref,
|
|
strides_long,
|
|
dilations_long,
|
|
pads_long,
|
|
pads_long,
|
|
InElementOp{},
|
|
WeiElementOp{},
|
|
out_element_op,
|
|
{}, // A tensors
|
|
{}, // B tensors
|
|
d_tensors_ref);
|
|
ref_invoker.Run(ref_arg);
|
|
|
|
// Copy result from device and compare
|
|
Tensor<OutDataType> output_gpu(output_ref.mDesc);
|
|
output_dev.FromDevice(output_gpu.mData.data());
|
|
HIP_CHECK_ERROR(hipDeviceSynchronize());
|
|
|
|
// Compare results
|
|
return ck::utils::check_err(output_gpu, output_ref);
|
|
}
|
|
|
|
// Forward convolution with multiple A/B tensor support
|
|
template <index_t NDimSpatial,
|
|
typename InDataType,
|
|
typename WeiDataType,
|
|
typename OutDataType,
|
|
typename InLayout,
|
|
typename WeiLayout,
|
|
typename OutLayout,
|
|
typename InElementOp,
|
|
typename WeiElementOp>
|
|
bool test_conv_fwd_with_multi_ab_impl(const ck::utils::conv::ConvParam& params,
|
|
const Tensor<InDataType>& input_cpu,
|
|
const Tensor<WeiDataType>& weight_cpu,
|
|
const Tensor<InDataType>& a_extra_cpu,
|
|
const Tensor<WeiDataType>& b_extra_cpu,
|
|
DeviceMem& input_dev,
|
|
DeviceMem& weight_dev,
|
|
DeviceMem& a_extra_dev,
|
|
DeviceMem& b_extra_dev,
|
|
DeviceMem& output_dev,
|
|
InElementOp in_element_op,
|
|
WeiElementOp wei_element_op)
|
|
{
|
|
using OutElementOp = tensor_operation::element_wise::PassThrough;
|
|
|
|
// Call GPU reference with extra A and B tensors
|
|
std::array<const InDataType*, 2> in_ptrs = {
|
|
reinterpret_cast<const InDataType*>(input_dev.GetDeviceBuffer()),
|
|
reinterpret_cast<const InDataType*>(a_extra_dev.GetDeviceBuffer())};
|
|
std::array<const WeiDataType*, 2> wei_ptrs = {
|
|
reinterpret_cast<const WeiDataType*>(weight_dev.GetDeviceBuffer()),
|
|
reinterpret_cast<const WeiDataType*>(b_extra_dev.GetDeviceBuffer())};
|
|
std::array<const OutDataType*, 0> d_ptrs = {};
|
|
std::array<std::vector<index_t>, 0> d_lengths = {};
|
|
std::array<std::vector<index_t>, 0> d_strides = {};
|
|
|
|
ref::naive_conv_fwd_multi_abd<1, 1, 0, InLayout, WeiLayout, OutLayout>(
|
|
in_ptrs,
|
|
wei_ptrs,
|
|
d_ptrs,
|
|
reinterpret_cast<OutDataType*>(output_dev.GetDeviceBuffer()),
|
|
params,
|
|
d_lengths,
|
|
d_strides,
|
|
in_element_op,
|
|
wei_element_op,
|
|
OutElementOp{});
|
|
|
|
HIP_CHECK_ERROR(hipDeviceSynchronize());
|
|
|
|
// Run CPU reference
|
|
std::vector<long_index_t> strides_long(params.conv_filter_strides_.begin(),
|
|
params.conv_filter_strides_.end());
|
|
std::vector<long_index_t> dilations_long(params.conv_filter_dilations_.begin(),
|
|
params.conv_filter_dilations_.end());
|
|
std::vector<long_index_t> pads_long(params.input_left_pads_.begin(),
|
|
params.input_left_pads_.end());
|
|
|
|
Tensor<InDataType> input_ref = input_cpu;
|
|
Tensor<WeiDataType> weight_ref = weight_cpu;
|
|
Tensor<OutDataType> output_ref(
|
|
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(params));
|
|
|
|
std::array<Tensor<InDataType>, 1> a_tensors_ref = {a_extra_cpu};
|
|
std::array<Tensor<WeiDataType>, 1> b_tensors_ref = {b_extra_cpu};
|
|
|
|
auto ref_conv = tensor_operation::host::ReferenceConvFwd<NDimSpatial,
|
|
InDataType,
|
|
WeiDataType,
|
|
OutDataType,
|
|
InElementOp,
|
|
WeiElementOp,
|
|
OutElementOp,
|
|
1, // NumA
|
|
1, // NumB
|
|
0 // NumD
|
|
>();
|
|
auto ref_invoker = ref_conv.MakeInvoker();
|
|
auto ref_arg = ref_conv.MakeArgument(input_ref,
|
|
weight_ref,
|
|
output_ref,
|
|
strides_long,
|
|
dilations_long,
|
|
pads_long,
|
|
pads_long,
|
|
in_element_op,
|
|
wei_element_op,
|
|
OutElementOp{},
|
|
a_tensors_ref,
|
|
b_tensors_ref,
|
|
{});
|
|
ref_invoker.Run(ref_arg);
|
|
|
|
// Copy result from device and compare
|
|
Tensor<OutDataType> output_gpu(output_ref.mDesc);
|
|
output_dev.FromDevice(output_gpu.mData.data());
|
|
HIP_CHECK_ERROR(hipDeviceSynchronize());
|
|
|
|
// Compare results
|
|
return ck::utils::check_err(output_gpu, output_ref);
|
|
}
|
|
|
|
} // namespace test
|
|
} // namespace ck
|