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Adding Instances and Examples for FP8-based Scaled Convolution with ReLU Activation and AMAX Reduction. (#1469)
* Enable CMakePresets build * Verify Convolution, Scaling and ReLU algorithms. * Add tensor element-wise scale and type cast operation. * Reduction implemented but does not work. * Exploration of Reduction functionality. * Completed example for Convolution scaled with ReLu activation and AMAX reduction. * WIP: Add required instances for convolution. * WIP: Create client example. Implement convolution stage. * Add elementwise instances. * Add elementwise scale + convert example. * Add reduction instances. * WIP: Client example for AMAX reduction. * WIP: Add instances for multistage reduction. * WIP: Implementation of multistage reduction. * Refactoring. * Clean up. * Guard off FP8 instances when the data type is not available. * Improve output readability. * Addressing reviewer's comments.
This commit is contained in:
Andriy Roshchenko
committed by
GitHub
GitHub
parent
f48529b511
commit
a94113a941
@@ -3,6 +3,7 @@ add_subdirectory(convinvscale)
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add_subdirectory(convscale)
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add_subdirectory(convscale_relu)
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add_subdirectory(convscale_add)
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add_subdirectory(convscale_reduce)
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add_subdirectory(multi_AB)
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add_subdirectory(unary)
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11
example/62_convnd_activ/convscale_reduce/CMakeLists.txt
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11
example/62_convnd_activ/convscale_reduce/CMakeLists.txt
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@@ -0,0 +1,11 @@
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list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942)
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set(target 0)
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foreach(gpu IN LISTS GPU_TARGETS)
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if(gpu IN_LIST gpu_list AND target EQUAL 0)
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add_custom_target(example_convnd_activ_xdl_convscale_reduce)
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add_example_executable(example_convnd_fwd_xdl_convscale_relu_amax_fp8 convnd_fwd_xdl_convscale_relu_amax_fp8.cpp)
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add_example_dependencies(example_convnd_activ_xdl_convscale_reduce example_convnd_fwd_xdl_convscale_relu_amax_fp8 )
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set(target 1)
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endif()
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endforeach()
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@@ -0,0 +1,502 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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#include <cstdlib>
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#include <iostream>
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#include "ck/ck.hpp"
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#include "ck/library/utility/algorithm.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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#include "ck/library/utility/convolution_parameter.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_reduce.hpp"
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#include "ck/tensor_operation/gpu/element/combined_element_wise_operation.hpp"
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#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_elementwise_dynamic_vector_dims_impl.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_reduce_multiblock.hpp"
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#include "ck/utility/reduction_operator.hpp"
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#include "ck/utility/reduction_enums.hpp"
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#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
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#include "ck/utility/type.hpp"
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namespace ew = ck::tensor_operation::element_wise;
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using PassThrough = ew::PassThrough;
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using ConvScaleRelu = ew::UnaryCombinedOp<ew::Scale, ew::Scale, ew::Relu>;
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using ConvScale = ew::UnaryCombinedOp<ew::Scale, ew::Scale, PassThrough>;
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using UnaryScaleConvert = ew::Scale;
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void print_helper_msg()
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{
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std::cout << "arg1: verification (0=no, 1=yes)\n"
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<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
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<< "arg3: time kernel (0=no, 1=yes)\n"
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<< ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
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}
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template <typename DataType>
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inline __host__ __device__ constexpr double get_rtol()
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{
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if constexpr(std::is_same_v<DataType, float>)
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{
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return 1e-3;
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}
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else if constexpr(std::is_same_v<DataType, double>)
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{
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return 1e-6;
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}
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else if constexpr(std::is_same_v<DataType, ck::half_t>)
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{
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return 1e-3;
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}
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else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
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{
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return 5e-2;
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}
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else if constexpr(std::is_same_v<DataType, int32_t>)
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{
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return 1e-1;
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}
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else if constexpr(std::is_same_v<DataType, int8_t>)
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{
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return 1e-1;
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}
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else if constexpr(std::is_same_v<DataType, ck::f8_t>)
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{
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return 1e-1; // 240 and 224 are acceptable
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}
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else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
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{
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return 1.5e-1; // 57344 and 49152 are acceptable
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}
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else
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{
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return 1e-3;
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}
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}
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template <typename DataType>
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inline __host__ __device__ constexpr double get_atol()
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{
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if constexpr(std::is_same_v<DataType, float>)
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{
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return 1e-3;
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}
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else if constexpr(std::is_same_v<DataType, double>)
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{
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return 1e-6;
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}
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else if constexpr(std::is_same_v<DataType, ck::half_t>)
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{
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return 1e-3;
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}
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else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
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{
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return 5e-2;
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}
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else if constexpr(std::is_same_v<DataType, int32_t>)
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{
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return 1e-1;
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}
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else if constexpr(std::is_same_v<DataType, int8_t>)
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{
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return 1e-1;
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}
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else if constexpr(std::is_same_v<DataType, ck::f8_t>)
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{
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return 16.1; // 240 and 224 are acceptable
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}
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else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
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{
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return 8192.1; // 57344 and 49152 are acceptable
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}
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else
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{
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return 1e-3;
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}
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}
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template <ck::index_t NDimSpatial,
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typename InDataType,
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typename WeiDataType,
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typename ConvOutDataType,
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typename OutDataType,
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typename InElementOp,
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typename WeiElementOp,
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typename ConvElementOp,
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typename DeviceConvNDFwdInstance>
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bool run_grouped_conv_fwd(bool do_verification,
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int init_method,
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bool time_kernel,
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const ck::utils::conv::ConvParam& conv_param,
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const HostTensorDescriptor& in_g_n_c_wis_desc,
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const HostTensorDescriptor& wei_g_k_c_xs_desc,
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const HostTensorDescriptor& out_g_n_k_wos_desc,
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const InElementOp& in_element_op,
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const WeiElementOp& wei_element_op)
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{
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Tensor<InDataType> in(in_g_n_c_wis_desc);
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Tensor<WeiDataType> wei(wei_g_k_c_xs_desc);
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Tensor<ConvOutDataType> host_conv(out_g_n_k_wos_desc);
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Tensor<ConvOutDataType> device_conv(out_g_n_k_wos_desc);
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Tensor<OutDataType> out_host(out_g_n_k_wos_desc);
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Tensor<OutDataType> out_device(out_g_n_k_wos_desc);
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std::cout << "in: " << in.mDesc << std::endl;
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std::cout << "wei: " << wei.mDesc << std::endl;
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std::cout << "out: " << out_host.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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in.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
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wei.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
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break;
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case 11: // used for debugging
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in.GenerateTensorValue(GeneratorTensor_1<InDataType>{1});
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wei.GenerateTensorValue(GeneratorTensor_1<WeiDataType>{1});
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break;
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default:
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in.GenerateTensorValue(GeneratorTensor_3<InDataType>{-1.0, 1.0});
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wei.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
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}
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DeviceMem in_device_buf(sizeof(InDataType) * in.mDesc.GetElementSpaceSize());
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DeviceMem wei_device_buf(sizeof(WeiDataType) * wei.mDesc.GetElementSpaceSize());
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DeviceMem conv_device_buf(conv_param.GetOutputByte<ConvOutDataType>());
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DeviceMem out_device_buf(conv_param.GetOutputByte<OutDataType>());
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in_device_buf.ToDevice(in.mData.data());
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wei_device_buf.ToDevice(wei.mData.data());
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std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_lengths{};
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std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_strides{};
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std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_lengths{};
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std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_strides{};
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std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_lengths{};
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std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_strides{};
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std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
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std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
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std::array<ck::index_t, NDimSpatial> input_left_pads{};
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std::array<ck::index_t, NDimSpatial> input_right_pads{};
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auto copy = [](const auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
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copy(in_g_n_c_wis_desc.GetLengths(), a_g_n_c_wis_lengths);
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copy(in_g_n_c_wis_desc.GetStrides(), a_g_n_c_wis_strides);
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copy(wei_g_k_c_xs_desc.GetLengths(), b_g_k_c_xs_lengths);
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copy(wei_g_k_c_xs_desc.GetStrides(), b_g_k_c_xs_strides);
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copy(out_g_n_k_wos_desc.GetLengths(), e_g_n_k_wos_lengths);
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copy(out_g_n_k_wos_desc.GetStrides(), e_g_n_k_wos_strides);
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copy(conv_param.conv_filter_strides_, conv_filter_strides);
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copy(conv_param.conv_filter_dilations_, conv_filter_dilations);
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copy(conv_param.input_left_pads_, input_left_pads);
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copy(conv_param.input_right_pads_, input_right_pads);
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// random scale values
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float scale_in = float(std::rand()) / float(RAND_MAX);
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float scale_wei = float(std::rand()) / float(RAND_MAX);
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float scale_out = float(std::rand()) / float(RAND_MAX);
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std::cout << std::endl;
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std::cout << "scale_in: " << scale_in << std::endl;
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std::cout << "scale_wei: " << scale_wei << std::endl;
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std::cout << "scale_out: " << scale_out << std::endl;
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// convolution elementwise operation
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auto conv_element_op = ConvElementOp{ew::Scale{scale_in}, ew::Scale{scale_wei}, {}};
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auto scale_convert = UnaryScaleConvert{scale_out}; // elementwise scale and type cast
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// do Conv
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auto conv = DeviceConvNDFwdInstance{};
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auto conv_invoker = conv.MakeInvoker();
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auto conv_argument =
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conv.MakeArgument(in_device_buf.GetDeviceBuffer(),
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wei_device_buf.GetDeviceBuffer(),
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std::array<const void*, 0>{},
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conv_device_buf.GetDeviceBuffer(),
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a_g_n_c_wis_lengths,
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a_g_n_c_wis_strides,
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b_g_k_c_xs_lengths,
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b_g_k_c_xs_strides,
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std::array<std::array<ck::index_t, NDimSpatial + 3>, 0>{},
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std::array<std::array<ck::index_t, NDimSpatial + 3>, 0>{},
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e_g_n_k_wos_lengths,
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e_g_n_k_wos_strides,
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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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conv_element_op);
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if(!conv.IsSupportedArgument(conv_argument))
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{
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throw std::runtime_error(
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"wrong! device_conv with the specified compilation parameters does "
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"not support this Conv problem");
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}
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std::string kernels = conv.GetTypeString();
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float avg_time = conv_invoker.Run(conv_argument, StreamConfig{nullptr, time_kernel});
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using DeviceElementwiseScale = ck::tensor_operation::device::DeviceElementwiseImpl<
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ck::Tuple<ConvOutDataType>, // InDataTypeTuple
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ck::Tuple<OutDataType>, // OutDataTypeTuple
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UnaryScaleConvert, // UnaryScaleConvert
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NDimSpatial + 3, // NumDim
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256, // BlockSize
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128, // M0PerBlock
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128, // M1PerBlock
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8, // M0PerThread
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8, // M1PerThread
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ck::Sequence<1, 0>, // ThreadClusterArrangeOrder
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ck::Sequence<8>, // InScalarPerVectorSeq
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ck::Sequence<8>>; // OutScalarPerVectorSeq
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auto device_ew_scale = DeviceElementwiseScale{};
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auto scale_invoker = device_ew_scale.MakeInvoker();
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auto scale_argument = device_ew_scale.MakeArgument(e_g_n_k_wos_lengths,
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{e_g_n_k_wos_strides},
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{e_g_n_k_wos_strides},
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{conv_device_buf.GetDeviceBuffer()},
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{out_device_buf.GetDeviceBuffer()},
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scale_convert);
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if(!device_ew_scale.IsSupportedArgument(scale_argument))
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{
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throw std::runtime_error(
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"wrong! DeviceElementwiseScale with the specified compilation parameters does "
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"not support this problem");
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}
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kernels += std::string("\n\t\t ") + device_ew_scale.GetTypeString();
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avg_time += scale_invoker.Run(scale_argument, StreamConfig{nullptr, time_kernel});
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constexpr auto ReduceOpId = ck::ReduceTensorOp::AMAX;
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using ReduceOperation = typename ck::reduce_binary_operator<ReduceOpId>::opType;
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using InElementwiseOperation =
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typename ck::reduce_unary_operator<ReduceOpId, true, true>::InElementwiseOperation;
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using AccElementwiseOperation =
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typename ck::reduce_unary_operator<ReduceOpId, true, true>::AccElementwiseOperation;
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using DeviceReduceInstance =
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ck::tensor_operation::device::DeviceReduceMultiBlock<ConvOutDataType,
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ConvOutDataType,
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ConvOutDataType,
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NDimSpatial + 3,
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NDimSpatial + 3,
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ReduceOperation,
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InElementwiseOperation,
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AccElementwiseOperation,
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ck::InMemoryDataOperationEnum::Set,
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true, // PropagateNan
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false, // OutputIndex
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false, // HaveIndexInputIfOutputIndex
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256, // BlockSize
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4, // MThreadClusterSize
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64, // KThreadClusterSize
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1, // MThreadSliceSize
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1, // KThreadSliceSize
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1, // InSrcVectorDim
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1, // InSrceVectorSize
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1>; // OutDstVectorSize
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std::vector<size_t> outLengths = {1};
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Tensor<ConvOutDataType> amax_host(outLengths);
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Tensor<ConvOutDataType> amax_from_device(outLengths);
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auto amax_host_strides = amax_host.mDesc.GetStrides();
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std::array<int, NDimSpatial + 3> reduce_dims;
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std::iota(reduce_dims.begin(), reduce_dims.end(), 0); // 0,..., NDimSpatial+3-1
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std::array<ck::index_t, 1> reduce_out_lengths{1};
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std::array<ck::index_t, 1> reduce_out_strides{static_cast<ck::index_t>(amax_host_strides[0])};
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DeviceMem amax_device(sizeof(ConvOutDataType) * amax_host.mDesc.GetElementSpaceSize());
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DeviceMem index_device;
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InElementwiseOperation in_elementwise_op;
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AccElementwiseOperation acc_elementwise_op;
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std::tie(in_elementwise_op, acc_elementwise_op) =
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ck::reduce_unary_operator<ReduceOpId, true, true>::GetElementwiseOperator(
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static_cast<int32_t>(host_conv.mDesc.GetElementSize()));
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// Hack convolution output strides for reduction as kernel expects stride 1 for the last
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// dimension. It only works because the reduction is done on the whole tensor and result is
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// independent of the order of elements.
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std::array<ck::index_t, NDimSpatial + 3> reduction_strides{};
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copy(HostTensorDescriptor(e_g_n_k_wos_lengths).GetStrides(), reduction_strides);
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auto device_reduce = DeviceReduceInstance{};
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auto reduce_invoker = device_reduce.MakeInvokerPointer();
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auto reduce_argument = device_reduce.MakeArgumentPointer(e_g_n_k_wos_lengths,
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reduction_strides,
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reduce_out_lengths,
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reduce_out_strides,
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reduce_dims,
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1.0,
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0.0,
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conv_device_buf.GetDeviceBuffer(),
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nullptr,
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amax_device.GetDeviceBuffer(),
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nullptr,
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in_elementwise_op,
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acc_elementwise_op);
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if(!device_reduce.IsSupportedArgument(reduce_argument.get()))
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{
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throw std::runtime_error(
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"wrong! DeviceReduceInstance with the specified compilation parameters does "
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"not support this runtime parameters!");
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};
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kernels += std::string("\n\t\t ") + device_reduce.GetTypeString();
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float reduce_time =
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reduce_invoker->Run(reduce_argument.get(), StreamConfig{nullptr, time_kernel});
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if(time_kernel)
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std::cout << "\nReduce time: " << reduce_time << " ms" << std::endl;
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avg_time += reduce_time;
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|
||||
std::size_t flop = conv_param.GetFlops(); // convolution FLOPs
|
||||
auto conv_out_elems = host_conv.GetElementSize(); // number of elements in conv result tensor
|
||||
|
||||
// 3 element-wise scale multipliers + 1 AMAX
|
||||
std::size_t elementwise_ops = 3 + 1;
|
||||
if constexpr(ck::is_same_v<ConvElementOp, ConvScaleRelu>)
|
||||
{
|
||||
elementwise_ops += 1; // +1 element-wise relu
|
||||
}
|
||||
|
||||
flop += elementwise_ops * conv_out_elems;
|
||||
|
||||
// convolution + elementwise scaling (in + wei + output byte count)
|
||||
std::size_t num_btype = conv_param.GetByte<InDataType, WeiDataType, ConvOutDataType>();
|
||||
num_btype += sizeof(float) + sizeof(float); // + 2 scales
|
||||
|
||||
// elementwise scaling + F8 conversion
|
||||
num_btype += conv_param.GetOutputByte<ConvOutDataType>() + sizeof(float) +
|
||||
conv_param.GetOutputByte<OutDataType>();
|
||||
|
||||
// AMAX
|
||||
num_btype += conv_param.GetOutputByte<ConvOutDataType>() + sizeof(float);
|
||||
|
||||
if(time_kernel)
|
||||
{
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
|
||||
float gb_per_sec = num_btype / 1.E6 / avg_time;
|
||||
std::cout << "Perf: " << avg_time << " ms, " << tflops << " TFlops, " << gb_per_sec
|
||||
<< " GB/s, " << std::endl;
|
||||
}
|
||||
|
||||
std::cout << "\nKernels: " << kernels << std::endl;
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<NDimSpatial,
|
||||
InDataType,
|
||||
WeiDataType,
|
||||
ConvOutDataType,
|
||||
InElementOp,
|
||||
WeiElementOp,
|
||||
ConvElementOp>();
|
||||
|
||||
auto ref_invoker = ref_conv.MakeInvoker();
|
||||
auto ref_argument = ref_conv.MakeArgument(in,
|
||||
wei,
|
||||
host_conv,
|
||||
conv_param.conv_filter_strides_,
|
||||
conv_param.conv_filter_dilations_,
|
||||
conv_param.input_left_pads_,
|
||||
conv_param.input_right_pads_,
|
||||
in_element_op,
|
||||
wei_element_op,
|
||||
conv_element_op);
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
conv_device_buf.FromDevice(device_conv.mData.data());
|
||||
|
||||
out_device_buf.FromDevice(out_device.mData.data());
|
||||
|
||||
out_host.ForEach([&](auto&, auto idx) { scale_convert(out_host(idx), host_conv(idx)); });
|
||||
|
||||
std::cout << "\nComparing output to reference: " << std::endl;
|
||||
auto tight_tol_check = ck::utils::check_err(out_device, out_host, "Error: ");
|
||||
if(!tight_tol_check)
|
||||
{
|
||||
std::cout << "\n\tRecompare applying tolerances...\n";
|
||||
std::cout << "\t\trtol = " << get_rtol<OutDataType>() << std::endl;
|
||||
std::cout << "\t\tatol = " << get_atol<OutDataType>() << std::endl;
|
||||
auto loose_tol_check = ck::utils::check_err(out_device,
|
||||
out_host,
|
||||
"Error: incorrect convolution results!",
|
||||
get_rtol<OutDataType>(),
|
||||
get_atol<OutDataType>());
|
||||
if(!loose_tol_check)
|
||||
{
|
||||
return false;
|
||||
}
|
||||
}
|
||||
std::cout << "Success!" << std::endl;
|
||||
|
||||
/// Verify AMAX
|
||||
|
||||
using RefReduceInstance =
|
||||
ck::tensor_operation::host::ReferenceReduce<ConvOutDataType,
|
||||
ConvOutDataType,
|
||||
ConvOutDataType,
|
||||
NDimSpatial + 3,
|
||||
NDimSpatial + 3,
|
||||
ReduceOperation,
|
||||
InElementwiseOperation,
|
||||
AccElementwiseOperation,
|
||||
true,
|
||||
false>;
|
||||
|
||||
auto ref_reduce = RefReduceInstance{};
|
||||
auto ref_reduce_invoker = ref_reduce.MakeInvokerPointer();
|
||||
auto ref_reduce_argument = ref_reduce.MakeArgumentPointer(e_g_n_k_wos_lengths,
|
||||
e_g_n_k_wos_strides,
|
||||
reduce_out_lengths,
|
||||
reduce_out_strides,
|
||||
reduce_dims,
|
||||
1.0,
|
||||
0.0,
|
||||
host_conv.mData.data(),
|
||||
nullptr,
|
||||
amax_host.mData.data(),
|
||||
nullptr,
|
||||
in_elementwise_op,
|
||||
acc_elementwise_op);
|
||||
|
||||
if(!ref_reduce.IsSupportedArgument(ref_reduce_argument.get()))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! RefReduceInstance with the specified compilation parameters does "
|
||||
"not support this runtime parameters!");
|
||||
};
|
||||
|
||||
ref_reduce_invoker->Run(ref_reduce_argument.get());
|
||||
|
||||
amax_device.FromDevice(amax_from_device.mData.data());
|
||||
|
||||
std::cout << "\namax: " << amax_from_device.mData[0] << std::endl;
|
||||
std::cout << "amax_ref: " << amax_host.mData[0] << std::endl;
|
||||
|
||||
return ck::utils::check_err(amax_from_device, amax_host, "Error: incorrect AMAX results!");
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
@@ -0,0 +1,82 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include "convnd_fwd_convscale_reduce_common.hpp"
|
||||
|
||||
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_abd_xdl_cshuffle.hpp"
|
||||
|
||||
using InDataType = ck::f8_t;
|
||||
using WeiDataType = ck::f8_t;
|
||||
using AccDataType = float;
|
||||
using CShuffleDataType = float;
|
||||
using ConvOutDataType = float; // data type of convolution result
|
||||
using OutDataType = ck::f8_t; // data type of final result
|
||||
using AComputeDataType = ck::f8_t;
|
||||
using BComputeDataType = ck::f8_t;
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using InElementOp = PassThrough;
|
||||
using WeiElementOp = PassThrough;
|
||||
using OutElementOp = ConvScaleRelu;
|
||||
|
||||
static constexpr auto ConvSpec =
|
||||
ck::tensor_operation::device::ConvolutionForwardSpecialization::Default;
|
||||
|
||||
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
|
||||
|
||||
template <ck::index_t NDimSpatial, typename InLayout, typename WeiLayout, typename OutLayout>
|
||||
using DeviceGroupedConvNDFwdInstance =
|
||||
ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<
|
||||
NDimSpatial,
|
||||
InLayout,
|
||||
WeiLayout,
|
||||
ck::Tuple<>,
|
||||
OutLayout,
|
||||
InDataType,
|
||||
WeiDataType,
|
||||
AccDataType,
|
||||
CShuffleDataType,
|
||||
ck::Tuple<>,
|
||||
ConvOutDataType,
|
||||
InElementOp,
|
||||
WeiElementOp,
|
||||
OutElementOp,
|
||||
ConvSpec, // ConvForwardSpecialization
|
||||
GemmSpec, // GemmSpecialization
|
||||
1, //
|
||||
256, // BlockSize
|
||||
128, // MPerBlock
|
||||
256, // NPerBlock
|
||||
32, // KPerBlock
|
||||
8, // AK1
|
||||
8, // BK1
|
||||
32, // MPerXdl
|
||||
32, // NPerXdl
|
||||
2, // MXdlPerWave
|
||||
4, // NXdlPerWave
|
||||
S<4, 64, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1
|
||||
S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
|
||||
S<1, 0, 2>, // ABlockTransferSrcAccessOrder
|
||||
2, // ABlockTransferSrcVectorDim
|
||||
8, // ABlockTransferSrcScalarPerVector
|
||||
8, // ABlockTransferDstScalarPerVector_AK1
|
||||
1, // ABlockLdsExtraM
|
||||
S<4, 64, 1>, // BBlockTransferThreadClusterLengths_BK0_N_BK1
|
||||
S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
|
||||
S<1, 0, 2>, // BBlockTransferSrcAccessOrder
|
||||
2, // BBlockTransferSrcVectorDim
|
||||
8, // BBlockTransferSrcScalarPerVector
|
||||
8, // BBlockTransferDstScalarPerVector_BK1
|
||||
1, // BBlockLdsExtraN
|
||||
1,
|
||||
1,
|
||||
S<1, 32, 1, 8>,
|
||||
8,
|
||||
AComputeDataType,
|
||||
BComputeDataType>;
|
||||
|
||||
#include "run_convnd_fwd_example.inc"
|
||||
|
||||
int main(int argc, char* argv[]) { return run_convnd_fwd_example(argc, argv) ? 0 : 1; }
|
||||
@@ -0,0 +1,98 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
bool run_convnd_fwd_example(int argc, char* argv[])
|
||||
{
|
||||
print_helper_msg();
|
||||
|
||||
bool do_verification = true;
|
||||
int init_method = 1;
|
||||
bool time_kernel = false;
|
||||
|
||||
ck::utils::conv::ConvParam conv_param{
|
||||
2, 1, 128, 256, 192, {3, 3}, {71, 71}, {2, 2}, {1, 1}, {1, 1}, {1, 1}};
|
||||
|
||||
if(argc == 1)
|
||||
{
|
||||
// use default
|
||||
}
|
||||
else if(argc == 4)
|
||||
{
|
||||
do_verification = std::stoi(argv[1]);
|
||||
init_method = std::stoi(argv[2]);
|
||||
time_kernel = std::stoi(argv[3]);
|
||||
}
|
||||
else
|
||||
{
|
||||
do_verification = std::stoi(argv[1]);
|
||||
init_method = std::stoi(argv[2]);
|
||||
time_kernel = std::stoi(argv[3]);
|
||||
const ck::index_t num_dim_spatial = std::stoi(argv[4]);
|
||||
|
||||
conv_param = ck::utils::conv::parse_conv_param(num_dim_spatial, 5, argv);
|
||||
}
|
||||
|
||||
// instantiate in and wei element ops, will
|
||||
// instantiate out_element_op below for every iteration
|
||||
const auto in_element_op = InElementOp{};
|
||||
const auto wei_element_op = WeiElementOp{};
|
||||
|
||||
const auto run = [&](auto ndim_spatial, auto in_layout, auto wei_layout, auto out_layout) {
|
||||
constexpr ck::index_t ndim_spatial_value = ndim_spatial.value;
|
||||
|
||||
using InLayout = decltype(in_layout);
|
||||
using WeiLayout = decltype(wei_layout);
|
||||
using OutLayout = decltype(out_layout);
|
||||
|
||||
const auto in_g_n_c_wis_desc =
|
||||
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(
|
||||
conv_param);
|
||||
|
||||
const auto wei_g_k_c_xs_desc =
|
||||
ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(
|
||||
conv_param);
|
||||
|
||||
const auto out_g_n_k_wos_desc =
|
||||
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
|
||||
conv_param);
|
||||
|
||||
return run_grouped_conv_fwd<
|
||||
ndim_spatial_value,
|
||||
InDataType,
|
||||
WeiDataType,
|
||||
ConvOutDataType,
|
||||
OutDataType,
|
||||
InElementOp,
|
||||
WeiElementOp,
|
||||
OutElementOp,
|
||||
DeviceGroupedConvNDFwdInstance<ndim_spatial_value, InLayout, WeiLayout, OutLayout>>(
|
||||
do_verification,
|
||||
init_method,
|
||||
time_kernel,
|
||||
conv_param,
|
||||
in_g_n_c_wis_desc,
|
||||
wei_g_k_c_xs_desc,
|
||||
out_g_n_k_wos_desc,
|
||||
in_element_op,
|
||||
wei_element_op);
|
||||
};
|
||||
|
||||
namespace ctc = ck::tensor_layout::convolution;
|
||||
|
||||
if(conv_param.num_dim_spatial_ == 1)
|
||||
{
|
||||
return run(ck::Number<1>{}, ctc::GNWC{}, ctc::GKXC{}, ctc::GNWK{});
|
||||
}
|
||||
else if(conv_param.num_dim_spatial_ == 2)
|
||||
{
|
||||
return run(ck::Number<2>{}, ctc::GNHWC{}, ctc::GKYXC{}, ctc::GNHWK{});
|
||||
}
|
||||
else if(conv_param.num_dim_spatial_ == 3)
|
||||
{
|
||||
return run(ck::Number<3>{}, ctc::GNDHWC{}, ctc::GKZYXC{}, ctc::GNDHWK{});
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
Reference in New Issue
Block a user