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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
@@ -47,6 +47,14 @@ target_link_libraries(client_conv3d_fwd_convscale_add_fp8 PRIVATE composable_ker
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add_executable(client_conv3d_fwd_convscale_relu_fp8
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grouped_convnd_fwd_convscale_relu/conv3d_fwd_convscale_relu_fp8.cpp)
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target_link_libraries(client_conv3d_fwd_convscale_relu_fp8 PRIVATE composable_kernel::device_conv_operations)
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# Fwd convscale + ReLU + AMAX
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add_executable(client_conv3d_fwd_convscale_relu_amax_fp8
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grouped_convnd_fwd_convscale_reduce/conv3d_fwd_convscale_relu_amax_fp8.cpp)
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target_link_libraries(client_conv3d_fwd_convscale_relu_amax_fp8
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PRIVATE composable_kernel::device_conv_operations
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composable_kernel::device_other_operations
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composable_kernel::device_reduction_operations
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utility)
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# Fwd convscale
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add_executable(client_conv3d_fwd_convscale_fp8
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grouped_convnd_fwd_convscale/conv3d_fwd_convscale_fp8.cpp)
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@@ -0,0 +1,835 @@
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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 <algorithm>
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#include <cstdlib>
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#include <iomanip>
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#include <iostream>
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#include <iterator>
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#include <numeric>
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#include <string>
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#include <vector>
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#include "ck/ck.hpp"
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#include "ck/library/utility/algorithm.hpp"
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#include "ck/tensor_operation/gpu/device/device_elementwise.hpp"
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#include "ck/tensor_operation/gpu/device/device_reduce.hpp"
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#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.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/utility/tuple.hpp"
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#include "ck/utility/type.hpp"
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#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_convscale_relu.hpp"
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#include "ck/utility/reduction_enums.hpp"
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#include "ck/library/tensor_operation_instance/gpu/permute_scale.hpp"
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#include "ck/library/tensor_operation_instance/gpu/reduce/reduce.hpp"
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#include "ck/library/utility/host_tensor.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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struct SimpleDeviceMem
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{
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SimpleDeviceMem() = delete;
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SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
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{
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(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
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}
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void* GetDeviceBuffer() { return p_mem_; }
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~SimpleDeviceMem() { (void)hipFree(p_mem_); }
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void* p_mem_;
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};
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template <ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
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std::size_t
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GetFlops(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& output_lengths,
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const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& weights_lengths,
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const std::size_t& ds_size)
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{
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// 2 * G * N * K * C * <output spatial lengths product> * <filter spatial lengths product> +
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// + ds_size * <output tensor size> =>
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// => <output tensor size> * ( 2 * C * <filter spatial lengths product> + ds_size) =>
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// => G * N * K * <output spatial lengths product> * (2 * C * <filter spatial lengths product> +
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// ds_size)
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ck::index_t G = weights_lengths[0];
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ck::index_t N = output_lengths[1];
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ck::index_t K = weights_lengths[1];
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ck::index_t C = weights_lengths[2];
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return G * N * K *
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std::accumulate(std::next(std::begin(output_lengths), NumNonSpatialDim),
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std::end(output_lengths),
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static_cast<std::size_t>(1),
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std::multiplies<>()) *
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(ds_size + static_cast<std::size_t>(2) * C *
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std::accumulate(std::next(std::begin(weights_lengths), NumNonSpatialDim),
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std::end(weights_lengths),
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static_cast<std::size_t>(1),
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std::multiplies<>()));
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}
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template <ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
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std::size_t GetTensorSize(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& lengths)
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{
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return std::accumulate(std::begin(lengths),
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std::end(lengths),
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static_cast<std::size_t>(1),
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std::multiplies<std::size_t>());
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}
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template <typename InDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
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std::size_t
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GetInputByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& input_lengths)
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{
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// sizeof(InDataType) * (G * N * C * <input spatial lengths product>) +
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return sizeof(InDataType) * GetTensorSize<NumDimSpatial>(input_lengths);
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}
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template <typename WeiDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
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std::size_t
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GetWeightByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& weights_lengths)
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{
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// sizeof(WeiDataType) * (G * K * C * <filter spatial lengths product>) +
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return sizeof(WeiDataType) * GetTensorSize<NumDimSpatial>(weights_lengths);
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}
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template <typename OutDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
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std::size_t
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GetOutputByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& output_lengths)
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{
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// sizeof(OutDataType) * (G * N * K * <output spatial lengths product>);
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return sizeof(OutDataType) * GetTensorSize<NumDimSpatial>(output_lengths);
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}
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template <typename InDataType,
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typename WeiDataType,
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typename OutDataType,
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typename ConvElementOp,
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typename InLayout,
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typename WeiLayout,
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typename OutLayout,
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ck::index_t NumDimSpatial,
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ck::index_t NumNonSpatialDim = 3,
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typename AComputeType = InDataType,
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typename BComputeType = AComputeType>
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bool ConvolutionScale(SimpleDeviceMem& in,
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SimpleDeviceMem& wei,
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SimpleDeviceMem& out,
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ConvElementOp elementwise_op,
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const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& in_lengths,
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const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& in_strides,
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const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& wei_lengths,
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const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& wei_strides,
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const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& out_lengths,
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const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& out_strides);
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template <typename InDataType,
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typename OutDataType,
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ck::index_t NumDimSpatial,
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ck::index_t NumNonSpatialDim = 3>
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bool TensorScaleConvert(SimpleDeviceMem& in,
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SimpleDeviceMem& out,
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float scale_out,
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const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& lengths,
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const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& strides);
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template <typename InDataType,
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typename OutDataType,
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ck::ReduceTensorOp ReduceOpId,
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ck::index_t NumDimSpatial,
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ck::index_t NumNonSpatialDim = 3>
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bool TensorFullReduction(SimpleDeviceMem& tensor,
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SimpleDeviceMem& out_amax,
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const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& lengths,
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const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& strides);
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template <ck::index_t NumDimSpatial,
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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 ConvElementOp,
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ck::ReduceTensorOp ReduceOp,
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typename InLayout,
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typename WeiLayout,
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typename OutLayout,
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ck::index_t NumNonSpatialDim = 3,
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typename AComputeType = InDataType,
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typename BComputeType = AComputeType>
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bool run_grouped_conv_fwd_convscale_reduce(
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std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> in_lengths,
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std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> wei_lengths,
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std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> out_lengths)
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{
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namespace ctc = ck::tensor_layout::convolution;
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static_assert(NumDimSpatial == 3 && ck::is_same_v<InLayout, ctc::NDHWGC> &&
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ck::is_same_v<WeiLayout, ctc::GKZYXC> &&
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ck::is_same_v<OutLayout, ctc::NDHWGK>,
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"Unsupported configuration");
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const ck::index_t G = in_lengths[4];
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const ck::index_t N = in_lengths[0];
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const ck::index_t K = wei_lengths[1];
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const ck::index_t C = in_lengths[5];
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const ck::index_t Z = wei_lengths[2];
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const ck::index_t Y = wei_lengths[3];
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const ck::index_t X = wei_lengths[4];
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const ck::index_t Di = in_lengths[1];
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const ck::index_t Hi = in_lengths[2];
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const ck::index_t Wi = in_lengths[3];
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const ck::index_t Do = out_lengths[1];
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const ck::index_t Ho = out_lengths[2];
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const ck::index_t Wo = out_lengths[3];
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const std::size_t in_mem_size = sizeof(InDataType) * N * Di * Hi * Wi * G * C;
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const std::size_t wei_mem_size = sizeof(WeiDataType) * G * K * Z * Y * X * C;
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const std::size_t conv_out_mem_size = sizeof(ConvOutDataType) * N * Do * Ho * Wo * G * K;
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const std::size_t out_mem_size = sizeof(OutDataType) * N * Do * Ho * Wo * G * K;
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SimpleDeviceMem in(in_mem_size);
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SimpleDeviceMem wei(wei_mem_size);
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SimpleDeviceMem conv_out(conv_out_mem_size);
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SimpleDeviceMem out(out_mem_size);
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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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// We have NDHWGC/GKZYXC/NDHWGK (x, weight, y) in memory space.
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// However, CK's API only accepts lengths and strides with order of GNCDHW/GKCZYX/GNKDHW.
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// Hence, we need to adjust the order of strides.
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const std::array<ck::index_t, NumDimSpatial + 3> input_lengths{G, N, C, Di, Hi, Wi};
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const std::array<ck::index_t, NumDimSpatial + 3> input_strides{
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C, Di * Hi * Wi * G * C, 1, Hi * Wi * G * C, Wi * G * C, G * C};
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const std::array<ck::index_t, NumDimSpatial + 3> weights_lengths{G, K, C, Z, Y, X};
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const std::array<ck::index_t, NumDimSpatial + 3> weights_strides{
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K * Z * Y * X * C, Z * Y * X * C, 1, Y * X * C, X * C, C};
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const std::array<ck::index_t, NumDimSpatial + 3> output_lengths{G, N, K, Do, Ho, Wo};
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const std::array<ck::index_t, NumDimSpatial + 3> output_strides{
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K, Do * Ho * Wo * G * K, 1, Ho * Wo * G * K, Wo * G * K, G * K};
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/*
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* FP8 Convolution with Scaling
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*/
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std::cout << "\n\nConvolution with scale Benchmarking:" << std::endl;
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auto elementwise_op = ConvElementOp{ew::Scale{scale_in}, ew::Scale{scale_wei}, {}};
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auto conv_ok = ConvolutionScale<InDataType,
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WeiDataType,
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ConvOutDataType,
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ConvElementOp,
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InLayout,
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WeiLayout,
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OutLayout,
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NumDimSpatial>(in,
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wei,
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conv_out,
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elementwise_op,
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input_lengths,
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input_strides,
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weights_lengths,
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weights_strides,
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output_lengths,
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output_strides);
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if(!conv_ok)
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return false;
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/*
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* Scale with output weight and convert to FP8
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*/
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std::cout << "\n\nElement-wise scale + convert Benchmarking:" << std::endl;
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auto elem_wise_ok = TensorScaleConvert<ConvOutDataType, OutDataType, NumDimSpatial>(
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conv_out, out, scale_out, output_lengths, output_strides);
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if(!elem_wise_ok)
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return false;
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/*
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* Compute AMAX
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*/
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std::cout << "\n\nAMAX Benchmarking:" << std::endl;
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SimpleDeviceMem amax_device(sizeof(ConvOutDataType));
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auto reduction_ok =
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TensorFullReduction<ConvOutDataType,
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ConvOutDataType,
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ck::ReduceTensorOp::AMAX,
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NumDimSpatial>(conv_out, amax_device, output_lengths, output_strides);
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if(!reduction_ok)
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return false;
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return true;
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}
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template <typename InDataType,
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typename WeiDataType,
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typename OutDataType,
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typename ConvElementOp,
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typename InLayout,
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typename WeiLayout,
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typename OutLayout,
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ck::index_t NumDimSpatial,
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ck::index_t NumNonSpatialDim,
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typename AComputeType,
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typename BComputeType>
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bool ConvolutionScale(SimpleDeviceMem& in,
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SimpleDeviceMem& wei,
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SimpleDeviceMem& out,
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ConvElementOp elementwise_op,
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const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& in_lengths,
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const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& in_strides,
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const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& wei_lengths,
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const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& wei_strides,
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const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& out_lengths,
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const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& out_strides)
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{
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const std::array<ck::index_t, NumDimSpatial> conv_filter_strides{1, 1, 1};
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const std::array<ck::index_t, NumDimSpatial> conv_filter_dilations{1, 1, 1};
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const std::array<ck::index_t, NumDimSpatial> input_left_pads{1, 1, 1};
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const std::array<ck::index_t, NumDimSpatial> input_right_pads{1, 1, 1};
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const auto in_mem_size = GetInputByte<InDataType, NumDimSpatial>(in_lengths);
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const auto wei_mem_size = GetWeightByte<WeiDataType, NumDimSpatial>(wei_lengths);
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const auto out_mem_size = GetOutputByte<OutDataType, NumDimSpatial>(out_lengths);
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std::size_t ds_size = 2; // 2 element-wise scale multipliers
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if constexpr(ck::is_same_v<ConvElementOp, ConvScaleRelu>)
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{
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ds_size += 1; // +1 element-wise relu
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}
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std::size_t flop = GetFlops<NumDimSpatial>(out_lengths, wei_lengths, ds_size);
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std::size_t num_bytes =
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in_mem_size + wei_mem_size + sizeof(float) + sizeof(float) + out_mem_size;
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using ConvDeviceOp =
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ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<NumDimSpatial,
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InLayout,
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WeiLayout,
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ck::Tuple<>,
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OutLayout,
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InDataType,
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WeiDataType,
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ck::Tuple<>,
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OutDataType,
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PassThrough,
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PassThrough,
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ConvElementOp,
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AComputeType,
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BComputeType>;
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// get device op instances
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const auto conv_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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ConvDeviceOp>::GetInstances();
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std::cout << "found " << conv_ptrs.size() << " instances" << std::endl;
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std::string conv_best_op_name;
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int conv_best_op_id = -1;
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float conv_best_avg_time = std::numeric_limits<float>::max();
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float conv_best_gb_per_sec = 0;
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float conv_best_tflops = 0;
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// profile device operation instances
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std::cout << "Run all convolution instances and do timing" << std::endl;
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for(int i = 0; i < conv_ptrs.size(); ++i)
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{
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auto& op_ptr = conv_ptrs[i];
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auto argument_ptr = op_ptr->MakeArgumentPointer(
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in.GetDeviceBuffer(),
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wei.GetDeviceBuffer(),
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std::array<const void*, 0>{},
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out.GetDeviceBuffer(),
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in_lengths,
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in_strides,
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wei_lengths,
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wei_strides,
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std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{},
|
||||
std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{},
|
||||
out_lengths,
|
||||
out_strides,
|
||||
conv_filter_strides,
|
||||
conv_filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
PassThrough{},
|
||||
PassThrough{},
|
||||
elementwise_op);
|
||||
|
||||
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
||||
std::string op_name = op_ptr->GetTypeString();
|
||||
|
||||
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
|
||||
float gb_per_sec = num_bytes / 1.E6 / avg_time;
|
||||
|
||||
std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
|
||||
<< gb_per_sec << " GB/s, " << op_name << std::endl;
|
||||
|
||||
if(tflops > conv_best_tflops)
|
||||
{
|
||||
conv_best_op_id = i;
|
||||
conv_best_op_name = op_name;
|
||||
conv_best_avg_time = avg_time;
|
||||
conv_best_gb_per_sec = gb_per_sec;
|
||||
conv_best_tflops = tflops;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cerr << op_name << " does not support this problem" << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
if(conv_best_op_id < 0)
|
||||
{
|
||||
std::cerr << "no suitable instance" << std::endl;
|
||||
return false;
|
||||
}
|
||||
|
||||
std::cout << "Best Perf: " << std::setw(10) << conv_best_avg_time << " ms, " << conv_best_tflops
|
||||
<< " TFlops, " << conv_best_gb_per_sec << " GB/s, " << conv_best_op_name << std::endl;
|
||||
|
||||
// run the best instance
|
||||
{
|
||||
auto& op_ptr = conv_ptrs[conv_best_op_id];
|
||||
std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
|
||||
<< std::endl;
|
||||
auto argument_ptr = op_ptr->MakeArgumentPointer(
|
||||
in.GetDeviceBuffer(),
|
||||
wei.GetDeviceBuffer(),
|
||||
std::array<const void*, 0>{},
|
||||
out.GetDeviceBuffer(),
|
||||
in_lengths,
|
||||
in_strides,
|
||||
wei_lengths,
|
||||
wei_strides,
|
||||
std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{},
|
||||
std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{},
|
||||
out_lengths,
|
||||
out_strides,
|
||||
conv_filter_strides,
|
||||
conv_filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
PassThrough{},
|
||||
PassThrough{},
|
||||
elementwise_op);
|
||||
|
||||
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
||||
|
||||
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
|
||||
}
|
||||
|
||||
std::cout << "Done" << std::endl;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
template <typename InDataType,
|
||||
typename OutDataType,
|
||||
ck::index_t NumDimSpatial,
|
||||
ck::index_t NumNonSpatialDim>
|
||||
bool TensorScaleConvert(SimpleDeviceMem& in,
|
||||
SimpleDeviceMem& out,
|
||||
float scale_out,
|
||||
const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& lengths,
|
||||
const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& strides)
|
||||
{
|
||||
|
||||
const auto tensor_size = GetTensorSize<NumDimSpatial>(lengths);
|
||||
|
||||
const std::size_t in_mem_size = sizeof(InDataType) * tensor_size;
|
||||
const std::size_t out_mem_size = sizeof(OutDataType) * tensor_size;
|
||||
|
||||
std::size_t flop = 2 * tensor_size; // element-wise scale + convert
|
||||
|
||||
std::size_t bytes =
|
||||
in_mem_size + sizeof(float) + out_mem_size; // read from in, scale, write to out
|
||||
|
||||
using DeviceScaleConvert =
|
||||
ck::tensor_operation::device::DeviceElementwise<ck::Tuple<InDataType>,
|
||||
ck::Tuple<OutDataType>,
|
||||
ew::Scale,
|
||||
NumDimSpatial + NumNonSpatialDim>;
|
||||
|
||||
// get device op instances
|
||||
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
|
||||
DeviceScaleConvert>::GetInstances();
|
||||
|
||||
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
|
||||
|
||||
std::string best_op_name;
|
||||
int best_op_id = -1;
|
||||
float best_avg_time = std::numeric_limits<float>::max();
|
||||
float best_gb_per_sec = 0;
|
||||
float best_tflops = 0;
|
||||
|
||||
// profile device operation instances
|
||||
std::cout << "Run all DeviceScaleConvert instances and do timing" << std::endl;
|
||||
|
||||
auto scale_convert = ew::Scale{scale_out};
|
||||
|
||||
for(int i = 0; i < op_ptrs.size(); ++i)
|
||||
{
|
||||
auto& op_ptr = op_ptrs[i];
|
||||
auto argument_ptr = op_ptr->MakeArgumentPointer(lengths,
|
||||
{strides},
|
||||
{strides},
|
||||
{in.GetDeviceBuffer()},
|
||||
{out.GetDeviceBuffer()},
|
||||
scale_convert);
|
||||
|
||||
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
||||
std::string op_name = op_ptr->GetTypeString();
|
||||
|
||||
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
|
||||
float gb_per_sec = bytes / 1.E6 / avg_time;
|
||||
|
||||
std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
|
||||
<< gb_per_sec << " GB/s, " << op_name << std::endl;
|
||||
|
||||
if(tflops > best_tflops)
|
||||
{
|
||||
best_op_id = i;
|
||||
best_op_name = op_name;
|
||||
best_avg_time = avg_time;
|
||||
best_gb_per_sec = gb_per_sec;
|
||||
best_tflops = tflops;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cerr << op_name << " does not support this problem" << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
if(best_op_id < 0)
|
||||
{
|
||||
std::cerr << "no suitable instance found." << std::endl;
|
||||
return false;
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
|
||||
<< " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
|
||||
|
||||
// run the best intance
|
||||
auto& op_ptr = op_ptrs[best_op_id];
|
||||
std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
|
||||
<< std::endl;
|
||||
auto argument_ptr = op_ptr->MakeArgumentPointer(lengths,
|
||||
{strides},
|
||||
{strides},
|
||||
{in.GetDeviceBuffer()},
|
||||
{out.GetDeviceBuffer()},
|
||||
scale_convert);
|
||||
|
||||
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
||||
|
||||
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
|
||||
}
|
||||
|
||||
std::cout << "Done" << std::endl;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
template <typename InDataType,
|
||||
typename OutDataType,
|
||||
ck::ReduceTensorOp ReduceOpId,
|
||||
ck::index_t NumDimSpatial,
|
||||
ck::index_t NumNonSpatialDim>
|
||||
bool TensorFullReduction(SimpleDeviceMem& tensor,
|
||||
SimpleDeviceMem& out_amax,
|
||||
const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& lengths,
|
||||
const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& strides)
|
||||
{
|
||||
const auto spatial_dim_size = std::accumulate(std::next(std::begin(lengths), NumNonSpatialDim),
|
||||
std::end(lengths),
|
||||
static_cast<std::size_t>(1),
|
||||
std::multiplies<>());
|
||||
const auto tensor_size = GetTensorSize<NumDimSpatial>(lengths);
|
||||
|
||||
auto copy = [](const auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
|
||||
|
||||
// Get the reduction operation
|
||||
using ReduceOperation = typename ck::reduce_binary_operator<ReduceOpId>::opType;
|
||||
using InElementwiseOperation =
|
||||
typename ck::reduce_unary_operator<ReduceOpId, true, true>::InElementwiseOperation;
|
||||
using AccElementwiseOperation =
|
||||
typename ck::reduce_unary_operator<ReduceOpId, true, true>::AccElementwiseOperation;
|
||||
|
||||
InElementwiseOperation in_elementwise_op;
|
||||
AccElementwiseOperation acc_elementwise_op;
|
||||
std::tie(in_elementwise_op, acc_elementwise_op) =
|
||||
ck::reduce_unary_operator<ReduceOpId, true, true>::GetElementwiseOperator(
|
||||
static_cast<int32_t>(tensor_size));
|
||||
|
||||
std::array<ck::index_t, 1> reduce_out_lengths{1};
|
||||
std::array<ck::index_t, 1> reduce_out_strides{1};
|
||||
|
||||
SimpleDeviceMem partial_reduce_tensor(sizeof(OutDataType) * spatial_dim_size);
|
||||
std::array<ck::index_t, NumDimSpatial> reduce_part_lengths;
|
||||
std::copy(std::next(std::begin(lengths), NumNonSpatialDim),
|
||||
std::end(lengths),
|
||||
std::begin(reduce_part_lengths));
|
||||
std::array<ck::index_t, NumDimSpatial> reduce_part_strides;
|
||||
copy(HostTensorDescriptor(reduce_part_lengths).GetStrides(), reduce_part_strides);
|
||||
|
||||
{
|
||||
std::cout << "\nReduction of nonspatial dimensions:" << std::endl;
|
||||
using DeviceOp =
|
||||
ck::tensor_operation::device::DeviceReduce<InDataType,
|
||||
OutDataType,
|
||||
OutDataType,
|
||||
NumDimSpatial + NumNonSpatialDim,
|
||||
NumNonSpatialDim,
|
||||
ReduceOperation,
|
||||
InElementwiseOperation,
|
||||
PassThrough,
|
||||
true, // PropagateNan
|
||||
false>; // OutputIndex
|
||||
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
|
||||
DeviceOp>::GetInstances();
|
||||
|
||||
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
|
||||
|
||||
std::string best_op_name;
|
||||
int best_op_id = -1;
|
||||
float best_ave_time = std::numeric_limits<float>::max();
|
||||
float best_gb_per_sec = 0;
|
||||
|
||||
std::array<int, NumNonSpatialDim> reduce_dims;
|
||||
std::iota(reduce_dims.begin(), reduce_dims.end(), 0); // 0,..., NumNonSpatialDim-1
|
||||
|
||||
ck::index_t num_in_elements = tensor_size;
|
||||
ck::index_t num_out_elements = spatial_dim_size;
|
||||
|
||||
// profile device operation instances
|
||||
std::cout << "Run partial reduction and do timing" << std::endl;
|
||||
|
||||
for(int i = 0; i < op_ptrs.size(); ++i)
|
||||
{
|
||||
auto& op_ptr = op_ptrs[i];
|
||||
|
||||
auto argument_ptr = op_ptr->MakeArgumentPointer(lengths,
|
||||
strides,
|
||||
reduce_part_lengths,
|
||||
reduce_part_strides,
|
||||
reduce_dims,
|
||||
1.0,
|
||||
0.0,
|
||||
tensor.GetDeviceBuffer(),
|
||||
nullptr,
|
||||
partial_reduce_tensor.GetDeviceBuffer(),
|
||||
nullptr,
|
||||
in_elementwise_op,
|
||||
PassThrough{});
|
||||
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
||||
std::string op_name = op_ptr->GetTypeString();
|
||||
|
||||
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
|
||||
std::size_t num_bytes =
|
||||
num_in_elements * sizeof(InDataType) + num_out_elements * sizeof(OutDataType);
|
||||
|
||||
float gb_per_sec = num_bytes / 1.E6 / ave_time;
|
||||
|
||||
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << gb_per_sec
|
||||
<< " GB/s, " << op_name << std::endl;
|
||||
|
||||
if(ave_time < best_ave_time)
|
||||
{
|
||||
best_op_id = i;
|
||||
best_op_name = op_name;
|
||||
best_ave_time = ave_time;
|
||||
best_gb_per_sec = gb_per_sec;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cout << op_name << " does not support this problem" << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
if(best_op_id < 0)
|
||||
{
|
||||
std::cerr << "no suitable instance found." << std::endl;
|
||||
return false;
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, "
|
||||
<< best_op_name << std::endl;
|
||||
|
||||
// run the best instance
|
||||
auto& op_ptr = op_ptrs[best_op_id];
|
||||
std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
|
||||
<< std::endl;
|
||||
auto argument_ptr = op_ptr->MakeArgumentPointer(lengths,
|
||||
strides,
|
||||
reduce_part_lengths,
|
||||
reduce_part_strides,
|
||||
reduce_dims,
|
||||
1.0,
|
||||
0.0,
|
||||
tensor.GetDeviceBuffer(),
|
||||
nullptr,
|
||||
partial_reduce_tensor.GetDeviceBuffer(),
|
||||
nullptr,
|
||||
in_elementwise_op,
|
||||
PassThrough{});
|
||||
|
||||
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
||||
|
||||
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
|
||||
}
|
||||
|
||||
std::cout << "Done" << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
{
|
||||
std::cout << "\nReduction of spatial dimensions:" << std::endl;
|
||||
using DeviceOp = ck::tensor_operation::device::DeviceReduce<OutDataType,
|
||||
OutDataType,
|
||||
OutDataType,
|
||||
NumDimSpatial,
|
||||
NumDimSpatial,
|
||||
ReduceOperation,
|
||||
PassThrough,
|
||||
AccElementwiseOperation,
|
||||
true, // PropagateNan
|
||||
false>; // OutputIndex
|
||||
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
|
||||
DeviceOp>::GetInstances();
|
||||
|
||||
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
|
||||
|
||||
std::string best_op_name;
|
||||
int best_op_id = -1;
|
||||
float best_ave_time = std::numeric_limits<float>::max();
|
||||
float best_gb_per_sec = 0;
|
||||
|
||||
std::array<int, NumDimSpatial> reduce_dims;
|
||||
std::iota(reduce_dims.begin(), reduce_dims.end(), 0); // 0,..., NumDimSpatial-1
|
||||
|
||||
ck::index_t num_in_elements = spatial_dim_size;
|
||||
ck::index_t num_out_elements = 1;
|
||||
|
||||
// profile device operation instances
|
||||
std::cout << "Run final reduction and do timing" << std::endl;
|
||||
|
||||
for(int i = 0; i < op_ptrs.size(); ++i)
|
||||
{
|
||||
auto& op_ptr = op_ptrs[i];
|
||||
|
||||
auto argument_ptr = op_ptr->MakeArgumentPointer(reduce_part_lengths,
|
||||
reduce_part_strides,
|
||||
reduce_out_lengths,
|
||||
reduce_out_strides,
|
||||
reduce_dims,
|
||||
1.0,
|
||||
0.0,
|
||||
partial_reduce_tensor.GetDeviceBuffer(),
|
||||
nullptr,
|
||||
out_amax.GetDeviceBuffer(),
|
||||
nullptr,
|
||||
PassThrough{},
|
||||
acc_elementwise_op);
|
||||
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
||||
std::string op_name = op_ptr->GetTypeString();
|
||||
|
||||
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
|
||||
|
||||
std::size_t num_bytes =
|
||||
num_in_elements * sizeof(OutDataType) + num_out_elements * sizeof(OutDataType);
|
||||
|
||||
float gb_per_sec = num_bytes / 1.E6 / ave_time;
|
||||
|
||||
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << gb_per_sec
|
||||
<< " GB/s, " << op_name << std::endl;
|
||||
|
||||
if(ave_time < best_ave_time)
|
||||
{
|
||||
best_op_id = i;
|
||||
best_op_name = op_name;
|
||||
best_ave_time = ave_time;
|
||||
best_gb_per_sec = gb_per_sec;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cout << op_name << " does not support this problem" << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
if(best_op_id < 0)
|
||||
{
|
||||
std::cerr << "no suitable instance found." << std::endl;
|
||||
return false;
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, "
|
||||
<< best_op_name << std::endl;
|
||||
|
||||
// run the best instance
|
||||
auto& op_ptr = op_ptrs[best_op_id];
|
||||
std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
|
||||
<< std::endl;
|
||||
auto argument_ptr = op_ptr->MakeArgumentPointer(reduce_part_lengths,
|
||||
reduce_part_strides,
|
||||
reduce_out_lengths,
|
||||
reduce_out_strides,
|
||||
reduce_dims,
|
||||
1.0,
|
||||
0.0,
|
||||
partial_reduce_tensor.GetDeviceBuffer(),
|
||||
nullptr,
|
||||
out_amax.GetDeviceBuffer(),
|
||||
nullptr,
|
||||
PassThrough{},
|
||||
acc_elementwise_op);
|
||||
|
||||
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
||||
|
||||
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
|
||||
}
|
||||
|
||||
std::cout << "Done" << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
@@ -0,0 +1,58 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include "common.hpp"
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
|
||||
using InDataType = ck::f8_t;
|
||||
using WeiDataType = ck::f8_t;
|
||||
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;
|
||||
|
||||
using ConvElementOp = ConvScaleRelu;
|
||||
|
||||
using InLayout = ck::tensor_layout::convolution::NDHWGC;
|
||||
using WeiLayout = ck::tensor_layout::convolution::GKZYXC;
|
||||
using OutLayout = ck::tensor_layout::convolution::NDHWGK;
|
||||
|
||||
constexpr auto ReduceOpId = ck::ReduceTensorOp::AMAX;
|
||||
|
||||
static constexpr ck::index_t NumDimSpatial = 3;
|
||||
static constexpr ck::index_t G = 1;
|
||||
static constexpr ck::index_t N = 64;
|
||||
static constexpr ck::index_t K = 128;
|
||||
static constexpr ck::index_t C = 64;
|
||||
static constexpr ck::index_t Z = 3;
|
||||
static constexpr ck::index_t Y = 3;
|
||||
static constexpr ck::index_t X = 3;
|
||||
static constexpr ck::index_t Di = 28;
|
||||
static constexpr ck::index_t Hi = 28;
|
||||
static constexpr ck::index_t Wi = 3;
|
||||
static constexpr ck::index_t Do = 28;
|
||||
static constexpr ck::index_t Ho = 28;
|
||||
static constexpr ck::index_t Wo = 3;
|
||||
|
||||
int main()
|
||||
{
|
||||
return run_grouped_conv_fwd_convscale_reduce<NumDimSpatial,
|
||||
InDataType,
|
||||
WeiDataType,
|
||||
ConvOutDataType,
|
||||
OutDataType,
|
||||
ConvElementOp,
|
||||
ReduceOpId,
|
||||
InLayout,
|
||||
WeiLayout,
|
||||
OutLayout,
|
||||
3,
|
||||
AComputeDataType,
|
||||
BComputeDataType>(
|
||||
{N, Di, Hi, Wi, G, C}, {G, K, Z, Y, X, C}, {N, Do, Ho, Wo, G, K})
|
||||
? EXIT_SUCCESS
|
||||
: EXIT_FAILURE;
|
||||
}
|
||||
Reference in New Issue
Block a user