mirror of
https://github.com/ROCm/composable_kernel.git
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Batchnorm-forward implemented using welford method to calculate variance (#403)
* Update to the batchnorm-forward API and base class * Fix leeked header including in gridwise_set_buffer_value.hpp * Add kernels and device file for batchnorm-forward welford supporting both blockwise and multi-block reduction * Update to the batchnorm-forward example to use the new batchnorm-forward device interface * Change the batchnorm-forward reference to use sequential welford method * Change to assign the workspace into four buffers in the host layer * Use GetReduceCountPerThread functor to replace the initial count for Blockwise and Multiblock welford * Tiny correction and remove un-used file under example/34_batchnorm * Renaming in the kernel arguments * Explicitly use ck::math::sqrt in batchnorm-forward kernels * Add some comments to some kernels * Tiny fix * Generalize the data types in reference_batchnorm_forward_nhwc_c * Use ck::ignore to mark un-used parameters * Move GetReduceCountPerThread functor codes from kernel to device * Remove some un-used codes in device_batchnorm_forward_impl.hpp * Tiny fix in batchnorm_forward example * Move GetReduceCountPerThread() to welford_helper.hpp * Use seperate data type for Scale and Bias * Renaming in device Op * Tiny fix in forward example * Updata to batchnorm-infer (type spliting, renaming) * Add time and bandwidth measurement to the batchnorm-forward example * Add support of elementwise operation for batchnorm forward output * Reduce object copying by passing object as reference type * Tiny change for performance * Updates for performance again * Some Renamings * Add GetActualVariance template parameter for ThreadwiseWelfordMerge * Tiny update in reference batchnorm forward nhwc/c * Move batchnorm multiblock kernel files to grid/batchnorm_multiblock sub-directory * Fuse mean and bias in the normalization calculation Co-authored-by: root <root@dc-smc-18.amd.com> Co-authored-by: rocking5566 <ChunYu.Lai@amd.com>
This commit is contained in:
Qianfeng
@@ -13,31 +13,36 @@ namespace ck {
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namespace tensor_operation {
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namespace device {
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template <index_t Rank, index_t NumBatchNormReduceDim>
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template <index_t Rank, index_t NumBatchNormReduceDim, typename YElementwiseOp>
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struct DeviceBatchNormFwd : public BaseOperator
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{
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virtual std::unique_ptr<BaseArgument> MakeArgumentPointer(
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const std::array<index_t, Rank> xyLengths,
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const std::array<index_t, Rank> xStrides,
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const std::array<index_t, Rank> yStrides,
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const std::array<int, NumBatchNormReduceDim> reduceDims,
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const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleBiasMeanVarLengths,
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const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleBiasMeanVarStrides,
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const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleStrides,
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const std::array<index_t, Rank - NumBatchNormReduceDim> bnBiasStrides,
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const std::array<index_t, Rank - NumBatchNormReduceDim> bnMeanVarStrides,
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const void* p_x,
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const void* bnScale,
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const void* bnBias,
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double epsilon,
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const YElementwiseOp y_elementwise_op,
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void* p_y,
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void* resultSaveMean,
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void* resultSaveInvVariance,
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double exponentialAverageFactor,
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void* resultRunningMean,
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void* resultRunningVariance,
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double epsilon,
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void* resultSaveMean,
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void* resultSaveInvVariance) = 0;
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void* resultRunningVariance) = 0;
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virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
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};
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template <index_t Rank, index_t NumBatchNormReduceDim>
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using DeviceBatchNormFwdPtr = std::unique_ptr<DeviceBatchNormFwd<Rank, NumBatchNormReduceDim>>;
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template <index_t Rank, index_t NumBatchNormReduceDim, typename YElementwiseOp>
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using DeviceBatchNormFwdPtr =
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std::unique_ptr<DeviceBatchNormFwd<Rank, NumBatchNormReduceDim, YElementwiseOp>>;
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} // namespace device
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} // namespace tensor_operation
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@@ -21,7 +21,9 @@ struct DeviceBatchNormInfer : public BaseOperator
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const std::array<index_t, Rank> xStrides,
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const std::array<index_t, Rank> yStrides,
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const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleBiasMeanVarLengths,
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const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleBiasMeanVarStrides,
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const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleStrides,
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const std::array<index_t, Rank - NumBatchNormReduceDim> bnBiasStrides,
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const std::array<index_t, Rank - NumBatchNormReduceDim> bnMeanVarStrides,
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const void* p_x,
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const void* bnScale,
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const void* bnBias,
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@@ -0,0 +1,711 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <iostream>
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#include <sstream>
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#include "ck/utility/reduction_operator.hpp"
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#include "ck/tensor_operation/gpu/device/device_batchnorm_forward.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_reduce_common.hpp"
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#include "ck/tensor_operation/gpu/device/welford_helper.hpp"
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#include "ck/tensor_operation/gpu/grid/batchnorm_multiblock/gridwise_multiblock_welford_first_half.hpp"
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#include "ck/tensor_operation/gpu/grid/batchnorm_multiblock/gridwise_multiblock_welford_second_half_batchnorm_forward_final.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_batchnorm_forward_blockwise_welford.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/host_utility/device_prop.hpp"
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#include "ck/host_utility/kernel_launch.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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template <typename XDataType,
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typename YDataType,
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typename AccDataType,
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typename ScaleDataType,
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typename BiasDataType,
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typename MeanVarDataType,
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typename YElementwiseOp,
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index_t Rank,
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index_t NumBatchNormReduceDim,
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bool UseMultiblockInK,
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index_t BlockSize,
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index_t MThreadClusterSize,
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index_t KThreadClusterSize,
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index_t MThreadSliceSize,
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index_t KThreadSliceSize,
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index_t XSrcYDstVectorDim,
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index_t XSrcVectorSize,
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index_t YDstVectorSize,
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index_t ScaleSrcVectorSize,
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index_t BiasSrcVectorSize,
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index_t MeanVarSrcDstVectorSize>
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struct DeviceBatchNormFwdImpl
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: public DeviceBatchNormFwd<Rank, NumBatchNormReduceDim, YElementwiseOp>
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{
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static_assert(Rank <= 6, "Bigger Rank size is not supported!");
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static_assert(BlockSize == MThreadClusterSize * KThreadClusterSize,
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"Invalid thread cluster size assignments!");
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static_assert((XSrcYDstVectorDim == 0 && MThreadSliceSize % XSrcVectorSize == 0) ||
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(XSrcYDstVectorDim == 1 && KThreadSliceSize % XSrcVectorSize == 0),
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"Invalid thread slice sizes and/or vector sizes configuration, please check!");
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static constexpr index_t NumInvariantDim = Rank - NumBatchNormReduceDim;
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static constexpr index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
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static constexpr index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
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static auto MakeXY2dDescriptor(const std::array<index_t, Rank>& xyLengths,
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const std::array<index_t, Rank>& xyStrides,
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int blkGroupSize,
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int numBlockTileIteration)
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{
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const auto tupleXYLengths =
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generate_tuple([&](auto I) { return xyLengths[I]; }, Number<Rank>{});
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const auto tupleXYStrides =
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generate_tuple([&](auto I) { return xyStrides[I]; }, Number<Rank>{});
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const auto raw_grid_desc = make_naive_tensor_descriptor(tupleXYLengths, tupleXYStrides);
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const auto grid_desc_m_k = [&]() {
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using InvariantDims = typename arithmetic_sequence_gen<0, NumInvariantDim, 1>::type;
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using ReduceDims = typename arithmetic_sequence_gen<NumInvariantDim, Rank, 1>::type;
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const auto reduceDimLengths =
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generate_tuple([&](auto I) { return xyLengths[NumInvariantDim + I]; },
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Number<NumBatchNormReduceDim>{});
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const auto invariantDimLengths =
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generate_tuple([&](auto I) { return xyLengths[I]; }, Number<NumInvariantDim>{});
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return transform_tensor_descriptor(raw_grid_desc,
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make_tuple(make_merge_transform(invariantDimLengths),
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make_merge_transform(reduceDimLengths)),
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make_tuple(InvariantDims{}, ReduceDims{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}));
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}();
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const auto invariantLength = grid_desc_m_k.GetLength(Number<0>{});
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const auto reduceLength = grid_desc_m_k.GetLength(Number<1>{});
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const int workSizePerBlock = K_BlockTileSize * numBlockTileIteration;
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const auto mPad =
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math::integer_least_multiple(invariantLength, M_BlockTileSize) - invariantLength;
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const auto kPad = workSizePerBlock * blkGroupSize - reduceLength;
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auto grid_desc_m_k_padded =
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transform_tensor_descriptor(grid_desc_m_k,
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make_tuple(make_right_pad_transform(invariantLength, mPad),
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make_right_pad_transform(reduceLength, kPad)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}));
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return (grid_desc_m_k_padded);
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};
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static auto MakeMeanVarCountOutputMG2dDescriptor(int invariantLength, int blkGroupSize)
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{
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const auto grid_desc_m_g =
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make_naive_tensor_descriptor_packed(make_tuple(invariantLength, blkGroupSize));
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const auto mPad =
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math::integer_least_multiple(invariantLength, M_BlockTileSize) - invariantLength;
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auto grid_desc_m_g_padded =
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transform_tensor_descriptor(grid_desc_m_g,
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make_tuple(make_right_pad_transform(invariantLength, mPad),
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make_pass_through_transform(blkGroupSize)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}));
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return (grid_desc_m_g_padded);
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};
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static auto MakeMeanVarCountInputMK2dDescriptor(int invariantLength, int blkGroupSize)
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{
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const auto reduceLength = blkGroupSize;
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const auto grid_desc_m_k =
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make_naive_tensor_descriptor_packed(make_tuple(invariantLength, reduceLength));
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const auto mPad =
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math::integer_least_multiple(invariantLength, M_BlockTileSize) - invariantLength;
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const auto kPad =
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math::integer_least_multiple(reduceLength, KThreadClusterSize) - reduceLength;
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auto grid_desc_m_k_padded =
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transform_tensor_descriptor(grid_desc_m_k,
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make_tuple(make_right_pad_transform(invariantLength, mPad),
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make_right_pad_transform(reduceLength, kPad)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}));
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return (grid_desc_m_k_padded);
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};
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static auto
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MakeScaleBiasMeanVar1dDescriptor(const std::array<index_t, NumInvariantDim>& lengths,
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const std::array<index_t, NumInvariantDim>& strides)
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{
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const auto tupleLengths =
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generate_tuple([&](auto I) { return lengths[I]; }, Number<NumInvariantDim>{});
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const auto tupleStrides =
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generate_tuple([&](auto I) { return strides[I]; }, Number<NumInvariantDim>{});
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auto raw_grid_desc = make_naive_tensor_descriptor(tupleLengths, tupleStrides);
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auto grid_desc_m = transform_tensor_descriptor(
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raw_grid_desc,
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make_tuple(make_merge_transform(tupleLengths)),
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make_tuple(typename arithmetic_sequence_gen<0, NumInvariantDim, 1>::type{}),
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make_tuple(Sequence<0>{}));
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const auto invariantLength = grid_desc_m.GetLength(Number<0>{});
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const auto mPad =
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math::integer_least_multiple(invariantLength, M_BlockTileSize) - invariantLength;
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auto grid_desc_m_padded =
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transform_tensor_descriptor(grid_desc_m,
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make_tuple(make_right_pad_transform(invariantLength, mPad)),
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make_tuple(Sequence<0>{}),
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make_tuple(Sequence<0>{}));
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return (grid_desc_m_padded);
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};
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using XYGridDesc_M_K = decltype(MakeXY2dDescriptor({1}, {1}, 1, 1));
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using ScaleBiasMeanVarGridDesc_M = decltype(MakeScaleBiasMeanVar1dDescriptor({1}, {1}));
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struct Argument : public BaseArgument
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{
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Argument(const std::array<index_t, Rank> xyLengths,
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const std::array<index_t, Rank> xStrides,
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const std::array<index_t, Rank> yStrides,
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const std::array<int, NumBatchNormReduceDim> reduceDims,
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const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleBiasMeanVarLengths,
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const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleStrides,
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const std::array<index_t, Rank - NumBatchNormReduceDim> bnBiasStrides,
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const std::array<index_t, Rank - NumBatchNormReduceDim> bnMeanVarStrides,
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const XDataType* p_x,
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const ScaleDataType* p_scale,
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const BiasDataType* p_bias,
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const YElementwiseOp y_elementwise_op,
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double epsilon,
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YDataType* p_y,
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MeanVarDataType* resultSaveMean,
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MeanVarDataType* resultSaveInvVariance,
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double averageFactor,
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MeanVarDataType* resultRunningMean,
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MeanVarDataType* resultRunningVariance)
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: bnScaleBiasMeanVarLengths_(bnScaleBiasMeanVarLengths),
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bnScaleStrides_(bnScaleStrides),
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bnBiasStrides_(bnBiasStrides),
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bnMeanVarStrides_(bnMeanVarStrides),
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p_x_(p_x),
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p_scale_(p_scale),
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p_bias_(p_bias),
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y_elementwise_op_(y_elementwise_op),
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p_y_(p_y),
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resultSaveMean_(resultSaveMean),
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resultSaveInvVariance_(resultSaveInvVariance),
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resultRunningMean_(resultRunningMean),
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resultRunningVariance_(resultRunningVariance)
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{
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xyLengths_ =
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shuffle_tensor_dimensions<Rank, NumBatchNormReduceDim>(xyLengths, reduceDims);
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xStrides_ =
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shuffle_tensor_dimensions<Rank, NumBatchNormReduceDim>(xStrides, reduceDims);
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yStrides_ =
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shuffle_tensor_dimensions<Rank, NumBatchNormReduceDim>(yStrides, reduceDims);
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std::tie(invariant_length_, reduce_length_) =
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get_2d_lengths<Rank, NumBatchNormReduceDim>(xyLengths_);
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epsilon_ = type_convert<AccDataType>(epsilon);
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averageFactor_ = type_convert<AccDataType>(averageFactor);
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updateMovingAverage_ =
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(resultRunningMean != nullptr && resultRunningVariance != nullptr);
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saveMeanInvVariance_ = (resultSaveMean != nullptr && resultSaveInvVariance_ != nullptr);
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if(UseMultiblockInK)
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{
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int iterations = 1;
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while(true)
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{
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int testBlkGroupSize = (reduce_length_ + (K_BlockTileSize * iterations) - 1) /
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(K_BlockTileSize * iterations);
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// we want the blkGroupSize be not more than 128
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if(testBlkGroupSize <= 128)
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break;
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iterations++;
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};
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blkGroupSize_ = (reduce_length_ + (K_BlockTileSize * iterations) - 1) /
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(K_BlockTileSize * iterations);
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numBlockTileIteration_ = iterations;
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}
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else
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{
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blkGroupSize_ = 1;
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numBlockTileIteration_ = (reduce_length_ + K_BlockTileSize - 1) / K_BlockTileSize;
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};
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gridSize_ = (invariant_length_ + M_BlockTileSize - 1) / M_BlockTileSize * blkGroupSize_;
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x_grid_desc_m_k_ =
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MakeXY2dDescriptor(xyLengths_, xStrides_, blkGroupSize_, numBlockTileIteration_);
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y_grid_desc_m_k_ =
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MakeXY2dDescriptor(xyLengths_, yStrides_, blkGroupSize_, numBlockTileIteration_);
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scale_grid_desc_m_ =
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MakeScaleBiasMeanVar1dDescriptor(bnScaleBiasMeanVarLengths, bnScaleStrides_);
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bias_grid_desc_m_ =
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MakeScaleBiasMeanVar1dDescriptor(bnScaleBiasMeanVarLengths, bnBiasStrides_);
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mean_var_grid_desc_m_ =
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MakeScaleBiasMeanVar1dDescriptor(bnScaleBiasMeanVarLengths, bnMeanVarStrides_);
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}
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AccDataType epsilon_;
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AccDataType averageFactor_;
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bool updateMovingAverage_;
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bool saveMeanInvVariance_;
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std::array<index_t, Rank> xyLengths_;
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std::array<index_t, Rank> xStrides_;
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std::array<index_t, Rank> yStrides_;
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std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleBiasMeanVarLengths_;
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std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleStrides_;
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std::array<index_t, Rank - NumBatchNormReduceDim> bnBiasStrides_;
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std::array<index_t, Rank - NumBatchNormReduceDim> bnMeanVarStrides_;
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const XDataType* p_x_;
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const ScaleDataType* p_scale_;
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const BiasDataType* p_bias_;
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const YElementwiseOp y_elementwise_op_;
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YDataType* p_y_;
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MeanVarDataType* resultSaveMean_;
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MeanVarDataType* resultSaveInvVariance_;
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MeanVarDataType* resultRunningMean_;
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MeanVarDataType* resultRunningVariance_;
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long_index_t invariant_length_;
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long_index_t reduce_length_;
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int blkGroupSize_;
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int numBlockTileIteration_;
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size_t gridSize_;
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XYGridDesc_M_K x_grid_desc_m_k_;
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XYGridDesc_M_K y_grid_desc_m_k_;
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ScaleBiasMeanVarGridDesc_M scale_grid_desc_m_;
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ScaleBiasMeanVarGridDesc_M bias_grid_desc_m_;
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ScaleBiasMeanVarGridDesc_M mean_var_grid_desc_m_;
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void* workspace_mean_;
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void* workspace_variance_;
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void* workspace_count_;
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};
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size_t GetWorkSpaceSize(const BaseArgument* pArg) const override
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{
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const Argument* pArg_ = dynamic_cast<const Argument*>(pArg);
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size_t workspace_size = 0;
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if(UseMultiblockInK && pArg_->blkGroupSize_ > 1)
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{
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// workspace for welford intermediate mean
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workspace_size +=
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pArg_->invariant_length_ * pArg_->blkGroupSize_ * sizeof(MeanVarDataType) + 64;
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// workspace for welford intermediate variance
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workspace_size +=
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pArg_->invariant_length_ * pArg_->blkGroupSize_ * sizeof(MeanVarDataType) + 64;
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// workspace for welford intermediate count
|
||||
workspace_size +=
|
||||
pArg_->invariant_length_ * pArg_->blkGroupSize_ * sizeof(int32_t) + 64;
|
||||
}
|
||||
|
||||
return (workspace_size);
|
||||
};
|
||||
|
||||
void SetWorkSpacePointer(BaseArgument* pArg, void* p_workspace) const override
|
||||
{
|
||||
Argument* pArg_ = dynamic_cast<Argument*>(pArg);
|
||||
|
||||
pArg_->p_workspace_ = p_workspace;
|
||||
|
||||
if(UseMultiblockInK && pArg_->blkGroupSize_ > 1)
|
||||
{
|
||||
|
||||
// setup buffer used for intermediate welford mean
|
||||
pArg_->workspace_mean_ = static_cast<char*>(pArg_->p_workspace_);
|
||||
|
||||
index_t mean_space_sz =
|
||||
pArg_->invariant_length_ * pArg_->blkGroupSize_ * sizeof(MeanVarDataType);
|
||||
|
||||
mean_space_sz = math::integer_least_multiple(mean_space_sz, 64);
|
||||
|
||||
// setup buffer used for intermediate welford varirance
|
||||
pArg_->workspace_variance_ =
|
||||
reinterpret_cast<char*>(pArg_->workspace_mean_) + mean_space_sz;
|
||||
|
||||
index_t variance_space_sz =
|
||||
pArg_->invariant_length_ * pArg_->blkGroupSize_ * sizeof(MeanVarDataType);
|
||||
|
||||
variance_space_sz = math::integer_least_multiple(variance_space_sz, 64);
|
||||
|
||||
// setup buffer used for intermediate welfor count
|
||||
pArg_->workspace_count_ =
|
||||
reinterpret_cast<char*>(pArg_->workspace_variance_) + variance_space_sz;
|
||||
};
|
||||
};
|
||||
|
||||
struct Invoker : public BaseInvoker
|
||||
{
|
||||
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
|
||||
{
|
||||
float avg_time = 0;
|
||||
|
||||
if(UseMultiblockInK && arg.blkGroupSize_ > 1)
|
||||
{
|
||||
using GetReduceCountPerThreadFunctor =
|
||||
GetReduceCountPerThreadForMultiblockWelford<K_BlockTileSize, KThreadSliceSize>;
|
||||
|
||||
GetReduceCountPerThreadFunctor get_reduce_count_per_thread(
|
||||
arg.blkGroupSize_, arg.numBlockTileIteration_, arg.reduce_length_);
|
||||
|
||||
const auto mean_var_count_grid_desc_m_g =
|
||||
DeviceBatchNormFwdImpl::MakeMeanVarCountOutputMG2dDescriptor(
|
||||
arg.invariant_length_, arg.blkGroupSize_);
|
||||
|
||||
const auto mean_var_count_grid_desc_m_k =
|
||||
DeviceBatchNormFwdImpl::MakeMeanVarCountInputMK2dDescriptor(
|
||||
arg.invariant_length_, arg.blkGroupSize_);
|
||||
|
||||
using MeanVarCountGridDesc_M_G = decltype(mean_var_count_grid_desc_m_g);
|
||||
using MeanVarCountGridDesc_M_K = decltype(mean_var_count_grid_desc_m_k);
|
||||
|
||||
using GridwiseMultiblockWelfordFirstHalf_ =
|
||||
GridwiseMultiblockWelfordFirstHalf<XDataType,
|
||||
AccDataType,
|
||||
MeanVarDataType,
|
||||
XYGridDesc_M_K,
|
||||
MeanVarCountGridDesc_M_G,
|
||||
GetReduceCountPerThreadFunctor,
|
||||
BlockSize,
|
||||
MThreadClusterSize,
|
||||
KThreadClusterSize,
|
||||
MThreadSliceSize,
|
||||
KThreadSliceSize,
|
||||
XSrcYDstVectorDim,
|
||||
XSrcVectorSize>;
|
||||
|
||||
using GridwiseWelfordSecondHalfBatchNormForwardFinal_ =
|
||||
GridwiseWelfordSecondHalfBatchNormForwardFinal<XDataType,
|
||||
YDataType,
|
||||
AccDataType,
|
||||
ScaleDataType,
|
||||
BiasDataType,
|
||||
MeanVarDataType,
|
||||
YElementwiseOp,
|
||||
XYGridDesc_M_K,
|
||||
MeanVarCountGridDesc_M_K,
|
||||
ScaleBiasMeanVarGridDesc_M,
|
||||
ScaleBiasMeanVarGridDesc_M,
|
||||
BlockSize,
|
||||
MThreadClusterSize,
|
||||
KThreadClusterSize,
|
||||
MThreadSliceSize,
|
||||
KThreadSliceSize,
|
||||
XSrcYDstVectorDim,
|
||||
XSrcVectorSize,
|
||||
YDstVectorSize,
|
||||
ScaleSrcVectorSize,
|
||||
BiasSrcVectorSize,
|
||||
MeanVarSrcDstVectorSize>;
|
||||
|
||||
index_t numMeanVarCountBlockTileIteration =
|
||||
(arg.blkGroupSize_ + KThreadClusterSize - 1) / KThreadClusterSize;
|
||||
|
||||
const auto kern_multiblock_welford_first_half =
|
||||
kernel_multiblock_welford_first_half<GridwiseMultiblockWelfordFirstHalf_,
|
||||
XDataType,
|
||||
MeanVarDataType,
|
||||
XYGridDesc_M_K,
|
||||
MeanVarCountGridDesc_M_G,
|
||||
GetReduceCountPerThreadFunctor>;
|
||||
|
||||
const auto kern_welford_second_half_batchnorm_forward_final =
|
||||
kernel_welford_second_half_batchnorm_forward_final<
|
||||
GridwiseWelfordSecondHalfBatchNormForwardFinal_,
|
||||
XDataType,
|
||||
YDataType,
|
||||
AccDataType,
|
||||
ScaleDataType,
|
||||
BiasDataType,
|
||||
MeanVarDataType,
|
||||
YElementwiseOp,
|
||||
XYGridDesc_M_K,
|
||||
MeanVarCountGridDesc_M_K,
|
||||
ScaleBiasMeanVarGridDesc_M,
|
||||
ScaleBiasMeanVarGridDesc_M>;
|
||||
|
||||
avg_time +=
|
||||
launch_and_time_kernel(stream_config,
|
||||
kern_multiblock_welford_first_half,
|
||||
dim3(arg.gridSize_),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
arg.x_grid_desc_m_k_,
|
||||
mean_var_count_grid_desc_m_g,
|
||||
get_reduce_count_per_thread,
|
||||
arg.numBlockTileIteration_,
|
||||
arg.p_x_,
|
||||
static_cast<MeanVarDataType*>(arg.workspace_mean_),
|
||||
static_cast<MeanVarDataType*>(arg.workspace_variance_),
|
||||
static_cast<int32_t*>(arg.workspace_count_));
|
||||
|
||||
avg_time +=
|
||||
launch_and_time_kernel(stream_config,
|
||||
kern_welford_second_half_batchnorm_forward_final,
|
||||
dim3(arg.gridSize_),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
arg.x_grid_desc_m_k_,
|
||||
arg.y_grid_desc_m_k_,
|
||||
mean_var_count_grid_desc_m_k,
|
||||
arg.scale_grid_desc_m_,
|
||||
arg.bias_grid_desc_m_,
|
||||
arg.mean_var_grid_desc_m_,
|
||||
arg.blkGroupSize_,
|
||||
arg.numBlockTileIteration_,
|
||||
numMeanVarCountBlockTileIteration,
|
||||
arg.epsilon_,
|
||||
static_cast<MeanVarDataType*>(arg.workspace_mean_),
|
||||
static_cast<MeanVarDataType*>(arg.workspace_variance_),
|
||||
static_cast<int32_t*>(arg.workspace_count_),
|
||||
arg.p_x_,
|
||||
arg.p_scale_,
|
||||
arg.p_bias_,
|
||||
arg.y_elementwise_op_,
|
||||
arg.p_y_,
|
||||
arg.updateMovingAverage_,
|
||||
arg.averageFactor_,
|
||||
arg.resultRunningMean_,
|
||||
arg.resultRunningVariance_,
|
||||
arg.saveMeanInvVariance_,
|
||||
arg.resultSaveMean_,
|
||||
arg.resultSaveInvVariance_);
|
||||
}
|
||||
else
|
||||
{
|
||||
using GetReduceCountPerThreadFunctor =
|
||||
GetReduceCountPerThreadForBlockwiseWelford<K_BlockTileSize, KThreadSliceSize>;
|
||||
|
||||
GetReduceCountPerThreadFunctor get_reduce_count_per_thread(
|
||||
arg.numBlockTileIteration_, arg.reduce_length_);
|
||||
|
||||
using GridwiseBatchNormForwardWithBlockwiseWelford_ =
|
||||
GridwiseBatchNormForwardWithBlockwiseWelford<XDataType,
|
||||
YDataType,
|
||||
AccDataType,
|
||||
ScaleDataType,
|
||||
BiasDataType,
|
||||
MeanVarDataType,
|
||||
YElementwiseOp,
|
||||
XYGridDesc_M_K,
|
||||
ScaleBiasMeanVarGridDesc_M,
|
||||
ScaleBiasMeanVarGridDesc_M,
|
||||
GetReduceCountPerThreadFunctor,
|
||||
BlockSize,
|
||||
MThreadClusterSize,
|
||||
KThreadClusterSize,
|
||||
MThreadSliceSize,
|
||||
KThreadSliceSize,
|
||||
XSrcYDstVectorDim,
|
||||
XSrcVectorSize,
|
||||
YDstVectorSize,
|
||||
ScaleSrcVectorSize,
|
||||
BiasSrcVectorSize,
|
||||
MeanVarSrcDstVectorSize>;
|
||||
|
||||
const auto kern_batchnorm_fwd = kernel_batchnorm_forward_with_blockwise_welford<
|
||||
GridwiseBatchNormForwardWithBlockwiseWelford_,
|
||||
XDataType,
|
||||
YDataType,
|
||||
AccDataType,
|
||||
ScaleDataType,
|
||||
BiasDataType,
|
||||
MeanVarDataType,
|
||||
YElementwiseOp,
|
||||
XYGridDesc_M_K,
|
||||
ScaleBiasMeanVarGridDesc_M,
|
||||
ScaleBiasMeanVarGridDesc_M,
|
||||
GetReduceCountPerThreadFunctor>;
|
||||
|
||||
avg_time += launch_and_time_kernel(stream_config,
|
||||
kern_batchnorm_fwd,
|
||||
dim3(arg.gridSize_),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
arg.x_grid_desc_m_k_,
|
||||
arg.y_grid_desc_m_k_,
|
||||
arg.scale_grid_desc_m_,
|
||||
arg.bias_grid_desc_m_,
|
||||
arg.mean_var_grid_desc_m_,
|
||||
get_reduce_count_per_thread,
|
||||
arg.numBlockTileIteration_,
|
||||
arg.epsilon_,
|
||||
arg.p_x_,
|
||||
arg.p_scale_,
|
||||
arg.p_bias_,
|
||||
arg.y_elementwise_op_,
|
||||
arg.p_y_,
|
||||
arg.updateMovingAverage_, // true or false
|
||||
arg.averageFactor_,
|
||||
arg.resultRunningMean_,
|
||||
arg.resultRunningVariance_,
|
||||
arg.saveMeanInvVariance_, // true or false
|
||||
arg.resultSaveMean_,
|
||||
arg.resultSaveInvVariance_);
|
||||
};
|
||||
|
||||
return (avg_time);
|
||||
};
|
||||
|
||||
float Run(const BaseArgument* pArg,
|
||||
const StreamConfig& stream_config = StreamConfig{}) override
|
||||
{
|
||||
return Run(*dynamic_cast<const Argument*>(pArg), stream_config);
|
||||
};
|
||||
};
|
||||
|
||||
bool IsSupportedArgument(const BaseArgument* pArg) override
|
||||
{
|
||||
const Argument* pArg_ = dynamic_cast<const Argument*>(pArg);
|
||||
|
||||
if constexpr(XSrcYDstVectorDim == 0)
|
||||
{
|
||||
if(pArg_->xStrides_[NumInvariantDim - 1] != 1 ||
|
||||
pArg_->yStrides_[NumInvariantDim - 1] != 1)
|
||||
return false;
|
||||
|
||||
if(pArg_->xyLengths_[NumInvariantDim - 1] % XSrcVectorSize != 0 ||
|
||||
pArg_->xyLengths_[NumInvariantDim - 1] % YDstVectorSize != 0)
|
||||
return false;
|
||||
}
|
||||
else
|
||||
{
|
||||
if(pArg_->xStrides_[Rank - 1] != 1 || pArg_->yStrides_[Rank - 1] != 1)
|
||||
return false;
|
||||
|
||||
if(pArg_->xyLengths_[Rank - 1] % XSrcVectorSize != 0 ||
|
||||
pArg_->xyLengths_[Rank - 1] % YDstVectorSize != 0)
|
||||
return false;
|
||||
};
|
||||
|
||||
if(pArg_->bnScaleStrides_[NumInvariantDim - 1] != 1 && ScaleSrcVectorSize != 1)
|
||||
return false;
|
||||
if(pArg_->bnBiasStrides_[NumInvariantDim - 1] != 1 && BiasSrcVectorSize != 1)
|
||||
return false;
|
||||
|
||||
if(pArg_->bnScaleBiasMeanVarLengths_[NumInvariantDim - 1] % ScaleSrcVectorSize != 0)
|
||||
return false;
|
||||
if(pArg_->bnScaleBiasMeanVarLengths_[NumInvariantDim - 1] % BiasSrcVectorSize != 0)
|
||||
return false;
|
||||
|
||||
if(pArg_->bnMeanVarStrides_[NumInvariantDim - 1] != 1 && MeanVarSrcDstVectorSize != 1)
|
||||
return false;
|
||||
|
||||
if(pArg_->bnScaleBiasMeanVarLengths_[NumInvariantDim - 1] % MeanVarSrcDstVectorSize != 0)
|
||||
return false;
|
||||
|
||||
bool is_valid = true;
|
||||
|
||||
static_for<0, NumInvariantDim, 1>{}([&](auto I) {
|
||||
if(pArg_->xyLengths_[I] != pArg_->bnScaleBiasMeanVarLengths_[I])
|
||||
is_valid = false;
|
||||
});
|
||||
|
||||
if(!is_valid)
|
||||
return false;
|
||||
|
||||
return true;
|
||||
};
|
||||
|
||||
std::unique_ptr<BaseArgument> MakeArgumentPointer(
|
||||
const std::array<index_t, Rank> xyLengths,
|
||||
const std::array<index_t, Rank> xStrides,
|
||||
const std::array<index_t, Rank> yStrides,
|
||||
const std::array<int, NumBatchNormReduceDim> reduceDims,
|
||||
const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleBiasMeanVarLengths,
|
||||
const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleStrides,
|
||||
const std::array<index_t, Rank - NumBatchNormReduceDim> bnBiasStrides,
|
||||
const std::array<index_t, Rank - NumBatchNormReduceDim> bnMeanVarStrides,
|
||||
const void* p_x,
|
||||
const void* p_scale,
|
||||
const void* p_bias,
|
||||
double epsilon,
|
||||
const YElementwiseOp y_elementwise_op,
|
||||
void* p_y,
|
||||
void* resultSaveMean,
|
||||
void* resultSaveInvVariance,
|
||||
double averageFactor,
|
||||
void* resultRunningMean,
|
||||
void* resultRunningVariance) override
|
||||
{
|
||||
return std::make_unique<Argument>(xyLengths,
|
||||
xStrides,
|
||||
yStrides,
|
||||
reduceDims,
|
||||
bnScaleBiasMeanVarLengths,
|
||||
bnScaleStrides,
|
||||
bnBiasStrides,
|
||||
bnMeanVarStrides,
|
||||
static_cast<const XDataType*>(p_x),
|
||||
static_cast<const ScaleDataType*>(p_scale),
|
||||
static_cast<const BiasDataType*>(p_bias),
|
||||
y_elementwise_op,
|
||||
epsilon,
|
||||
static_cast<YDataType*>(p_y),
|
||||
static_cast<MeanVarDataType*>(resultSaveMean),
|
||||
static_cast<MeanVarDataType*>(resultSaveInvVariance),
|
||||
averageFactor,
|
||||
static_cast<MeanVarDataType*>(resultRunningMean),
|
||||
static_cast<MeanVarDataType*>(resultRunningVariance));
|
||||
};
|
||||
|
||||
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
||||
{
|
||||
return std::make_unique<Invoker>();
|
||||
};
|
||||
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
// clang-format off
|
||||
str << "DeviceBatchNormFwdImpl<" << BlockSize << ",";
|
||||
str << "M_C" << MThreadClusterSize << "_S" << MThreadSliceSize << ",";
|
||||
str << "K_C" << KThreadClusterSize << "_S" << KThreadSliceSize << ",";
|
||||
str << "XSrcYDstVectorDim_" << XSrcYDstVectorDim << ",";
|
||||
str << "VectorSize_X" << XSrcVectorSize << "_scale_" << ScaleSrcVectorSize << "_bias_" << BiasSrcVectorSize << "_mean_var_" << MeanVarSrcDstVectorSize << "_Y" << YDstVectorSize << ">";
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
89
include/ck/tensor_operation/gpu/device/welford_helper.hpp
Normal file
89
include/ck/tensor_operation/gpu/device/welford_helper.hpp
Normal file
@@ -0,0 +1,89 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
|
||||
template <index_t K_BlockTileSize, index_t KThreadSliceSize>
|
||||
struct GetReduceCountPerThreadForBlockwiseWelford
|
||||
{
|
||||
GetReduceCountPerThreadForBlockwiseWelford(index_t numBlockTileIteration,
|
||||
long_index_t reduce_length)
|
||||
: numBlockTileIteration_{numBlockTileIteration}
|
||||
{
|
||||
count_in_last_tile_ = reduce_length % K_BlockTileSize;
|
||||
};
|
||||
|
||||
__device__ index_t operator()(index_t thread_k_cluster_id) const
|
||||
{
|
||||
if(count_in_last_tile_ == 0)
|
||||
return (KThreadSliceSize * numBlockTileIteration_);
|
||||
else
|
||||
{
|
||||
index_t num_complete_slice = count_in_last_tile_ / KThreadSliceSize;
|
||||
index_t count_in_last_slice = count_in_last_tile_ % KThreadSliceSize;
|
||||
|
||||
if(thread_k_cluster_id < num_complete_slice)
|
||||
return (KThreadSliceSize * numBlockTileIteration_);
|
||||
else if(thread_k_cluster_id == num_complete_slice)
|
||||
return (KThreadSliceSize * (numBlockTileIteration_ - 1) + count_in_last_slice);
|
||||
else
|
||||
return (KThreadSliceSize * (numBlockTileIteration_ - 1));
|
||||
};
|
||||
};
|
||||
|
||||
index_t numBlockTileIteration_;
|
||||
index_t count_in_last_tile_;
|
||||
};
|
||||
|
||||
template <index_t K_BlockTileSize, index_t KThreadSliceSize>
|
||||
struct GetReduceCountPerThreadForMultiblockWelford
|
||||
{
|
||||
GetReduceCountPerThreadForMultiblockWelford(index_t blkGroupSize,
|
||||
index_t numBlockTileIteration,
|
||||
long_index_t reduce_length)
|
||||
: blkGroupSize_(blkGroupSize), numBlockTileIteration_{numBlockTileIteration}
|
||||
{
|
||||
last_block_reduce_length_ =
|
||||
reduce_length - K_BlockTileSize * numBlockTileIteration_ * (blkGroupSize_ - 1);
|
||||
numBlockTileIterationByLastBlock_ =
|
||||
(last_block_reduce_length_ + K_BlockTileSize - 1) / K_BlockTileSize;
|
||||
};
|
||||
|
||||
__device__ index_t operator()(index_t block_local_id, index_t thread_k_cluster_id) const
|
||||
{
|
||||
if(last_block_reduce_length_ == K_BlockTileSize * numBlockTileIteration_ ||
|
||||
block_local_id < blkGroupSize_ - 1)
|
||||
return (KThreadSliceSize * numBlockTileIteration_);
|
||||
|
||||
index_t count_in_last_tile = last_block_reduce_length_ % K_BlockTileSize;
|
||||
|
||||
if(count_in_last_tile == 0)
|
||||
return (KThreadSliceSize * numBlockTileIterationByLastBlock_);
|
||||
else
|
||||
{
|
||||
index_t num_complete_slice = count_in_last_tile / KThreadSliceSize;
|
||||
|
||||
if(thread_k_cluster_id < num_complete_slice)
|
||||
return (KThreadSliceSize * numBlockTileIterationByLastBlock_);
|
||||
else if(thread_k_cluster_id == num_complete_slice)
|
||||
return (KThreadSliceSize * (numBlockTileIterationByLastBlock_ - 1) +
|
||||
count_in_last_tile);
|
||||
else
|
||||
return (KThreadSliceSize * (numBlockTileIterationByLastBlock_ - 1));
|
||||
};
|
||||
};
|
||||
|
||||
index_t blkGroupSize_;
|
||||
index_t numBlockTileIteration_;
|
||||
|
||||
index_t last_block_reduce_length_;
|
||||
index_t numBlockTileIterationByLastBlock_;
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user