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Commit bbd19d89 authored by Qianfeng Zhang's avatar Qianfeng Zhang
Browse files

Implemented batchnorm-backward Blockwise and Multiblock kernels

parent b8825547
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/utility/data_type.hpp"
#include "ck/utility/math_v2.hpp"
#include "ck/utility/reduction_operator.hpp"
#include "ck/tensor_operation/gpu/block/blockwise_welford.hpp"
#include "ck/tensor_operation/gpu/block/reduction_functions_blockwise.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_welford.hpp"
#include "ck/tensor_operation/gpu/thread/reduction_functions_threadwise.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
namespace ck {
template <typename GridwiseBatchrNormBackwardWithBlockwiseWelford_,
typename XDataType,
typename DyDataType,
typename DxDataType,
typename AccDataType,
typename ScaleDataType,
typename BiasDataType,
typename MeanVarDataType,
typename XYGridDesc_M_K,
typename ScaleBiasGridDesc_M,
typename MeanVarGridDesc_M,
typename GetReduceCountPerThreadFunctor>
__global__ void kernel_batchnorm_backward_with_blockwise_welford(
const XYGridDesc_M_K x_grid_desc_m_k,
const XYGridDesc_M_K dy_grid_desc_m_k,
const XYGridDesc_M_K dx_grid_desc_m_k,
const ScaleBiasGridDesc_M scale_grid_desc_m,
const ScaleBiasGridDesc_M bias_grid_desc_m,
const MeanVarGridDesc_M mean_var_grid_desc_m,
const GetReduceCountPerThreadFunctor get_reduce_count_per_thread,
long_index_t reduce_size,
index_t num_k_block_tile_iteration,
AccDataType epsilon,
const XDataType* const __restrict__ p_x,
const DyDataType* const __restrict__ p_dy,
const ScaleDataType* const __restrict__ p_scale,
bool haveSavedMeanInvVar,
const MeanVarDataType* const __restrict__ p_savedMean,
const MeanVarDataType* const __restrict__ p_savedInvVar,
DxDataType* const __restrict__ p_dx,
ScaleDataType* const __restrict__ p_scale_diff,
BiasDataType* const __restrict__ p_bias_diff)
{
GridwiseBatchrNormBackwardWithBlockwiseWelford_::Run(x_grid_desc_m_k,
dy_grid_desc_m_k,
dx_grid_desc_m_k,
scale_grid_desc_m,
bias_grid_desc_m,
mean_var_grid_desc_m,
get_reduce_count_per_thread,
reduce_size,
num_k_block_tile_iteration,
epsilon,
p_x,
p_dy,
p_scale,
haveSavedMeanInvVar,
p_savedMean,
p_savedInvVar,
p_dx,
p_scale_diff,
p_bias_diff);
};
template <typename XDataType,
typename DyDataType,
typename DxDataType,
typename AccDataType,
typename ScaleDataType,
typename BiasDataType,
typename MeanVarDataType,
typename XYGridDesc_M_K,
typename ScaleBiasGridDesc_M,
typename MeanVarGridDesc_M,
typename GetReduceCountPerThreadFunctor,
index_t BlockSize,
index_t MThreadClusterSize,
index_t KThreadClusterSize,
index_t MThreadSliceSize,
index_t KThreadSliceSize,
index_t XDyDxVectorDim,
index_t XSrcVectorSize,
index_t DySrcVectorSize,
index_t DxDstVectorSize,
index_t ScaleSrcDstVectorSize,
index_t BiasDstVectorSize,
index_t MeanVarSrcVectorSize>
struct GridwiseBatchNormBackwardWithBlockwiseWelford
{
static_assert((XDyDxVectorDim == 0 && MThreadSliceSize % XSrcVectorSize == 0 &&
MThreadSliceSize % DySrcVectorSize == 0 &&
MThreadSliceSize % DxDstVectorSize == 0) ||
(XDyDxVectorDim == 1 && KThreadSliceSize % XSrcVectorSize == 0 &&
KThreadSliceSize % DySrcVectorSize == 0 &&
KThreadSliceSize % DxDstVectorSize == 0),
"Invalid thread slice sizes and/or vector sizes configuration, please check!");
static constexpr bool reorder_thread_cluster = (XDyDxVectorDim == 0);
using ThreadClusterLengths_M_K = Sequence<MThreadClusterSize, KThreadClusterSize>;
using ThreadBufferDimAccessOrder =
typename conditional<reorder_thread_cluster, Sequence<1, 0>, Sequence<0, 1>>::type;
using ThreadClusterArrangeOrder =
typename conditional<reorder_thread_cluster, Sequence<1, 0>, Sequence<0, 1>>::type;
static constexpr auto thread_cluster_desc =
make_cluster_descriptor(ThreadClusterLengths_M_K{}, ThreadClusterArrangeOrder{});
using ThreadReduceSrcDesc_M_K = decltype(make_naive_tensor_descriptor_packed(
make_tuple(Number<MThreadSliceSize>{}, Number<KThreadSliceSize>{})));
using ThreadReduceDstDesc_M =
decltype(make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{})));
using ThreadwiseWelford =
ThreadwiseWelford<AccDataType, ThreadReduceSrcDesc_M_K, ThreadReduceDstDesc_M>;
using BlockwiseWelford = BlockwiseWelford<AccDataType,
BlockSize,
ThreadClusterLengths_M_K,
ThreadClusterArrangeOrder>;
using BlockwiseReduce = PartitionedBlockwiseReduction<AccDataType,
BlockSize,
ThreadClusterLengths_M_K,
ThreadClusterArrangeOrder,
ck::reduce::Add,
false>;
using ThreadwiseReduce = ThreadwiseReduction<AccDataType,
ThreadReduceSrcDesc_M_K,
ThreadReduceDstDesc_M,
ck::reduce::Add,
false>;
using PassThroughOp = tensor_operation::element_wise::PassThrough;
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
static constexpr index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
static constexpr index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
__device__ static void Run(const XYGridDesc_M_K x_grid_desc_m_k,
const XYGridDesc_M_K dy_grid_desc_m_k,
const XYGridDesc_M_K dx_grid_desc_m_k,
const ScaleBiasGridDesc_M scale_grid_desc_m,
const ScaleBiasGridDesc_M bias_grid_desc_m,
const MeanVarGridDesc_M mean_var_grid_desc_m,
const GetReduceCountPerThreadFunctor get_reduce_count_per_thread,
long_index_t reduce_size,
index_t num_k_block_tile_iteration,
AccDataType epsilon,
const XDataType* const __restrict__ p_x,
const DyDataType* const __restrict__ p_dy,
const ScaleDataType* const __restrict__ p_scale,
bool haveSavedMeanInvVar,
const MeanVarDataType* const __restrict__ p_savedMean,
const MeanVarDataType* const __restrict__ p_savedInvVar,
DxDataType* const __restrict__ p_dx,
ScaleDataType* const __restrict__ p_scale_diff,
BiasDataType* const __restrict__ p_bias_diff)
{
using ck::math::sqrt;
__shared__ AccDataType p_reduce_work_buffer[BlockSize];
auto reduce_work_buf =
make_dynamic_buffer<AddressSpaceEnum::Lds>(p_reduce_work_buffer, BlockSize);
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
x_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
dy_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
dx_thread_buf;
// buffer of values of dy * (x-mean) * invVariance, used as input of Blockwise reduction
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
tmp1_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> scale_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> mean_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> var_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>&
inv_var_thread_buf = var_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
scale_diff_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
bias_diff_thread_buf;
const index_t thread_local_id = get_thread_local_1d_id();
const index_t block_global_id = get_block_1d_id();
const auto thread_cluster_idx =
thread_cluster_desc.CalculateBottomIndex(make_multi_index(thread_local_id));
const auto thread_m_cluster_id = thread_cluster_idx[I0];
const auto thread_k_cluster_id = thread_cluster_idx[I1];
using ThreadBufferLengths_M_K = Sequence<MThreadSliceSize, KThreadSliceSize>;
using ThreadBufferLengths_M = Sequence<MThreadSliceSize>;
constexpr auto thread_buffer_desc_m_k = make_naive_tensor_descriptor_packed(
make_tuple(Number<MThreadSliceSize>{}, Number<KThreadSliceSize>{}));
constexpr auto thread_buffer_desc_m =
make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}));
auto threadwise_x_load = ThreadwiseTensorSliceTransfer_v2<XDataType,
AccDataType,
XYGridDesc_M_K,
decltype(thread_buffer_desc_m_k),
ThreadBufferLengths_M_K,
ThreadBufferDimAccessOrder,
XDyDxVectorDim,
XSrcVectorSize,
1,
true>(
x_grid_desc_m_k,
make_multi_index(block_global_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize,
thread_k_cluster_id * KThreadSliceSize));
auto threadwise_dy_load = ThreadwiseTensorSliceTransfer_v2<DyDataType,
AccDataType,
XYGridDesc_M_K,
decltype(thread_buffer_desc_m_k),
ThreadBufferLengths_M_K,
ThreadBufferDimAccessOrder,
XDyDxVectorDim,
XSrcVectorSize,
1,
true>(
x_grid_desc_m_k,
make_multi_index(block_global_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize,
thread_k_cluster_id * KThreadSliceSize));
auto threadwise_dx_store =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
DxDataType,
decltype(thread_buffer_desc_m_k),
XYGridDesc_M_K,
PassThroughOp,
ThreadBufferLengths_M_K,
ThreadBufferDimAccessOrder,
XDyDxVectorDim,
DxDstVectorSize,
InMemoryDataOperationEnum::Set,
1,
true>(
dy_grid_desc_m_k,
make_multi_index(block_global_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize,
thread_k_cluster_id * KThreadSliceSize),
PassThroughOp{});
auto threadwise_scale_load =
ThreadwiseTensorSliceTransfer_v2<ScaleDataType,
AccDataType,
ScaleBiasGridDesc_M,
decltype(thread_buffer_desc_m),
ThreadBufferLengths_M,
Sequence<0>,
0,
ScaleSrcDstVectorSize,
1,
true>(
scale_grid_desc_m,
make_multi_index(block_global_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize));
auto threadwise_scale_diff_store =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
ScaleDataType,
decltype(thread_buffer_desc_m),
ScaleBiasGridDesc_M,
PassThroughOp,
ThreadBufferLengths_M,
Sequence<0>,
0,
ScaleSrcDstVectorSize,
InMemoryDataOperationEnum::Set,
1,
true>(
scale_grid_desc_m,
make_multi_index(block_global_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize),
PassThroughOp{});
auto threadwise_bias_diff_store =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
BiasDataType,
decltype(thread_buffer_desc_m),
ScaleBiasGridDesc_M,
PassThroughOp,
ThreadBufferLengths_M,
Sequence<0>,
0,
BiasDstVectorSize,
InMemoryDataOperationEnum::Set,
1,
true>(
bias_grid_desc_m,
make_multi_index(block_global_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize),
PassThroughOp{});
constexpr auto thread_copy_fwd_step_m_k = make_multi_index(0, K_BlockTileSize);
constexpr auto thread_copy_bwd_step_m_k = make_multi_index(0, -K_BlockTileSize);
const auto x_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_x, x_grid_desc_m_k.GetElementSpaceSize());
const auto dy_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_dy, dy_grid_desc_m_k.GetElementSpaceSize());
auto dx_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_dx, dx_grid_desc_m_k.GetElementSpaceSize());
const auto scale_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_scale, scale_grid_desc_m.GetElementSpaceSize());
auto scale_diff_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_scale_diff, scale_grid_desc_m.GetElementSpaceSize());
auto bias_diff_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_bias_diff, bias_grid_desc_m.GetElementSpaceSize());
if(haveSavedMeanInvVar)
{
const auto mean_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_savedMean, mean_var_grid_desc_m.GetElementSpaceSize());
const auto inv_var_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_savedInvVar, mean_var_grid_desc_m.GetElementSpaceSize());
auto threadwise_mean_inv_var_load =
ThreadwiseTensorSliceTransfer_v2<MeanVarDataType,
AccDataType,
MeanVarGridDesc_M,
decltype(thread_buffer_desc_m),
ThreadBufferLengths_M,
Sequence<0>,
0,
MeanVarSrcVectorSize,
1,
true>(
mean_var_grid_desc_m,
make_multi_index(block_global_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize));
threadwise_mean_inv_var_load.Run(mean_var_grid_desc_m,
mean_global_val_buf,
thread_buffer_desc_m,
make_tuple(I0),
mean_thread_buf);
threadwise_mean_inv_var_load.Run(mean_var_grid_desc_m,
inv_var_global_val_buf,
thread_buffer_desc_m,
make_tuple(I0),
inv_var_thread_buf);
}
else
{
auto threadwise_welford = ThreadwiseWelford();
threadwise_welford.max_count_ = get_reduce_count_per_thread(thread_k_cluster_id);
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
mean_thread_buf(I) = type_convert<AccDataType>(0.0f);
var_thread_buf(I) = type_convert<AccDataType>(0.0f);
});
for(index_t reducedTiles = 0; reducedTiles < num_k_block_tile_iteration; ++reducedTiles)
{
threadwise_x_load.Run(x_grid_desc_m_k,
x_global_val_buf,
thread_buffer_desc_m_k,
make_tuple(I0, I0),
x_thread_buf);
threadwise_x_load.MoveSrcSliceWindow(x_grid_desc_m_k, thread_copy_fwd_step_m_k);
threadwise_welford.Run(x_thread_buf, mean_thread_buf, var_thread_buf);
}
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
if constexpr(I > 0)
block_sync_lds();
int count = threadwise_welford.cur_count_;
BlockwiseWelford::Run(mean_thread_buf(I), var_thread_buf(I), count);
});
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
inv_var_thread_buf(I) =
type_convert<AccDataType>(1.0) / sqrt(var_thread_buf[I] + epsilon);
});
threadwise_x_load.SetSrcSliceOrigin(
x_grid_desc_m_k,
make_multi_index(block_global_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize,
thread_k_cluster_id * KThreadSliceSize));
};
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
scale_diff_thread_buf(I) = type_convert<AccDataType>(0);
bias_diff_thread_buf(I) = type_convert<AccDataType>(0);
});
for(index_t reducedTiles = 0; reducedTiles < num_k_block_tile_iteration; ++reducedTiles)
{
threadwise_x_load.Run(x_grid_desc_m_k,
x_global_val_buf,
thread_buffer_desc_m_k,
make_tuple(I0, I0),
x_thread_buf);
threadwise_dy_load.Run(dx_grid_desc_m_k,
dy_global_val_buf,
thread_buffer_desc_m_k,
make_tuple(I0, I0),
dy_thread_buf);
static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
static_for<0, KThreadSliceSize, 1>{}([&](auto iK) {
constexpr auto offset =
thread_buffer_desc_m_k.CalculateOffset(make_tuple(iM, iK));
AccDataType norm_x = (x_thread_buf[Number<offset>{}] - mean_thread_buf[iM]) *
inv_var_thread_buf[iM];
tmp1_thread_buf(Number<offset>{}) = norm_x * dy_thread_buf[Number<offset>{}];
});
});
ThreadwiseReduce::Reduce(tmp1_thread_buf, scale_diff_thread_buf);
ThreadwiseReduce::Reduce(dy_thread_buf, bias_diff_thread_buf);
threadwise_x_load.MoveSrcSliceWindow(x_grid_desc_m_k, thread_copy_fwd_step_m_k);
threadwise_dy_load.MoveSrcSliceWindow(dy_grid_desc_m_k, thread_copy_fwd_step_m_k);
};
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
BlockwiseReduce::Reduce(reduce_work_buf, scale_diff_thread_buf(I));
block_sync_lds();
BlockwiseReduce::Reduce(reduce_work_buf, bias_diff_thread_buf(I));
});
if(thread_k_cluster_id == 0)
{
threadwise_scale_diff_store.Run(thread_buffer_desc_m,
make_tuple(I0),
scale_diff_thread_buf,
scale_grid_desc_m,
scale_diff_global_val_buf);
threadwise_bias_diff_store.Run(thread_buffer_desc_m,
make_tuple(I0),
bias_diff_thread_buf,
bias_grid_desc_m,
bias_diff_global_val_buf);
};
threadwise_scale_load.Run(scale_grid_desc_m,
scale_global_val_buf,
thread_buffer_desc_m,
make_tuple(I0),
scale_thread_buf);
auto thread_copy_tail_m_k = (num_k_block_tile_iteration - 1) * thread_copy_fwd_step_m_k;
threadwise_x_load.MoveSrcSliceWindow(x_grid_desc_m_k, thread_copy_bwd_step_m_k);
threadwise_dy_load.MoveSrcSliceWindow(dy_grid_desc_m_k, thread_copy_bwd_step_m_k);
threadwise_dx_store.MoveDstSliceWindow(dx_grid_desc_m_k, thread_copy_tail_m_k);
for(index_t reducedTiles = 0; reducedTiles < num_k_block_tile_iteration; ++reducedTiles)
{
threadwise_x_load.Run(x_grid_desc_m_k,
x_global_val_buf,
thread_buffer_desc_m_k,
make_tuple(I0, I0),
x_thread_buf);
threadwise_dy_load.Run(dy_grid_desc_m_k,
dy_global_val_buf,
thread_buffer_desc_m_k,
make_tuple(I0, I0),
dy_thread_buf);
static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
static_for<0, KThreadSliceSize, 1>{}([&](auto iK) {
constexpr auto offset =
thread_buffer_desc_m_k.CalculateOffset(make_tuple(iM, iK));
AccDataType norm_x = (x_thread_buf[Number<offset>{}] - mean_thread_buf[iM]) *
inv_var_thread_buf[iM];
AccDataType tmpVal = norm_x * scale_diff_thread_buf[iM];
dx_thread_buf(Number<offset>{}) =
type_convert<AccDataType>(1.0) / type_convert<AccDataType>(reduce_size) *
inv_var_thread_buf[iM] * scale_thread_buf[iM] *
(type_convert<AccDataType>(reduce_size) * dy_thread_buf[Number<offset>{}] -
bias_diff_thread_buf[iM] - tmpVal);
});
});
threadwise_dx_store.Run(thread_buffer_desc_m_k,
make_tuple(I0, I0),
dx_thread_buf,
dx_grid_desc_m_k,
dx_global_val_buf);
threadwise_x_load.MoveSrcSliceWindow(x_grid_desc_m_k, thread_copy_bwd_step_m_k);
threadwise_dy_load.MoveSrcSliceWindow(dy_grid_desc_m_k, thread_copy_bwd_step_m_k);
threadwise_dx_store.MoveDstSliceWindow(dx_grid_desc_m_k, thread_copy_bwd_step_m_k);
}
}
};
} // namespace ck
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/utility/data_type.hpp"
#include "ck/tensor_operation/gpu/block/reduction_functions_blockwise.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_welford.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
namespace ck {
template <typename GridwiseReduceSecondHalfBatchNormBackwardFinal_,
typename XDataType,
typename DyDataType,
typename DxDataType,
typename ScaleDataType,
typename BiasDataType,
typename MeanVarDataType,
typename XYGridDesc_M_K,
typename ScaleBiasDiffGridDesc_M_K,
typename MeanVarGridDesc_M,
typename ScaleBiasGridDesc_M>
__global__ void kernel_reduce_second_half_batchnorm_backward_final(
const XYGridDesc_M_K x_grid_desc_m_k,
const XYGridDesc_M_K dy_grid_desc_m_k,
const XYGridDesc_M_K dx_grid_desc_m_k,
const ScaleBiasDiffGridDesc_M_K scale_bias_diff_grid_desc_m_k,
const MeanVarGridDesc_M mean_var_grid_desc_m,
const ScaleBiasGridDesc_M scale_grid_desc_m,
const ScaleBiasGridDesc_M bias_grid_desc_m,
index_t blkgroup_size,
long_index_t reduce_size,
index_t num_xy_k_block_tile_iteration,
index_t num_scale_bias_diff_k_block_tile_iteration,
const ScaleDataType* const __restrict__ p_reduce_scale_diff,
const BiasDataType* const __restrict__ p_reduce_bias_diff,
const MeanVarDataType* const __restrict__ p_mean,
const MeanVarDataType* const __restrict__ p_inv_var,
const XDataType* const __restrict__ p_x,
const DyDataType* const __restrict__ p_dy,
const ScaleDataType* const __restrict__ p_scale,
DxDataType* const __restrict__ p_dx,
ScaleDataType* const __restrict__ p_scale_diff,
BiasDataType* const __restrict__ p_bias_diff)
{
GridwiseReduceSecondHalfBatchNormBackwardFinal_::Run(x_grid_desc_m_k,
dy_grid_desc_m_k,
dx_grid_desc_m_k,
scale_bias_diff_grid_desc_m_k,
mean_var_grid_desc_m,
scale_grid_desc_m,
bias_grid_desc_m,
blkgroup_size,
reduce_size,
num_xy_k_block_tile_iteration,
num_scale_bias_diff_k_block_tile_iteration,
p_reduce_scale_diff,
p_reduce_bias_diff,
p_mean,
p_inv_var,
p_x,
p_dy,
p_scale,
p_dx,
p_scale_diff,
p_bias_diff);
};
template <typename XDataType,
typename DyDataType,
typename DxDataType,
typename AccDataType,
typename ScaleDataType,
typename BiasDataType,
typename MeanVarDataType,
typename XYGridDesc_M_K,
typename ScaleBiasDiffGridDesc_M_K,
typename MeanVarGridDesc_M,
typename ScaleBiasGridDesc_M,
index_t BlockSize,
index_t MThreadClusterSize,
index_t KThreadClusterSize,
index_t MThreadSliceSize,
index_t KThreadSliceSize,
index_t XDyDxVectorDim,
index_t XSrcVectorSize,
index_t DySrcVectorSize,
index_t DxDstVectorSize,
index_t ScaleSrcDstVectorSize,
index_t BiasDstVectorSize,
index_t MeanVarSrcVectorSize>
struct GridwiseReduceSecondHalfBatchNormBackwardFinal
{
static_assert((XDyDxVectorDim == 0 && MThreadSliceSize % XSrcVectorSize == 0 &&
MThreadSliceSize % DySrcVectorSize == 0 &&
MThreadSliceSize % DxDstVectorSize == 0) ||
(XDyDxVectorDim == 1 && KThreadSliceSize % XSrcVectorSize == 0 &&
KThreadSliceSize % DySrcVectorSize == 0 &&
KThreadSliceSize % DxDstVectorSize == 0),
"Invalid thread slice sizes and/or vector sizes configuration, please check!");
static constexpr bool reorder_thread_cluster = (XDyDxVectorDim == 0);
using ThreadClusterLengths_M_K = Sequence<MThreadClusterSize, KThreadClusterSize>;
using ThreadBufferDimAccessOrder =
typename conditional<reorder_thread_cluster, Sequence<1, 0>, Sequence<0, 1>>::type;
using ThreadClusterArrangeOrder =
typename conditional<reorder_thread_cluster, Sequence<1, 0>, Sequence<0, 1>>::type;
static constexpr auto thread_cluster_desc =
make_cluster_descriptor(ThreadClusterLengths_M_K{}, ThreadClusterArrangeOrder{});
using ThreadReduceSrcDesc_M_1 = decltype(
make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}, Number<1>{})));
using ThreadReduceDstDesc_M =
decltype(make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{})));
using BlockwiseReduce = PartitionedBlockwiseReduction<AccDataType,
BlockSize,
ThreadClusterLengths_M_K,
ThreadClusterArrangeOrder,
ck::reduce::Add,
false>;
using ThreadwiseReduce = ThreadwiseReduction<AccDataType,
ThreadReduceSrcDesc_M_1,
ThreadReduceDstDesc_M,
ck::reduce::Add,
false>;
using PassThroughOp = tensor_operation::element_wise::PassThrough;
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
static constexpr index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
static constexpr index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
__device__ static void Run(const XYGridDesc_M_K& x_grid_desc_m_k,
const XYGridDesc_M_K& dy_grid_desc_m_k,
const XYGridDesc_M_K& dx_grid_desc_m_k,
const ScaleBiasDiffGridDesc_M_K& scale_bias_diff_grid_desc_m_k,
const MeanVarGridDesc_M& mean_var_grid_desc_m,
const ScaleBiasGridDesc_M& scale_grid_desc_m,
const ScaleBiasGridDesc_M& bias_grid_desc_m,
index_t blkgroup_size,
long_index_t reduce_size,
index_t num_xy_k_block_tile_iteration,
index_t num_scale_bias_diff_k_block_tile_iteration,
const ScaleDataType* const __restrict__ p_reduce_scale_diff,
const BiasDataType* const __restrict__ p_reduce_bias_diff,
const MeanVarDataType* const __restrict__ p_mean,
const MeanVarDataType* const __restrict__ p_inv_var,
const XDataType* const __restrict__ p_x,
const DyDataType* const __restrict__ p_dy,
const ScaleDataType* const __restrict__ p_scale,
DxDataType* const __restrict__ p_dx,
ScaleDataType* const __restrict__ p_scale_diff,
BiasDataType* const __restrict__ p_bias_diff)
{
__shared__ AccDataType p_reduce_work_buffer[BlockSize];
auto reduce_work_buf =
make_dynamic_buffer<AddressSpaceEnum::Lds>(p_reduce_work_buffer, BlockSize);
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * 1, true>
reduce_scale_diff_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * 1, true>
reduce_bias_diff_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
scale_diff_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
bias_diff_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
x_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
dy_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
dx_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> mean_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
inv_var_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> scale_thread_buf;
const index_t thread_local_id = get_thread_local_1d_id();
const index_t block_global_id = get_block_1d_id();
const index_t blkgroup_id = block_global_id / blkgroup_size;
const index_t block_local_id = block_global_id % blkgroup_size;
const auto thread_cluster_idx =
thread_cluster_desc.CalculateBottomIndex(make_multi_index(thread_local_id));
const auto thread_m_cluster_id = thread_cluster_idx[I0];
const auto thread_k_cluster_id = thread_cluster_idx[I1];
using ThreadBufferLengths_M_K = Sequence<MThreadSliceSize, KThreadSliceSize>;
using ThreadBufferLengths_M = Sequence<MThreadSliceSize>;
using ThreadBufferLengths_M_1 = Sequence<MThreadSliceSize, 1>;
constexpr auto thread_buffer_desc_m_k = make_naive_tensor_descriptor_packed(
make_tuple(Number<MThreadSliceSize>{}, Number<KThreadSliceSize>{}));
constexpr auto thread_buffer_desc_m =
make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}));
constexpr auto thread_buffer_desc_m_1 = make_naive_tensor_descriptor_packed(
make_tuple(Number<MThreadSliceSize>{}, Number<1>{}));
// Step 1: do final reduction for scale_diff and bias_diff and output
auto threadwise_scale_diff_load_m_k =
ThreadwiseTensorSliceTransfer_v2<ScaleDataType,
AccDataType,
ScaleBiasDiffGridDesc_M_K,
decltype(thread_buffer_desc_m_1),
ThreadBufferLengths_M_1,
Sequence<0, 1>,
1,
1,
1,
true>(
scale_bias_diff_grid_desc_m_k,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize,
thread_k_cluster_id * 1));
auto threadwise_bias_diff_load_m_k =
ThreadwiseTensorSliceTransfer_v2<BiasDataType,
AccDataType,
ScaleBiasDiffGridDesc_M_K,
decltype(thread_buffer_desc_m_1),
ThreadBufferLengths_M_1,
Sequence<0, 1>,
1,
1,
1,
true>(
scale_bias_diff_grid_desc_m_k,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize,
thread_k_cluster_id * 1));
auto threadwise_scale_diff_store_m =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
ScaleDataType,
decltype(thread_buffer_desc_m),
ScaleBiasGridDesc_M,
PassThroughOp,
ThreadBufferLengths_M,
Sequence<0>,
0,
ScaleSrcDstVectorSize,
InMemoryDataOperationEnum::Set,
1,
true>(
scale_grid_desc_m,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize),
PassThroughOp{});
auto threadwise_bias_diff_store_m =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
BiasDataType,
decltype(thread_buffer_desc_m),
ScaleBiasGridDesc_M,
PassThroughOp,
ThreadBufferLengths_M,
Sequence<0>,
0,
BiasDstVectorSize,
InMemoryDataOperationEnum::Set,
1,
true>(
bias_grid_desc_m,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize),
PassThroughOp{});
const auto reduce_scale_diff_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_reduce_scale_diff, scale_bias_diff_grid_desc_m_k.GetElementSpaceSize());
const auto reduce_bias_diff_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_reduce_bias_diff, scale_bias_diff_grid_desc_m_k.GetElementSpaceSize());
auto scale_diff_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_scale_diff, scale_grid_desc_m.GetElementSpaceSize());
auto bias_diff_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_bias_diff, bias_grid_desc_m.GetElementSpaceSize());
constexpr auto scale_bias_diff_thread_copy_step_m_k =
make_multi_index(0, KThreadClusterSize * 1);
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
scale_diff_thread_buf(I) = type_convert<AccDataType>(0.0f);
bias_diff_thread_buf(I) = type_convert<AccDataType>(0.0f);
});
for(index_t reducedTiles = 0; reducedTiles < num_scale_bias_diff_k_block_tile_iteration;
++reducedTiles)
{
threadwise_scale_diff_load_m_k.Run(scale_bias_diff_grid_desc_m_k,
reduce_scale_diff_global_val_buf,
thread_buffer_desc_m_1,
make_tuple(I0, I0),
reduce_scale_diff_thread_buf);
threadwise_bias_diff_load_m_k.Run(scale_bias_diff_grid_desc_m_k,
reduce_bias_diff_global_val_buf,
thread_buffer_desc_m_1,
make_tuple(I0, I0),
reduce_bias_diff_thread_buf);
ThreadwiseReduce::Reduce(reduce_scale_diff_thread_buf, scale_diff_thread_buf);
ThreadwiseReduce::Reduce(reduce_bias_diff_thread_buf, bias_diff_thread_buf);
threadwise_scale_diff_load_m_k.MoveSrcSliceWindow(scale_bias_diff_grid_desc_m_k,
scale_bias_diff_thread_copy_step_m_k);
threadwise_bias_diff_load_m_k.MoveSrcSliceWindow(scale_bias_diff_grid_desc_m_k,
scale_bias_diff_thread_copy_step_m_k);
}
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
BlockwiseReduce::Reduce(reduce_work_buf, scale_diff_thread_buf(I));
block_sync_lds();
BlockwiseReduce::Reduce(reduce_work_buf, bias_diff_thread_buf(I));
});
threadwise_scale_diff_store_m.Run(thread_buffer_desc_m,
make_tuple(I0),
scale_diff_thread_buf,
scale_grid_desc_m,
scale_diff_global_val_buf);
threadwise_bias_diff_store_m.Run(thread_buffer_desc_m,
make_tuple(I0),
bias_diff_thread_buf,
bias_grid_desc_m,
bias_diff_global_val_buf);
// Step 2: calculate dx = 1/N * invVar * scale * (N * dy - biasDiff - scaleDiff * (x - mean)
// * invVar) and output
const index_t workSizePerBlock = K_BlockTileSize * num_xy_k_block_tile_iteration;
auto threadwise_x_load = ThreadwiseTensorSliceTransfer_v2<XDataType,
AccDataType,
XYGridDesc_M_K,
decltype(thread_buffer_desc_m_k),
ThreadBufferLengths_M_K,
ThreadBufferDimAccessOrder,
XDyDxVectorDim,
XSrcVectorSize,
1,
true>(
x_grid_desc_m_k,
make_multi_index(blkgroup_id * M_BlockTileSize + thread_m_cluster_id * MThreadSliceSize,
workSizePerBlock * block_local_id +
thread_k_cluster_id * KThreadSliceSize));
auto threadwise_dy_load = ThreadwiseTensorSliceTransfer_v2<DyDataType,
AccDataType,
XYGridDesc_M_K,
decltype(thread_buffer_desc_m_k),
ThreadBufferLengths_M_K,
ThreadBufferDimAccessOrder,
XDyDxVectorDim,
DySrcVectorSize,
1,
true>(
dy_grid_desc_m_k,
make_multi_index(blkgroup_id * M_BlockTileSize + thread_m_cluster_id * MThreadSliceSize,
workSizePerBlock * block_local_id +
thread_k_cluster_id * KThreadSliceSize));
auto threadwise_dx_store =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
DxDataType,
decltype(thread_buffer_desc_m_k),
XYGridDesc_M_K,
PassThroughOp,
ThreadBufferLengths_M_K,
ThreadBufferDimAccessOrder,
XDyDxVectorDim,
DxDstVectorSize,
InMemoryDataOperationEnum::Set,
1,
true>(
dx_grid_desc_m_k,
make_multi_index(
blkgroup_id * M_BlockTileSize + thread_m_cluster_id * MThreadSliceSize,
workSizePerBlock * block_local_id + thread_k_cluster_id * KThreadSliceSize),
PassThroughOp{});
auto threadwise_scale_load =
ThreadwiseTensorSliceTransfer_v2<ScaleDataType,
AccDataType,
ScaleBiasGridDesc_M,
decltype(thread_buffer_desc_m),
ThreadBufferLengths_M,
Sequence<0>,
0,
ScaleSrcDstVectorSize,
1,
true>(
scale_grid_desc_m,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize));
auto threadwise_mean_var_load =
ThreadwiseTensorSliceTransfer_v2<MeanVarDataType,
AccDataType,
MeanVarGridDesc_M,
decltype(thread_buffer_desc_m),
ThreadBufferLengths_M,
Sequence<0>,
0,
MeanVarSrcVectorSize,
1,
true>(
mean_var_grid_desc_m,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize));
const auto x_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_x, x_grid_desc_m_k.GetElementSpaceSize());
const auto dy_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_dy, dy_grid_desc_m_k.GetElementSpaceSize());
auto dx_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_dx, dx_grid_desc_m_k.GetElementSpaceSize());
const auto scale_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_scale, scale_grid_desc_m.GetElementSpaceSize());
const auto mean_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_mean, mean_var_grid_desc_m.GetElementSpaceSize());
const auto inv_var_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_inv_var, mean_var_grid_desc_m.GetElementSpaceSize());
threadwise_scale_load.Run(scale_grid_desc_m,
scale_global_val_buf,
thread_buffer_desc_m,
make_tuple(I0),
scale_thread_buf);
threadwise_mean_var_load.Run(mean_var_grid_desc_m,
mean_global_val_buf,
thread_buffer_desc_m,
make_tuple(I0),
mean_thread_buf);
threadwise_mean_var_load.Run(mean_var_grid_desc_m,
inv_var_global_val_buf,
thread_buffer_desc_m,
make_tuple(I0),
inv_var_thread_buf);
constexpr auto xy_thread_copy_step_m_k = make_multi_index(0, K_BlockTileSize);
for(index_t reducedTiles = 0; reducedTiles < num_xy_k_block_tile_iteration; ++reducedTiles)
{
threadwise_x_load.Run(x_grid_desc_m_k,
x_global_val_buf,
thread_buffer_desc_m_k,
make_tuple(I0, I0),
x_thread_buf);
threadwise_dy_load.Run(dy_grid_desc_m_k,
dy_global_val_buf,
thread_buffer_desc_m_k,
make_tuple(I0, I0),
dy_thread_buf);
static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
AccDataType multiplier = type_convert<AccDataType>(1.0) /
type_convert<AccDataType>(reduce_size) *
inv_var_thread_buf[iM] * scale_thread_buf[iM];
static_for<0, KThreadSliceSize, 1>{}([&](auto iK) {
constexpr auto offset =
thread_buffer_desc_m_k.CalculateOffset(make_tuple(iM, iK));
AccDataType norm_x = (x_thread_buf[Number<offset>{}] - mean_thread_buf[iM]) *
inv_var_thread_buf[iM];
AccDataType tmpVal = norm_x * scale_diff_thread_buf[iM];
dx_thread_buf(Number<offset>{}) =
multiplier *
(type_convert<AccDataType>(reduce_size) * dy_thread_buf[Number<offset>{}] -
bias_diff_thread_buf[iM] - tmpVal);
});
});
threadwise_dx_store.Run(thread_buffer_desc_m_k,
make_tuple(I0, I0),
dx_thread_buf,
dx_grid_desc_m_k,
dx_global_val_buf);
threadwise_x_load.MoveSrcSliceWindow(x_grid_desc_m_k, xy_thread_copy_step_m_k);
threadwise_dy_load.MoveSrcSliceWindow(dy_grid_desc_m_k, xy_thread_copy_step_m_k);
threadwise_dx_store.MoveDstSliceWindow(dx_grid_desc_m_k, xy_thread_copy_step_m_k);
}
};
};
} // namespace ck
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/utility/data_type.hpp"
#include "ck/utility/math.hpp"
#include "ck/tensor_operation/gpu/block/blockwise_welford.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_welford.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
namespace ck {
template <typename GridwiseMultiblockWelfordFirstHalf_,
typename XDataType,
typename MeanVarDataType,
typename XGridDesc_M_K,
typename MeanVarCountGridDesc_M_G,
typename GetReduceCountPerThreadFunctor>
__global__ void kernel_multiblock_welford_first_half(
const XGridDesc_M_K x_grid_desc_m_k,
const MeanVarCountGridDesc_M_G mean_var_count_grid_desc_m_g,
const GetReduceCountPerThreadFunctor get_reduce_count_per_thread,
index_t num_k_block_tile_iteration,
const XDataType* const __restrict__ p_x,
MeanVarDataType* const p_welford_mean,
MeanVarDataType* const p_welford_variance,
int32_t* const p_welford_count)
{
GridwiseMultiblockWelfordFirstHalf_::Run(x_grid_desc_m_k,
mean_var_count_grid_desc_m_g,
get_reduce_count_per_thread,
num_k_block_tile_iteration,
p_x,
p_welford_mean,
p_welford_variance,
p_welford_count);
};
template <typename XDataType,
typename AccDataType,
typename MeanVarDataType,
typename XGridDesc_M_K,
typename MeanVarCountGridDesc_M_G,
typename GetReduceCountPerThreadFunctor,
index_t BlockSize,
index_t MThreadClusterSize,
index_t KThreadClusterSize,
index_t MThreadSliceSize,
index_t KThreadSliceSize,
index_t XSrcCountSrcVectorDim,
index_t XSrcCountSrcVectorSize>
struct GridwiseMultiblockWelfordFirstHalf
{
static_assert((XSrcCountSrcVectorDim == 0 && MThreadSliceSize % XSrcCountSrcVectorSize == 0) ||
(XSrcCountSrcVectorDim == 1 &&
KThreadSliceSize % XSrcCountSrcVectorSize == 0),
"Invalid thread slice sizes and/or vector sizes configuration, please check!");
static constexpr bool reorder_thread_cluster = (XSrcCountSrcVectorDim == 0);
using ThreadClusterLengths_M_K = Sequence<MThreadClusterSize, KThreadClusterSize>;
using ThreadBufferDimAccessOrder =
typename conditional<reorder_thread_cluster, Sequence<1, 0>, Sequence<0, 1>>::type;
using ThreadClusterArrangeOrder =
typename conditional<reorder_thread_cluster, Sequence<1, 0>, Sequence<0, 1>>::type;
static constexpr auto thread_cluster_desc =
make_cluster_descriptor(ThreadClusterLengths_M_K{}, ThreadClusterArrangeOrder{});
using ThreadReduceSrcDesc_M_K = decltype(make_naive_tensor_descriptor_packed(
make_tuple(Number<MThreadSliceSize>{}, Number<KThreadSliceSize>{})));
using ThreadReduceDstDesc_M =
decltype(make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{})));
using ThreadwiseWelford =
ThreadwiseWelford<AccDataType, ThreadReduceSrcDesc_M_K, ThreadReduceDstDesc_M>;
using BlockwiseWelford = BlockwiseWelford<AccDataType,
BlockSize,
ThreadClusterLengths_M_K,
ThreadClusterArrangeOrder,
false>;
using PassThroughOp = tensor_operation::element_wise::PassThrough;
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
static constexpr index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
static constexpr index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
__device__ static void Run(const XGridDesc_M_K& x_grid_desc_m_k,
const MeanVarCountGridDesc_M_G& mean_var_count_grid_desc_m_g,
const GetReduceCountPerThreadFunctor& get_reduce_count_per_thread,
index_t num_k_block_tile_iteration,
const XDataType* const __restrict__ p_x,
MeanVarDataType* const p_welford_mean,
MeanVarDataType* const p_welford_variance,
int32_t* const p_welford_count)
{
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
x_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
welford_mean_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
welford_var_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, int32_t, MThreadSliceSize, true>
welford_count_thread_buf;
const index_t blkgroup_size = mean_var_count_grid_desc_m_g.GetLength(I1);
const index_t thread_local_id = get_thread_local_1d_id();
const index_t block_global_id = get_block_1d_id();
const index_t blkgroup_id = block_global_id / blkgroup_size;
const index_t block_local_id = block_global_id % blkgroup_size;
const auto thread_cluster_idx =
thread_cluster_desc.CalculateBottomIndex(make_multi_index(thread_local_id));
const auto thread_m_cluster_id = thread_cluster_idx[I0];
const auto thread_k_cluster_id = thread_cluster_idx[I1];
using ThreadBufferLengths_M_K = Sequence<MThreadSliceSize, KThreadSliceSize>;
using ThreadBufferLengths_M_1 = Sequence<MThreadSliceSize, 1>;
constexpr auto thread_buffer_desc_m_k = make_naive_tensor_descriptor_packed(
make_tuple(Number<MThreadSliceSize>{}, Number<KThreadSliceSize>{}));
constexpr auto thread_buffer_desc_m_1 = make_naive_tensor_descriptor_packed(
make_tuple(Number<MThreadSliceSize>{}, Number<1>{}));
const index_t reduceSizePerBlock = K_BlockTileSize * num_k_block_tile_iteration;
auto threadwise_x_load = ThreadwiseTensorSliceTransfer_v2<XDataType,
AccDataType,
XGridDesc_M_K,
decltype(thread_buffer_desc_m_k),
ThreadBufferLengths_M_K,
ThreadBufferDimAccessOrder,
XSrcCountSrcVectorDim,
XSrcCountSrcVectorSize,
1,
true>(
x_grid_desc_m_k,
make_multi_index(blkgroup_id * M_BlockTileSize + thread_m_cluster_id * MThreadSliceSize,
block_local_id * reduceSizePerBlock +
thread_k_cluster_id * KThreadSliceSize));
auto threadwise_welford_mean_var_store =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
MeanVarDataType,
decltype(thread_buffer_desc_m_1),
MeanVarCountGridDesc_M_G,
PassThroughOp,
ThreadBufferLengths_M_1,
Sequence<0, 1>,
1,
1,
InMemoryDataOperationEnum::Set,
1,
true>(
mean_var_count_grid_desc_m_g,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize,
block_local_id),
PassThroughOp{});
auto threadwise_welford_count_store =
ThreadwiseTensorSliceTransfer_v1r3<int32_t,
int32_t,
decltype(thread_buffer_desc_m_1),
MeanVarCountGridDesc_M_G,
PassThroughOp,
ThreadBufferLengths_M_1,
Sequence<0, 1>,
1,
1,
InMemoryDataOperationEnum::Set,
1,
true>(
mean_var_count_grid_desc_m_g,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize,
block_local_id),
PassThroughOp{});
constexpr auto thread_copy_fwd_step_m_k = make_multi_index(0, K_BlockTileSize);
const auto x_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_x, x_grid_desc_m_k.GetElementSpaceSize());
auto welford_mean_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_welford_mean, mean_var_count_grid_desc_m_g.GetElementSpaceSize());
auto welford_var_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_welford_variance, mean_var_count_grid_desc_m_g.GetElementSpaceSize());
auto welford_count_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_welford_count, mean_var_count_grid_desc_m_g.GetElementSpaceSize());
auto threadwise_welford = ThreadwiseWelford();
threadwise_welford.max_count_ =
get_reduce_count_per_thread(block_local_id, thread_k_cluster_id);
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
welford_mean_thread_buf(I) = type_convert<AccDataType>(0.0f);
welford_var_thread_buf(I) = type_convert<AccDataType>(0.0f);
});
for(index_t reducedTiles = 0; reducedTiles < num_k_block_tile_iteration; ++reducedTiles)
{
threadwise_x_load.Run(x_grid_desc_m_k,
x_global_val_buf,
thread_buffer_desc_m_k,
make_tuple(I0, I0),
x_thread_buf);
threadwise_x_load.MoveSrcSliceWindow(x_grid_desc_m_k, thread_copy_fwd_step_m_k);
threadwise_welford.Run(x_thread_buf, welford_mean_thread_buf, welford_var_thread_buf);
}
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
if constexpr(I > 0)
block_sync_lds();
welford_count_thread_buf(I) = threadwise_welford.cur_count_;
BlockwiseWelford::Run(
welford_mean_thread_buf(I), welford_var_thread_buf(I), welford_count_thread_buf(I));
});
if(thread_k_cluster_id == 0)
{
threadwise_welford_mean_var_store.Run(thread_buffer_desc_m_1,
make_tuple(I0, I0),
welford_mean_thread_buf,
mean_var_count_grid_desc_m_g,
welford_mean_global_val_buf);
threadwise_welford_mean_var_store.Run(thread_buffer_desc_m_1,
make_tuple(I0, I0),
welford_var_thread_buf,
mean_var_count_grid_desc_m_g,
welford_var_global_val_buf);
threadwise_welford_count_store.Run(thread_buffer_desc_m_1,
make_tuple(I0, I0),
welford_count_thread_buf,
mean_var_count_grid_desc_m_g,
welford_count_global_val_buf);
};
}
};
} // namespace ck
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/utility/data_type.hpp"
#include "ck/tensor_operation/gpu/block/blockwise_welford.hpp"
#include "ck/tensor_operation/gpu/block/reduction_functions_blockwise.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_welford.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
namespace ck {
template <typename GridwiseWelfordSecondHalfReduceFirstHalf_,
typename XDataType,
typename DyDataType,
typename AccDataType,
typename ScaleDataType,
typename BiasDataType,
typename MeanVarDataType,
typename XYGridDesc_M_K,
typename MeanVarGridDesc_M,
typename MeanVarCountGridDesc_M_K,
typename ScaleBiasDiffGridDesc_M_G>
__global__ void kernel_welford_second_half_reduce_first_half(
const XYGridDesc_M_K x_grid_desc_m_k,
const XYGridDesc_M_K dy_grid_desc_m_k,
const MeanVarGridDesc_M mean_var_grid_desc_m,
const MeanVarCountGridDesc_M_K mean_var_count_grid_desc_m_k,
const ScaleBiasDiffGridDesc_M_G scale_bias_grid_desc_m_g,
index_t blkgroup_size,
index_t num_xy_k_block_tile_iteration,
index_t num_mean_var_count_k_block_tile_iteration,
AccDataType epsilon,
bool haveSavedMeanInvVar,
const MeanVarDataType* const __restrict__ p_savedMean,
const MeanVarDataType* const __restrict__ p_savedInvVar,
const MeanVarDataType* const __restrict__ p_in_welford_mean,
const MeanVarDataType* const __restrict__ p_in_welford_variance,
const int32_t* const __restrict__ p_in_welford_count,
MeanVarDataType* const __restrict__ p_out_welford_mean,
MeanVarDataType* const __restrict__ p_out_welford_inv_variance,
const XDataType* const __restrict__ p_x,
const DyDataType* const __restrict__ p_dy,
ScaleDataType* const __restrict__ p_reduce_scale_diff,
BiasDataType* const __restrict__ p_reduce_bias_diff)
{
GridwiseWelfordSecondHalfReduceFirstHalf_::Run(x_grid_desc_m_k,
dy_grid_desc_m_k,
mean_var_grid_desc_m,
mean_var_count_grid_desc_m_k,
scale_bias_grid_desc_m_g,
blkgroup_size,
num_xy_k_block_tile_iteration,
num_mean_var_count_k_block_tile_iteration,
epsilon,
haveSavedMeanInvVar,
p_savedMean,
p_savedInvVar,
p_in_welford_mean,
p_in_welford_variance,
p_in_welford_count,
p_out_welford_mean,
p_out_welford_inv_variance,
p_x,
p_dy,
p_reduce_scale_diff,
p_reduce_bias_diff);
};
template <typename XDataType,
typename DyDataType,
typename AccDataType,
typename ScaleDataType,
typename BiasDataType,
typename MeanVarDataType,
typename XYGridDesc_M_K,
typename MeanVarGridDesc_M,
typename MeanVarCountGridDesc_M_K,
typename ScaleBiasDiffGridDesc_M_G,
index_t BlockSize,
index_t MThreadClusterSize,
index_t KThreadClusterSize,
index_t MThreadSliceSize,
index_t KThreadSliceSize,
index_t XDyVectorDim,
index_t XSrcVectorSize,
index_t DySrcVectorSize,
index_t MeanVarSrcVectorSize>
struct GridwiseWelfordSecondHalfReduceFirstHalf
{
static_assert((XDyVectorDim == 0 && MThreadSliceSize % XSrcVectorSize == 0 &&
MThreadSliceSize % DySrcVectorSize == 0) ||
(XDyVectorDim == 1 && KThreadSliceSize % XSrcVectorSize == 0 &&
KThreadSliceSize % DySrcVectorSize == 0),
"Invalid thread slice sizes and/or vector sizes configuration, please check!");
static constexpr bool reorder_thread_cluster = (XDyVectorDim == 0);
using ThreadClusterLengths_M_K = Sequence<MThreadClusterSize, KThreadClusterSize>;
using ThreadBufferDimAccessOrder =
typename conditional<reorder_thread_cluster, Sequence<1, 0>, Sequence<0, 1>>::type;
using ThreadClusterArrangeOrder =
typename conditional<reorder_thread_cluster, Sequence<1, 0>, Sequence<0, 1>>::type;
static constexpr auto thread_cluster_desc =
make_cluster_descriptor(ThreadClusterLengths_M_K{}, ThreadClusterArrangeOrder{});
using ThreadReduceSrcDesc_M_K = decltype(make_naive_tensor_descriptor_packed(
make_tuple(Number<MThreadSliceSize>{}, Number<KThreadSliceSize>{})));
using ThreadReduceSrcDesc_M_1 = decltype(
make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}, Number<1>{})));
using ThreadReduceDstDesc_M =
decltype(make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{})));
using ThreadwiseWelford =
ThreadwiseWelford_2<AccDataType, ThreadReduceSrcDesc_M_1, ThreadReduceDstDesc_M>;
using BlockwiseWelford = BlockwiseWelford<AccDataType,
BlockSize,
ThreadClusterLengths_M_K,
ThreadClusterArrangeOrder>;
using BlockwiseReduce = PartitionedBlockwiseReduction<AccDataType,
BlockSize,
ThreadClusterLengths_M_K,
ThreadClusterArrangeOrder,
ck::reduce::Add,
false>;
using ThreadwiseReduce = ThreadwiseReduction<AccDataType,
ThreadReduceSrcDesc_M_K,
ThreadReduceDstDesc_M,
ck::reduce::Add,
false>;
using PassThroughOp = tensor_operation::element_wise::PassThrough;
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
static constexpr index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
static constexpr index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
__device__ static void Run(const XYGridDesc_M_K& x_grid_desc_m_k,
const XYGridDesc_M_K& dy_grid_desc_m_k,
const MeanVarGridDesc_M& mean_var_grid_desc_m,
const MeanVarCountGridDesc_M_K& mean_var_count_grid_desc_m_k,
const ScaleBiasDiffGridDesc_M_G& scale_bias_diff_grid_desc_m_g,
index_t blkgroup_size,
index_t num_xy_k_block_tile_iteration,
index_t num_mean_var_count_k_block_tile_iteration,
AccDataType epsilon,
bool haveSavedMeanInvVar,
const MeanVarDataType* const __restrict__ p_savedMean,
const MeanVarDataType* const __restrict__ p_savedInvVar,
const MeanVarDataType* const __restrict__ p_in_welford_mean,
const MeanVarDataType* const __restrict__ p_in_welford_variance,
const int32_t* const __restrict__ p_in_welford_count,
MeanVarDataType* const __restrict__ p_out_welford_mean,
MeanVarDataType* const __restrict__ p_out_welford_inv_variance,
const XDataType* const __restrict__ p_x,
const DyDataType* const __restrict__ p_dy,
ScaleDataType* const __restrict__ p_reduce_scale_diff,
BiasDataType* const __restrict__ p_reduce_bias_diff)
{
__shared__ AccDataType p_reduce_work_buffer[BlockSize];
auto reduce_work_buf =
make_dynamic_buffer<AddressSpaceEnum::Lds>(p_reduce_work_buffer, BlockSize);
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * 1, true>
in_welford_mean_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * 1, true>
in_welford_var_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, int32_t, MThreadSliceSize * 1, true>
in_welford_count_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
welford_mean_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
welford_var_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, int32_t, MThreadSliceSize, true>
welford_count_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>& mean_thread_buf =
welford_mean_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>&
inv_var_thread_buf = welford_var_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
x_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
dy_thread_buf;
// buffer of values of dy * (x-mean) * invVariance, used as input of Blockwise reduction
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
tmp1_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
reduce_scale_diff_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
reduce_bias_diff_thread_buf;
const index_t thread_local_id = get_thread_local_1d_id();
const index_t block_global_id = get_block_1d_id();
const index_t blkgroup_id = block_global_id / blkgroup_size;
const index_t block_local_id = block_global_id % blkgroup_size;
const auto thread_cluster_idx =
thread_cluster_desc.CalculateBottomIndex(make_multi_index(thread_local_id));
const auto thread_m_cluster_id = thread_cluster_idx[I0];
const auto thread_k_cluster_id = thread_cluster_idx[I1];
using ThreadBufferLengths_M_K = Sequence<MThreadSliceSize, KThreadSliceSize>;
using ThreadBufferLengths_M = Sequence<MThreadSliceSize>;
using ThreadBufferLengths_M_1 = Sequence<MThreadSliceSize, 1>;
constexpr auto thread_buffer_desc_m_k = make_naive_tensor_descriptor_packed(
make_tuple(Number<MThreadSliceSize>{}, Number<KThreadSliceSize>{}));
constexpr auto thread_buffer_desc_m =
make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}));
constexpr auto thread_buffer_desc_m_1 = make_naive_tensor_descriptor_packed(
make_tuple(Number<MThreadSliceSize>{}, Number<1>{}));
// Step 1: load existing mean and inv-variance do final welford reduction on mean and
// variance
if(haveSavedMeanInvVar)
{
const auto mean_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_savedMean, mean_var_grid_desc_m.GetElementSpaceSize());
const auto inv_var_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_savedInvVar, mean_var_grid_desc_m.GetElementSpaceSize());
auto threadwise_mean_inv_var_load =
ThreadwiseTensorSliceTransfer_v2<MeanVarDataType,
AccDataType,
MeanVarGridDesc_M,
decltype(thread_buffer_desc_m),
ThreadBufferLengths_M,
Sequence<0>,
0,
MeanVarSrcVectorSize,
1,
true>(
mean_var_grid_desc_m,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize));
threadwise_mean_inv_var_load.Run(mean_var_grid_desc_m,
mean_global_val_buf,
thread_buffer_desc_m,
make_tuple(I0),
mean_thread_buf);
threadwise_mean_inv_var_load.Run(mean_var_grid_desc_m,
inv_var_global_val_buf,
thread_buffer_desc_m,
make_tuple(I0),
inv_var_thread_buf);
}
else
{
const auto welford_mean_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_in_welford_mean, mean_var_count_grid_desc_m_k.GetElementSpaceSize());
const auto welford_var_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_in_welford_variance, mean_var_count_grid_desc_m_k.GetElementSpaceSize());
const auto welford_count_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_in_welford_count, mean_var_count_grid_desc_m_k.GetElementSpaceSize());
auto threadwise_mean_var_load_m_k =
ThreadwiseTensorSliceTransfer_v2<AccDataType,
AccDataType,
MeanVarCountGridDesc_M_K,
decltype(thread_buffer_desc_m_1),
ThreadBufferLengths_M_1,
Sequence<0, 1>,
1,
1,
1,
true>(
mean_var_count_grid_desc_m_k,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize,
thread_k_cluster_id * 1));
auto threadwise_count_load_m_k =
ThreadwiseTensorSliceTransfer_v2<int32_t,
int32_t,
MeanVarCountGridDesc_M_K,
decltype(thread_buffer_desc_m_1),
ThreadBufferLengths_M_1,
Sequence<0, 1>,
1,
1,
1,
true>(
mean_var_count_grid_desc_m_k,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize,
thread_k_cluster_id * 1));
constexpr auto mean_var_count_thread_copy_step_m_k =
make_multi_index(0, KThreadClusterSize * 1);
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
welford_mean_thread_buf(I) = type_convert<AccDataType>(0.0f);
welford_var_thread_buf(I) = type_convert<AccDataType>(0.0f);
welford_count_thread_buf(I) = 0;
});
for(index_t reducedTiles = 0; reducedTiles < num_mean_var_count_k_block_tile_iteration;
++reducedTiles)
{
threadwise_mean_var_load_m_k.Run(mean_var_count_grid_desc_m_k,
welford_mean_global_val_buf,
thread_buffer_desc_m_1,
make_tuple(I0, I0),
in_welford_mean_thread_buf);
threadwise_mean_var_load_m_k.Run(mean_var_count_grid_desc_m_k,
welford_var_global_val_buf,
thread_buffer_desc_m_1,
make_tuple(I0, I0),
in_welford_var_thread_buf);
threadwise_count_load_m_k.Run(mean_var_count_grid_desc_m_k,
welford_count_global_val_buf,
thread_buffer_desc_m_1,
make_tuple(I0, I0),
in_welford_count_thread_buf);
ThreadwiseWelford::Run(in_welford_mean_thread_buf,
in_welford_var_thread_buf,
in_welford_count_thread_buf,
welford_mean_thread_buf,
welford_var_thread_buf,
welford_count_thread_buf);
threadwise_mean_var_load_m_k.MoveSrcSliceWindow(
mean_var_count_grid_desc_m_k, mean_var_count_thread_copy_step_m_k);
threadwise_count_load_m_k.MoveSrcSliceWindow(mean_var_count_grid_desc_m_k,
mean_var_count_thread_copy_step_m_k);
}
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
if constexpr(I > 0)
block_sync_lds();
BlockwiseWelford::Run(welford_mean_thread_buf(I),
welford_var_thread_buf(I),
welford_count_thread_buf(I));
});
// calculate inv-variance from variance, stored in place
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
welford_var_thread_buf(I) =
type_convert<AccDataType>(1.0) / sqrt(welford_var_thread_buf[I] + epsilon);
});
if(block_local_id == 0 && thread_k_cluster_id == 0)
{
auto threadwise_mean_inv_var_store =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
MeanVarDataType,
decltype(thread_buffer_desc_m),
MeanVarGridDesc_M,
PassThroughOp,
ThreadBufferLengths_M,
Sequence<0>,
0,
1,
InMemoryDataOperationEnum::Set,
1,
true>(
mean_var_grid_desc_m,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize),
PassThroughOp{});
auto mean_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_out_welford_mean, mean_var_grid_desc_m.GetElementSpaceSize());
auto inv_var_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_out_welford_inv_variance, mean_var_grid_desc_m.GetElementSpaceSize());
threadwise_mean_inv_var_store.Run(thread_buffer_desc_m,
make_tuple(I0),
mean_thread_buf,
mean_var_grid_desc_m,
mean_global_val_buf);
threadwise_mean_inv_var_store.Run(thread_buffer_desc_m,
make_tuple(I0),
inv_var_thread_buf,
mean_var_grid_desc_m,
inv_var_global_val_buf);
};
};
const index_t workSizePerBlock = K_BlockTileSize * num_xy_k_block_tile_iteration;
auto threadwise_x_load = ThreadwiseTensorSliceTransfer_v2<XDataType,
AccDataType,
XYGridDesc_M_K,
decltype(thread_buffer_desc_m_k),
ThreadBufferLengths_M_K,
ThreadBufferDimAccessOrder,
XDyVectorDim,
XSrcVectorSize,
1,
true>(
x_grid_desc_m_k,
make_multi_index(blkgroup_id * M_BlockTileSize + thread_m_cluster_id * MThreadSliceSize,
workSizePerBlock * block_local_id +
thread_k_cluster_id * KThreadSliceSize));
auto threadwise_dy_load = ThreadwiseTensorSliceTransfer_v2<DyDataType,
AccDataType,
XYGridDesc_M_K,
decltype(thread_buffer_desc_m_k),
ThreadBufferLengths_M_K,
ThreadBufferDimAccessOrder,
XDyVectorDim,
DySrcVectorSize,
1,
true>(
dy_grid_desc_m_k,
make_multi_index(blkgroup_id * M_BlockTileSize + thread_m_cluster_id * MThreadSliceSize,
workSizePerBlock * block_local_id +
thread_k_cluster_id * KThreadSliceSize));
const auto x_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_x, x_grid_desc_m_k.GetElementSpaceSize());
const auto dy_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_dy, dy_grid_desc_m_k.GetElementSpaceSize());
constexpr auto xy_thread_copy_step_m_k = make_multi_index(0, K_BlockTileSize);
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
reduce_scale_diff_thread_buf(I) = type_convert<AccDataType>(0);
reduce_bias_diff_thread_buf(I) = type_convert<AccDataType>(0);
});
// Step 2: do first-half reduction on dy and dy * (x-mean) * inv-variance
for(index_t reducedTiles = 0; reducedTiles < num_xy_k_block_tile_iteration; ++reducedTiles)
{
threadwise_x_load.Run(x_grid_desc_m_k,
x_global_val_buf,
thread_buffer_desc_m_k,
make_tuple(I0, I0),
x_thread_buf);
threadwise_dy_load.Run(dy_grid_desc_m_k,
dy_global_val_buf,
thread_buffer_desc_m_k,
make_tuple(I0, I0),
dy_thread_buf);
static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
static_for<0, KThreadSliceSize, 1>{}([&](auto iK) {
constexpr auto offset =
thread_buffer_desc_m_k.CalculateOffset(make_tuple(iM, iK));
AccDataType norm_x = (x_thread_buf[Number<offset>{}] - mean_thread_buf[iM]) *
inv_var_thread_buf[iM];
tmp1_thread_buf(Number<offset>{}) = norm_x * dy_thread_buf[Number<offset>{}];
});
});
ThreadwiseReduce::Reduce(tmp1_thread_buf, reduce_scale_diff_thread_buf);
ThreadwiseReduce::Reduce(dy_thread_buf, reduce_bias_diff_thread_buf);
threadwise_x_load.MoveSrcSliceWindow(x_grid_desc_m_k, xy_thread_copy_step_m_k);
threadwise_dy_load.MoveSrcSliceWindow(dy_grid_desc_m_k, xy_thread_copy_step_m_k);
};
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
BlockwiseReduce::Reduce(reduce_work_buf, reduce_scale_diff_thread_buf(I));
block_sync_lds();
BlockwiseReduce::Reduce(reduce_work_buf, reduce_bias_diff_thread_buf(I));
});
auto threadwise_scale_diff_store =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
ScaleDataType,
decltype(thread_buffer_desc_m_1),
ScaleBiasDiffGridDesc_M_G,
PassThroughOp,
ThreadBufferLengths_M_1,
Sequence<0, 1>,
1,
1,
InMemoryDataOperationEnum::Set,
1,
true>(
scale_bias_diff_grid_desc_m_g,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize,
block_local_id),
PassThroughOp{});
auto threadwise_bias_diff_store =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
BiasDataType,
decltype(thread_buffer_desc_m_1),
ScaleBiasDiffGridDesc_M_G,
PassThroughOp,
ThreadBufferLengths_M_1,
Sequence<0, 1>,
1,
1,
InMemoryDataOperationEnum::Set,
1,
true>(
scale_bias_diff_grid_desc_m_g,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize,
block_local_id),
PassThroughOp{});
auto reduce_scale_diff_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_reduce_scale_diff, scale_bias_diff_grid_desc_m_g.GetElementSpaceSize());
auto reduce_bias_diff_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_reduce_bias_diff, scale_bias_diff_grid_desc_m_g.GetElementSpaceSize());
if(thread_k_cluster_id == 0)
{
threadwise_scale_diff_store.Run(thread_buffer_desc_m_1,
make_tuple(I0, I0),
reduce_scale_diff_thread_buf,
scale_bias_diff_grid_desc_m_g,
reduce_scale_diff_global_val_buf);
threadwise_bias_diff_store.Run(thread_buffer_desc_m_1,
make_tuple(I0, I0),
reduce_bias_diff_thread_buf,
scale_bias_diff_grid_desc_m_g,
reduce_bias_diff_global_val_buf);
};
};
};
} // namespace ck
......@@ -75,4 +75,55 @@ struct ThreadwiseWelford
int max_count_;
};
template <typename T, typename SrcMeanVarCountThreadDesc_M_K, typename DstMeanVarThreadDesc_M>
struct ThreadwiseWelford_2
{
static constexpr auto src_thread_desc_m_k = SrcMeanVarCountThreadDesc_M_K{};
static constexpr auto dst_thread_desc_m = DstMeanVarThreadDesc_M{};
static constexpr auto src_length_m = src_thread_desc_m_k.GetLength(Number<0>{});
static constexpr auto src_length_k = src_thread_desc_m_k.GetLength(Number<1>{});
static constexpr auto dst_length_m = dst_thread_desc_m.GetLength(Number<0>{});
static_assert(src_length_m == dst_length_m, "lengths of source and dst buffer must match!");
__device__ static void
Merge(T& mean_a, T& var_a, int32_t& count_a, T mean_b, T var_b, int32_t count_b)
{
int count = count_a + count_b;
T count_b_over_count = count == 0 ? type_convert<T>(0) : type_convert<T>(count_b) / count;
T delta = mean_b - mean_a;
mean_a += delta * count_b_over_count;
var_a += var_b + delta * delta * count_a * count_b_over_count;
count_a = count;
}
template <typename SrcMeanBufferType,
typename SrcVarBufferType,
typename SrcCountBufferType,
typename DstMeanBufferType,
typename DstVarBufferType,
typename DstCountBufferType>
__device__ static void Run(const SrcMeanBufferType& src_mean_buf,
const SrcVarBufferType& src_var_buf,
const SrcCountBufferType& src_count_buf,
DstMeanBufferType& dst_mean_buf,
DstVarBufferType& dst_var_buf,
DstCountBufferType& dst_count_buf)
{
static_for<0, src_length_m, 1>{}([&](auto iM) {
static_for<0, src_length_k, 1>{}([&](auto iK) {
constexpr auto src_offset = src_thread_desc_m_k.CalculateOffset(make_tuple(iM, iK));
Merge(dst_mean_buf(iM),
dst_var_buf(iM),
dst_count_buf(iM),
src_mean_buf[Number<src_offset>{}],
src_var_buf[Number<src_offset>{}],
src_count_buf[Number<src_offset>{}]);
});
});
};
};
} // namespace ck
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