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Add elementwise with dynamic vector dim (#1198)
* Add elementwise with dynamic vector dim * Reduce number of instaces * Fixes * Fixes
This commit is contained in:
Bartłomiej Kocot
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "ck/utility/common_header.hpp"
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#include "ck/tensor_description/tensor_descriptor.hpp"
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#include "ck/tensor_description/tensor_descriptor_helper.hpp"
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#include "ck/tensor_description/cluster_descriptor.hpp"
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#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer_v3r2.hpp"
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namespace ck {
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/**
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* @brief Blockwise data transfer
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*
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* This version does following things to avoid scratch memory issue
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* 1. Use StaticallyIndexedArray instead of C array for thread buffer
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* 2. ThreadwiseTensorSliceTransfer_v3 does not keep reference to tensor descriptor
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* 3. ThreadwiseTensorSliceTransfer_v3::Run() does not construct new tensor coordinate
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*
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*/
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template <typename ThreadGroup,
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typename ElementwiseOperation,
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typename DstInMemOps, // Sequence
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typename BlockSliceLengths,
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typename ThreadClusterLengths,
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typename ThreadClusterArrangeOrder,
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typename SrcDatas,
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typename DstDatas,
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typename SrcDescs,
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typename DstDescs,
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typename SrcDimAccessOrder,
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typename DstDimAccessOrder,
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index_t SrcVectorDim,
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index_t DstVectorDim,
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typename SrcsScalarPerVector, // Sequence
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typename DstsScalarPerVector, // Sequence
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typename SrcsScalarStrideInVector, // Sequence
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typename DstsScalarStrideInVector, // Sequence
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typename ThreadTransferSrcsResetCoordinateAfterRun, // Sequence
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typename ThreadTransferDstsResetCoordinateAfterRun, // Sequence
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index_t NumThreadScratch = 1>
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struct ThreadGroupTensorSliceTransfer_v4r2
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{
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static constexpr index_t nDim =
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remove_reference_t<tuple_element_t<0, SrcDescs>>::GetNumOfDimension();
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static constexpr index_t nSrc = SrcDescs::Size();
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static constexpr index_t nDst = DstDescs::Size();
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static constexpr auto thread_slice_lengths = BlockSliceLengths{} / ThreadClusterLengths{};
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using Index = MultiIndex<nDim>;
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__device__ constexpr ThreadGroupTensorSliceTransfer_v4r2(
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const SrcDescs& src_descs,
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const StaticallyIndexedArray<Index, nSrc>& src_block_slice_origins,
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const DstDescs& dst_descs,
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const StaticallyIndexedArray<Index, nDst>& dst_block_slice_origins,
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const ElementwiseOperation& element_op)
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: threadwise_transfer_(src_descs,
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StaticallyIndexedArray<Index, nSrc>{},
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dst_descs,
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StaticallyIndexedArray<Index, nDst>{},
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element_op)
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{
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static_assert(nDim == ThreadClusterLengths::Size() &&
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nDim == ThreadClusterArrangeOrder::Size() &&
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nDim == SrcDimAccessOrder::Size() && nDim == SrcDimAccessOrder::Size(),
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"wrong! nDim not consistent");
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static_for<0, nSrc, 1>{}([&](auto src_i) {
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static_assert(nDim ==
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remove_cvref_t<tuple_element_t<src_i, SrcDescs>>::GetNumOfDimension(),
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"wrong! nDim not consistent");
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});
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static_for<0, nDst, 1>{}([&](auto dst_i) {
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static_assert(nDim ==
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remove_cvref_t<tuple_element_t<dst_i, DstDescs>>::GetNumOfDimension(),
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"wrong! nDim not consistent");
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});
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static_assert(
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is_same<BlockSliceLengths, decltype(thread_slice_lengths * ThreadClusterLengths{})>{},
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"wrong! threads should be mapped to cover entire slicing window");
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static_assert(ThreadGroup::GetNumOfThread() >= thread_cluster_desc_.GetElementSize(),
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"wrong! ThreadGroup::GetNumOfThread() too small");
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if(ThreadGroup::GetNumOfThread() == thread_cluster_desc_.GetElementSize() or
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ThreadGroup::GetThreadId() < thread_cluster_desc_.GetElementSize())
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{
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const auto thread_cluster_idx = thread_cluster_desc_.CalculateBottomIndex(
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make_multi_index(ThreadGroup::GetThreadId()));
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const auto thread_data_idx_begin = thread_cluster_idx * thread_slice_lengths;
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const auto src_thread_slice_origins = generate_tuple(
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[&](auto i) { return src_block_slice_origins[i] + thread_data_idx_begin; },
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Number<nSrc>{});
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const auto dst_thread_slice_origins = generate_tuple(
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[&](auto i) { return dst_block_slice_origins[i] + thread_data_idx_begin; },
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Number<nDst>{});
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threadwise_transfer_.SetSrcSliceOrigins(src_descs, src_thread_slice_origins);
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threadwise_transfer_.SetDstSliceOrigins(dst_descs, dst_thread_slice_origins);
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}
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}
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template <typename SrcBuffers, index_t ThreadScratchId = 0>
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__device__ void RunRead(const SrcDescs& src_descs,
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const SrcBuffers& src_bufs,
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Number<ThreadScratchId> thread_scratch_id = Number<ThreadScratchId>{})
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{
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if(ThreadGroup::GetNumOfThread() == thread_cluster_desc_.GetElementSize() or
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ThreadGroup::GetThreadId() < thread_cluster_desc_.GetElementSize())
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{
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threadwise_transfer_.RunRead(src_descs, src_bufs, thread_scratch_id);
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}
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}
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template <typename DstBuffers, index_t ThreadScratchId = 0>
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__device__ void RunWrite(const DstDescs& dst_descs,
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DstBuffers& dst_bufs,
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Number<ThreadScratchId> thread_scratch_id = Number<ThreadScratchId>{})
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{
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if(ThreadGroup::GetNumOfThread() == thread_cluster_desc_.GetElementSize() or
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ThreadGroup::GetThreadId() < thread_cluster_desc_.GetElementSize())
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{
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threadwise_transfer_.RunWrite(dst_descs, dst_bufs, thread_scratch_id);
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}
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}
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template <typename SrcBuffer, typename DstBuffer, index_t ThreadScratchId>
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__device__ void Run(const SrcDescs& src_descs,
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const SrcBuffer& src_bufs,
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const DstDescs& dst_descs,
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DstBuffer& dst_bufs,
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Number<ThreadScratchId> thread_scratch_id)
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{
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RunRead(src_descs, src_bufs, thread_scratch_id);
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RunWrite(dst_descs, dst_bufs, thread_scratch_id);
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}
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__device__ void MoveSrcSliceWindow(const SrcDescs& src_descs, const Index& step)
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{
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if(ThreadGroup::GetNumOfThread() == thread_cluster_desc_.GetElementSize() or
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ThreadGroup::GetThreadId() < thread_cluster_desc_.GetElementSize())
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{
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threadwise_transfer_.MoveSrcSliceWindow(src_descs, step);
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}
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}
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__device__ void MoveDstSliceWindow(const DstDescs& dst_descs, const Index& step)
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{
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if(ThreadGroup::GetNumOfThread() == thread_cluster_desc_.GetElementSize() or
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ThreadGroup::GetThreadId() < thread_cluster_desc_.GetElementSize())
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{
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threadwise_transfer_.MoveDstSliceWindow(dst_descs, step);
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}
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}
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private:
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static constexpr auto thread_cluster_desc_ =
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make_cluster_descriptor(ThreadClusterLengths{}, ThreadClusterArrangeOrder{});
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using ThreadwiseTransfer =
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ThreadwiseTensorSliceTransfer_v3r2<decltype(thread_slice_lengths),
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ElementwiseOperation,
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DstInMemOps,
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SrcDatas,
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DstDatas,
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SrcDescs,
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DstDescs,
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SrcDimAccessOrder,
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DstDimAccessOrder,
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SrcVectorDim,
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DstVectorDim,
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SrcsScalarPerVector,
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DstsScalarPerVector,
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SrcsScalarStrideInVector,
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DstsScalarStrideInVector,
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ThreadTransferSrcsResetCoordinateAfterRun,
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ThreadTransferDstsResetCoordinateAfterRun,
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NumThreadScratch>;
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ThreadwiseTransfer threadwise_transfer_;
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};
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} // namespace ck
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@@ -0,0 +1,422 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <iostream>
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#include <sstream>
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#include "ck/utility/math.hpp"
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#include "ck/utility/sequence.hpp"
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#include "ck/tensor_operation/gpu/device/device_elementwise.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_elementwise_dynamic_vector_dims.hpp"
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#include "ck/tensor_description/tensor_descriptor_helper.hpp"
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#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
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#include "ck/host_utility/kernel_launch.hpp"
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#include "ck/host_utility/stream_utility.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 InDataTypeTuple,
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typename OutDataTypeTuple,
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typename ElementwiseOperation,
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index_t NumDim,
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index_t BlockSize,
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index_t M0PerBlock,
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index_t M1PerBlock,
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index_t M0PerThread,
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index_t M1PerThread,
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typename ThreadClusterArrangeOrder,
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typename InScalarPerVectorSeq,
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typename OutScalarPerVectorSeq>
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struct DeviceElementwiseImpl
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: public DeviceElementwise<InDataTypeTuple, OutDataTypeTuple, ElementwiseOperation, NumDim>
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{
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static constexpr int NumInput = InDataTypeTuple::Size();
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static constexpr int NumOutput = OutDataTypeTuple::Size();
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static constexpr auto I0 = Number<0>{};
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static constexpr auto I1 = Number<1>{};
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static_assert(NumInput == InScalarPerVectorSeq::Size() &&
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NumOutput == OutScalarPerVectorSeq::Size(),
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"Tuple size is inconsistent with the number of in/out!");
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static auto GenerateInDataTypePointerTuple()
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{
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return generate_tuple(
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[&](auto I) {
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using DataType = remove_cvref_t<decltype(InDataTypeTuple{}[I])>;
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return static_cast<const DataType*>(nullptr);
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},
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Number<NumInput>{});
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};
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static auto GenerateOutDataTypePointerTuple()
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{
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return generate_tuple(
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[&](auto I) {
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using DataType = remove_cvref_t<decltype(OutDataTypeTuple{}[I])>;
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return static_cast<DataType*>(nullptr);
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},
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Number<NumOutput>{});
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};
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using InDataTypePointerTuple = decltype(GenerateInDataTypePointerTuple());
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using OutDataTypePointerTuple = decltype(GenerateOutDataTypePointerTuple());
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static index_t GetLowestStrideDim(const std::array<index_t, NumDim>& strides)
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{
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index_t most_continous_dim = NumDim - 1;
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index_t most_continous_dim_stride = strides[most_continous_dim];
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for(index_t dim = 0; dim < NumDim; dim++)
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{
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if(strides[dim] < most_continous_dim_stride)
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{
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most_continous_dim_stride = strides[dim];
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most_continous_dim = dim;
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}
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}
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return most_continous_dim;
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}
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template <typename InOutDescriptor>
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static auto PadInputOutputDescriptor(const InOutDescriptor& desc)
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{
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const auto M0 = desc.GetLength(I0);
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const auto M1 = desc.GetLength(I1);
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const auto pad_M0 = math::integer_divide_ceil(M0, M0PerThread) * M0PerThread - M0;
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const auto pad_M1 = math::integer_divide_ceil(M1, M1PerThread) * M1PerThread - M1;
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const auto padded_desc = transform_tensor_descriptor(
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desc,
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make_tuple(make_right_pad_transform(M0, pad_M0), make_right_pad_transform(M1, pad_M1)),
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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 padded_desc;
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}
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static auto GenerateBatchDimsLenghtsTuple(const std::array<index_t, NumDim>& lengths,
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const index_t M0_dim,
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const index_t M1_dim)
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{
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// Generate batch dims, they will be merged to M0
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// Add one more dim than needed in case that M0 is equal to M1
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// If M0 is equal to M1, then will be one more batch dim
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std::array<index_t, NumDim - 1> batch_dims;
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index_t batch_dim = 0;
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for(index_t i = 0; i < NumDim; i++)
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{
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if(i != M0_dim && i != M1_dim)
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{
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batch_dims[batch_dim] = lengths[i];
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batch_dim++;
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}
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}
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// Add dummy dim if M0_dim is not equal to M1_dim
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if(M0_dim != M1_dim && NumDim >= 2)
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batch_dims[NumDim - 2] = 1;
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return generate_tuple([&](auto I) { return batch_dims[I]; }, Number<NumDim - 1>{});
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}
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static auto MakeDescriptor(const std::array<index_t, NumDim>& lengths,
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const std::array<index_t, NumDim>& in_strides,
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const std::array<index_t, NumDim>& out_strides,
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const std::array<index_t, NumDim>& desc_strides)
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{
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const auto M0_dim = GetLowestStrideDim(out_strides);
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const auto M1_dim = GetLowestStrideDim(in_strides);
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// If M0_dim is equal to M1_dim, then make M0_dim dummy
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const auto M0 = M0_dim == M1_dim ? I1 : lengths[M0_dim];
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const auto M1 = lengths[M1_dim];
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const auto M0_stride = M0_dim == M1_dim ? I1 : desc_strides[M0_dim];
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const auto M1_stride = desc_strides[M1_dim];
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const auto batch_dims_lenghts = GenerateBatchDimsLenghtsTuple(lengths, M0_dim, M1_dim);
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const auto batch_dims_strides = GenerateBatchDimsLenghtsTuple(desc_strides, M0_dim, M1_dim);
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const auto desc = make_naive_tensor_descriptor(
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concat_tuple(batch_dims_lenghts, make_tuple(M0), make_tuple(M1)),
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concat_tuple(batch_dims_strides, make_tuple(M0_stride), make_tuple(M1_stride)));
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// Merged batch dims with M0
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const auto transforms =
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make_tuple(make_merge_transform(concat_tuple(batch_dims_lenghts, make_tuple(M0))),
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make_pass_through_transform(M1));
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using BatchElemsSequence =
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typename arithmetic_sequence_gen<0, decltype(batch_dims_lenghts)::Size() + 1, 1>::type;
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const auto lower_dims = make_tuple(BatchElemsSequence{}, Sequence<NumDim>{});
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const auto upper_dims = make_tuple(Sequence<0>{}, Sequence<1>{});
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// desc: (merged_dims + M0, M1)
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auto merged_desc = transform_tensor_descriptor(desc, transforms, lower_dims, upper_dims);
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return PadInputOutputDescriptor(merged_desc);
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}
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template <index_t NumTensors>
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static auto GenerateInOutGridDescTuple()
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{
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std::array<index_t, NumDim> ones;
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for(index_t d = 0; d < NumDim; d++)
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{
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ones[d] = 1;
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}
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return generate_tuple([&](auto) { return MakeDescriptor(ones, ones, ones, ones); },
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Number<NumTensors>{});
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};
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using InGridDescTuple = decltype(GenerateInOutGridDescTuple<NumInput>());
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using OutGridDescTuple = decltype(GenerateInOutGridDescTuple<NumOutput>());
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using Block2TileMap = BlockToCTileMap_M00_N0_M01Adapt<M0PerBlock, M1PerBlock>;
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using GridwiseElementwiseOp = GridwiseElementwise<InGridDescTuple,
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OutGridDescTuple,
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InDataTypePointerTuple,
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OutDataTypePointerTuple,
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Block2TileMap,
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ElementwiseOperation,
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BlockSize,
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M0PerBlock,
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M1PerBlock,
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M0PerThread,
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M1PerThread,
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ThreadClusterArrangeOrder,
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InScalarPerVectorSeq,
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OutScalarPerVectorSeq,
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false>;
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using GridwiseElementwiseOpSameInOutVectorDim = GridwiseElementwise<InGridDescTuple,
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OutGridDescTuple,
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InDataTypePointerTuple,
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OutDataTypePointerTuple,
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Block2TileMap,
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ElementwiseOperation,
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BlockSize,
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M0PerBlock,
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M1PerBlock,
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M0PerThread,
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M1PerThread,
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ThreadClusterArrangeOrder,
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InScalarPerVectorSeq,
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OutScalarPerVectorSeq,
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true>;
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struct Argument : public BaseArgument
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{
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Argument(const std::array<index_t, NumDim> lengths,
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const std::array<std::array<index_t, NumDim>, NumInput> inStridesArray,
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const std::array<std::array<index_t, NumDim>, NumOutput> outStridesArray,
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const std::array<const void*, NumInput> in_dev_buffers,
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const std::array<void*, NumOutput> out_dev_buffers,
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ElementwiseOperation elementwise_op)
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: lengths_(lengths),
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inStridesArray_(inStridesArray),
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outStridesArray_(outStridesArray),
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elementwise_op_(elementwise_op)
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{
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in_dev_buffers_ = generate_tuple(
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[&](auto I) {
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using DataType = remove_cvref_t<decltype(InDataTypeTuple{}[I])>;
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return static_cast<const DataType*>(in_dev_buffers[I.value]);
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},
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Number<NumInput>{});
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out_dev_buffers_ = generate_tuple(
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[&](auto I) {
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using DataType = remove_cvref_t<decltype(OutDataTypeTuple{}[I])>;
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return static_cast<DataType*>(out_dev_buffers[I.value]);
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},
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Number<NumOutput>{});
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}
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InDataTypePointerTuple in_dev_buffers_;
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OutDataTypePointerTuple out_dev_buffers_;
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std::array<index_t, NumDim> lengths_;
|
||||
std::array<std::array<index_t, NumDim>, NumInput> inStridesArray_;
|
||||
std::array<std::array<index_t, NumDim>, NumOutput> outStridesArray_;
|
||||
|
||||
ElementwiseOperation elementwise_op_;
|
||||
};
|
||||
|
||||
struct Invoker : public BaseInvoker
|
||||
{
|
||||
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
|
||||
{
|
||||
auto in_grid_desc_tuple = generate_tuple(
|
||||
[&](auto src_i) {
|
||||
// Use Strides from first tensor to assert that M0 dim and
|
||||
// M1 dim are the same for each tensor.
|
||||
return MakeDescriptor(arg.lengths_,
|
||||
arg.inStridesArray_[I0],
|
||||
arg.outStridesArray_[I0],
|
||||
arg.inStridesArray_[src_i]);
|
||||
},
|
||||
Number<NumInput>{});
|
||||
|
||||
auto out_grid_desc_tuple = generate_tuple(
|
||||
[&](auto dst_i) {
|
||||
return MakeDescriptor(arg.lengths_,
|
||||
arg.inStridesArray_[I0],
|
||||
arg.outStridesArray_[I0],
|
||||
arg.outStridesArray_[dst_i]);
|
||||
},
|
||||
Number<NumOutput>{});
|
||||
|
||||
const index_t M0 = in_grid_desc_tuple.At(I0).GetLength(Number<I0>{});
|
||||
const index_t M1 = in_grid_desc_tuple.At(I0).GetLength(Number<I1>{});
|
||||
|
||||
const auto block_2_tile_map = Block2TileMap(M0, M1);
|
||||
const index_t grid_size = block_2_tile_map.CalculateGridSize(M0, M1);
|
||||
|
||||
const bool in_out_same_vector_dim = GetLowestStrideDim(arg.inStridesArray_[I0]) ==
|
||||
GetLowestStrideDim(arg.outStridesArray_[I0]);
|
||||
|
||||
const auto kernel = in_out_same_vector_dim
|
||||
? kernel_elementwise<GridwiseElementwiseOpSameInOutVectorDim,
|
||||
InGridDescTuple,
|
||||
OutGridDescTuple,
|
||||
InDataTypePointerTuple,
|
||||
OutDataTypePointerTuple,
|
||||
Block2TileMap,
|
||||
ElementwiseOperation>
|
||||
: kernel_elementwise<GridwiseElementwiseOp,
|
||||
InGridDescTuple,
|
||||
OutGridDescTuple,
|
||||
InDataTypePointerTuple,
|
||||
OutDataTypePointerTuple,
|
||||
Block2TileMap,
|
||||
ElementwiseOperation>;
|
||||
|
||||
float elapsed_time = launch_and_time_kernel(stream_config,
|
||||
kernel,
|
||||
dim3(grid_size),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
in_grid_desc_tuple,
|
||||
out_grid_desc_tuple,
|
||||
arg.in_dev_buffers_,
|
||||
arg.out_dev_buffers_,
|
||||
block_2_tile_map,
|
||||
arg.elementwise_op_);
|
||||
return elapsed_time;
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
float Run(const BaseArgument* p_arg,
|
||||
const StreamConfig& stream_config = StreamConfig{}) override
|
||||
{
|
||||
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
|
||||
}
|
||||
};
|
||||
|
||||
static bool IsSupportedArgument(const Argument& arg)
|
||||
{
|
||||
const index_t M0_dim = GetLowestStrideDim(arg.inStridesArray_[I0]);
|
||||
const index_t M1_dim = GetLowestStrideDim(arg.outStridesArray_[I0]);
|
||||
|
||||
auto IsScalarPerVectorValid = [&](const std::array<index_t, NumDim>& lengths,
|
||||
const std::array<index_t, NumDim>& strides,
|
||||
index_t scalarPerVector,
|
||||
index_t M_dim) {
|
||||
if(scalarPerVector == 1)
|
||||
{
|
||||
return true;
|
||||
}
|
||||
if(strides[M_dim] == 1 && lengths[M_dim] % scalarPerVector == 0)
|
||||
{
|
||||
return true;
|
||||
}
|
||||
return false;
|
||||
};
|
||||
|
||||
bool is_valid = true;
|
||||
static_for<0, NumInput, 1>{}([&](auto I) {
|
||||
static_assert(M0PerThread % InScalarPerVectorSeq::At(I) == 0 &&
|
||||
M1PerThread % InScalarPerVectorSeq::At(I) == 0);
|
||||
is_valid &= IsScalarPerVectorValid(
|
||||
arg.lengths_, arg.inStridesArray_[I.value], InScalarPerVectorSeq::At(I), M0_dim);
|
||||
});
|
||||
|
||||
static_for<0, NumOutput, 1>{}([&](auto I) {
|
||||
static_assert(M0PerThread % OutScalarPerVectorSeq::At(I) == 0 &&
|
||||
M1PerThread % OutScalarPerVectorSeq::At(I) == 0);
|
||||
is_valid &= IsScalarPerVectorValid(
|
||||
arg.lengths_, arg.outStridesArray_[I.value], OutScalarPerVectorSeq::At(I), M1_dim);
|
||||
});
|
||||
|
||||
return is_valid;
|
||||
};
|
||||
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
|
||||
static auto
|
||||
MakeArgument(const std::array<index_t, NumDim> lengths,
|
||||
const std::array<std::array<index_t, NumDim>, NumInput> inStridesArray,
|
||||
const std::array<std::array<index_t, NumDim>, NumOutput> outStridesArray,
|
||||
const std::array<const void*, NumInput> in_dev_buffers,
|
||||
const std::array<void*, NumOutput> out_dev_buffers,
|
||||
ElementwiseOperation elementwise_op)
|
||||
{
|
||||
return Argument{lengths,
|
||||
inStridesArray,
|
||||
outStridesArray,
|
||||
in_dev_buffers,
|
||||
out_dev_buffers,
|
||||
elementwise_op};
|
||||
}
|
||||
|
||||
std::unique_ptr<BaseArgument>
|
||||
MakeArgumentPointer(const std::array<index_t, NumDim> lengths,
|
||||
const std::array<std::array<index_t, NumDim>, NumInput> inStridesArray,
|
||||
const std::array<std::array<index_t, NumDim>, NumOutput> outStridesArray,
|
||||
const std::array<const void*, NumInput> in_dev_buffers,
|
||||
const std::array<void*, NumOutput> out_dev_buffers,
|
||||
ElementwiseOperation elementwise_op) override
|
||||
{
|
||||
return std::make_unique<Argument>(lengths,
|
||||
inStridesArray,
|
||||
outStridesArray,
|
||||
in_dev_buffers,
|
||||
out_dev_buffers,
|
||||
elementwise_op);
|
||||
}
|
||||
|
||||
static auto MakeInvoker() { return Invoker{}; }
|
||||
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
||||
{
|
||||
return std::make_unique<Invoker>();
|
||||
};
|
||||
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
// clang-format off
|
||||
str << "DeviceElementwiseImpl<";
|
||||
str << NumDim << ", ";
|
||||
str << BlockSize << ", ";
|
||||
str << M0PerBlock << ", ";
|
||||
str << M1PerBlock << ", ";
|
||||
str << M0PerThread << ", ";
|
||||
str << M1PerThread << ">";
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,169 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ck/tensor_description/cluster_descriptor.hpp"
|
||||
#include "ck/utility/data_type.hpp"
|
||||
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v7r2.hpp"
|
||||
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r2.hpp"
|
||||
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
|
||||
#include "ck/tensor/static_tensor.hpp"
|
||||
#include "ck/utility/common_header.hpp"
|
||||
|
||||
namespace ck {
|
||||
|
||||
template <typename GridwiseElementwiseFunctor,
|
||||
typename InGridDescTuple,
|
||||
typename OutGridDescTuple,
|
||||
typename InDataTypePointerTuple,
|
||||
typename OutDataTypePointerTuple,
|
||||
typename Block2TileMap,
|
||||
typename ElementwiseOperation>
|
||||
__global__ void
|
||||
#if CK_USE_LAUNCH_BOUNDS
|
||||
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
|
||||
#endif
|
||||
kernel_elementwise(const InGridDescTuple in_grid_desc_tuple,
|
||||
const OutGridDescTuple out_grid_desc_tuple,
|
||||
const InDataTypePointerTuple p_in_global_tuple,
|
||||
const OutDataTypePointerTuple p_out_global_tuple,
|
||||
const Block2TileMap block_2_tile_map,
|
||||
const ElementwiseOperation elementwise_op)
|
||||
{
|
||||
GridwiseElementwiseFunctor::Run(in_grid_desc_tuple,
|
||||
out_grid_desc_tuple,
|
||||
p_in_global_tuple,
|
||||
p_out_global_tuple,
|
||||
block_2_tile_map,
|
||||
elementwise_op);
|
||||
}
|
||||
|
||||
template <typename InGridDescTuple,
|
||||
typename OutGridDescTuple,
|
||||
typename InDataTypePointerTuple,
|
||||
typename OutDataTypePointerTuple,
|
||||
typename Block2TileMap,
|
||||
typename ElementwiseOperation,
|
||||
index_t BlockSize,
|
||||
index_t M0PerBlock,
|
||||
index_t M1PerBlock,
|
||||
index_t M0PerThread,
|
||||
index_t M1PerThread,
|
||||
typename ThreadClusterArrangeOrder,
|
||||
typename InScalarPerVectorSeq,
|
||||
typename OutScalarPerVectorSeq,
|
||||
bool InOutSameVectorDim>
|
||||
struct GridwiseElementwise
|
||||
{
|
||||
static constexpr index_t NumInput = InDataTypePointerTuple::Size();
|
||||
static constexpr index_t NumOutput = OutDataTypePointerTuple::Size();
|
||||
|
||||
static_assert(NumInput == InScalarPerVectorSeq::Size() &&
|
||||
NumOutput == OutScalarPerVectorSeq::Size() &&
|
||||
NumInput == InGridDescTuple::Size() && NumOutput == OutGridDescTuple::Size(),
|
||||
"Tuple size is inconsistent with the number of in/out!");
|
||||
|
||||
static constexpr auto I0 = Number<0>{};
|
||||
static constexpr auto I1 = Number<1>{};
|
||||
|
||||
using PassThroughOp = tensor_operation::element_wise::PassThrough;
|
||||
|
||||
__device__ static void Run(const InGridDescTuple& in_grid_desc_tuple,
|
||||
const OutGridDescTuple& out_grid_desc_tuple,
|
||||
const InDataTypePointerTuple& p_in_global_tuple,
|
||||
const OutDataTypePointerTuple& p_out_global_tuple,
|
||||
const Block2TileMap& block_2_tile_map,
|
||||
const ElementwiseOperation& elementwise_op)
|
||||
{
|
||||
|
||||
constexpr auto src_datas = generate_tuple(
|
||||
[&](auto I) {
|
||||
using DataTypePointer = remove_cvref_t<decltype(InDataTypePointerTuple{}[I])>;
|
||||
using DataType = remove_cv_t<remove_pointer_t<DataTypePointer>>;
|
||||
|
||||
return DataType{};
|
||||
},
|
||||
Number<NumInput>{});
|
||||
|
||||
constexpr auto dst_datas = generate_tuple(
|
||||
[&](auto I) {
|
||||
using DataTypePointer = remove_cvref_t<decltype(OutDataTypePointerTuple{}[I])>;
|
||||
using DataType = remove_pointer_t<DataTypePointer>;
|
||||
|
||||
return DataType{};
|
||||
},
|
||||
Number<NumOutput>{});
|
||||
|
||||
const auto in_global_buf_tuple = generate_tuple(
|
||||
[&](auto I) {
|
||||
return make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_in_global_tuple[I], in_grid_desc_tuple[I].GetElementSpaceSize());
|
||||
},
|
||||
Number<NumInput>{});
|
||||
|
||||
auto out_global_buf_tuple = generate_tuple(
|
||||
[&](auto I) {
|
||||
return make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_out_global_tuple[I], out_grid_desc_tuple[I].GetElementSpaceSize());
|
||||
},
|
||||
Number<NumOutput>{});
|
||||
|
||||
const auto block_work_idx =
|
||||
block_2_tile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
|
||||
|
||||
const index_t m0_block_data_idx_on_grid =
|
||||
__builtin_amdgcn_readfirstlane(block_work_idx[I0] * M0PerBlock);
|
||||
const index_t m1_block_data_idx_on_grid =
|
||||
__builtin_amdgcn_readfirstlane(block_work_idx[I1] * M1PerBlock);
|
||||
const auto thread_grid_offset =
|
||||
make_multi_index(m0_block_data_idx_on_grid, m1_block_data_idx_on_grid);
|
||||
|
||||
using ThisThreadBlock = ThisThreadBlock<BlockSize>;
|
||||
// If src and dst have same vector dim, then:
|
||||
// M0 dim - for src and dst vector load/store
|
||||
// else:
|
||||
// M0 dim - for dst vector load
|
||||
// M1 dim - for src vector store
|
||||
using SrcDimAccessOrder = Sequence<0, 1>;
|
||||
using DstDimAccessOrder =
|
||||
std::conditional_t<InOutSameVectorDim, Sequence<0, 1>, Sequence<1, 0>>;
|
||||
using SrcVectorDim = Number<1>;
|
||||
using DstVectorDim = std::conditional_t<InOutSameVectorDim, Number<1>, Number<0>>;
|
||||
|
||||
using ThreadClusterLengths =
|
||||
Sequence<Number<M0PerBlock / M0PerThread>{}, Number<M1PerBlock / M1PerThread>{}>;
|
||||
|
||||
auto global_to_global_transfer = ThreadGroupTensorSliceTransfer_v4r2<
|
||||
ThisThreadBlock,
|
||||
ElementwiseOperation,
|
||||
uniform_sequence_gen_t<NumOutput, static_cast<index_t>(InMemoryDataOperationEnum::Set)>,
|
||||
Sequence<M0PerBlock, M1PerBlock>,
|
||||
ThreadClusterLengths,
|
||||
ThreadClusterArrangeOrder,
|
||||
decltype(src_datas),
|
||||
decltype(dst_datas),
|
||||
InGridDescTuple,
|
||||
OutGridDescTuple,
|
||||
SrcDimAccessOrder,
|
||||
DstDimAccessOrder,
|
||||
SrcVectorDim{},
|
||||
DstVectorDim{},
|
||||
InScalarPerVectorSeq,
|
||||
OutScalarPerVectorSeq,
|
||||
uniform_sequence_gen_t<NumInput, 1>,
|
||||
uniform_sequence_gen_t<NumOutput, 1>,
|
||||
uniform_sequence_gen_t<NumInput, false>,
|
||||
uniform_sequence_gen_t<NumOutput, false>>{in_grid_desc_tuple,
|
||||
thread_grid_offset,
|
||||
out_grid_desc_tuple,
|
||||
thread_grid_offset,
|
||||
elementwise_op};
|
||||
global_to_global_transfer.Run(
|
||||
in_grid_desc_tuple, in_global_buf_tuple, out_grid_desc_tuple, out_global_buf_tuple, I0);
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,804 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ck/utility/common_header.hpp"
|
||||
#include "ck/tensor_description/tensor_descriptor.hpp"
|
||||
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
|
||||
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
|
||||
#include "ck/tensor/static_tensor.hpp"
|
||||
#include "ck/utility/is_detected.hpp"
|
||||
|
||||
namespace ck {
|
||||
|
||||
// Assume:
|
||||
// 1. src_desc and dst_desc are not known at compile-time
|
||||
// 2. SrcBuffer and DstBuffer are DynamicBuffer
|
||||
// 3. src_slice_origin and dst_slice_origin are not known at compile-time,
|
||||
// 4. Use thread buffer
|
||||
template <typename SliceLengths,
|
||||
typename ElementwiseOperation,
|
||||
typename DstInMemOps, // Sequence
|
||||
typename SrcDatas,
|
||||
typename DstDatas,
|
||||
typename SrcDescs,
|
||||
typename DstDescs,
|
||||
typename SrcDimAccessOrder,
|
||||
typename DstDimAccessOrder,
|
||||
index_t SrcVectorDim,
|
||||
index_t DstVectorDim,
|
||||
typename SrcsScalarPerVector, // Sequence
|
||||
typename DstsScalarPerVector, // Sequence
|
||||
typename SrcsScalarStrideInVector, // Sequence
|
||||
typename DstsScalarStrideInVector, // Sequence
|
||||
typename SrcsResetCoordinateAfterRun, // control whether to move back src coordinate after
|
||||
// each RunRead(), will be fused with
|
||||
// MoveSrcSliceWindow to save addr computation
|
||||
typename DstsResetCoordinateAfterRun, // control whether to move back dst coordinate after
|
||||
// each RunWrite(), will be fused with
|
||||
// MoveDstSliceWindow to save addr computation
|
||||
index_t NumThreadScratch = 1>
|
||||
struct ThreadwiseTensorSliceTransfer_v3r2
|
||||
{
|
||||
static constexpr index_t nDim = SliceLengths::Size();
|
||||
using Index = MultiIndex<nDim>;
|
||||
|
||||
static constexpr index_t nSrc = SrcDescs::Size();
|
||||
static constexpr index_t nDst = DstDescs::Size();
|
||||
|
||||
// return a tuple of coordiantes for a tuple of tensor
|
||||
template <typename Descs,
|
||||
typename Indices,
|
||||
enable_if_t<Descs::Size() == Indices::Size(), bool> = false>
|
||||
static constexpr auto MakeCoordinates(const Descs& descs, const Indices& indices)
|
||||
{
|
||||
return generate_tuple([&](auto i) { return make_tensor_coordinate(descs[i], indices[i]); },
|
||||
Number<Descs::Size()>{});
|
||||
}
|
||||
|
||||
using SrcCoords = decltype(MakeCoordinates(SrcDescs{}, StaticallyIndexedArray<Index, nSrc>{}));
|
||||
using DstCoords = decltype(MakeCoordinates(DstDescs{}, StaticallyIndexedArray<Index, nDst>{}));
|
||||
|
||||
static constexpr auto I0 = Number<0>{};
|
||||
|
||||
__device__ constexpr ThreadwiseTensorSliceTransfer_v3r2(
|
||||
const SrcDescs& src_descs,
|
||||
const StaticallyIndexedArray<Index, nSrc>& src_slice_origins,
|
||||
const DstDescs& dst_descs,
|
||||
const StaticallyIndexedArray<Index, nDst>& dst_slice_origins,
|
||||
const ElementwiseOperation& element_op)
|
||||
: src_coords_(MakeCoordinates(src_descs, src_slice_origins)),
|
||||
dst_coords_(MakeCoordinates(dst_descs, dst_slice_origins)),
|
||||
element_op_(element_op)
|
||||
{
|
||||
}
|
||||
|
||||
template <typename Indices, enable_if_t<SrcDescs::Size() == Indices::Size(), bool> = false>
|
||||
__device__ void SetSrcSliceOrigins(const SrcDescs& src_descs,
|
||||
const Indices& src_slice_origin_idxs)
|
||||
{
|
||||
static_for<0, nSrc, 1>{}([&](auto src_i) {
|
||||
src_coords_(src_i) =
|
||||
make_tensor_coordinate(src_descs.At(src_i), src_slice_origin_idxs[src_i]);
|
||||
});
|
||||
}
|
||||
|
||||
template <typename Indices, enable_if_t<DstDescs::Size() == Indices::Size(), bool> = false>
|
||||
__device__ void SetDstSliceOrigins(const DstDescs& dst_descs,
|
||||
const Indices& dst_slice_origin_idxs)
|
||||
{
|
||||
static_for<0, nDst, 1>{}([&](auto dst_i) {
|
||||
dst_coords_(dst_i) =
|
||||
make_tensor_coordinate(dst_descs.At(dst_i), dst_slice_origin_idxs[dst_i]);
|
||||
});
|
||||
}
|
||||
|
||||
template <typename SrcBuffers, index_t ThreadScratchId = 0>
|
||||
__device__ void RunRead(const SrcDescs& src_descs,
|
||||
const SrcBuffers& src_bufs,
|
||||
Number<ThreadScratchId> thread_scratch_id = Number<ThreadScratchId>{})
|
||||
{
|
||||
// scalar per access on each dim
|
||||
// TODO: don't use lambda_scalar_per_access
|
||||
constexpr auto src_scalar_per_access_tuple = generate_tuple(
|
||||
[&](auto src_i) {
|
||||
return generate_sequence(
|
||||
detail::lambda_scalar_per_access<SrcVectorDim,
|
||||
SrcsScalarPerVector::At(src_i)>{},
|
||||
Number<nDim>{});
|
||||
},
|
||||
Number<nSrc>{});
|
||||
|
||||
constexpr auto src_access_lengths_tuple = generate_tuple(
|
||||
[&](auto src_i) {
|
||||
return SliceLengths{} / src_scalar_per_access_tuple.At(src_i);
|
||||
static_assert(
|
||||
SliceLengths::At(SrcVectorDim) % SrcsScalarPerVector::At(src_i) == 0,
|
||||
"SliceLengths[SrcVectorDim] must be divisible by SrcsScalarPerVector");
|
||||
},
|
||||
Number<nSrc>{});
|
||||
|
||||
constexpr auto src_dim_access_order = SrcDimAccessOrder{};
|
||||
|
||||
constexpr auto ordered_src_access_lengths_tuple = generate_tuple(
|
||||
[&](auto src_i) {
|
||||
return container_reorder_given_new2old(src_access_lengths_tuple.At(src_i),
|
||||
src_dim_access_order);
|
||||
},
|
||||
Number<nSrc>{});
|
||||
|
||||
// make forward steps
|
||||
const auto src_forward_steps_tuple = generate_tuple(
|
||||
[&](auto src_i) {
|
||||
return generate_tuple(
|
||||
[&](auto i) {
|
||||
Index forward_step_idx;
|
||||
|
||||
static_for<0, nDim, 1>{}([&](auto j) {
|
||||
forward_step_idx(j) =
|
||||
(i.value == j.value) ? src_scalar_per_access_tuple.At(src_i)[i] : 0;
|
||||
});
|
||||
|
||||
return make_tensor_coordinate_step(src_descs.At(src_i), forward_step_idx);
|
||||
},
|
||||
Number<nDim>{});
|
||||
},
|
||||
Number<nSrc>{});
|
||||
|
||||
// make backward steps
|
||||
const auto src_backward_steps_tuple = generate_tuple(
|
||||
[&](auto src_i) {
|
||||
return generate_tuple(
|
||||
[&](auto i) {
|
||||
Index backward_step_idx;
|
||||
|
||||
static_for<0, nDim, 1>{}([&](auto j) {
|
||||
backward_step_idx(j) = (i.value == j.value)
|
||||
? -src_scalar_per_access_tuple.At(src_i)[i]
|
||||
: 0;
|
||||
});
|
||||
|
||||
return make_tensor_coordinate_step(src_descs.At(src_i), backward_step_idx);
|
||||
},
|
||||
Number<nDim>{});
|
||||
},
|
||||
Number<nSrc>{});
|
||||
|
||||
// loop over tensor and copy
|
||||
static_for<0, nSrc, 1>{}([&](auto src_i) {
|
||||
static_ford<remove_cvref_t<decltype(ordered_src_access_lengths_tuple.At(src_i))>>{}(
|
||||
[&](auto ordered_src_access_idx) {
|
||||
// judge move forward or move backward
|
||||
constexpr auto forward_sweep = [&]() {
|
||||
StaticallyIndexedArray<bool, nDim> forward_sweep_;
|
||||
|
||||
forward_sweep_(I0) = true;
|
||||
|
||||
static_for<1, nDim, 1>{}([&](auto i) {
|
||||
index_t tmp = ordered_src_access_idx[I0];
|
||||
|
||||
static_for<1, i, 1>{}([&](auto j) {
|
||||
tmp = tmp * ordered_src_access_lengths_tuple[j] +
|
||||
ordered_src_access_idx[j];
|
||||
});
|
||||
|
||||
forward_sweep_(i) = tmp % 2 == 0;
|
||||
});
|
||||
|
||||
return forward_sweep_;
|
||||
}();
|
||||
|
||||
// calculate src data index
|
||||
constexpr auto src_data_idx = [&]() {
|
||||
Index ordered_idx;
|
||||
|
||||
static_for<0, nDim, 1>{}([&](auto i) {
|
||||
ordered_idx(i) = forward_sweep[i]
|
||||
? ordered_src_access_idx[i]
|
||||
: ordered_src_access_lengths_tuple.At(src_i)[i] -
|
||||
1 - ordered_src_access_idx[i];
|
||||
});
|
||||
|
||||
return container_reorder_given_old2new(ordered_idx, src_dim_access_order) *
|
||||
src_scalar_per_access_tuple.At(src_i);
|
||||
}();
|
||||
|
||||
constexpr auto src_data_idx_seq =
|
||||
generate_sequence_v2([&](auto i) { return Number<src_data_idx[i]>{}; },
|
||||
Number<src_data_idx.Size()>{});
|
||||
|
||||
const bool is_src_valid =
|
||||
coordinate_has_valid_offset_assuming_visible_index_is_valid(
|
||||
src_descs.At(src_i), src_coords_.At(src_i));
|
||||
|
||||
using src_vector_type = vector_type_maker_t<tuple_element_t<src_i, SrcDatas>,
|
||||
SrcsScalarPerVector::At(src_i)>;
|
||||
using src_vector_t = typename src_vector_type::type;
|
||||
|
||||
// copy data from src_buf into src_vector_container
|
||||
auto src_vector_container =
|
||||
src_vector_type{src_bufs.At(src_i).template Get<src_vector_t>(
|
||||
src_coords_.At(src_i).GetOffset(), is_src_valid)};
|
||||
|
||||
// copy data from src_vector_container into src_thread_scratch_
|
||||
src_thread_scratch_tuple_(thread_scratch_id)
|
||||
.At(src_i)
|
||||
.template SetAsType<src_vector_t>(
|
||||
src_data_idx_seq,
|
||||
src_vector_container.template AsType<src_vector_t>()[I0]);
|
||||
|
||||
constexpr auto move_on_dim = [&]() constexpr
|
||||
{
|
||||
StaticallyIndexedArray<bool, nDim> move_on_dim_;
|
||||
|
||||
static_for<0, nDim, 1>{}([&](auto i) {
|
||||
move_on_dim_(i) = ordered_src_access_idx[i] <
|
||||
ordered_src_access_lengths_tuple.At(src_i)[i] - 1;
|
||||
|
||||
static_for<i + 1, nDim, 1>{}([&](auto j) {
|
||||
move_on_dim_(i) &=
|
||||
ordered_src_access_idx[j] ==
|
||||
ordered_src_access_lengths_tuple.At(src_i)[j] - 1;
|
||||
});
|
||||
});
|
||||
|
||||
return move_on_dim_;
|
||||
}
|
||||
();
|
||||
|
||||
// move src coord
|
||||
static_for<0, nDim, 1>{}([&](auto i) {
|
||||
if constexpr(move_on_dim[i])
|
||||
{
|
||||
if constexpr(forward_sweep[i])
|
||||
{
|
||||
move_tensor_coordinate(
|
||||
src_descs.At(src_i),
|
||||
src_coords_.At(src_i),
|
||||
src_forward_steps_tuple.At(src_i)[src_dim_access_order[i]]);
|
||||
}
|
||||
else
|
||||
{
|
||||
move_tensor_coordinate(
|
||||
src_descs.At(src_i),
|
||||
src_coords_.At(src_i),
|
||||
src_backward_steps_tuple.At(src_i)[src_dim_access_order[i]]);
|
||||
}
|
||||
}
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
static_for<0, nSrc, 1>{}([&](auto src_i) {
|
||||
// move src coordinate back to slice origin (or not)
|
||||
if constexpr(SrcsResetCoordinateAfterRun::At(src_i))
|
||||
{
|
||||
const auto src_reset_step = make_tensor_coordinate_step(
|
||||
src_descs.At(src_i), GetSrcCoordinateResetStep<src_i>());
|
||||
|
||||
move_tensor_coordinate(src_descs.At(src_i), src_coords_.At(src_i), src_reset_step);
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
template <index_t ThreadScratchId>
|
||||
__device__ void
|
||||
TransferDataFromSrcThreadScratchToDstThreadScratch(Number<ThreadScratchId> thread_scratch_id)
|
||||
{
|
||||
// TODO: Add support for CK_EXPERIMENTAL_USE_IN_REGISTER_SUB_DWORD_TRANSPOSE
|
||||
// (it requires to add Elementwise support in transpose_vectors)
|
||||
static_ford<SliceLengths>{}([&](auto idx) {
|
||||
const auto src_data_refs = generate_tie(
|
||||
[&](auto src_i) -> const auto& {
|
||||
return src_thread_scratch_tuple_[thread_scratch_id].At(src_i)[idx];
|
||||
},
|
||||
Number<nSrc>{});
|
||||
|
||||
auto dst_data_refs = generate_tie(
|
||||
[&](auto dst_i) -> auto& { return dst_thread_scratch_tuple_.At(dst_i)(idx); },
|
||||
Number<nDst>{});
|
||||
unpack2(element_op_, dst_data_refs, src_data_refs);
|
||||
});
|
||||
}
|
||||
|
||||
template <typename DstBuffers, index_t ThreadScratchId = 0>
|
||||
__device__ void RunWrite(const DstDescs& dst_descs,
|
||||
DstBuffers& dst_bufs,
|
||||
Number<ThreadScratchId> thread_scratch_id = Number<ThreadScratchId>{})
|
||||
{
|
||||
// if there is transpose, it's done here
|
||||
// TODO move this elsewhere
|
||||
TransferDataFromSrcThreadScratchToDstThreadScratch(thread_scratch_id);
|
||||
|
||||
// src scalar per access on each dim
|
||||
// TODO: don't use this
|
||||
constexpr auto dst_scalar_per_access_tuple = generate_tuple(
|
||||
[&](auto dst_i) {
|
||||
return generate_sequence(
|
||||
detail::lambda_scalar_per_access<DstVectorDim,
|
||||
DstsScalarPerVector::At(dst_i)>{},
|
||||
Number<nDim>{});
|
||||
},
|
||||
Number<nDst>{});
|
||||
|
||||
constexpr auto dst_access_lengths_tuple = generate_tuple(
|
||||
[&](auto dst_i) { return SliceLengths{} / dst_scalar_per_access_tuple.At(dst_i); },
|
||||
Number<nDst>{});
|
||||
|
||||
constexpr auto dst_dim_access_order = DstDimAccessOrder{};
|
||||
|
||||
constexpr auto ordered_dst_access_lengths_tuple = generate_tuple(
|
||||
[&](auto dst_i) {
|
||||
return container_reorder_given_new2old(dst_access_lengths_tuple.At(dst_i),
|
||||
dst_dim_access_order);
|
||||
},
|
||||
Number<nDst>{});
|
||||
|
||||
// make forward steps
|
||||
const auto dst_forward_steps_tuple = generate_tuple(
|
||||
[&](auto dst_i) {
|
||||
return generate_tuple(
|
||||
[&](auto i) {
|
||||
Index forward_step_idx;
|
||||
|
||||
static_for<0, nDim, 1>{}([&](auto j) {
|
||||
forward_step_idx(j) =
|
||||
(i.value == j.value) ? dst_scalar_per_access_tuple.At(dst_i)[i] : 0;
|
||||
});
|
||||
|
||||
return make_tensor_coordinate_step(dst_descs.At(dst_i), forward_step_idx);
|
||||
},
|
||||
Number<nDim>{});
|
||||
},
|
||||
Number<nDst>{});
|
||||
|
||||
// make backward steps
|
||||
const auto dst_backward_steps_tuple = generate_tuple(
|
||||
[&](auto dst_i) {
|
||||
return generate_tuple(
|
||||
[&](auto i) {
|
||||
Index backward_step_idx;
|
||||
|
||||
static_for<0, nDim, 1>{}([&](auto j) {
|
||||
backward_step_idx(j) = (i.value == j.value)
|
||||
? -dst_scalar_per_access_tuple.At(dst_i)[i]
|
||||
: 0;
|
||||
});
|
||||
|
||||
return make_tensor_coordinate_step(dst_descs.At(dst_i), backward_step_idx);
|
||||
},
|
||||
Number<nDim>{});
|
||||
},
|
||||
Number<nDst>{});
|
||||
|
||||
// loop over tensor and copy
|
||||
static_for<0, nDst, 1>{}([&](auto dst_i) {
|
||||
static_ford<remove_cvref_t<decltype(ordered_dst_access_lengths_tuple.At(dst_i))>>{}(
|
||||
[&](auto ordered_dst_access_idx) {
|
||||
// judge move forward or move backward
|
||||
constexpr auto forward_sweep = [&]() {
|
||||
StaticallyIndexedArray<bool, nDim> forward_sweep_;
|
||||
|
||||
forward_sweep_(I0) = true;
|
||||
|
||||
static_for<1, nDim, 1>{}([&](auto i) {
|
||||
index_t tmp = ordered_dst_access_idx[I0];
|
||||
|
||||
static_for<1, i, 1>{}([&](auto j) {
|
||||
tmp = tmp * ordered_dst_access_lengths_tuple.At(dst_i)[j] +
|
||||
ordered_dst_access_idx[j];
|
||||
});
|
||||
|
||||
forward_sweep_(i) = tmp % 2 == 0;
|
||||
});
|
||||
|
||||
return forward_sweep_;
|
||||
}();
|
||||
|
||||
// calculate dst data index
|
||||
constexpr auto dst_data_idx = [&]() {
|
||||
Index ordered_idx;
|
||||
|
||||
static_for<0, nDim, 1>{}([&](auto i) {
|
||||
ordered_idx(i) = forward_sweep[i]
|
||||
? ordered_dst_access_idx[i]
|
||||
: ordered_dst_access_lengths_tuple.At(dst_i)[i] -
|
||||
1 - ordered_dst_access_idx[i];
|
||||
});
|
||||
|
||||
return container_reorder_given_old2new(ordered_idx, dst_dim_access_order) *
|
||||
dst_scalar_per_access_tuple.At(dst_i);
|
||||
}();
|
||||
|
||||
constexpr auto dst_data_idx_seq =
|
||||
generate_sequence_v2([&](auto i) { return Number<dst_data_idx[i]>{}; },
|
||||
Number<dst_data_idx.Size()>{});
|
||||
|
||||
const bool is_dst_valid =
|
||||
coordinate_has_valid_offset_assuming_visible_index_is_valid(
|
||||
dst_descs.At(dst_i), dst_coords_.At(dst_i));
|
||||
|
||||
using dst_vector_type = vector_type_maker_t<tuple_element_t<dst_i, DstDatas>,
|
||||
DstsScalarPerVector::At(dst_i)>;
|
||||
using dst_vector_t = typename dst_vector_type::type;
|
||||
|
||||
// copy data from dst_thread_scratch_ into dst_vector_container
|
||||
auto dst_vector_container = dst_vector_type{
|
||||
dst_thread_scratch_tuple_.At(dst_i).template GetAsType<dst_vector_t>(
|
||||
dst_data_idx_seq)};
|
||||
|
||||
constexpr InMemoryDataOperationEnum DstInMemOp =
|
||||
static_cast<InMemoryDataOperationEnum>(DstInMemOps::At(dst_i.value));
|
||||
|
||||
// copy data from dst_vector_container to dst_buf
|
||||
dst_bufs.At(dst_i).template Update<DstInMemOp, dst_vector_t>(
|
||||
dst_coords_.At(dst_i).GetOffset(),
|
||||
is_dst_valid,
|
||||
dst_vector_container.template AsType<dst_vector_t>()[I0]);
|
||||
|
||||
constexpr auto move_on_dim = [&]() constexpr
|
||||
{
|
||||
StaticallyIndexedArray<bool, nDim> move_on_dim_;
|
||||
|
||||
static_for<0, nDim, 1>{}([&](auto i) {
|
||||
move_on_dim_(i) = ordered_dst_access_idx[i] <
|
||||
ordered_dst_access_lengths_tuple.At(dst_i)[i] - 1;
|
||||
|
||||
static_for<i + 1, nDim, 1>{}([&](auto j) {
|
||||
move_on_dim_(i) &=
|
||||
ordered_dst_access_idx[j] ==
|
||||
ordered_dst_access_lengths_tuple.At(dst_i)[j] - 1;
|
||||
});
|
||||
});
|
||||
|
||||
return move_on_dim_;
|
||||
}
|
||||
();
|
||||
|
||||
// move dst coord
|
||||
static_for<0, nDim, 1>{}([&](auto i) {
|
||||
if constexpr(move_on_dim[i])
|
||||
{
|
||||
if constexpr(forward_sweep[i])
|
||||
{
|
||||
move_tensor_coordinate(
|
||||
dst_descs.At(dst_i),
|
||||
dst_coords_.At(dst_i),
|
||||
dst_forward_steps_tuple.At(dst_i)[dst_dim_access_order[i]]);
|
||||
}
|
||||
else
|
||||
{
|
||||
move_tensor_coordinate(
|
||||
dst_descs.At(dst_i),
|
||||
dst_coords_.At(dst_i),
|
||||
dst_backward_steps_tuple.At(dst_i)[dst_dim_access_order[i]]);
|
||||
}
|
||||
}
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
// move dst coordinate back to slice origin (or not)
|
||||
static_for<0, nDst, 1>{}([&](auto dst_i) {
|
||||
if constexpr(DstsResetCoordinateAfterRun::At(dst_i))
|
||||
{
|
||||
const auto dst_reset_step = make_tensor_coordinate_step(
|
||||
dst_descs.At(dst_i), GetDstCoordinateResetStep<dst_i>());
|
||||
|
||||
move_tensor_coordinate(dst_descs.At(dst_i), dst_coords_.At(dst_i), dst_reset_step);
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
template <index_t src_i>
|
||||
__device__ static constexpr auto GetSrcCoordinateResetStep()
|
||||
{
|
||||
// scalar per access on each dim
|
||||
// TODO: don't use lambda_scalar_per_access
|
||||
constexpr auto src_scalar_per_access = generate_sequence(
|
||||
detail::lambda_scalar_per_access<SrcVectorDim, SrcsScalarPerVector::At(src_i)>{},
|
||||
Number<nDim>{});
|
||||
|
||||
constexpr auto src_access_lengths = SliceLengths{} / src_scalar_per_access;
|
||||
|
||||
constexpr auto src_dim_access_order = SrcDimAccessOrder{};
|
||||
|
||||
constexpr auto ordered_src_access_lengths =
|
||||
container_reorder_given_new2old(src_access_lengths, src_dim_access_order);
|
||||
|
||||
// judge move forward or move backward during the last iteration
|
||||
constexpr auto forward_sweep = [&]() {
|
||||
StaticallyIndexedArray<bool, nDim> forward_sweep_;
|
||||
|
||||
forward_sweep_(I0) = true;
|
||||
|
||||
static_for<1, nDim, 1>{}([&](auto i) {
|
||||
index_t tmp = ordered_src_access_lengths[I0] - 1;
|
||||
|
||||
static_for<1, i, 1>{}([&](auto j) {
|
||||
tmp = tmp * ordered_src_access_lengths[j] + ordered_src_access_lengths[j] - 1;
|
||||
});
|
||||
|
||||
forward_sweep_(i) = tmp % 2 == 0;
|
||||
});
|
||||
|
||||
return forward_sweep_;
|
||||
}();
|
||||
|
||||
// calculate src data index after last iteration in RunRead(), if it has not being reset by
|
||||
// RunRead()
|
||||
constexpr auto src_data_idx = [&]() {
|
||||
Index ordered_idx;
|
||||
|
||||
static_for<0, nDim, 1>{}([&](auto i) {
|
||||
ordered_idx(i) = forward_sweep[i] ? ordered_src_access_lengths[i] - 1 : 0;
|
||||
});
|
||||
|
||||
return container_reorder_given_old2new(ordered_idx, src_dim_access_order) *
|
||||
src_scalar_per_access;
|
||||
}();
|
||||
|
||||
//
|
||||
constexpr auto reset_src_data_step = [&]() {
|
||||
Index reset_src_data_step_;
|
||||
|
||||
static_for<0, nDim, 1>{}([&](auto i) { reset_src_data_step_(i) = -src_data_idx[i]; });
|
||||
|
||||
return reset_src_data_step_;
|
||||
}();
|
||||
|
||||
return reset_src_data_step;
|
||||
}
|
||||
|
||||
template <index_t dst_i>
|
||||
__device__ static constexpr auto GetDstCoordinateResetStep()
|
||||
{
|
||||
// scalar per access on each dim
|
||||
// TODO: don't use lambda_scalar_per_access
|
||||
constexpr auto dst_scalar_per_access = generate_sequence(
|
||||
detail::lambda_scalar_per_access<DstVectorDim, DstsScalarPerVector::At(dst_i)>{},
|
||||
Number<nDim>{});
|
||||
|
||||
constexpr auto dst_access_lengths = SliceLengths{} / dst_scalar_per_access;
|
||||
|
||||
constexpr auto dst_dim_access_order = DstDimAccessOrder{};
|
||||
|
||||
constexpr auto ordered_dst_access_lengths =
|
||||
container_reorder_given_new2old(dst_access_lengths, dst_dim_access_order);
|
||||
|
||||
// judge move forward or move backward during the last iteration
|
||||
constexpr auto forward_sweep = [&]() {
|
||||
StaticallyIndexedArray<bool, nDim> forward_sweep_;
|
||||
|
||||
forward_sweep_(I0) = true;
|
||||
|
||||
static_for<1, nDim, 1>{}([&](auto i) {
|
||||
index_t tmp = ordered_dst_access_lengths[I0] - 1;
|
||||
|
||||
static_for<1, i, 1>{}([&](auto j) {
|
||||
tmp = tmp * ordered_dst_access_lengths[j] + ordered_dst_access_lengths[j] - 1;
|
||||
});
|
||||
|
||||
forward_sweep_(i) = tmp % 2 == 0;
|
||||
});
|
||||
|
||||
return forward_sweep_;
|
||||
}();
|
||||
|
||||
// calculate dst data index after last iteration in RunWrite(), if it has not being reset by
|
||||
// RunWrite()
|
||||
constexpr auto dst_data_idx = [&]() {
|
||||
Index ordered_idx;
|
||||
|
||||
static_for<0, nDim, 1>{}([&](auto i) {
|
||||
ordered_idx(i) = forward_sweep[i] ? ordered_dst_access_lengths[i] - 1 : 0;
|
||||
});
|
||||
|
||||
return container_reorder_given_old2new(ordered_idx, dst_dim_access_order) *
|
||||
dst_scalar_per_access.At(dst_i);
|
||||
}();
|
||||
|
||||
//
|
||||
constexpr auto reset_dst_data_step = [&]() {
|
||||
Index reset_dst_data_step_;
|
||||
|
||||
static_for<0, nDim, 1>{}([&](auto i) { reset_dst_data_step_(i) = -dst_data_idx[i]; });
|
||||
|
||||
return reset_dst_data_step_;
|
||||
}();
|
||||
|
||||
return reset_dst_data_step;
|
||||
}
|
||||
|
||||
// src_slice_origin_step_idx need to be known at compile-time, for performance reason
|
||||
__device__ void MoveSrcSliceWindow(const SrcDescs& src_descs,
|
||||
const Index& src_slice_origin_step_idx)
|
||||
{
|
||||
static_for<0, nSrc, 1>{}([&](auto src_i) {
|
||||
// if src coord was not reset by RunRead(), then need to adjust the step here
|
||||
const auto adjusted_step_idx =
|
||||
SrcsResetCoordinateAfterRun::At(src_i)
|
||||
? src_slice_origin_step_idx
|
||||
: src_slice_origin_step_idx + GetSrcCoordinateResetStep<src_i>();
|
||||
|
||||
// is it OK to construct a new step every time?
|
||||
const auto adjusted_step =
|
||||
make_tensor_coordinate_step(src_descs.At(src_i), adjusted_step_idx);
|
||||
|
||||
move_tensor_coordinate(src_descs.At(src_i), src_coords_.At(src_i), adjusted_step);
|
||||
});
|
||||
}
|
||||
|
||||
// dst_slice_origin_step_idx need to be known at compile-time, for performance reason
|
||||
__device__ void MoveDstSliceWindow(const DstDescs& dst_descs,
|
||||
const Index& dst_slice_origin_step_idx)
|
||||
{
|
||||
static_for<0, nDst, 1>{}([&](auto dst_i) {
|
||||
// if dst coord was not reset by RunWrite(), then need to adjust the step here
|
||||
const auto adjusted_step_idx =
|
||||
DstsResetCoordinateAfterRun::At(dst_i)
|
||||
? dst_slice_origin_step_idx
|
||||
: dst_slice_origin_step_idx + GetDstCoordinateResetStep<dst_i>();
|
||||
|
||||
// is it OK to construct a new step every time?
|
||||
const auto adjusted_step =
|
||||
make_tensor_coordinate_step(dst_descs.At(dst_i), adjusted_step_idx);
|
||||
|
||||
move_tensor_coordinate(dst_descs.At(dst_i), dst_coords_.At(dst_i), adjusted_step);
|
||||
});
|
||||
}
|
||||
|
||||
template <index_t src_i>
|
||||
__device__ static constexpr auto GetSrcThreadScratchDescriptor()
|
||||
{
|
||||
constexpr auto src_scalar_per_access = generate_sequence(
|
||||
detail::lambda_scalar_per_access<SrcVectorDim, SrcsScalarPerVector::At(src_i)>{},
|
||||
Number<nDim>{});
|
||||
|
||||
constexpr auto src_access_lengths = SliceLengths{} / src_scalar_per_access;
|
||||
|
||||
constexpr auto src_access_lengths_and_vector_length =
|
||||
container_push_back(sequence_to_tuple_of_number(src_access_lengths),
|
||||
Number<SrcsScalarPerVector::At(src_i)>{});
|
||||
|
||||
// 1st stage of transforms
|
||||
constexpr auto desc0 =
|
||||
make_naive_tensor_descriptor_packed(src_access_lengths_and_vector_length);
|
||||
|
||||
// 2nd stage of transforms
|
||||
constexpr auto transforms = generate_tuple(
|
||||
[&](auto i) {
|
||||
if constexpr(i == SrcVectorDim)
|
||||
{
|
||||
return make_merge_transform_v3_division_mod(
|
||||
make_tuple(src_access_lengths_and_vector_length[i],
|
||||
src_access_lengths_and_vector_length[Number<nDim>{}]));
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_pass_through_transform(src_access_lengths_and_vector_length[i]);
|
||||
}
|
||||
},
|
||||
Number<nDim>{});
|
||||
|
||||
constexpr auto low_dim_idss = generate_tuple(
|
||||
[&](auto i) {
|
||||
if constexpr(i == SrcVectorDim)
|
||||
{
|
||||
return Sequence<i.value, nDim>{};
|
||||
}
|
||||
else
|
||||
{
|
||||
return Sequence<i.value>{};
|
||||
}
|
||||
},
|
||||
Number<nDim>{});
|
||||
|
||||
constexpr auto up_dim_idss =
|
||||
generate_tuple([&](auto i) { return Sequence<i.value>{}; }, Number<nDim>{});
|
||||
|
||||
return transform_tensor_descriptor(desc0, transforms, low_dim_idss, up_dim_idss);
|
||||
}
|
||||
|
||||
template <index_t dst_i>
|
||||
__device__ static constexpr auto GetDstThreadScratchDescriptor()
|
||||
{
|
||||
// 1st stage of transforms
|
||||
constexpr auto dst_scalar_per_access = generate_sequence(
|
||||
detail::lambda_scalar_per_access<DstVectorDim, DstsScalarPerVector::At(dst_i)>{},
|
||||
Number<nDim>{});
|
||||
|
||||
constexpr auto dst_access_lengths = SliceLengths{} / dst_scalar_per_access;
|
||||
|
||||
constexpr auto dst_access_lengths_and_vector_length =
|
||||
container_push_back(sequence_to_tuple_of_number(dst_access_lengths),
|
||||
Number<DstsScalarPerVector::At(dst_i)>{});
|
||||
|
||||
constexpr auto desc0 =
|
||||
make_naive_tensor_descriptor_packed(dst_access_lengths_and_vector_length);
|
||||
|
||||
// 2nd stage of transforms
|
||||
constexpr auto transforms = generate_tuple(
|
||||
[&](auto i) {
|
||||
if constexpr(i == DstVectorDim)
|
||||
{
|
||||
return make_merge_transform_v3_division_mod(
|
||||
make_tuple(dst_access_lengths_and_vector_length[i],
|
||||
dst_access_lengths_and_vector_length[Number<nDim>{}]));
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_pass_through_transform(dst_access_lengths_and_vector_length[i]);
|
||||
}
|
||||
},
|
||||
Number<nDim>{});
|
||||
|
||||
constexpr auto low_dim_idss = generate_tuple(
|
||||
[&](auto i) {
|
||||
if constexpr(i == DstVectorDim)
|
||||
{
|
||||
return Sequence<i.value, nDim>{};
|
||||
}
|
||||
else
|
||||
{
|
||||
return Sequence<i.value>{};
|
||||
}
|
||||
},
|
||||
Number<nDim>{});
|
||||
|
||||
constexpr auto up_dim_idss =
|
||||
generate_tuple([&](auto i) { return Sequence<i.value>{}; }, Number<nDim>{});
|
||||
|
||||
return transform_tensor_descriptor(desc0, transforms, low_dim_idss, up_dim_idss);
|
||||
}
|
||||
|
||||
__device__ static constexpr auto MakeSrcThreadScratchTuple()
|
||||
{
|
||||
return generate_tuple(
|
||||
[&](auto src_i) {
|
||||
constexpr auto src_thread_scratch_desc =
|
||||
decltype(GetSrcThreadScratchDescriptor<src_i>()){};
|
||||
using SrcThreadScratch =
|
||||
StaticTensorTupleOfVectorBuffer<AddressSpaceEnum::Vgpr,
|
||||
tuple_element_t<src_i, SrcDatas>,
|
||||
SrcsScalarPerVector::At(src_i),
|
||||
decltype(src_thread_scratch_desc),
|
||||
true>;
|
||||
return SrcThreadScratch{};
|
||||
},
|
||||
Number<nSrc>{});
|
||||
}
|
||||
|
||||
__device__ static constexpr auto MakeDstThreadScratchTuple()
|
||||
{
|
||||
return generate_tuple(
|
||||
[&](auto dst_i) {
|
||||
constexpr auto dst_thread_scratch_desc =
|
||||
decltype(GetDstThreadScratchDescriptor<dst_i>()){};
|
||||
using DstThreadScratch =
|
||||
StaticTensorTupleOfVectorBuffer<AddressSpaceEnum::Vgpr,
|
||||
tuple_element_t<dst_i, DstDatas>,
|
||||
DstsScalarPerVector::At(dst_i),
|
||||
decltype(dst_thread_scratch_desc),
|
||||
true>;
|
||||
return DstThreadScratch{};
|
||||
},
|
||||
Number<nDst>{});
|
||||
}
|
||||
|
||||
private:
|
||||
using SrcThreadScratchTuple = decltype(MakeSrcThreadScratchTuple());
|
||||
using DstThreadScratchTuple = decltype(MakeDstThreadScratchTuple());
|
||||
|
||||
StaticallyIndexedArray<SrcThreadScratchTuple, NumThreadScratch> src_thread_scratch_tuple_;
|
||||
|
||||
DstThreadScratchTuple dst_thread_scratch_tuple_;
|
||||
|
||||
SrcCoords src_coords_;
|
||||
DstCoords dst_coords_;
|
||||
const ElementwiseOperation element_op_;
|
||||
};
|
||||
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user