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https://github.com/ROCm/composable_kernel.git
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Transpose 3d (#984)
* added working example for 5D input using 1D kernel * example with 5D input tensor and 2d kernel - not working: issues with arguments * added updated version of 3d device op - changed descriptors/dims * added example file to check kernel * fixed descriptor and isSupportedArgument stride problem * added and modified kernel for 3d - updated tids/loop * adding some more 5d example files * fixed some issues * changes made for testing * working version: fixed error in stride for A, still a bit inefficient * cleaned up formatting/comments * updating formatting * more formatting fixes * fixing cmake, adding back gpu targets in cmake script * adding client example * added instances for client example * fixed errors in client example * implemented client ex with device_elementwise.hpp and device_elementwise_3d_impl.hpp * removed extra files * minor formatting and naming fixes * adding test files and profiler * fixing minor error * minor fix * removed unneccesary comments, renamed files * updated instance list for client example, added different layout example * removing instances * fixed error in instance generation * remove comments * update profiler and client example tensor layouts * fixed errors in test/profiler * updated vector dim access to enable vector load * updated test/profiler files * updated example with 1d kernel * updating profiler * renamed files --------- Co-authored-by: Jing Zhang <jizha@amd.com>
This commit is contained in:
arai713
264
include/ck/tensor_operation/gpu/grid/gridwise_elementwise_3d.hpp
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264
include/ck/tensor_operation/gpu/grid/gridwise_elementwise_3d.hpp
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// SPDX-License-Identifier: MIT
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// // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
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//
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#pragma once
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#include "ck/tensor_description/cluster_descriptor.hpp"
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#include "ck/utility/data_type.hpp"
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#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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namespace ck {
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template <typename GridwiseElementwise3dFunctor,
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typename InGrid3dDescTuple,
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typename OutGrid3dDescTuple,
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typename InDataTypePointerTuple,
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typename OutDataTypePointerTuple,
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typename ElementwiseOperation>
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__global__ void kernel_elementwise_3d(const InGrid3dDescTuple in_grid_3d_desc_tuple,
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const OutGrid3dDescTuple out_grid_3d_desc_tuple,
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const InDataTypePointerTuple p_in_global_tuple,
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const OutDataTypePointerTuple p_out_global_tuple,
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const ElementwiseOperation elementwise_op,
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const index_t num_threads_m,
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const index_t num_threads_n,
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const index_t num_threads_k)
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{
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GridwiseElementwise3dFunctor::Run(in_grid_3d_desc_tuple,
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out_grid_3d_desc_tuple,
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p_in_global_tuple,
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p_out_global_tuple,
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elementwise_op,
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num_threads_m,
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num_threads_n,
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num_threads_k);
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}
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template <typename InGrid3dDescTuple,
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typename OutGrid3dDescTuple,
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typename InDataTypePointerTuple,
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typename OutDataTypePointerTuple,
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typename ElementwiseOperation,
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index_t MPerThread,
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index_t NPerThread,
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index_t KPerThread,
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typename InScalarPerVectorSeq,
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typename OutScalarPerVectorSeq>
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struct GridwiseElementwise_3D
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{
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static constexpr index_t NumInput = InDataTypePointerTuple::Size();
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static constexpr index_t NumOutput = OutDataTypePointerTuple::Size();
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static_assert(NumInput == InScalarPerVectorSeq::Size() &&
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NumOutput == OutScalarPerVectorSeq::Size() &&
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NumInput == InGrid3dDescTuple::Size() &&
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NumOutput == OutGrid3dDescTuple::Size(),
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"Tuple size is inconsistent with the number of in/out!");
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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 constexpr auto I2 = Number<2>{};
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static constexpr auto thread_buffer_desc_mnk = make_naive_tensor_descriptor_packed(
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make_tuple(Number<MPerThread>{}, Number<NPerThread>{}, Number<KPerThread>{}));
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using PassThroughOp = tensor_operation::element_wise::PassThrough;
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__device__ static void Run(const InGrid3dDescTuple in_grid_3d_desc_tuple,
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const OutGrid3dDescTuple out_grid_3d_desc_tuple,
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const InDataTypePointerTuple p_in_global_tuple,
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const OutDataTypePointerTuple p_out_global_tuple,
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const ElementwiseOperation elementwise_op,
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const index_t num_threads_m,
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const index_t num_threads_n,
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const index_t num_threads_k)
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{
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auto in_thread_buf_tuple = generate_tuple(
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[&](auto I) {
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using DataTypePointer = remove_cvref_t<decltype(InDataTypePointerTuple{}[I])>;
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using DataType = remove_cv_t<remove_pointer_t<DataTypePointer>>;
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return StaticBuffer<AddressSpaceEnum::Vgpr,
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DataType,
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MPerThread * NPerThread * KPerThread,
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true>{};
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},
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Number<NumInput>{});
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auto out_thread_buf_tuple = generate_tuple(
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[&](auto I) {
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using DataTypePointer = remove_cvref_t<decltype(OutDataTypePointerTuple{}[I])>;
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using DataType = remove_pointer_t<DataTypePointer>;
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return StaticBuffer<AddressSpaceEnum::Vgpr,
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DataType,
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MPerThread * NPerThread * KPerThread,
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true>{};
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},
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Number<NumOutput>{});
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auto in_global_buf_tuple = generate_tuple(
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[&](auto I) {
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return make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_in_global_tuple[I], in_grid_3d_desc_tuple[I].GetElementSpaceSize());
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},
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Number<NumInput>{});
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auto out_global_buf_tuple = generate_tuple(
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[&](auto I) {
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return make_dynamic_buffer<AddressSpaceEnum::Global>(
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p_out_global_tuple[I], out_grid_3d_desc_tuple[I].GetElementSpaceSize());
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},
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Number<NumOutput>{});
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const auto M = in_grid_3d_desc_tuple[I0].GetLength(I0);
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const auto N = in_grid_3d_desc_tuple[I0].GetLength(I1);
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const auto K = in_grid_3d_desc_tuple[I0].GetLength(I2);
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const index_t loop_step_m = num_threads_m * MPerThread;
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const index_t loop_step_n = num_threads_n * NPerThread;
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const index_t loop_step_k = num_threads_k * KPerThread;
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const index_t thread_1d_id = get_thread_global_1d_id();
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const index_t tid_m = thread_1d_id / (num_threads_n * num_threads_k);
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const index_t tid_nk = thread_1d_id % (num_threads_n * num_threads_k);
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const index_t tid_n = tid_nk / num_threads_k;
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const index_t tid_k = tid_nk % num_threads_k;
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const auto thread_global_offset =
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make_multi_index(tid_m * MPerThread, tid_n * NPerThread, tid_k * KPerThread);
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auto in_global_load_tuple = generate_tuple(
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[&](auto I) {
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using DataTypePointer = remove_cvref_t<decltype(InDataTypePointerTuple{}[I])>;
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using DataType = remove_cv_t<remove_pointer_t<DataTypePointer>>;
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return ThreadwiseTensorSliceTransfer_v2<
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DataType,
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DataType,
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decltype(in_grid_3d_desc_tuple[I]),
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decltype(thread_buffer_desc_mnk),
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Sequence<MPerThread, NPerThread, KPerThread>, // SliceLengths
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Sequence<0, 1, 2>, // DimAccessOrder
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01, // SrcVectorDim
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InScalarPerVectorSeq::At(I), // InScalarPerVectorSeq::At(I), //
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// ScalarPerVector
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1, // SrcScalarStrideInVector
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true>{in_grid_3d_desc_tuple[I], thread_global_offset};
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},
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Number<NumInput>{});
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auto out_global_store_tuple = generate_tuple(
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[&](auto I) {
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using DataTypePointer = remove_cvref_t<decltype(OutDataTypePointerTuple{}[I])>;
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using DataType = remove_pointer_t<DataTypePointer>;
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return ThreadwiseTensorSliceTransfer_v1r3<
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DataType,
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DataType,
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decltype(thread_buffer_desc_mnk),
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decltype(out_grid_3d_desc_tuple[I]),
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PassThroughOp,
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Sequence<MPerThread, NPerThread, KPerThread>, // SliceLengths
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Sequence<0, 1, 2>, // DimAccessOrder
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2, // SrcVectorDim
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OutScalarPerVectorSeq::At(I), // OutScalarPerVectorSeq::At(I),
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InMemoryDataOperationEnum::Set,
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1,
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true>(out_grid_3d_desc_tuple[I], thread_global_offset, PassThroughOp{});
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},
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Number<NumOutput>{});
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index_t num_iter_m = M / (loop_step_m);
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do
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{
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index_t num_iter_n = N / (loop_step_n);
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do
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{
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index_t num_iter_k = K / (loop_step_k);
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do
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{
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static_for<0, NumInput, 1>{}([&](auto I) {
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in_global_load_tuple(I).Run(in_grid_3d_desc_tuple[I],
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in_global_buf_tuple[I],
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thread_buffer_desc_mnk,
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make_tuple(I0, I0, I0),
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in_thread_buf_tuple(I));
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in_global_load_tuple(I).MoveSrcSliceWindow(
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in_grid_3d_desc_tuple[I], make_multi_index(0, 0, loop_step_k));
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});
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static_for<0, MPerThread, 1>{}([&](auto iM) {
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static_for<0, NPerThread, 1>{}([&](auto iN) {
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static_for<0, KPerThread, 1>{}([&](auto iK) {
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constexpr auto offset =
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thread_buffer_desc_mnk.CalculateOffset(make_tuple(iM, iN, iK));
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// get reference to in data
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const auto in_data_refs = generate_tie(
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// return type should be lvalue
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[&](auto I) -> const auto& {
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return in_thread_buf_tuple(I)(Number<offset>{});
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},
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Number<NumInput>{});
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// get referenec to dst data
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auto out_data_refs = generate_tie(
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// return type should be lvalue
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[&](auto I) -> auto& {
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return out_thread_buf_tuple(I)(Number<offset>{});
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},
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Number<NumOutput>{});
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unpack2(elementwise_op, out_data_refs, in_data_refs);
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});
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});
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});
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static_for<0, NumOutput, 1>{}([&](auto I) {
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out_global_store_tuple(I).Run(thread_buffer_desc_mnk,
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make_tuple(I0, I0, I0),
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out_thread_buf_tuple[I],
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out_grid_3d_desc_tuple[I],
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out_global_buf_tuple(I));
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out_global_store_tuple(I).MoveDstSliceWindow(
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out_grid_3d_desc_tuple[I], make_multi_index(0, 0, loop_step_k));
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});
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} while(--num_iter_k);
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static_for<0, NumInput, 1>{}([&](auto I) {
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in_global_load_tuple(I).MoveSrcSliceWindow(
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in_grid_3d_desc_tuple[I],
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make_multi_index(0, loop_step_n, -(K / loop_step_k) * loop_step_k));
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});
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static_for<0, NumOutput, 1>{}([&](auto I) {
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out_global_store_tuple(I).MoveDstSliceWindow(
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out_grid_3d_desc_tuple[I],
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make_multi_index(0, loop_step_n, -(K / loop_step_k) * loop_step_k));
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});
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} while(--num_iter_n);
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static_for<0, NumInput, 1>{}([&](auto I) {
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in_global_load_tuple(I).MoveSrcSliceWindow(
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in_grid_3d_desc_tuple[I],
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make_multi_index(loop_step_m,
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-(N / loop_step_n) * loop_step_n,
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-(K / loop_step_k) * loop_step_k));
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});
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static_for<0, NumOutput, 1>{}([&](auto I) {
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out_global_store_tuple(I).MoveDstSliceWindow(
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out_grid_3d_desc_tuple[I],
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make_multi_index(loop_step_m,
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-(N / loop_step_n) * loop_step_n,
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-(K / loop_step_k) * loop_step_k));
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});
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} while(--num_iter_m);
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}
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};
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} // namespace ck
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