mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-03 21:21:22 +00:00
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
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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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#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_3d.hpp"
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#include "ck/tensor_description/tensor_descriptor_helper.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_m, // choose how to set dims
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index_t NumDim_n,
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index_t NumDim_k,
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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 DeviceElementwise3dImpl : public DeviceElementwise<InDataTypeTuple,
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OutDataTypeTuple,
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ElementwiseOperation,
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NumDim_m + NumDim_n + NumDim_k>
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{
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static constexpr index_t NumDim = NumDim_m + NumDim_n + NumDim_k;
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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 constexpr auto I2 = Number<2>{};
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static constexpr auto I3 = Number<3>{};
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static constexpr auto I4 = Number<4>{};
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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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template <typename Desc_MNK>
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static auto PadDescriptor_MNK(Desc_MNK desc_mnk,
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index_t gridSize,
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index_t blockSize,
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index_t num_threads_m,
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index_t num_threads_n,
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index_t num_threads_k)
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{
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std::ignore = blockSize;
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std::ignore = gridSize;
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const auto m = desc_mnk.GetLength(I0);
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const auto n = desc_mnk.GetLength(I1);
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const auto k = desc_mnk.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 auto pad_m = math::integer_least_multiple(m, loop_step_m) - m;
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const auto pad_n = math::integer_least_multiple(n, loop_step_n) - n;
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const auto pad_k = math::integer_least_multiple(k, loop_step_k) - k;
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const auto desc_mnk_pad =
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transform_tensor_descriptor(desc_mnk,
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make_tuple(make_right_pad_transform(m, pad_m),
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make_right_pad_transform(n, pad_n),
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make_right_pad_transform(k, pad_k)),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
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return desc_mnk_pad;
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}
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static auto MakeDescriptor_MNK(const std::array<index_t, NumDim>& lengths,
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const std::array<index_t, NumDim>& stride,
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index_t gridSize,
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index_t blockSize,
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index_t num_threads_m,
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index_t num_threads_n,
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index_t num_threads_k)
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{
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auto tupleOfShape = generate_tuple([&](auto I) { return lengths[I]; }, Number<NumDim>{});
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auto tupleOfStride = generate_tuple([&](auto I) { return stride[I]; }, Number<NumDim>{});
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// nd desc - [s0, s1, s2, ...]
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const auto desc = make_naive_tensor_descriptor(tupleOfShape, tupleOfStride);
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constexpr auto mDimIds = typename arithmetic_sequence_gen<0, NumDim_m, 1>::type();
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constexpr auto nDimIds =
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typename arithmetic_sequence_gen<NumDim_m, NumDim_m + NumDim_n, 1>::type();
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constexpr auto kDimIds =
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typename arithmetic_sequence_gen<NumDim_m + NumDim_n, NumDim, 1>::type();
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const auto mLengths = get_container_subset(tupleOfShape, mDimIds);
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const auto nLengths = get_container_subset(tupleOfShape, nDimIds);
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const auto kLengths = get_container_subset(tupleOfShape, kDimIds);
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// merge nd to 3d desc - [s0 * s1 * ...]
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if constexpr(NumDim > 3)
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{
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const auto desc_mnk = transform_tensor_descriptor(
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desc,
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make_tuple(make_merge_transform(mLengths),
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make_merge_transform(nLengths),
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make_merge_transform(kLengths)),
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make_tuple(mDimIds, nDimIds, kDimIds),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
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return PadDescriptor_MNK(
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desc_mnk, gridSize, blockSize, num_threads_m, num_threads_n, num_threads_k);
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}
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else
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return PadDescriptor_MNK(
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desc, gridSize, blockSize, num_threads_m, num_threads_n, num_threads_k);
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}
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template <index_t TupleSize>
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static auto GenerateInOutGrid3dDescTuple(Number<TupleSize>)
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{
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return generate_tuple(
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[&](auto) {
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if constexpr(NumDim > 3)
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{
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return MakeDescriptor_MNK({1, 1, 1}, {1, 1, 1}, 1, 1, 1, 1, 1);
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}
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else
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{
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return MakeDescriptor_MNK({1}, {1}, 1, 1, 1, 1, 1);
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};
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},
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Number<TupleSize>{});
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}
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using OutGrid3dDescTuple = decltype(GenerateInOutGrid3dDescTuple(Number<NumOutput>{}));
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using InGrid3dDescTuple = decltype(GenerateInOutGrid3dDescTuple(Number<NumInput>{}));
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using GridwiseElementwise = GridwiseElementwise_3D<InGrid3dDescTuple,
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OutGrid3dDescTuple,
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InDataTypePointerTuple,
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OutDataTypePointerTuple,
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ElementwiseOperation,
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MPerThread,
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NPerThread,
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KPerThread,
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InScalarPerVectorSeq,
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OutScalarPerVectorSeq>;
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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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blockSize_(256)
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{
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static_assert(NumDim_m > 0, "");
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static_assert(NumDim_n > 0, "");
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static_assert(NumDim_k > 0, "");
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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_;
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std::array<std::array<index_t, NumDim>, NumInput> inStridesArray_;
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std::array<std::array<index_t, NumDim>, NumOutput> outStridesArray_;
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ElementwiseOperation elementwise_op_;
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index_t blockSize_;
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};
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struct Invoker : public BaseInvoker
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{
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float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
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{
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index_t gridSize = getAvailableComputeUnitCount(stream_config) * arg.blockSize_;
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index_t num_threads_m = gridSize / (16 * 16);
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index_t num_threads_n = 16;
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index_t num_threads_k = 16;
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auto in_grid_3d_desc_tuple = generate_tuple(
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[&](auto I) {
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return MakeDescriptor_MNK(arg.lengths_,
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arg.inStridesArray_[I.value],
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gridSize,
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arg.blockSize_,
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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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Number<NumInput>{});
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auto out_grid_3d_desc_tuple = generate_tuple(
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[&](auto I) {
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return MakeDescriptor_MNK(arg.lengths_,
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arg.outStridesArray_[I.value],
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gridSize,
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arg.blockSize_,
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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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Number<NumOutput>{});
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const auto kernel = kernel_elementwise_3d<GridwiseElementwise,
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InGrid3dDescTuple,
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OutGrid3dDescTuple,
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InDataTypePointerTuple,
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OutDataTypePointerTuple,
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ElementwiseOperation>;
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float elapsed_time = launch_and_time_kernel(stream_config,
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kernel,
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dim3(gridSize),
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dim3(arg.blockSize_),
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0,
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in_grid_3d_desc_tuple,
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out_grid_3d_desc_tuple,
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arg.in_dev_buffers_,
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arg.out_dev_buffers_,
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arg.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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return elapsed_time;
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}
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// polymorphic
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float Run(const BaseArgument* p_arg,
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const StreamConfig& stream_config = StreamConfig{}) override
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{
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return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
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}
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};
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bool IsSupportedArgument(const BaseArgument* p_arg) override
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{
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const Argument* pArg = dynamic_cast<const Argument*>(p_arg);
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if(pArg == nullptr)
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return false;
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if(pArg->lengths_.back() % MPerThread != 0)
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return false;
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auto IsScalarPerVectorValid = [&](const std::array<index_t, NumDim>& lengths,
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const std::array<index_t, NumDim>& strides,
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index_t scalarPerVector,
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index_t vectorDim) {
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if(strides[vectorDim] == 1 &&
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(lengths[vectorDim] % scalarPerVector == 0 ||
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lengths[vectorDim] % scalarPerVector == lengths[vectorDim]))
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{
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return true;
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}
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if(strides[vectorDim] >= scalarPerVector)
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{
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return true;
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}
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return false;
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};
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bool valid = true;
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static_for<0, NumInput, 1>{}([&](auto I) {
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valid = valid && IsScalarPerVectorValid(pArg->lengths_,
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pArg->inStridesArray_[I.value],
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InScalarPerVectorSeq::At(I),
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NumDim_m - 1);
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});
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static_for<0, NumOutput, 1>{}([&](auto I) {
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valid = valid && IsScalarPerVectorValid(pArg->lengths_,
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pArg->outStridesArray_[I.value],
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OutScalarPerVectorSeq::At(I),
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NumDim - 1);
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});
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return valid;
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}
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std::unique_ptr<BaseArgument>
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MakeArgumentPointer(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) override
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{
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return std::make_unique<Argument>(lengths,
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inStridesArray,
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outStridesArray,
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in_dev_buffers,
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out_dev_buffers,
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elementwise_op);
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}
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static auto MakeInvoker() { return Invoker{}; }
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std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
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{
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return std::make_unique<Invoker>();
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}
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}; // namespace device
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} // namespace device
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} // namespace tensor_operation
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} // namespace ck
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