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GEMM with Multiple Source, GEMM+Bias+Add+FastGeLU example and ckProfiler (#241)
* ad gelu and fast_gelu * added GeLU and fast GeLU * clean up * add gemm+fastgelu example * add gemm+gelu instances * update profiler * clean up * clean up * adding gemm+bias+activation * clean * adding bias * clean * adding gemm multiple d * debugging * add gemm bias add fastgelu * rename, clean * refactoring; add readme * refactor * refactor * refactor * refactor * refactor * refactor * fix * fix * update example * update example * rename * update example * add ckProfiler * clean * clean * clean * clean * add comment * use type_convert * clean * clean element wise op
This commit is contained in:
Chao Liu
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#pragma once
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#include "common_header.hpp"
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#include "tensor_descriptor.hpp"
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#include "tensor_descriptor_helper.hpp"
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#include "tensor_space_filling_curve.hpp"
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namespace ck {
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// Thread-level multi-source, multi-destination tensor slice data movement
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// Assume:
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// 1. All sources and destinations are DynamicBuffer
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// 2. Same VectorDim and ScalerPerVector for all sources and destinations
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// 3. DstInMemOps are per destination tensor
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// 4. ThreadTransferSrcResetCoordinateAfterRunFlags are per source tensor
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// 5. ThreadTransferDstResetCoordinateAfterRunFlags are per destination tensor
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// 6. Does not need to know src_descs and dst_descs at compile-time
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// 7. Does not need to know src_slice_origins and dst_slice_origins at compile-time,
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//
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// Does following things to avoid scratch memory issue
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// 1. Use StaticallyIndexedArray or vector_type instead of C array for thread buffer
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// 2. Pass tensor descritpors by reference (or tuple of references)
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// 3. Does not keep reference to tensor descriptor
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// 4. Does not construct new tensor coordinate when call Run()
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template <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 ElementwiseOperation,
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typename DstInMemOps, // Sequence<InMemoryDataOperationEnum ...>
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typename SliceLengths,
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typename DimAccessOrder,
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index_t VectorDim,
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index_t ScalarPerVector,
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typename SrcResetCoordinateAfterRunFlags, // Sequence<bool ...>
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typename DstResetCoordinateAfterRunFlags> // Sequence<bool ...>
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struct ThreadwiseTensorSliceTransfer_v7
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{
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static constexpr auto I0 = Number<0>{};
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static constexpr index_t nDim = SliceLengths::Size();
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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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using Index = MultiIndex<nDim>;
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// return a tuple of coordiantes for a tuple of tensor
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template <typename Descs,
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typename Indices,
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enable_if_t<Descs::Size() == Indices::Size(), bool> = false>
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static constexpr auto MakeCoordinates(const Descs& descs, const Indices& indices)
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{
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return generate_tuple([&](auto i) { return make_tensor_coordinate(descs[i], indices[i]); },
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Number<Descs::Size()>{});
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}
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using SrcCoords = decltype(MakeCoordinates(SrcDescs{}, StaticallyIndexedArray<Index, nSrc>{}));
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using DstCoords = decltype(MakeCoordinates(DstDescs{}, StaticallyIndexedArray<Index, nDst>{}));
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// scalar per access on each dim
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// FIXME: don't use lambda_scalar_per_access
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static constexpr auto scalar_per_access = generate_sequence(
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detail::lambda_scalar_per_access<VectorDim, ScalarPerVector>{}, Number<nDim>{});
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using SpaceFillingCurve =
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SpaceFillingCurve<SliceLengths, DimAccessOrder, remove_cv_t<decltype(scalar_per_access)>>;
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__device__ constexpr ThreadwiseTensorSliceTransfer_v7(
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const SrcDescs& src_descs,
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const StaticallyIndexedArray<Index, nSrc>& src_slice_origins,
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const DstDescs& dst_descs,
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const StaticallyIndexedArray<Index, nDst>& dst_slice_origins,
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const ElementwiseOperation& element_op)
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: src_coords_(MakeCoordinates(src_descs, src_slice_origins)),
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dst_coords_(MakeCoordinates(dst_descs, dst_slice_origins)),
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element_op_(element_op)
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{
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static_assert(SliceLengths::At(Number<VectorDim>{}) % ScalarPerVector == 0,
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"wrong! cannot evenly divide");
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}
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template <typename Indices, enable_if_t<SrcDescs::Size() == Indices::Size(), bool> = false>
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__device__ void SetSrcSliceOrigins(const SrcDescs& src_descs,
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const Indices& src_slice_origin_idxs)
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{
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static_for<0, nSrc, 1>{}([&](auto i) {
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src_coords_(i) = make_tensor_coordinate(src_descs[i], src_slice_origin_idxs[i]);
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});
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}
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template <typename Indices, enable_if_t<DstDescs::Size() == Indices::Size(), bool> = false>
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__device__ void SetDstSliceOrigins(const DstDescs& dst_descs,
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const Indices& dst_slice_origin_idxs)
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{
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static_for<0, nDst, 1>{}([&](auto i) {
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dst_coords_(i) = make_tensor_coordinate(dst_descs[i], dst_slice_origin_idxs[i]);
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});
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}
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// SrcDescs: Tuple<const SrcDesc0&, const SrcDesc1&, ...>
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// SrcBuffers: Tuple<const SrcBuffer0&, const SrcBuffer1&, ...>
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// DstDescs: Tuple<const DstDesc0&, const DstDesc1&, ...>
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// DstBuffers: Tuple<const DstBuffer0&, const DstBuffer1&, ...>
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template <typename SrcBuffers,
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typename DstBuffers,
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enable_if_t<SrcDescs::Size() == SrcBuffers::Size() &&
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DstDescs::Size() == DstBuffers::Size(),
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bool> = false>
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__device__ void Run(const SrcDescs& src_descs,
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const SrcBuffers& src_bufs,
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const DstDescs& dst_descs,
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DstBuffers dst_bufs)
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{
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auto generate_vectors = [&](auto data_types) {
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constexpr index_t num = data_types.Size();
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return generate_tuple(
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[&](auto i) {
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using DataType = remove_cvref_t<decltype(data_types[i])>;
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return vector_type_maker_t<DataType, ScalarPerVector>{};
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},
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Number<num>{});
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};
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constexpr auto num_access = SpaceFillingCurve::GetNumOfAccess();
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// loop over space-filling curve
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static_for<0, num_access, 1>{}([&](auto iAccess) {
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auto src_vectors = generate_vectors(SrcDatas{});
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auto dst_vectors = generate_vectors(DstDatas{});
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// copy data from src_bufs into src_vectors
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static_for<0, nSrc, 1>{}([&](auto i) {
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using src_vector_t = typename remove_cvref_t<decltype(src_vectors[i])>::type;
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const bool is_src_valid =
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coordinate_has_valid_offset_assuming_visible_index_is_valid(src_descs[i],
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src_coords_[i]);
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src_vectors(i).template AsType<src_vector_t>()(I0) =
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src_bufs[i].template Get<src_vector_t>(src_coords_[i].GetOffset(),
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is_src_valid);
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});
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// apply pointwise function
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static_for<0, ScalarPerVector, 1>{}([&](auto i) {
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// get reference to src data
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const auto src_data_refs = generate_tie(
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// return type should be lvalue
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[&](auto iSrc) -> const auto& {
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using SrcData = remove_cvref_t<tuple_element_t<iSrc.value, SrcDatas>>;
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return src_vectors[iSrc].template AsType<SrcData>()[i];
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},
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Number<nSrc>{});
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// get reference to dst data
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auto dst_data_refs = generate_tie(
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// return type should be lvalue
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[&](auto iDst) -> auto& {
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using DstData = remove_cvref_t<tuple_element_t<iDst.value, DstDatas>>;
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return dst_vectors(iDst).template AsType<DstData>()(i);
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},
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Number<nDst>{});
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// apply pointwise function
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// pointwise function signature:
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// element_op_(dst_data_refs[I0],
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// dst_data_refs[I1],
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// ...,
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// src_data_refs[I0],
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// src_data_refs[I1],
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// ...)
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unpack2(element_op_, dst_data_refs, src_data_refs);
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});
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// copy data from buf_vectors into dst_bufs
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static_for<0, nDst, 1>{}([&](auto i) {
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using dst_vector_t = typename remove_cvref_t<decltype(dst_vectors[i])>::type;
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const bool is_dst_valid =
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coordinate_has_valid_offset_assuming_visible_index_is_valid(dst_descs[i],
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dst_coords_[i]);
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constexpr InMemoryDataOperationEnum DstInMemOp =
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static_cast<InMemoryDataOperationEnum>(DstInMemOps::At(i.value));
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dst_bufs(i).template Update<DstInMemOp, dst_vector_t>(
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dst_coords_[i].GetOffset(),
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is_dst_valid,
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dst_vectors[i].template AsType<dst_vector_t>()[I0]);
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});
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// move coordinate
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if constexpr(iAccess.value != num_access - 1)
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{
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constexpr auto forward_step = SpaceFillingCurve::GetForwardStep(iAccess);
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static_for<0, nSrc, 1>{}([&](auto i) {
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move_tensor_coordinate(src_descs[i],
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src_coords_(i),
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make_tensor_coordinate_step(src_descs[i], forward_step));
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});
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static_for<0, nDst, 1>{}([&](auto i) {
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move_tensor_coordinate(dst_descs[i],
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dst_coords_(i),
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make_tensor_coordinate_step(dst_descs[i], forward_step));
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});
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}
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});
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// move coordinate back to slice origin (or not)
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static_for<0, nSrc, 1>{}([&](auto i) {
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if constexpr(SrcResetCoordinateAfterRunFlags::At(i))
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{
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const auto src_reset_step =
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make_tensor_coordinate_step(src_descs[i], GetCoordinateResetStep());
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move_tensor_coordinate(src_descs[i], src_coords_(i), src_reset_step);
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}
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});
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static_for<0, nDst, 1>{}([&](auto i) {
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if constexpr(DstResetCoordinateAfterRunFlags::At(i))
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{
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const auto dst_reset_step =
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make_tensor_coordinate_step(dst_descs[i], GetCoordinateResetStep());
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move_tensor_coordinate(dst_descs[i], dst_coords_(i), dst_reset_step);
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}
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});
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}
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__device__ static constexpr auto GetCoordinateResetStep()
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{
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constexpr auto num_access = SpaceFillingCurve::GetNumOfAccess();
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if constexpr(num_access == 0)
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{
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return typename SpaceFillingCurve::Index{};
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}
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else
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{
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constexpr auto reset_step =
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SpaceFillingCurve::GetStepBetween(Number<num_access - 1>{}, Number<0>{});
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return reset_step;
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}
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}
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// src_slice_origin_step_idx need to be known at compile-time, for performance reason
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template <index_t ISrc>
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__device__ void MoveSrcSliceWindow(const SrcDescs& src_descs,
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Number<ISrc> iSrc,
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const Index& src_slice_origin_step_idx)
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{
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// if src coord was not reset by RunRead(), then need to adjust the step here
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const auto adjusted_step_idx = SrcResetCoordinateAfterRunFlags::At(iSrc)
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? src_slice_origin_step_idx
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: src_slice_origin_step_idx + GetCoordinateResetStep();
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// is it OK to construct a new step every time?
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const auto adjusted_step = make_tensor_coordinate_step(src_descs[iSrc], adjusted_step_idx);
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move_tensor_coordinate(src_descs[iSrc], src_coords_(iSrc), adjusted_step);
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}
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// dst_slice_origin_step_idx need to be known at compile-time, for performance reason
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template <index_t IDst>
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__device__ void MoveDstSliceWindow(const DstDescs& dst_descs,
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Number<IDst> iDst,
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const Index& dst_slice_origin_step_idx)
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{
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// if dst coord was not reset by Run(), then need to adjust the step here
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const auto adjusted_step_idx = DstResetCoordinateAfterRunFlags::At(iDst)
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? dst_slice_origin_step_idx
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: dst_slice_origin_step_idx + GetCoordinateResetStep();
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// is it OK to construct a new step every time?
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const auto adjusted_step = make_tensor_coordinate_step(dst_descs[iDst], adjusted_step_idx);
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move_tensor_coordinate(dst_descs[iDst], dst_coords_(iDst), adjusted_step);
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}
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private:
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SrcCoords src_coords_;
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DstCoords dst_coords_;
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const ElementwiseOperation element_op_;
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};
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} // namespace ck
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