mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-12 09:16:52 +00:00
4234b3a691
* Add tensor partition and generic copy for ck wrapper * Update changelog * Stylistic fixes * Change shape/strides logic to descriptor transforms * Fixes * Fix client example * Fix comments
335 lines
13 KiB
C++
335 lines
13 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "utils/tensor_utils.hpp"
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#include "utils/tensor_partition.hpp"
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#include "utils/layout_utils.hpp"
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namespace ck {
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namespace wrapper {
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/**
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* \brief Tensor wrapper that performs static and dynamic buffer logic.
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*
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* \tparam BufferAddressSpace Memory type (Generic, Global, LDS, VGPR, SGPR).
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* \tparam ElementType Element data type.
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* \tparam Shape Tensor shape (layout component).
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* \tparam UnnestedDescriptorType Unnested descriptor (layout component).
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* \tparam NumVectors Number of vectors (only for VGPR, SGPR).
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* \tparam ScalarPerVector Scalars per vector (only for VGPR, SGPR).
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*/
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template <MemoryTypeEnum BufferAddressSpace,
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typename ElementType,
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typename Shape,
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typename UnnestedDescriptorType,
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index_t NumVectors, // param for Register memory
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index_t ScalarPerVector // param for Register memory
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>
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struct Tensor
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{
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private:
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// Check if Tuple contains Slice object
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template <typename T>
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__host__ __device__ constexpr static bool IsSlicing(T&&)
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{
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return is_detected<is_slice, T>::value;
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}
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template <typename... Ts>
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__host__ __device__ constexpr static bool IsSlicing(Tuple<Ts...>&&)
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{
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return (IsSlicing(Ts{}) || ...);
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}
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// Calculate new tensor shape after slice
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template <typename... Ts, typename ShapeTmpType>
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__host__ __device__ constexpr auto GetShapeFromSlicedTensor(const Tuple<Ts...>& idx,
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const ShapeTmpType& shape) const
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{
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// Pack each value in tuple to remove empty tuples after generation
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auto new_shape = generate_tuple(
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[&](auto i) {
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constexpr auto num_i = Number<i>{};
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if constexpr(is_detected<is_tuple, tuple_element_t<i.value, Tuple<Ts...>>>::value)
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{
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if constexpr(!IsSlicing(tuple_element_t<i.value, Tuple<Ts...>>{}))
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{
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// if tuple does not have any slice then we can remove dimension
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return Tuple<>{};
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}
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else
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{
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// if tuple then recurrence
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return make_tuple(GetShapeFromSlicedTensor(idx.At(num_i), shape.At(num_i)));
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}
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}
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else if constexpr(is_detected<is_slice,
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tuple_element_t<i.value, Tuple<Ts...>>>::value)
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{
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// calculate new dimension
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const auto& dim = size(shape.At(num_i));
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const auto val = idx.At(num_i).range(dim);
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return make_tuple(val);
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}
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else
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{
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// remove dimension for just value
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return Tuple<>{};
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}
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},
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Number<Tuple<Ts...>::Size()>{});
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// Remove empty tuples (deleted elements) and return
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return UnrollNestedTuple<0, 1>(new_shape);
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}
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// Generate Freeze for each of nested shape
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template <typename T, typename ShapeTmpType>
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__host__ __device__ constexpr auto GenerateMultipleFreeze(T idx,
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const ShapeTmpType& shape) const
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{
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const auto unrolled_shape = UnrollNestedTuple(shape);
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return generate_tuple(
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[&](auto i) {
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// dimension offset from idx
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const auto dim = unrolled_shape.At(Number<i>{});
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const auto dim_idx = idx % dim;
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idx /= dim;
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return make_freeze_transform(dim_idx);
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},
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Number<decltype(unrolled_shape)::Size()>{});
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}
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template <typename... Ts, typename ShapeTmpType>
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__host__ __device__ constexpr auto
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GetTransformsFromSlicedTensor(const Tuple<Ts...>& idx, const ShapeTmpType& shape) const
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{
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// Pack each value in tuple to remove empty tuples after generation
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auto transforms = generate_tuple(
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[&](auto i) {
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constexpr auto num_i = Number<i>{};
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if constexpr(is_detected<is_tuple, tuple_element_t<i.value, Tuple<Ts...>>>::value)
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{
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return GetTransformsFromSlicedTensor(idx.At(num_i), shape.At(num_i));
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}
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else if constexpr(is_detected<is_slice,
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tuple_element_t<i.value, Tuple<Ts...>>>::value)
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{
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const auto from = idx.At(num_i).from_;
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const auto dim = shape.At(num_i);
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const auto range = idx.At(num_i).range(dim);
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return make_slice_transform(range, from, from + range);
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}
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else
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{
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// remove dimension for just value
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return GenerateMultipleFreeze(idx.At(num_i), shape.At(num_i));
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}
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},
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Number<Tuple<Ts...>::Size()>{});
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// Remove empty tuples (deleted elements) and return
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return UnrollNestedTuple(transforms);
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}
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// There is no output for Freeze transform
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template <index_t i, typename LowerIndex>
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__host__ __device__ constexpr auto GetSequenceVal(const ck::Freeze<LowerIndex>&) const
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{
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return Sequence<>{};
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}
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template <index_t i, typename LowLength, typename SliceBegin, typename SliceEnd>
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__host__ __device__ constexpr auto
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GetSequenceVal(const ck::Slice<LowLength, SliceBegin, SliceEnd>&) const
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{
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return Sequence<i>{};
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}
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template <index_t i>
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__host__ __device__ constexpr auto GenerateUpperDims(const Tuple<>&) const
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{
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return Tuple<>{};
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}
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template <index_t i, typename... Transforms>
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__host__ __device__ constexpr auto
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GenerateUpperDims(const Tuple<Transforms...>& transforms) const
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{
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constexpr auto num_transforms = Tuple<Transforms...>::Size();
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// Deduce Sequence element for specific transform
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const auto currect_elem = GetSequenceVal<i>(transforms.At(Number<0>{}));
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if constexpr(is_same_v<decltype(currect_elem), const Sequence<>>)
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{
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const auto next_tuple = GenerateUpperDims<i>(TupleSlice<1, num_transforms>(transforms));
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return concat_tuple(make_tuple(currect_elem), next_tuple);
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}
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else
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{
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// Increase i if current_elem is Slice transform
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const auto next_tuple =
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GenerateUpperDims<i + 1>(TupleSlice<1, num_transforms>(transforms));
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return concat_tuple(make_tuple(currect_elem), next_tuple);
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}
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}
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template <typename... Ts, typename ShapeTmpType, typename FlattenDescriptor>
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__host__ __device__ constexpr auto
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GetDescriptorFromSlicedTensor(const Tuple<Ts...>& idx,
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const ShapeTmpType& shape,
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const FlattenDescriptor& flatten_desc) const
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{
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constexpr auto old_shape_dims = decltype(UnrollNestedTuple(shape))::Size();
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const auto transforms = GetTransformsFromSlicedTensor(idx, shape);
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using TransformsTupleType = decltype(transforms);
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const auto lower_dims =
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generate_tuple([&](auto i) { return Sequence<i.value>{}; }, Number<old_shape_dims>{});
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const auto upper_dims = decltype(GenerateUpperDims<0>(TransformsTupleType{})){};
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return transform_tensor_descriptor(flatten_desc, transforms, lower_dims, upper_dims);
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}
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public:
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using ElementSpaceSize = decltype(Layout<Shape, UnnestedDescriptorType>{
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Shape{}, UnnestedDescriptorType{}}.GetElementSpaceSize()); // SpaceSize type for buffer
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using TensorElementType = ElementType; // DataType
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static constexpr MemoryTypeEnum TensorBufferAddressSpace = BufferAddressSpace;
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static constexpr bool IsDynamicBuffer = !(BufferAddressSpace == MemoryTypeEnum ::Sgpr ||
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BufferAddressSpace == MemoryTypeEnum ::Vgpr);
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__host__ __device__ Tensor() = delete;
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__host__ __device__ Tensor(ElementType* pointer,
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const Layout<Shape, UnnestedDescriptorType>& layout)
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: layout_(layout),
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buffer_(make_dynamic_buffer<BufferAddressSpace>(pointer, layout.GetElementSpaceSize()))
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{
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}
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__host__ __device__ Tensor(const Layout<Shape, UnnestedDescriptorType>& layout)
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: layout_(layout)
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{
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static_assert(!IsDynamicBuffer, "Wrong BufferAddressSpace for register.");
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}
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__host__ __device__ constexpr const Layout<Shape, UnnestedDescriptorType>& GetLayout() const
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{
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return layout_;
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}
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// Getter for new sliced tensor
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template <typename... Ts, enable_if_t<IsSlicing(Tuple<Ts...>{}), bool> = false>
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__host__ __device__ auto operator[](const Tuple<Ts...>& idx) const
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{
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static_assert(IsDynamicBuffer, "Register slice is not supported");
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const auto& shape = layout_.GetShape();
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auto new_shape = GetShapeFromSlicedTensor(idx, shape);
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const auto& flatten_desc = layout_.GetUnnestedDescriptor();
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auto new_desc = GetDescriptorFromSlicedTensor(idx, shape, flatten_desc);
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const auto new_layout =
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Layout<decltype(new_shape), decltype(new_desc)>(new_shape, new_desc);
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return make_tensor<BufferAddressSpace>(buffer_.p_data_, new_layout);
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}
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template <typename... Ts, enable_if_t<IsSlicing(Tuple<Ts...>{}), bool> = false>
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__host__ __device__ auto operator()(const Tuple<Ts...>& idx) const
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{
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return this->operator[](idx);
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}
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template <typename... Idxs, enable_if_t<IsSlicing(Tuple<Idxs...>{}), bool> = false>
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__host__ __device__ auto operator()(Idxs... idxs) const
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{
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return this->operator[](make_tuple(idxs...));
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}
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// Getter for the const value
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template <typename... Ts, enable_if_t<!IsSlicing(Tuple<Ts...>{}), bool> = false>
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__host__ __device__ const ElementType& operator[](const Tuple<Ts...>& idx) const
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{
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if constexpr(IsDynamicBuffer)
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{
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const index_t offset = layout_(idx);
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return buffer_[offset];
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}
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else
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{
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constexpr index_t offset = Layout<Shape, UnnestedDescriptorType>{
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Shape{},
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UnnestedDescriptorType{}}.template operator()<Tuple<Ts...>>();
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return buffer_[Number<offset>{}];
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}
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}
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template <typename... Ts, enable_if_t<!IsSlicing(Tuple<Ts...>{}), bool> = false>
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__host__ __device__ const ElementType& operator()(const Tuple<Ts...>& idx) const
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{
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return this->operator[](idx);
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}
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template <typename... Idxs, enable_if_t<!IsSlicing(Tuple<Idxs...>{}), bool> = false>
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__host__ __device__ const ElementType& operator()(Idxs... idxs) const
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{
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return this->operator[](make_tuple(idxs...));
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}
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// Getter for the value reference
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template <typename... Ts, enable_if_t<!IsSlicing(Tuple<Ts...>{}), bool> = false>
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__host__ __device__ ElementType& operator[](const Tuple<Ts...>& idx)
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{
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if constexpr(IsDynamicBuffer)
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{
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const index_t offset = layout_(idx);
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return buffer_(offset);
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}
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else
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{
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constexpr index_t offset = Layout<Shape, UnnestedDescriptorType>{
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Shape{},
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UnnestedDescriptorType{}}.template operator()<Tuple<Ts...>>();
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return buffer_(Number<offset>{});
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}
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}
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template <typename... Ts, enable_if_t<!IsSlicing(Tuple<Ts...>{}), bool> = false>
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__host__ __device__ ElementType& operator()(const Tuple<Ts...>& idx)
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{
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return this->operator[](idx);
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}
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template <typename... Idxs, enable_if_t<!IsSlicing(Tuple<Idxs...>{}), bool> = false>
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__host__ __device__ ElementType& operator()(Idxs... idxs)
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{
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return this->operator[](make_tuple(idxs...));
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}
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__host__ __device__ constexpr auto GetDefaultDescriptor()
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{
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return layout_.GetDefaultDescriptor();
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}
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__host__ __device__ ElementType* GetPointer() const { return buffer_.p_data_; }
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private:
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using DynamicBufferType = DynamicBuffer<BufferAddressSpace,
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ElementType,
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ElementSpaceSize,
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true /*InvalidElementUseNumericalZeroValue*/>;
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using StaticBufferType =
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StaticBufferTupleOfVector<BufferAddressSpace,
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ElementType,
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NumVectors,
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ScalarPerVector,
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true /*InvalidElementUseNumericalZeroValue*/>;
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// If register use static buffer, else use dynamic buffer
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using Buffer = std::conditional_t<IsDynamicBuffer, DynamicBufferType, StaticBufferType>;
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const Layout<Shape, UnnestedDescriptorType> layout_;
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Buffer buffer_;
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};
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} // namespace wrapper
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} // namespace ck
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