mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-04-19 22:39:03 +00:00
* Add example folder for 'DeviceElementwise' * Re-structure example files * Move common parts into common.hpp * Use more strict input * Add more helper methods in 'DeviceElementwise' * Use more specific method to write example * Allow specify problem through command line argument * Allow specify problem 'axes' through command line argument * Add check to template type argument * Add transpose_shape() to generalize shape permute * Generalize transpose utility functions * Use better name for tensor indices * Add checks in helper functions * Remove debug messages * Refine error message for check_err() * Generalize variable naming in example code * Add device op 'DevicePermute' This device op is clone of 'DeviceElementwise' * Use 'DevicePermute' device op in example * Remove 'elementwise' from identifiers * Remove 'elementwise' from file paths * Remove base class of 'DevicePermute' * Let 'DevicePermute' inherit from 'BaseOperator' * Add simple type traits to validate device op type * Add static_assert() to check type constraints * Create 'DevicePermuteBase' to generate methods * Use indirect base type to generate methods * Remove 'is_device_op<>' type traits * Only accept single-input-single-output for 'DervicePermute' * Simplify 'DevicePermute' interface * Re-format 'DeviceElementwise' * Use CRTP to generate overridden virtual method * Remove unnecessary include directives * Distinguish input & output shape in 'DevicePermute' * Passing 'axes' to 'DevicePermute' * Use more reasonable return value for Invoker::Run() * Add 'GridwisePermute' kernel This kernel is a clone of 'GridwiseElementwise_1D' * Remove no-longer used type argument * Check if input/output shape meet the requirement * Remove no-longer used method * Remove never-entered-if-clause * Change problem description for 'DevicePermute' * Transform descriptor into 3 dimensions * Add debug code the verify result * Add comment to indicate template argument location * Add N/H/WPerBlock template parameter to 'DevicePermute' * Rename 'GridwisePermute' to 'GridwiseCopy' * Check tensor descriptor dimensions in 'GridwiseElementwise_1D' * Add missing include directive * Add 'BlockSize' parameter to 'DevicePermute' * Remove no-longer used method * Add 'BlockToTileMap' for 'GridwiseCopy' * Use the normal Block2TileMap convention * Rename 'BlockToTileMap' as 'Block2TileMap' * Fix most of compilation errors * Let 'Block2TileMap' map block to 2d coordinate * Allow data transfer in 'GridwiseCopy' * Fix wrong output descriptor for 2nd blockwise copy * Rename 'GridwiseCopy' as 'GridwisePermute' * Remove '1d' in identifiers * Remove commented-out codes * Remove 'MPerThread' template parameter * Seperate template parameters * Unify variable namming convention * Use more verbose way to create expressions * Add template parameter 'InBlockLdsExtraW' * Release the constraint on In/OutGridDesc * Use date type directly as template argument * Re-arrange template arguments for blockwise copy * Remove no-longer used template parameters * Embed layout in the variable names * Add GridwisePermute::CheckValidity() * Extract local types as template parameters * Rename local type alias * Add more template parameters (vector width related) * Calculate new SrcVectorDim/DstVectorDim after merge descriptor dimensions * Fill tensor values start from 1 * Re-formate example code * Avoid too-large block id * Add comment * Make sure 'SrcVectorDim' is not same as 'DstVectorDim' * Add check for the 'VectorDim' & 'ScalarPerVector' template params * Let 'DstVectorDim' equals 'SrcVectorDim' after transpose out grid desc * Remove no-longer used template parameter 'NPerBlock' * Fix wrong descriptor creation logics * Specify problem in each examples * Use better example name * Add new example 'example_permute_NxHxW_fp32' * Add example for demonstrating bundle multiple elems in tensor * Add support to permute multiple elements together * Change the default problem size * Add span<> class template * Use span<> to generalize check_err() interface * Fix ambiguous ctor call * Avoid create necessary objects * Use helper functions to simplify example code * Add example for 4xfp16 permute * Disable failed-to-compile example * Add check for the NUM_ELEMS_IN_BUNDLE * Remove redundant parameter in helper lambda function * Add check for the input tensor type's byte-size * Check scalar-per-vector with padded length * Use more verbose name to avoid name collision * Use fixed 'VectorDim' & 'ScalarPerVector' for LDS * Embed shape info in name of descriptor constructor * Rename example folder '36_permute' into '37_permute' * Avoid using too-large LDS in kernel code * Remove redundant example * Usw switch() to group similar codes * Add const to the span<> type arguement * Simply initialize tensor with floating point values * Use fp16 as data type in all examples * Enlarge tensor size in example * Enalrge N-dim in example * Add check for the bundled type in example * Use more stricter error threshold * Remove global load/store loop in kernel code * Measure execution time by default * Use faster device op config for example 'NxHxW_fp16' * Use faster device op config for example '1xHxW_fp16' * Use faster device op config for example 'HxWx4_fp16' * Remove cmd arg parsing logics * Rename functions * Extract bundle permutation logic out * Simplify permute bundle example * Add Tensor<>::GetElementSpaceSizeInBytes() * Add Tensor<>::data() * Use new methods to simplify code * Use type alias to replace duplicated code * Use existing method to shorten code * Allow FillUniformDistribution accept range arugment * Intialize random values in range * Add Tensor<>::size() * Use more meaningful names in permute bundle example * Use more meaningful names in permute element examples * Use rangified copy() to copy elements * Use function return value directly to eliminate variables * Add to_array() conversion tool to eliminate more variables * Add Tensor<>::AsSpan<>() to create view of tensor values * Use AsSpan() to shorten check_err() calls * Remove no-longer-used 'using' directives * Move 'using' directive to proper code position * Remove redudant variables * Remove useless static_assert() * Add check for range types * Declare variable right before first use * Move long return type as tailing return type * Add BaseInvokerCRTP<> class template to generate method * Create new base type for 'DervicePermute' implementations * Move 'NumDim' template param to the first * Rename 'DevicePermute' to 'DevicePermuteImpl' * Add 'noexcept' specifier to CRTP generated method * Move 'Block2TileMap' definition into 'GridwisePermute' * Use type alias to reduce code * Unify naming style in 'DevicePermute' * Add comments in 'GridwisePermute' * Rename permute example folder * Use std::cerr to report error * Use larger shape in examples * Rename '38_permute' to '39_permute' * Make sure we use unsigned type for shape & indices * Remove opt-ed out assertion * Remove template BaseInvokerCRTP<>
469 lines
13 KiB
C++
469 lines
13 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <algorithm>
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#include <cassert>
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#include <cstddef>
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#include <cstdlib>
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#include <cstring>
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#include <iostream>
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#include <iterator>
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#include <numeric>
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#include <type_traits>
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#include <utility>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_permute_impl.hpp"
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#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
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#include "ck/utility/type.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/fill.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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using F16 = ck::half_t;
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using F32 = float;
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using F64 = double;
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struct Problem final
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{
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static constexpr std::size_t NumDim = 3;
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using Shape = std::array<std::size_t, NumDim>;
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using Axes = Shape;
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Problem() = delete;
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explicit Problem(const Shape& default_shape, const Axes& default_axes)
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: shape(default_shape), axes(default_axes)
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{
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}
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Shape shape;
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Axes axes;
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};
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template <ck::index_t... Is>
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using S = ck::Sequence<Is...>;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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namespace detail {
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template <typename Array, std::size_t Difference>
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struct enlarge_array_size;
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template <typename T, std::size_t Size, std::size_t Difference>
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struct enlarge_array_size<std::array<T, Size>, Difference>
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{
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using type = std::array<T, Size + Difference>;
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};
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template <typename Array, std::size_t Difference>
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using enlarge_array_size_t = typename enlarge_array_size<Array, Difference>::type;
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template <typename Array>
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struct get_array_size;
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template <typename T, std::size_t Size>
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struct get_array_size<std::array<T, Size>> : std::integral_constant<std::size_t, Size>
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{
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};
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template <typename Array>
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inline constexpr std::size_t get_array_size_v = get_array_size<Array>::value;
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template <typename T, typename = void>
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struct is_iterator : std::false_type
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{
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};
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template <typename T>
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struct is_iterator<T,
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std::void_t<decltype(*std::declval<T>()),
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decltype(++std::declval<std::add_lvalue_reference_t<T>>()),
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decltype(std::declval<std::add_lvalue_reference_t<T>>()++)>>
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: std::true_type
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{
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};
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template <typename T>
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inline constexpr bool is_iterator_v = is_iterator<T>::value;
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struct Placeholder final
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{
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template <typename T>
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constexpr inline operator T() const noexcept;
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};
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template <typename Iterator, typename = void>
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struct is_output_iterator : std::false_type
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{
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};
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template <typename Iterator>
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struct is_output_iterator<
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Iterator,
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std::void_t<decltype(*std::declval<Iterator>() = std::declval<Placeholder>())>>
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: std::bool_constant<is_iterator_v<Iterator>>
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{
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};
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template <typename T>
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inline constexpr bool is_output_iterator_v = is_output_iterator<T>::value;
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template <typename Iterator, typename = void>
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struct is_bidirectional_iterator : std::false_type
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{
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};
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template <typename Iterator>
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struct is_bidirectional_iterator<
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Iterator,
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std::void_t<decltype(--std::declval<std::add_lvalue_reference_t<Iterator>>()),
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decltype(std::declval<std::add_lvalue_reference_t<Iterator>>()--)>>
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: std::bool_constant<is_iterator_v<Iterator>>
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{
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};
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template <typename Iterator>
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inline constexpr bool is_bidirectional_iterator_v = is_bidirectional_iterator<Iterator>::value;
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template <typename Iterator, typename = void>
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struct is_random_access_iterator : std::false_type
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{
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};
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template <typename Iterator>
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struct is_random_access_iterator<Iterator,
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std::void_t<decltype(std::declval<Iterator>() + 1),
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decltype(std::declval<Iterator>() - 1),
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decltype(std::declval<Iterator>()[1])>>
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: std::bool_constant<is_iterator_v<Iterator>>
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{
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};
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template <typename Iterator>
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inline constexpr bool is_random_access_iterator_v = is_random_access_iterator<Iterator>::value;
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template <typename T, typename = void>
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struct is_range : std::false_type
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{
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};
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template <typename T>
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struct is_range<T,
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std::void_t<decltype(begin(std::declval<T>())),
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decltype(end(std::declval<T>())),
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decltype(begin(std::declval<T>()) != end(std::declval<T>()))>>
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: std::bool_constant<is_iterator_v<ck::remove_cvref_t<decltype(begin(std::declval<T>()))>>>
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{
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};
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template <typename T>
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inline constexpr bool is_range_v = is_range<T>::value;
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template <typename Range, typename = void>
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struct is_sized_range : std::false_type
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{
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};
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template <typename Range>
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struct is_sized_range<Range, std::void_t<decltype(size(std::declval<Range>()))>>
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: std::bool_constant<is_range_v<Range>>
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{
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};
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template <typename Range>
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inline constexpr bool is_sized_range_v = is_sized_range<Range>::value;
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template <typename Range, typename = void>
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struct is_bidirectional_range : std::false_type
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{
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};
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template <typename Range>
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struct is_bidirectional_range<Range, std::void_t<>>
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: std::bool_constant<
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is_range_v<Range> &&
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is_bidirectional_iterator_v<ck::remove_cvref_t<decltype(begin(std::declval<Range>()))>>>
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{
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};
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template <typename Range>
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inline constexpr bool is_bidirectional_range_v = is_bidirectional_range<Range>::value;
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template <typename Range, typename = void>
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struct is_random_access_range : std::false_type
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{
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};
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template <typename Range>
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struct is_random_access_range<Range, std::void_t<>>
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: std::bool_constant<
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is_range_v<Range> &&
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is_random_access_iterator_v<ck::remove_cvref_t<decltype(begin(std::declval<Range>()))>>>
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{
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};
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template <typename Range>
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inline constexpr bool is_random_access_range_v = is_random_access_range<Range>::value;
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template <typename Range>
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class to_array_proxy
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{
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static_assert(is_range_v<Range>);
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public:
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explicit to_array_proxy(const Range& source) noexcept : source_(source) {}
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template <typename T, std::size_t Size>
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operator std::array<T, Size>() const
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{
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std::array<T, Size> destination;
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std::copy_n(std::begin(source_),
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std::min<std::size_t>(Size, std::size(source_)),
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std::begin(destination));
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return destination;
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}
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private:
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const Range& source_;
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};
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} // namespace detail
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template <typename Range>
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inline auto to_array(Range& range) noexcept
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-> std::enable_if_t<detail::is_range_v<Range>,
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detail::to_array_proxy<ck::remove_cvref_t<Range>>>
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{
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return detail::to_array_proxy<ck::remove_cvref_t<Range>>{range};
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}
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namespace ranges {
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template <typename InputRange, typename OutputIterator>
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inline auto copy(InputRange&& range, OutputIterator iter)
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-> decltype(std::copy(std::begin(std::forward<InputRange>(range)),
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std::end(std::forward<InputRange>(range)),
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iter))
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{
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return std::copy(std::begin(std::forward<InputRange>(range)),
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std::end(std::forward<InputRange>(range)),
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iter);
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}
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} // namespace ranges
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template <typename Axes>
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inline auto is_valid_axes(const Axes& axes)
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-> std::enable_if_t<detail::is_random_access_range_v<Axes>, bool>
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{
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using std::empty;
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if(empty(axes))
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{
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return false;
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}
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using std::begin, std::end;
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std::vector<std::size_t> sorted_axes(begin(axes), end(axes));
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std::sort(begin(sorted_axes), end(sorted_axes));
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const auto last = std::unique(begin(sorted_axes), end(sorted_axes));
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return (last == end(sorted_axes)) && (*begin(sorted_axes) == 0) &&
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(*std::prev(last) == size(axes) - 1);
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}
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template <typename Shape>
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inline auto is_valid_shape(const Shape& shape) -> std::enable_if_t<detail::is_range_v<Shape>, bool>
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{
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static_assert(std::is_unsigned_v<ck::remove_cvref_t<decltype(*std::begin(shape))>>);
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using std::begin, std::end;
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using std::empty;
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return !empty(shape) && std::all_of(begin(shape), end(shape), [](auto dim) { return 0 < dim; });
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}
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template <typename Shape, typename Indices>
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inline auto is_valid_indices(const Shape& shape, const Indices& indices)
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-> std::enable_if_t<detail::is_sized_range_v<Shape> && detail::is_sized_range_v<Indices>, bool>
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{
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static_assert(std::is_unsigned_v<ck::remove_cvref_t<decltype(*std::begin(indices))>>);
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if(!is_valid_shape(shape))
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{
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return false;
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}
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using std::empty;
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if(empty(indices))
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{
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return false;
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}
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using std::size;
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if(size(shape) != size(indices))
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{
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return false;
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}
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using std::begin, std::end;
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auto dim = begin(shape);
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auto idx = begin(indices);
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for(; dim != end(shape) && idx != end(indices); ++dim, ++idx)
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{
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if(*dim <= *idx)
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{
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return false;
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}
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}
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return true;
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}
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template <std::size_t Size>
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std::array<std::size_t, Size> transpose(const std::array<std::size_t, Size>& shape,
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const std::array<std::size_t, Size>& axes)
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{
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assert(is_valid_shape(shape) && is_valid_axes(axes));
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std::array<std::size_t, Size> transposed;
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auto iter = std::begin(transposed);
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for(const auto axis : axes)
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{
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*iter++ = shape[axis];
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}
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return transposed;
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}
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auto extend_shape(const Problem::Shape& shape, std::size_t new_dim)
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{
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detail::enlarge_array_size_t<Problem::Shape, 1> extended_shape;
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using std::begin, std::end;
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std::copy(begin(shape), end(shape), begin(extended_shape));
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extended_shape.back() = new_dim;
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return extended_shape;
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}
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auto extend_axes(const Problem::Axes& axes)
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{
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detail::enlarge_array_size_t<Problem::Axes, 1> extended_axes;
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using std::begin, std::end;
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std::copy(begin(axes), end(axes), begin(extended_axes));
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extended_axes.back() = detail::get_array_size_v<Problem::Axes>;
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return extended_axes;
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}
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template <typename Shape, typename Indices>
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auto advance_indices(const Shape& shape, Indices& indices) -> std::enable_if_t<
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detail::is_bidirectional_range_v<Shape> && detail::is_sized_range_v<Shape> &&
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detail::is_bidirectional_range_v<Indices> && detail::is_sized_range_v<Indices>,
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bool>
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{
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using std::size;
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if(!(is_valid_shape(shape) && is_valid_indices(shape, indices) && size(shape) == size(indices)))
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{
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return false;
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}
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bool carry = true;
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using std::rbegin, std::rend;
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auto dim = rbegin(shape);
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auto idx = rbegin(indices);
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for(; carry && dim != rend(shape) && idx != rend(indices); ++dim, ++idx)
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{
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*idx = (*idx + carry);
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carry = ((*idx == *dim) ? (*idx = 0, true) : false);
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}
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return !carry;
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}
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template <typename Src, typename Axes, typename Functor, typename Dest>
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auto host_permute(const Tensor<Src>& src, const Axes& axes, Functor functor, Tensor<Dest>& dest)
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-> std::enable_if_t<detail::is_random_access_range_v<Axes> && detail::is_sized_range_v<Axes> &&
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std::is_invocable_v<Functor,
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std::add_lvalue_reference_t<Dest>,
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std::add_lvalue_reference_t<Src>>,
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bool>
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{
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const auto& shape = src.mDesc.GetLengths();
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const auto& transposed_shape = dest.mDesc.GetLengths();
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if(!(is_valid_shape(shape) && is_valid_shape(transposed_shape)))
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{
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return false;
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}
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using std::size;
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if(!is_valid_axes(axes))
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{
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return false;
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}
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static_assert(detail::is_sized_range_v<ck::remove_cvref_t<decltype(shape)>> &&
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detail::is_sized_range_v<ck::remove_cvref_t<decltype(transposed_shape)>>);
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if(size(shape) != size(transposed_shape))
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{
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return false;
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}
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static_assert(detail::is_random_access_range_v<ck::remove_cvref_t<decltype(shape)>> &&
|
|
detail::is_random_access_range_v<ck::remove_cvref_t<decltype(transposed_shape)>>);
|
|
{
|
|
for(std::size_t idx = 0; idx < size(shape); ++idx)
|
|
{
|
|
if(transposed_shape[idx] != shape[axes[idx]])
|
|
{
|
|
return false;
|
|
}
|
|
}
|
|
}
|
|
|
|
std::vector<std::size_t> indices(size(shape), 0);
|
|
if(!is_valid_indices(shape, indices))
|
|
{
|
|
return false;
|
|
}
|
|
|
|
switch(size(shape))
|
|
{
|
|
case 3: {
|
|
do
|
|
{
|
|
Dest output = 0;
|
|
functor(output, src(indices[0], indices[1], indices[2]));
|
|
dest(indices[axes[0]], indices[axes[1]], indices[axes[2]]) = output;
|
|
} while(advance_indices(shape, indices));
|
|
}
|
|
break;
|
|
case 4: {
|
|
do
|
|
{
|
|
Dest output = 0;
|
|
functor(output, src(indices[0], indices[1], indices[2], indices[3]));
|
|
dest(indices[axes[0]], indices[axes[1]], indices[axes[2]], indices[axes[3]]) = output;
|
|
} while(advance_indices(shape, indices));
|
|
}
|
|
break;
|
|
default: return false;
|
|
}
|
|
|
|
return true;
|
|
}
|