mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-19 04:19:36 +00:00
0cc91c73d8
* GEMM + Reduce max fp16+fp32
* GEmm + Max bf16 + int8
* Refactor common definitions.
* Refactor common func of mean meansquare example.
* More examples for mean meansquare.
* Update int8 examples and skip them cause of random errors.
* Int4 examples.
* Fix examples for max int4/8
* Tensor conversion for int4 input data for mean meansquare example.
* Remove int4 mean_meansquare example
* Fix int8 mean_meansquare example.
-All ReductionAccData and R<N>DataType have to be F32. The INT32 data
type is giving wrong results.
* Guard int4 with ifdef
* Change int8 example to add_addsquare due to div rounding err.
* Clang format
* Change the return type of common function.
* Get back int8 example with division.
* Remove int8 mean meansquare.
* Use proper cast for BF16 data type.
* Use ck::literals.
* Use proper data type for host tensors & reference.
- Use ReduceAccDataType for reference gemm output data type.
- Cast host reference output tensor to EDataType
- Fix ifdefs for int4.
Co-authored-by: Adam Osewski <aosewski@amd.com>
[ROCm/composable_kernel commit: d00e6115b9]
439 lines
13 KiB
C++
439 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 <thread>
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#include <vector>
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#include <numeric>
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#include <algorithm>
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#include <utility>
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#include <cassert>
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#include <iostream>
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#include "ck/utility/data_type.hpp"
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template <typename Range>
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std::ostream& LogRange(std::ostream& os, Range&& range, std::string delim)
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{
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bool first = true;
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for(auto&& v : range)
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{
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if(first)
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first = false;
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else
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os << delim;
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os << v;
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}
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return os;
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}
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template <typename T, typename Range>
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std::ostream& LogRangeAsType(std::ostream& os, Range&& range, std::string delim)
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{
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bool first = true;
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for(auto&& v : range)
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{
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if(first)
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first = false;
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else
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os << delim;
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os << static_cast<T>(v);
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}
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return os;
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}
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template <typename F, typename T, std::size_t... Is>
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auto call_f_unpack_args_impl(F f, T args, std::index_sequence<Is...>)
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{
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return f(std::get<Is>(args)...);
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}
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template <typename F, typename T>
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auto call_f_unpack_args(F f, T args)
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{
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constexpr std::size_t N = std::tuple_size<T>{};
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return call_f_unpack_args_impl(f, args, std::make_index_sequence<N>{});
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}
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template <typename F, typename T, std::size_t... Is>
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auto construct_f_unpack_args_impl(T args, std::index_sequence<Is...>)
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{
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return F(std::get<Is>(args)...);
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}
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template <typename F, typename T>
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auto construct_f_unpack_args(F, T args)
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{
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constexpr std::size_t N = std::tuple_size<T>{};
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return construct_f_unpack_args_impl<F>(args, std::make_index_sequence<N>{});
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}
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struct HostTensorDescriptor
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{
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HostTensorDescriptor() = default;
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void CalculateStrides();
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template <typename X, typename = std::enable_if_t<std::is_convertible_v<X, std::size_t>>>
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HostTensorDescriptor(const std::initializer_list<X>& lens) : mLens(lens.begin(), lens.end())
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{
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this->CalculateStrides();
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}
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template <typename Range,
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typename = std::enable_if_t<
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std::is_convertible_v<decltype(*std::begin(std::declval<Range>())), std::size_t>>>
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HostTensorDescriptor(const Range& lens) : mLens(lens.begin(), lens.end())
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{
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this->CalculateStrides();
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}
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template <typename X,
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typename Y,
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typename = std::enable_if_t<std::is_convertible_v<X, std::size_t> &&
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std::is_convertible_v<Y, std::size_t>>>
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HostTensorDescriptor(const std::initializer_list<X>& lens,
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const std::initializer_list<Y>& strides)
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: mLens(lens.begin(), lens.end()), mStrides(strides.begin(), strides.end())
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{
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}
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template <
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typename Range1,
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typename Range2,
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typename = std::enable_if_t<
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std::is_convertible_v<decltype(*std::begin(std::declval<Range1>())), std::size_t> &&
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std::is_convertible_v<decltype(*std::begin(std::declval<Range2>())), std::size_t>>>
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HostTensorDescriptor(const Range1& lens, const Range2& strides)
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: mLens(lens.begin(), lens.end()), mStrides(strides.begin(), strides.end())
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{
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}
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std::size_t GetNumOfDimension() const;
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std::size_t GetElementSize() const;
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std::size_t GetElementSpaceSize() const;
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const std::vector<std::size_t>& GetLengths() const;
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const std::vector<std::size_t>& GetStrides() const;
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template <typename... Is>
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std::size_t GetOffsetFromMultiIndex(Is... is) const
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{
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assert(sizeof...(Is) == this->GetNumOfDimension());
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std::initializer_list<std::size_t> iss{static_cast<std::size_t>(is)...};
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return std::inner_product(iss.begin(), iss.end(), mStrides.begin(), std::size_t{0});
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}
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std::size_t GetOffsetFromMultiIndex(std::vector<std::size_t> iss) const
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{
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return std::inner_product(iss.begin(), iss.end(), mStrides.begin(), std::size_t{0});
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}
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friend std::ostream& operator<<(std::ostream& os, const HostTensorDescriptor& desc);
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private:
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std::vector<std::size_t> mLens;
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std::vector<std::size_t> mStrides;
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};
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template <typename New2Old>
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HostTensorDescriptor transpose_host_tensor_descriptor_given_new2old(const HostTensorDescriptor& a,
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const New2Old& new2old)
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{
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std::vector<std::size_t> new_lengths(a.GetNumOfDimension());
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std::vector<std::size_t> new_strides(a.GetNumOfDimension());
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for(std::size_t i = 0; i < a.GetNumOfDimension(); i++)
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{
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new_lengths[i] = a.GetLengths()[new2old[i]];
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new_strides[i] = a.GetStrides()[new2old[i]];
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}
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return HostTensorDescriptor(new_lengths, new_strides);
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}
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struct joinable_thread : std::thread
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{
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template <typename... Xs>
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joinable_thread(Xs&&... xs) : std::thread(std::forward<Xs>(xs)...)
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{
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}
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joinable_thread(joinable_thread&&) = default;
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joinable_thread& operator=(joinable_thread&&) = default;
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~joinable_thread()
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{
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if(this->joinable())
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this->join();
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}
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};
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template <typename F, typename... Xs>
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struct ParallelTensorFunctor
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{
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F mF;
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static constexpr std::size_t NDIM = sizeof...(Xs);
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std::array<std::size_t, NDIM> mLens;
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std::array<std::size_t, NDIM> mStrides;
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std::size_t mN1d;
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ParallelTensorFunctor(F f, Xs... xs) : mF(f), mLens({static_cast<std::size_t>(xs)...})
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{
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mStrides.back() = 1;
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std::partial_sum(mLens.rbegin(),
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mLens.rend() - 1,
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mStrides.rbegin() + 1,
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std::multiplies<std::size_t>());
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mN1d = mStrides[0] * mLens[0];
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}
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std::array<std::size_t, NDIM> GetNdIndices(std::size_t i) const
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{
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std::array<std::size_t, NDIM> indices;
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for(std::size_t idim = 0; idim < NDIM; ++idim)
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{
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indices[idim] = i / mStrides[idim];
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i -= indices[idim] * mStrides[idim];
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}
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return indices;
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}
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void operator()(std::size_t num_thread = 1) const
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{
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std::size_t work_per_thread = (mN1d + num_thread - 1) / num_thread;
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std::vector<joinable_thread> threads(num_thread);
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for(std::size_t it = 0; it < num_thread; ++it)
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{
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std::size_t iw_begin = it * work_per_thread;
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std::size_t iw_end = std::min((it + 1) * work_per_thread, mN1d);
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auto f = [=] {
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for(std::size_t iw = iw_begin; iw < iw_end; ++iw)
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{
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call_f_unpack_args(mF, GetNdIndices(iw));
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}
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};
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threads[it] = joinable_thread(f);
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}
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}
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};
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template <typename F, typename... Xs>
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auto make_ParallelTensorFunctor(F f, Xs... xs)
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{
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return ParallelTensorFunctor<F, Xs...>(f, xs...);
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}
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template <typename T>
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struct Tensor
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{
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template <typename X>
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Tensor(std::initializer_list<X> lens) : mDesc(lens), mData(mDesc.GetElementSpaceSize())
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{
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}
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template <typename X>
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Tensor(std::vector<X> lens) : mDesc(lens), mData(mDesc.GetElementSpaceSize())
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{
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}
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template <typename X, typename Y>
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Tensor(std::vector<X> lens, std::vector<Y> strides)
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: mDesc(lens, strides), mData(mDesc.GetElementSpaceSize())
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{
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}
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Tensor(const HostTensorDescriptor& desc) : mDesc(desc), mData(mDesc.GetElementSpaceSize()) {}
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template <typename OutT>
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Tensor<OutT> CopyAsType() const
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{
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Tensor<OutT> ret(mDesc);
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for(size_t i = 0; i < mData.size(); i++)
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{
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ret.mData[i] = ck::type_convert<OutT>(mData[i]);
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}
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return ret;
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}
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Tensor() = delete;
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Tensor(const Tensor&) = default;
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Tensor(Tensor&&) = default;
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~Tensor() = default;
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Tensor& operator=(const Tensor&) = default;
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Tensor& operator=(Tensor&&) = default;
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template <typename FromT>
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explicit Tensor(const Tensor<FromT>& other) : Tensor(other.template CopyAsType<T>())
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{
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}
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const std::vector<std::size_t>& GetLengths() const { return mDesc.GetLengths(); }
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const std::vector<std::size_t>& GetStrides() const { return mDesc.GetStrides(); }
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std::size_t GetNumOfDimension() const { return mDesc.GetNumOfDimension(); }
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std::size_t GetElementSize() const { return mDesc.GetElementSize(); }
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std::size_t GetElementSpaceSize() const { return mDesc.GetElementSpaceSize(); }
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void SetZero()
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{
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for(auto& v : mData)
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{
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v = T{0};
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}
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}
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template <typename F>
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void ForEach_impl(F&& f, std::vector<size_t>& idx, size_t rank)
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{
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if(rank == mDesc.GetNumOfDimension())
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{
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f(*this, idx);
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return;
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}
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// else
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for(size_t i = 0; i < mDesc.GetLengths()[rank]; i++)
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{
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idx[rank] = i;
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ForEach_impl(std::forward<F>(f), idx, rank + 1);
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}
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}
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template <typename F>
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void ForEach(F&& f)
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{
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std::vector<size_t> idx(mDesc.GetNumOfDimension(), 0);
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ForEach_impl(std::forward<F>(f), idx, size_t(0));
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}
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template <typename F>
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void ForEach_impl(const F&& f, std::vector<size_t>& idx, size_t rank) const
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{
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if(rank == mDesc.GetNumOfDimension())
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{
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f(*this, idx);
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return;
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}
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// else
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for(size_t i = 0; i < mDesc.GetLengths()[rank]; i++)
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{
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idx[rank] = i;
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ForEach_impl(std::forward<const F>(f), idx, rank + 1);
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}
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}
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template <typename F>
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void ForEach(const F&& f) const
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{
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std::vector<size_t> idx(mDesc.GetNumOfDimension(), 0);
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ForEach_impl(std::forward<const F>(f), idx, size_t(0));
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}
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template <typename G>
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void GenerateTensorValue(G g, std::size_t num_thread = 1)
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{
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switch(mDesc.GetNumOfDimension())
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{
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case 1: {
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auto f = [&](auto i) { (*this)(i) = g(i); };
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make_ParallelTensorFunctor(f, mDesc.GetLengths()[0])(num_thread);
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break;
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}
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case 2: {
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auto f = [&](auto i0, auto i1) { (*this)(i0, i1) = g(i0, i1); };
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make_ParallelTensorFunctor(f, mDesc.GetLengths()[0], mDesc.GetLengths()[1])(num_thread);
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break;
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}
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case 3: {
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auto f = [&](auto i0, auto i1, auto i2) { (*this)(i0, i1, i2) = g(i0, i1, i2); };
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make_ParallelTensorFunctor(
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f, mDesc.GetLengths()[0], mDesc.GetLengths()[1], mDesc.GetLengths()[2])(num_thread);
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break;
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}
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case 4: {
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auto f = [&](auto i0, auto i1, auto i2, auto i3) {
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(*this)(i0, i1, i2, i3) = g(i0, i1, i2, i3);
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};
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make_ParallelTensorFunctor(f,
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mDesc.GetLengths()[0],
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mDesc.GetLengths()[1],
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mDesc.GetLengths()[2],
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mDesc.GetLengths()[3])(num_thread);
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break;
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}
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case 5: {
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auto f = [&](auto i0, auto i1, auto i2, auto i3, auto i4) {
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(*this)(i0, i1, i2, i3, i4) = g(i0, i1, i2, i3, i4);
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};
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make_ParallelTensorFunctor(f,
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mDesc.GetLengths()[0],
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mDesc.GetLengths()[1],
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mDesc.GetLengths()[2],
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mDesc.GetLengths()[3],
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mDesc.GetLengths()[4])(num_thread);
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break;
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}
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case 6: {
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auto f = [&](auto i0, auto i1, auto i2, auto i3, auto i4, auto i5) {
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(*this)(i0, i1, i2, i3, i4) = g(i0, i1, i2, i3, i4, i5);
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};
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make_ParallelTensorFunctor(f,
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mDesc.GetLengths()[0],
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mDesc.GetLengths()[1],
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mDesc.GetLengths()[2],
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mDesc.GetLengths()[3],
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mDesc.GetLengths()[4],
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mDesc.GetLengths()[5])(num_thread);
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break;
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}
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default: throw std::runtime_error("unspported dimension");
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}
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}
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template <typename... Is>
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T& operator()(Is... is)
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{
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return mData[mDesc.GetOffsetFromMultiIndex(is...)];
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}
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template <typename... Is>
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const T& operator()(Is... is) const
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{
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return mData[mDesc.GetOffsetFromMultiIndex(is...)];
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}
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T& operator()(std::vector<std::size_t> idx)
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{
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return mData[mDesc.GetOffsetFromMultiIndex(idx)];
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}
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const T& operator()(std::vector<std::size_t> idx) const
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{
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return mData[mDesc.GetOffsetFromMultiIndex(idx)];
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}
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typename std::vector<T>::iterator begin() { return mData.begin(); }
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typename std::vector<T>::iterator end() { return mData.end(); }
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typename std::vector<T>::const_iterator begin() const { return mData.begin(); }
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typename std::vector<T>::const_iterator end() const { return mData.end(); }
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HostTensorDescriptor mDesc;
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std::vector<T> mData;
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};
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