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composable_kernel/include/ck/library/utility/host_tensor.hpp
Illia Silin 504b101da3 upgrade from clang-format-12 to clang-format-18 (#2568) 
* upgrade to clang-format-18

* update to clang-format-18 in pre-commit-config
2025-07-28 11:34:07 -07:00

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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <algorithm>
#include <cassert>
#include <iostream>
#include <fstream>
#include <numeric>
#include <random>
#include <thread>
#include <utility>
#include <vector>
#include "ck/utility/data_type.hpp"
#include "ck/utility/span.hpp"
#include "ck/utility/type_convert.hpp"
#include "ck/library/utility/algorithm.hpp"
#include "ck/library/utility/ranges.hpp"
#include "ck/library/utility/thread.hpp"
template <typename Range>
std::ostream& LogRange(std::ostream& os, Range&& range, std::string delim)
{
bool first = true;
for(auto&& v : range)
{
if(first)
first = false;
else
os << delim;
os << v;
}
return os;
}
template <typename T, typename Range>
std::ostream& LogRangeAsType(std::ostream& os, Range&& range, std::string delim)
{
bool first = true;
for(auto&& v : range)
{
if(first)
first = false;
else
os << delim;
using RangeType = ck::remove_cvref_t<decltype(v)>;
if constexpr(std::is_same_v<RangeType, ck::f8_t> || std::is_same_v<RangeType, ck::bf8_t> ||
std::is_same_v<RangeType, ck::bhalf_t>)
{
os << ck::type_convert<float>(v);
}
else if constexpr(std::is_same_v<RangeType, ck::pk_i4_t> ||
std::is_same_v<RangeType, ck::f4x2_pk_t>)
{
const auto packed_floats = ck::type_convert<ck::float2_t>(v);
const ck::vector_type<float, 2> vector_of_floats{packed_floats};
os << vector_of_floats.template AsType<float>()[ck::Number<0>{}] << delim
<< vector_of_floats.template AsType<float>()[ck::Number<1>{}];
}
else
{
os << static_cast<T>(v);
}
}
return os;
}
template <typename F, typename T, std::size_t... Is>
auto call_f_unpack_args_impl(F f, T args, std::index_sequence<Is...>)
{
return f(std::get<Is>(args)...);
}
template <typename F, typename T>
auto call_f_unpack_args(F f, T args)
{
constexpr std::size_t N = std::tuple_size<T>{};
return call_f_unpack_args_impl(f, args, std::make_index_sequence<N>{});
}
template <typename F, typename T, std::size_t... Is>
auto construct_f_unpack_args_impl(T args, std::index_sequence<Is...>)
{
return F(std::get<Is>(args)...);
}
template <typename F, typename T>
auto construct_f_unpack_args(F, T args)
{
constexpr std::size_t N = std::tuple_size<T>{};
return construct_f_unpack_args_impl<F>(args, std::make_index_sequence<N>{});
}
struct HostTensorDescriptor
{
HostTensorDescriptor() = default;
void CalculateStrides();
template <typename X, typename = std::enable_if_t<std::is_convertible_v<X, std::size_t>>>
HostTensorDescriptor(const std::initializer_list<X>& lens) : mLens(lens.begin(), lens.end())
{
this->CalculateStrides();
}
HostTensorDescriptor(const std::initializer_list<ck::long_index_t>& lens)
: mLens(lens.begin(), lens.end())
{
this->CalculateStrides();
}
template <typename Lengths,
typename = std::enable_if_t<
std::is_convertible_v<ck::ranges::range_value_t<Lengths>, std::size_t> ||
std::is_convertible_v<ck::ranges::range_value_t<Lengths>, ck::long_index_t>>>
HostTensorDescriptor(const Lengths& lens) : mLens(lens.begin(), lens.end())
{
this->CalculateStrides();
}
template <typename X,
typename Y,
typename = std::enable_if_t<std::is_convertible_v<X, std::size_t> &&
std::is_convertible_v<Y, std::size_t>>>
HostTensorDescriptor(const std::initializer_list<X>& lens,
const std::initializer_list<Y>& strides)
: mLens(lens.begin(), lens.end()), mStrides(strides.begin(), strides.end())
{
}
HostTensorDescriptor(const std::initializer_list<ck::long_index_t>& lens,
const std::initializer_list<ck::long_index_t>& strides)
: mLens(lens.begin(), lens.end()), mStrides(strides.begin(), strides.end())
{
}
template <typename Lengths,
typename Strides,
typename = std::enable_if_t<
(std::is_convertible_v<ck::ranges::range_value_t<Lengths>, std::size_t> &&
std::is_convertible_v<ck::ranges::range_value_t<Strides>, std::size_t>) ||
(std::is_convertible_v<ck::ranges::range_value_t<Lengths>, ck::long_index_t> &&
std::is_convertible_v<ck::ranges::range_value_t<Strides>, ck::long_index_t>)>>
HostTensorDescriptor(const Lengths& lens, const Strides& strides)
: mLens(lens.begin(), lens.end()), mStrides(strides.begin(), strides.end())
{
}
std::size_t GetNumOfDimension() const;
std::size_t GetElementSize() const;
std::size_t GetElementSpaceSize() const;
const std::vector<std::size_t>& GetLengths() const;
const std::vector<std::size_t>& GetStrides() const;
template <typename... Is>
std::size_t GetOffsetFromMultiIndex(Is... is) const
{
assert(sizeof...(Is) == this->GetNumOfDimension());
std::initializer_list<std::size_t> iss{static_cast<std::size_t>(is)...};
return std::inner_product(iss.begin(), iss.end(), mStrides.begin(), std::size_t{0});
}
std::size_t GetOffsetFromMultiIndex(const std::vector<std::size_t>& iss) const
{
return std::inner_product(iss.begin(), iss.end(), mStrides.begin(), std::size_t{0});
}
friend std::ostream& operator<<(std::ostream& os, const HostTensorDescriptor& desc);
private:
std::vector<std::size_t> mLens;
std::vector<std::size_t> mStrides;
};
template <typename New2Old>
HostTensorDescriptor transpose_host_tensor_descriptor_given_new2old(const HostTensorDescriptor& a,
const New2Old& new2old)
{
std::vector<std::size_t> new_lengths(a.GetNumOfDimension());
std::vector<std::size_t> new_strides(a.GetNumOfDimension());
for(std::size_t i = 0; i < a.GetNumOfDimension(); i++)
{
new_lengths[i] = a.GetLengths()[new2old[i]];
new_strides[i] = a.GetStrides()[new2old[i]];
}
return HostTensorDescriptor(new_lengths, new_strides);
}
struct joinable_thread : std::thread
{
template <typename... Xs>
joinable_thread(Xs&&... xs) : std::thread(std::forward<Xs>(xs)...)
{
}
joinable_thread(joinable_thread&&) = default;
joinable_thread& operator=(joinable_thread&&) = default;
~joinable_thread()
{
if(this->joinable())
this->join();
}
};
template <typename F, typename... Xs>
struct ParallelTensorFunctor
{
F mF;
static constexpr std::size_t NDIM = sizeof...(Xs);
std::array<std::size_t, NDIM> mLens;
std::array<std::size_t, NDIM> mStrides;
std::size_t mN1d;
ParallelTensorFunctor(F f, Xs... xs) : mF(f), mLens({static_cast<std::size_t>(xs)...})
{
mStrides.back() = 1;
std::partial_sum(mLens.rbegin(),
mLens.rend() - 1,
mStrides.rbegin() + 1,
std::multiplies<std::size_t>());
mN1d = mStrides[0] * mLens[0];
}
std::array<std::size_t, NDIM> GetNdIndices(std::size_t i) const
{
std::array<std::size_t, NDIM> indices;
for(std::size_t idim = 0; idim < NDIM; ++idim)
{
indices[idim] = i / mStrides[idim];
i -= indices[idim] * mStrides[idim];
}
return indices;
}
void operator()(std::size_t num_thread = 1) const
{
std::size_t work_per_thread = (mN1d + num_thread - 1) / num_thread;
std::vector<joinable_thread> threads(num_thread);
for(std::size_t it = 0; it < num_thread; ++it)
{
std::size_t iw_begin = it * work_per_thread;
std::size_t iw_end = std::min((it + 1) * work_per_thread, mN1d);
auto f = [=, *this] {
for(std::size_t iw = iw_begin; iw < iw_end; ++iw)
{
call_f_unpack_args(mF, GetNdIndices(iw));
}
};
threads[it] = joinable_thread(f);
}
}
};
template <typename F, typename... Xs>
auto make_ParallelTensorFunctor(F f, Xs... xs)
{
return ParallelTensorFunctor<F, Xs...>(f, xs...);
}
template <typename T>
struct Tensor
{
using Descriptor = HostTensorDescriptor;
using Data = std::vector<T>;
template <typename X>
Tensor(std::initializer_list<X> lens) : mDesc(lens), mData(GetElementSpaceSize())
{
}
template <typename X, typename Y>
Tensor(std::initializer_list<X> lens, std::initializer_list<Y> strides)
: mDesc(lens, strides), mData(GetElementSpaceSize())
{
}
template <typename Lengths>
Tensor(const Lengths& lens) : mDesc(lens), mData(GetElementSpaceSize())
{
}
template <typename Lengths, typename Strides>
Tensor(const Lengths& lens, const Strides& strides)
: mDesc(lens, strides), mData(GetElementSpaceSize())
{
}
Tensor(const Descriptor& desc) : mDesc(desc), mData(GetElementSpaceSize()) {}
template <typename OutT>
Tensor<OutT> CopyAsType() const
{
Tensor<OutT> ret(mDesc);
ck::ranges::transform(
mData, ret.mData.begin(), [](auto value) { return ck::type_convert<OutT>(value); });
return ret;
}
Tensor() = delete;
Tensor(const Tensor&) = default;
Tensor(Tensor&&) = default;
~Tensor() = default;
Tensor& operator=(const Tensor&) = default;
Tensor& operator=(Tensor&&) = default;
template <typename FromT>
explicit Tensor(const Tensor<FromT>& other) : Tensor(other.template CopyAsType<T>())
{
}
void savetxt(std::string file_name, std::string dtype = "float")
{
std::ofstream file(file_name);
if(file.is_open())
{
for(auto& itm : mData)
{
if(dtype == "float")
file << ck::type_convert<float>(itm) << std::endl;
else if(dtype == "int")
file << ck::type_convert<int>(itm) << std::endl;
else
// TODO: we didn't implement operator<< for all custom
// data types, here fall back to float in case compile error
file << ck::type_convert<float>(itm) << std::endl;
}
file.close();
}
else
{
// Print an error message to the standard error
// stream if the file cannot be opened.
throw std::runtime_error(std::string("unable to open file:") + file_name);
}
}
decltype(auto) GetLengths() const { return mDesc.GetLengths(); }
decltype(auto) GetStrides() const { return mDesc.GetStrides(); }
std::size_t GetNumOfDimension() const { return mDesc.GetNumOfDimension(); }
std::size_t GetElementSize() const { return mDesc.GetElementSize(); }
std::size_t GetElementSpaceSize() const
{
if constexpr(ck::is_packed_type_v<ck::remove_cvref_t<T>>)
{
return (mDesc.GetElementSpaceSize() + 1) / ck::packed_size_v<ck::remove_cvref_t<T>>;
}
else
{
return mDesc.GetElementSpaceSize();
}
}
std::size_t GetElementSpaceSizeInBytes() const { return sizeof(T) * GetElementSpaceSize(); }
void SetZero() { ck::ranges::fill<T>(mData, T{0}); }
template <typename F>
void ForEach_impl(F&& f, std::vector<size_t>& idx, size_t rank)
{
if(rank == mDesc.GetNumOfDimension())
{
f(*this, idx);
return;
}
// else
for(size_t i = 0; i < mDesc.GetLengths()[rank]; i++)
{
idx[rank] = i;
ForEach_impl(std::forward<F>(f), idx, rank + 1);
}
}
template <typename F>
void ForEach(F&& f)
{
std::vector<size_t> idx(mDesc.GetNumOfDimension(), 0);
ForEach_impl(std::forward<F>(f), idx, size_t(0));
}
template <typename F>
void ForEach_impl(const F&& f, std::vector<size_t>& idx, size_t rank) const
{
if(rank == mDesc.GetNumOfDimension())
{
f(*this, idx);
return;
}
// else
for(size_t i = 0; i < mDesc.GetLengths()[rank]; i++)
{
idx[rank] = i;
ForEach_impl(std::forward<const F>(f), idx, rank + 1);
}
}
template <typename F>
void ForEach(const F&& f) const
{
std::vector<size_t> idx(mDesc.GetNumOfDimension(), 0);
ForEach_impl(std::forward<const F>(f), idx, size_t(0));
}
template <typename G>
void GenerateTensorValue(G g, std::size_t num_thread = 1)
{
switch(mDesc.GetNumOfDimension())
{
case 1: {
auto f = [&](auto i) { (*this)(i) = g(i); };
make_ParallelTensorFunctor(f, mDesc.GetLengths()[0])(num_thread);
break;
}
case 2: {
auto f = [&](auto i0, auto i1) { (*this)(i0, i1) = g(i0, i1); };
make_ParallelTensorFunctor(f, mDesc.GetLengths()[0], mDesc.GetLengths()[1])(num_thread);
break;
}
case 3: {
auto f = [&](auto i0, auto i1, auto i2) { (*this)(i0, i1, i2) = g(i0, i1, i2); };
make_ParallelTensorFunctor(
f, mDesc.GetLengths()[0], mDesc.GetLengths()[1], mDesc.GetLengths()[2])(num_thread);
break;
}
case 4: {
auto f = [&](auto i0, auto i1, auto i2, auto i3) {
(*this)(i0, i1, i2, i3) = g(i0, i1, i2, i3);
};
make_ParallelTensorFunctor(f,
mDesc.GetLengths()[0],
mDesc.GetLengths()[1],
mDesc.GetLengths()[2],
mDesc.GetLengths()[3])(num_thread);
break;
}
case 5: {
auto f = [&](auto i0, auto i1, auto i2, auto i3, auto i4) {
(*this)(i0, i1, i2, i3, i4) = g(i0, i1, i2, i3, i4);
};
make_ParallelTensorFunctor(f,
mDesc.GetLengths()[0],
mDesc.GetLengths()[1],
mDesc.GetLengths()[2],
mDesc.GetLengths()[3],
mDesc.GetLengths()[4])(num_thread);
break;
}
case 6: {
auto f = [&](auto i0, auto i1, auto i2, auto i3, auto i4, auto i5) {
(*this)(i0, i1, i2, i3, i4, i5) = g(i0, i1, i2, i3, i4, i5);
};
make_ParallelTensorFunctor(f,
mDesc.GetLengths()[0],
mDesc.GetLengths()[1],
mDesc.GetLengths()[2],
mDesc.GetLengths()[3],
mDesc.GetLengths()[4],
mDesc.GetLengths()[5])(num_thread);
break;
}
case 12: {
auto f = [&](auto i0,
auto i1,
auto i2,
auto i3,
auto i4,
auto i5,
auto i6,
auto i7,
auto i8,
auto i9,
auto i10,
auto i11) {
(*this)(i0, i1, i2, i3, i4, i5, i6, i7, i8, i9, i10, i11) =
g(i0, i1, i2, i3, i4, i5, i6, i7, i8, i9, i10, i11);
};
make_ParallelTensorFunctor(f,
mDesc.GetLengths()[0],
mDesc.GetLengths()[1],
mDesc.GetLengths()[2],
mDesc.GetLengths()[3],
mDesc.GetLengths()[4],
mDesc.GetLengths()[5],
mDesc.GetLengths()[6],
mDesc.GetLengths()[7],
mDesc.GetLengths()[8],
mDesc.GetLengths()[9],
mDesc.GetLengths()[10],
mDesc.GetLengths()[11])(num_thread);
break;
}
default: throw std::runtime_error("unspported dimension");
}
}
// Generate random values with multiple threads. Guaranteed to give the same sequence with any
// number of threads provided.
template <typename Distribution = std::uniform_real_distribution<float>,
typename Mapping = ck::identity,
typename Generator = std::minstd_rand>
void GenerateTensorDistr(Distribution dis = {0.f, 1.f},
Mapping fn = {},
const Generator g = Generator(0), // default seed 0
std::size_t num_thread = -1)
{
using ck::math::integer_divide_ceil;
using ck::math::min;
if(num_thread == -1ULL)
num_thread = min(ck::get_available_cpu_cores(), 80U); // max 80 threads
// At least 2MB per thread
num_thread = min(num_thread, integer_divide_ceil(this->GetElementSpaceSize(), 0x200000));
constexpr std::size_t BLOCK_BYTES = 64;
constexpr std::size_t BLOCK_SIZE = BLOCK_BYTES / sizeof(T);
const std::size_t num_blocks = integer_divide_ceil(this->GetElementSpaceSize(), BLOCK_SIZE);
const std::size_t blocks_per_thread = integer_divide_ceil(num_blocks, num_thread);
std::vector<std::thread> threads;
threads.reserve(num_thread - 1);
const auto dst = const_cast<T*>(this->mData.data());
const auto element_space_size = this->GetElementSpaceSize();
for(int it = num_thread - 1; it >= 0; --it)
{
std::size_t ib_begin = it * blocks_per_thread;
std::size_t ib_end = min(ib_begin + blocks_per_thread, num_blocks);
auto job = [=]() {
auto g_ = g; // copy
auto dis_ = dis; // copy
g_.discard(ib_begin * BLOCK_SIZE * ck::packed_size_v<T>);
auto t_fn = [&]() {
// As user can pass integer distribution in dis, we must ensure that the correct
// constructor/converter is called at all times. For f4/f6/f8 types, to ensure
// correct results, we convert from float to the target type. In these cases
// integer constructors are interpreted as direct initialization of the internal
// storage with binary values instead of treating integers as subset of floats.
if constexpr(ck::is_same_v<T, ck::f8_t> || ck::is_same_v<T, ck::bf8_t>)
return ck::type_convert<T>(static_cast<float>(fn(dis_(g_))));
else if constexpr(ck::packed_size_v<T> == 1)
return ck::type_convert<T>(fn(dis_(g_)));
else if constexpr(ck::is_same_v<T, ck::f4x2_pk_t>)
return ck::f4x2_pk_t{ck::type_convert<ck::f4x2_t>(
ck::float2_t{ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_)))})};
else if constexpr(ck::is_same_v<T, ck::f6x32_pk_t> ||
ck::is_same_v<T, ck::bf6x32_pk_t>)
{
return ck::type_convert<T>(
ck::float32_t{ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_)))});
}
else if constexpr(ck::is_same_v<T, ck::f6x16_pk_t> ||
ck::is_same_v<T, ck::bf6x16_pk_t>)
{
return ck::type_convert<T>(
ck::float16_t{ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_))),
ck::type_convert<float>(fn(dis_(g_)))});
}
else
static_assert(false, "Unsupported packed size for T");
};
std::size_t ib = ib_begin;
for(; ib < ib_end - 1; ++ib)
ck::static_for<0, BLOCK_SIZE, 1>{}([&](auto iw_) {
constexpr size_t iw = iw_.value;
dst[ib * BLOCK_SIZE + iw] = t_fn();
});
for(std::size_t iw = 0; iw < BLOCK_SIZE; ++iw)
if(ib * BLOCK_SIZE + iw < element_space_size)
dst[ib * BLOCK_SIZE + iw] = t_fn();
};
if(it > 0)
threads.emplace_back(std::move(job));
else
job(); // last job run in the main thread
}
for(auto& t : threads)
t.join();
}
template <typename... Is>
std::size_t GetOffsetFromMultiIndex(Is... is) const
{
return mDesc.GetOffsetFromMultiIndex(is...) / ck::packed_size_v<ck::remove_cvref_t<T>>;
}
template <typename... Is>
T& operator()(Is... is)
{
return mData[mDesc.GetOffsetFromMultiIndex(is...) /
ck::packed_size_v<ck::remove_cvref_t<T>>];
}
template <typename... Is>
const T& operator()(Is... is) const
{
return mData[mDesc.GetOffsetFromMultiIndex(is...) /
ck::packed_size_v<ck::remove_cvref_t<T>>];
}
T& operator()(const std::vector<std::size_t>& idx)
{
return mData[mDesc.GetOffsetFromMultiIndex(idx) / ck::packed_size_v<ck::remove_cvref_t<T>>];
}
const T& operator()(const std::vector<std::size_t>& idx) const
{
return mData[mDesc.GetOffsetFromMultiIndex(idx) / ck::packed_size_v<ck::remove_cvref_t<T>>];
}
typename Data::iterator begin() { return mData.begin(); }
typename Data::iterator end() { return mData.end(); }
typename Data::pointer data() { return mData.data(); }
typename Data::const_iterator begin() const { return mData.begin(); }
typename Data::const_iterator end() const { return mData.end(); }
typename Data::const_pointer data() const { return mData.data(); }
typename Data::size_type size() const { return mData.size(); }
template <typename U = T>
auto AsSpan() const
{
constexpr std::size_t FromSize = sizeof(T);
constexpr std::size_t ToSize = sizeof(U);
using Element = std::add_const_t<std::remove_reference_t<U>>;
return ck::span<Element>{reinterpret_cast<Element*>(data()), size() * FromSize / ToSize};
}
template <typename U = T>
auto AsSpan()
{
constexpr std::size_t FromSize = sizeof(T);
constexpr std::size_t ToSize = sizeof(U);
using Element = std::remove_reference_t<U>;
return ck::span<Element>{reinterpret_cast<Element*>(data()), size() * FromSize / ToSize};
}
Descriptor mDesc;
Data mData;
};
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