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adding ConstantMergedTensorDescriptor, refactering ConstantTensorDescriptor, Sequence

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This commit is contained in:
Chao Liu
2019-05-21 16:17:58 -05:00
parent cd29b09a82
commit acd7082fe1
38 changed files with 1238 additions and 768 deletions
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309
src/include/ConstantTensorDescriptor.hip.hpp
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@@ -2,40 +2,25 @@
#include "common.hip.hpp"
template <class Lengths>
__host__ __device__ constexpr auto calculate_default_strides(Lengths)
__host__ __device__ constexpr auto calculate_packed_tensor_strides(Lengths)
{
return reverse_inclusive_scan_sequence(Lengths{}.PopFront().PushBack(Number<1>{}),
std::multiplies<index_t>{});
return reverse_inclusive_scan_sequence(Lengths{}.PopFront(), std::multiplies<index_t>{})
.PushBack(Number<1>{});
}
// this is ugly, only for 2d
template <index_t L0, index_t L1, index_t Align>
__host__ __device__ constexpr auto calculate_default_strides_aligned(Sequence<L0, L1>,
Number<Align>)
template <class Lengths, index_t Align>
__host__ __device__ constexpr auto
calculate_rank_tensor_default_strides_with_alignment(Lengths, Number<Align>)
{
constexpr index_t L1_align = Align * ((L1 + Align - 1) / Align);
return Sequence<L1_align, 1>{};
constexpr index_t L_back_align =
Align * mod_conv::integer_divide_ceiler<index_t>{}(Lengths{}.Back(), Align);
return calculate_packed_tensor_strides(
Lengths{}.Modify(Number<Lengths{}.GetSize() - 1>{}, Number<L_back_align>{}));
}
// this is ugly, only for 3d
template <index_t L0, index_t L1, index_t L2, index_t Align>
__host__ __device__ constexpr auto calculate_default_strides_aligned(Sequence<L0, L1, L2>,
Number<Align>)
{
constexpr index_t L2_align = Align * ((L2 + Align - 1) / Align);
return Sequence<L1 * L2_align, L2_align, 1>{};
}
// this is ugly, only for 4d
template <index_t L0, index_t L1, index_t L2, index_t L3, index_t Align>
__host__ __device__ constexpr auto calculate_default_strides_aligned(Sequence<L0, L1, L2, L3>,
Number<Align>)
{
constexpr index_t L3_align = Align * ((L3 + Align - 1) / Align);
return Sequence<L1 * L2 * L3_align, L2 * L3_align, L3_align, 1>{};
}
template <class Lengths, class Strides>
// MemoryRanks of dimensions is for conversion from offset to multi-index
template <class Lengths, class Strides, class MemoryRanks>
struct ConstantTensorDescriptor
{
using Type = ConstantTensorDescriptor;
@@ -44,14 +29,24 @@ struct ConstantTensorDescriptor
__host__ __device__ constexpr ConstantTensorDescriptor()
{
static_assert(Lengths::GetSize() == Strides::GetSize(), "nDim not consistent");
static_assert(Lengths::GetSize() == Strides::GetSize() &&
Lengths::GetSize() == MemoryRanks::GetSize(),
"nDim not consistent");
#if 0 // require sequence_sort, but it's not implemented yet
static_assert(is_same<typename sequence_sort<MemoryRanks>::SortedSeqType,
typename arithmetic_sequence_gen<0, nDim, 1>::SeqType>::value,
"wrong! invalid MemoryRanks");
#endif
}
__host__ __device__ static constexpr index_t GetNumOfDimension() { return nDim; }
__host__ __device__ static constexpr Lengths GetLengths() { return Lengths{}; }
__host__ __device__ static constexpr auto GetLengths() { return Lengths{}; }
__host__ __device__ static constexpr Strides GetStrides() { return Strides{}; }
__host__ __device__ static constexpr auto GetStrides() { return Strides{}; }
__host__ __device__ static constexpr auto GetMemoryRanks() { return MemoryRanks{}; }
template <index_t I>
__host__ __device__ static constexpr index_t GetLength(Number<I>)
@@ -65,47 +60,58 @@ struct ConstantTensorDescriptor
return Strides{}.Get(Number<I>{});
}
template <index_t I>
__host__ __device__ static constexpr index_t GetMemoryRank(Number<I>)
{
return MemoryRanks{}.Get(Number<I>{});
}
__host__ __device__ static constexpr index_t GetElementSize()
{
return accumulate_on_sequence(Lengths{}, std::multiplies<index_t>{}, Number<1>{});
}
// WRONG! ReorderGivenOld2New is broken
template <class Align = Number<1>>
__host__ __device__ static constexpr index_t GetElementSpace(Align align = Align{})
{
#if 0
constexpr auto lengths_in_rank = GetLengths().ReorderGivenOld2New(MemoryRank{});
constexpr auto strides_in_rank = GetStrides().ReorderGivenOld2new(MemoryRank{});
constexpr index_t element_space_unaligned = accumulate_on_sequence(
(lengths_in_rank - Number<1>{}) * strides_in_rank, std::plus<index_t>{}, Number<1>{});
#else // WRONG! align shouldbe applied to the last memory rank, not the last tensor dimension
constexpr index_t element_space_unaligned = accumulate_on_sequence(
(GetLengths() - Number<1>{}) * GetStrides(), std::plus<index_t>{}, Number<1>{});
#endif
return align.Get() * ((element_space_unaligned + align.Get() - 1) / align.Get());
}
template <index_t NSize>
__host__ __device__ static index_t Get1dIndex(Array<index_t, NSize> multi_id)
__host__ __device__ static index_t GetOffsetFromMultiIndex(Array<index_t, NSize> multi_id)
{
static_assert(NSize == nDim, "wrong! Dimension not consistent");
index_t id = 0;
index_t offset = 0;
static_for<0, nDim, 1>{}([&](auto IDim) {
constexpr index_t idim = IDim.Get();
id += multi_id[idim] * GetStride(IDim);
offset += multi_id[idim] * GetStride(IDim);
});
return id;
return offset;
}
template <class... Is>
__host__ __device__ static index_t Get1dIndex(Is... is)
__host__ __device__ static index_t GetOffsetFromMultiIndex(Is... is)
{
static_assert(sizeof...(Is) == nDim, "number of multi-index is wrong");
const auto multi_id = Array<index_t, nDim>(is...);
return Get1dIndex(multi_id);
return GetOffsetFromMultiIndex(Array<index_t, sizeof...(Is)>{is...});
}
template <index_t... Is>
__host__ __device__ static constexpr index_t Get1dIndex(Sequence<Is...> /*multi_id*/)
__host__ __device__ static constexpr index_t GetOffsetFromMultiIndex(Sequence<Is...>)
{
static_assert(sizeof...(Is) == nDim, "wrong! Dimension not consistent");
@@ -114,44 +120,84 @@ struct ConstantTensorDescriptor
return accumulate_on_sequence(multi_id * GetStrides(), std::plus<index_t>{}, Number<0>{});
}
__host__ __device__ static Array<index_t, nDim> GetMultiIndex(index_t id)
#if 0 // ReorderGivenOld2new is broken
__host__ __device__ static Array<index_t, nDim> GetMultiIndexFromOffset(index_t offset)
{
Array<index_t, nDim> ranked_multi_id;
constexpr auto ranked_strides =
GetStrides().ReorderGivenOld2new(MemoryRanks{}); // check this
// calculate index in each of the dimensions in the order of their rank (not dimension)
static_for<0, nDim - 1, 1>{}([&](auto IDim) {
constexpr index_t idim = IDim.Get();
constexpr index_t stride = ranked_strides.Get(Number<idim>{});
ranked_multi_id[idim] = offset / stride;
offset -= ranked_multi_id[idim] * stride;
});
ranked_multi_id[nDim - 1] = offset / ranked_strides.Get(Number<nDim - 1>{});
return reorder_array_given_new2old(ranked_multi_id, MemoryRanks{}); // check this
}
#endif
__host__ __device__ static Array<index_t, nDim> GetMultiIndexFrom1dIndex(index_t id)
{
Array<index_t, nDim> multi_id;
constexpr auto dummy_strides = calculate_packed_tensor_strides(GetLengths());
// calculate index in each of the dimensions in the order of their dimension (not rank)
static_for<0, nDim - 1, 1>{}([&](auto IDim) {
constexpr index_t idim = IDim.Get();
multi_id[idim] = id / GetStride(IDim);
id -= multi_id[idim] * GetStride(IDim);
constexpr index_t idim = IDim.Get();
constexpr index_t stride = dummy_strides.Get(Number<idim>{});
multi_id[idim] = id / stride;
id -= multi_id[idim] * stride;
});
multi_id[nDim - 1] = id / GetStride(Number<nDim - 1>{});
multi_id[nDim - 1] = id / dummy_strides.Get(Number<nDim - 1>{});
return multi_id;
}
__host__ __device__ static constexpr auto Pack()
{
constexpr auto default_strides = calculate_default_strides(Lengths{});
return ConstantTensorDescriptor<Lengths, decltype(default_strides)>{};
}
// WRONG! Ranks is broken
template <index_t... IDims>
__host__ __device__ static constexpr auto Extract(Number<IDims>... extract_dims)
{
static_assert(sizeof...(IDims) <= GetNumOfDimension(),
"wrong! too many number of dimensions to be extracted");
return make_ConstantTensorDescriptor(Lengths{}.Extract(extract_dims...),
Strides{}.Extract(extract_dims...));
using extract_lengths = decltype(Lengths{}.Extract(extract_dims...));
using extract_strides = decltype(Strides{}.Extract(extract_dims...));
using extract_ranks = decltype(MemoryRanks{}.Extract(extract_dims...));
#if 0
using new_ranks = typename sequence_sort<extract_ranks>::Original2SortedType;
#else // WRONG! TODO:: implement sequence_sort
using new_ranks = typename arithmetic_sequence_gen<0, sizeof...(IDims), 1>::SeqType;
#endif
return ConstantTensorDescriptor<extract_lengths, extract_strides, new_ranks>{};
}
template <index_t IDim, index_t SliceLen>
__host__ __device__ static constexpr auto Slice(Number<IDim>, Number<SliceLen>)
{
return make_ConstantTensorDescriptor(Lengths{}.Modify(Number<IDim>{}, Number<SliceLen>{}),
Strides{});
using slice_lengths = decltype(Lengths{}.Modify(Number<IDim>{}, Number<SliceLen>{}));
return ConstantTensorDescriptor<slice_lengths, Strides, MemoryRanks>{};
}
template <index_t Threashold, index_t Delta>
struct f_fold_impl
{
__host__ __device__ constexpr index_t operator()(index_t x) const
{
return x > Threashold ? x + Delta : x;
}
};
template <index_t IDim, index_t... FoldIntervals>
__host__ __device__ static constexpr auto Fold(Number<IDim>, Number<FoldIntervals>...)
{
@@ -162,6 +208,7 @@ struct ConstantTensorDescriptor
constexpr auto unfold_length = GetLength(Number<IDim>{});
constexpr auto unfold_stride = GetStride(Number<IDim>{});
constexpr auto unfold_rank = GetMemoryRank(Number<IDim>{});
// length of the dimension to be folded needs to be dividable by fold_interval_product,
// otherwise, folding is invalid
@@ -178,16 +225,45 @@ struct ConstantTensorDescriptor
reverse_inclusive_scan_sequence(fold_intervals.PushBack(Number<1>{}),
std::multiplies<index_t>{});
// left and right
constexpr auto left = make_increasing_sequence(Number<0>{}, Number<IDim>{}, Number<1>{});
constexpr auto right = make_increasing_sequence(
Number<IDim + 1>{}, Number<GetNumOfDimension()>{}, Number<1>{});
// folded_ranks
constexpr auto fold_ranks =
typename arithmetic_sequence_gen<unfold_rank,
unfold_rank + fold_intervals.GetSize() + 1,
1>::SeqType{};
return make_ConstantTensorDescriptor(
GetLengths().Extract(left).Append(fold_lengths).Append(GetLengths().Extract(right)),
GetStrides().Extract(left).Append(fold_strides).Append(GetStrides().Extract(right)));
// increase the ranks that are larger than unfold_rank
constexpr auto tmp_ranks = transform_sequences(
f_fold_impl<unfold_rank, fold_intervals.GetSize()>{}, GetMemoryRanks());
// left and right
constexpr auto left = typename arithmetic_sequence_gen<0, IDim, 1>::SeqType{};
constexpr auto right =
typename arithmetic_sequence_gen<IDim + 1, GetNumOfDimension(), 1>::SeqType{};
constexpr auto new_lengths =
GetLengths().Extract(left).Append(fold_lengths).Append(GetLengths().Extract(right));
constexpr auto new_strides =
GetStrides().Extract(left).Append(fold_strides).Append(GetStrides().Extract(right));
constexpr auto new_ranks =
tmp_ranks.Extract(left).Append(fold_ranks).Append(tmp_ranks.Extract(right));
static_assert(new_ranks.GetSize() == new_lengths.GetSize(), "wrong!");
static_assert(fold_ranks.GetSize() == fold_lengths.GetSize(), "wrong!");
return ConstantTensorDescriptor<decltype(new_lengths),
decltype(new_strides),
decltype(new_ranks)>{};
}
template <index_t Threashold, index_t Delta>
struct f_unfold_impl
{
__host__ __device__ constexpr index_t operator()(index_t x) const
{
return x > Threashold ? x - Delta : x;
}
};
template <index_t FirstUnfoldDim, index_t LastUnfoldDim>
__host__ __device__ static constexpr auto Unfold(Number<FirstUnfoldDim>, Number<LastUnfoldDim>)
{
@@ -198,66 +274,109 @@ struct ConstantTensorDescriptor
// dimensions to be unfold need to be in descending order (w.r.t. strides), and need to be
// packed in memory, otherwise, unfolding is invalid
static_for<FirstUnfoldDim, LastUnfoldDim, 1>{}([&](auto IDim) {
constexpr auto IDim_p1 = IDim + Number<1>{};
// check stride
static_assert(
GetStride(IDim) >= GetStride(Number<IDim.Get() + 1>{}),
GetStride(IDim) >= GetStride(IDim_p1),
"wrong! dimensions to be unfolded need to be in descending order w.r.t strides");
static_assert(GetStride(IDim + 1) * GetLength(IDim + 1) == GetStride(IDim),
// check if packed
static_assert(GetStride(IDim_p1) * GetLength(IDim_p1) == GetStride(IDim),
"wrong! dimensions to be unfolded need to be packed");
// checkt ranks
static_assert(GetMemoryRank(IDim_p1) = GetMemoryRank(IDim) + 1,
"wrong! ranks of dimensions to be "
"unfolded need to be in increasing "
"and continuous ranks");
});
// left and right
constexpr auto left =
make_increasing_sequence(Number<0>{}, Number<FirstUnfoldDim>{}, Number<1>{});
constexpr auto middle = make_increasing_sequence(
Number<FirstUnfoldDim>{}, Number<LastUnfoldDim + 1>{}, Number<1>{});
constexpr auto right = make_increasing_sequence(
Number<LastUnfoldDim + 1>{}, Number<GetNumOfDimension()>{}, Number<1>{});
constexpr auto left = typename arithmetic_sequence_gen<0, FirstUnfoldDim, 1>::SeqType{};
constexpr auto middle =
typename arithmetic_sequence_gen<FirstUnfoldDim, LastUnfoldDim + 1, 1>::SeqType{};
constexpr auto right =
typename arithmetic_sequence_gen<LastUnfoldDim + 1, GetNumOfDimension(), 1>::SeqType{};
// length and stride
// unfolded length, stride and rank
constexpr index_t unfold_length = accumulate_on_sequence(
GetLengths().Extract(middle), std::multiplies<index_t>{}, Number<1>{});
constexpr index_t unfold_stride = GetStride(Number<LastUnfoldDim>{});
return make_ConstantTensorDescriptor(GetLengths()
.Extract(left)
.PushBack(Number<unfold_length>{})
.Append(GetLengths().Extract(right)),
GetStrides()
.Extract(left)
.PushBack(Number<unfold_stride>{})
.Append(GetStrides().Extract(right)));
constexpr index_t unfold_rank = GetMemoryRank(Number<FirstUnfoldDim>{});
// decrease the ranks that are larger than the rank of LastUnfoldDim
constexpr auto tmp_ranks =
transform_sequences(GetMemoryRanks(),
f_unfold_impl<GetMemoryRank(Number<LastUnfoldDim>{}),
LastUnfoldDim - FirstUnfoldDim + 1>{});
// new lengths, strides and ranks
constexpr auto new_lengths = GetLengths()
.Extract(left)
.PushBack(Number<unfold_length>{})
.Append(GetLengths().Extract(right));
constexpr auto new_strides = GetStrides()
.Extract(left)
.PushBack(Number<unfold_stride>{})
.Append(GetStrides().Extract(right));
constexpr auto new_ranks = tmp_ranks.Extract(left)
.PushBack(Number<unfold_rank>{})
.Append(tmp_ranks.Extract(right));
return ConstantTensorDescriptor<decltype(new_lengths),
decltype(new_strides),
decltype(new_ranks)>{};
}
template <index_t... IRs>
__host__ __device__ static constexpr auto ReorderGivenNew2Old(Sequence<IRs...> /*new2old*/)
template <class MapNew2Old>
__host__ __device__ static constexpr auto ReorderGivenNew2Old(MapNew2Old)
{
static_assert(sizeof...(IRs) == GetNumOfDimension(), "wrong! dimension is wrong");
constexpr auto map_new2old = Sequence<IRs...>{};
return make_ConstantTensorDescriptor(Lengths{}.ReorderGivenNew2Old(map_new2old),
Strides{}.ReorderGivenNew2Old(map_new2old));
return ConstantTensorDescriptor<decltype(Lengths{}.ReorderGivenNew2Old(MapNew2Old{})),
decltype(Strides{}.ReorderGivenNew2Old(MapNew2Old{})),
decltype(
MemoryRanks{}.ReorderGivenNew2Old(MapNew2Old{}))>{};
}
#if 0 // require sequence_sort, which is not implemented yet
template <class MapOld2New>
__host__ __device__ static constexpr auto ReorderGivenOld2New(MapOld2New)
{
return ConstantTensorDescriptor<decltype(Lengths{}.ReorderGivenOld2New(MapOld2New{})),
decltype(Strides{}.ReorderGivenOld2New(MapOld2New{})),
decltype(
MemoryRanks{}.ReorderGivenOld2New(MapOld2New{}))>{};
}
#endif
};
template <class Lengths>
__host__ __device__ constexpr auto make_ConstantTensorDescriptor(Lengths)
__host__ __device__ constexpr auto make_packed_ConstantTensorDescriptor(Lengths)
{
using Strides = decltype(calculate_default_strides(Lengths{}));
return ConstantTensorDescriptor<Lengths, Strides>{};
using Strides = decltype(calculate_packed_tensor_strides(Lengths{}));
using MemoryRanks = typename arithmetic_sequence_gen<0, Lengths::GetSize(), 1>::SeqType;
return ConstantTensorDescriptor<Lengths, Strides, MemoryRanks>{};
}
template <class Lengths, class Strides>
__host__ __device__ constexpr auto make_ConstantTensorDescriptor(Lengths, Strides)
__host__ __device__ constexpr auto make_ranked_ConstantTensorDescriptor(Lengths, Strides)
{
return ConstantTensorDescriptor<Lengths, Strides>{};
using MemoryRanks = typename arithmetic_sequence_gen<0, Lengths::GetSize(), 1>::SeqType;
return ConstantTensorDescriptor<Lengths, Strides, MemoryRanks>{};
}
template <class Lengths, index_t Align>
__host__ __device__ constexpr auto make_ConstantTensorDescriptor_aligned(Lengths, Number<Align>)
__host__ __device__ constexpr auto
make_ranked_ConstantTensorDescriptor_with_alignment(Lengths, Number<Align>)
{
using Strides = decltype(calculate_default_strides_aligned(Lengths{}, Number<Align>{}));
return ConstantTensorDescriptor<Lengths, Strides>{};
using Strides =
decltype(calculate_rank_tensor_default_strides_with_alignment(Lengths{}, Number<Align>{}));
using MemoryRanks = typename arithmetic_sequence_gen<0, Lengths::GetSize(), 1>::SeqType;
return ConstantTensorDescriptor<Lengths, Strides, MemoryRanks>{};
}
template <class TDesc>