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mma_traits_sm100.hpp
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/***************************************************************************************************
* Copyright (c) 2022 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* 3. Neither the name of the copyright holder nor the names of its
* contributors may be used to endorse or promote products derived from
* this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
#pragma once
#include <cute/config.hpp>
#include <cute/pointer_sparse.hpp>
#include <cute/tensor_impl.hpp>
#include <cute/arch/mma_sm100.hpp>
#include <cute/arch/mma_sm100_desc.hpp>
#include <cute/arch/mma_sm100_umma.hpp>
#include <cute/atom/copy_traits_sm100.hpp> // cute::TMEM::
#include <cute/atom/mma_traits.hpp>
#include <cute/atom/mma_traits_sm90_gmma.hpp> // cute::GMMA::
#include <cute/atom/mma_traits_sm90_gmma_sparse.hpp> // cute::GMMA::
#include <cute/numeric/numeric_types.hpp>
// Check that aggregate initialization in .with() initializes all fields
#if defined(__GNUG__)
#pragma GCC diagnostic warning "-Wmissing-field-initializers"
#pragma GCC diagnostic error "-Wmissing-field-initializers"
#endif
namespace cute {
namespace UMMA {
//////////////////////////////////////////////////
// Common layouts for UMMA Shared Memory //
//////////////////////////////////////////////////
using cute::GMMA::Layout_MN_INTER_Atom;
using cute::GMMA::Layout_MN_SW32_Atom;
using cute::GMMA::Layout_MN_SW64_Atom;
using cute::GMMA::Layout_MN_SW128_Atom;
using cute::GMMA::Layout_K_INTER_Atom;
using cute::GMMA::Layout_K_SW32_Atom;
using cute::GMMA::Layout_K_SW64_Atom;
using cute::GMMA::Layout_K_SW128_Atom;
using Layout_MN_SW128_32B_Atom_Bits = ComposedLayout<Swizzle<2,5,2>, smem_ptr_flag, Layout<Shape< _1024,_4>,Stride<_1, _1024>>>;
template <class Type>
using Layout_MN_SW128_32B_Atom = decltype(upcast<sizeof_bits<Type>::value>(Layout_MN_SW128_32B_Atom_Bits{}));
//////////////////////////////////////////////////
// Common layouts for Sparse UMMA Shared Memory //
//////////////////////////////////////////////////
using cute::GMMA::Layout_MN_INTER_SpAtom;
using cute::GMMA::Layout_MN_SW32_SpAtom;
using cute::GMMA::Layout_MN_SW64_SpAtom;
using cute::GMMA::Layout_MN_SW128_SpAtom;
using cute::GMMA::Layout_K_INTER_SpAtom;
using cute::GMMA::Layout_K_SW32_SpAtom;
using cute::GMMA::Layout_K_SW64_SpAtom;
using cute::GMMA::Layout_K_SW128_SpAtom;
template <class Type, int S>
using Layout_MN_SW128_32B_SpAtom = ComposedLayout<Swizzle<2,5,2>, smem_sparse_ptr_flag_bits<S,sizeof_bits_v<Type>>,
decltype(blocked_product(Layout<Shape<_1,Int<S>>>{}, Layout_MN_SW128_32B_Atom<Type>{}.layout_b()))>;
// With UMMA::Major param
template <class Type, int S, UMMA::Major tnsp>
using Layout_INTER_SpAtom = typename conditional<tnsp == UMMA::Major::MN,
Layout_MN_INTER_SpAtom<Type,S>,
Layout_K_INTER_SpAtom<Type,S>>::type;
template <class Type, int S, UMMA::Major tnsp>
using Layout_SW32_SpAtom = typename conditional<tnsp == UMMA::Major::MN,
Layout_MN_SW32_SpAtom<Type,S>,
Layout_K_SW32_SpAtom<Type,S>>::type;
template <class Type, int S, UMMA::Major tnsp>
using Layout_SW64_SpAtom = typename conditional<tnsp == UMMA::Major::MN,
Layout_MN_SW64_SpAtom<Type,S>,
Layout_K_SW64_SpAtom<Type,S>>::type;
template <class Type, int S, UMMA::Major tnsp>
using Layout_SW128_SpAtom = typename conditional<tnsp == UMMA::Major::MN,
Layout_MN_SW128_SpAtom<Type,S>,
Layout_K_SW128_SpAtom<Type,S>>::type;
// Tile a MN-logical layout atom to an MMA Tile Shape ((MMA_M,MMA_N),M_MMAs,N_MMAs,...)
template <class LayoutAtom, class MMATileShape, class ModeOrder = GenColMajor>
CUTE_HOST_DEVICE constexpr
auto
tile_to_mma_shape(LayoutAtom const& atom, MMATileShape const& mma_tile_shape, ModeOrder const& order = {})
{
constexpr int R = decltype(rank(mma_tile_shape))::value;
auto mn_shape = cute::tuple_cat(zip(shape<0>(mma_tile_shape), take<1,3>(mma_tile_shape)), take<3,R>(mma_tile_shape));
auto mn_tiled = tile_to_shape(atom, mn_shape, order); // (BLK_M,BLK_N,...)
return tiled_divide(mn_tiled, product_each(shape<0>(mma_tile_shape))); // ((MMA_M,MMA_N),M_MMAs,N_MMAs,...)
}
//
// Tensor (position-dependent swizzle) to LayoutType utility
//
template <class Engine, class Shape, class Stride>
CUTE_HOST_DEVICE constexpr
LayoutType
layout_type(Tensor<Engine, Layout<Shape,Stride>> const&)
{
static_assert(is_same<uint128_t, typename Engine::value_type>::value,
"Expected uint128_t type in LayoutType conversion.");
using Swizzle = get_swizzle_t<Engine>;
constexpr int B = Swizzle::num_bits;
constexpr int M = Swizzle::num_base;
constexpr int S = Swizzle::num_shft;
if constexpr (M == 4) {
static_assert(S == 3, "Expected S = 3 when M == 4. Unsupported layout swizzle.");
switch (B) {
default: static_assert(0 <= B && B <= 3, "Expected B = 0,1,2, or 3 when M == 4. Unsupported layout swizzle.");
case 0: return LayoutType::SWIZZLE_NONE;
case 1: return LayoutType::SWIZZLE_32B;
case 2: return LayoutType::SWIZZLE_64B;
case 3: return LayoutType::SWIZZLE_128B;
}
} else
if constexpr (M == 5) {
static_assert(B == 2, "Expected B = 2 when M == 5. Unsupported layout swizzle.");
static_assert(S == 2, "Expected S = 2 when M == 5. Unsupported layout swizzle.");
return LayoutType::SWIZZLE_128B_BASE32B;
} else {
static_assert(M==5, "Only 16B and 32B Atoms are supported for UMMA. Unsupported layout swizzle.");
return LayoutType::SWIZZLE_NONE; // ERROR
}
}
///////////////////////////////////////////////////////////////////////////////
// Construction method for UMMA Descriptors
///////////////////////////////////////////////////////////////////////////////
/**
* ///////////////////////////////
* // make_umma_desc<Major::MN> //
* ///////////////////////////////
* Each UmmaDescriptor Major-MN describes a canonical layout of the form
*
* LayoutType::INTERLEAVE : Swizzle<0,4,3> o smem_ptr o ((T,1,m),(8,k)):((1,T,SBO),(1T,LBO))
* LayoutType::B32 : Swizzle<1,4,3> o smem_ptr o ((T,2,m),(8,k)):((1,T,LBO),(2T,SBO))
* LayoutType::B64 : Swizzle<2,4,3> o smem_ptr o ((T,4,m),(8,k)):((1,T,LBO),(4T,SBO))
* LayoutType::B128 : Swizzle<3,4,3> o smem_ptr o ((T,8,m),(8,k)):((1,T,LBO),(8T,SBO))
* LayoutType::128B_BASE32B : Swizzle<2,5,2> o smem_ptr o ((T,8,m),(4,k)):((1,T,LBO),(?T,SBO))
*
* where
* T : sizeof(uint128_t) / sizeof(value_type)
* m : integer in [1,16] corresponding to UMMA shape
* k : integer in [1,32] corresponding to UMMA shape
* SBO: stride byte offset
* LBO: leading byte offset
*
* See UMMA::Layout_MN_XXX_Atom<value_type> for building canonical UmmaDescriptor Major-MN layouts.
* For example,
* auto smem_layout = tile_to_shape(Layout_MN_SW128_Atom<value_type>{}, Shape<_128,_64>{});
* is guaranteed to be accepted by make_umma_desc<Major::MN> for appropriate value_type.
*
* //////////////////////////////
* // make_umma_desc<Major::K> //
* //////////////////////////////
* Each UmmaDescriptor Major-K describes a canonical layout of the form
*
* LayoutType::INTERLEAVE : Swizzle<0,4,3> o smem_ptr o ((8,m),(T,2)):((1T,SBO),(1,LBO))
* LayoutType::B32 : Swizzle<1,4,3> o smem_ptr o ((8,m),(T,2)):((2T,SBO),(1, T ))
* LayoutType::B64 : Swizzle<2,4,3> o smem_ptr o ((8,m),(T,2)):((4T,SBO),(1, T ))
* LayoutType::B128 : Swizzle<3,4,3> o smem_ptr o ((8,m),(T,2)):((8T,SBO),(1, T ))
*
* See UMMA::Layout_K_XXX_Atom<value_type> for building canonical UmmaDescriptor Major-K layouts.
* For example,
* auto smem_layout = tile_to_shape(Layout_K_SW128_Atom<value_type>{}, Shape<_128,_64>{});
* is guaranteed to be accepted by make_umma_desc<Major::K> for appropriate value_type.
*/
template <UMMA::Major MajorMode, class TEngine, class TLayout>
CUTE_HOST_DEVICE constexpr
SmemDescriptor
make_umma_desc(Tensor<TEngine,TLayout> const& tensor)
{
static_assert(is_smem<TEngine>::value, "UMMA Descriptors can only be constructed on smem.");
static_assert(TLayout::rank == 2, "UMMA Descriptors can only be constructed on rank-2 tensors.");
using value_type = typename TEngine::value_type;
Tensor u128_tensor = recast<uint128_t const>(tensor);
// Result
SmemDescriptor desc;
desc.version_ = 1; // Set the version for blackwell
desc.lbo_mode_ = 0; // set to legacy mode by default
// Layout type
constexpr UMMA::LayoutType LAYOUT_TYPE = UMMA::layout_type(u128_tensor);
desc.layout_type_ = uint8_t(LAYOUT_TYPE);
// Start address (4LSB not included)
uint32_t start_address = cast_smem_ptr_to_uint(raw_pointer_cast(u128_tensor.data()));
desc.start_address_ = static_cast<uint16_t>(start_address >> 4);
constexpr uint8_t base_offset = 0;
desc.base_offset_ = base_offset;
// LayoutType meta
constexpr int SwizzleAtomMNSize = LAYOUT_TYPE == UMMA::LayoutType::SWIZZLE_NONE ? 1 :
LAYOUT_TYPE == UMMA::LayoutType::SWIZZLE_32B ? 2 :
LAYOUT_TYPE == UMMA::LayoutType::SWIZZLE_64B ? 4 :
LAYOUT_TYPE == UMMA::LayoutType::SWIZZLE_128B ? 8 :
LAYOUT_TYPE == UMMA::LayoutType::SWIZZLE_128B_BASE32B ? 8 : -1;
if constexpr (MajorMode == UMMA::Major::MN)
{
/* In units of uint128_t, each UmmaDescriptor Major-MN describes a canonical layout of the form
*
* LayoutType::INTERLEAVE : Swizzle<0,4,3> o smem_ptr o ((1,n),(8,k)):((X,SBO),(1,LBO))
* LayoutType::B32 : Swizzle<1,4,3> o smem_ptr o ((2,n),(8,k)):((1,LBO),(2,SBO))
* LayoutType::B64 : Swizzle<2,4,3> o smem_ptr o ((4,n),(8,k)):((1,LBO),(4,SBO))
* LayoutType::B128 : Swizzle<3,4,3> o smem_ptr o ((8,n),(8,k)):((1,LBO),(8,SBO))
* LayoutType::B128_BASE32B : Swizzle<2,5,2> o smem_ptr o ((8,n),(4,k)):((1,LBO),(4,SBO))
*/
constexpr int SwizzleAtomKSize = LAYOUT_TYPE == UMMA::LayoutType::SWIZZLE_128B_BASE32B ? 4 : 8;
// Construct the canonical UMMA T Layout with shape ((SwizzleAtomMNSize,n),(SwizzleAtomKSize,2))
Layout canonical_layout = logical_divide(layout(u128_tensor), Tile<Layout<Int<SwizzleAtomMNSize>>,Layout<Int<SwizzleAtomKSize>>>{});
// Check profile of canonical
CUTE_STATIC_ASSERT_V(congruent(canonical_layout, Shape<Shape<_1,_1>,Shape<_1,_1>>{}), "Not a canonical UMMA_MN Layout: Expected profile failure.");
// Check canonical mode strides
constexpr uint32_t stride_00 = stride<0,0>(canonical_layout);
constexpr uint32_t expected_stride_00 = LAYOUT_TYPE == UMMA::LayoutType::SWIZZLE_NONE ? stride<0,0>(canonical_layout) : 1;
static_assert(stride_00 == expected_stride_00, "Not a canonical UMMA_MN Layout: Expected stride failure.");
constexpr uint32_t stride_10 = stride<1,0>(canonical_layout);
constexpr uint32_t expected_stride_10 = SwizzleAtomMNSize;
static_assert(stride_10 == expected_stride_10, "Not a canonical UMMA_MN Layout: Expected stride failure.");
// stride dimension byte offset and leading dimension byte offset (4LSB not included == uint128_t units)
constexpr uint32_t stride_01 = stride<0,1>(canonical_layout);
constexpr uint32_t stride_11 = stride<1,1>(canonical_layout);
desc.stride_byte_offset_ = (LAYOUT_TYPE == UMMA::LayoutType::SWIZZLE_NONE) ? stride_01 : stride_11;
desc.leading_byte_offset_ = (LAYOUT_TYPE == UMMA::LayoutType::SWIZZLE_NONE) ? stride_11 : stride_01;
} else
if constexpr (MajorMode == UMMA::Major::K)
{
/* In units of uint128_t, each UmmaDescriptor Major-K describes a canonical layout of the form
*
* LayoutType::INTERLEAVE : Swizzle<0,4,3> o smem_ptr o ((8,n),2):((1,SBO),LBO)
* LayoutType::B32 : Swizzle<1,4,3> o smem_ptr o ((8,n),2):((2,SBO),1)
* LayoutType::B64 : Swizzle<2,4,3> o smem_ptr o ((8,n),2):((4,SBO),1)
* LayoutType::B128 : Swizzle<3,4,3> o smem_ptr o ((8,n),2):((8,SBO),1)
* LayoutType::B128_BASE32B : Not applicable for Major-K
*/
static_assert(LAYOUT_TYPE != UMMA::LayoutType::SWIZZLE_128B_BASE32B, "SWIZZLE_128B_BASE32B is invalid for Major-K");
CUTE_STATIC_ASSERT_V(size<0>(u128_tensor) % Int<8>{} == Int<0>{}, // N|M size
"Not a canonical UMMA_K Layout: Expected MN-size multiple of 8.");
// Construct the canonical UMMA N Layout with shape ((8,n),(2,1))
Layout canonical_layout = logical_divide(layout(u128_tensor), Tile<Layout<_8,_1>,Layout<_2,_1>>{});
// Check profile of canonical
CUTE_STATIC_ASSERT_V(congruent(canonical_layout, Shape<Shape<_1,_1>,Shape<_1,_1>>{}), "Not a canonical UMMA_K Layout: Expected profile failure.");
// Check canonical mode strides
constexpr uint32_t stride_00 = stride<0,0>(canonical_layout);
constexpr uint32_t expected_stride_00 = SwizzleAtomMNSize;
static_assert(stride_00 == expected_stride_00, "Not a canonical UMMA_K Layout: Expected stride failure.");
constexpr uint32_t stride_10 = stride<1,0>(canonical_layout);
constexpr uint32_t expected_stride_10 = (LAYOUT_TYPE == UMMA::LayoutType::SWIZZLE_NONE) ? stride<1,0>(canonical_layout) : 1;
static_assert(stride_10 == expected_stride_10, "Not a canonical UMMA_K Layout: Expected stride failure.");
// stride dimension byte offset and leading dimension byte offset (4LSB not included == uint128_t units)
constexpr uint32_t stride_01 = stride<0,1>(canonical_layout);
desc.stride_byte_offset_ = stride_01;
desc.leading_byte_offset_ = stride_10;
} else {
static_assert(MajorMode != UMMA::Major::MN && MajorMode != UMMA::Major::K, "Unrecognized MajorMode!");
}
return desc;
}
///////////////////////////////////////////////////////////////////////////////
// Higher level UMMA Descriptor utilities
///////////////////////////////////////////////////////////////////////////////
struct DescriptorIterator
{
using reference = SmemDescriptor;
using element_type = SmemDescriptor;
using value_type = SmemDescriptor;
SmemDescriptor desc_;
// Dereference returns the UmmaDescriptor
CUTE_HOST_DEVICE constexpr
reference operator*() const { return desc_; }
// Advance and return a new UmmaDescriptor
template <class Index>
CUTE_HOST_DEVICE constexpr
reference operator[](Index const& i) const { return *(*this + i); }
// Return an advanced iterator
template <class Index>
CUTE_HOST_DEVICE constexpr
DescriptorIterator operator+(Index const& offset) const
{
// Use 32bit calculation rather than 64 bit calculation as we only update the part of desc
SmemDescriptor ret;
ret.lo = desc_.lo + uint32_t(offset);
ret.hi = desc_.hi;
return { ret };
}
};
template <class T>
CUTE_HOST_DEVICE constexpr
SmemDescriptor
raw_pointer_cast(DescriptorIterator const& ptr) {
return ptr.desc_;
}
CUTE_HOST_DEVICE void
print(DescriptorIterator const&) {
printf("UMMA::DescriptorIterator");
}
// Flag for smem descriptor allocation/creation
template <UMMA::Major>
struct smem_desc : DescriptorIterator {};
template <UMMA::Major>
struct sparse_smem_desc : DescriptorIterator {};
} // end namespace UMMA
// Customization point for creating a UMMA::smem_desc Tensor
template <UMMA::Major MajorMode>
struct MakeTensor<UMMA::smem_desc<MajorMode>>
{
template <class TEngine, class TLayout>
CUTE_HOST_DEVICE constexpr auto
operator()(Tensor<TEngine,TLayout> const& smem_tensor)
{
static_assert(is_smem<TEngine>::value, "Expected SMEM Tensor to construct a UMMA Desc Tensor");
return make_tensor(UMMA::DescriptorIterator{UMMA::make_umma_desc<MajorMode>(tensor<0>(smem_tensor))},
replace<0>(recast<uint128_t const>(smem_tensor).layout(), Layout<_1,_0>{}));
}
};
// Customization point for creating a UMMA::sparse_smem_desc Tensor
template <UMMA::Major MajorMode>
struct MakeTensor<UMMA::sparse_smem_desc<MajorMode>>
{
// Note that this is the exact same as UMMA::smem_desc above.
// Only the interface validates that we are passed a sparse_ptr, which is recast away to construct
// the smem desc tensor
template <class TEngine, class TLayout>
CUTE_HOST_DEVICE constexpr auto
operator()(Tensor<TEngine,TLayout> const& smem_tensor)
{
static_assert(is_smem<TEngine>::value, "Expected SMEM Tensor to construct a UMMA Desc Tensor");
static_assert(is_sparse<typename TEngine::value_type>::value, "Expected sparse value_type.");
static_assert(is_sparse_ptr<TEngine>::value, "Expected sparse iter.");
return make_tensor(UMMA::DescriptorIterator{UMMA::make_umma_desc<MajorMode>(tensor<0>(smem_tensor))},
replace<0>(recast<uint128_t const>(smem_tensor).layout(), Layout<_1,_0>{}));
}
};
// Special smem_desc_iter tensor entry for UTCCP copy.
template <class UtccpOp, class TEngine, class TLayout>
constexpr auto get_utccp_smem_desc_tensor(Tensor<TEngine, TLayout> const& smem_utccp_partitioned_tensor) {
using VecLayout = decltype(layout<0>(TLayout{}));
static_assert(VecLayout::rank == 2 && shape<1>(VecLayout{}) == 1, "Mismatched vec_mode tensor.");
static_assert(is_smem<TEngine>::value, "Expect vec_mode smem_tesnor.");
static_assert(is_static<VecLayout>::value, "Utccp copy tensor's vec_mode should be static.");
using value_type = typename TEngine::value_type;
using UtccpTaits = Copy_Traits<UtccpOp>;
// UtccpTaits::ValID: logical_bit_idx -> tmem_offset.
// We arrange the logical_bit_idx in order of (core_matrix_strided, core_matrix_leading, repeat(only in 64dplw01), broadcast).
// So we only need the first two modes for src smem_tensor.
auto utccp_core_matrix_shape = take<0,2>(upcast<sizeof_bits_v<value_type>>(typename UtccpTaits::ValID{}).shape());
// logical_bit_idx -> smem_addr
Layout vec_v_layout = flatten(layout<0>(VecLayout{}));
Layout utccp_core_matrix_layout = vec_v_layout.with_shape(utccp_core_matrix_shape);
Tensor utccp_core_matrix_tensor = group_modes<0,2>(make_tensor(smem_utccp_partitioned_tensor.data(), utccp_core_matrix_layout));
Tensor core_matrix_desc_tensor = make_tensor<UMMA::smem_desc<UMMA::Major::K>>(utccp_core_matrix_tensor);
return make_tensor(core_matrix_desc_tensor.data(), recast_layout<value_type, uint128_t>(smem_utccp_partitioned_tensor.layout()));
}
namespace UMMA {
enum class TmemAllocMode {
// Default allocation mode.
// If a TMEM Atom uses a half-subpartition (16DPs), then multiple atoms can be
// interleaved by using the top-half-subpartition and the bottom-half-subpartition.
// Full utilization of TMEM capacity.
Interleaved = 0,
// Prevents interleaving.
// If a TMEM Atom uses a half-subpartition (16DPs), then multiple atoms will not be
// interleaved.
// Required for DP-address equivalence in TMEM-A and TMEM-C allocations in UMMA_TS.
NonInterleaved = 1,
// Duplicates the TMEM allocation across subpartitions.
// E.g. UMMA_2SM_128xNx16_TS uses a "2x2 DP" TMEM Layout, but the TMEM allocation is
// actually doubled and the input data must be duplicated between the
// subpartitions [0,1]<->[2,3], i.e., each subpartition holds all columns
// of the A matrix needed for a single UMMA operation.
// For UMMA_2SM_128xNx16_TS, the distribution of the data is as follows.
// SM0:
// Subpart0 = A[0:32, 0:16], Subpart1 = A[32:64, 0:16],
// Subpart2 = A[A:32, 0:16], Subpart3 = A[32:64, 0:16]
// SM1:
// Subpart0 = A[64:96, 0:16], Subpart1 = A[96:128, 0:16],
// Subpart2 = A[64:96, 0:16], Subpart3 = A[96:128, 0:16]
Duplicated = 2,
// Duplicates the TMEM allocation across subpartitions for scale factor.
// Scale factor TMEM allocation for 4x1 data path
ScaleFactorDuplicated4by1 = 3,
// Scale factor TMEM allocation for 2x2 data path
ScaleFactorDuplicated2by2 = 4
};
struct tmem_frg_base {};
// The UMMA Traits below have custom fragment type flags for their tmem tensors.
// These flags specialize a MakeTensor customization point to correctly make the fragment that is desired.
template <class ValueType, class StorageType, int N_SM, UMMA::TmemAllocMode TmemAlloc = UMMA::TmemAllocMode::Interleaved>
struct tmem_frg : tmem_frg_base
{
static_assert(sizeof_bits_v<ValueType> <= sizeof_bits_v<StorageType>, "TMEM MMA allocations require StorageType big enough for ValueType.");
// UMMA TMEM Allocator
// Each UMMA expects a specific MxN layout of TMEM for accumulators
// and sometimes a specific MxK layout of TMEM for A-values.
// @tparam ValueType The value type of the TMEM Tensor to allocate.
// @tparam StorageType The storage type of the TMEM Tensor to allocate.
// "Sparse" allocations often allocate ValueType=half_t within StorageType=uint32_t.
// "Dense" allocations often allocate ValueType=half_t within StorageType=half_t.
// @tparam N_SM The number of SMs in this UMMA_XSM instruction.
// @tparam TmemAlloc UMMA-specific allocation modifier for special cases.
// Some UMMA instructions expect strange atoms or tilings of atoms.
// @param tmem_shape ((M_MMA_SM,N_MMA_SM),MMA_M,MMA_N,...)
// The post-MMA-partitioned shape of TMEM to allocate.
// Note for UMMA_2SM_128xNx16, that M_MMA_SM will be 64, for example.
template <class TmemShape>
CUTE_HOST_DEVICE constexpr static auto
make(TmemShape const& tmem_shape)
{
CUTE_STATIC_ASSERT_V(size(tmem_shape)*Int<int(sizeof_bits_v<StorageType>)>{} <= TMEM::MAX_CAPACITY_BITS{},
"Requesting more TMEM than is available.");
CUTE_STATIC_ASSERT_V(rank<0>(tmem_shape) == Int<2>{}, "Expected post-partitioned shape ((M_MMA,N_MMA),...).");
constexpr int R = decltype(rank(tmem_shape))::value;
constexpr int M_MMA = decltype(size<0,0>(tmem_shape))::value;
constexpr int N_MMA = decltype(size<0,1>(tmem_shape))::value;
// It's convenient to use "virtual tensor memory addressing"
// with DP_STRIDE=1, COL_STRIDE=128 to define the tmem_atom,
// then convert to "logical tensor memory addressing" on return.
using COL_ADDR = C<sizeof_bits<StorageType>::value / sizeof_bits<ValueType>::value>;
Layout tmem_restride = Layout<Shape < _128, _16384>,
Stride<TMEM::DP<ValueType>, COL_ADDR>>{};
static_assert(N_SM == 1 || N_SM == 2, "UMMA expects N_SM == 1 or N_SM == 2");
if constexpr (N_SM == 1)
{
static_assert(TmemAlloc == UMMA::TmemAllocMode::Interleaved || TmemAlloc == UMMA::TmemAllocMode::NonInterleaved,
"UMMA_1SM only accepts Interleaved or NonInterleaved");
static_assert(M_MMA == 64 || M_MMA == 128, "UMMA_1SM M-mode size should be 64 or 128.");
if constexpr (M_MMA == 64)
{
// Half subpartitions layout atom: (M,N) -> tmem_addr
Layout tmem_atom = Layout<Shape <Shape <_16, _4>, Int<N_MMA>>,
Stride<Stride< _1, _32>, _128>>{};
// tile_stride = 2 causes the tiling to "skip" the first tile in DPs
constexpr int tile_stride = TmemAlloc == UMMA::TmemAllocMode::Interleaved ? 1 : 2;
// This will tile in DPs first, then COLs
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape),
compact_col_major(take<1,R>(tmem_shape),Int<tile_stride>{})));
// Restride for the DP/COL addressing and return
return make_tensor(make_tmem_ptr<ValueType>(), composition(tmem_restride, tmem_logical_layout));
} else
if constexpr (M_MMA == 128)
{
// For M_MMA = 128, all datapaths are occupied. TmemAllocMode doesn't change the allocation.
// Full subpartitions layout atom: (M,N) -> tmem_addr
Layout tmem_atom = Layout<Shape <_128,Int<N_MMA>>,
Stride< _1, _128>>{};
// This will tile in DPs first, then COLs
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
// Restride for the DP/COL addressing and return
return make_tensor(make_tmem_ptr<ValueType>(), composition(tmem_restride, tmem_logical_layout));
}
} else
if constexpr (N_SM == 2)
{
static_assert(TmemAlloc == UMMA::TmemAllocMode::Interleaved || TmemAlloc == UMMA::TmemAllocMode::Duplicated,
"UMMA_2SM only accepts Interleaved or Duplicated");
static_assert(M_MMA == 32 || M_MMA == 64 || M_MMA == 128, "UMMA_2SM M-mode size should be 32 or 64 or 128.");
if constexpr (M_MMA == 32)
{
static_assert(TmemAlloc == UMMA::TmemAllocMode::Interleaved, "Only TmemAllocMode::Interleaved is supported for UMMA_2SM M_MMA=32");
// The "1x4" layout atom: (M,N) -> tmem_addr
Layout tmem_atom = Layout<Shape <_32,Shape <Int<N_MMA/4>, _4>>,
Stride< _1,Stride< _128,_32>>>{};
// This will tile in DPs first, then COLs
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
// Restride for the DP/COL addressing and return
return make_tensor(make_tmem_ptr<ValueType>(), composition(tmem_restride, tmem_logical_layout));
} else
if constexpr (M_MMA == 64 && TmemAlloc == UMMA::TmemAllocMode::Interleaved)
{
// The "2x2" layout atom: (M,N) -> tmem_addr
Layout tmem_atom = Layout<Shape <_64,Shape <Int<N_MMA/2>, _2>>,
Stride< _1,Stride< _128,_64>>>{};
// This will tile in DPs first, then COLs
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
// Restride for the DP/COL addressing and return
return make_tensor(make_tmem_ptr<ValueType>(), composition(tmem_restride, tmem_logical_layout));
} else
if constexpr (M_MMA == 64 && TmemAlloc == UMMA::TmemAllocMode::Duplicated)
{
// The "2x2" duplicated layout atom: (M,N) -> tmem_addr
Layout tmem_atom = Layout<Shape <_128,Int<N_MMA>>,
Stride< _1, _128>>{};
// This will tile in DPs first, then COLs
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
// Restride for the DP/COL addressing and return
return make_tensor(make_tmem_ptr<ValueType>(), composition(tmem_restride, tmem_logical_layout));
} else
if constexpr (M_MMA == 128)
{
// For M_MMA = 128, all datapaths are occupied. TmemAllocMode doesn't change the allocation.
// The "4x1" layout atom: (M,N) -> tmem_addr
Layout tmem_atom = Layout<Shape <_128,Int<N_MMA>>,
Stride< _1, _128>>{};
// This will tile in DPs first, then COLs
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
// Restride for the DP/COL addressing and return
return make_tensor(make_tmem_ptr<ValueType>(), composition(tmem_restride, tmem_logical_layout));
}
}
CUTE_GCC_UNREACHABLE;
}
};
// Convenient aliases for common cases in the UMMA::ElementXFrg below
template <class ValueType, class StorageType = uint32_t, UMMA::TmemAllocMode TmemAlloc = UMMA::TmemAllocMode::Interleaved>
using tmem_frg_1sm = tmem_frg<ValueType, StorageType, 1, TmemAlloc>;
template <class ValueType, class StorageType = uint32_t, UMMA::TmemAllocMode TmemAlloc = UMMA::TmemAllocMode::Interleaved>
using tmem_frg_2sm = tmem_frg<ValueType, StorageType, 2, TmemAlloc>;
// Make metadata TMEM fragments for sparse MMAs.
// Also note that the TMEM fragment addresses are assumed to be COL-4 aligned -- working with arch to remove this condition
template <class ValueType>
struct tmem_e_frg : tmem_frg_base
{
template <class TmemShape>
CUTE_HOST_DEVICE constexpr static auto
make(TmemShape const& tmem_shape)
{
CUTE_STATIC_ASSERT_V(rank<0>(tmem_shape) == Int<2>{}, "Expected post-partitioned shape ((M_MMA,N_MMA),...).");
constexpr int R = decltype(rank(tmem_shape))::value;
constexpr int M_MMA = decltype(size<0,0>(tmem_shape))::value;
constexpr int N_MMA = decltype(size<0,1>(tmem_shape))::value;
static_assert(M_MMA == 128, "Only 128 implemented right now.");
// It's convenient to use "virtual tensor memory addressing"
// with DP_STRIDE=1, COL_STRIDE=128 to define the tmem_atom,
// then convert to "logical tensor memory addressing" on return.
[[maybe_unused]] Layout tmem_restride = Layout<Shape < _128, _16384>,
Stride<TMEM::DP_b, _1>>{};
if constexpr (sizeof_bits<ValueType>::value == 32) // TF32: 128x16 atom
{
static_assert(N_MMA == 16);
Layout tmem_atom = Layout<Shape <Shape <_8, _2, _8>, Shape < _8,_2>>,
Stride<Stride<_1,_1024,_16>, Stride<_128,_8>>>{};
// Tile to MMA tiling
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
// Address transformations with upcast<2> for 2-bit base types
Layout tmem_layout = composition(upcast<2>(tmem_restride), tmem_logical_layout);
// Sparsity wrap, no sparse_ptr because tmem_ptr handles it's own subword addressing
return make_tensor(make_tmem_ptr<sparse_elem<4,uint8_t>>(), tmem_layout);
} else
if constexpr (sizeof_bits<ValueType>::value == 16) // FP16: 128x32 atom
{
static_assert(N_MMA == 32);
Layout tmem_atom = Layout<Shape <Shape <_8, _2, _8>, Shape < _16,_2>>,
Stride<Stride<_1,_2048,_16>, Stride<_128,_8>>>{};
// Tile to MMA tiling
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
// Address transformations
Layout tmem_layout = composition(tmem_restride, tmem_logical_layout);
// Sparsity wrap, no sparse_ptr because tmem_ptr handles it's own subword addressing
return make_tensor(make_tmem_ptr<sparse_elem<8,uint8_t>>(), tmem_layout);
} else
if constexpr (sizeof_bits<ValueType>::value == 8) // S8|Mix.F4/F6/F8: 128x64 atom
{
// For Mix 8bit f4/f6/f8, will pass in ValueType = uint8_t
static_assert(N_MMA == 64);
Layout tmem_atom = Layout<Shape <_128, _64>,
Stride< _1,_128>>{};
// Tile to MMA tiling
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
// Address transformations
Layout tmem_layout = composition(tmem_restride, tmem_logical_layout);
// Sparsity wrap, no sparse_ptr because tmem_ptr handles it's own subword addressing
return make_tensor(make_tmem_ptr<sparse_elem<8,uint8_t>>(), tmem_layout);
}
if constexpr (sizeof_bits<ValueType>::value == 4) // F4: 128x128 atom
{
// For F4, will pass in ValueType = fp4
Layout tmem_restride1 = Layout<Shape < _128, Int<32768>>,
Stride<cute::C<int32_t(1) << 22>, _1>>{};
// F4 has roughly same TMEM layout as Mix8bit.F4/F6/F8, the only difference is that K is multiplied by two
static_assert(N_MMA == 128);
Layout tmem_atom = Layout<Shape <_128, _128>,
Stride< _1, _128>>{};
// Tile to MMA tiling
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
// Address transformations
Layout tmem_layout = composition(tmem_restride1, tmem_logical_layout);
// Sparsity wrap, no sparse_ptr because tmem_ptr handles it's own subword addressing
return make_tensor(make_tmem_ptr<sparse_elem<16,uint8_t>>(), tmem_layout);
}
CUTE_GCC_UNREACHABLE;
}
};
template <class ValueType>
struct tmem_e_frg_ws : tmem_frg_base
{
template <class TmemShape>
CUTE_HOST_DEVICE constexpr static auto
make(TmemShape const& tmem_shape)
{
CUTE_STATIC_ASSERT_V(rank<0>(tmem_shape) == Int<2>{}, "Expected post-partitioned shape ((M_MMA,N_MMA),...).");
constexpr int R = decltype(rank(tmem_shape))::value;
constexpr int M_MMA = decltype(size<0,0>(tmem_shape))::value;
constexpr int N_MMA = decltype(size<0,1>(tmem_shape))::value;
static_assert(M_MMA == 128 || M_MMA == 64 || M_MMA == 32, "Weight stationary UMMA_1SM M-mode size should be 32 or 64 or 128.");
// It's convenient to use "virtual tensor memory addressing"
// with DP_STRIDE=1, COL_STRIDE=128 to define the tmem_atom,
// then convert to "logical tensor memory addressing" on return.
Layout tmem_restride = Layout<Shape < _128, _16384>,
Stride<TMEM::DP_b, _1>>{};
if constexpr (sizeof_bits<ValueType>::value == 32) // TF32
{
// MMA_M x MMA_K: 128x16 atom / 64x16 atom / 32x16 atom
static_assert(N_MMA == 16);
if constexpr (M_MMA == 128) {
Layout tmem_atom = Layout<Shape <Shape <_8, _2, _8>, Shape < _8,_2>>,
Stride<Stride<_1,_1024,_16>, Stride<_128,_8>>>{};
// Tile to MMA tiling
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
// Address transformations with upcast<2> for 2-bit base types
Layout tmem_layout = composition(upcast<2>(tmem_restride), tmem_logical_layout);
// Sparsity wrap, no sparse_ptr because tmem_ptr handles it's own subword addressing
return make_tensor(make_tmem_ptr<sparse_elem<4,uint8_t>>(), tmem_layout);
}
else if constexpr (M_MMA == 64) {
Layout tmem_atom = Layout<Shape <Shape <_8, _2, _4>, Shape < _8,_2>, _2>,
Stride<Stride<_1,_1024,_16>, Stride<_128,_8>,_64>>{};
// Tile to MMA tiling
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
// Address transformations with upcast<2> for 2-bit base types
Layout tmem_layout = composition(upcast<2>(tmem_restride), tmem_logical_layout);
// Sparsity wrap, no sparse_ptr because tmem_ptr handles its own subword addressing
return make_tensor(make_tmem_ptr<sparse_elem<4,uint8_t>>(), tmem_layout);
}
else if constexpr (M_MMA == 32) {
Layout tmem_atom = Layout<Shape <Shape <_8, _2, _2>, Shape < _8,_2>, _4>,
Stride<Stride<_1,_1024,_16>, Stride<_128,_8>,_32>>{};
// Tile to MMA tiling
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
// Address transformations with upcast<2> for 2-bit base types
Layout tmem_layout = composition(upcast<2>(tmem_restride), tmem_logical_layout);
// Sparsity wrap, no sparse_ptr because tmem_ptr handles its own subword addressing
return make_tensor(make_tmem_ptr<sparse_elem<4,uint8_t>>(), tmem_layout);
}
else {
static_assert(dependent_false<TmemShape>, "Invalid M_MMA value");
}
}
else if constexpr (sizeof_bits<ValueType>::value == 16) // FP16
{
// MMA_M x MMA_K: 128x32 atom / 64x32 atom / 32x32 atom
static_assert(N_MMA == 32);
if constexpr (M_MMA == 128) {
Layout tmem_atom = Layout<Shape <Shape <_8, _2, _8>, Shape < _16,_2>>,
Stride<Stride<_1,_2048,_16>, Stride<_128,_8>>>{};
// Tile to MMA tiling
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
// Address transformations
Layout tmem_layout = composition(tmem_restride, tmem_logical_layout);
// Sparsity wrap, no sparse_ptr because tmem_ptr handles it's own subword addressing
return make_tensor(make_tmem_ptr<sparse_elem<8,uint8_t>>(), tmem_layout);
}
else if constexpr (M_MMA == 64) {
Layout tmem_atom = Layout<Shape <Shape <_8, _2, _4>, Shape < _16,_2>, _2>,
Stride<Stride<_1,_2048,_16>, Stride<_128,_8>,_64>>{};
// Tile to MMA tiling
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
// Address transformations
Layout tmem_layout = composition(tmem_restride, tmem_logical_layout);
// Sparsity wrap, no sparse_ptr because tmem_ptr handles it's own subword addressing
return make_tensor(make_tmem_ptr<sparse_elem<8,uint8_t>>(), tmem_layout);
}
else if constexpr (M_MMA == 32) {
Layout tmem_atom = Layout<Shape <Shape <_8, _2, _2>, Shape < _16,_2>, _4>,
Stride<Stride<_1,_2048,_16>, Stride<_128,_8>,_32>>{};
// Tile to MMA tiling
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
// Address transformations
Layout tmem_layout = composition(tmem_restride, tmem_logical_layout);
// Sparsity wrap, no sparse_ptr because tmem_ptr handles it's own subword addressing
return make_tensor(make_tmem_ptr<sparse_elem<8,uint8_t>>(), tmem_layout);
}
else {
static_assert(dependent_false<TmemShape>, "Invalid M_MMA value");
}
}
else if constexpr (sizeof_bits<ValueType>::value == 8) // I8|F8
{
// MMA_M x MMA_K: 128x64 atom / 64x64 atom / 32x64 atom
static_assert(N_MMA == 64);
if constexpr (M_MMA == 128) {
Layout tmem_atom = Layout<Shape <_128, _64>,
Stride< _1,_128>>{};
// Tile to MMA tiling
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
// Address transformations
Layout tmem_layout = composition(tmem_restride, tmem_logical_layout);
// Sparsity wrap, no sparse_ptr because tmem_ptr handles it's own subword addressing
return make_tensor(make_tmem_ptr<sparse_elem<8,uint8_t>>(), tmem_layout);
}
else if constexpr (M_MMA == 64) {
Layout tmem_atom = Layout<Shape <_64, Shape < _64, _2>>,
Stride< _1, Stride<_128, _64>>>{};
// Tile to MMA tiling
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
// Address transformations
Layout tmem_layout = composition(tmem_restride, tmem_logical_layout);
// Sparsity wrap, no sparse_ptr because tmem_ptr handles it's own subword addressing
return make_tensor(make_tmem_ptr<sparse_elem<8,uint8_t>>(), tmem_layout);
}
else if constexpr (M_MMA == 32) {
Layout tmem_atom = Layout<Shape <_32, Shape < _64, _4>>,
Stride< _1, Stride<_128, _32>>>{};
// Tile to MMA tiling
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
// Address transformations
Layout tmem_layout = composition(tmem_restride, tmem_logical_layout);
// Sparsity wrap, no sparse_ptr because tmem_ptr handles it's own subword addressing
return make_tensor(make_tmem_ptr<sparse_elem<8,uint8_t>>(), tmem_layout);
}
else {
static_assert(dependent_false<TmemShape>, "Invalid M_MMA value");
}
}
else {
static_assert(dependent_false<TmemShape>, "Invalid ValueType");
}
CUTE_GCC_UNREACHABLE;
}
};
template <class ValueType, int SFVecSize, int N_SM, bool Is_SFA,
UMMA::TmemAllocMode TmemAlloc = UMMA::TmemAllocMode::ScaleFactorDuplicated4by1>
struct tmem_sf_frg: tmem_frg_base
{
// UMMA TMEM Allocator for Scale Factor A for Mxf4Nvf4 and Mxf8f6f4 instructions
// We expect a tensor that has the same layout as A matrix
// @tparam ValueType: data type of scaling factor
// Note that the StorageType is the same as ValueType, i.e., we always use a compact allocation
// @tparam SFVecSize: The number of values that is scaled by a single scaling factor.
// Valid values are (16, 32)
// @tparam N_SM: Number of SMs in UMMA instruction
// @param tmem_shape: An MMA partitioned shape where first mode encodes, A layout of the MMA instruction.
// Note that the shape doesn't match the actual allocation. size<0,1>(tmem_shape) will give us the number of
// elements in K-mode of MMA rather than the number of scaling factors.
template <class TmemShape>
CUTE_HOST_DEVICE constexpr static auto
make(TmemShape const& tmem_shape)
{
CUTE_STATIC_ASSERT_V(rank<0>(tmem_shape) == Int<2>{}, "Expected post-partitioned shape ((M_MMA,N_MMA),...).");
constexpr int MMA_MN = decltype(size<0,0>(tmem_shape))::value;
constexpr int MMA_VS = decltype(size<0,1,0>(tmem_shape))::value;
constexpr int MMA_NSF = decltype(size<0,1,1>(tmem_shape))::value;
constexpr int R_MMA_K = decltype(rank(get<0,1>(tmem_shape)))::value;
constexpr int R = decltype(rank(tmem_shape))::value;
// We expect an MMA-SF partitioned tensor
// ((MMA_MN, (VecSize, NSF)), num_MMA_MN, num_MMA_K, ...)
// where VecSize*NSF = MMA_K
static_assert(R >= 3, "Expected an MMA partitioned tensor"); // ((MMA), num_MMA_MN, num_MMA_K, ...)
static_assert(R_MMA_K == 2, "Expected an MMA-SF partitioned tensor"); // (VecSize, NSF)
using REP = _4; // Replication factor. Data is always replicated across subpartitions
constexpr int SUBPART_DPs = 32; // Number of DPs in a subpartition
using COL_ADDR = C<sizeof_bits<ValueType>::value / sizeof_bits<ValueType>::value>;
Layout tmem_restride = Layout<Shape < _128, _16384>,
Stride<TMEM::DP<ValueType>, COL_ADDR>>{};
if constexpr (Is_SFA || (!Is_SFA && TmemAlloc == UMMA::TmemAllocMode::ScaleFactorDuplicated4by1)) {
// SFA, 2x2 and 4x1 data path
// SFB, 4x1 data path
auto tmem_atom = Layout < Shape< Shape< Shape<Int<SUBPART_DPs>, Int<MMA_MN/SUBPART_DPs>>, REP>, Shape<Int<MMA_VS>, Int<MMA_NSF>>>,
Stride<Stride<Stride< _1, _512>, _32>, Stride< _0, _128>>>{};
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
auto final_tmem_layout = composition(tmem_restride, tmem_logical_layout);
return make_tensor(make_tmem_ptr<ValueType>(), final_tmem_layout);
}
else {
// SFB, 2x2 datapath
static_assert(!Is_SFA and TmemAlloc == UMMA::TmemAllocMode::ScaleFactorDuplicated2by2);
static_assert(N_SM == 2, "Should be 2x2 Datapath");
// 2x2 Datapth
auto tmem_atom = Layout < Shape< Shape< Shape<Int<SUBPART_DPs>, Int<MMA_MN/2/SUBPART_DPs>>, _2, _2>, Shape<Int<MMA_VS>, Int<MMA_NSF>>>,
Stride<Stride<Stride< _1 , _512>, _64, _32>, Stride< _0, _128>>>{};
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
auto final_tmem_layout = composition(tmem_restride, tmem_logical_layout);
return make_tensor(make_tmem_ptr<ValueType>(), final_tmem_layout);
}
}
};
// Make C/D Tmem fragment for weight-stationary MMAs
template <class ValueType, class StorageType, int N_SM>
struct tmem_frg_ws : tmem_frg_base
{
static_assert(sizeof_bits_v<ValueType> <= sizeof_bits_v<StorageType>, "TMEM MMA allocations require StorageType big enough for ValueType.");
// UMMA TMEM Allocator
// Each UMMA expects a specific MxN layout of TMEM for accumulators
// and sometimes a specific MxK layout of TMEM for A-values.
// @tparam ValueType The value type of the TMEM Tensor to allocate.
// @tparam StorageType The storage type of the TMEM Tensor to allocate.
// "Sparse" allocations often allocate ValueType=half_t within StorageType=uint32_t.
// "Dense" allocations often allocate ValueType=half_t within StorageType=half_t.
// @tparam N_SM The number of SMs in this UMMA_XSM instruction.
// @tparam TmemAlloc UMMA-specific allocation modifier for special cases.
// Some UMMA instructions expect strange atoms or tilings of atoms.
// @param tmem_shape ((M_MMA_SM,N_MMA_SM),MMA_M,MMA_N,...)
// The post-MMA-partitioned shape of TMEM to allocate.
// Note for UMMA_2SM_128xNx16, that M_MMA_SM will be 64, for example.
template <class TmemShape>
CUTE_HOST_DEVICE constexpr static auto
make(TmemShape const& tmem_shape)
{
CUTE_STATIC_ASSERT_V(size(tmem_shape)*Int<int(sizeof_bits_v<StorageType>)>{} <= TMEM::MAX_CAPACITY_BITS{},
"Requesting more TMEM than is available.");
CUTE_STATIC_ASSERT_V(rank<0>(tmem_shape) == Int<2>{}, "Expected post-partitioned shape ((M_MMA,N_MMA),...).");
constexpr int R = decltype(rank(tmem_shape))::value;
constexpr int M_MMA = decltype(size<0,0>(tmem_shape))::value;
constexpr int N_MMA = decltype(size<0,1>(tmem_shape))::value;
// It's convenient to use "virtual tensor memory addressing"
// with DP_STRIDE=1, COL_STRIDE=128 to define the tmem_atom,
// then convert to "logical tensor memory addressing" on return.
using COL_ADDR = C<sizeof_bits<StorageType>::value / sizeof_bits<ValueType>::value>;
Layout tmem_restride = Layout<Shape < _128, _16384>,
Stride<TMEM::DP<ValueType>, COL_ADDR>>{};
static_assert(N_SM == 1, "UMMA.WS expects N_SM == 1");
static_assert(M_MMA == 32 || M_MMA == 64 || M_MMA == 128,
"Weight stationary UMMA_1SM M-mode size should be 32 or 64 or 128.");
static_assert(N_MMA == 64 || N_MMA == 128 || N_MMA == 256,
"Dense weight stationary UMMA_1SM N-mode size should be 64 or 128 or 256.");
// Weight Stationary MMA config
if constexpr (M_MMA == 32)
{
// 1x4 datapath
Layout tmem_atom = Layout<Shape <_32, Shape<Int<N_MMA/4>, _4>>,
Stride< _1, Stride< _128,_32>>
>{};
constexpr int tile_stride = 1;
// This will tile in DPs first, then COLs
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape),
compact_col_major(take<1,R>(tmem_shape), Int<tile_stride>{})));
// Restride for the DP/COL addressing and return
return make_tensor(make_tmem_ptr<ValueType>(), composition(tmem_restride, tmem_logical_layout));
} else
if constexpr (M_MMA == 64)
{
// 2x2 datapath
Layout tmem_atom = Layout<Shape <_64, Shape<Int<N_MMA/2>, _2>>,
Stride< _1, Stride< _128,_64>>
>{};
constexpr int tile_stride = 1;
// This will tile in DPs first, then COLs
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape),
compact_col_major(take<1,R>(tmem_shape), Int<tile_stride>{})));
// Restride for the DP/COL addressing and return
return make_tensor(make_tmem_ptr<ValueType>(), composition(tmem_restride, tmem_logical_layout));
} else
if constexpr (M_MMA == 128)
{
// For M_MMA = 128, all datapaths are occupied. TmemAllocMode doesn't change the allocation.
// Full subpartitions layout atom: (M,N) -> tmem_addr
Layout tmem_atom = Layout<Shape <_128,Int<N_MMA>>,
Stride< _1, _128>>{};
// This will tile in DPs first, then COLs
Layout tmem_logical_layout = tiled_product(tmem_atom, make_layout(take<1,R>(tmem_shape)));
// Restride for the DP/COL addressing and return
return make_tensor(make_tmem_ptr<ValueType>(), composition(tmem_restride, tmem_logical_layout));
}
CUTE_GCC_UNREACHABLE;
}
};
// Convenient aliases for common cases in the UMMA::ElementXFrg below
template <class ValueType, class StorageType = uint32_t>
using tmem_frg_ws_1sm = tmem_frg_ws<ValueType, StorageType, 1>;
} // end namespace UMMA
// Customization point for creating a UMMA::tmem_frg Tensor
template <class ValueType, class StorageType, int N_SM, UMMA::TmemAllocMode TmemAlloc>
struct MakeTensor<UMMA::tmem_frg<ValueType, StorageType, N_SM, TmemAlloc>>
{
template <class Shape>
CUTE_HOST_DEVICE constexpr auto
operator()(Shape const& tmem_shape) {
return UMMA::tmem_frg<ValueType, StorageType, N_SM, TmemAlloc>::make(shape(tmem_shape));
}
};
template <class ValueType, class StorageType, int N_SM>
struct MakeTensor<UMMA::tmem_frg_ws<ValueType, StorageType, N_SM>>
{
template <class Shape>
CUTE_HOST_DEVICE constexpr auto
operator()(Shape const& tmem_shape) {
return UMMA::tmem_frg_ws<ValueType, StorageType, N_SM>::make(shape(tmem_shape));
}
};
// Customization point for creating a UMMA::tmem_frg Tensor
template <class ValueType>
struct MakeTensor<UMMA::tmem_e_frg<ValueType>>
{
template <class Shape>
CUTE_HOST_DEVICE constexpr auto
operator()(Shape const& tmem_shape) {
return UMMA::tmem_e_frg<ValueType>::make(shape(tmem_shape));
}
};
template <class ValueType>
struct MakeTensor<UMMA::tmem_e_frg_ws<ValueType>>
{
template <class Shape>
CUTE_HOST_DEVICE constexpr auto
operator()(Shape const& tmem_shape) {
return UMMA::tmem_e_frg_ws<ValueType>::make(shape(tmem_shape));