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kernel_forward.h
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kernel_forward.h
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/***************************************************************************************************
* Copyright (c) 2017 - 2023 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 holdvr 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
#ifdef HAS_PYTORCH
#include <ATen/cuda/CUDAGeneratorImpl.h>
#include <ATen/cuda/CUDAGraphsUtils.cuh>
#endif
#include <curand_kernel.h>
#include <cmath>
#include <vector>
#include "cutlass/bfloat16.h"
#include "cutlass/fast_math.h"
#include "cutlass/gemm/gemm.h"
#include "cutlass/layout/matrix.h"
#include "cutlass/layout/vector.h"
#include "cutlass/matrix.h"
#include "cutlass/numeric_types.h"
#include "cutlass/tensor_ref.h"
#include "cutlass/epilogue/threadblock/default_epilogue_simt.h"
#include "cutlass/epilogue/threadblock/default_epilogue_tensor_op.h"
#include "cutlass/epilogue/threadblock/default_epilogue_volta_tensor_op.h"
#include "cutlass/gemm/device/default_gemm_configuration.h"
#include "cutlass/gemm/kernel/default_gemm.h"
#include "cutlass/gemm/threadblock/default_mma.h"
#include "cutlass/gemm/threadblock/default_mma_core_simt.h"
#include "cutlass/gemm/threadblock/default_mma_core_sm70.h"
#include "cutlass/gemm/threadblock/default_mma_core_sm75.h"
#include "cutlass/gemm/threadblock/default_mma_core_sm80.h"
#include "cutlass/gemm/threadblock/threadblock_swizzle.h"
#include "cutlass/matrix_shape.h"
#include "cutlass/platform/platform.h"
#include "cutlass/transform/threadblock/predicated_tile_iterator.h"
#include "debug_utils.h"
#include "epilogue/epilogue_pipelined.h"
#include "epilogue/epilogue_rescale_output.h"
#include "gemm/custom_mma.h"
#include "gemm/find_default_mma.h"
#include "gemm/mma_from_smem.h"
#include "gemm_kernel_utils.h"
#include "transform/tile_smem_loader.h"
#include <inttypes.h>
using namespace gemm_kernel_utils;
namespace {
template <typename scalar_t, typename Arch>
constexpr int getWarpsPerSmFw() {
return (
Arch::kMinComputeCapability >= 80 &&
!cutlass::platform::is_same<scalar_t, float>::value
? 16
: 12);
}
static CUTLASS_DEVICE float atomicMaxFloat(float* addr, float value) {
// source: https://stackoverflow.com/a/51549250
return (value >= 0)
? __int_as_float(atomicMax((int*)addr, __float_as_int(value)))
: __uint_as_float(atomicMin((unsigned int*)addr, __float_as_uint(value)));
}
} // namespace
// If ToBatchHookType_ is supplied other than this default (which is
// never the case in the xformers library) then the user is
// defining the logic which each block uses to find its data to work on,
// with the advance_to_batch function with the following signature.
// It should return false if there is no work to do for this block.
// In general this will not work with saving for backward due to fixed layout
// for logsumexp and incompatible rngs for dropout, so is likely only useful for
// custom inference.
struct DefaultToBatchHook {
template <typename Params>
CUTLASS_DEVICE static bool advance_to_batch(
Params&,
int64_t& /* q_start */,
int64_t& /* k_start */) {
return true;
}
};
template <
// The datatype of Q/K/V
typename scalar_t_,
// Architecture we are targeting (eg `cutlass::arch::Sm80`)
typename ArchTag,
// If Q/K/V are correctly aligned in memory and we can run a fast kernel
bool isAligned_,
int kQueriesPerBlock_,
int kKeysPerBlock_,
// upperbound on `max(value.shape[-1], query.shape[-1])`
int kMaxK_ = (int)cutlass::platform::numeric_limits<uint32_t>::max(),
// This is quite slower on V100 for some reason
// Set to false if you know at compile-time you will never need dropout
bool kSupportsDropout_ = true,
bool kSupportsBias_ = true,
typename ToBatchHookType_ = DefaultToBatchHook>
struct AttentionKernel {
enum CustomMaskType {
NoCustomMask = 0,
CausalFromTopLeft = 1,
CausalFromBottomRight = 2,
NumCustomMaskTypes,
};
using scalar_t = scalar_t_;
using accum_t = float;
using lse_scalar_t = float;
using output_t = scalar_t;
// Accumulator between 2 iterations
// Using `accum_t` improves perf on f16 at the cost of
// numerical errors
using output_accum_t = accum_t;
static constexpr bool kSupportsDropout = kSupportsDropout_;
static constexpr bool kSupportsBias = kSupportsBias_;
static constexpr int kKeysPerBlock = kKeysPerBlock_;
static constexpr int kQueriesPerBlock = kQueriesPerBlock_;
static constexpr int kMaxK = kMaxK_;
static constexpr bool kIsAligned = isAligned_;
static constexpr bool kSingleValueIteration = kMaxK <= kKeysPerBlock;
static constexpr int32_t kAlignLSE = 32; // block size of backward
static constexpr bool kIsHalf = cutlass::sizeof_bits<scalar_t>::value == 16;
static constexpr bool kPreloadV =
ArchTag::kMinComputeCapability >= 80 && kIsHalf;
static constexpr bool kKeepOutputInRF = kSingleValueIteration;
static constexpr bool kNeedsOutputAccumulatorBuffer = !kKeepOutputInRF &&
!cutlass::platform::is_same<output_accum_t, output_t>::value;
static_assert(kQueriesPerBlock % 32 == 0, "");
static_assert(kKeysPerBlock % 32 == 0, "");
static constexpr int kNumWarpsPerBlock =
kQueriesPerBlock * kKeysPerBlock / (32 * 32);
static constexpr int kWarpSize = 32;
// Launch bounds
static constexpr int kNumThreads = kWarpSize * kNumWarpsPerBlock;
static constexpr int kMinBlocksPerSm =
getWarpsPerSmFw<scalar_t, ArchTag>() / kNumWarpsPerBlock;
struct Params {
// Input tensors
scalar_t* query_ptr = nullptr; // [num_queries, num_heads, head_dim]
scalar_t* key_ptr = nullptr; // [num_keys, num_heads, head_dim]
scalar_t* value_ptr = nullptr; // [num_keys, num_heads, head_dim_value]
scalar_t* attn_bias_ptr = nullptr; // [num_heads, num_queries, num_keys]
int32_t* seqstart_q_ptr = nullptr;
int32_t* seqstart_k_ptr = nullptr;
int32_t* seqlen_k_ptr = nullptr;
uint32_t causal_diagonal_offset = 0;
// Output tensors
output_t* output_ptr = nullptr; // [num_queries, num_heads, head_dim_value]
// [num_queries, num_heads, head_dim_value]
output_accum_t* output_accum_ptr = nullptr;
// [num_heads, num_queries] - can be null
lse_scalar_t* logsumexp_ptr = nullptr;
// Scale
accum_t scale = 0.0;
// Dimensions/strides
int32_t head_dim = 0;
int32_t head_dim_value = 0;
int32_t num_queries = 0;
int32_t num_keys = 0;
int32_t num_keys_absolute = 0;
uint8_t custom_mask_type = NoCustomMask;
int32_t q_strideM = 0;
int32_t k_strideM = 0;
int32_t v_strideM = 0;
int32_t bias_strideM = 0;
int32_t o_strideM = 0;
// Everything below is only used in `advance_to_block`
// and shouldn't use registers
int32_t q_strideH = 0;
int32_t k_strideH = 0;
int32_t v_strideH = 0;
int64_t bias_strideH = 0;
int64_t q_strideB = 0;
int64_t k_strideB = 0;
int64_t v_strideB = 0;
int64_t bias_strideB = 0;
int32_t num_batches = 0;
int32_t num_heads = 0;
// dropout
bool use_dropout = false;
unsigned long long dropout_batch_head_rng_offset = 0;
float dropout_prob = 0.0f;
#ifdef HAS_PYTORCH
at::PhiloxCudaState rng_engine_inputs = at::PhiloxCudaState(0, 0);
#endif
// Moves pointers to what we should process
// Returns "false" if there is no work to do
CUTLASS_DEVICE bool advance_to_block() {
auto batch_id = blockIdx.z;
auto head_id = blockIdx.y;
auto query_start = blockIdx.x * kQueriesPerBlock;
auto lse_dim = ceil_div((int32_t)num_queries, kAlignLSE) * kAlignLSE;
if (kSupportsDropout) {
dropout_batch_head_rng_offset =
batch_id * num_heads * num_queries * num_keys +
head_id * num_queries * num_keys;
}
int64_t q_start = 0, k_start = 0;
// Advance to current batch - in case of different sequence lengths
constexpr bool kToBatchHook =
!cutlass::platform::is_same<ToBatchHookType_, DefaultToBatchHook>::
value;
if (kToBatchHook) {
// Call out to a custom implementation.
if (!ToBatchHookType_::advance_to_batch(*this, q_start, k_start)) {
return false;
}
} else if (seqstart_q_ptr != nullptr) {
assert(seqstart_k_ptr != nullptr);
seqstart_q_ptr += batch_id;
q_start = seqstart_q_ptr[0];
int64_t q_next_start = seqstart_q_ptr[1];
int64_t k_end;
seqstart_k_ptr += batch_id;
if (seqlen_k_ptr) {
k_start = seqstart_k_ptr[0];
k_end = k_start + seqlen_k_ptr[batch_id];
} else {
k_start = seqstart_k_ptr[0];
k_end = seqstart_k_ptr[1];
}
num_queries = q_next_start - q_start;
num_keys = k_end - k_start;
if (query_start >= num_queries) {
return false;
}
} else {
query_ptr += batch_id * q_strideB;
key_ptr += batch_id * k_strideB;
value_ptr += batch_id * v_strideB;
output_ptr += int64_t(batch_id * num_queries) * o_strideM;
if (output_accum_ptr != nullptr) {
output_accum_ptr +=
int64_t(batch_id * num_queries) * (head_dim_value * num_heads);
}
q_start = 0;
k_start = 0;
}
// Advance to the current batch / head / query_start
query_ptr += (q_start + query_start) * q_strideM + head_id * q_strideH;
key_ptr += k_start * k_strideM + head_id * k_strideH;
value_ptr += k_start * v_strideM + head_id * v_strideH;
output_ptr +=
int64_t(q_start + query_start) * o_strideM + head_id * head_dim_value;
if (kSupportsBias && attn_bias_ptr != nullptr) {
attn_bias_ptr += (batch_id * bias_strideB) + (head_id * bias_strideH);
}
if (output_accum_ptr != nullptr) {
output_accum_ptr +=
int64_t(q_start + query_start) * (head_dim_value * num_heads) +
head_id * head_dim_value;
} else {
// Accumulate directly in the destination buffer (eg for f32)
output_accum_ptr = (accum_t*)output_ptr;
}
if (logsumexp_ptr != nullptr) {
// lse[batch_id, head_id, query_start]
logsumexp_ptr +=
batch_id * lse_dim * num_heads + head_id * lse_dim + query_start;
}
// Custom masking
if (custom_mask_type == CausalFromBottomRight) {
causal_diagonal_offset = num_keys - num_queries;
}
// We use num_keys_absolute to index into the rng_state
// We need this index to match between forward and backwards
num_keys_absolute = num_keys;
if (custom_mask_type == CausalFromTopLeft ||
custom_mask_type == CausalFromBottomRight) {
// the bottom row of the current block is query_start + kQueriesPerBlock
// the last active key is then query_start + causal_diagonal_offset +
// kQueriesPerBlock so num_keys is the min between actual num_keys and
// this to avoid extra computations
num_keys = cutlass::fast_min(
int32_t(query_start + causal_diagonal_offset + kQueriesPerBlock),
num_keys);
}
num_queries -= query_start;
num_batches = 0; // no longer used after
// If num_queries == 1, and there is only one key head we're wasting
// 15/16th of tensor core compute In that case :
// - we only launch kernels for head_id % kQueriesPerBlock == 0
// - we iterate over heads instead of queries (strideM = strideH)
if (num_queries == 1 && k_strideH == 0 && v_strideH == 0) {
if (head_id % kQueriesPerBlock != 0)
return false;
q_strideM = q_strideH;
num_queries = num_heads;
num_heads = 1; // unused but here for intent
// remove causal since n_query = 1
// otherwise, offset would change with head !
custom_mask_type = NoCustomMask;
o_strideM = head_dim_value;
}
// Make sure the compiler knows these variables are the same on all
// the threads of the warp.
// Only worth doing if they could have been modified above.
query_ptr = warp_uniform(query_ptr);
key_ptr = warp_uniform(key_ptr);
value_ptr = warp_uniform(value_ptr);
if (kSupportsBias) {
attn_bias_ptr = warp_uniform(attn_bias_ptr);
}
output_ptr = warp_uniform(output_ptr);
output_accum_ptr = warp_uniform(output_accum_ptr);
logsumexp_ptr = warp_uniform(logsumexp_ptr);
num_queries = warp_uniform(num_queries);
num_keys = warp_uniform(num_keys);
num_heads = warp_uniform(num_heads);
o_strideM = warp_uniform(o_strideM);
custom_mask_type = warp_uniform(custom_mask_type);
return true;
}
__host__ dim3 getBlocksGrid() const {
return dim3(
ceil_div(num_queries, (int32_t)kQueriesPerBlock),
num_heads,
num_batches);
}
__host__ dim3 getThreadsGrid() const {
return dim3(kWarpSize, kNumWarpsPerBlock, 1);
}
};
struct MM0 {
/*
In this first matmul, we compute a block of `Q @ K.T`.
While the calculation result is still hot in registers, we update
`mi`, `m_prime`, `s_prime` in shared-memory, and then store this value
into a shared-memory ("AccumulatorSharedStorage") that is used later as
operand A for the second matmul (see MM1)
*/
using GemmType = DefaultGemmType<ArchTag, scalar_t>;
using OpClass = typename GemmType::OpClass;
using DefaultConfig =
typename cutlass::gemm::device::DefaultGemmConfiguration<
OpClass,
ArchTag,
scalar_t,
scalar_t,
scalar_t, // ElementC
accum_t // ElementAccumulator
>;
static constexpr int kAlignmentA =
kIsAligned ? DefaultConfig::kAlignmentA : GemmType::kMinimumAlignment;
static constexpr int kAlignmentB =
kIsAligned ? DefaultConfig::kAlignmentB : GemmType::kMinimumAlignment;
using ThreadblockShape = cutlass::gemm::
GemmShape<kQueriesPerBlock, kKeysPerBlock, GemmType::ThreadK>;
using WarpShape = cutlass::gemm::GemmShape<32, 32, GemmType::WarpK>;
using DefaultMma = typename cutlass::gemm::threadblock::FindDefaultMma<
scalar_t, // ElementA,
cutlass::layout::RowMajor, // LayoutA,
kAlignmentA,
scalar_t, // ElementB,
cutlass::layout::ColumnMajor, // LayoutB,
kAlignmentB,
accum_t,
cutlass::layout::RowMajor, // LayoutC,
OpClass,
ArchTag, // ArchTag
ThreadblockShape, // ThreadblockShape
WarpShape, // WarpShape
typename GemmType::InstructionShape, // InstructionShape
ArchTag::kMinComputeCapability >= 80 && kIsHalf
? 4
: DefaultConfig::kStages,
typename GemmType::Operator // Operator
>::DefaultMma;
using MmaCore = typename DefaultMma::MmaCore;
using IteratorA = typename DefaultMma::IteratorA;
using IteratorB = typename DefaultMma::IteratorB;
using DefaultThreadblockMma = typename DefaultMma::ThreadblockMma;
using Mma = typename cutlass::platform::conditional<
kSingleValueIteration,
typename MakeCustomMma<DefaultThreadblockMma, kMaxK>::Mma,
DefaultThreadblockMma>::type;
using AccumLambdaIterator = typename DefaultMmaAccumLambdaIterator<
typename Mma::Operator::IteratorC,
accum_t,
kWarpSize>::Iterator;
static_assert(
MmaCore::WarpCount::kM * MmaCore::WarpCount::kN *
MmaCore::WarpCount::kK ==
kNumWarpsPerBlock,
"");
// used for efficient load of bias tile Bij from global to shared memory
using BiasLoader = TileSmemLoader<
scalar_t,
cutlass::MatrixShape<kQueriesPerBlock, kKeysPerBlock>,
MmaCore::kThreads,
// input restriction: kv_len has to be a multiple of this value
128 / cutlass::sizeof_bits<scalar_t>::value>;
// Epilogue to store to shared-memory in a format that we can use later for
// the second matmul
using B2bGemm = typename cutlass::gemm::threadblock::B2bGemm<
typename Mma::Operator::IteratorC,
typename Mma::Operator,
scalar_t,
WarpShape,
ThreadblockShape>;
using AccumulatorSharedStorage = typename B2bGemm::AccumulatorSharedStorage;
};
struct MM1 {
/**
Second matmul: perform `attn @ V` where `attn` is the attention (not
normalized) and stored in shared memory
*/
using GemmType = DefaultGemmType<ArchTag, scalar_t>;
using OpClass = typename GemmType::OpClass;
using DefaultConfig =
typename cutlass::gemm::device::DefaultGemmConfiguration<
OpClass,
ArchTag,
scalar_t,
scalar_t,
output_accum_t, // ElementC
accum_t // ElementAccumulator
>;
static constexpr int kAlignmentA = DefaultConfig::kAlignmentA; // from smem
static constexpr int kAlignmentB =
kIsAligned ? DefaultConfig::kAlignmentB : GemmType::kMinimumAlignment;
using ThreadblockShape = cutlass::gemm::
GemmShape<kQueriesPerBlock, kKeysPerBlock, GemmType::ThreadK>;
using WarpShape = cutlass::gemm::GemmShape<32, 32, GemmType::WarpK>;
using InstructionShape = typename GemmType::InstructionShape;
using LayoutB = cutlass::layout::RowMajor;
using DefaultGemm = cutlass::gemm::kernel::DefaultGemm<
scalar_t, // ElementA,
cutlass::layout::RowMajor, // LayoutA,
kAlignmentA,
scalar_t, // ElementB,
LayoutB, // LayoutB,
kAlignmentB,
output_accum_t,
cutlass::layout::RowMajor, // LayoutC,
accum_t,
OpClass,
ArchTag,
ThreadblockShape,
WarpShape,
typename GemmType::InstructionShape,
typename DefaultConfig::EpilogueOutputOp,
void, // ThreadblockSwizzle - not used
ArchTag::kMinComputeCapability >= 80 && kIsHalf
? 4
: DefaultConfig::kStages,
false, // SplitKSerial
typename GemmType::Operator>;
using WarpIteratorA = typename cutlass::gemm::threadblock::
DefaultWarpIteratorAFromSharedMemory<
typename DefaultGemm::Mma::Policy::Operator::Shape, // WarpShape
typename DefaultGemm::Mma::Policy::Operator::InstructionShape,
typename DefaultGemm::Mma::Policy::Operator::IteratorA,
typename DefaultGemm::Mma::Policy>::WarpIterator;
using DefaultMmaFromSmem =
typename cutlass::gemm::threadblock::DefaultMmaFromSharedMemory<
typename DefaultGemm::Mma,
MM0::AccumulatorSharedStorage::Shape::kN, // kMaxK
WarpIteratorA,
false>; // kScaleOperandA
using Mma = typename DefaultMmaFromSmem::Mma;
using IteratorB = typename Mma::IteratorB;
using WarpCount = typename Mma::WarpCount;
static_assert(
WarpCount::kM * WarpCount::kN * WarpCount::kK == kNumWarpsPerBlock,
"");
using DefaultEpilogue = typename DefaultGemm::Epilogue;
using OutputTileIterator =
typename cutlass::epilogue::threadblock::PredicatedTileIterator<
typename DefaultEpilogue::OutputTileIterator::ThreadMap,
output_t>;
using OutputTileIteratorAccum =
typename cutlass::epilogue::threadblock::PredicatedTileIterator<
typename DefaultEpilogue::OutputTileIterator::ThreadMap,
output_accum_t>;
};
static constexpr int64_t kAlignmentQ = MM0::kAlignmentA;
static constexpr int64_t kAlignmentK = MM0::kAlignmentB;
static constexpr int64_t kAlignmentV = 1;
// Shared storage - depends on kernel params
struct ScalingCoefs {
cutlass::Array<accum_t, kQueriesPerBlock> m_prime;
cutlass::Array<accum_t, kQueriesPerBlock> s_prime;
cutlass::Array<accum_t, kQueriesPerBlock> mi;
cutlass::Array<accum_t, kQueriesPerBlock> out_rescale;
cutlass::Array<accum_t, kQueriesPerBlock * MM0::MmaCore::WarpCount::kN>
addition_storage;
};
struct SharedStorageEpilogueAtEnd : ScalingCoefs {
struct SharedStorageAfterMM0 {
// Everything here might be overwritten during MM0
union {
typename MM0::BiasLoader::SmemTile bias;
typename MM0::AccumulatorSharedStorage si;
};
typename MM1::Mma::SharedStorage mm1;
};
union {
typename MM0::Mma::SharedStorage mm0;
SharedStorageAfterMM0 after_mm0;
typename MM1::DefaultEpilogue::SharedStorage epilogue;
};
CUTLASS_DEVICE typename MM1::DefaultEpilogue::SharedStorage&
epilogue_shared_storage() {
return epilogue;
}
};
struct SharedStorageEpilogueInLoop : ScalingCoefs {
struct SharedStorageAfterMM0 {
// Everything here might be overwritten during MM0
union {
typename MM0::BiasLoader::SmemTile bias;
typename MM0::AccumulatorSharedStorage si;
};
typename MM1::Mma::SharedStorage mm1;
typename MM1::DefaultEpilogue::SharedStorage epilogue;
};
union {
typename MM0::Mma::SharedStorage mm0;
SharedStorageAfterMM0 after_mm0;
};
CUTLASS_DEVICE typename MM1::DefaultEpilogue::SharedStorage&
epilogue_shared_storage() {
return after_mm0.epilogue;
}
};
using SharedStorage = typename cutlass::platform::conditional<
kSingleValueIteration || kKeepOutputInRF,
SharedStorageEpilogueAtEnd,
SharedStorageEpilogueInLoop>::type;
static bool __host__ check_supported(Params const& p) {
CHECK_ALIGNED_PTR(p.query_ptr, kAlignmentQ);
CHECK_ALIGNED_PTR(p.key_ptr, kAlignmentK);
CHECK_ALIGNED_PTR(p.value_ptr, kAlignmentV);
if (kSupportsBias) {
CHECK_ALIGNED_PTR(p.attn_bias_ptr, kAlignmentQ);
XFORMERS_CHECK(
p.num_batches <= 1 || p.bias_strideB % kAlignmentQ == 0,
"attn_bias is not correctly aligned (strideB)");
XFORMERS_CHECK(
p.num_heads <= 1 || p.bias_strideH % kAlignmentQ == 0,
"attn_bias is not correctly aligned (strideH)");
XFORMERS_CHECK(
p.bias_strideM % kAlignmentQ == 0,
"attn_bias is not correctly aligned");
}
XFORMERS_CHECK(
p.q_strideM % kAlignmentQ == 0,
"query is not correctly aligned (strideM)");
XFORMERS_CHECK(
p.k_strideM % kAlignmentK == 0,
"key is not correctly aligned (strideM)");
XFORMERS_CHECK(
p.v_strideM % kAlignmentV == 0,
"value is not correctly aligned (strideM)");
XFORMERS_CHECK(
p.num_heads <= 1 || p.q_strideH % kAlignmentQ == 0,
"query is not correctly aligned (strideH)");
XFORMERS_CHECK(
p.num_heads <= 1 || p.k_strideH % kAlignmentK == 0,
"key is not correctly aligned (strideH)");
XFORMERS_CHECK(
p.num_heads <= 1 || p.v_strideH % kAlignmentV == 0,
"value is not correctly aligned (strideH)");
XFORMERS_CHECK(
p.custom_mask_type < NumCustomMaskTypes,
"invalid value for `custom_mask_type`");
return true;
}
static void CUTLASS_DEVICE attention_kernel(Params& p) {
// In this block, we will only ever:
// - read query[query_start:query_end, :]
// - write to output[query_start:query_end, :]
extern __shared__ char smem_buffer[];
SharedStorage& shared_storage = *((SharedStorage*)smem_buffer);
auto& m_prime = shared_storage.m_prime;
auto& s_prime = shared_storage.s_prime;
auto& mi = shared_storage.mi;
auto& out_rescale = shared_storage.out_rescale;
const uint32_t query_start = blockIdx.x * kQueriesPerBlock;
static_assert(kQueriesPerBlock < kNumWarpsPerBlock * kWarpSize, "");
if (thread_id() < kQueriesPerBlock) {
s_prime[thread_id()] = accum_t(0);
out_rescale[thread_id()] = accum_t(1.0);
m_prime[thread_id()] =
-cutlass::platform::numeric_limits<accum_t>::infinity();
mi[thread_id()] = -cutlass::platform::numeric_limits<accum_t>::infinity();
}
typename MM1::Mma::FragmentC accum_o;
accum_o.clear();
auto createOutputIter = [&](int col) -> typename MM1::OutputTileIterator {
using OutputTileIterator = typename MM1::OutputTileIterator;
return OutputTileIterator(
typename OutputTileIterator::Params{(int32_t)p.o_strideM},
p.output_ptr,
typename OutputTileIterator::TensorCoord{
p.num_queries, p.head_dim_value},
thread_id(),
{0, col});
};
auto createOutputAccumIter = [&](int col) ->
typename MM1::OutputTileIteratorAccum {
using OutputTileIteratorAccum = typename MM1::OutputTileIteratorAccum;
return OutputTileIteratorAccum(
typename OutputTileIteratorAccum::Params{
(int32_t)(p.head_dim_value * p.num_heads)},
p.output_accum_ptr,
typename OutputTileIteratorAccum::TensorCoord{
p.num_queries, p.head_dim_value},
thread_id(),
{0, col});
};
#ifdef HAS_PYTORCH
curandStatePhilox4_32_10_t curand_state_init;
if (kSupportsDropout && p.use_dropout) {
const auto seeds = at::cuda::philox::unpack(p.rng_engine_inputs);
// each element of the attention matrix P with shape
// (batch_sz, n_heads, n_queries, n_keys) is associated with a single
// offset in RNG sequence. we initialize the RNG state with offset that
// starts at the beginning of a (n_queries, n_keys) matrix for this
// block's batch_id and head_id
// initializing rng state is very expensive, so we run once per kernel,
// rather than once per iteration. each iteration takes a copy of the
// initialized RNG state and offsets it as needed.
curand_init(
std::get<0>(seeds),
0,
std::get<1>(seeds) + p.dropout_batch_head_rng_offset,
&curand_state_init);
}
#endif
// Iterate through keys
for (int32_t iter_key_start = 0; iter_key_start < p.num_keys;
iter_key_start += kKeysPerBlock) {
int32_t problem_size_0_m =
cutlass::fast_min((int32_t)kQueriesPerBlock, p.num_queries);
int32_t problem_size_0_n = cutlass::fast_min(
int32_t(kKeysPerBlock), p.num_keys - iter_key_start);
int32_t const& problem_size_0_k = p.head_dim;
int32_t const& problem_size_1_n = p.head_dim_value;
int32_t const& problem_size_1_k = problem_size_0_n;
auto prologueV = [&](int blockN) {
typename MM1::Mma::IteratorB iterator_V(
typename MM1::IteratorB::Params{MM1::LayoutB(p.v_strideM)},
p.value_ptr + iter_key_start * p.v_strideM,
{problem_size_1_k, problem_size_1_n},
thread_id(),
cutlass::MatrixCoord{0, blockN * MM1::Mma::Shape::kN});
MM1::Mma::prologue(
shared_storage.after_mm0.mm1,
iterator_V,
thread_id(),
problem_size_1_k);
};
__syncthreads(); // Need to have shared memory initialized, and `m_prime`
// updated from end of prev iter
//
// MATMUL: Q.K_t
//
// Computes the block-matrix product of:
// (a) query[query_start:query_end, :]
// with
// (b) key[iter_key_start:iter_key_start + kKeysPerBlock]
// and stores that into `shared_storage.si`
//
// Compute threadblock location
cutlass::gemm::GemmCoord tb_tile_offset = {0, 0, 0};
cutlass::MatrixCoord tb_offset_A{
tb_tile_offset.m() * MM0::Mma::Shape::kM, tb_tile_offset.k()};
cutlass::MatrixCoord tb_offset_B{
tb_tile_offset.k(), tb_tile_offset.n() * MM0::Mma::Shape::kN};
// Construct iterators to A and B operands
typename MM0::IteratorA iterator_A(
typename MM0::IteratorA::Params(
typename MM0::MmaCore::LayoutA(p.q_strideM)),
p.query_ptr,
{problem_size_0_m, problem_size_0_k},
thread_id(),
tb_offset_A);
typename MM0::IteratorB iterator_B(
typename MM0::IteratorB::Params(
typename MM0::MmaCore::LayoutB(p.k_strideM)),
p.key_ptr + iter_key_start * p.k_strideM,
{problem_size_0_k, problem_size_0_n},
thread_id(),
tb_offset_B);
auto my_warp_id = warp_uniform(warp_id());
auto my_lane_id = lane_id();
// Construct thread-scoped matrix multiply
typename MM0::Mma mma(
shared_storage.mm0, thread_id(), my_warp_id, my_lane_id);
typename MM0::Mma::FragmentC accum;
accum.clear();
auto gemm_k_iterations =
(problem_size_0_k + MM0::Mma::Shape::kK - 1) / MM0::Mma::Shape::kK;
// Compute threadblock-scoped matrix multiply-add
mma(gemm_k_iterations, accum, iterator_A, iterator_B, accum);
__syncthreads();
if (kPreloadV) {
prologueV(0);
} else {
MM1::Mma::drain_cp_asyncs();
}
typename MM0::Mma::Operator::IteratorC::TensorCoord
iteratorC_tile_offset = {
(tb_tile_offset.m() * MM0::Mma::WarpCount::kM) +
(my_warp_id % MM0::Mma::WarpCount::kM),
(tb_tile_offset.n() * MM0::Mma::WarpCount::kN) +
(my_warp_id / MM0::Mma::WarpCount::kM)};
// multiply by scaling factor
if (kSupportsBias) {
accum =
cutlass::multiplies<typename MM0::Mma::FragmentC>()(p.scale, accum);
}
// apply attention bias if applicable
if (kSupportsBias && p.attn_bias_ptr != nullptr) {
// load bias tile Bij into shared memory
typename MM0::BiasLoader::GmemTileIterator bias_iter(
{cutlass::layout::RowMajor(p.bias_strideM)},
// attn_bias_pointer points to matrix of size (n_queries, n_keys)
// for the relevant batch_id and head_id
p.attn_bias_ptr + query_start * p.bias_strideM + iter_key_start,
{problem_size_0_m, problem_size_0_n},
thread_id());
cutlass::TensorRef<scalar_t, cutlass::layout::RowMajor> bias_tensor_ref(
shared_storage.after_mm0.bias.data(),
cutlass::layout::RowMajor(MM0::ThreadblockShape::kN));
typename MM0::BiasLoader::SmemTileIterator smem_tile_iter(
bias_tensor_ref, thread_id());
MM0::BiasLoader::load(bias_iter, smem_tile_iter);
// Pij += Bij, Pij is in register fragment and Bij is in shared memory
auto lane_offset = MM0::AccumLambdaIterator::get_lane_offset(
my_lane_id, my_warp_id, iteratorC_tile_offset);
MM0::AccumLambdaIterator::iterateRows(
lane_offset,
[&](int accum_m) {},
[&](int accum_m, int accum_n, int idx) {
if (accum_m < problem_size_0_m && accum_n < problem_size_0_n) {
accum[idx] += bias_tensor_ref.at({accum_m, accum_n});
}
},
[&](int accum_m) {});
}
// Mask out last if causal
// This is only needed if upper-right corner of current query / key block
// intersects the mask Coordinates of upper-right corner of current block
// is y=query_start x=min(iter_key_start + kKeysPerBlock, num_keys)) The
// first masked element is x = y + offset -> query_start + offset There is
// intersection (and we need to mask) if min(iter_key_start +
// kKeysPerBlock, num_keys)) >= query_start + offset
if (p.custom_mask_type &&
cutlass::fast_min(iter_key_start + kKeysPerBlock, p.num_keys) >=
(query_start + p.causal_diagonal_offset)) {
auto query_start = blockIdx.x * kQueriesPerBlock;
auto lane_offset = MM0::AccumLambdaIterator::get_lane_offset(
my_lane_id, my_warp_id, iteratorC_tile_offset);
int32_t last_col;
MM0::AccumLambdaIterator::iterateRows(
lane_offset,
[&](int accum_m) {
// last absolute col is (last absolute query + offset)
// last local col is (last absolute query + offset -
// iter_key_start)
last_col = query_start + accum_m + p.causal_diagonal_offset -
iter_key_start;
},
[&](int accum_m, int accum_n, int idx) {
if (accum_n > last_col) {
accum[idx] =
-cutlass::platform::numeric_limits<accum_t>::infinity();
}
},
[&](int accum_m) {});
}
// Update `mi` from accum stored in registers
// Also does accum[i] <- exp(accum[i] - mi)
iterative_softmax<typename MM0::Mma::Operator::IteratorC>(
accum_o,
accum,
mi,
m_prime,
s_prime,
out_rescale,
shared_storage.addition_storage,
my_lane_id,
thread_id(),
my_warp_id,
p.num_keys - iter_key_start,
iter_key_start == 0,
iteratorC_tile_offset,
kSupportsBias ? 1.0f : p.scale);
// Output results to shared-memory
int warp_idx_mn_0 = my_warp_id %
(MM0::Mma::Base::WarpCount::kM * MM0::Mma::Base::WarpCount::kN);
auto output_tile_coords = cutlass::MatrixCoord{
warp_idx_mn_0 % MM0::Mma::Base::WarpCount::kM,
warp_idx_mn_0 / MM0::Mma::Base::WarpCount::kM};
MM0::B2bGemm::accumToSmem(
shared_storage.after_mm0.si, accum, my_lane_id, output_tile_coords);
__syncthreads();
#ifdef HAS_PYTORCH
// apply dropout (if applicable) after we've written Pij to smem.
// dropout is applied by multiplying each element of Pij by:
// - 0 with probability dropout_p
// - 1 / (1 - dropout_p) with probability 1 - dropout_p
//
// for backward purposes we want to be able to map each element of the
// attention matrix to the same random uniform number as the one we used
// in forward, without needing to use the same iteration order or having
// to store the dropout matrix. its possible to do this in registers but
// it ends up being very slow because each thread having noncontiguous
// strips of the Pij tile means we have to skip around a lot, and also
// have to generate a single random number at a time
if (kSupportsDropout && p.use_dropout) {
auto si = shared_storage.after_mm0.si.accum_ref();
// each thread handles a contiguous sequence of elements from Sij, all
// coming from the same row. the reason they have to come from the same
// row is that the sampling random numbers from a contiguous random
// number sequence is much more efficient than jumping around, and the
// linear offset of each element of S (the global matrix) maps to an
// offset in a random number sequence. for S, the end of a row and the
// beginning of the next have adjacent offsets, but for Sij, this is not
// necessarily the case.
const int num_threads = blockDim.x * blockDim.y * blockDim.z;
const int threads_per_row =
cutlass::fast_min(num_threads / problem_size_0_m, problem_size_0_n);
const int elts_per_thread = cutlass::round_nearest(
cutlass::ceil_div(problem_size_0_n, threads_per_row), 4);
const int thread_i = thread_id() / threads_per_row;
const int thread_start_j =
(thread_id() % threads_per_row) * elts_per_thread;
if (thread_i < problem_size_0_m && thread_start_j < problem_size_0_n) {
curandStatePhilox4_32_10_t curand_state = curand_state_init;
skipahead(
static_cast<unsigned long long>(
(query_start + thread_i) * p.num_keys_absolute +
(iter_key_start + thread_start_j)),
&curand_state);
const float dropout_scale = 1.0 / (1.0 - p.dropout_prob);
// apply dropout scaling to elements this thread is responsible for,
// in chunks of 4
for (int sij_start_col_idx = thread_start_j; sij_start_col_idx <
cutlass::fast_min(thread_start_j + elts_per_thread,
problem_size_0_n);
sij_start_col_idx += 4) {
const float4 rand_uniform_quad = curand_uniform4(&curand_state);
CUTLASS_PRAGMA_UNROLL
for (int quad_idx = 0; quad_idx < 4; ++quad_idx) {
si.at({thread_i, sij_start_col_idx + quad_idx}) *=
static_cast<scalar_t>(
dropout_scale *
((&rand_uniform_quad.x)[quad_idx] > p.dropout_prob));
}
}
}
__syncthreads(); // p.use_dropout should have same value kernel-wide
}
#endif
//
// MATMUL: Attn . V
// Run the matmul `attn @ V` for a block of attn and V.
// `attn` is read from shared memory (in `shared_storage_si`)
// `V` is read from global memory (with iterator_B)
//
const int64_t nBlockN = kSingleValueIteration
? 1
: ceil_div(
(int64_t)problem_size_1_n, int64_t(MM1::ThreadblockShape::kN));
for (int blockN = 0; blockN < nBlockN; ++blockN) {
int gemm_k_iterations =
(problem_size_1_k + MM1::Mma::Shape::kK - 1) / MM1::Mma::Shape::kK;
// Compute threadblock-scoped matrix multiply-add and store it in accum
// (in registers)
if (!kPreloadV) {
__syncthreads(); // we share shmem between mma and epilogue
}
typename MM1::Mma::IteratorB iterator_V(
typename MM1::IteratorB::Params{MM1::LayoutB(p.v_strideM)},