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Block-sparse runtime utilities for CUTE DSL kernels.
This module contains runtime execution functions for block-sparse attention kernels.
These utilities are used by CUTE DSL kernels to produce and consume block-sparse loads.
"""
from typing import Callable, Optional
from functools import partial
import math
import cutlass
import cutlass.cute as cute
from cutlass import Float32, Int32, const_expr
# Import data structures from block_sparsity
from .block_sparsity import BlockSparseTensors
from . import utils
@cute.jit
def load_block_list(
block_indices: cute.Tensor,
block_count,
load_q_with_first: cutlass.Constexpr,
first_block_preloaded: cutlass.Constexpr,
kv_producer_state,
load_Q,
load_K,
load_V,
pipeline_k,
pipeline_v,
use_tma_q: cutlass.Constexpr,
tma_q_bytes: cutlass.Constexpr,
intra_wg_overlap: cutlass.Constexpr,
):
"""Iterate over the sparse blocks and load K, V (and Q) into the pipeline.
for the intra_wg_overlap case, we overlap the loads of K and V. And this
means we need to pipeline the last V load from the partial block case,
with the loads for the full blocks. Set first_block_preloaded when the
caller has already issued the first K load for the list.
Note:
we iterate along the block_n indices in reverse.
Returns:
Updated kv_producer_state after processing the block list.
"""
if block_count > 0:
if const_expr(not intra_wg_overlap):
# Peel first iteration: the first block may need to load Q alongside K,
# Parameters are already Constexpr, so no need to wrap in const_expr()
n_block_first = block_indices[block_count - 1]
extra_tx = tma_q_bytes if const_expr(load_q_with_first) and const_expr(use_tma_q) else 0
pipeline_k.producer_acquire(kv_producer_state, extra_tx_count=extra_tx)
if const_expr(load_q_with_first and use_tma_q):
load_Q(tma_bar_ptr=pipeline_k.producer_get_barrier(kv_producer_state))
load_K(src_idx=n_block_first, producer_state=kv_producer_state)
pipeline_v.producer_acquire(kv_producer_state)
load_V(src_idx=n_block_first, producer_state=kv_producer_state)
kv_producer_state.advance()
for offset in cutlass.range(1, block_count):
n_block = block_indices[block_count - 1 - offset]
pipeline_k.producer_acquire(kv_producer_state)
load_K(src_idx=n_block, producer_state=kv_producer_state)
pipeline_v.producer_acquire(kv_producer_state)
load_V(src_idx=n_block, producer_state=kv_producer_state)
kv_producer_state.advance()
else:
n_block_first = block_indices[block_count - 1]
if const_expr(not first_block_preloaded):
extra_tx = (
tma_q_bytes if const_expr(load_q_with_first) and const_expr(use_tma_q) else 0
)
pipeline_k.producer_acquire(kv_producer_state, extra_tx_count=extra_tx)
if const_expr(load_q_with_first and use_tma_q):
load_Q(tma_bar_ptr=pipeline_k.producer_get_barrier(kv_producer_state))
load_K(src_idx=n_block_first, producer_state=kv_producer_state)
for idx in cutlass.range(block_count - 1, unroll=1):
n_block_prev = block_indices[block_count - 1 - idx]
n_block = block_indices[block_count - 2 - idx]
kv_producer_state_prev = kv_producer_state.clone()
kv_producer_state.advance()
pipeline_k.producer_acquire(kv_producer_state)
load_K(src_idx=n_block, producer_state=kv_producer_state)
pipeline_v.producer_acquire(kv_producer_state_prev)
load_V(src_idx=n_block_prev, producer_state=kv_producer_state_prev)
return kv_producer_state
@cute.jit
def finish_overlap_v_load(
block_indices: cute.Tensor,
block_count,
load_V,
pipeline_v,
kv_producer_state,
):
"""Load the final V block after overlapped K/V loads."""
if block_count > 0:
n_block_last = block_indices[0]
pipeline_v.producer_acquire(kv_producer_state)
load_V(src_idx=n_block_last, producer_state=kv_producer_state)
kv_producer_state.advance()
return kv_producer_state
@cute.jit
def produce_block_sparse_loads(
blocksparse_tensors: BlockSparseTensors,
batch_idx,
head_idx,
m_block,
kv_producer_state,
load_Q,
load_K,
load_V,
pipeline_k,
pipeline_v,
use_tma_q: cutlass.Constexpr,
tma_q_bytes: cutlass.Constexpr,
intra_wg_overlap: cutlass.Constexpr,
):
"""Iterate over the mask and full block lists for a single tile.
The masked (partial) list may leave the last V load pending when intra-warp-group
overlap is enabled. The first full block must consume that pending V while
issuing its own K load on the next pipeline stage.
In the intra-wg-overlap path, the last masked block leaves its V copy in flight
while we advance the producer state to start the next full K. Either the full list
overlaps that pending V load, or, if no full blocks exist, we explicitly drain it.
"""
mask_block_cnt, mask_block_idx, full_block_cnt, full_block_idx = blocksparse_tensors
curr_mask_block_cnt = mask_block_cnt[batch_idx, head_idx, m_block]
curr_mask_block_idx = mask_block_idx[batch_idx, head_idx, m_block, None]
if const_expr(full_block_cnt is not None):
curr_full_block_cnt = full_block_cnt[batch_idx, head_idx, m_block]
curr_full_block_idx = full_block_idx[batch_idx, head_idx, m_block, None]
else:
curr_full_block_cnt = Int32(0)
curr_full_block_idx = None
mask_empty = curr_mask_block_cnt == 0
full_empty = curr_full_block_cnt == 0
if mask_empty:
# No masked blocks: the full list owns the initial Q+K load.
kv_producer_state = load_block_list(
curr_full_block_idx,
curr_full_block_cnt,
load_q_with_first=True,
first_block_preloaded=False,
kv_producer_state=kv_producer_state,
load_Q=load_Q,
load_K=load_K,
load_V=load_V,
pipeline_k=pipeline_k,
pipeline_v=pipeline_v,
use_tma_q=use_tma_q,
tma_q_bytes=tma_q_bytes,
intra_wg_overlap=intra_wg_overlap,
)
if const_expr(intra_wg_overlap) and curr_full_block_cnt > 0:
kv_producer_state = finish_overlap_v_load(
curr_full_block_idx,
curr_full_block_cnt,
load_V,
pipeline_v,
kv_producer_state,
)
else:
# Masked blocks present: load Q together with the first masked K so consumers can
# start immediately. When overlap is disabled this fully drains the list.
kv_producer_state = load_block_list(
curr_mask_block_idx,
curr_mask_block_cnt,
load_q_with_first=True,
first_block_preloaded=False,
kv_producer_state=kv_producer_state,
load_Q=load_Q,
load_K=load_K,
load_V=load_V,
pipeline_k=pipeline_k,
pipeline_v=pipeline_v,
use_tma_q=use_tma_q,
tma_q_bytes=tma_q_bytes,
intra_wg_overlap=intra_wg_overlap,
)
if full_empty:
if const_expr(intra_wg_overlap):
kv_producer_state = finish_overlap_v_load(
curr_mask_block_idx,
curr_mask_block_cnt,
load_V,
pipeline_v,
kv_producer_state,
)
else:
if const_expr(intra_wg_overlap):
# Bridge the masked list to the full list by overlapping the pending masked V
# with the first full K load.
n_block_mask_last = curr_mask_block_idx[0]
n_block_full_first = curr_full_block_idx[curr_full_block_cnt - 1]
kv_producer_state_prev = kv_producer_state.clone()
kv_producer_state.advance()
pipeline_k.producer_acquire(kv_producer_state)
load_K(src_idx=n_block_full_first, producer_state=kv_producer_state)
pipeline_v.producer_acquire(kv_producer_state_prev)
load_V(src_idx=n_block_mask_last, producer_state=kv_producer_state_prev)
kv_producer_state = load_block_list(
curr_full_block_idx,
curr_full_block_cnt,
load_q_with_first=False,
first_block_preloaded=True,
kv_producer_state=kv_producer_state,
load_Q=load_Q,
load_K=load_K,
load_V=load_V,
pipeline_k=pipeline_k,
pipeline_v=pipeline_v,
use_tma_q=use_tma_q,
tma_q_bytes=tma_q_bytes,
intra_wg_overlap=intra_wg_overlap,
)
kv_producer_state = finish_overlap_v_load(
curr_full_block_idx,
curr_full_block_cnt,
load_V,
pipeline_v,
kv_producer_state,
)
else:
# Non-overlap path with both lists: run the full list normally (skipping the Q
# reload because the masked list already issued it).
kv_producer_state = load_block_list(
curr_full_block_idx,
curr_full_block_cnt,
load_q_with_first=False,
first_block_preloaded=False,
kv_producer_state=kv_producer_state,
load_Q=load_Q,
load_K=load_K,
load_V=load_V,
pipeline_k=pipeline_k,
pipeline_v=pipeline_v,
use_tma_q=use_tma_q,
tma_q_bytes=tma_q_bytes,
intra_wg_overlap=intra_wg_overlap,
)
return kv_producer_state
@cute.jit
def consume_block_sparse_loads(
blocksparse_tensors: BlockSparseTensors,
batch_idx,
head_idx,
m_block,
kv_consumer_state,
mma_pv_fn,
mma_one_n_block,
process_first_half_block,
process_last_half_block,
mask_fn,
score_mod_fn,
O_should_accumulate,
mask_mod,
fastdiv_mods,
intra_wg_overlap: cutlass.Constexpr,
warp_scheduler_barrier_sync: Callable,
warp_scheduler_barrier_arrive: Callable,
):
"""Consume the mask and full block lists for a single tile on the consumer side.
Mirrors `produce_block_sparse_loads` so that the consumer pipeline
"""
mask_block_cnt, mask_block_idx, full_block_cnt, full_block_idx = blocksparse_tensors
curr_mask_block_cnt = mask_block_cnt[batch_idx, head_idx, m_block]
curr_mask_block_idx = mask_block_idx[batch_idx, head_idx, m_block, None]
curr_full_block_cnt = full_block_cnt[batch_idx, head_idx, m_block]
curr_full_block_idx = full_block_idx[batch_idx, head_idx, m_block, None]
processed_any = curr_mask_block_cnt + curr_full_block_cnt > 0
if const_expr(not intra_wg_overlap):
if curr_mask_block_cnt > 0:
mask_n_block = curr_mask_block_idx[curr_mask_block_cnt - 1]
warp_scheduler_barrier_sync()
kv_consumer_state = mma_one_n_block(
kv_consumer_state,
n_block=mask_n_block,
mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate),
mask_fn=partial(
mask_fn,
mask_mod=mask_mod,
mask_seqlen=True,
fastdiv_mods=fastdiv_mods if cutlass.const_expr(mask_mod is not None) else None,
),
is_first_n_block=True,
)
O_should_accumulate = True
for i in cutlass.range(1, curr_mask_block_cnt):
mask_n_block = curr_mask_block_idx[curr_mask_block_cnt - 1 - i]
kv_consumer_state = mma_one_n_block(
kv_consumer_state,
n_block=mask_n_block,
mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate),
mask_fn=partial(mask_fn, mask_mod=mask_mod, mask_seqlen=False),
is_first_n_block=False,
)
O_should_accumulate = True
if curr_full_block_cnt == 0:
warp_scheduler_barrier_arrive()
if curr_full_block_cnt > 0:
full_n_block = curr_full_block_idx[curr_full_block_cnt - 1]
if curr_mask_block_cnt == 0:
warp_scheduler_barrier_sync()
kv_consumer_state = mma_one_n_block(
kv_consumer_state,
n_block=full_n_block,
mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate),
mask_fn=partial(mask_fn, mask_seqlen=True),
is_first_n_block=True,
)
O_should_accumulate = True
for i in cutlass.range(1, curr_full_block_cnt):
full_n_block = curr_full_block_idx[curr_full_block_cnt - 1 - i]
kv_consumer_state = mma_one_n_block(
kv_consumer_state,
n_block=full_n_block,
mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate),
mask_fn=partial(mask_fn, mask_seqlen=False),
is_first_n_block=False,
)
O_should_accumulate = True
else:
kv_consumer_state = mma_one_n_block(
kv_consumer_state,
n_block=full_n_block,
mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate),
mask_fn=partial(mask_fn, mask_mod=None, mask_seqlen=True),
is_first_n_block=False,
)
O_should_accumulate = True
for i in cutlass.range(1, curr_full_block_cnt):
full_n_block = curr_full_block_idx[curr_full_block_cnt - 1 - i]
kv_consumer_state = mma_one_n_block(
kv_consumer_state,
n_block=full_n_block,
mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate),
mask_fn=partial(mask_fn, mask_mod=None, mask_seqlen=False),
is_first_n_block=False,
)
O_should_accumulate = True
warp_scheduler_barrier_arrive()
else:
if curr_mask_block_cnt > 0:
mask_n_block = curr_mask_block_idx[curr_mask_block_cnt - 1]
kv_consumer_state = process_first_half_block(
n_block=mask_n_block,
kv_consumer_state=kv_consumer_state,
mask_fn=partial(
mask_fn,
mask_mod=mask_mod,
mask_seqlen=True,
fastdiv_mods=fastdiv_mods if cutlass.const_expr(mask_mod is not None) else None,
),
score_mod_fn=score_mod_fn,
is_first_block=True,
)
for i in cutlass.range(1, curr_mask_block_cnt):
mask_n_block = curr_mask_block_idx[curr_mask_block_cnt - 1 - i]
kv_consumer_state = mma_one_n_block(
kv_consumer_state,
n_block=mask_n_block,
mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate),
mask_fn=partial(mask_fn, mask_mod=mask_mod, mask_seqlen=False),
)
O_should_accumulate = True
if curr_full_block_cnt > 0:
full_n_block = curr_full_block_idx[curr_full_block_cnt - 1]
if curr_mask_block_cnt == 0:
kv_consumer_state = process_first_half_block(
n_block=full_n_block,
kv_consumer_state=kv_consumer_state,
mask_fn=partial(mask_fn, mask_mod=None, mask_seqlen=True),
score_mod_fn=score_mod_fn,
is_first_block=True,
)
else:
kv_consumer_state = mma_one_n_block(
kv_consumer_state,
n_block=full_n_block,
mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate),
mask_fn=partial(mask_fn, mask_mod=None, mask_seqlen=True),
)
O_should_accumulate = True
for i in cutlass.range(1, curr_full_block_cnt):
full_n_block = curr_full_block_idx[curr_full_block_cnt - 1 - i]
kv_consumer_state = mma_one_n_block(
kv_consumer_state,
n_block=full_n_block,
mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate),
mask_fn=partial(mask_fn, mask_mod=None, mask_seqlen=False),
)
O_should_accumulate = True
if curr_mask_block_cnt + curr_full_block_cnt > 0:
kv_consumer_state = process_last_half_block(
kv_consumer_state=kv_consumer_state,
zero_init=not O_should_accumulate,
)
O_should_accumulate = True
return kv_consumer_state, O_should_accumulate, processed_any
@cute.jit
def load_block_list_sm100(
block_indices: cute.Tensor,
block_count,
load_q_with_first: cutlass.Constexpr,
m_block,
q_stage: cutlass.Constexpr,
kv_producer_state,
load_Q,
load_K,
load_V,
pipeline_kv,
):
"""SM100 version of load_block_list (no intra_wg_overlap, no extra_tx_count)."""
if block_count > 0:
# First iteration: load Q alongside K if requested
n_block_first = block_indices[block_count - 1]
if const_expr(load_q_with_first):
# SM100 loads Q0 and optionally Q1
load_Q(block=q_stage * m_block + 0, stage=0)
if const_expr(q_stage == 2):
load_Q(block=q_stage * m_block + 1, stage=1)
# SM100 doesn't use producer_acquire for pipeline_kv in load path
# The pipeline barriers are handled inside load_KV
load_K(block=n_block_first, producer_state=kv_producer_state, page_idx=None)
kv_producer_state.advance()
load_V(block=n_block_first, producer_state=kv_producer_state, page_idx=None)
kv_producer_state.advance()
# Remaining blocks
for offset in cutlass.range(1, block_count):
n_block = block_indices[block_count - 1 - offset]
load_K(block=n_block, producer_state=kv_producer_state, page_idx=None)
kv_producer_state.advance()
load_V(block=n_block, producer_state=kv_producer_state, page_idx=None)
kv_producer_state.advance()
return kv_producer_state
# SM100-specific tile processor using SM100 helpers
@cute.jit
def produce_block_sparse_loads_sm100(
blocksparse_tensors: BlockSparseTensors,
batch_idx,
head_idx,
m_block,
kv_producer_state,
load_Q,
load_K,
load_V,
pipeline_kv,
q_stage: cutlass.Constexpr,
q_producer_phase: Int32,
):
"""SM100 entry point for sparse block iteration.
SM100 uses PipelineTmaUmma which doesn't support extra_tx_count, so we use
simplified block processing that just calls producer_acquire without extras.
"""
mask_block_cnt, mask_block_idx, full_block_cnt, full_block_idx = blocksparse_tensors
curr_mask_block_cnt = mask_block_cnt[batch_idx, head_idx, m_block]
curr_mask_block_idx = mask_block_idx[batch_idx, head_idx, m_block, None]
if const_expr(full_block_cnt is not None):
curr_full_block_cnt = full_block_cnt[batch_idx, head_idx, m_block]
curr_full_block_idx = full_block_idx[batch_idx, head_idx, m_block, None]
else:
curr_full_block_cnt = Int32(0)
curr_full_block_idx = None
mask_empty = curr_mask_block_cnt == 0
full_empty = curr_full_block_cnt == 0
q_phase_flipped = False
if mask_empty:
# No masked blocks: process full list with Q loading
kv_producer_state = load_block_list_sm100(
curr_full_block_idx,
curr_full_block_cnt,
load_q_with_first=True,
m_block=m_block,
q_stage=q_stage,
kv_producer_state=kv_producer_state,
load_Q=load_Q,
load_K=load_K,
load_V=load_V,
pipeline_kv=pipeline_kv,
)
q_phase_flipped = not full_empty
else:
# Process masked blocks with Q loading
kv_producer_state = load_block_list_sm100(
curr_mask_block_idx,
curr_mask_block_cnt,
load_q_with_first=True,
m_block=m_block,
q_stage=q_stage,
kv_producer_state=kv_producer_state,
load_Q=load_Q,
load_K=load_K,
load_V=load_V,
pipeline_kv=pipeline_kv,
)
q_phase_flipped = True
if not full_empty:
# Process full blocks without Q loading
kv_producer_state = load_block_list_sm100(
curr_full_block_idx,
curr_full_block_cnt,
load_q_with_first=False,
m_block=m_block,
q_stage=q_stage,
kv_producer_state=kv_producer_state,
load_Q=load_Q,
load_K=load_K,
load_V=load_V,
pipeline_kv=pipeline_kv,
)
if q_phase_flipped:
q_producer_phase ^= 1
return kv_producer_state, q_producer_phase
@cute.jit
def get_total_block_count(
blocksparse_tensors: BlockSparseTensors,
batch_idx,
head_idx,
m_block,
):
mask_block_cnt, mask_block_idx, full_block_cnt, full_block_idx = blocksparse_tensors
if const_expr(full_block_cnt is not None):
return (
mask_block_cnt[batch_idx, head_idx, m_block]
+ full_block_cnt[batch_idx, head_idx, m_block]
)
else:
return mask_block_cnt[batch_idx, head_idx, m_block]
@cute.jit
def handle_block_sparse_empty_tile_correction_sm100(
tidx: Int32,
q_stage: cutlass.Constexpr,
m_block_size: cutlass.Constexpr,
qhead_per_kvhead,
pack_gqa: cutlass.Constexpr,
is_split_kv: cutlass.Constexpr,
learnable_sink,
mLSE,
seqlen,
m_block: Int32,
head_idx: Int32,
batch_idx: Int32,
split_idx: Int32,
sScale: cute.Tensor,
stats: list,
correction_epilogue: Callable,
thr_mma_pv: cute.core.ThrMma,
tOtOs: tuple[cute.Tensor],
sO: cute.Tensor,
mbar_ptr,
mbar_softmax_corr_full_offset: Int32,
mbar_softmax_corr_empty_offset: Int32,
mbar_P_full_O_rescaled_offset: Int32,
mbar_P_full_2_offset: Int32,
mbar_corr_epi_full_offset: Int32,
mbar_corr_epi_empty_offset: Int32,
softmax_corr_consumer_phase: Int32,
o_corr_consumer_phase: Int32,
corr_epi_producer_phase: Int32,
softmax_scale_log2: Float32,
mO_cur: Optional[cute.Tensor] = None,
gO: Optional[cute.Tensor] = None,
gmem_tiled_copy_O: Optional[cute.TiledCopy] = None,
):
"""Handle the block-sparse case where a tile is fully masked:
* zero staged results
* seed stats
* satisfy the usual barrier protocol so downstream warps continue to make progress.
"""
LOG2_E = Float32(math.log2(math.e))
for stage in cutlass.range_constexpr(q_stage):
row_sum_value = Float32(1.0)
row_max_value = (
-Float32.inf if const_expr(mLSE is not None or learnable_sink is not None) else None
)
if const_expr(learnable_sink is not None):
sink_val = -Float32.inf
if const_expr(not pack_gqa):
sink_val = Float32(learnable_sink[head_idx])
elif tidx < m_block_size:
q_head_idx = (
(q_stage * m_block + stage) * m_block_size + tidx
) % qhead_per_kvhead + head_idx * qhead_per_kvhead
sink_val = Float32(learnable_sink[q_head_idx])
if sink_val != -Float32.inf and (const_expr(not is_split_kv) or split_idx == 0):
if row_max_value == -Float32.inf:
row_max_value = sink_val * (LOG2_E / softmax_scale_log2)
row_sum_value = Float32(1.0)
else:
row_sum_value = row_sum_value + utils.exp2f(
sink_val * LOG2_E - row_max_value * softmax_scale_log2
)
if tidx < m_block_size:
scale_row_idx = tidx + stage * m_block_size
sScale[scale_row_idx] = row_sum_value
if const_expr(mLSE is not None or learnable_sink is not None):
sScale[scale_row_idx + m_block_size * 2] = row_max_value
acc_flag = row_sum_value == Float32(0.0) or row_sum_value != row_sum_value
stats[stage] = (row_sum_value, row_max_value, acc_flag)
cute.arch.mbarrier_wait(
mbar_ptr + mbar_softmax_corr_full_offset + stage,
softmax_corr_consumer_phase,
)
cute.arch.mbarrier_arrive(mbar_ptr + mbar_softmax_corr_empty_offset + stage)
if const_expr(gmem_tiled_copy_O is None):
cute.arch.mbarrier_wait(
mbar_ptr + mbar_corr_epi_empty_offset + stage,
corr_epi_producer_phase,
)
correction_epilogue(
thr_mma_pv,
tOtOs[stage],
tidx,
stage,
m_block,
seqlen.seqlen_q,
Float32(0.0), # zero scale ensures empty tile writes zeros into staged outputs
sO[None, None, stage],
mO_cur,
gO,
gmem_tiled_copy_O,
)
if const_expr(gmem_tiled_copy_O is None):
cute.arch.mbarrier_arrive(mbar_ptr + mbar_corr_epi_full_offset + stage)
cute.arch.mbarrier_arrive(mbar_ptr + mbar_P_full_O_rescaled_offset + stage)
cute.arch.mbarrier_arrive(mbar_ptr + mbar_P_full_2_offset + stage)
softmax_corr_consumer_phase ^= 1
o_corr_consumer_phase ^= 1
corr_epi_producer_phase ^= 1
return (
softmax_corr_consumer_phase,
o_corr_consumer_phase,
corr_epi_producer_phase,
)
@cute.jit
def softmax_block_sparse_sm100(
blocksparse_tensors: BlockSparseTensors,
batch_idx,
head_idx,
m_block,
softmax_step: Callable,
mask_fn: Callable,
mask_fn_none: Callable,
mma_si_consumer_phase: Int32,
si_corr_producer_phase: Int32,
s0_s1_sequence_phase: Int32,
mbar_ptr,
mbar_softmax_corr_full_offset: Int32,
mbar_softmax_corr_empty_offset: Int32,
mbar_P_full_O_rescaled_offset: Int32,
mbar_P_full_2_offset: Int32,
q_stage: cutlass.Constexpr,
stage_idx: Int32,
):
mask_block_cnt, mask_block_idx, full_block_cnt, full_block_idx = blocksparse_tensors
curr_mask_block_cnt = mask_block_cnt[batch_idx, head_idx, m_block]
curr_mask_block_idx = mask_block_idx[batch_idx, head_idx, m_block, None]
if const_expr(full_block_cnt is not None):
curr_full_block_cnt = full_block_cnt[batch_idx, head_idx, m_block]
curr_full_block_idx = full_block_idx[batch_idx, head_idx, m_block, None]
else:
curr_full_block_cnt = Int32(0)
curr_full_block_idx = None
total_block_cnt = curr_mask_block_cnt + curr_full_block_cnt
if total_block_cnt == 0:
cute.arch.mbarrier_arrive(mbar_ptr + mbar_softmax_corr_full_offset + stage_idx)
cute.arch.mbarrier_arrive(mbar_ptr + mbar_P_full_O_rescaled_offset + stage_idx)
cute.arch.mbarrier_arrive(mbar_ptr + mbar_P_full_2_offset + stage_idx)
cute.arch.mbarrier_arrive(mbar_ptr + mbar_softmax_corr_empty_offset + stage_idx)
else:
if curr_mask_block_cnt > 0:
mask_n_block = curr_mask_block_idx[curr_mask_block_cnt - 1]
(
mma_si_consumer_phase,
si_corr_producer_phase,
s0_s1_sequence_phase,
) = softmax_step(
mma_si_consumer_phase,
si_corr_producer_phase,
s0_s1_sequence_phase,
mask_n_block,
is_first=True,
mask_fn=partial(mask_fn, mask_seqlen=True), # last block could oob
)
for i in cutlass.range(1, curr_mask_block_cnt):
mask_n_block = curr_mask_block_idx[curr_mask_block_cnt - 1 - i]
(
mma_si_consumer_phase,
si_corr_producer_phase,
s0_s1_sequence_phase,
) = softmax_step(
mma_si_consumer_phase,
si_corr_producer_phase,
s0_s1_sequence_phase,
mask_n_block,
mask_fn=partial(mask_fn, mask_seqlen=False),
)
if curr_full_block_cnt > 0:
full_n_block = curr_full_block_idx[curr_full_block_cnt - 1]
if curr_mask_block_cnt == 0:
(
mma_si_consumer_phase,
si_corr_producer_phase,
s0_s1_sequence_phase,
) = softmax_step(
mma_si_consumer_phase,
si_corr_producer_phase,
s0_s1_sequence_phase,
full_n_block,
is_first=True,
mask_fn=partial(mask_fn_none, mask_seqlen=True),
)
else:
(
mma_si_consumer_phase,
si_corr_producer_phase,
s0_s1_sequence_phase,
) = softmax_step(
mma_si_consumer_phase,
si_corr_producer_phase,
s0_s1_sequence_phase,
full_n_block,
is_first=False,
mask_fn=partial(mask_fn_none, mask_seqlen=False),
)
for i in cutlass.range(1, curr_full_block_cnt):
full_n_block = curr_full_block_idx[curr_full_block_cnt - 1 - i]
(
mma_si_consumer_phase,
si_corr_producer_phase,
s0_s1_sequence_phase,
) = softmax_step(
mma_si_consumer_phase,
si_corr_producer_phase,
s0_s1_sequence_phase,
full_n_block,
mask_fn=partial(mask_fn_none, mask_seqlen=False),
)
return (
mma_si_consumer_phase,
si_corr_producer_phase,
s0_s1_sequence_phase,
total_block_cnt == 0,
)
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