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Revert "D1: move O rescale atoms outside const_expr guard (match CUTLASS pattern)"

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This reverts commit aaf21d8ac1.
This commit is contained in:
biondizzle
2026-05-24 22:15:38 +00:00
parent aaf21d8ac1
commit 0f30319e06
1 changed files with 28 additions and 30 deletions
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58
dsv4/kernels/attention/fmha.py
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@@ -362,35 +362,36 @@ class FmhaKernel:
row_sum = Float32(0.0)
scale_log2 = Float32(self.scale_softmax_log2)
# O rescale atoms (CUTLASS correction_rescale pattern)
# Always defined — const_expr guards the kt>0 execution, not the atom setup.
# CUTLASS correction_rescale defines these unconditionally.
# O rescale atoms (hand-constructed, using composition layout like CUTLASS correction_rescale)
# Only needed when there are multiple KV tiles (O must be rescaled per-kt).
# With n_kv_tiles=1, no rescale is needed (kt is always 0).
corr_tile_size = 16
n_corr_tiles = self.pv_n_tile // corr_tile_size
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
tOcO = pv_thr.partition_C(cO)
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
tmem_load_o_atom = cute.make_copy_atom(
tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)),
self.acc_dtype,
)
tmem_store_o_atom = cute.make_copy_atom(
tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)),
self.acc_dtype,
)
tiled_tmem_load_o = tcgen05.make_tmem_copy(tmem_load_o_atom, tOtO_i)
tiled_tmem_store_o = tcgen05.make_tmem_copy(tmem_store_o_atom, tOtO_i)
thr_tmem_load_o = tiled_tmem_load_o.get_slice(sfw_idx)
thr_tmem_store_o = tiled_tmem_store_o.get_slice(sfw_idx)
tTMEM_LOADtO = thr_tmem_load_o.partition_S(tOtO_i)
tTMEM_LOADcO = thr_tmem_load_o.partition_D(tOcO_i)
tTMEM_STOREtO = thr_tmem_store_o.partition_D(tOtO_i)
tTMrO = cute.make_rmem_tensor(
(tTMEM_LOADcO.shape, 128 // corr_tile_size), self.acc_dtype
)
if const_expr(self.n_kv_tiles > 1):
cO = cute.make_identity_tensor((self.pv_mma_tiler[0], self.pv_mma_tiler[1]))
tOcO = pv_thr.partition_C(cO)
tOtO_i_layout = cute.composition(tOtO0.layout, cute.make_layout((128, corr_tile_size)))
tOcO_i_layout = cute.composition(tOcO.layout, cute.make_layout((128, corr_tile_size)))
tOtO_i = cute.make_tensor(tOtO0.iterator, tOtO_i_layout)
tOcO_i = cute.make_tensor(tOcO.iterator, tOcO_i_layout)
tmem_load_o_atom = cute.make_copy_atom(
tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)),
self.acc_dtype,
)
tmem_store_o_atom = cute.make_copy_atom(
tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)),
self.acc_dtype,
)
tiled_tmem_load_o = tcgen05.make_tmem_copy(tmem_load_o_atom, tOtO_i)
tiled_tmem_store_o = tcgen05.make_tmem_copy(tmem_store_o_atom, tOtO_i)
thr_tmem_load_o = tiled_tmem_load_o.get_slice(sfw_idx)
thr_tmem_store_o = tiled_tmem_store_o.get_slice(sfw_idx)
tTMEM_LOADtO = thr_tmem_load_o.partition_S(tOtO_i)
tTMEM_LOADcO = thr_tmem_load_o.partition_D(tOcO_i)
tTMEM_STOREtO = thr_tmem_store_o.partition_D(tOtO_i)
tTMrO = cute.make_rmem_tensor(
(tTMEM_LOADcO.shape, 128 // corr_tile_size), self.acc_dtype
)
for kt in range(self.n_kv_tiles):
si_handle = s_cons.wait_and_advance()
@@ -446,9 +447,6 @@ class FmhaKernel:
k2 = k_coord // 64
_sP_nostage[(m_coord, k0), 0, (k1, k2)] = rP_bf16[(j0, 0), j1, 0, 0]
cute.arch.fence_proxy("async.shared", space="cta")
# O rescale: multiply existing O accumulator by acc_scale
# when processing a new KV tile (kt > 0).
# Same pattern as CUTLASS correction_rescale.
if const_expr(self.n_kv_tiles > 1):
if kt > 0:
for i in range(n_corr_tiles):