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[AutoWS] Move p computation closer to use #564

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[AutoWS] Move p computation closer to use #564

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df55ea7
Added tmp change
njriasan Oct 15, 2025
f54077c
revert data_partition_factor change
njriasan Oct 15, 2025
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7 changes: 4 additions & 3 deletions tritonbench/kernels/blackwell_triton_fused_attention.py
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Original file line number Diff line number Diff line change
Expand Up @@ -76,11 +76,8 @@ def _attn_fwd_subtile(
qk = _fma_f32x2(qk, qk_scale, -m_ij[:, None])
else:
qk = qk * qk_scale - m_ij[:, None]
p = tl.math.exp2(qk)
# -- compute correction factor
alpha = tl.math.exp2(m_i - m_ij)
if not FADD2_REDUCE:
l_ij = tl.sum(p, 1)

# -- update output accumulator --
BM: tl.constexpr = acc.shape[0]
Expand All @@ -98,13 +95,16 @@ def _attn_fwd_subtile(
else:
acc = acc * alpha[:, None]

p = tl.math.exp2(qk)
PM: tl.constexpr = p.shape[0]
PN: tl.constexpr = p.shape[1]
if FADD2_REDUCE:
p0, p1 = p.reshape([PM, 2, PN // 2]).permute(0, 2, 1).split()
l_ij0, l_ij1 = tl.reduce((p0, p1), axis=1, combine_fn=_reduce_fadd2)
l_i0 = l_i0 * alpha + l_ij0
l_i1 = l_i1 * alpha + l_ij1
else:
l_ij = tl.sum(p, 1)

# prepare p and v for the dot
p = p.to(dtype)
Expand Down Expand Up @@ -707,6 +707,7 @@ def grid_debug(META):
warp_specialize=warp_specialize,
OUTER_LOOP=True,
dtype=torch_dtype_to_triton(q.dtype),
data_partition_factor=2,
**extra_kern_args,
)
else:
Expand Down
6 changes: 3 additions & 3 deletions tritonbench/kernels/blackwell_triton_fused_attention_dp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,11 +80,8 @@ def _attn_fwd_subtile(
qk = _fma_f32x2(qk, qk_scale, -m_ij[:, None])
else:
qk = qk * qk_scale - m_ij[:, None]
p = tl.math.exp2(qk)
# -- compute correction factor
alpha = tl.math.exp2(m_i - m_ij)
if not FADD2_REDUCE:
l_ij = tl.sum(p, 1)

# -- update output accumulator --
BM: tl.constexpr = acc.shape[0]
Expand All @@ -104,13 +101,16 @@ def _attn_fwd_subtile(

# update m_i and l_i
# place this at the end of the loop to reduce register pressure
p = tl.math.exp2(qk)
PM: tl.constexpr = p.shape[0]
PN: tl.constexpr = p.shape[1]
if FADD2_REDUCE:
p0, p1 = p.reshape([PM, 2, PN // 2]).permute(0, 2, 1).split()
l_ij0, l_ij1 = tl.reduce((p0, p1), axis=1, combine_fn=_reduce_fadd2)
l_i0 = l_i0 * alpha + l_ij0
l_i1 = l_i1 * alpha + l_ij1
else:
l_ij = tl.sum(p, 1)

# We can potentially move these to be before updating l_ij, so the dot
# is not blocked.
Expand Down