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Fix flash/sage varlen prep under torch.compile with dynamic shapes #14568

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Fix flash/sage varlen prep under torch.compile with dynamic shapes #14568

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Fix flash/sage varlen prep under torch.compile with dynamic shapes
ShivamShrirao Aug 23, 2026
f3bfd56
Merge branch 'main' into varlen-compile-fix
ShivamShrirao Aug 24, 2026
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Merge branch 'main' into varlen-compile-fix
ShivamShrirao Aug 24, 2026
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21 changes: 10 additions & 11 deletions src/diffusers/models/attention_dispatch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -598,13 +598,12 @@ def _prepare_for_flash_attn_or_sage_varlen_without_mask(
):
seqlens_q = torch.full((batch_size,), seq_len_q, dtype=torch.int32, device=device)
seqlens_k = torch.full((batch_size,), seq_len_kv, dtype=torch.int32, device=device)
cu_seqlens_q = torch.zeros(batch_size + 1, dtype=torch.int32, device=device)
cu_seqlens_k = torch.zeros(batch_size + 1, dtype=torch.int32, device=device)
cu_seqlens_q[1:] = torch.cumsum(seqlens_q, dim=0)
cu_seqlens_k[1:] = torch.cumsum(seqlens_k, dim=0)
max_seqlen_q = seqlens_q.max().item()
max_seqlen_k = seqlens_k.max().item()
return (seqlens_q, seqlens_k), (cu_seqlens_q, cu_seqlens_k), (max_seqlen_q, max_seqlen_k)
# Built with arange instead of cumsum(full(...)): inductor rewrites that pattern into
# `arange * fill_value`, which raises under dynamic shapes because the fill value is a
# symbolic sequence length. The lengths are uniform here, so arange is also cheaper.
cu_seqlens_q = torch.arange(0, (batch_size + 1) * seq_len_q, seq_len_q, dtype=torch.int32, device=device)
cu_seqlens_k = torch.arange(0, (batch_size + 1) * seq_len_kv, seq_len_kv, dtype=torch.int32, device=device)
return (seqlens_q, seqlens_k), (cu_seqlens_q, cu_seqlens_k), (seq_len_q, seq_len_kv)


def _prepare_for_flash_attn_or_sage_varlen_with_mask(
Expand All @@ -615,13 +614,13 @@ def _prepare_for_flash_attn_or_sage_varlen_with_mask(
):
seqlens_q = torch.full((batch_size,), seq_len_q, dtype=torch.int32, device=device)
seqlens_k = attn_mask.sum(dim=1, dtype=torch.int32)
cu_seqlens_q = torch.zeros(batch_size + 1, dtype=torch.int32, device=device)
# Queries are uniform, so arange (see the no-mask helper: cumsum(full(...)) breaks inductor
# under dynamic shapes). Keys are data-dependent and keep the cumsum.
cu_seqlens_q = torch.arange(0, (batch_size + 1) * seq_len_q, seq_len_q, dtype=torch.int32, device=device)
cu_seqlens_k = torch.zeros(batch_size + 1, dtype=torch.int32, device=device)
cu_seqlens_q[1:] = torch.cumsum(seqlens_q, dim=0)
cu_seqlens_k[1:] = torch.cumsum(seqlens_k, dim=0)
max_seqlen_q = seqlens_q.max().item()
max_seqlen_k = seqlens_k.max().item()
return (seqlens_q, seqlens_k), (cu_seqlens_q, cu_seqlens_k), (max_seqlen_q, max_seqlen_k)
return (seqlens_q, seqlens_k), (cu_seqlens_q, cu_seqlens_k), (seq_len_q, max_seqlen_k)


def _prepare_for_flash_attn_or_sage_varlen(
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