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Modify output shape in nsa for decoding #565

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5d01b70
Modify output shape in nsa for decoding
Espere-1119-Song Aug 14, 2025
1b3483c
Modify output shape in nsa for decoding with the true format
Espere-1119-Song Aug 14, 2025
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8 changes: 4 additions & 4 deletions fla/ops/nsa/parallel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -542,7 +542,7 @@ def parallel_nsa_fwd(
token_indices: Optional[torch.LongTensor] = None,
):
B, T, H, K, V, S = *k.shape, v.shape[-1], block_indices.shape[-1]
HQ = q.shape[2]
_, T_q, HQ, _ = q.shape
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@gemini-code-assist gemini-code-assist bot Aug 14, 2025

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medium

Using _ to ignore dimensions can be concise, but it's less explicit and misses an opportunity to validate tensor shapes. For better robustness and readability, it's recommended to explicitly unpack all dimensions and add assertions to ensure that the batch size and key/value dimensions of q and k are compatible.

Suggested change
_, T_q, HQ, _ = q.shape
B_q, T_q, HQ, K_q = q.shape
assert B == B_q, f"q and k must have the same batch size, but got {B_q} and {B}"
assert K == K_q, f"q and k must have the same key dimension, but got {K_q} and {K}"

G = HQ // H
BS = block_size
if check_shared_mem('hopper', q.device.index):
Expand All @@ -555,9 +555,9 @@ def parallel_nsa_fwd(
NV = triton.cdiv(V, BV)
assert NK == 1, "The key dimension can not be larger than 256"

grid = (T, NV, B * H)
o = torch.empty(B, T, HQ, V, dtype=v.dtype, device=q.device)
lse = torch.empty(B, T, HQ, dtype=torch.float, device=q.device)
grid = (T_q, NV, B * H)
o = torch.empty(B, T_q, HQ, V, dtype=v.dtype, device=q.device)
lse = torch.empty(B, T_q, HQ, dtype=torch.float, device=q.device)

parallel_nsa_fwd_kernel[grid](
q=q,
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