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fix(generation): handle CUDA multinomial limit in beam search sampling #45369

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fix(generation): handle CUDA multinomial limit in beam search sampling #45369

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13 changes: 10 additions & 3 deletions src/transformers/generation/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2973,9 +2973,16 @@ def _get_top_k_continuations(

# Gather the top K scores from _all_ beams.
if do_sample:
topk_indices = torch.multinomial(
nn.functional.softmax(accumulated_log_probs, dim=-1), num_samples=beams_to_keep
)
probs = nn.functional.softmax(accumulated_log_probs, dim=-1)
# torch.multinomial on CUDA requires the last dimension to be <= 2**24.
# When num_beams * vocab_size exceeds this, pre-filter to the top candidates.
_MULTINOMIAL_MAX = 2**24
if probs.shape[-1] > _MULTINOMIAL_MAX:
top_values, top_indices = torch.topk(probs, k=_MULTINOMIAL_MAX, dim=-1)
sampled = torch.multinomial(top_values, num_samples=beams_to_keep)
topk_indices = torch.gather(top_indices, dim=1, index=sampled)
else:
topk_indices = torch.multinomial(probs, num_samples=beams_to_keep)
topk_log_probs = torch.gather(input=accumulated_log_probs, dim=1, index=topk_indices)
else:
topk_log_probs, topk_indices = torch.topk(accumulated_log_probs, k=beams_to_keep)
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