refactor: remove inference redundancy and fix cache leaks
- drop Executor unused tokenizer field, _head_dim, stale metrics docstring - unify greedy sampling via SamplingPipeline.sample, drop top-level duplicate - drop Task.flush_remaining no-op and unreachable prompt-length branch - drop ProtocolHandler redundant chunks list (reuse body) - fix page_size=1 token-slot leak on task_free - clear _task_pages/_task_slots on alloc-failure paths - reset _bind_state on task_free to avoid stale steady-state reuse - remove unreachable contiguous branches in paged-only helpers
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@@ -363,20 +363,6 @@ def sample(
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``True`` — a ``(token_ids, chosen_logprobs)`` tuple where
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``chosen_logprobs`` has shape ``[batch]``.
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"""
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greedy = (
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bool((temperature == 0).all())
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if isinstance(temperature, Tensor)
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else temperature == 0
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)
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if greedy:
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tokens = logits.argmax(dim=-1)
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if not return_logprobs:
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return tokens
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log_probs = torch.log_softmax(logits.float(), dim=-1)
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chosen = torch.gather(log_probs, -1, tokens.unsqueeze(-1)).squeeze(-1)
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return tokens, chosen
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has_freq = (
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(isinstance(frequency_penalty, Tensor) and (frequency_penalty != 0).any())
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if isinstance(frequency_penalty, Tensor)
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