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[None][fix] Hide trailing EOS from generated text #14336

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[None][fix] Hide trailing EOS from generated text #14336

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22 changes: 19 additions & 3 deletions tensorrt_llm/executor/result.py
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Original file line number Diff line number Diff line change
Expand Up @@ -378,6 +378,8 @@ def _handle_sequence(self,

if sequence_is_finished:
if finish_reasons[src_idx] == tllm.FinishReason.END_ID:
# Keep the trailing EOS in token_ids; the detokenizer hides
# it from text. stop_reason stays None to mark "natural EOS".
output.finish_reason = 'stop'
elif finish_reasons[src_idx] == tllm.FinishReason.STOP_WORDS:
output.finish_reason = 'stop'
Expand Down Expand Up @@ -734,22 +736,36 @@ def _handle_response(self, response: "GenerationExecutor.Response"):
'spaces_between_special_tokens':
self.sampling_params.spaces_between_special_tokens
}
end_id = self.sampling_params.end_id
include_stop = self.sampling_params.include_stop_str_in_output

if self.sampling_params.detokenize and self.tokenizer is not None:
for beam_output in self.outputs:
beam_output._last_text_len = len(beam_output.text)
# Hide trailing EOS from decoded text without mutating
# token_ids (matches vLLM). stop_reason is None gates on
# END_ID; STOP_WORDS sets it and was stripped upstream.
trim_eos = (beam_output.finish_reason == 'stop'
and beam_output.stop_reason is None
and not include_stop and end_id is not None)
if hasattr(
self.tokenizer, 'decode_incrementally'
) and self._streaming and not self.sampling_params.use_beam_search:
diff = beam_output.token_ids_diff
if trim_eos and diff and diff[-1] == end_id:
diff = diff[:-1]
beam_output.text, beam_output._incremental_states = self.tokenizer.decode_incrementally(
beam_output.token_ids_diff,
diff,
prev_text=beam_output.text,
states=beam_output._incremental_states,
flush=self._done,
stream_interval=self.sampling_params._stream_interval,
**kwargs)
else:
beam_output.text = self.tokenizer.decode(
beam_output.token_ids, **kwargs)
tids = beam_output.token_ids
if trim_eos and tids and tids[-1] == end_id:
tids = tids[:-1]
beam_output.text = self.tokenizer.decode(tids, **kwargs)

is_generating = not self._done
is_finished_with_stop_or_length = (
Expand Down
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