Heuristic:Iamhankai Forest of Thought Input Length Overflow Recovery
| Knowledge Sources | |
|---|---|
| Domains | Debugging, LLMs, Inference |
| Last Updated | 2026-02-14 03:30 GMT |
Overview
Error recovery pattern that catches `ValueError` from input length exceeding `max_length`, parses the actual input length from the error message, and retries with an extended buffer of +100 tokens.
Description
When running inference on long prompts (multi-turn reasoning histories, few-shot examples), the tokenized input can exceed the configured `max_length` parameter. Instead of failing, the Pipeline class catches the specific `ValueError` pattern `"Input length of input_ids is X, but max_length is set to Y"`, extracts the actual input length X from the error message string, and retries generation with `max_length = X + 100`. This adds a small buffer to accommodate the output tokens.
This pattern appears in both the pipeline-based Mistral/Game24 path and the direct model GLM path, indicating it was encountered and solved across multiple model architectures.
Usage
This heuristic is applied automatically during inference. It is relevant when:
- Running with long conversation histories (MCTS multi-step reasoning chains)
- Using few-shot prompting with verbose examples
- Processing questions with long problem descriptions
No user action is needed — the recovery is built into the generation code.
The Insight (Rule of Thumb)
- Action: Wrap `model.generate()` or `pipeline()` in a try/except that catches `ValueError` with the specific "Input length" message pattern.
- Value: Parse the actual input length from the error message and add a buffer of +100 tokens.
- Trade-off: The retry adds one failed forward pass overhead. The +100 buffer is conservative; if the model needs more than 100 output tokens, truncation occurs silently. For long-form answers, a larger buffer may be needed.
- Fragility: This relies on parsing the exact error message string from HuggingFace Transformers, which could break with library version updates.
Reasoning
The `max_length` parameter in HuggingFace Transformers controls the total sequence length (input + output). When the input alone exceeds this limit, the library raises a ValueError rather than silently truncating. Since the Forest-of-Thought framework builds up multi-turn conversation histories during MCTS search (each step appends hints and refined answers), the input length is unpredictable and can vary significantly between problems. The error-and-retry approach is more robust than pre-computing exact token counts, though it incurs the cost of one failed forward pass.
Code Evidence
Pipeline path recovery from `models/load_local_model.py:L72-85`:
except ValueError as e:
if "Input length of input_ids is" in str(e) and "but `max_length` is set to" in str(e):
max_length = int(str(e).split("Input length of input_ids is ")[1].split(",")[0])
max_length = max_length + 100 # Add some buffer
outputs = self.pipeline(
messages,
max_new_tokens=max_length,
eos_token_id=terminators,
temperature=0.95,
do_sample=True,
pad_token_id=self.pipeline.tokenizer.eos_token_id,
)
else:
raise
GLM model path recovery from `models/load_local_model.py:L146-152`:
except ValueError as e:
if "Input length of input_ids is" in str(e) and "but `max_length` is set to" in str(e):
input_length = int(str(e).split("Input length of input_ids is ")[1].split(",")[0])
gen_kwargs["max_length"] = input_length + 100 # Add some buffer
generated_ids = self.model.generate(**inputs, **gen_kwargs)
else:
raise