83 lines
3.8 KiB
Python
83 lines
3.8 KiB
Python
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#!/usr/bin/env python3
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"""Generate Lean proofs with the paper's prompt and last-complete-block parser."""
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from __future__ import annotations
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import argparse
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import json
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from pathlib import Path
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from prompt_utils import (
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LEAN4_DEFAULT_HEADER, DEFAULT_GENERATION_TOP_P,
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build_prover_prompt_for_example, default_generation_temperature,
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default_generation_max_tokens, default_lean_stop_sequences,
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parse_generated_lean, split_context_and_command,
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)
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DEFAULT_MODEL = 'EPFL-LARA/lean-standard-prover-qwen3-8b-reasoning-grpo'
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FEEDBACK = False
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REASONING = True
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def build_prompt(source: str) -> str:
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context, declaration, found = split_context_and_command(source)
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if not found:
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raise ValueError("Supply a Lean theorem, lemma, or example ending in 'by sorry'.")
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example = {"formal_statement": declaration, "header": context or LEAN4_DEFAULT_HEADER}
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_, prompt = build_prover_prompt_for_example(
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example, use_informal_statement=False, is_deepseek=False,
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tgat_prompt=FEEDBACK, reasoning=REASONING,
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)
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return prompt
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def main() -> None:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("files", nargs="+", type=Path, help="Lean problem files, including imports and a theorem to complete")
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parser.add_argument("--model", default=DEFAULT_MODEL)
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parser.add_argument("--samples", type=int, default=1)
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parser.add_argument("--temperature", type=float)
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parser.add_argument("--top-p", type=float, default=DEFAULT_GENERATION_TOP_P)
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parser.add_argument("--max-new-tokens", type=int, default=default_generation_max_tokens(False))
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parser.add_argument("--tensor-parallel-size", type=int, default=1)
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parser.add_argument("--seed", type=int, default=0)
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parser.add_argument("--print-prompt", action="store_true", help="Show the exact raw prompt without loading the model")
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args = parser.parse_args()
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if args.samples < 1 or args.max_new_tokens < 1:
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parser.error("--samples and --max-new-tokens must be positive")
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prompts = [build_prompt(path.read_text(encoding="utf-8")) for path in args.files]
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if args.print_prompt:
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for prompt in prompts:
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print(prompt, end="")
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return
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from vllm import LLM, SamplingParams
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llm = LLM(model=args.model, tokenizer=args.model,
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tensor_parallel_size=args.tensor_parallel_size, dtype="auto",
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max_model_len=32768, trust_remote_code=False, seed=args.seed,
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gpu_memory_utilization=0.90)
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tokenizer = llm.get_tokenizer()
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tokenized = [tokenizer.encode(prompt, add_special_tokens=False) for prompt in prompts]
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if any(len(ids) >= 32768 for ids in tokenized):
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raise ValueError("The problem prompt must leave space in the 32,768-token context for a proof.")
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temperature = args.temperature if args.temperature is not None else default_generation_temperature(args.samples)
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params = [SamplingParams(n=args.samples, temperature=temperature,
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top_p=args.top_p,
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max_tokens=min(args.max_new_tokens, 32768-len(ids)),
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stop=default_lean_stop_sequences(False),
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include_stop_str_in_output=True, seed=args.seed)
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for ids in tokenized]
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outputs = llm.generate([{"prompt_token_ids": ids} for ids in tokenized], params)
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for path, output in zip(args.files, outputs, strict=True):
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for i, completion in enumerate(output.outputs):
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print(json.dumps({
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"source": str(path), "sample_id": i,
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"response": completion.text,
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"lean_code": parse_generated_lean(completion.text, is_deepseek=False),
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"finish_reason": completion.finish_reason,
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}, ensure_ascii=False), flush=True)
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if __name__ == "__main__":
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main()
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