426 lines
15 KiB
Python
426 lines
15 KiB
Python
import os
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import subprocess
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import sys
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def _install_bundled_deps() -> None:
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"""Install transformers from bundled wheels (eval sandbox has no PyPI access)."""
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wheels_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "wheels")
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if not os.path.isdir(wheels_dir):
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return
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subprocess.run(
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[
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sys.executable,
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"-m",
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"pip",
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"install",
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"-q",
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"--no-index",
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f"--find-links={wheels_dir}",
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"transformers==4.56.2",
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],
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check=True,
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)
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_install_bundled_deps()
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import re
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import csv
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import json
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import shutil
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import tempfile
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import unicodedata
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from collections import Counter
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# The repo is the working directory at run time, and there is no network.
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os.environ["HF_HUB_OFFLINE"] = "1"
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os.environ["TRANSFORMERS_OFFLINE"] = "1"
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MODEL_ID = "."
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MAX_NEW_TOKENS = 1200 # was 2000; cut to fit T4 wall clock
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MAX_NEW_TOKENS_EXPLAIN = 600
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TEMPERATURE = 0.8
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TOP_P = 0.95
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NUM_SAMPLES = 3 # independent rollouts per problem (was 5; timeout)
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MAX_ATTEMPTS = 1 # no per-sample retry; majority vote absorbs misses
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def load_tokenizer(model_id: str = "."):
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"""Load tokenizer, converting tokenizer.json for older tokenizers if needed."""
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tokenizer_path = os.path.join(model_id, "tokenizer.json")
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with open(tokenizer_path, encoding="utf-8") as handle:
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data = json.load(handle)
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merges = data.get("model", {}).get("merges", [])
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if not merges or not isinstance(merges[0], list):
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return AutoTokenizer.from_pretrained(model_id)
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# Older tokenizers expect merge pairs as "a b" strings, not ["a", "b"] lists.
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data["model"]["merges"] = [" ".join(piece) for piece in merges]
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tmpdir = tempfile.mkdtemp()
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for name in ("tokenizer_config.json", "special_tokens_map.json"):
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src = os.path.join(model_id, name)
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if os.path.isfile(src):
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shutil.copy(src, tmpdir)
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with open(os.path.join(tmpdir, "tokenizer.json"), "w", encoding="utf-8") as handle:
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json.dump(data, handle)
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return AutoTokenizer.from_pretrained(tmpdir)
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SYSTEM = (
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"You solve International Linguistics Olympiad problems by reasoning from the "
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"data in CONTEXT you are given to solve the problems in QUERY. \n"
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"There are common TASK TYPES that we specify below, but "
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"you may meet a TASK TYPE you have never seen: read the "
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"instruction and the examples, and answer the QUERY in the same form they use.\n\n"
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"Common TASK TYPES and what to return: \n"
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"`translation`: return the translated form only, in the language the task asks for; \n"
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"`fill_blanks`: return only the missing form for each indicated blank "
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"(beware: this could be many different things: a word, a part of a word or a phonetic transcription---pay close attention to what part of the CONTEXT is missing in QUERY); \n"
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"`match_letters`: return only the option letter (for example A, B, C); \n"
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"`text_to_num`: return the number in digits; \n"
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"`num_to_text`: return the number written out in words, in the language asked; \n"
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"any other type: return exactly what the instruction asks for, nothing else. \n\n"
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"As the first part of your answer, reason step by step about (1) the linguistic "
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"rules that can be deduced from the given examples in CONTEXT, and (2) "
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"how to apply them to the given problems in QUERY, and (3) in what format answers need to be returned (words, numbers, phonetic transcriptions, ...). \n"
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"Then write a draft of the final answer. "
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"Subsequently, compare it with the format requirements again, "
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"and verify it's compliant with the deduced rules, and it is complete, i.e. has an answer for each element in QUERY. "
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"If necessary, correct and refine."
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"Finally, write a line that says exactly `FINAL ANSWERS:` "
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"and, below it, write the answers to the items requested in QUERY (not those in CONTEXT),"
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"one answer per line (separated by \n) in the order the items are asked for in the QUERY -- the "
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"bare answer only, no numbering, no quotes, no extra text, according to the given TASK TYPE."
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)
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SYSTEM_POST_EXPLAIN = (
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"You are a helpful assistant that explains the reasoning behind the answers to the International Linguistics Olympiad problems.\n"
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"You are given the following information:\n"
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"- The context of the problem\n"
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"- The task type\n"
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"- The query\n"
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"- The full model output (reasoning draft and answers)\n"
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"You need to condense that output into a concise explanation focused on the key insights "
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"and linguistic rules deduced and applied.\n"
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"Do not include any other text, do not include the answer in the explanation, "
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"and do not invent any new information."
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)
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# Prefer a dedicated header line; also allow same-line answers after the colon.
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FINAL_ANSWERS_LINE_RE = re.compile(
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r"(?im)^[^\w\n]*final answers?[^\w\n]*:?[ \t]*(?=\n|$)|"
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r"(?im)^[^\w\n]*final answers?\s*:\s*"
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)
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FINAL_ANSWERS_INLINE_RE = re.compile(
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r"(?is)\bfinal answers?\s*:\s*"
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)
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def extract_raw_final(text: str) -> str:
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"""Return text after the last final-answers marker, or '' if none found."""
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line_matches = list(FINAL_ANSWERS_LINE_RE.finditer(text))
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if line_matches:
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return text[line_matches[-1].end() :]
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inline_matches = list(FINAL_ANSWERS_INLINE_RE.finditer(text))
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if inline_matches:
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return text[inline_matches[-1].end() :]
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return ""
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def expected_answer_count(query: str, task_type: str) -> int:
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if task_type == "match_letters":
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numbered = re.findall(r"^\s*\d+\.", query, re.MULTILINE)
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return len(numbered) or 1
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if "blanks" in query.lower():
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range_match = re.search(r"\((\d+)-(\d+)\)", query)
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if range_match:
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return int(range_match.group(2)) - int(range_match.group(1)) + 1
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return len(re.findall(r"\(\d+\)", query)) or 1
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numbered = re.findall(r"^\s*\d+[.)]", query, re.MULTILINE)
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return len(numbered) or 1
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def split_single_line_answer(text: str, expected: int, task_type: str) -> list[str]:
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text = text.strip()
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if expected <= 1:
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return [text]
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def try_split(pattern: str) -> list[str] | None:
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parts = [part.strip() for part in re.split(pattern, text) if part.strip()]
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return parts if len(parts) == expected else None
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if task_type == "match_letters":
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for pattern in (r"\s+", r",\s*", r";\s*"):
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if result := try_split(pattern):
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return result
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letters = re.findall(r"[A-Za-z]", text)
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if len(letters) == expected:
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return [letter.upper() for letter in letters]
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return [text]
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if task_type in ("text_to_num", "num_to_text"):
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for pattern in (r",\s*", r";\s*", r"\s+"):
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if result := try_split(pattern):
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return result
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return [text]
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for pattern in (r";\s*", r",\s*"):
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if result := try_split(pattern):
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return result
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return [text]
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def parse_answer_lines(text_after_marker: str, query: str, task_type: str) -> list[str]:
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"""Parse cleaned answer lines from the raw final-answers section."""
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answers = []
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for line in text_after_marker.splitlines():
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stripped_line = line.strip("`").strip()
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if stripped_line == "":
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continue
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match_numbered_prefix = re.match(r"^\s*\d+[.)]\s+(.*)", stripped_line)
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if match_numbered_prefix:
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cleaned_line = match_numbered_prefix.group(1).strip()
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else:
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cleaned_line = stripped_line
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cleaned_line = re.sub(r"\*\*", "", cleaned_line).strip()
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if task_type == "match_letters":
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parts = [
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part.strip("().[]")
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for part in re.split(r"[\s,;]+", cleaned_line)
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if part.strip()
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]
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if not (
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len(parts) > 1
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and all(re.fullmatch(r"[A-Za-z]", part) for part in parts)
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):
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match_letter_word = re.match(
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r"^\s*(?:\(([A-Za-z])\)|\[([A-Za-z])\]|([A-Za-z]))\.?:?\s*(.*)$",
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cleaned_line,
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)
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if match_letter_word:
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letter = (
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match_letter_word.group(1)
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or match_letter_word.group(2)
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or match_letter_word.group(3)
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)
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cleaned_line = letter.upper()
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if cleaned_line:
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answers.append(cleaned_line)
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expected = expected_answer_count(query, task_type)
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if len(answers) == 1 and expected > 1:
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answers = split_single_line_answer(answers[0], expected, task_type)
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return answers
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def postprocess_answer(text, query, task_type):
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"""Keep only the content after the last 'FINAL ANSWERS' marker."""
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text_after_marker = extract_raw_final(text)
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if not text_after_marker.strip():
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return []
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return parse_answer_lines(text_after_marker, query, task_type)
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def normalize_for_vote(text: str, task_type: str) -> str:
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text = unicodedata.normalize("NFC", text.strip())
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if task_type == "match_letters":
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return text.upper()
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return " ".join(text.split())
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def majority_vote(
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rollouts: list[list[str]],
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expected: int,
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task_type: str,
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) -> list[str]:
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"""Per-item majority vote; ties break toward the first sample."""
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if expected <= 0:
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return []
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# Prefer rollouts whose length matches the expected answer count.
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eligible_idx = [
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i
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for i, rollout in enumerate(rollouts)
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if len(rollout) == expected and any(a.strip() for a in rollout)
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]
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if not eligible_idx:
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eligible_idx = [
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i
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for i, rollout in enumerate(rollouts)
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if any(a.strip() for a in rollout)
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]
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if not eligible_idx:
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return []
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final: list[str] = []
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for slot in range(expected):
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tagged = [
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(i, rollouts[i][slot])
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for i in eligible_idx
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if slot < len(rollouts[i]) and rollouts[i][slot].strip()
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]
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if not tagged:
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final.append("")
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continue
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pairs = [
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(i, normalize_for_vote(ans, task_type), ans) for i, ans in tagged
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]
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counter = Counter(norm for _, norm, _ in pairs)
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top_count = max(counter.values())
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tied_norms = {norm for norm, count in counter.items() if count == top_count}
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# Tie-break: earliest sample whose answer is among the tied winners.
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for i, norm, ans in sorted(pairs, key=lambda p: p[0]):
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if norm in tied_norms:
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final.append(ans)
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break
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return final
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def select_source_sample(
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rollouts: list[list[str]],
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voted: list[str],
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task_type: str,
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) -> int:
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"""Pick the sample closest to the voted answers (tie: earliest index)."""
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if not rollouts:
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return 0
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best_i = 0
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best_score = -1
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for i, rollout in enumerate(rollouts):
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score = 0
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for slot, ans in enumerate(voted):
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if (
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slot < len(rollout)
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and rollout[slot].strip()
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and normalize_for_vote(rollout[slot], task_type)
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== normalize_for_vote(ans, task_type)
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):
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score += 1
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if score > best_score:
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best_score = score
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best_i = i
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return best_i
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def generate_sample(tok, model, ids) -> str:
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"""Sample one completion, retrying if FINAL ANSWERS cannot be extracted."""
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text = ""
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for attempt in range(1, MAX_ATTEMPTS + 1):
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with torch.no_grad():
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out = model.generate(
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ids,
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max_new_tokens=MAX_NEW_TOKENS,
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do_sample=True,
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temperature=TEMPERATURE,
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top_p=TOP_P,
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)
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text = tok.decode(out[0][ids.shape[-1] :], skip_special_tokens=True).strip()
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if extract_raw_final(text).strip():
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return text
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print(f" retry {attempt}/{MAX_ATTEMPTS}: no FINAL ANSWERS", flush=True)
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return text
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def generate_explanation(
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tok, model, context: str, task_type: str, query: str, source_text: str,
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) -> str:
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"""Condense one selected sample's full output into a short explanation."""
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messages = [
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{"role": "system", "content": SYSTEM_POST_EXPLAIN},
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{
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"role": "user",
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"content": (
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f"CONTEXT:{context.strip()}\n"
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f"TASK TYPE:`{task_type}`\n\n"
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f"QUERY:{query.strip()}\n\n"
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f"MODEL OUTPUT:{source_text.strip()}"
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),
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},
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]
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ids = tok.apply_chat_template(
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messages, add_generation_prompt=True, return_tensors="pt",
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).to(model.device)
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with torch.no_grad():
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out = model.generate(
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ids,
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max_new_tokens=MAX_NEW_TOKENS_EXPLAIN,
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do_sample=False,
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)
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return tok.decode(out[0][ids.shape[-1] :], skip_special_tokens=True).strip()
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tok = load_tokenizer(MODEL_ID)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID, torch_dtype=torch.float16, device_map="auto"
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).eval()
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with open("/tmp/data/test.csv", encoding="utf-8", newline="") as f:
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test_rows = list(csv.DictReader(f))
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# Write incrementally so a wall-clock kill still leaves a partial submission.csv.
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with open("submission.csv", "w", encoding="utf-8", newline="") as f:
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writer = csv.DictWriter(f, fieldnames=["id", "pred", "explanation"])
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writer.writeheader()
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f.flush()
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for idx, r in enumerate(test_rows, start=1):
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messages = [
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{"role": "system", "content": SYSTEM},
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{
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"role": "user",
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"content": (
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f"CONTEXT:{r['context'].strip()}\n"
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f"TASK TYPE:`{r['task_type']}`\n\n"
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f"QUERY:{r['query'].strip()}"
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),
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},
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]
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ids = tok.apply_chat_template(
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messages, add_generation_prompt=True, return_tensors="pt",
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).to(model.device)
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print(f"{idx}/{len(test_rows)} sampling {NUM_SAMPLES}x", flush=True)
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raw_texts: list[str] = []
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rollouts: list[list[str]] = []
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for s in range(1, NUM_SAMPLES + 1):
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text = generate_sample(tok, model, ids)
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answers = postprocess_answer(text, r["query"], r["task_type"])
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raw_texts.append(text)
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rollouts.append(answers)
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print(f" sample {s}/{NUM_SAMPLES} parsed={answers!r}", flush=True)
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expected = expected_answer_count(r["query"], r["task_type"])
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voted = majority_vote(rollouts, expected, r["task_type"])
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source_i = select_source_sample(rollouts, voted, r["task_type"])
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print(f" vote -> {voted!r} (explain from sample {source_i + 1})", flush=True)
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explanation = generate_explanation(
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tok, model, r["context"], r["task_type"], r["query"], raw_texts[source_i],
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)
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writer.writerow(
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{
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"id": r["id"],
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"pred": json.dumps(voted, ensure_ascii=False),
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"explanation": explanation,
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}
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)
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f.flush()
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print("wrote submission.csv", flush=True)
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