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import re |
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import torch |
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import argparse |
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import jsonlines |
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import numpy as np |
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import datasets |
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from datasets import load_from_disk, load_dataset |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from transformers.generation import GenerationConfig |
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ANS_RE = re.compile(r"#### (\-?[0-9\.\,]+)") |
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INVALID_ANS = "[invalid]" |
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def doc_to_text(doc): |
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return ( |
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fewshot_prompt |
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+ "\nQuestion: " |
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+ doc["question"] |
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+ "\nLet's think step by step\n" |
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) |
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def decode(tokens_list, tokenizer, raw_text_len): |
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sents = [] |
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for tokens in tokens_list: |
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tokens = tokens.cpu().numpy().tolist() |
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sent = tokenizer.tokenizer.decode(tokens[raw_text_len:]) |
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sent = sent.split("<|endoftext|>")[0] |
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sent = sent.split("\n\n\n")[0] |
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sent = sent.split("\n\n")[0] |
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sent = sent.split("Question:")[0] |
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sents.append(sent) |
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return sents |
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def generate_sample(model, tokenizer, input_txt): |
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input_ids = tokenizer.tokenizer.encode(input_txt) |
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raw_text_len = len(input_ids) |
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context_enc = torch.tensor([input_ids]).to(model.device) |
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print(f"Input text: {input_txt}\n") |
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outputs = model.generate(context_enc) |
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output_text = decode(outputs, tokenizer, raw_text_len)[0] |
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print(f"\nOutput text: {output_text}\n") |
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return output_text |
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def extract_answer_hf(completion): |
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match = ANS_RE.search(completion) |
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if match: |
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match_str = match.group(1).strip() |
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match_str = match_str.replace(",", "") |
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return eval(match_str) |
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else: |
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return INVALID_ANS |
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def extract_answer(completion): |
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try: |
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last_number = re.findall(r"\d+", completion)[-1] |
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return eval(last_number) |
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except: |
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return INVALID_ANS |
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def is_correct(completion, answer): |
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gold = extract_answer_hf(answer) |
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assert gold != INVALID_ANS, "No ground truth answer found in the document." |
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return extract_answer(completion) == gold |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser(description="Test HF checkpoint.") |
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parser.add_argument( |
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"-c", |
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"--checkpoint-path", |
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type=str, |
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help="Checkpoint path", |
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default="Qwen/Qwen-7B", |
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) |
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parser.add_argument("-f", "--sample-input-file", type=str, default=None) |
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parser.add_argument( |
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"-o", "--sample-output-file", type=str, default="gsm8k_res.jsonl" |
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) |
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args = parser.parse_args() |
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fewshot_prompt = open("gsm8k_prompt.txt").read() |
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if args.sample_input_file is not None: |
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dataset = load_from_disk(args.sample_input_file) |
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else: |
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config = datasets.DownloadConfig(resume_download=True, max_retries=100) |
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dataset = load_dataset("gsm8k", "main", download_config=config) |
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test = dataset["test"] |
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print("Loading tokenizer ...") |
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tokenizer = AutoTokenizer.from_pretrained( |
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args.checkpoint_path, trust_remote_code=True |
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) |
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print("Loading model ...") |
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model = AutoModelForCausalLM.from_pretrained( |
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args.checkpoint_path, device_map="auto", trust_remote_code=True |
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).eval() |
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model.generation_config = GenerationConfig.from_pretrained( |
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args.checkpoint_path, trust_remote_code=True |
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) |
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model.generation_config.do_sample = False |
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f_output = jsonlines.Writer(open(args.sample_output_file, "w", encoding="utf-8")) |
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tot_length = test.num_rows |
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acc_res = [] |
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for doc in test: |
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context = doc_to_text(doc) |
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completion = generate_sample(model, tokenizer, context) |
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answer = doc["answer"] |
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acc = is_correct(completion, answer) |
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doc["completion"] = completion |
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doc["acc"] = acc |
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f_output.write(doc) |
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acc_res.append(acc) |
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f_output.close() |
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print("Acc: ", np.mean(acc_res)) |
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