mirror of
https://github.com/nomic-ai/gpt4all
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109 lines
3.6 KiB
Python
109 lines
3.6 KiB
Python
import json
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import torch
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import pickle
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import numpy as np
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from tqdm import tqdm
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from read import read_config
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from argparse import ArgumentParser
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from peft import PeftModelForCausalLM
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from transformers import AutoModelForCausalLM, AutoTokenizer
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'''
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Evaluates perplexity on the outputs of:
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https://github.com/yizhongw/self-instruct/blob/main/human_eval/user_oriented_instructions.jsonl
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'''
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def read_jsonl_file(file_path):
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data = []
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with open(file_path, 'r', encoding='utf-8') as file:
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for line in file:
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json_object = json.loads(line.strip())
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data.append(json_object)
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return data
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def setup_model(config):
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model = AutoModelForCausalLM.from_pretrained(config["model_name"], device_map="auto", torch_dtype=torch.float16, output_hidden_states=True)
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tokenizer = AutoTokenizer.from_pretrained(config["tokenizer_name"])
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added_tokens = tokenizer.add_special_tokens({"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>"})
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if added_tokens > 0:
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model.resize_token_embeddings(len(tokenizer))
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if 'lora' in config and config['lora']:
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model = PeftModelForCausalLM.from_pretrained(model, config["lora_path"], device_map="auto", torch_dtype=torch.float16, return_hidden_states=True)
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model.to(dtype=torch.float16)
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print(f"Mem needed: {model.get_memory_footprint() / 1024 / 1024 / 1024:.2f} GB")
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return model, tokenizer
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def eval_example(model, tokenizer, example, config):
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prompt = example['instruction'] + ' ' + example['instances'][0]['input']
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gt = prompt + ' ' + example['instances'][0]['output']
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#decode several continuations and compute their page trajectories
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input = tokenizer(prompt, return_tensors="pt")
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input = {k: v.to(model.device) for k, v in input.items()}
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#compute the ground truth perplexity
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gt_input = tokenizer(gt, return_tensors="pt")
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gt_input = {k: v.to(model.device) for k, v in gt_input.items()}
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nlls = []
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prev_end_loc = 0
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stride = 512
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seq_len = gt_input['input_ids'].size(1)
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for begin_loc in tqdm(range(input['input_ids'].size(1), gt_input['input_ids'].size(1), stride)):
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end_loc = min(begin_loc + stride, seq_len)
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trg_len = end_loc - prev_end_loc # may be different from stride on last loop
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input_ids = gt_input['input_ids'][:, begin_loc:end_loc].to(model.device)
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target_ids = input_ids.clone()
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target_ids[:, :-trg_len] = -100
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with torch.no_grad():
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outputs = model(input_ids, labels=target_ids)
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neg_log_likelihood = outputs.loss * trg_len
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nlls.append(neg_log_likelihood)
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prev_end_loc = end_loc
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if end_loc == seq_len:
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break
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ppl = torch.exp(torch.stack(nlls).sum() / end_loc).item()
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print('ppl: ', ppl)
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print(prompt)
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print(80*'-')
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return ppl
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def do_eval(config):
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eval_data = read_jsonl_file('eval_data/user_oriented_instructions.jsonl')
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model, tokenizer = setup_model(config)
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all_perplexities = []
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for example in tqdm(eval_data):
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gt_perplexity = eval_example(model, tokenizer, example, config)
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all_perplexities.append(gt_perplexity)
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name = f"eval_data/eval__model-{config['model_name'].replace('/', '_')}{'__lora-' + config['lora_path'].replace('/', '_') if config['lora'] else ''}.pkl"
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with open(name, 'wb') as f:
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r = {'perplexities': all_perplexities}
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pickle.dump(r, f)
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if __name__ == '__main__':
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parser = ArgumentParser()
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parser.add_argument("--config", type=str, required=True)
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args = parser.parse_args()
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config = read_config(args.config)
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do_eval(config)
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