gpt4all/gpt4all-training/eval_self_instruct.py

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#!/usr/bin/env python3
import json
import torch
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import pickle
import numpy as np
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from tqdm import tqdm
from read import read_config
from argparse import ArgumentParser
from peft import PeftModelForCausalLM
from transformers import AutoModelForCausalLM, AutoTokenizer
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'''
Evaluates perplexity on the outputs of:
https://github.com/yizhongw/self-instruct/blob/main/human_eval/user_oriented_instructions.jsonl
'''
def read_jsonl_file(file_path):
data = []
with open(file_path, 'r', encoding='utf-8') as file:
for line in file:
json_object = json.loads(line.strip())
data.append(json_object)
return data
def setup_model(config):
model = AutoModelForCausalLM.from_pretrained(config["model_name"], device_map="auto", torch_dtype=torch.float16, output_hidden_states=True)
tokenizer = AutoTokenizer.from_pretrained(config["tokenizer_name"])
added_tokens = tokenizer.add_special_tokens({"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>"})
if added_tokens > 0:
model.resize_token_embeddings(len(tokenizer))
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if 'lora' in config and config['lora']:
model = PeftModelForCausalLM.from_pretrained(model, config["lora_path"], device_map="auto", torch_dtype=torch.float16, return_hidden_states=True)
model.to(dtype=torch.float16)
print(f"Mem needed: {model.get_memory_footprint() / 1024 / 1024 / 1024:.2f} GB")
return model, tokenizer
def eval_example(model, tokenizer, example, config):
prompt = example['instruction'] + ' ' + example['instances'][0]['input']
gt = prompt + ' ' + example['instances'][0]['output']
#decode several continuations and compute their page trajectories
input = tokenizer(prompt, return_tensors="pt")
input = {k: v.to(model.device) for k, v in input.items()}
#compute the ground truth perplexity
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gt_input = tokenizer(gt, return_tensors="pt")
gt_input = {k: v.to(model.device) for k, v in gt_input.items()}
nlls = []
prev_end_loc = 0
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stride = 512
seq_len = gt_input['input_ids'].size(1)
for begin_loc in tqdm(range(input['input_ids'].size(1), gt_input['input_ids'].size(1), stride)):
end_loc = min(begin_loc + stride, seq_len)
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)
target_ids = input_ids.clone()
target_ids[:, :-trg_len] = -100
with torch.no_grad():
outputs = model(input_ids, labels=target_ids)
neg_log_likelihood = outputs.loss * trg_len
nlls.append(neg_log_likelihood)
prev_end_loc = end_loc
if end_loc == seq_len:
break
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ppl = torch.exp(torch.stack(nlls).sum() / end_loc).item()
print('ppl: ', ppl)
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print(prompt)
print(80*'-')
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return ppl
def do_eval(config):
eval_data = read_jsonl_file('eval_data/user_oriented_instructions.jsonl')
model, tokenizer = setup_model(config)
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all_perplexities = []
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"
with open(name, 'wb') as f:
r = {'perplexities': all_perplexities}
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pickle.dump(r, f)
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument("--config", type=str, required=True)
args = parser.parse_args()
config = read_config(args.config)
do_eval(config)