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evaluate_main.py
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from data_utils.prompt_datasets import PromptDataset
from transformers import GenerationConfig, mpu
import os
import nltk
nltk.download("punkt")
import torch
import torch.nn as nn
import torch.distributed as dist
from torch.utils.data import DataLoader, DistributedSampler
import torch.nn.functional as F
from tqdm import tqdm
import numpy as np
import json
from utils import print_rank, save_rank, all_gather
from rouge_metric import compute_metrics
torch.set_num_threads(4)
def prepare_dataset_main(args, tokenizer):
data = {}
data["test"] = PromptDataset(args, tokenizer, "valid", args.data_dir, args.dev_num)
return data
def run_model(args, tokenizer, model, dataset: PromptDataset, epoch, device):
collate_fn = dataset.collate
if args.model_parallel:
dp_world_size = mpu.get_data_parallel_world_size()
dp_rank = mpu.get_data_parallel_rank()
dp_group = mpu.get_data_parallel_group()
else:
dp_world_size = dist.get_world_size()
dp_rank = dist.get_rank()
dp_group = None
sampler = DistributedSampler(dataset, shuffle=False, drop_last=False, rank=dp_rank, num_replicas=dp_world_size)
dataloader = DataLoader(
dataset, sampler=sampler, batch_size=args.eval_batch_size, num_workers=args.num_workers, collate_fn=collate_fn)
model.eval()
all_query_ids = []
all_response_ids = []
all_lm_losses = []
generation_config = GenerationConfig (
do_sample=args.do_sample,
top_p=args.top_p,
top_k=args.top_k,
temperature=args.temperature,
no_repeat_ngram_size=args.no_repeat_ngram_size,
repetition_penalty=args.repetition_penalty,
max_length=args.max_length,
min_length=None,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
return_dict_in_generate=True,
output_scores=True
)
with torch.no_grad():
for it, (model_batch, no_model_batch) in enumerate(tqdm(dataloader, desc=f"Evaluating {args.data_names} ", disable=(dist.get_rank() != 0))):
if it == 0:
print_rank("############### Example ###############")
print_rank(tokenizer.decode(model_batch["input_ids"][0], skip_special_tokens=True))
print_rank("############### End ###############")
dataset.move_to_device(model_batch, no_model_batch, device)
all_ids = torch.cat([model_batch["input_ids"], no_model_batch["rest_ids"]], dim=-1)
input_ids = all_ids[:, :-1]
attention_mask = (input_ids != tokenizer.pad_token_id).long()
label_ids = all_ids[:, 1:]
label_ids = torch.masked_fill(label_ids, label_ids==tokenizer.pad_token_id, -100)
label_ids[:, :model_batch["input_ids"].size(1)-1] = -100
if args.model_type in ["gpt2"]:
position_ids = (torch.cumsum(attention_mask, dim=-1) - 1) * attention_mask
out = model(input_ids=input_ids, position_ids=position_ids, attention_mask=attention_mask, return_dict=True)
else:
out = model(input_ids=input_ids, attention_mask=attention_mask, return_dict=True)
logits = out.logits
loss_mask = (label_ids != -100).float()
if args.model_parallel:
lm_loss = mpu.parallel_cross_entropy(logits, label_ids)
lm_loss = torch.sum(lm_loss * loss_mask, dim=-1) / torch.sum(loss_mask, dim=-1)
else:
loss_func = nn.CrossEntropyLoss(reduction="none")
lm_loss = loss_func(logits.view(-1, logits.size(-1)), label_ids.view(-1)).view(label_ids.size())
lm_loss = torch.sum(lm_loss * loss_mask, -1) / torch.sum(loss_mask, -1)
all_lm_losses.append(lm_loss)
query_ids = model_batch["input_ids"]
max_new_tokens = args.max_length - query_ids.size(1)
gen_out = model.generate(
**model_batch,
generation_config=generation_config,
max_new_tokens=max_new_tokens
)
full_ids = gen_out.sequences
response_ids = full_ids[:, query_ids.size(1):] # remove prompt (may include start token)
query_ids = F.pad(query_ids, (args.max_prompt_length-query_ids.size(1), 0, 0, 0), value=tokenizer.pad_token_id)
response_ids = F.pad(response_ids, (0, args.max_length-args.max_prompt_length-response_ids.size(1), 0, 0), value=tokenizer.pad_token_id)
all_query_ids.append(query_ids)
all_response_ids.append(response_ids)
all_lm_losses = torch.cat(all_lm_losses)
mean_lm_loss = all_lm_losses.mean()
dist.all_reduce(mean_lm_loss, dist.ReduceOp.SUM, group=dp_group)
mean_lm_loss = mean_lm_loss.item() / dp_world_size
all_query_ids = torch.cat(all_query_ids)
all_query_ids = all_gather(all_query_ids, dim=1, group=dp_group, world_size=dp_world_size, op="stack")
all_query_ids = all_query_ids.view(-1, all_query_ids.size(-1))
all_query_ids = all_query_ids[:len(dataset)]
all_response_ids = torch.cat(all_response_ids)
all_response_ids = all_gather(all_response_ids, dim=1, group=dp_group, world_size=dp_world_size, op="stack")
all_response_ids = all_response_ids.view(-1, all_response_ids.size(-1))
all_response_ids = all_response_ids[:len(dataset)]
return (
mean_lm_loss,
all_query_ids,
all_response_ids)
def evaluate_main(args, tokenizer, model, dataset: PromptDataset, split, epoch, device):
lm_loss, query_ids, response_ids = run_model(args, tokenizer, model, dataset, epoch, device)
query_strs = tokenizer.batch_decode(query_ids, skip_special_tokens=True)
response_strs = tokenizer.batch_decode(response_ids, skip_special_tokens=True)
with open(os.path.join(args.save, "preds.txt"), "w") as f:
for q, r in zip(query_strs, response_strs):
f.write(q.replace("\n", "<n>") + "\t\t" + r.replace("\n", "<n>") + "\n")
all_preds = [[]]
for q, r in zip(query_strs, response_strs):
all_preds[0].append((q, q + r))
torch.save(all_preds, os.path.join(args.save, "preds.pt"))
all_responses = []
with open(os.path.join(args.save, "answers.jsonl"), "w") as f:
for p in all_preds[0]:
q, r = p
r = r[len(q):]
idx = r.find("<|endoftext|>")
if idx >= 0:
r = r[:idx]
f.write(json.dumps({
"text": r.replace("<n>", "\n").strip()
}) + "\n")
all_responses.append(r.replace("<n>", "\n").strip())
gen_res = compute_metrics(all_responses, dataset.answers)
mean_gen_length = np.mean([len(tokenizer.encode(s)) for s in response_strs])
log_str = f"{split} | name: {args.data_names} | {gen_res} | lm_loss {round(lm_loss, 4)} | avg. gen lenth: {mean_gen_length}"
print_rank(log_str)
save_rank(log_str, os.path.join(args.save, "log.txt"))