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evaluate_main.py
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import os
import json
import numpy as np
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as dist
from torch.utils.data import DataLoader, DistributedSampler
from transformers import (
GenerationConfig,
PreTrainedTokenizer,
PreTrainedTokenizerFast,
PreTrainedModel,
mpu,
)
from utils import print_rank, save_rank, all_gather
from data_utils.prompt_datasets import PromptDataset
from rouge_metric import compute_metrics
torch.set_num_threads(4)
def prepare_dataset_main(args, tokenizer: PreTrainedTokenizerFast | PreTrainedTokenizer):
data = {}
data["test"] = PromptDataset(args, tokenizer, "valid", args.data_dir, args.dev_num)
return data
def run_model(args, tokenizer: PreTrainedTokenizerFast | PreTrainedTokenizer, 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: PreTrainedTokenizer | PreTrainedTokenizerFast,
model: PreTrainedModel, dataset: PromptDataset, split: str, epoch: int, device: int):
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"))