lfqa1 / training /run_retriever_no_trainer_gpl.py
Achyut Tiwari
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import argparse
import logging
import math
from dataclasses import dataclass
from typing import List, Any, Union, Optional
import torch
import ujson
from accelerate import Accelerator
from accelerate.utils import set_seed
from torch import nn, Tensor
from torch.nn import functional as F
from torch.utils.data import Dataset, RandomSampler, DataLoader, SequentialSampler
from tqdm.auto import tqdm
from transformers import get_scheduler, AutoTokenizer, AutoModel, AdamW, SchedulerType, PreTrainedTokenizerBase, AutoModelForSequenceClassification, BatchEncoding
from transformers.file_utils import PaddingStrategy
logger = logging.getLogger(__name__)
def get_parser():
parser = argparse.ArgumentParser(description="Train LFQA retriever")
parser.add_argument(
"--dpr_input_file",
type=str,
help="DPR formatted input file with question/positive/negative pairs in a JSONL file",
)
parser.add_argument(
"--per_device_train_batch_size",
type=int,
default=32,
)
parser.add_argument(
"--per_device_eval_batch_size",
type=int,
default=32,
help="Batch size (per device) for the evaluation dataloader.",
)
parser.add_argument(
"--max_length",
type=int,
default=128,
)
parser.add_argument(
"--pretrained_model_name",
type=str,
default="sentence-transformers/all-MiniLM-L6-v2",
)
parser.add_argument(
"--ce_model_name",
type=str,
default="cross-encoder/ms-marco-MiniLM-L-6-v2",
)
parser.add_argument(
"--model_save_name",
type=str,
default="eli5_retriever_model_l-12_h-768_b-512-512",
)
parser.add_argument(
"--learning_rate",
type=float,
default=2e-5,
)
parser.add_argument(
"--weight_decay",
type=float,
default=0.01,
)
parser.add_argument(
"--log_freq",
type=int,
default=500,
help="Log train/validation loss every log_freq update steps"
)
parser.add_argument(
"--num_train_epochs",
type=int,
default=4,
)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--lr_scheduler_type",
type=SchedulerType,
default="linear", # this is linear with warmup
help="The scheduler type to use.",
choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
)
parser.add_argument(
"--num_warmup_steps",
type=int,
default=100,
help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--warmup_percentage",
type=float,
default=0.08,
help="Number of steps for the warmup in the lr scheduler."
)
return parser
@dataclass
class InputExample:
guid: str = ""
texts: List[str] = None
label: Union[int, float] = 0
class DPRDataset(Dataset):
"""
Dataset DPR format of question, answers, positive, negative, and hard negative passages
See https://github.com/facebookresearch/DPR#retriever-input-data-format for more details
"""
def __init__(self, file_path: str, include_all_positive: bool = False) -> None:
super().__init__()
with open(file_path, "r") as fp:
self.data = []
def dpr_example_to_input_example(idx, dpr_item):
examples = []
for p_idx, p_item in enumerate(dpr_item["positive_ctxs"]):
for n_idx, n_item in enumerate(dpr_item["negative_ctxs"]):
examples.append(InputExample(guid=[idx, p_idx, n_idx], texts=[dpr_item["question"],
p_item["text"],
n_item["text"]]))
if not include_all_positive:
break
return examples
for idx, line in enumerate(fp):
self.data.extend(dpr_example_to_input_example(idx, ujson.loads(line)))
def __len__(self):
return len(self.data)
def __getitem__(self, index):
return self.data[index]
def dpr_collate_fn(batch):
query_id, pos_id, neg_id = zip(*[example.guid for example in batch])
query, pos, neg = zip(*[example.texts for example in batch])
return (query_id, pos_id, neg_id), (query, pos, neg)
# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] # First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
return sum_embeddings / sum_mask
@dataclass
class CrossEncoderCollator:
tokenizer: PreTrainedTokenizerBase
model: Any
target_tokenizer: PreTrainedTokenizerBase
padding: Union[bool, str, PaddingStrategy] = True
max_length: Optional[int] = None
pad_to_multiple_of: Optional[int] = None
return_tensors: str = "pt"
def __call__(self, batch):
query_id, pos_id, neg_id = zip(*[example.guid for example in batch])
query, pos_passage, neg_passage = zip(*[example.texts for example in batch])
batch_input: List[List[str]] = list(zip(query, pos_passage)) + list(zip(query, neg_passage))
features = self.tokenizer(batch_input, padding=self.padding, truncation=True,
return_tensors=self.return_tensors)
with torch.no_grad():
scores = self.model(**features).logits
labels = scores[:len(query)] - scores[len(query):]
batch_input: List[str] = list(query) + list(pos_passage) + list(neg_passage)
#breakpoint()
encoded_input = self.target_tokenizer(batch_input, padding=True, truncation=True,
max_length=256, return_tensors='pt')
encoded_input["labels"] = labels
return encoded_input
class RetrievalQAEmbedder(torch.nn.Module):
def __init__(self, sent_encoder, sent_tokenizer, batch_size:int = 32):
super(RetrievalQAEmbedder, self).__init__()
dim = sent_encoder.config.hidden_size
self.model = sent_encoder
self.tokenizer = sent_tokenizer
self.scale = 1
self.similarity_fct = 'dot'
self.batch_size = 32
self.loss_fct = nn.MSELoss()
def forward(self, examples: BatchEncoding):
# Tokenize sentences
labels = examples.pop("labels")
# Compute token embeddings
model_output = self.model(**examples)
examples["labels"] = labels
# Perform pooling. In this case, mean pooling
sentence_embeddings = mean_pooling(model_output, examples['attention_mask'])
target_shape = (3, self.batch_size, sentence_embeddings.shape[-1])
sentence_embeddings_reshaped = torch.reshape(sentence_embeddings, target_shape)
#breakpoint()
embeddings_query = sentence_embeddings_reshaped[0]
embeddings_pos = sentence_embeddings_reshaped[1]
embeddings_neg = sentence_embeddings_reshaped[2]
if self.similarity_fct == 'cosine':
embeddings_query = F.normalize(embeddings_query, p=2, dim=1)
embeddings_pos = F.normalize(embeddings_pos, p=2, dim=1)
embeddings_neg = F.normalize(embeddings_neg, p=2, dim=1)
scores_pos = (embeddings_query * embeddings_pos).sum(dim=-1) * self.scale
scores_neg = (embeddings_query * embeddings_neg).sum(dim=-1) * self.scale
margin_pred = scores_pos - scores_neg
#breakpoint()
return self.loss_fct(margin_pred, labels.squeeze())
def evaluate_qa_retriever(model, data_loader):
# make iterator
epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True)
tot_loss = 0.0
with torch.no_grad():
for step, batch in enumerate(epoch_iterator):
q_ids, q_mask, a_ids, a_mask = batch
loss = model(q_ids, q_mask, a_ids, a_mask)
tot_loss += loss.item()
return tot_loss / (step + 1)
def train(config):
set_seed(42)
args = config["args"]
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
accelerator = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
logger.info(accelerator.state)
# prepare torch Dataset objects
train_dataset = DPRDataset(file_path=args.dpr_input_file)
valid_dataset = Dataset()
base_tokenizer = AutoTokenizer.from_pretrained(args.pretrained_model_name)
base_model = AutoModel.from_pretrained(args.pretrained_model_name)
ce_tokenizer = AutoTokenizer.from_pretrained(args.ce_model_name)
ce_model = AutoModelForSequenceClassification.from_pretrained(args.ce_model_name)
_ = ce_model.eval()
model = RetrievalQAEmbedder(base_model, base_tokenizer)
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
'weight_decay': args.weight_decay},
{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
cec = CrossEncoderCollator(model=ce_model, tokenizer=ce_tokenizer, target_tokenizer=base_tokenizer)
train_dataloader = DataLoader(train_dataset, batch_size=args.per_device_train_batch_size,
sampler=RandomSampler(train_dataset), collate_fn=cec)
eval_dataloader = DataLoader(valid_dataset, batch_size=args.per_device_eval_batch_size,
sampler=SequentialSampler(valid_dataset), collate_fn=cec)
# train the model
model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(model, optimizer,
train_dataloader, eval_dataloader)
# Scheduler and math around the number of training steps.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
else:
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
num_warmup_steps = args.num_warmup_steps if args.num_warmup_steps else math.ceil(args.max_train_steps *
args.warmup_percentage)
scheduler = get_scheduler(
name=args.lr_scheduler_type,
optimizer=optimizer,
num_warmup_steps=args.num_warmup_steps,
num_training_steps=args.max_train_steps,
)
# Train!
total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
logger.info(f" Warmup steps = {num_warmup_steps}")
logger.info(f" Logging training progress every {args.log_freq} optimization steps")
loc_loss = 0.0
current_loss = 0.0
checkpoint_step = 0
completed_steps = checkpoint_step
progress_bar = tqdm(range(args.max_train_steps), initial=checkpoint_step,
disable=not accelerator.is_local_main_process)
for epoch in range(args.num_train_epochs):
model.train()
for step, batch in enumerate(train_dataloader, start=checkpoint_step):
# model inputs
pre_loss = model(batch)
loss = pre_loss / args.gradient_accumulation_steps
accelerator.backward(loss)
loc_loss += loss.item()
if ((step + 1) % args.gradient_accumulation_steps == 0) or (step + 1 == len(train_dataloader)):
current_loss = loc_loss
optimizer.step()
scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)
progress_bar.set_postfix(loss=loc_loss)
loc_loss = 0
completed_steps += 1
if step % (args.log_freq * args.gradient_accumulation_steps) == 0:
# accelerator.wait_for_everyone()
# unwrapped_model = accelerator.unwrap_model(model)
# eval_loss = evaluate_qa_retriever(unwrapped_model, eval_dataloader)
eval_loss = 0
logger.info(f"Train loss {current_loss} , eval loss {eval_loss}")
if args.wandb and accelerator.is_local_main_process:
import wandb
wandb.log({"loss": current_loss, "eval_loss": eval_loss, "step": completed_steps})
if completed_steps >= args.max_train_steps:
break
logger.info("Saving model {}".format(args.model_save_name))
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
accelerator.save(unwrapped_model.state_dict(), "{}_{}.bin".format(args.model_save_name, epoch))
eval_loss = evaluate_qa_retriever(unwrapped_model, eval_dataloader)
logger.info("Evaluation loss epoch {:4d}: {:.3f}".format(epoch, eval_loss))
if __name__ == "__main__":
parser = get_parser()
parser.add_argument(
"--wandb",
action="store_true",
help="Whether to use W&B logging",
)
main_args, _ = parser.parse_known_args()
config = {"args": main_args}
if main_args.wandb:
import wandb
wandb.init(project="Retriever")
train(config=config)