gpt-pretrain/lit_module.py

102 lines
3.5 KiB
Python

from functools import cache
from typing import Dict, Optional
import pytorch_lightning as pl
import torch
import torchmetrics
from utils import init_model
class LitModule(pl.LightningModule):
def __init__(
self,
model_name: str,
learning_rate: float = 0.0001,
use_tril_attention_mask: str = False,
):
super().__init__()
self.save_hyperparameters()
self.llm = self.register_core_module(init_model(model_name))
self.learning_rate = learning_rate
self.use_tril_attention_mask = use_tril_attention_mask
self.metric_loss = torchmetrics.MeanMetric()
self.metric_accuracy = torchmetrics.Accuracy(
task='multiclass',
num_classes=self.llm.config.vocab_size,
)
@cache
def get_batch_tril_matrix(
self, block_size: int, batch_size: Optional[int] = None
) -> torch.Tensor:
matrix = torch.ones(block_size, block_size).tril()
if batch_size is not None:
matrix = matrix.repeat(batch_size, 1, 1)
return matrix
def register_core_module(self, module: torch.nn.Module) -> torch.nn.Module:
object.__setattr__(self, '__core_module__', module)
return module
def training_step(self, batch: Dict[str, torch.Tensor], batch_idx):
batch_size, block_size = batch['input_ids'].shape
if self.use_tril_attention_mask:
batch['attention_mask'] = self.get_batch_tril_matrix(
block_size, batch_size=batch_size
).to(self.device)
outputs = self.llm(**batch, return_dict=True)
loss = outputs.loss
self.log('train_loss', loss, rank_zero_only=True)
return loss
def validation_step(self, batch: Dict[str, torch.Tensor], batch_idx):
outputs = self.llm(**batch, return_dict=True)
loss = outputs.loss
logits = outputs.logits[..., :-1, :]
labels = batch['labels'][..., 1:]
self.metric_loss.update(loss)
label_mask = labels != -100
self.metric_accuracy.update(logits[label_mask], labels[label_mask])
def on_validation_epoch_end(self) -> None:
self.log('val_loss', self.metric_loss, rank_zero_only=True)
self.log('accuracy', self.metric_accuracy, rank_zero_only=True)
def configure_optimizers(self):
strategy = self.trainer.strategy
if isinstance(strategy, pl.strategies.DeepSpeedStrategy):
assert "optimizer" not in strategy.config
zero_config = strategy.config.get("zero_optimization")
if zero_config is not None:
if "offload_optimizer" in zero_config:
import deepspeed
optimizer = deepspeed.ops.adam.DeepSpeedCPUAdam(
self.trainer.model.parameters(), lr=self.learning_rate
)
return optimizer
optimizer = torch.optim.AdamW(
self.trainer.model.parameters(), lr=self.learning_rate
)
return optimizer
def configure_callbacks(self):
checkpoint_callback = pl.callbacks.ModelCheckpoint(
monitor='accuracy',
mode='max',
filename='{epoch:02d}-{accuracy:.4f}',
)
early_stop_callback = pl.callbacks.EarlyStopping(
monitor='accuracy',
min_delta=0.001,
patience=3,
mode='max',
stopping_threshold=1,
)
return [checkpoint_callback, early_stop_callback]