Witllm/wit/lit_module.py

105 lines
3.8 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
# from custom_models.gpt2.modeling_gpt2 import GPT2LMHeadModel
from modeling_wit import QWenLMHeadModel
from configuration_qwen import QWenConfig
from transformers import AutoConfig
class LitModule(pl.LightningModule):
def __init__(
self,
model_dir: str,
learning_rate: float = 0.0001,
use_tril_attention_mask: str = False,
):
super().__init__()
self.save_hyperparameters()
config = QWenConfig()
model = QWenLMHeadModel(config)
model = model.from_pretrained(model_dir)
self.llm = self.register_core_module(model)
self.learning_rate = learning_rate
self.use_tril_attention_mask = use_tril_attention_mask
self.metric_loss = torchmetrics.MeanMetric()
self.vocab_size = self.llm.config.vocab_size
self.metric_accuracy = torchmetrics.Accuracy(
task="multiclass",
num_classes=self.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, loss = self.llm(**batch)
self.log("train_loss", loss, rank_zero_only=True)
return loss
def validation_step(self, batch: Dict[str, torch.Tensor], batch_idx):
outputs, loss = self.llm(**batch, return_dict=True)
logits = outputs[..., :-1, :]
logits = logits.contiguous().view(-1, logits.size(-1))
labels = batch["labels"][..., 1:]
labels = labels.contiguous().view(-1)
label_mask = labels < self.vocab_size
logits = logits[label_mask]
labels = labels[label_mask]
self.metric_accuracy.update(logits, labels)
self.metric_loss.update(loss)
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]
# return [checkpoint_callback, early_stop_callback]