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"""
PyTorch Lightning module for Multimodal Gemma training
"""
import torch
import lightning as L
from typing import Dict, Any, Optional, List
from transformers import get_linear_schedule_with_warmup
import logging
from .multimodal_gemma import MultimodalGemma
logger = logging.getLogger(__name__)
class MultimodalGemmaLightning(L.LightningModule):
"""Lightning module for Multimodal Gemma training"""
def __init__(self, config: Dict[str, Any]):
super().__init__()
self.save_hyperparameters()
self.config = config
# Initialize model
self.model = MultimodalGemma(config)
# Training metrics tracking
self.training_step_outputs = []
self.validation_step_outputs = []
# Setup automatic optimization
self.automatic_optimization = True
logger.info("MultimodalGemmaLightning initialized")
def forward(self, batch: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""Forward pass"""
return self.model(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
images=batch.get("images"),
labels=batch["labels"]
)
def training_step(self, batch: Dict[str, torch.Tensor], batch_idx: int) -> torch.Tensor:
"""Training step"""
outputs = self(batch)
loss = outputs["loss"]
# Log metrics
self.log("train/loss", loss, on_step=True, on_epoch=True, prog_bar=True, sync_dist=True)
self.log("train/learning_rate", self.optimizers().param_groups[0]["lr"], on_step=True)
# Store outputs for epoch end
self.training_step_outputs.append(loss.detach())
return loss
def validation_step(self, batch: Dict[str, torch.Tensor], batch_idx: int) -> torch.Tensor:
"""Validation step"""
outputs = self(batch)
loss = outputs["loss"]
# Log metrics
self.log("val/loss", loss, on_step=False, on_epoch=True, prog_bar=True, sync_dist=True)
# Store outputs for epoch end
self.validation_step_outputs.append(loss.detach())
return loss
def on_train_epoch_end(self) -> None:
"""Called at the end of each training epoch"""
if self.training_step_outputs:
avg_loss = torch.stack(self.training_step_outputs).mean()
self.log("train/epoch_loss", avg_loss, prog_bar=False, sync_dist=True)
self.training_step_outputs.clear()
def on_validation_epoch_end(self) -> None:
"""Called at the end of each validation epoch"""
if self.validation_step_outputs:
avg_loss = torch.stack(self.validation_step_outputs).mean()
self.log("val/epoch_loss", avg_loss, prog_bar=False, sync_dist=True)
self.validation_step_outputs.clear()
def configure_optimizers(self):
"""Configure optimizer and scheduler"""
# Collect trainable parameters with different learning rates
param_groups = []
# Ensure learning rates are floats
projector_lr = float(self.config["training"]["projector_lr"])
lora_lr = float(self.config["training"]["lora_lr"])
# Vision projector parameters
vision_proj_params = list(self.model.vision_projector.parameters())
if vision_proj_params:
param_groups.append({
"params": vision_proj_params,
"lr": projector_lr,
"name": "vision_projector"
})
# Audio projector parameters (if enabled)
if hasattr(self.model, 'audio_projector'):
audio_proj_params = list(self.model.audio_projector.parameters())
if audio_proj_params:
param_groups.append({
"params": audio_proj_params,
"lr": projector_lr,
"name": "audio_projector"
})
# LoRA parameters from language model
lora_params = []
for name, param in self.model.language_model.named_parameters():
if param.requires_grad:
lora_params.append(param)
if lora_params:
param_groups.append({
"params": lora_params,
"lr": lora_lr,
"name": "lora_adapters"
})
if not param_groups:
raise ValueError("No trainable parameters found!")
# Log parameter counts
for group in param_groups:
param_count = sum(p.numel() for p in group["params"])
logger.info(f"{group['name']}: {param_count:,} parameters, lr={group['lr']}")
# Create optimizer
optimizer_class = torch.optim.AdamW
if self.config.get("optimization", {}).get("use_fused_adamw", False):
try:
optimizer_class = torch.optim.AdamW # Fused AdamW is default in recent PyTorch
except AttributeError:
logger.warning("Fused AdamW not available, using regular AdamW")
optimizer = optimizer_class(
param_groups,
weight_decay=self.config["training"]["weight_decay"],
eps=1e-8,
betas=(0.9, 0.999)
)
# Calculate total steps for scheduler
if self.trainer.datamodule is not None:
steps_per_epoch = len(self.trainer.datamodule.train_dataloader())
else:
# Fallback estimation
steps_per_epoch = self.config["training"].get("steps_per_epoch", 1000)
max_epochs = self.config["training"]["max_epochs"]
accumulate_grad_batches = self.config["training"].get("accumulate_grad_batches", 1)
total_steps = (steps_per_epoch // accumulate_grad_batches) * max_epochs
warmup_steps = int(total_steps * self.config["training"]["warmup_ratio"])
logger.info(f"Scheduler setup: {total_steps} total steps, {warmup_steps} warmup steps")
# Create scheduler
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=warmup_steps,
num_training_steps=total_steps
)
return {
"optimizer": optimizer,
"lr_scheduler": {
"scheduler": scheduler,
"interval": "step",
"frequency": 1,
"name": "learning_rate"
}
}
def lr_scheduler_step(self, scheduler, metric):
"""Custom learning rate scheduler step"""
scheduler.step()
def on_before_optimizer_step(self, optimizer):
"""Called before optimizer step"""
# Log gradient norms
if self.global_step % 100 == 0:
grad_norm = 0.0
param_count = 0
for param_group in optimizer.param_groups:
for param in param_group["params"]:
if param.grad is not None:
param_norm = param.grad.data.norm(2)
grad_norm += param_norm.item() ** 2
param_count += 1
if param_count > 0:
grad_norm = (grad_norm / param_count) ** 0.5
self.log("train/grad_norm", grad_norm, on_step=True, prog_bar=False)
def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
"""Called when saving checkpoint"""
# Save additional model components
checkpoint["model_config"] = self.config
checkpoint["tokenizer_vocab_size"] = len(self.model.tokenizer)
def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
"""Called when loading checkpoint"""
# Restore model configuration if needed
if "model_config" in checkpoint:
logger.info("Loaded model configuration from checkpoint")
def predict_step(self, batch: Dict[str, torch.Tensor], batch_idx: int) -> Dict[str, Any]:
"""Prediction step for inference"""
outputs = self.model.generate(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
images=batch.get("images"),
max_new_tokens=150,
temperature=0.7,
do_sample=True
)
# Decode generated text
generated_text = []
for i, output in enumerate(outputs):
# Remove input tokens from output
input_length = batch["input_ids"][i].shape[0]
generated_tokens = output[input_length:]
text = self.model.tokenizer.decode(generated_tokens, skip_special_tokens=True)
generated_text.append(text)
return {
"generated_text": generated_text,
"input_ids": batch["input_ids"],
}
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