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"""
Multimodal Gemma model implementation
"""
import torch
import torch.nn as nn
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
CLIPVisionModel,
CLIPProcessor,
BitsAndBytesConfig
)
from peft import LoraConfig, get_peft_model, TaskType
from typing import Dict, Any, Optional, Tuple
import logging
from .projectors import VisionProjector
logger = logging.getLogger(__name__)
class MultimodalGemma(nn.Module):
"""Multimodal Gemma model with vision and audio capabilities"""
def __init__(self, config: Dict[str, Any]):
super().__init__()
self.config = config
# Initialize tokenizer first
self._setup_tokenizer()
# Initialize language model
self._setup_language_model()
# Initialize vision components
self._setup_vision_components()
# Initialize projectors
self._setup_projectors()
# Freeze encoders
self._freeze_encoders()
# Setup LoRA
self._setup_lora()
logger.info("MultimodalGemma model initialized successfully")
# Move projectors to the same device as the language model
self._move_to_device()
def _setup_tokenizer(self):
"""Initialize and configure tokenizer"""
model_name = self.config["model"]["gemma_model_name"]
self.tokenizer = AutoTokenizer.from_pretrained(
model_name,
trust_remote_code=True,
use_fast=True
)
# Set padding token
if self.tokenizer.pad_token is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
# Add special tokens
special_tokens = self.config.get("special_tokens", {})
new_tokens = []
for token_name, token_value in special_tokens.items():
if token_value not in self.tokenizer.get_vocab():
new_tokens.append(token_value)
if new_tokens:
self.tokenizer.add_special_tokens({"additional_special_tokens": new_tokens})
logger.info(f"Added special tokens: {new_tokens}")
def _setup_language_model(self):
"""Initialize language model with quantization if specified"""
model_name = self.config["model"]["gemma_model_name"]
# Setup quantization config
quantization_config = None
if self.config["model"].get("use_4bit", False):
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=getattr(torch, self.config["model"]["bnb_4bit_compute_dtype"]),
bnb_4bit_quant_type=self.config["model"]["bnb_4bit_quant_type"],
bnb_4bit_use_double_quant=self.config["model"]["use_nested_quant"]
)
self.language_model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=quantization_config,
torch_dtype=torch.bfloat16,
device_map=None, # Lightning handles device placement
trust_remote_code=True,
attn_implementation="eager" # Use eager attention (flash_attn not required)
)
# Resize embeddings if we added special tokens
if len(self.tokenizer) > self.language_model.config.vocab_size:
self.language_model.resize_token_embeddings(len(self.tokenizer))
logger.info(f"Resized embeddings to {len(self.tokenizer)}")
# Store image token ID for later use
self.image_token_id = self.tokenizer.convert_tokens_to_ids(
self.config.get("special_tokens", {}).get("image_token", "<image>")
)
def _setup_vision_components(self):
"""Initialize vision encoder and processor"""
vision_model_name = self.config["model"]["vision_model_name"]
self.vision_encoder = CLIPVisionModel.from_pretrained(
vision_model_name,
torch_dtype=torch.bfloat16
)
self.vision_processor = CLIPProcessor.from_pretrained(vision_model_name)
logger.info(f"Loaded vision model: {vision_model_name}")
def _setup_projectors(self):
"""Initialize projection layers"""
vision_dim = self.vision_encoder.config.hidden_size
language_dim = self.language_model.config.hidden_size
# Vision projector
self.vision_projector = VisionProjector(
vision_dim=vision_dim,
language_dim=language_dim,
hidden_dim=self.config["model"].get("projector_hidden_dim", language_dim)
).to(torch.bfloat16) # Match the model dtype
logger.info("Initialized vision projection layer")
def _freeze_encoders(self):
"""Freeze vision encoder"""
# Freeze vision encoder
for param in self.vision_encoder.parameters():
param.requires_grad = False
logger.info("Froze vision encoder parameters")
def _setup_lora(self):
"""Setup LoRA for the language model"""
lora_config = LoraConfig(
r=self.config["model"]["lora"]["r"],
lora_alpha=self.config["model"]["lora"]["alpha"],
target_modules=self.config["model"]["lora"]["target_modules"],
lora_dropout=self.config["model"]["lora"]["dropout"],
bias="none",
task_type=TaskType.CAUSAL_LM,
)
self.language_model = get_peft_model(self.language_model, lora_config)
self.language_model.print_trainable_parameters()
logger.info("Setup LoRA adapters")
def _move_to_device(self):
"""Move all components to the same device as the language model"""
device = next(self.language_model.parameters()).device
# Move vision components
self.vision_encoder = self.vision_encoder.to(device)
self.vision_projector = self.vision_projector.to(device)
logger.info(f"Moved vision components to device: {device}")
def encode_images(self, images: torch.Tensor) -> torch.Tensor:
"""
Encode images using CLIP and project to language space
Args:
images: [batch_size, 3, height, width]
Returns:
projected_features: [batch_size, language_dim]
"""
with torch.no_grad():
vision_outputs = self.vision_encoder(pixel_values=images)
# Use the pooled output (CLS token equivalent)
image_features = vision_outputs.pooler_output
# Project to language model space
projected_features = self.vision_projector(image_features)
return projected_features
def forward(
self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
images: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
) -> Dict[str, torch.Tensor]:
"""
Forward pass with multimodal inputs
Args:
input_ids: [batch_size, seq_len]
attention_mask: [batch_size, seq_len]
images: [batch_size, 3, height, width] or None
labels: [batch_size, seq_len] or None
Returns:
Dictionary with loss and logits
"""
if images is not None:
# Encode images and project to language space
image_features = self.encode_images(images) # [batch_size, language_dim]
# Replace <image> tokens with actual image features
input_embeds, attention_mask, labels = self._merge_image_features(
input_ids, image_features, attention_mask, labels
)
# Forward through language model with merged embeddings
outputs = self.language_model(
inputs_embeds=input_embeds,
attention_mask=attention_mask,
labels=labels,
)
else:
# Standard text-only forward pass
outputs = self.language_model(
input_ids=input_ids,
attention_mask=attention_mask,
labels=labels,
)
return {
"loss": outputs.loss,
"logits": outputs.logits,
}
def _merge_image_features(
self,
input_ids: torch.Tensor,
image_features: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Merge image features with text embeddings at <image> token positions
Args:
input_ids: [batch_size, seq_len]
image_features: [batch_size, language_dim]
attention_mask: [batch_size, seq_len]
labels: [batch_size, seq_len]
Returns:
input_embeds: [batch_size, seq_len, hidden_size]
attention_mask: [batch_size, seq_len]
labels: [batch_size, seq_len]
"""
batch_size, seq_len = input_ids.shape
# Get text embeddings
text_embeds = self.language_model.get_input_embeddings()(input_ids)
# Find positions of <image> tokens
image_token_mask = (input_ids == self.image_token_id)
# Replace <image> token embeddings with projected image features
for batch_idx in range(batch_size):
image_positions = torch.where(image_token_mask[batch_idx])[0]
if len(image_positions) > 0:
# Use the first <image> token position (assuming one image per sample)
img_pos = image_positions[0]
text_embeds[batch_idx, img_pos] = image_features[batch_idx]
return text_embeds, attention_mask, labels
def generate(
self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
images: Optional[torch.Tensor] = None,
max_new_tokens: int = 150,
temperature: float = 0.7,
do_sample: bool = True,
**kwargs
) -> torch.Tensor:
"""Generate text with multimodal context"""
if images is not None:
# Encode images and merge with text embeddings
image_features = self.encode_images(images)
input_embeds, attention_mask, _ = self._merge_image_features(
input_ids, image_features, attention_mask, None
)
# Generate using language model with merged embeddings
with torch.no_grad():
outputs = self.language_model.generate(
inputs_embeds=input_embeds,
attention_mask=attention_mask,
max_new_tokens=max_new_tokens,
temperature=temperature,
do_sample=do_sample,
pad_token_id=self.tokenizer.pad_token_id,
eos_token_id=self.tokenizer.eos_token_id,
**kwargs
)
else:
# Standard text-only generation
with torch.no_grad():
outputs = self.language_model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
max_new_tokens=max_new_tokens,
temperature=temperature,
do_sample=do_sample,
pad_token_id=self.tokenizer.pad_token_id,
eos_token_id=self.tokenizer.eos_token_id,
**kwargs
)
return outputs
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