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import timm
import torch
from torch import nn
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from .local_store import runtime_text_model_path
def resolve_dtype(dtype_name: str) -> torch.dtype:
if dtype_name == "float16":
return torch.float16
if dtype_name == "float32":
return torch.float32
raise ValueError(f"Unsupported dtype: {dtype_name}")
def infer_hidden_size(llama: AutoModelForCausalLM) -> int:
if hasattr(llama.config, "hidden_size"):
return int(llama.config.hidden_size)
if hasattr(llama.config, "n_embd"):
return int(llama.config.n_embd)
raise ValueError("Could not infer hidden size from the language model config")
class ImageEmbeddingModel(nn.Module):
def __init__(self, model_name: str, embedding_dim: int) -> None:
super().__init__()
self.embedding = timm.create_model(model_name, pretrained=True, num_classes=embedding_dim)
def forward(self, images: torch.Tensor) -> torch.Tensor:
return self.embedding(images)
class LlamaTextProjector(nn.Module):
def __init__(self, llama: AutoModelForCausalLM, embedding_dim: int) -> None:
super().__init__()
self.text_base = getattr(llama, "model", llama)
hidden_size = infer_hidden_size(llama)
self.projection = nn.Sequential(
nn.Linear(hidden_size, 512, bias=False),
nn.Dropout(0.5),
nn.GELU(),
nn.LayerNorm(512),
nn.Linear(512, embedding_dim, bias=False),
)
def forward(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
outputs = self.text_base(input_ids=input_ids, attention_mask=attention_mask)
hidden = outputs.last_hidden_state
mask = attention_mask.unsqueeze(-1).to(hidden.dtype)
pooled = (hidden * mask).sum(dim=1) / mask.sum(dim=1).clamp_min(1.0)
pooled = pooled.to(self.projection[0].weight.dtype)
return self.projection(pooled)
class FusionHead(nn.Module):
def __init__(self, fusion: str, embedding_dim: int) -> None:
super().__init__()
self.fusion = fusion
if fusion == "concat":
fused_dim = embedding_dim * 2
elif fusion == "element":
fused_dim = embedding_dim
elif fusion == "avgpool":
self.w_image = nn.Linear(embedding_dim, 64, bias=False)
self.w_text = nn.Linear(embedding_dim, 64, bias=False)
self.pool = nn.AvgPool1d(kernel_size=4)
fused_dim = 16
elif fusion == "gated":
self.u_text = nn.Linear(embedding_dim, 64, bias=False)
self.v_image = nn.Linear(embedding_dim, 64, bias=False)
self.w_fused = nn.Linear(64, 8, bias=False)
self.sigmoid = nn.Sigmoid()
fused_dim = 8
else:
raise ValueError(f"Unsupported fusion method: {fusion}")
hidden_dim = max(fused_dim // 2, 1)
self.initial = nn.Linear(fused_dim, hidden_dim, bias=True)
self.final = nn.Linear(hidden_dim, 1, bias=True)
def forward(self, text_features: torch.Tensor, image_features: torch.Tensor) -> torch.Tensor:
target_dtype = self.initial.weight.dtype
text_features = text_features.to(target_dtype)
image_features = image_features.to(target_dtype)
if self.fusion == "concat":
fused = torch.cat([text_features, image_features], dim=-1)
elif self.fusion == "element":
fused = text_features * image_features
elif self.fusion == "avgpool":
text_encoded = self.w_text(text_features)
image_encoded = self.w_image(image_features)
fused = self.pool((text_encoded * image_encoded).unsqueeze(1)).squeeze(1)
else:
gate = self.sigmoid(self.u_text(text_features))
image_encoded = self.v_image(image_features)
fused = self.w_fused(gate * image_encoded)
hidden = self.initial(fused)
return self.final(hidden).squeeze(-1)
class FuseMD(nn.Module):
def __init__(
self,
text_encoder: LlamaTextProjector,
image_encoder: ImageEmbeddingModel,
fusion_head: FusionHead,
) -> None:
super().__init__()
self.text_encoder = text_encoder
self.image_encoder = image_encoder
self.fusion_head = fusion_head
def forward(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
images: torch.Tensor,
) -> torch.Tensor:
text_features = self.text_encoder(input_ids, attention_mask)
image_features = self.image_encoder(images)
return self.fusion_head(text_features, image_features)
def load_tokenizer_and_llama(
text_model_name: str,
use_8bit: bool,
dtype: torch.dtype,
device: torch.device,
) -> Tuple[AutoTokenizer, AutoModelForCausalLM]:
resolved_model_path = runtime_text_model_path(text_model_name)
tokenizer = AutoTokenizer.from_pretrained(resolved_model_path, local_files_only=True)
if tokenizer.pad_token is None:
if tokenizer.eos_token is not None:
tokenizer.pad_token = tokenizer.eos_token
else:
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
quantization_config = None
if use_8bit and device.type == "cuda":
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
model_dtype = dtype if device.type == "cuda" else torch.float32
llama = AutoModelForCausalLM.from_pretrained(
resolved_model_path,
low_cpu_mem_usage=True,
local_files_only=True,
quantization_config=quantization_config,
torch_dtype=model_dtype,
)
if len(tokenizer) != llama.get_input_embeddings().num_embeddings:
llama.resize_token_embeddings(len(tokenizer), mean_resizing=False)
return tokenizer, llama
def freeze_base_models(model: FuseMD) -> None:
for param in model.text_encoder.text_base.parameters():
param.requires_grad = False
for param in model.text_encoder.projection.parameters():
param.requires_grad = True
for param in model.image_encoder.embedding.parameters():
param.requires_grad = False
if hasattr(model.image_encoder.embedding, "head"):
for param in model.image_encoder.embedding.head.parameters():
param.requires_grad = True
for param in model.fusion_head.parameters():
param.requires_grad = True
def trainable_parameters(model: FuseMD) -> List[nn.Parameter]:
return [param for param in model.parameters() if param.requires_grad]
def get_trainable_checkpoint(model: FuseMD) -> Dict[str, object]:
vit_state = model.image_encoder.state_dict()
vit_head_state = {key: value.cpu() for key, value in vit_state.items() if key.startswith("embedding.head")}
return {
"text_projection_state_dict": {
key: value.cpu() for key, value in model.text_encoder.projection.state_dict().items()
},
"vit_head_state_dict": vit_head_state,
"fusion_head_state_dict": {
key: value.cpu() for key, value in model.fusion_head.state_dict().items()
},
}
def load_trainable_checkpoint(model: FuseMD, checkpoint: Dict[str, object]) -> None:
text_projection_state = checkpoint.get("text_projection_state_dict")
if text_projection_state is None:
text_projection_state = checkpoint.get("text_clf_head_state_dict")
if text_projection_state is None:
raise KeyError("Checkpoint is missing both 'text_projection_state_dict' and 'text_clf_head_state_dict'.")
model.text_encoder.projection.load_state_dict(text_projection_state)
vit_current = model.image_encoder.state_dict()
vit_current.update(checkpoint["vit_head_state_dict"])
model.image_encoder.load_state_dict(vit_current, strict=False)
model.fusion_head.load_state_dict(checkpoint["fusion_head_state_dict"])
def build_model(
llama: AutoModelForCausalLM,
image_model_name: str,
fusion_method: str,
embedding_dim: int,
) -> FuseMD:
text_encoder = LlamaTextProjector(llama=llama, embedding_dim=embedding_dim)
image_encoder = ImageEmbeddingModel(image_model_name, embedding_dim=embedding_dim)
fusion_head = FusionHead(fusion=fusion_method, embedding_dim=embedding_dim)
model = FuseMD(text_encoder=text_encoder, image_encoder=image_encoder, fusion_head=fusion_head)
freeze_base_models(model)
return model
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