import os import torch import math from abc import ABC import torch.nn as nn from typing import List from transformers import AutoTokenizer from transformers import SiglipTextModel from transformers import T5EncoderModel, T5Tokenizer from transformers import CLIPTextModel, AutoModel, AutoModelForCausalLM from transformers import Qwen3VLForConditionalGeneration class TextEmbedder(ABC, nn.Module): """ Abstract base class for text embedders. Subclasses must set: self.model (nn.Module), self.tokenizer, self.emb_dim (int). This class provides a shared forward() that returns hidden states with shape (b, n, d). """ emb_dim: int # required max_length: int # required tokenizer: object # tokenizer model: nn.Module # HF model (encoder or LM; must support output_hidden_states=True) @property def device(self) -> torch.device: return next(self.model.parameters()).device @torch.no_grad() def forward(self, txt: List[str]): tok_out = self.tokenizer( txt, return_tensors="pt", padding="max_length", max_length=self.max_length, truncation=True ) tok_out = tok_out.to(self.device) txt_emb = self.model(**tok_out, output_hidden_states=True) if hasattr(txt_emb, "last_hidden_state"): return txt_emb.last_hidden_state return txt_emb.hidden_states[-1] # =================================================================================================== class ClipTextEmbedder(TextEmbedder): def __init__(self, max_length: int = 77, compile: bool = False, dtype: torch.dtype = torch.bfloat16): super().__init__() os.environ["TOKENIZERS_PARALLELISM"] = "false" self.emb_dim = 768 self.max_length = max_length self.path = "openai/clip-vit-large-patch14" self.tokenizer = AutoTokenizer.from_pretrained(self.path) self.model = CLIPTextModel.from_pretrained(self.path, torch_dtype=dtype) self.model.requires_grad_(False) self.model.eval() if compile: torch.compile(self.model) print(f"[ClipTextEmbedder] {sum([p.numel() for p in self.parameters()]):,}") class SigLipTextEmbedder(TextEmbedder): def __init__(self, max_length: int = 64, compile: bool = False, dtype: torch.dtype = torch.bfloat16): super().__init__() os.environ["TOKENIZERS_PARALLELISM"] = "false" self.emb_dim = 1152 self.max_length = max_length self.path = "google/siglip-so400m-patch14-384" self.tokenizer = AutoTokenizer.from_pretrained(self.path) self.model = SiglipTextModel.from_pretrained(self.path, torch_dtype=dtype, attn_implementation="sdpa") self.model.requires_grad_(False) self.model.eval() if compile: torch.compile(self.model) print(f"[SigLipTextEmbedder] {sum([p.numel() for p in self.parameters()]):,}") class T5XXL(TextEmbedder): def __init__(self, max_length: int = 512, compile: bool = False, dtype: torch.dtype = torch.bfloat16): super().__init__() os.environ["TOKENIZERS_PARALLELISM"] = "false" self.emb_dim = 4096 self.max_length = max_length self.path = "google/t5-xxl-lm-adapt" self.tokenizer: T5Tokenizer = T5Tokenizer.from_pretrained(self.path, max_length=max_length) self.model = T5EncoderModel.from_pretrained(self.path, torch_dtype=dtype) self.model.requires_grad_(False) self.model.eval() if compile: torch.compile(self.model) print(f"[T5XXL] {sum([p.numel() for p in self.parameters()]):,}") class InternVL3(TextEmbedder): def __init__(self, max_length: int = 160, compile: bool = False, dtype: torch.dtype = torch.bfloat16): super().__init__() os.environ["TOKENIZERS_PARALLELISM"] = "false" self.emb_dim = 896 self.max_length = max_length self.path = "OpenGVLab/InternVL3-1B" self.tokenizer = AutoTokenizer.from_pretrained(self.path, trust_remote_code=True, use_fast=True) model = AutoModel.from_pretrained(self.path, trust_remote_code=True, torch_dtype=dtype) text_tower = getattr(model, "language_model", None) or getattr(model, "text_model", None) assert text_tower is not None, "Could not find text tower (language_model/text_model)." self.model = text_tower self.model.requires_grad_(False) self.model.eval() if compile: torch.compile(self.model) print(f"[InternVL3] {sum([p.numel() for p in self.parameters()]):,}") class Gemma2B(TextEmbedder): def __init__(self, max_length: int = 160, compile: bool = False, dtype: torch.dtype = torch.bfloat16): super().__init__() os.environ["TOKENIZERS_PARALLELISM"] = "false" self.emb_dim = 2048 self.max_length = max_length self.path = "google/gemma-2b" self.tokenizer = AutoTokenizer.from_pretrained(self.path) self.model = AutoModelForCausalLM.from_pretrained(self.path, torch_dtype=dtype) self.model.requires_grad_(False) self.model.eval() if compile: torch.compile(self.model) print(f"[Gemma2B] {sum([p.numel() for p in self.parameters()]):,}") class Qwen3VLEmbedder2B(TextEmbedder): def __init__( self, repo: str = "Qwen/Qwen3-VL-Embedding-2B", max_length: int = 256, compile: bool = False, dtype: torch.dtype = torch.bfloat16, ): super().__init__() os.environ["TOKENIZERS_PARALLELISM"] = "false" self.path = repo self.max_length = max_length self.tokenizer = AutoTokenizer.from_pretrained(self.path) full_model = Qwen3VLForConditionalGeneration.from_pretrained(self.path, dtype=dtype) text_tower = full_model.model.language_model del full_model self.model = text_tower self.emb_dim = int(self.model.config.hidden_size) self.model.requires_grad_(False) self.model.eval() if compile: self.model = torch.compile(self.model) print(f"[Qwen3VLEmbedder2B] {sum([p.numel() for p in self.parameters()]):,}") # =================================================================================================== if __name__ == "__main__": DEV = "cuda:0" if torch.cuda.is_available() else "cpu" batch_text = ["a red cube on a wooden table, studio lighting, 35mm", "image of a dog"] def check(model_cls): model = model_cls().to(DEV).eval() with torch.no_grad(): out = model(batch_text) print(f"[{model.__class__.__name__}] output shape: {tuple(out.shape)}") assert out.shape[-1] == model.emb_dim, f"Mismatch emb_dim: {model.emb_dim} != {out.shape[-1]}" assert out.shape[1] == model.max_length, f"Mismatch max_length: {model.max_length} != {out.shape[1]}" check(ClipTextEmbedder) check(SigLipTextEmbedder) check(T5XXL) check(Gemma2B) check(InternVL3) check(Qwen3VLEmbedder2B)