File size: 7,075 Bytes
b910c09 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 | 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)
|