| import json |
| import os |
| from typing import Any, Dict |
|
|
| from transformers import PretrainedConfig |
|
|
|
|
| class M2EncoderConfig(PretrainedConfig): |
| model_type = "m2_encoder" |
|
|
| def __init__( |
| self, |
| loss_names=None, |
| beit_version="large", |
| encoder_embed_dim=1024, |
| out_embed_dim=1024, |
| encoder_layers=21, |
| beit3_vl_layers=3, |
| image_size=224, |
| visual_mask_size=14, |
| tokenizer_type="GLMChineseTokenizer", |
| tokenizer=".", |
| vocab_size=115244, |
| whole_word_masking=False, |
| precision=32, |
| test_only=True, |
| flash_attn=False, |
| model_file="m2_encoder_1B.safetensors", |
| architectures=None, |
| auto_map=None, |
| **kwargs, |
| ): |
| super().__init__(**kwargs) |
| self.loss_names = loss_names or {"itc": 1} |
| self.beit_version = beit_version |
| self.encoder_embed_dim = encoder_embed_dim |
| self.out_embed_dim = out_embed_dim |
| self.encoder_layers = encoder_layers |
| self.beit3_vl_layers = beit3_vl_layers |
| self.image_size = image_size |
| self.visual_mask_size = visual_mask_size |
| self.tokenizer_type = tokenizer_type |
| self.tokenizer = tokenizer |
| self.vocab_size = vocab_size |
| self.whole_word_masking = whole_word_masking |
| self.precision = precision |
| self.test_only = test_only |
| self.flash_attn = flash_attn |
| self.model_file = model_file |
| self.architectures = architectures or ["M2EncoderModel"] |
| self.auto_map = auto_map or { |
| "AutoConfig": "configuration_m2_encoder.M2EncoderConfig", |
| "AutoModel": "modeling_m2_encoder.M2EncoderModel", |
| "AutoProcessor": "processing_m2_encoder.M2EncoderProcessor", |
| } |
|
|
| @classmethod |
| def from_encoder_json(cls, config_path: str, **kwargs) -> "M2EncoderConfig": |
| with open(config_path, "r", encoding="utf-8") as f: |
| data = json.load(f) |
| data.update(kwargs) |
| return cls(**data) |
|
|
| def to_vlmo_overrides(self, model_dir: str) -> Dict[str, Any]: |
| return { |
| "loss_names": self.loss_names, |
| "beit_version": self.beit_version, |
| "encoder_embed_dim": self.encoder_embed_dim, |
| "out_embed_dim": self.out_embed_dim, |
| "encoder_layers": self.encoder_layers, |
| "beit3_vl_layers": self.beit3_vl_layers, |
| "image_size": self.image_size, |
| "visual_mask_size": self.visual_mask_size, |
| "tokenizer_type": self.tokenizer_type, |
| "tokenizer": self._resolve_tokenizer_dir(model_dir), |
| "vocab_size": self.vocab_size, |
| "whole_word_masking": self.whole_word_masking, |
| "precision": self.precision, |
| "test_only": self.test_only, |
| "flash_attn": self.flash_attn, |
| "load_path": os.path.join(model_dir, self.model_file), |
| } |
|
|
| def _resolve_tokenizer_dir(self, model_dir: str) -> str: |
| if os.path.isabs(self.tokenizer): |
| return self.tokenizer |
| if self.tokenizer in (".", "./", ""): |
| return model_dir |
| return os.path.join(model_dir, self.tokenizer) |
|
|
