File size: 5,174 Bytes
f471fb4 | 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 | import os
import sys
import importlib
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from huggingface_hub import snapshot_download
from safetensors.torch import load_file
from transformers import PreTrainedModel
from transformers.modeling_outputs import ModelOutput
from .configuration_m2_encoder import M2EncoderConfig
@dataclass
class M2EncoderOutput(ModelOutput):
loss: Optional[torch.FloatTensor] = None
text_embeds: Optional[torch.FloatTensor] = None
image_embeds: Optional[torch.FloatTensor] = None
logits_per_image: Optional[torch.FloatTensor] = None
logits_per_text: Optional[torch.FloatTensor] = None
class M2EncoderModel(PreTrainedModel):
config_class = M2EncoderConfig
base_model_prefix = "m2_encoder"
main_input_name = "pixel_values"
def __init__(self, config: M2EncoderConfig):
super().__init__(config)
model_dir = getattr(config, "_model_dir", None)
if model_dir is None:
raise ValueError(
"M2EncoderConfig is missing `_model_dir`. Use "
"`M2EncoderModel.from_pretrained(...)` so the checkpoint path can be resolved."
)
if model_dir not in sys.path:
sys.path.insert(0, model_dir)
vlmo_default_config = importlib.import_module("vlmo.config").config
VLMo = importlib.import_module("vlmo.modules").VLMo
vlmo_config = vlmo_default_config()
vlmo_config.update(config.to_vlmo_overrides(model_dir))
load_path = vlmo_config["load_path"]
use_safetensors = load_path.endswith(".safetensors")
if use_safetensors:
vlmo_config["load_path"] = ""
if vlmo_config["flash_attn"]:
patch_torch_scale_with_flash_attn = importlib.import_module(
"vlmo.utils.patch_utils"
).patch_torch_scale_with_flash_attn
patch_torch_scale_with_flash_attn()
self.model = VLMo(vlmo_config)
if use_safetensors:
state_dict = load_file(load_path)
self.model.load_state_dict(state_dict, strict=False)
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path,
*model_args,
config: Optional[M2EncoderConfig] = None,
**kwargs,
):
model_dir = pretrained_model_name_or_path
if not os.path.isdir(model_dir):
model_dir = snapshot_download(repo_id=pretrained_model_name_or_path)
if config is None:
config = M2EncoderConfig.from_pretrained(model_dir, **kwargs)
checkpoint_path = os.path.join(
model_dir,
kwargs.pop("m2_checkpoint_name", config.model_file),
)
if not os.path.exists(checkpoint_path):
raise FileNotFoundError(
f"Missing M2-Encoder checkpoint: {checkpoint_path}"
)
config._model_dir = model_dir
return cls(config, *model_args)
def get_text_features(
self,
input_ids: torch.LongTensor,
attention_mask: torch.LongTensor,
) -> torch.FloatTensor:
outputs = self.model.infer_text(
{
"text_ids": input_ids,
"text_masks": attention_mask,
"text_labels": None,
}
)
return outputs["cls_vlffn_feats"]
def get_image_features(self, pixel_values: torch.FloatTensor) -> torch.FloatTensor:
outputs = self.model.infer_image({"image": [pixel_values]})
return outputs["cls_vlffn_feats"]
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
return_dict: Optional[bool] = True,
**kwargs,
) -> Union[M2EncoderOutput, Tuple[torch.FloatTensor, ...]]:
text_embeds = None
image_embeds = None
if input_ids is not None:
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
text_embeds = self.get_text_features(
input_ids=input_ids, attention_mask=attention_mask
)
if pixel_values is not None:
image_embeds = self.get_image_features(pixel_values=pixel_values)
logits_per_image = None
logits_per_text = None
if image_embeds is not None and text_embeds is not None:
logit_scale = self.model.logit_scale.exp()
logits_per_image = logit_scale * image_embeds @ text_embeds.t()
logits_per_text = logits_per_image.t()
if not return_dict:
return tuple(
value
for value in (
text_embeds,
image_embeds,
logits_per_image,
logits_per_text,
)
if value is not None
)
return M2EncoderOutput(
text_embeds=text_embeds,
image_embeds=image_embeds,
logits_per_image=logits_per_image,
logits_per_text=logits_per_text,
)
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