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from io import BytesIO
from typing import Any, Dict, Optional, List
import torch
from PIL import Image
from sentence_transformers.models import Transformer as BaseTransformer
from transformers import AutoModelForVision2Seq, AutoProcessor
class MultiModalTransformer(BaseTransformer):
def __init__(
self,
model_name_or_path: str,
cache_dir: Optional[str] = None,
tokenizer_args: Optional[Dict[str, Any]] = None,
min_image_tokens: int = 256,
max_image_tokens: int = 1280,
max_length: int = 1800,
**kwargs,
):
super().__init__(model_name_or_path, **kwargs)
if tokenizer_args is None:
tokenizer_args = {}
tokenizer_args.pop("trust_remote_code", None)
# Initialize processor
min_pixels = min_image_tokens * 28 * 28
max_pixels = max_image_tokens * 28 * 28
self.processor = AutoProcessor.from_pretrained(
model_name_or_path, min_pixels=min_pixels, max_pixels=max_pixels, **kwargs
)
self.processor.tokenizer.padding_side = 'right'
self.sep = ' '
self.max_length = max_length
self.normalize = True
def _load_model(
self,
model_name_or_path: str,
config,
cache_dir: str,
backend: str,
is_peft_model: bool,
**model_args,
) -> None:
model_args.pop("trust_remote_code", None)
self.auto_model = AutoModelForVision2Seq.from_pretrained(
model_name_or_path, torch_dtype=torch.float16, **model_args
)
def forward(
self, features: Dict[str, torch.Tensor], **kwargs
) -> Dict[str, torch.Tensor]:
if features.get("inputs_embeds", None) is None:
features["inputs_embeds"] = self.auto_model.base_model.embed_tokens(features["input_ids"])
if features.get("pixel_values", None) is not None:
features["pixel_values"] = features["pixel_values"].type(self.auto_model.visual.get_dtype())
image_embeds = self.auto_model.visual(
features["pixel_values"], grid_thw=features["image_grid_thw"]
)
image_mask = features["input_ids"] == self.auto_model.config.image_token_id
features["inputs_embeds"][image_mask] = image_embeds
# features.pop("pixel_values")
# features.pop("image_grid_thw")
# features.pop("input_ids")
inputs = {k: v for k, v in features.items() if k in 'position_ids,attention_mask,inputs_embeds'}
outputs = self.auto_model.model(
**inputs,
return_dict=True,
output_hidden_states=True,
# **kwargs
)
# pooling_mask = features["attention_mask"] if features.get("pooling_mask", None) is None else features["pooling_mask"]
# left_padding = (pooling_mask[:, -1].sum() == pooling_mask.shape[0]) # TODO
# if left_padding:
# embeddings = outputs.last_hidden_state
# else:
# sequence_lengths = pooling_mask.sum(dim=1) - 1
# embeddings = outputs.last_hidden_state[torch.arange(
# outputs.last_hidden_state.shape[0], device=outputs.last_hidden_state.device
# ), sequence_lengths]
features.update({"token_embeddings": outputs.last_hidden_state})
return features
def tokenize(self, texts: List[List[Dict[str, Any]]] | List[str]) -> Dict[str, torch.Tensor]:
default_instruction = 'You are a helpful assistant.'
all_texts, all_images = list(), list()
for item in texts:
if isinstance(item, str):
txt, img, inst = item, None, default_instruction
elif isinstance(item, dict):
txt = item.get('text', None)
img = item.get('image', None)
inst = item.get('prompt', default_instruction)
else:
raise RuntimeError(f'Input format not supported! {item=}')
input_str = ''
if img is None:
all_images = None # All examples in the same batch are consistent
# or will have ValueError: Could not make a flat list of images from xxxx
else:
input_str += '<|vision_start|><|image_pad|><|vision_end|>'
img = fetch_image(img)
all_images.append(img)
if txt is not None:
input_str += txt
msg = f'<|im_start|>system\n{inst}<|im_end|>\n<|im_start|>user\n{input_str}<|im_end|>\n<|im_start|>assistant\n<|endoftext|>'
all_texts.append(msg)
inputs = self.processor(
text=all_texts,
images=all_images,
padding="longest",
truncation=True,
max_length=self.max_seq_length,
return_tensors='pt'
)
return inputs
### Copied from qwen_vl_utils.vision_process.py
import base64
from io import BytesIO
import requests
IMAGE_FACTOR = 28
MIN_PIXELS = 4 * 28 * 28
MAX_PIXELS = 16384 * 28 * 28
MAX_RATIO = 200
def round_by_factor(number: int, factor: int) -> int:
"""Returns the closest integer to 'number' that is divisible by 'factor'."""
return round(number / factor) * factor
def ceil_by_factor(number: int, factor: int) -> int:
"""Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
return math.ceil(number / factor) * factor
def floor_by_factor(number: int, factor: int) -> int:
"""Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
return math.floor(number / factor) * factor
def smart_resize(
height: int, width: int, factor: int = IMAGE_FACTOR, min_pixels: int = MIN_PIXELS, max_pixels: int = MAX_PIXELS
) -> tuple[int, int]:
"""
Rescales the image so that the following conditions are met:
1. Both dimensions (height and width) are divisible by 'factor'.
2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
3. The aspect ratio of the image is maintained as closely as possible.
"""
h_bar = max(factor, round_by_factor(height, factor))
w_bar = max(factor, round_by_factor(width, factor))
if h_bar * w_bar > max_pixels:
beta = math.sqrt((height * width) / max_pixels)
h_bar = floor_by_factor(height / beta, factor)
w_bar = floor_by_factor(width / beta, factor)
elif h_bar * w_bar < min_pixels:
beta = math.sqrt(min_pixels / (height * width))
h_bar = ceil_by_factor(height * beta, factor)
w_bar = ceil_by_factor(width * beta, factor)
if max(h_bar, w_bar) / min(h_bar, w_bar) > MAX_RATIO:
logging.warning(
f"Absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(h_bar, w_bar) / min(h_bar, w_bar)}"
)
if h_bar > w_bar:
h_bar = w_bar * MAX_RATIO
else:
w_bar = h_bar * MAX_RATIO
return h_bar, w_bar
def fetch_image(image: str | Image.Image, size_factor: int = IMAGE_FACTOR) -> Image.Image:
image_obj = None
if isinstance(image, Image.Image):
image_obj = image
elif image.startswith("http://") or image.startswith("https://"):
image_obj = Image.open(requests.get(image, stream=True).raw)
elif image.startswith("file://"):
image_obj = Image.open(image[7:])
elif image.startswith("data:image"):
if "base64," in image:
_, base64_data = image.split("base64,", 1)
data = base64.b64decode(base64_data)
image_obj = Image.open(BytesIO(data))
else:
image_obj = Image.open(image)
if image_obj is None:
raise ValueError(f"Unrecognized image input, support local path, http url, base64 and PIL.Image, got {image}")
image = image_obj.convert("RGB")
## resize
# if "resized_height" in ele and "resized_width" in ele:
# resized_height, resized_width = smart_resize(
# ele["resized_height"],
# ele["resized_width"],
# factor=size_factor,
# )
# else:
width, height = image.size
# min_pixels = ele.get("min_pixels", MIN_PIXELS)
# max_pixels = ele.get("max_pixels", MAX_PIXELS)
resized_height, resized_width = smart_resize(
height,
width,
factor=size_factor,
min_pixels=MIN_PIXELS,
max_pixels=MAX_PIXELS,
)
image = image.resize((resized_width, resized_height))
return image
###
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