393 lines
16 KiB
Python
393 lines
16 KiB
Python
from dataclasses import dataclass
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from typing import Optional, Tuple
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from copy import deepcopy
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import torch
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import torch.nn as nn
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from transformers import CLIPTextModel, CLIPTokenizer, AutoTokenizer, AutoModel, LlavaForConditionalGeneration, AutoProcessor
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from transformers.utils import ModelOutput
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from ..constants import TEXT_ENCODER_PATH, TOKENIZER_PATH
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from ..constants import PRECISION_TO_TYPE
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from ..utils.token_helper import find_subsequence, multi_slice_to_mask
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from PIL import Image
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def use_default(value, default):
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return value if value is not None else default
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def load_text_encoder(
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text_encoder_type,
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text_encoder_precision=None,
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text_encoder_path=None,
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logger=None,
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device=None,
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dtype=None,
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quantization_config=None,
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):
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if text_encoder_path is None:
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text_encoder_path = TEXT_ENCODER_PATH[text_encoder_type]
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if logger is not None:
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logger.info(
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f"Loading text encoder model ({text_encoder_type}) from: {text_encoder_path}"
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)
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if text_encoder_type == "clipL":
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text_encoder = CLIPTextModel.from_pretrained(text_encoder_path)
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text_encoder.final_layer_norm = text_encoder.text_model.final_layer_norm
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elif text_encoder_type == "llm":
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text_encoder = AutoModel.from_pretrained(
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text_encoder_path,
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low_cpu_mem_usage=True,
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quantization_config=quantization_config
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)
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text_encoder.final_layer_norm = text_encoder.norm
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elif text_encoder_type == "vlm":
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text_encoder = LlavaForConditionalGeneration.from_pretrained(
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text_encoder_path,
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low_cpu_mem_usage=True,
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quantization_config=quantization_config
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)
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else:
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raise ValueError(f"Unsupported text encoder type: {text_encoder_type}")
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# from_pretrained will ensure that the model is in eval mode.
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if text_encoder_precision is not None and quantization_config is None and dtype != torch.float8_e4m3fn:
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text_encoder = text_encoder.to(dtype)
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text_encoder.requires_grad_(False)
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if logger is not None:
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logger.info(f"Text encoder to dtype: {dtype}")
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if device is not None and quantization_config is None:
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text_encoder = text_encoder.to(device)
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return text_encoder, text_encoder_path
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def load_tokenizer(
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tokenizer_type, tokenizer_path=None, padding_side="right", logger=None
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):
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if tokenizer_path is None:
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tokenizer_path = TOKENIZER_PATH[tokenizer_type]
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if logger is not None:
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logger.info(f"Loading tokenizer ({tokenizer_type}) from: {tokenizer_path}")
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if tokenizer_type == "clipL":
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tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path, max_length=77)
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elif tokenizer_type == "llm" or tokenizer_type == "vlm":
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tokenizer = AutoTokenizer.from_pretrained(
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tokenizer_path, padding_side=padding_side
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)
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else:
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raise ValueError(f"Unsupported tokenizer type: {tokenizer_type}")
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return tokenizer, tokenizer_path
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@dataclass
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class TextEncoderModelOutput(ModelOutput):
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"""
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Base class for model's outputs that also contains a pooling of the last hidden states.
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Args:
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hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
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hidden_states_list (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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text_outputs (`list`, *optional*, returned when `return_texts=True` is passed):
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List of decoded texts.
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"""
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hidden_state: torch.FloatTensor = None
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attention_mask: Optional[torch.LongTensor] = None
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hidden_states_list: Optional[Tuple[torch.FloatTensor, ...]] = None
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text_outputs: Optional[list] = None
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class TextEncoder(nn.Module):
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def __init__(
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self,
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text_encoder_type: str,
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max_length: int,
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text_encoder_precision: Optional[str] = None,
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text_encoder_path: Optional[str] = None,
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tokenizer_type: Optional[str] = None,
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tokenizer_path: Optional[str] = None,
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output_key: Optional[str] = None,
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use_attention_mask: bool = True,
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input_max_length: Optional[int] = None,
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hidden_state_skip_layer: Optional[int] = None,
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apply_final_norm: bool = False,
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reproduce: bool = False,
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logger=None,
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device=None,
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dtype=torch.float16,
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quantization_config=None,
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):
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super().__init__()
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self.text_encoder_type = text_encoder_type
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self.max_length = max_length
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self.precision = text_encoder_precision
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self.model_path = text_encoder_path
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self.tokenizer_type = (
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tokenizer_type if tokenizer_type is not None else text_encoder_type
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)
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self.tokenizer_path = (
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tokenizer_path if tokenizer_path is not None else text_encoder_path
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)
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self.use_attention_mask = use_attention_mask
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self.input_max_length = (
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input_max_length if input_max_length is not None else max_length
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)
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self.hidden_state_skip_layer = hidden_state_skip_layer
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self.apply_final_norm = apply_final_norm
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self.reproduce = reproduce
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self.logger = logger
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self.is_fp8 = False
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self.processor = None
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if "t5" in text_encoder_type:
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self.output_key = output_key or "last_hidden_state"
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elif "clip" in text_encoder_type:
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self.output_key = output_key or "pooler_output"
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elif "llm" in text_encoder_type or "glm" in text_encoder_type or "vlm" in text_encoder_type:
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self.output_key = output_key or "last_hidden_state"
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if "glm" in text_encoder_type or "vlm" in text_encoder_type:
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self.processor = AutoProcessor.from_pretrained(text_encoder_path, device=device)
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else:
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raise ValueError(f"Unsupported text encoder type: {text_encoder_type}")
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self.model, self.model_path = load_text_encoder(
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text_encoder_type=self.text_encoder_type,
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text_encoder_precision=self.precision,
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text_encoder_path=self.model_path,
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logger=self.logger,
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device=device,
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dtype=dtype,
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quantization_config=quantization_config,
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)
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self.dtype = self.model.dtype
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self.device = self.model.device
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self.tokenizer, self.tokenizer_path = load_tokenizer(
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tokenizer_type=self.tokenizer_type,
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tokenizer_path=self.tokenizer_path,
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padding_side="right",
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logger=self.logger,
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)
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def __repr__(self):
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return f"{self.text_encoder_type} ({self.precision} - {self.model_path})"
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@staticmethod
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def apply_text_to_template(text, template, prevent_empty_text=True):
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"""
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Apply text to template.
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Args:
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text (str): Input text.
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template (str or list): Template string or list of chat conversation.
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prevent_empty_text (bool): If Ture, we will prevent the user text from being empty
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by adding a space. Defaults to True.
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"""
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if isinstance(template, str):
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# Will send string to tokenizer. Used for llm
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return template.format(text)
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else:
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raise TypeError(f"Unsupported template type: {type(template)}")
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def text2tokens(self, text, prompt_template, image1=None, image2=None, clip_text_override=None):
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"""
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Tokenize the input text.
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Args:
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text (str or list): Input text.
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"""
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if self.text_encoder_type != "vlm" and image1 is not None:
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raise ValueError("Only vision_languague models support image input")
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tokenize_input_type = "str"
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if prompt_template is not None and self.text_encoder_type == "llm" or self.text_encoder_type == "vlm":
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if isinstance(text, (list, tuple)):
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text = [
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self.apply_text_to_template(one_text, prompt_template["template"])
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for one_text in text
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]
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if isinstance(text[0], list):
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tokenize_input_type = "list"
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elif isinstance(text, str):
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text = self.apply_text_to_template(text, prompt_template["template"])
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if isinstance(text, list):
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tokenize_input_type = "list"
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else:
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raise TypeError(f"Unsupported text type: {type(text)}")
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elif clip_text_override is not None and self.text_encoder_type == "clipL":
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text = clip_text_override
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kwargs = dict(
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truncation=True,
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max_length=self.max_length,
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padding="max_length" if self.text_encoder_type != "vlm" else "do_not_pad",
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return_tensors="pt",
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)
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if tokenize_input_type == "str":
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text_tokens = self.tokenizer(
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text,
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return_length=False,
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return_overflowing_tokens=False,
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return_attention_mask=True,
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**kwargs,
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)
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if self.text_encoder_type == "vlm":
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raw_images = []
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if image1 is not None:
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raw_images.append(image1.squeeze(0)*255)
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if image2 is not None:
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raw_images.append(image2.squeeze(0)*255)
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text_tokens = self.processor(
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raw_images,
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text,
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**kwargs,
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).to(0, torch.float16)
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return text_tokens #text_tokens
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elif tokenize_input_type == "list":
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return self.tokenizer.apply_chat_template(
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text,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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**kwargs,
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)
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else:
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raise ValueError(f"Unsupported tokenize_input_type: {tokenize_input_type}")
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def encode(
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self,
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batch_encoding,
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use_attention_mask=None,
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output_hidden_states=False,
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do_sample=None,
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hidden_state_skip_layer=None,
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return_texts=False,
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prompt_template=None,
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image_token_selection_expr="::4",
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device=None,
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):
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"""
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Args:
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batch_encoding (dict): Batch encoding from tokenizer.
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use_attention_mask (bool): Whether to use attention mask. If None, use self.use_attention_mask.
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Defaults to None.
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output_hidden_states (bool): Whether to output hidden states. If False, return the value of
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self.output_key. If True, return the entire output. If set self.hidden_state_skip_layer,
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output_hidden_states will be set True. Defaults to False.
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do_sample (bool): Whether to sample from the model. Used for Decoder-Only LLMs. Defaults to None.
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When self.produce is False, do_sample is set to True by default.
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hidden_state_skip_layer (int): Number of hidden states to hidden_state_skip_layer. 0 means the last layer.
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If None, self.output_key will be used. Defaults to None.
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return_texts (bool): Whether to return the decoded texts. Defaults to False.
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"""
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device = self.model.device if device is None else device
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use_attention_mask = use_default(use_attention_mask, self.use_attention_mask)
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hidden_state_skip_layer = use_default(
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hidden_state_skip_layer, self.hidden_state_skip_layer
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)
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do_sample = use_default(do_sample, not self.reproduce)
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attention_mask = (
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batch_encoding["attention_mask"].to(device) if use_attention_mask else None
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)
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for k,v in batch_encoding.items():
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batch_encoding[k] = v.to(device) if isinstance(v, torch.Tensor) else v
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outputs = self.model(
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**batch_encoding,
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output_hidden_states=output_hidden_states
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or hidden_state_skip_layer is not None,
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)
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if hidden_state_skip_layer is not None:
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last_hidden_state = outputs.hidden_states[-(hidden_state_skip_layer + 1)]
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# Real last hidden state already has layer norm applied. So here we only apply it
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# for intermediate layers.
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if hidden_state_skip_layer > 0 and self.apply_final_norm:
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last_hidden_state = self.model.final_layer_norm(last_hidden_state)
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else:
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last_hidden_state = outputs[self.output_key]
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# Remove hidden states of instruction tokens, only keep prompt tokens.
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if prompt_template is not None and self.text_encoder_type == "llm":
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crop_start = prompt_template.get("crop_start", -1)
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if crop_start > 0:
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last_hidden_state = last_hidden_state[:, crop_start:]
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attention_mask = (
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attention_mask[:, crop_start:] if use_attention_mask else None
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)
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elif prompt_template is not None and self.text_encoder_type == "vlm":
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# Temporory implementation for one round chat template to get rid of system prompts aand chat header
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user_start_tokens = self.tokenizer(
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text="<|start_header_id|>user<|end_header_id|>",
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add_special_tokens=False,
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return_tensors="pt"
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)
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image_token = self.tokenizer(
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text="<image>",
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add_special_tokens=False,
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return_tensors="pt"
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)
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image_token = image_token["input_ids"].to(device)
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user_start_tokens["input_ids"] = user_start_tokens["input_ids"].to(device)
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tk_idx, tk_n, tk_len = find_subsequence(batch_encoding["input_ids"], user_start_tokens["input_ids"])
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if tk_n != 1:
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raise ValueError("Template seems not in the required format, do you have <|start_header_id|>user<|end_header_id|> in place, and only one round of user input?")
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user_tokens = batch_encoding["input_ids"][:,tk_idx[0]+tk_len:]
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img_idx, img_n, _ = find_subsequence(user_tokens, image_token)
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img_seq_len=outputs["image_hidden_states"].shape[1]
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last_hidden_state = last_hidden_state[:, tk_idx[0]+tk_len:]
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# create image_mask to subset non-image hidden state
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seq_mask = torch.ones_like(last_hidden_state, device=device, dtype=torch.bool)
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img_mask=torch.zeros_like(outputs["image_hidden_states"][0:1], device=device, dtype=torch.bool)
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img_mask[:, multi_slice_to_mask(image_token_selection_expr, img_mask.shape[1])]=True
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drift=0
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for i in img_idx:
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i = i+drift
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seq_mask[:,i:i+img_seq_len,:] = img_mask
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drift+=img_seq_len
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last_hidden_state = last_hidden_state[seq_mask].view(1,-1,outputs["image_hidden_states"].shape[-1])
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attention_mask = torch.ones(last_hidden_state.shape[0], last_hidden_state.shape[1], device=device, dtype=torch.int64)
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elif prompt_template is None and self.text_encoder_type == "vlm":
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raise ValueError("Vlm encoders must use compatiable chat template.")
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if output_hidden_states:
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return TextEncoderModelOutput(
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last_hidden_state, attention_mask, outputs.hidden_states
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)
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return TextEncoderModelOutput(last_hidden_state, attention_mask)
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def forward(
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self,
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text,
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use_attention_mask=None,
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output_hidden_states=False,
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do_sample=False,
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hidden_state_skip_layer=None,
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return_texts=False,
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):
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batch_encoding = self.text2tokens(text)
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return self.encode(
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batch_encoding,
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use_attention_mask=use_attention_mask,
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output_hidden_states=output_hidden_states,
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do_sample=do_sample,
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hidden_state_skip_layer=hidden_state_skip_layer,
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return_texts=return_texts,
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)
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