Create modeling_llamavision.py
Browse files- modeling_llamavision.py +148 -0
modeling_llamavision.py
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import torch
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import torch.nn as nn
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from transformers import (
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PreTrainedModel,
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AutoModelForCausalLM,
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AutoModel,
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SiglipImageProcessor,
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)
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from .configuration_llamavision import LlamavisionConfig
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class ProjectionModule(nn.Module):
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def __init__(self, mm_hidden_size=1152, hidden_size=4096):
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super(ProjectionModule, self).__init__()
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# Directly set up the sequential model
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self.model = nn.Sequential(
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nn.Linear(mm_hidden_size, hidden_size),
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nn.GELU(),
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nn.Linear(hidden_size, hidden_size),
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)
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def forward(self, x):
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return self.model(x)
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class Llamavision(PreTrainedModel):
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config_class = LlamavisionConfig
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def __init__(self, config):
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super().__init__(config)
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self.vision_model = AutoModel.from_config(self.config.vision_config)
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self.text_model = AutoModelForCausalLM.from_config(self.config.text_config)
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self.processor = SiglipImageProcessor()
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self.mm_projector = ProjectionModule(
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mm_hidden_size=config.vision_config.hidden_size,
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hidden_size=config.text_config.hidden_size,
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)
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@property
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def device(self):
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return self.text_model.device
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def encode_image(self, image):
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image = image.convert("RGB")
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image = self.processor(
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images=image,
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return_tensors="pt",
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do_resize=True,
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size={"height": 378, "width": 378},
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)["pixel_values"].to(
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device=self.vision_model.device, dtype=self.vision_model.dtype
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)
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with torch.no_grad():
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return self.vision_model(image, output_hidden_states=True).hidden_states[-2]
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def input_embeds(self, prompt, image_embeds, tokenizer):
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def _tokenize(txt):
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return tokenizer(
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txt, return_tensors="pt", add_special_tokens=False
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).input_ids.to(self.device)
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text_emb = self.text_model.get_input_embeddings()
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embeds = []
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tokenized_prompt = _tokenize(prompt)
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if (
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tokenizer.bos_token_id is not None
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and tokenized_prompt[0][0] != tokenizer.bos_token_id
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):
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embeds.append(
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text_emb(torch.tensor([[tokenizer.bos_token_id]], device=self.device))
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)
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projected_image_embeds = self.mm_projector(image_embeds.to(self.device))
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embeds.append(projected_image_embeds)
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embeds.append(text_emb(tokenized_prompt))
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return torch.cat(embeds, dim=1)
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def get_input_embeddings(self):
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return self.text_model.get_input_embeddings()
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def generate(
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self,
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image_embeds,
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prompt,
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tokenizer,
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max_new_tokens=128,
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**kwargs,
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):
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generate_config = {
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"eos_token_id": [
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tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids("<|eot_id|>"),
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],
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"bos_token_id": tokenizer.bos_token_id,
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"pad_token_id": tokenizer.pad_token_id,
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"max_new_tokens": max_new_tokens,
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**kwargs,
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}
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with torch.no_grad():
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inputs_embeds = self.input_embeds(prompt, image_embeds, tokenizer)
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attention_mask = torch.ones(
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inputs_embeds.shape[:2],
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dtype=torch.long,
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device=inputs_embeds.device
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)
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output_ids = self.text_model.generate(
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inputs_embeds=inputs_embeds,
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attention_mask=attention_mask,
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**generate_config
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)
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return tokenizer.batch_decode(output_ids, skip_special_tokens=True)
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def answer_question(self, image, question, tokenizer, **kwargs):
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image_embeds = self.encode_image(image)
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chat = [
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{
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"role": "system",
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"content": "You are a helpful AI assistant that can see images and answer questions about them.",
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},
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{"role": "user", "content": question},
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]
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prompt = tokenizer.apply_chat_template(
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chat, tokenize=False, add_generation_prompt=True
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)
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# Generate the answer
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with torch.no_grad():
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output = self.generate(
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image_embeds=image_embeds,
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prompt=prompt,
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tokenizer=tokenizer,
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**kwargs,
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)[0]
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# Clean and return the answer
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cleaned_answer = output.strip()
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return cleaned_answer
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