Create modeling_llamavision.py

modeling_llamavision.py

@@ -0,0 +1,150 @@
+# https://huggingface.co/qresearch/llama-3.1-8B-vision-378/blob/main/modeling_llamavision.py
+
+import torch
+import torch.nn as nn
+from transformers import (
+    PreTrainedModel,
+    AutoModelForCausalLM,
+    AutoModel,
+    SiglipImageProcessor,
+)
+from .configuration_llamavision import LlamavisionConfig
+
+
+class ProjectionModule(nn.Module):
+    def __init__(self, mm_hidden_size=1152, hidden_size=4096):
+        super(ProjectionModule, self).__init__()
+
+        # Directly set up the sequential model
+        self.model = nn.Sequential(
+            nn.Linear(mm_hidden_size, hidden_size),
+            nn.GELU(),
+            nn.Linear(hidden_size, hidden_size),
+        )
+
+    def forward(self, x):
+        return self.model(x)
+
+
+class Llamavision(PreTrainedModel):
+    config_class = LlamavisionConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.vision_model = AutoModel.from_config(self.config.vision_config)
+        self.text_model = AutoModelForCausalLM.from_config(self.config.text_config)
+        self.processor = SiglipImageProcessor()
+        self.mm_projector = ProjectionModule(
+            mm_hidden_size=config.vision_config.hidden_size,
+            hidden_size=config.text_config.hidden_size,
+        )
+
+    @property
+    def device(self):
+        return self.text_model.device
+
+    def encode_image(self, image):
+        image = image.convert("RGB")
+        image = self.processor(
+            images=image,
+            return_tensors="pt",
+            do_resize=True,
+            size={"height": 378, "width": 378},
+        )["pixel_values"].to(
+            device=self.vision_model.device, dtype=self.vision_model.dtype
+        )
+        with torch.no_grad():
+            return self.vision_model(image, output_hidden_states=True).hidden_states[-2]
+
+    def input_embeds(self, prompt, image_embeds, tokenizer):
+        def _tokenize(txt):
+            return tokenizer(
+                txt, return_tensors="pt", add_special_tokens=False
+            ).input_ids.to(self.device)
+
+        text_emb = self.text_model.get_input_embeddings()
+
+        embeds = []
+
+        tokenized_prompt = _tokenize(prompt)
+        if (
+            tokenizer.bos_token_id is not None
+            and tokenized_prompt[0][0] != tokenizer.bos_token_id
+        ):
+            embeds.append(
+                text_emb(torch.tensor([[tokenizer.bos_token_id]], device=self.device))
+            )
+
+        projected_image_embeds = self.mm_projector(image_embeds.to(self.device))
+        embeds.append(projected_image_embeds)
+
+        embeds.append(text_emb(tokenized_prompt))
+
+        return torch.cat(embeds, dim=1)
+
+    def get_input_embeddings(self):
+        return self.text_model.get_input_embeddings()
+
+    def generate(
+        self,
+        image_embeds,
+        prompt,
+        tokenizer,
+        max_new_tokens=128,
+        **kwargs,
+    ):
+        generate_config = {
+            "eos_token_id": [
+                tokenizer.eos_token_id,
+                tokenizer.convert_tokens_to_ids("<|eot_id|>"),
+            ],
+            "bos_token_id": tokenizer.bos_token_id,
+            "pad_token_id": tokenizer.pad_token_id,
+            "max_new_tokens": max_new_tokens,
+            **kwargs,
+        }
+
+        with torch.no_grad():
+            inputs_embeds = self.input_embeds(prompt, image_embeds, tokenizer)
+            
+            attention_mask = torch.ones(
+                inputs_embeds.shape[:2],
+                dtype=torch.long,
+                device=inputs_embeds.device
+            )
+            
+            output_ids = self.text_model.generate(
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                **generate_config
+            )
+
+        return tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+
+    def answer_question(self, image, question, tokenizer, **kwargs):
+        image_embeds = self.encode_image(image)
+
+        chat = [
+            {
+                "role": "system",
+                "content": "You are a helpful AI assistant that can see images and answer questions about them.",
+            },
+            {"role": "user", "content": question},
+        ]
+        prompt = tokenizer.apply_chat_template(
+            chat, tokenize=False, add_generation_prompt=True
+        )
+
+        # Generate the answer
+        with torch.no_grad():
+            output = self.generate(
+                image_embeds=image_embeds,
+                prompt=prompt,
+                tokenizer=tokenizer,
+                **kwargs,
+            )[0]
+
+        # Clean and return the answer
+        cleaned_answer = output.strip()
+        return cleaned_answer