patch llama32 class

nbs/llama.ipynb

@@ -2,26 +2,62 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2024-10-29 17:54:30.075\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mxinfer.models\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m63\u001b[0m - \u001b[1mModel: meta-llama/Llama-3.2-11B-Vision-Instruct\u001b[0m\n",
+      "\u001b[32m2024-10-29 17:54:30.076\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mxinfer.models\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m64\u001b[0m - \u001b[1mDevice: cuda\u001b[0m\n",
+      "\u001b[32m2024-10-29 17:54:30.076\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mxinfer.models\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m65\u001b[0m - \u001b[1mDtype: bfloat16\u001b[0m\n",
+      "The model weights are not tied. Please use the `tie_weights` method before using the `infer_auto_device` function.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a54363be84904348a857e9d36e3e1462",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import xinfer \n",
     "\n",
-    "model = xinfer.create_model(\"meta-llama/Llama-3.2-11B-Vision\", device=\"cuda\", dtype=\"bfloat16\")\n",
+    "model = xinfer.create_model(\"meta-llama/Llama-3.2-11B-Vision-Instruct\", device=\"cuda\", dtype=\"bfloat16\")\n",
     "# model = xinfer.create_model(\"Salesforce/blip2-opt-2.7b\", device=\"cuda\", dtype=\"bfloat16\")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'The image is a screenshot from an anime featuring a character with long white hair, elf-like ears, and green eyes. She'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "image = \"https://raw.githubusercontent.com/vikhyat/moondream/main/assets/demo-1.jpg\"\n",
     "prompt = \"Describe image.\"\n",
     "\n",
-    "# model.infer(image, prompt, max_new_tokens=25)"
+    "model.infer(image, prompt, max_new_tokens=25)"
    ]
   },
   {

xinfer/transformers/__init__.py

@@ -1,6 +1,6 @@
 from .blip2 import BLIP2
 from .joycaption import JoyCaption
-from .llama32 import Llama32
+from .llama32 import Llama32Vision, Llama32VisionInstruct
 from .moondream import Moondream
 from .vision2seq import Vision2SeqModel
 from .vlrm_blip2 import VLRMBlip2

xinfer/transformers/llama32.py

@@ -10,22 +10,12 @@
     "transformers",
     ModelInputOutput.IMAGE_TEXT_TO_TEXT,
 )
-@register_model(
-    "meta-llama/Llama-3.2-11B-Vision",
-    "transformers",
-    ModelInputOutput.IMAGE_TEXT_TO_TEXT,
-)
 @register_model(
     "meta-llama/Llama-3.2-90B-Vision-Instruct",
     "transformers",
     ModelInputOutput.IMAGE_TEXT_TO_TEXT,
 )
-@register_model(
-    "meta-llama/Llama-3.2-90B-Vision",
-    "transformers",
-    ModelInputOutput.IMAGE_TEXT_TO_TEXT,
-)
-class Llama32(BaseModel):
+class Llama32VisionInstruct(BaseModel):
     def __init__(
         self, model_id: str, device: str = "cpu", dtype: str = "float32", **kwargs
     ):
@@ -97,3 +87,47 @@ def infer_batch(self, images: list[str], prompts: list[str], **generate_kwargs):
         decoded = self.processor.batch_decode(outputs, skip_special_tokens=True)
         # Remove the prompt and assistant marker for each response
         return [d.split("assistant")[-1].strip() for d in decoded]
+
+
+@register_model(
+    "meta-llama/Llama-3.2-11B-Vision",
+    "transformers",
+    ModelInputOutput.IMAGE_TEXT_TO_TEXT,
+)
+@register_model(
+    "meta-llama/Llama-3.2-90B-Vision",
+    "transformers",
+    ModelInputOutput.IMAGE_TEXT_TO_TEXT,
+)
+class Llama32Vision(Llama32VisionInstruct):
+    @track_inference
+    def infer(self, image: str, prompt: str, **generate_kwargs) -> str:
+        image = super().parse_images(image)
+
+        # Format prompt for base vision model
+        input_text = f"<|image|><|begin_of_text|>{prompt}"
+
+        # Process inputs without adding special tokens
+        inputs = self.processor(image, input_text, return_tensors="pt").to(
+            self.model.device
+        )
+
+        with torch.inference_mode():
+            output = self.model.generate(**inputs, **generate_kwargs)
+
+        return self.processor.decode(output[0], skip_special_tokens=True)
+
+    def infer_batch(self, images: list[str], prompts: list[str], **generate_kwargs):
+        images = super().parse_images(images)
+
+        # Format prompts for base vision model
+        input_texts = [f"<|image|><|begin_of_text|>{prompt}" for prompt in prompts]
+
+        inputs = self.processor(images, input_texts, return_tensors="pt").to(
+            self.model.device
+        )
+
+        with torch.inference_mode():
+            outputs = self.model.generate(**inputs, **generate_kwargs)
+
+        return self.processor.batch_decode(outputs, skip_special_tokens=True)