Add example of LLava encoder (#2375)

Summary:
Pull Request resolved: #2375

Add the example to start enabling LLava, one multimodal model in generative AI area. In this example, we initiate the process of running LLava through ExecuTorch. Refer to the added README.md for details.

bypass-github-export-checks

Reviewed By: cccclai

Differential Revision: D54812717

fbshipit-source-id: 57f79a925f40594d6c0714b77aefb6193ee2890a

Author: Martin Yuan

examples/models/__init__.py

@@ -26,6 +26,7 @@
     "ic4": ("inception_v4", "InceptionV4Model"),
     "resnet18": ("resnet", "ResNet18Model"),
     "resnet50": ("resnet", "ResNet50Model"),
+    "llava": ("llava", "LlavaModel"),
 }
 
 __all__ = [

examples/models/llava_encoder/README.md

@@ -0,0 +1,17 @@
+## Summary
+In this example, we initiate the process of running multi modality through ExecuTorch.
+- Demonstrate how to export the image encoder model in the [LLava](https://github.com/haotian-liu/LLaVA) multimodal model.
+- Provide TODO steps on how to use the exported .pte file and the existing [exported Llama2 model](https://github.com/pytorch/executorch/tree/main/examples/models/llama2), to build the multimodal pipeline.
+
+## Instructions
+Note that this folder does not host the pretrained LLava model.
+- To have Llava available, follow the [Install instructions](https://github.com/haotian-liu/LLaVA?tab=readme-ov-file#install) in the LLava github. Follow the licence in the specific repo when using L
+- Since the pytorch model version may not be updated, `cd executorch`, run `./install_requirements.sh`.
+- Run `python3 -m examples.portable.scripts.export --model_name="llava_encoder"`. The llava_encoder.pte file will be generated.
+
+## TODO
+- Write the pipeline in cpp
+  - Have image and text prompts as inputs.
+  - Call image processing functions to preprocess the image tensor.
+  - Load the llava_encoder.pte model, run it using the image tensor.
+  - The output of the encoder can be combined with the prompt, as inputs to the llama model. Call functions in llama_runner.cpp to run the llama model and get outputs. 

examples/models/llava_encoder/__init__.py

@@ -0,0 +1,11 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .model import LlavaModel
+
+__all__ = [
+    LlavaModel,
+]

examples/models/llava_encoder/model.py

@@ -0,0 +1,52 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+
+from examples.models.model_base import EagerModelBase
+from llava.eval.run_llava import load_images, process_images
+from llava.mm_utils import get_model_name_from_path
+
+from llava.model.builder import load_pretrained_model
+from torch import nn
+
+
+class EncoderModel(nn.Module):
+    def __init__(self, llava_model):
+        super().__init__()
+        self.model_ = llava_model
+
+    def forward(self, images_tensor):
+        features = self.model_.get_model().get_vision_tower()(images_tensor)
+        features = self.model_.get_model().mm_projector(features)
+        return features
+
+
+class LlavaModel(EagerModelBase):
+    def __init__(self):
+        model_path = "liuhaotian/llava-v1.5-7b"
+        tokenizer, self.model_, self.image_processor_, context_len = (
+            load_pretrained_model(
+                model_path=model_path,
+                model_base=None,
+                model_name=get_model_name_from_path(model_path),
+            )
+        )
+        self.device = "cpu"
+        self.model_.to(self.device)
+        self.dtype = torch.float32
+
+    def get_eager_model(self):
+        model = EncoderModel(self.model_)
+        return model
+
+    def get_example_inputs(self):
+        image_file = "https://llava-vl.github.io/static/images/view.jpg"
+        images = load_images([image_file])
+        images_tensor = process_images(
+            images, self.image_processor_, self.model_.config
+        ).to(self.model_.device, dtype=torch.float32)
+        return (images_tensor,)