add readme for gemma3 cuda (#15349)

Summary: creates a readme for exporting and running gemma3 on cuda
backend

Differential Revision: D85220287

Author: Gasoonjia

examples/models/gemma3/README.md

@@ -0,0 +1,127 @@
+# Summary
+
+This example demonstrates how to export and run Google's [Gemma 3](https://huggingface.co/google/gemma-3-4b-it) vision-language multimodal model locally on ExecuTorch with CUDA backend support.
+
+# Exporting the model
+To export the model, we use [Optimum ExecuTorch](https://github.com/huggingface/optimum-executorch), a repo that enables exporting models straight from the source - from HuggingFace's Transformers repo.
+
+## Setting up Optimum ExecuTorch
+Install through pip package:
+```
+pip install optimum-executorch
+```
+
+Or install from source:
+```
+git clone https://github.com/huggingface/optimum-executorch.git
+cd optimum-executorch
+python install_dev.py
+```
+
+## CUDA Support
+This guide focuses on CUDA backend support for Gemma3, which provides accelerated performance on NVIDIA GPUs.
+
+### Exporting with CUDA
+```bash
+optimum-cli export executorch \
+  --model "google/gemma-3-4b-it" \
+  --task "multimodal-text-to-text" \
+  --recipe "cuda" \
+  --dtype bfloat16 \
+  --device cuda \
+  --output_dir="path/to/output/dir"
+```
+
+This will generate:
+- `model.pte` - The exported model
+- `aoti_cuda_blob.ptd` - The CUDA kernel blob required for runtime
+
+### Exporting with INT4 Quantization (Tile Packed)
+For improved performance and reduced memory footprint, you can export Gemma3 with INT4 weight quantization using tile-packed format:
+
+```bash
+optimum-cli export executorch \
+  --model "google/gemma-3-4b-it" \
+  --task "multimodal-text-to-text" \
+  --recipe "cuda" \
+  --dtype bfloat16 \
+  --device cuda \
+  --qlinear 4w \
+  --qlinear_encoder 4w \
+  --qlinear_packing_format tile_packed_to_4d \
+  --qlinear_encoder_packing_format tile_packed_to_4d \
+  --output_dir="path/to/output/dir"
+```
+
+This will generate the same files (`model.pte` and `aoti_cuda_blob.ptd`) in the `int4` directory.
+
+See the "Building the Gemma3 runner" section below for instructions on building with CUDA support, and the "Running the model" section for runtime instructions.
+
+# Running the model
+To run the model, we will use the Gemma3 runner, which utilizes ExecuTorch's MultiModal runner API.
+The Gemma3 runner will do the following:
+
+- **Image Input**: Load image files (PNG, JPG, etc.) and format them as input tensors for the model
+- **Text Input**: Process text prompts using the tokenizer
+- **Feed the formatted inputs** to the multimodal runner for inference
+
+## Obtaining the tokenizer
+You can download the `tokenizer.json` file from [Gemma 3's HuggingFace repo](https://huggingface.co/unsloth/gemma-3-1b-it):
+```bash
+curl -L https://huggingface.co/unsloth/gemma-3-1b-it/resolve/main/tokenizer.json -o tokenizer.json
+```
+
+## Building the Gemma3 runner
+
+### Prerequisites
+Ensure you have a CUDA-capable GPU and CUDA toolkit installed on your system.
+
+### Building for CUDA
+```bash
+# Install ExecuTorch.
+./install_executorch.sh
+
+# Build the multimodal runner with CUDA
+cmake --preset llm \
+      -DEXECUTORCH_BUILD_CUDA=ON \
+      -DCMAKE_INSTALL_PREFIX=cmake-out \
+      -DCMAKE_BUILD_TYPE=Release \
+      -Bcmake-out -S.
+cmake --build cmake-out -j$(nproc) --target install --config Release
+
+# Build the Gemma3 runner
+cmake -DEXECUTORCH_BUILD_CUDA=ON \
+      -DCMAKE_BUILD_TYPE=Release \
+      -Sexamples/models/gemma3 \
+      -Bcmake-out/examples/models/gemma3/
+cmake --build cmake-out/examples/models/gemma3 --target gemma3_e2e_runner --config Release
+```
+
+## Running the model
+You need to provide the following files to run Gemma3:
+- `model.pte` - The exported model file
+- `aoti_cuda_blob.ptd` - The CUDA kernel blob
+- `tokenizer.json` - The tokenizer file
+- An image file (PNG, JPG, etc.)
+
+### Example usage
+```bash
+./cmake-out/examples/models/gemma3/gemma3_e2e_runner \
+  --model_path path/to/model.pte \
+  --data_path path/to/aoti_cuda_blob.ptd \
+  --tokenizer_path path/to/tokenizer.json \
+  --image_path docs/source/_static/img/et-logo.png \ # here we use the ExecuTorch logo as an example
+  --temperature 0
+```
+
+# Example output
+```
+Okay, let's break down what's in the image!
+
+It appears to be a stylized graphic combining:
+
+*   **A Microchip:** The core shape is a representation of a microchip (the integrated circuit).
+*   **An "On" Symbol:**  There's an "On" symbol (often represented as a circle with a vertical line) incorporated into the microchip design.
+*   **Color Scheme:** The microchip is colored in gray, and
+PyTorchObserver {"prompt_tokens":271,"generated_tokens":99,"model_load_start_ms":0,"model_load_end_ms":0,"inference_start_ms":1761118126790,"inference_end_ms":1761118128385,"prompt_eval_end_ms":1761118127175,"first_token_ms":1761118127175,"aggregate_sampling_time_ms":86,"SCALING_FACTOR_UNITS_PER_SECOND":1000}
+```