Export mini phi3 LoRA model

examples/models/phi3-mini-lora/README.md

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+## Summary
+In this exmaple, we export a model ([phi-3-mini](https://github.com/pytorch/executorch/tree/main/examples/models/phi-3-mini)) appended with additional LoRA layers to ExecuTorch.
+
+## Instructions
+### Step 1: [Optional] Install ExecuTorch dependencies
+`./install_requirements.sh` in ExecuTorch root directory.
+
+### Step 2: Install TorchTune nightly
+The LoRA model used is recent and is not yet officially released on `TorchTune`. To be able to run this example, you will need to run the following to install TorchTune nighly:
+- `./examples/models/llava_encoder/install_requirements.sh`'
+
+### Step 3: Export and run the model
+1. Export the model to ExecuTorch.
+```
+python export_model.py
+```
+
+2. Run the model using an example runtime. For more more detailed steps on this, check out [Build & Run](https://pytorch.org/executorch/stable/getting-started-setup.html#build-run).
+```
+# Clean and configure the CMake build system. Compiled programs will appear in the executorch/cmake-out directory we create here.
+(rm -rf cmake-out && mkdir cmake-out && cd cmake-out && cmake ..)
+
+# Build the executor_runner target
+cmake --build cmake-out --target executor_runner -j9
+
+./cmake-out/executor_runner --model_path mini_phi3_lora.pte
+```

examples/models/phi3-mini-lora/export_model.py

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+# 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 executorch.exir import to_edge
+from torch import int64, long, no_grad, randint, Tensor, zeros
+from torch.export import export, ExportedProgram
+from torch.nn.attention import sdpa_kernel, SDPBackend
+from torchtune.models.phi3._model_builders import lora_phi3_mini
+
+
+@no_grad()
+def export_mini_phi3_lora(model) -> None:
+    """
+    Export the example mini-phi3 with LoRA model to executorch.
+
+    Note: need to use the SDPBackend's custom kernel for sdpa (scalable
+    dot product attention) because the default sdpa kernel used in the
+    model results in a internally mutating graph.
+    """
+    model.eval()
+    # 1. torch.export: Defines the program with the ATen operator set.
+    print("Exporting to aten dialect")
+    example_args = (randint(0, 100, (1, 100), dtype=long),)
+    with sdpa_kernel([SDPBackend.MATH]):
+        aten_dialect: ExportedProgram = export(model, example_args)
+
+        # 2. to_edge: Make optimizations for Edge devices.
+        print("Lowering to edge dialect")
+        edge_program = to_edge(aten_dialect)
+
+    # 3. to_executorch: Convert the graph to an ExecuTorch program.
+    print("Exporting to executorch")
+    executorch_program = edge_program.to_executorch()
+
+    # 4. Save the compiled .pte program.
+    print("Saving to mini_phi3_lora.pte")
+    with open("mini_phi3_lora.pte", "wb") as file:
+        file.write(executorch_program.buffer)
+
+    print("Done.")
+
+
+def run_mini_phi3_lora(model) -> Tensor:
+    """Run the model and return the result."""
+    args = zeros([3072, 1], dtype=int64)
+    model.eval()
+    res = model(args)
+    return res
+
+
+def main() -> None:
+    mini_lora_model = lora_phi3_mini(
+        lora_attn_modules=[
+            "q_proj",
+        ]
+    )
+    export_mini_phi3_lora(mini_lora_model)
+
+
+if __name__ == "__main__":
+    main()

examples/models/phi3-mini-lora/install_requirements.sh

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+#!/bin/bash
+# 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.
+
+# Install nightly build of TorchTune.
+pip install --pre torchtune --extra-index-url https://download.pytorch.org/whl/nightly/cpu --no-cache-dir
+pip install tiktoken