Update LocalInference to use public repos

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Dalton Flanagan 2024-09-25 11:05:03 -07:00
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commit b3b0349931
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# LocalInference
LocalInference provides a local inference implementation powered by [executorch](https://github.com/pytorch/executorch/).
Llama Stack currently supports on-device inference for iOS with Android coming soon. You can run on-device inference on Android today using [executorch](https://github.com/pytorch/executorch/tree/main/examples/demo-apps/android/LlamaDemo), PyTorchs on-device inference library.
## Installation
We're working on making LocalInference easier to set up. For now, you'll need to import it via `.xcframework`:
1. Clone the executorch submodule in this repo and its dependencies: `git submodule update --init --recursive`
1. Install [Cmake](https://cmake.org/) for the executorch build`
1. Drag `LocalInference.xcodeproj` into your project
1. Add `LocalInference` as a framework in your app target
1. Add a package dependency on https://github.com/pytorch/executorch (branch latest)
1. Add all the kernels / backends from executorch (but not exectuorch itself!) as frameworks in your app target:
- backend_coreml
- backend_mps
- backend_xnnpack
- kernels_custom
- kernels_optimized
- kernels_portable
- kernels_quantized
1. In "Build Settings" > "Other Linker Flags" > "Any iOS Simulator SDK", add:
```
-force_load
$(BUILT_PRODUCTS_DIR)/libkernels_optimized-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libkernels_custom-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libkernels_quantized-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libbackend_xnnpack-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libbackend_coreml-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libbackend_mps-simulator-release.a
```
1. In "Build Settings" > "Other Linker Flags" > "Any iOS SDK", add:
```
-force_load
$(BUILT_PRODUCTS_DIR)/libkernels_optimized-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libkernels_custom-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libkernels_quantized-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libbackend_xnnpack-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libbackend_coreml-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libbackend_mps-simulator-release.a
```
## Preparing a model
1. Prepare a `.pte` file [following the executorch docs](https://github.com/pytorch/executorch/blob/main/examples/models/llama2/README.md#step-2-prepare-model)
2. Bundle the `.pte` and `tokenizer.model` file into your app
## Using LocalInference
1. Instantiate LocalInference with a DispatchQueue. Optionally, pass it into your agents service:
```swift
init () {
runnerQueue = DispatchQueue(label: "org.meta.llamastack")
inferenceService = LocalInferenceService(queue: runnerQueue)
agentsService = LocalAgentsService(inference: inferenceService)
}
```
2. Before making any inference calls, load your model from your bundle:
```swift
let mainBundle = Bundle.main
inferenceService.loadModel(
modelPath: mainBundle.url(forResource: "llama32_1b_spinquant", withExtension: "pte"),
tokenizerPath: mainBundle.url(forResource: "tokenizer", withExtension: "model"),
completion: {_ in } // use to handle load failures
)
```
3. Make inference calls (or agents calls) as you normally would with LlamaStack:
```
for await chunk in try await agentsService.initAndCreateTurn(
messages: [
.UserMessage(Components.Schemas.UserMessage(
content: .case1("Call functions as needed to handle any actions in the following text:\n\n" + text),
role: .user))
]
) {
```
## Troubleshooting
If you receive errors like "missing package product" or "invalid checksum", try cleaning the build folder and resetting the Swift package cache:
(Opt+Click) Product > Clean Build Folder Immediately
```
rm -rf \
~/Library/org.swift.swiftpm \
~/Library/Caches/org.swift.swiftpm \
~/Library/Caches/com.apple.dt.Xcode \
~/Library/Developer/Xcode/DerivedData
```