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package","maintainers":[{"name":"markoristic","email":"markoristic@google.com"},{"name":"mattsoulanille","email":"matthew@soulanille.net"},{"name":"tmullen","email":"tmullen@google.com"},{"name":"pyu10055","email":"ping.yu.11@gmail.com"}],"readme":"# @litertjs/core\n\nCore web runtime for [LiteRT](https://ai.google.dev/edge/litert) ([previously\nTFLite](https://developers.googleblog.com/en/tensorflow-lite-is-now-litert/)),\nGoogle's open-source high performance runtime for on-device AI.\n\nThis package provides the core functionality to load and run arbitrary LiteRT\n(`.tflite`) models directly in the browser, with robust support for WebGPU\nacceleration, WebNN for dedicated hardware acceleration (such as NPUs), and\noptimal CPU execution.\n\nThis is the primary package for LiteRT.js. For integration with TensorFlow.js,\nsee\n[`@litertjs/tfjs-interop`](https://www.npmjs.com/package/@litertjs/tfjs-interop).\nTo test and benchmark your models, check out the\n[`@litertjs/model-tester`](https://www.npmjs.com/package/@litertjs/model-tester).\n\n## Features\n\n-   **Comprehensive Model Coverage**: Load and reliably run standardized LiteRT\n(`.tflite`) models directly in the web browser.\n-   **WebGPU Acceleration**: Leverage WebGPU to unlock high-performance GPU\nprocessing across modern desktops and mobile browsers.\n-   **WebNN Integration**: Harness the emerging WebNN API to target dedicated\nneural processing units (NPUs) or specialized hardware accelerators, enabling\nhighly power-efficient and ultra-low latency inference.\n-   **High-Performance CPU Inference**: Fallback to CPU kernels accelerated by [XNNPack](https://github.com/google/XNNPACK) running in WebAssembly, ensuring dependable, high-speed performance on any browser architecture.\n-   **Seamless TensorFlow.js Integration**: Slot into existing [TensorFlow.js](https://github.com/tensorflow/tfjs)\n    pipelines as a replacement for [TFJS Graph Models](https://js.tensorflow.org/api/latest/#loadGraphModel). See the\n    [`@litertjs/tfjs-interop`](https://www.npmjs.com/package/@litertjs/tfjs-interop) package for integration guidelines and types.\n\n## Usage\n\nFor a comprehensive guide covering model conversion, debugging, and advanced\npipeline development, see our official documentation at\n[ai.google.dev/edge/litert/web](https://ai.google.dev/edge/litert/web).\n\nThe following code snippet demonstrates how to initialize the LiteRT.js runtime,\nload a MobileNetV2 model, prepare inputs, and execute inference utilizing\navailable accelerators.\n\n```typescript\nimport {loadLiteRt, loadAndCompile, Tensor} from '@litertjs/core';\n\n// 1. Initialize LiteRT.js's Wasm runtime components.\n// These files are located in `node_modules/@litertjs/core/wasm/`\n// and need to be served statically by your web server.\n// Alternatively, they can be loaded via\n// https://cdn.jsdelivr.net/npm/@litertjs/core/wasm/\n// Pass `{jspi: true}` to enable JSPI-supporting WebAssembly, which is\n// required for WebNN or for model partitioning on WebGPU / WASM.\nawait loadLiteRt('/path/to/wasm/directory/', {jspi: true});\n\n// 2. Load and compile the model.\n// Determine the appropriate accelerator based on device support:\n// - 'wasm' acts as a high-performance CPU executor.\n// - 'webgpu' targets integrated or dedicated graphics acceleration.\n// - 'webnn' [experimental] targets integrated NPUs or OS-level acceleration.\n// Note on WebGPU delegation fallback:\n// - On JSPI-supporting browsers: If a model has operators unsupported by WebGPU,\n//   LiteRT.js will run supported ops on WebGPU and fall back to WASM on a per-op\n//   basis (model partitioning).\n// - On non-JSPI browsers: WebGPU/WASM partitioning is not supported due to\n//   synchronous boundary constraints. If a model cannot be fully delegated to WebGPU,\n//   the entire model will completely fall back to WASM execution (logging a warning).\nconst model = await loadAndCompile(\n  '/path/to/your/model/torchvision_mobilenet_v2.tflite',\n  {accelerator: 'webgpu'}, // Alternatively, use 'webnn' or 'wasm'\n  // If using 'webnn', or to support mixed execution on WebGPU, the 'jspi' option\n  // must be passed as true when calling loadLiteRt() in step 1\n);\n\n// 3. Construct the input tensor.\n// Dimensions map tightly to the underlying PyTorch or TensorFlow constraints,\n// but can vary depending on the model conversion process.\n// e.g. tensor is of the form (batch, channel, height, width)\nconst inputTypedArray = new Float32Array(1 * 3 * 224 * 224);\n/*\n  This comment is meant to represent some code which populates inputTypedArray\n  with the data intended for inference.\n*/\nconst inputTensor = new Tensor(inputTypedArray, [1, 3, 224, 224]);\n\n// 4. Run the model\nconst results = await model.run(gpuTensor);\n\n// 5. Clean up Tensors\n// LiteRT.js uses manual memory management, so all tensors must be manually\n// deleted when they are no longer needed.\ngpuTensor.delete();\n\n// 6. Read the model's output\nconst result = await results[0].moveTo('wasm');\nconsole.log('Prediction buffer:', result.toTypedArray());\n\n// 7. Clean up result Tensors.\nresults.delete();\nresult.delete();\n```\n","readmeFilename":"README.md"}