Refactor to use new rustnn MLContext API

.github/workflows/test.yml

@@ -17,6 +17,11 @@ jobs:
         python-version: ["3.10", "3.11", "3.12", "3.13"]
 
     steps:
+      # Allow checkout of Rust dependency snapshots whose paths exceed Windows' default limit.
+      - name: Enable Git longpaths (Windows)
+        if: runner.os == 'Windows'
+        run: git config --global core.longpaths true
+
       - uses: actions/checkout@v4
 
       - name: Set up Python

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "pywebnn"
-version = "0.5.11"
+version = "0.5.13"
 edition = "2021"
 description = "Python bindings for W3C WebNN specification via rustnn"
 license = "Apache-2.0"
@@ -9,28 +9,32 @@ keywords = ["webnn", "python", "machine-learning", "neural-networks", "ai"]
 categories = ["api-bindings", "science"]
 
 [lib]
-crate-type = ["cdylib"]
+crate-type = ["cdylib", "rlib"]
 name = "pywebnn"
 
 [features]
-default = ["onnx-runtime", "coreml-runtime", "dynamic-inputs"]
+default = ["onnx-runtime", "dynamic-inputs"]
 onnx-runtime = ["rustnn/onnx-runtime", "ort", "ndarray"]
-coreml-runtime = ["rustnn/coreml-runtime", "objc"]
+coreml-runtime = ["rustnn/coreml-runtime"]
 trtx-runtime = ["rustnn/trtx-runtime"]
 trtx-runtime-mock = ["rustnn/trtx-runtime-mock"]
+litert-runtime = ["rustnn/litert-runtime"]
+cann-runtime = ["rustnn/cann-runtime"]
+cann-runtime-mock = ["rustnn/cann-runtime-mock"]
 dynamic-inputs = ["rustnn/dynamic-inputs"]
 
 [dependencies]
 pyo3 = { version = "0.28", features = ["extension-module"] }
+bytemuck = "1.14"
 rustnn = { git = "https://github.com/rustnn/rustnn", branch = "main" }
 serde_json = "1.0"
 webnn-graph = { git = "https://github.com/rustnn/webnn-graph", branch = "main" }
 half = "2.4"
 regex = "1.11"
+pretty_env_logger = "0.5"
 # Optional runtime dependencies
 ndarray = { version = "0.16", optional = true }
-ort = { version = "=2.0.0-rc.11", optional = true, features = ["ndarray", "half", "load-dynamic"] }
-objc = { version = "0.2", optional = true }
+ort = { version = "=2.0.0-rc.13", optional = true, features = ["ndarray", "half", "load-dynamic"] }
 
 # Make this crate independent from the parent workspace
 [workspace]

README.md

@@ -10,6 +10,20 @@ Python bindings for the W3C WebNN API, powered by `rustnn`.
 pip install pywebnn
 ```
 
+## Development
+
+Install the development dependencies and native extension in editable mode:
+
+```powershell
+python -m pip install --upgrade pip
+python -m pip install maturin numpy tokenizers
+maturin develop
+```
+
+Re-run `maturin develop` after changing Rust code. To build with TensorRT RTX
+support, use `maturin develop --features trtx-runtime` and ensure the matching
+TensorRT runtime DLLs are available on `PATH` before launching Python.
+
 ## Docs
 
 Full documentation is published on GitHub Pages:
@@ -41,6 +55,27 @@ result = context.compute(
 print(result["output"])
 ```
 
+## TensorRT backend
+
+TensorRT is opt-in. Build an editable installation with RustNN's TensorRT feature,
+then request it when creating the context:
+
+```bash
+maturin develop --features trtx-runtime
+```
+
+```python
+context = webnn.ML().create_context(backend="trtx")
+```
+
+This requires a compatible TensorRT installation and GPU. Use
+`context.backend_info()` to inspect the requested backend and compiled features.
+On Windows, make the matching TensorRT RTX runtime DLLs available through
+`PATH` before starting Python. The DLL version must match the TensorRT headers
+used when building the extension.
+Other opt-in runtime plugins are `litert-runtime` and `cann-runtime`; select them
+with `backend="litert"` and `backend="cann"`, respectively.
+
 ## Links
 
 - GitHub: <https://github.com/rustnn/pywebnn>