examples/pyodide/bridge.py (1)

The file examples/pyodide/bridge.py has been replaced with a single line containing a relative path string ../../pyodide_bridge.py. However, examples/pyodide/main.js (line 18) reads this file's content and passes it directly to pyodide.runPython(). This will result in a SyntaxError in Python because a path string is not valid Python code.

You should either update main.js to read from the root pyodide_bridge.py or ensure that bridge.py contains valid Python code that imports or executes the logic from the root file.

pyodide_bridge.py (480-482)

_NNModule.__call__ attempts to invoke self._module.call(*js_args). In most JavaScript reimplementations of PyTorch, module instances are objects that expose a forward method rather than a call method. Unless the underlying JS library specifically adds a call method to all modules, this will fail. It is more standard and robust to have __call__ invoke self.forward(*args), which is already correctly implemented to call the JS forward method.

    def __call__(self, *args):
        return self.forward(*args)

pyodide_bridge.py (304-307)

The implementation of slicing for Tensor objects creates a new tensor by iterating over the range and converting each element to a Python list and then back to a JS tensor. This is highly inefficient for large tensors and, crucially, it returns a copy instead of a view. In PyTorch, slicing a tensor should return a view that shares the same underlying storage.

Since the JS Tensor.index method (line 351 in src/tensor.ts) already supports creating views for single indices, you should consider extending the JS implementation to support slicing or implementing a more efficient view-based slicing mechanism in the bridge to maintain PyTorch semantics.

pyodide_bridge.py (332-334)

The method wrapper returned by __getattr__ accepts **kwargs but does not pass them to the underlying JavaScript function. Many PyTorch operations rely on keyword arguments for important options (e.g., dim, keepdim, requires_grad). Ignoring these arguments will lead to incorrect behavior when they are used from Python.

Consider transforming kwargs into a JavaScript object (using to_js(kwargs)) and passing it as an argument if the JS API supports options objects.

pyodide_bridge.py (14-24)

_wrap_result automatically wraps any JsProxy returned from a JavaScript call into a Python Tensor. This is problematic if the JavaScript function returns something other than a tensor, such as a list of shapes, a boolean, or a plain configuration object. In such cases, the returned value will be incorrectly converted into a Tensor object.

You should add a check to verify that the JsProxy actually represents a tensor (e.g., by checking for the existence of a shape or id property) before wrapping it.