Missing torch.load

Similar with the discussion of the missing torch.save

pytorch torch.load refers to torch\serialization.py

which provide parameter instructions for loading

Example

        >>> torch.load('tensors.pt')
        # Load all tensors onto the CPU
        >>> torch.load('tensors.pt', map_location=torch.device('cpu'))
        # Load all tensors onto the CPU, using a function
        >>> torch.load('tensors.pt', map_location=lambda storage, loc: storage)
        # Load all tensors onto GPU 1
        >>> torch.load('tensors.pt', map_location=lambda storage, loc: storage.cuda(1))
        # Map tensors from GPU 1 to GPU 0
        >>> torch.load('tensors.pt', map_location={'cuda:1':'cuda:0'})
        # Load tensor from io.BytesIO object
        >>> with open('tensor.pt', 'rb') as f:
        ...     buffer = io.BytesIO(f.read())
        >>> torch.load(buffer)
        # Load a module with 'ascii' encoding for unpickling
        >>> torch.load('module.pt', encoding='ascii')

Currently LibTorchSharp implements one of the possible loading options listed above

Since pickling is an overkill as discussed for .NET

As I am still learning … is there a need to provide more loading options provided through torch.load instead of Module.load in TorchSharp?

I am raising this issue, as I fail to load a saved State_Dict created through exportsd.py back to TorchSharp using Module.Load

I did not get any error message, as the process crashes.

suggestions: is there a need for error messages when loading fail to assist in a more reliable loading state_dict.

[NiklasGustafsson]

@GeorgeS2019 – I suggest adding a print statement (on your machine) to the exportsd.py, something like:

    for entry in sd:
        print(entry)
        stream.write(leb128.u.encode(len(entry)))
        stream.write(bytes(entry, 'utf-8'))
        _write_tensor(sd[entry], stream)

and see what the names of all the state_dict entries are, then compare that to your .NET module that you are loading the weights into.

[NiklasGustafsson]

Anything that looks like a <String,Tensor> dictionary and was saved using the format that exportsd.py also uses should be possible to load, but when loading, the keys come from the model instance (either a custom module or Sequential) that the weights are being loaded into. On the saving side, the keys likewise come from the original model.

Thus, the two have to exactly match – that’s the key here. Without seeing the model definition on both sides, it’s hard to help debug it. The best I can do, and I will try to get that into the next release, is to improve the error messages so that they are more informative.