[WIP] feat: support graph encoding backend auto-detection in wasi-nn

[TE-7000026184]

No description provided.

[TE-7000026184]

In current implementation of this PR, we assume that loading will fail if the format is not matched.
However, after checking some code, it seems the result of "load" function cannot indicate whether the file is in the correct format, for some back-ends, "load" functions only do some memory copy works.

Wondering whether there is an additional API for each back-end to check whether the model file is encoded in its way?
Plan B is to look into the content of the model file to be loaded. For "tflite" and "h5" format, header of the file contains magic number to verify the format, but "onnx" and some other formats do not have such things.

[TE-7000026184]

How to check model file format:

openvino:
”load” function checks “builder“ number, should be two, one xml file and one binary weight file. Partial reliable.

onnx:
”load” function checks the status of ctx->ort_api->CreateSessionFromArray. Reliable.

tensorflow:
Is it really supported? It should be a directory containing a pb or pbtxt file.

pytorch:
Is it really supported? It should be a package.

tensorflowlite:
“TFL3” at 5-8 byte: “00 1c 00 00 46 54 33 4c 00 14 00 20 00 1c”. Need to add a check.

ggml:
“GGUF” at 1-4 byte: “47 47 46 55”. Need to add a check. But llama does not support “load“ function?

__attribute__((visibility("default"))) wasi_nn_error
load(void *ctx, graph_builder_array *builder, graph_encoding encoding,
     execution_target target, graph *g)
{
    return unsupported_operation;
}

[yamt]

i guess you should explain the motivation of auto-detection.

[TE-7000026184]

i guess you should explain the motivation of auto-detection.

The motivation is to implement auto-detect option in encoding when loading a model file. In case a binary model file without an extension is provided, or in case different types of model files are used by application user.