Remove num dim usages in xnnpack flatbuffer

[lucylq]

There's some redundancy here where num_dims is serialized into the flatbuffer. Use size from the dims array directly, as if we use num_dims, we have to check it matches up with the dims array size.

Hopefully prevent some security issues down the line.

[pytorch-bot]

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[lucylq]

ah this was recently added but if we use tensor_value->dims()->size() directly then we also don't need the check on tensor_value->num_dims()..

[Copilot]

Pull request overview

This PR updates the XNNPACK flatbuffer runtime deserialization path to stop relying on the serialized num_dims field and instead derive tensor rank directly from the dims vector length, reducing redundancy and avoiding mismatches between num_dims and dims.

Changes:

• Removes num_dims/dims consistency checks in defineTensor and builds rank from dims()->size().
• Passes dims_data.size() to XNNPACK xnn_define_* APIs instead of tensor_value->num_dims().
• Updates debug logging format to print size_t correctly.

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[Copilot]

PerChannelGroupQuant path still dereferences tensor_value->dims()->Get(0/1) earlier in this case block without any null / length validation. Since the earlier num_dims/dims consistency checks were removed, a malformed (or malicious) flatbuffer with dims == nullptr (or a shorter-than-expected dims vector) can now crash here instead of returning InvalidProgram. Please add an ET_CHECK_OR_RETURN_ERROR(dims_data.size() >= 2, InvalidProgram, ...) (and use dims_data[0]/dims_data[1]) before computing output_channels/input_channels so this remains robust against corrupted inputs.