def create_tensor_spec_sample_io()

in sdk/python/foundation-models/system/inference/text-generation/llama-files/score/default/score.py [0:0]

• 17 lines of code
• 11 McCabe index (conditional complexity)

def create_tensor_spec_sample_io(model_signature_io):
    # Create a sample numpy.ndarray based on shape/type of the tensor info of the model
    io = model_signature_io.inputs
    if not model_signature_io.has_input_names():
        # If the input is not a named tensor, the sample io value that we create will just be a numpy.ndarray
        shape = io[0].shape
        if shape and shape[0] == -1:
            # -1 for first dimension means the input data is batched
            # Create a numpy array with the first dimension of shape as 1 so that inference-schema
            # can correctly generate the swagger sample for the input
            shape = list(deepcopy(shape))
            shape[0] = 1
        sample_io = np.zeros(tuple(shape), dtype=io[0].type)
    else:
        # otherwise, the input is a named tensor, so the sample io value that we create will be
        # Dict[str, numpy.ndarray], which maps input name to a numpy.ndarray of the corresponding size
        sample_io = {}
        for io_val in io:
            shape = io_val.shape
            if shape and shape[0] == -1:
                # -1 for first dimension means the input data is batched
                # Create a numpy array with the first dimension of shape as 1 so that inference-schema
                # can correctly generate the swagger sample for the input
                shape = list(deepcopy(shape))
                shape[0] = 1
            sample_io[io_val.name] = np.zeros(tuple(shape), dtype=io_val.type)
    return sample_io