Method from_tensor

exir/tensor.py:193–213

(cls, tensor: torch.Tensor, const: bool = False)

    @classmethod
    def from_tensor(cls, tensor: torch.Tensor, const: bool = False) -> TensorSpec:
        if const:
            # for non-contigous tensors, convert to a contiguous one
            tensor = tensor.contiguous()
            # Weights cannot be views during emission or serialization
            if tensor.nbytes != tensor.untyped_storage().nbytes():
                tensor = tensor.clone()

        spec = cls(
            dtype=tensor.dtype,
            shape=tensor.shape,
            layout=tensor.layout,
            const=const,
            is_sparse=tensor.is_sparse,
        )
        spec.stride = tensor.stride()
        spec.dim_order = dim_order_from_stride(spec.stride)
        spec.requires_grad = tensor.requires_grad
        spec.storage = tensor.untyped_storage() if const else None

        return spec

    def init_mem_planning_fields(self) -> None:
        self.lifetime = [None, None]