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mixin 🔗

Torch operations.

TorchMixin 🔗

Mixin providing Torch operations for a Session.

This mixin is intended to be used in combination with BaseSession and should not be instantiated on its own.

Intended usage

class Session(BaseSession, TorchMixin): ...

torch_jitload_forward 🔗

torch_jitload_forward(
    resource: Resource,
    in_tensors: list[Tensor],
    nr_out_tensors: int = 1,
    run_options: Buffer | None = None,
) -> list[Tensor]

Performs the Torch jitload forward operation.

Wraps the vaccel_torch_jitload_forward() C operation.

Parameters:

Name Type Description Default

resource 🔗

 Resource

A resource with the model to run.

 required

in_tensors 🔗

 list[Tensor]

The input tensors for the inference.

 required

nr_out_tensors 🔗

 int

The number of output tensors. Defaults to 1.

 1

run_options 🔗

 Buffer | None

The inference options.

 None

Returns:

Type Description
list[Tensor]

The output tensors

Raises:

Type Description
FFIError

If the C operation fails.
Source code in vaccel/ops/torch/mixin.py 
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def torch_jitload_forward(
    self,
    resource: Resource,
    in_tensors: list[Tensor],
    nr_out_tensors: int = 1,
    run_options: Buffer | None = None,
) -> list[Tensor]:
    """Performs the Torch jitload forward operation.

    Wraps the `vaccel_torch_jitload_forward()` C operation.

    Args:
        resource: A resource with the model to run.
        in_tensors: The input tensors for the inference.
        nr_out_tensors: The number of output tensors. Defaults to 1.
        run_options: The inference options.

    Returns:
        The output tensors

    Raises:
        FFIError: If the C operation fails.
    """
    run_options_ptr = (
        ffi.NULL if run_options is None else run_options._c_ptr
    )
    c_in_tensors = CList.from_ptrs(in_tensors)
    c_out_tensors = CList.from_ptrs([Tensor.empty()] * nr_out_tensors)

    ret = lib.vaccel_torch_jitload_forward(
        self._c_ptr_or_raise,
        resource._c_ptr,
        run_options_ptr,
        c_in_tensors._c_ptr,
        len(c_in_tensors),
        c_out_tensors._c_ptr,
        len(c_out_tensors),
    )
    if ret != 0:
        raise FFIError(ret, "Torch jitload forward operation failed")

    return [Tensor.from_c_obj(t) for t in c_out_tensors.value]