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# Module: `torch_inference_workflow`

## Torch Inference Workflow

Workflow for running inference on Torch models.

This class is responsible for loading & running a Torch model.

Models can be loaded in two ways:

1. Preloading: The model is loaded in the setup method. This happens in the `setup()` method if model source and load args are provided when the class is instantiated.
2. On-demand: The model is loaded with an inference request. This happens if model source and load args are provided with the input (see the optional fields in the `TorchInferenceInput` class).

Loaded models are cached in-memory using an LRU cache. The cache size can be configured using the `TORCH_MODEL_LRU_CACHE_SIZE` environment variable.

### Additional Installations

Since this workflow uses some additional libraries, you'll need to install `infernet-ml[torch_inference]`. Alternatively, you can install those packages directly. The optional dependencies `"[torch_inference]"` are provided for your convenience.

uv pip

    uv pip install "infernet-ml[torch_inference]"

    pip install "infernet-ml[torch_inference]"

### Example

    from infernet_ml.utils.common_types import TensorInput
    from infernet_ml.workflows.inference.torch_inference_workflow import (
        TorchInferenceWorkflow,
        TorchInferenceInput,
    )
    from infernet_ml.utils.model_loader import ModelSource, HFLoadArgs

    def main():
        # Instantiate the workflow
        workflow = TorchInferenceWorkflow(
            model_source=ModelSource.HUGGINGFACE_HUB,
            load_args=HFLoadArgs(
                repo_id="Ritual-Net/california-housing",
                filename="california_housing.torch",
            ),
        )

        # Setup the workflow
        workflow.setup()

        # Run the model
        result = workflow.inference(
            TorchInferenceInput(
                input=TensorInput(
                    dtype="double",
                    shape=(1, 8),
                    values=[[-122.25, 37.85, 52.0, 1627.0, 322.0, 5.64, 2400.0, 9.0]],
                )
            )
        )

        print(result.outputs)

    if __name__ == "__main__":
        main()

Outputs:

    tensor([164.8323], dtype=torch.float64, grad_fn=<ViewBackward0>)

## `TorchInferenceInput`

Bases: `BaseModel`

Input data for Torch inference workflows. If model source and load args are provided, the model is loaded. Otherwise, if the class is instantiated with a model source and load args, the model is preloaded in the setup method.

##### Input Format

Input format is a dictionary of input tensors. Each key corresponds to the name of the input nodes defined in the Torch model. The values are of type `TensorInput`.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`input` |  |  | required
`model_source` |  | Optional[ModelSource]: Source of the model to be loaded | required
`load_args` |  | Optional[LoadArgs]: Arguments to be passed to the model loader | required

Source code in `src/infernet_ml/workflows/inference/torch_inference_workflow.py`

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    class TorchInferenceInput(BaseModel):
        """
        Input data for Torch inference workflows. If model source and load args are provided,
        the model is loaded. Otherwise, if the class is instantiated with a model source and
        load args, the model is preloaded in the setup method.

        ### Input Format
        Input format is a dictionary of input tensors. Each key corresponds to the name of
        the input nodes defined in the Torch model. The values are of type `TensorInput`.

        Args:
            input: TensorInput: Input tensor
            model_source: Optional[ModelSource]: Source of the model to be loaded
            load_args: Optional[LoadArgs]: Arguments to be passed to the model loader
        """

        input: TensorInput
        model_source: Optional[ModelSource] = None
        load_args: Optional[LoadArgs] = None

## `TorchInferenceResult`

Bases: `BaseModel`

Pydantic model for the result of a torch inference workflow.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`dtype` |  |  | required
`shape` |  | Tuple[int, ...]: Shape of the output tensor | required
`outputs` |  |  | required

Source code in `src/infernet_ml/workflows/inference/torch_inference_workflow.py`

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    class TorchInferenceResult(BaseModel):
        """
        Pydantic model for the result of a torch inference workflow.

        Args:
            dtype: str: Data type of the output tensor
            shape: Tuple[int, ...]: Shape of the output tensor
            outputs: Tensor: Output tensor
        """

        model_config = ConfigDict(arbitrary_types_allowed=True)

        dtype: str
        shape: Tuple[int, ...]
        outputs: Tensor

        @field_validator("outputs")
        def check_is_tensor(cls, v: Tensor) -> Tensor:
            if not isinstance(v, torch.Tensor):
                raise ValueError("Outputs must be a torch.Tensor")
            return v

## `TorchInferenceWorkflow`

Bases: `BaseInferenceWorkflow`

Inference workflow for Torch based models. models are loaded using the default torch pickling by default(i.e. torch.load).

Source code in `src/infernet_ml/workflows/inference/torch_inference_workflow.py`

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    class TorchInferenceWorkflow(BaseInferenceWorkflow):
        """
        Inference workflow for Torch based models. models are loaded using the default
        torch pickling by default(i.e. torch.load).
        """

        def __init__(
            self,
            model_source: Optional[ModelSource] = None,
            load_args: Optional[LoadArgs] = None,
            use_jit: bool = False,
            *args: Any,
            **kwargs: Any,
        ) -> None:
            """
            Args:
                model_source: Optional[ModelSource]: Source of the model to be loaded
                load_args: Optional[LoadArgs]: Arguments to be passed to the model loader
                use_jit: bool: Whether to use JIT for loading the model
                *args: Any: Additional arguments
                **kwargs: Any: Additional keyword arguments
            """
            super().__init__(*args, **kwargs)
            self.model: Optional[torch.nn.Module] = None
            self.model_source = model_source
            self.model_load_args = load_args
            self.use_jit = use_jit

            # This is so that tools like `isort` don't exclude the sk2torch import. This is
            # necessary for scikit-learn models to be present in pytorch's classpath.
            logger.debug(sk2torch.__name__)

        def inference(self, input_data: TorchInferenceInput) -> TorchInferenceResult:
            """
            Inference method for the torch workflow. Overridden to add type hints.
            """
            return cast(TorchInferenceResult, super().inference(input_data))

        def do_setup(self) -> "TorchInferenceWorkflow":
            """
            If model source and load args are provided, preloads the model & starts the
            session. Otherwise, does nothing & model is loaded with an inference request.
            """

            if self.model_source is None or self.model_load_args is None:
                logging.info(
                    "Model source or load args not provided, not preloading any models."
                )
                return self

            self.model = self._load_model(self.model_source, self.model_load_args)
            return self

        def _load_model(
            self, model_source: ModelSource, load_args: LoadArgs
        ) -> torch.nn.Module:
            """
            Loads the model from the model source and load args provided in the input.
            Uses an LRU cache to store the loaded models.

            Args:
                model_source: ModelSource: Source of the model to be loaded
                load_args: LoadArgs: Arguments to be passed to the model loader

            Returns:
                torch.nn.Module: Loaded model
            """
            # uses lru_cache
            return load_torch_model(model_source, load_args, self.use_jit)

        def do_run_model(
            self, inference_input: TorchInferenceInput
        ) -> TorchInferenceResult:
            """
            Runs the model on the input data.

            Args:
                inference_input: TorchInferenceInput: Input data for the inference workflow

            Returns:
                TorchInferenceResult: Result of the inference workflow
            """
            if (
                inference_input.model_source is not None
                and inference_input.load_args is not None
            ):
                model = self._load_model(
                    inference_input.model_source, inference_input.load_args
                )
            else:
                model = cast(torch.nn.Module, self.model)

            input_data = torch.tensor(
                inference_input.input.values, dtype=DTYPES[inference_input.input.dtype]
            )
            model_result = model(input_data)
            dtype = str(model_result.dtype)
            shape = tuple(model_result.shape)
            model_result = model_result.flatten()
            return TorchInferenceResult(dtype=dtype, shape=shape, outputs=model_result)

        def do_stream(self, preprocessed_input: Any) -> Iterator[Any]:
            """
            Streaming inference is not supported for Torch models.
            """
            raise NotImplementedError

### `__init__(model_source=None, load_args=None, use_jit=False, *args, **kwargs)`

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`model_source` | `Optional[ModelSource]` | Optional[ModelSource]: Source of the model to be loaded | `None`
`load_args` | `Optional[LoadArgs]` | Optional[LoadArgs]: Arguments to be passed to the model loader | `None`
`use_jit` | `bool` |  | `False`
`*args` | `Any` |  | `()`
`**kwargs` | `Any` |  | `{}`

Source code in `src/infernet_ml/workflows/inference/torch_inference_workflow.py`

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    def __init__(
        self,
        model_source: Optional[ModelSource] = None,
        load_args: Optional[LoadArgs] = None,
        use_jit: bool = False,
        *args: Any,
        **kwargs: Any,
    ) -> None:
        """
        Args:
            model_source: Optional[ModelSource]: Source of the model to be loaded
            load_args: Optional[LoadArgs]: Arguments to be passed to the model loader
            use_jit: bool: Whether to use JIT for loading the model
            *args: Any: Additional arguments
            **kwargs: Any: Additional keyword arguments
        """
        super().__init__(*args, **kwargs)
        self.model: Optional[torch.nn.Module] = None
        self.model_source = model_source
        self.model_load_args = load_args
        self.use_jit = use_jit

        # This is so that tools like `isort` don't exclude the sk2torch import. This is
        # necessary for scikit-learn models to be present in pytorch's classpath.
        logger.debug(sk2torch.__name__)

### `do_run_model(inference_input)`

Runs the model on the input data.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`inference_input` | `TorchInferenceInput` |  | required

Returns:

Name | Type | Description
--- | --- | ---
`TorchInferenceResult` | `TorchInferenceResult` | Result of the inference workflow

Source code in `src/infernet_ml/workflows/inference/torch_inference_workflow.py`

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    def do_run_model(
        self, inference_input: TorchInferenceInput
    ) -> TorchInferenceResult:
        """
        Runs the model on the input data.

        Args:
            inference_input: TorchInferenceInput: Input data for the inference workflow

        Returns:
            TorchInferenceResult: Result of the inference workflow
        """
        if (
            inference_input.model_source is not None
            and inference_input.load_args is not None
        ):
            model = self._load_model(
                inference_input.model_source, inference_input.load_args
            )
        else:
            model = cast(torch.nn.Module, self.model)

        input_data = torch.tensor(
            inference_input.input.values, dtype=DTYPES[inference_input.input.dtype]
        )
        model_result = model(input_data)
        dtype = str(model_result.dtype)
        shape = tuple(model_result.shape)
        model_result = model_result.flatten()
        return TorchInferenceResult(dtype=dtype, shape=shape, outputs=model_result)

### `do_setup()`

If model source and load args are provided, preloads the model & starts the session. Otherwise, does nothing & model is loaded with an inference request.

Source code in `src/infernet_ml/workflows/inference/torch_inference_workflow.py`

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    def do_setup(self) -> "TorchInferenceWorkflow":
        """
        If model source and load args are provided, preloads the model & starts the
        session. Otherwise, does nothing & model is loaded with an inference request.
        """

        if self.model_source is None or self.model_load_args is None:
            logging.info(
                "Model source or load args not provided, not preloading any models."
            )
            return self

        self.model = self._load_model(self.model_source, self.model_load_args)
        return self

### `do_stream(preprocessed_input)`

Streaming inference is not supported for Torch models.

Source code in `src/infernet_ml/workflows/inference/torch_inference_workflow.py`

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    def do_stream(self, preprocessed_input: Any) -> Iterator[Any]:
        """
        Streaming inference is not supported for Torch models.
        """
        raise NotImplementedError

### `inference(input_data)`

Inference method for the torch workflow. Overridden to add type hints.

Source code in `src/infernet_ml/workflows/inference/torch_inference_workflow.py`

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    def inference(self, input_data: TorchInferenceInput) -> TorchInferenceResult:
        """
        Inference method for the torch workflow. Overridden to add type hints.
        """
        return cast(TorchInferenceResult, super().inference(input_data))

## `load_torch_model(model_source, load_args, use_jit)` `cached`

Loads a torch model from the given source. Uses `torch.jit.load()` if use_jit is set, otherwise uses `torch.load()`.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`model_source` | `ModelSource` |  | required
`load_args` | `LoadArgs` |  | required
`use_jit` | `bool` |  | required

Returns:

Type | Description
--- | ---
`Module` | torch.nn.Module: Loaded model

Source code in `src/infernet_ml/workflows/inference/torch_inference_workflow.py`

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    @lru_cache(maxsize=TORCH_MODEL_LRU_CACHE_SIZE)
    def load_torch_model(
        model_source: ModelSource, load_args: LoadArgs, use_jit: bool
    ) -> torch.nn.Module:
        """
        Loads a torch model from the given source. Uses `torch.jit.load()` if use_jit is set,
        otherwise uses `torch.load()`.

        Args:
            model_source: ModelSource: Source of the model to be loaded
            load_args: LoadArgs: Arguments to be passed to the model loader
            use_jit: bool: Whether to use JIT for loading the model

        Returns:
            torch.nn.Module: Loaded model
        """
        path = download_model(model_source, load_args)
        logger.info(f"Loading model from path: {path}")

        model = torch.jit.load(path) if use_jit else torch.load(path)  # type: ignore

        # turn on inference mode
        model.eval()
        return cast(torch.nn.Module, model)