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Mojo struct

InferenceSession

Holds the context for MAX Engine in which you can load and run models.

For example, you can load a model like this:

var session = engine.InferenceSession()
var model = session.load("bert-base-uncased")
var session = engine.InferenceSession()
var model = session.load("bert-base-uncased")

Implemented traits​

AnyType, Copyable, Movable

Methods​

__init__​

__init__(inout self: Self, options: SessionOptions = SessionOptions())

Creates a new inference session.

Args:

  • ​options (SessionOptions): Session options to configure how session is created.

load​

load(self: Self, path: Path, *, custom_ops_paths: List[Path, 0] = List(), input_specs: Optional[List[InputSpec, 0]] = #kgen.none) -> Model

Compile and initialize a model in MAX Engine, with the given model path and config.

Note: PyTorch models must be in TorchScript format.

If you're loading a TorchScript model, you must specify the input_specs argument with a list of InputSpec objects that specify the model's input specs (which may have dynamic shapes). For details, see how to specify input specs.

Args:

  • ​path (Path): Location of model in filesystem. You may pass a string here because the Path object supports implicit casting from a string.
  • ​custom_ops_paths (List[Path, 0]): List of paths to Mojo custom op packages, to replace Modular kernels in models with user-defined kernels.
  • ​input_specs (Optional[List[InputSpec, 0]]): Provide shapes and dtypes for model inputs. Required for TorchScript models, optional for other input formats.

Returns:

Initialized model ready for inference.

load(self: Self, graph: Graph, *, custom_ops_paths: List[Path, 0] = List(), input_specs: Optional[List[InputSpec, 0]] = #kgen.none) -> Model

Compile and initialize a model in MAX Engine, with the given Graph and config.

Args:

  • ​graph (Graph): MAX Graph.
  • ​custom_ops_paths (List[Path, 0]): List of paths to Mojo custom op packages, to replace Modular kernels in models with user-defined kernels.
  • ​input_specs (Optional[List[InputSpec, 0]]): Provide shapes and dtypes for model inputs. Required for TorchScript models, optional for other input formats.

Returns:

Initialized model ready for inference.

get_as_engine_tensor_spec​

get_as_engine_tensor_spec(self: Self, name: String, spec: TensorSpec) -> EngineTensorSpec

Gets a TensorSpec compatible with MAX Engine.

Args:

  • ​name (String): Name of the Tensor.
  • ​spec (TensorSpec): Tensor specification in Mojo TensorSpec format.

Returns:

EngineTensorSpec to be used with MAX Engine APIs.

get_as_engine_tensor_spec(self: Self, name: String, shape: Optional[List[Optional[SIMD[int64, 1]], 0]], dtype: DType) -> EngineTensorSpec

Gets a TensorSpec compatible with MAX Engine.

Args:

  • ​name (String): Name of the Tensor.
  • ​shape (Optional[List[Optional[SIMD[int64, 1]], 0]]): Shape of the Tensor. Dynamic Dimensions can be represented with None and for Dynamic Rank Tensor use None as value for shape.
  • ​dtype (DType): DataType of the Tensor.

Returns:

EngineTensorSpec to be used with MAX Engine APIs.

new_tensor_map​

new_tensor_map(self: Self) -> TensorMap

Gets a new TensorMap. This can be used to pass inputs to model.

Returns:

A new instance of TensorMap.

new_borrowed_tensor_value​

new_borrowed_tensor_value[type: DType](self: Self, tensor: Tensor[type]) -> Value

Create a new Value representing data borrowed from given tensor.

The user must ensure the tensor stays live through the lifetime of the value.

Parameters:

  • ​type (DType): Data type of the tensor to turn into a Value.

Args:

  • ​tensor (Tensor[type]): Tensor to borrow into a value.

Returns:

A value borrowing the tensor.

new_bool_value​

new_bool_value(self: Self, value: Bool) -> Value

Create a new Value representing a Bool.

Args:

  • ​value (Bool): Boolean to wrap into a value.

Returns:

Value representing the given boolean.

new_list_value​

new_list_value(self: Self) -> Value

Create a new Value representing an empty list.

Returns:

A new value containing an empty list.

set_debug_print_options​

set_debug_print_options(inout self: Self, style: PrintStyle = 0, precision: UInt = 6, output_directory: String = "")

Sets the debug print options on the context.

This affects debug printing across all model execution using the same InferenceSession.

Warning: Even with style set to NONE, debug print ops in the graph can stop optimizations. If you see performance issues, try fully removing debug print ops.

Args:

  • ​style (PrintStyle): How the values will be printed.
  • ​precision (UInt): If the style is FULL, the digits of precision in the output.
  • ​output_directory (String): If the style is BINARY, the directory to store output tensors.

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