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Python API

compile API

Classes

RBLNCompiledModel

A class that holds the compile binaries. This class is generated by compile_from_* functions.

Functions
save(path)

Serialize and save the model to the disk, .rbln formatted file.

Parameters:

Name Type Description Default
path PathLike

Path to save serialized data

required
create_runtime(*, device=0, tensor_type='np')

Create runtime with this binaries. Note that this function is exclusive to create_async_runtime. Once you create a runtime by create_runtime with the instance, you can't call create_async_runtime.

Parameters:

Name Type Description Default
device int

The device ID of the NPU to use for execution. Defaults to 0.

0
tensor_type str

The object type of the tensor used in the run function. Possible values are:

  • "np": Uses np.ndarray type.
  • "pt": Uses torch.Tensor type.

Defaults to "np".

'np'

Returns:

Type Description
Runtime

Runtime object that can be run on the RBLN ATOM

create_async_runtime(*, device=0, tensor_type='np')

Create asynchronous version of runtime with this binaries. Note that this function is exclusive to create_runtime. Once you create an asynchronous runtime by create_async_runtime with the instance, you can't call create_runtime.

Parameters:

Name Type Description Default
device int

The device ID of the NPU to use for execution. Defaults to 0.

0
tensor_type str

The object type of the tensor used in the run function. Possible values are:

  • "np": Uses np.ndarray type.
  • "pt": Uses torch.Tensor type.

Defaults to "np".

'np'

Returns:

Type Description
AsyncRuntime

Asynchronous runtime object that can be run on the RBLN ATOM

Functions

compile_from_torch(mod, input_info, *, npu=None, tensor_parallel_size=None)

Compile a model from torch.nn.Module.

Parameters:

Name Type Description Default
mod Module

A pytorch function

required
input_info List[Tuple[str, List[int], DType]]

A list of input information, with each information described in a triple format (name, shape, dtype).

  • name : str
  • shape : List[int]
  • dtype : str or torch.dtype (i.e., "float32" or torch.float32)
required
npu str

The identifier of the target NPU for compilation. If None, the function will use the NPU installed on the host machine. If no NPU is installed on the host machine, an error will be raised. Defaults to None.

None
tensor_parallel_size int

The number of NPU devices to use for tensor parallelism. If None, tensor parallelism will not be used. Defaults to None.

None

Returns:

Type Description
RBLNCompiledModel

Compiled model that can be run on the RBLN NPU

compile_from_torchscript(mod, input_names=None, *, npu=None, tensor_parallel_size=None)

Compile a model from torch.jit.ScriptModule, a result of torch.jit.trace function.

Note that the input shape & dtype informations should be kept to compile torchscript model. When loading a torchscript model via torch.jit.load, these information are skipped from recover by default. You should explicitly add additional parameter _restore_shapes=True to recover these informations.

mod = torch.jit.load("model.pt", _restore_shapes=True)
compile_from_torchscript(mod)

Parameters:

Name Type Description Default
mod TorchScript

A pytorch jit-traced model.

required
input_names Optional[List[Optional[str]]]

A list of input names. If input_names is specified as None, it'll derive name from mod, name of corresponding input in forward function.

None
npu str

The identifier of the target NPU for compilation. If None, the function will use the NPU installed on the host machine. If no NPU is installed on the host machine, an error will be raised. Defaults to None.

None
tensor_parallel_size int

The number of NPU devices to use for tensor parallelism. If None, tensor parallelism will not be used. Defaults to None.

None

Returns:

Type Description
RBLNCompiledModel

Compiled model that can be run on the RBLN NPU

compile_from_tf_function(func, input_info, outputs=None, layout='NHWC', *, npu=None)

Compile a model from tf.function. Note that the input function should not be concretized by get_concrete_function method.

Parameters:

Name Type Description Default
func GenericFunction

A tensorflow function

required
input_info List[Tuple[str, List[int], DType]]

A list of input information, with each information described in triple format (name, shape, dtype). If the dtype is specified as None, it'll derive the dtype from dtype parameter.

required
outputs Optional[Union[str, List[str]]]

A string or list of the name of output node(s) (Optional). If not specified, then the last node is assumed to be the graph output. This may be useful when the graph has multiple outputs.

None
layout str

Layout of the tensor used internally in the model. One of "NHWC" or "NCHW"

'NHWC'
npu str

The identifier of the target NPU for compilation. If None, the function will use the NPU installed on the host machine. If no NPU is installed on the host machine, an error will be raised. Defaults to None.

None

Returns:

Type Description
RBLNCompiledModel

Compiled model that can be run on the RBLN NPU

compile_from_tf_graph_def(graph_def, outputs=None, layout='NHWC', *, npu=None)

Compile a model from TensorFlow GraphDef. This function allows you to compile TensorFlow V1.x legacy models. If you are using TensorFlow V2 as a default, we recommend compiling the model using compile_from_tf_function in its function form.

Parameters:

Name Type Description Default
graph_def GraphDef

A tensorflow graph definition in the form of a protocol buffer

required
outputs Optional[Union[str, List[str]]]

A string or list of the name of output node(s) (Optional). If not specified, then the last node is assumed to be the graph output. This may be useful when the graph has multiple outputs.

None
layout str

Layout of the tensor used internally in the model. One of "NHWC" or "NCHW"

'NHWC'
npu str

The identifier of the target NPU for compilation. If None, the function will use the NPU installed on the host machine. If no NPU is installed on the host machine, an error will be raised. Defaults to None.

None

Returns:

Type Description
RBLNCompiledModel

Compiled model that can be run on the RBLN NPU

runtime API

Classes

Runtime

A Runtime object for executing a compiled neural network on an NPU.

Functions
__init__(path, *, device=0, tensor_type='np')

Initializes a Runtime object for executing a compiled neural network on an NPU.

Parameters:

Name Type Description Default
path str

The path to the compiled rbln neural network file (*.rbln).

required
device int

The device ID of the NPU to use for execution. Defaults to 0.

0
tensor_type str

The object type of the tensor used in the run function. Possible values are:

  • "np": Uses np.ndarray type.
  • "pt": Uses torch.Tensor type.

Defaults to "np".

'np'
run(*input_args, **input_kwargs)

Runs the compiled neural network with the given input tensors.

Parameters:

Name Type Description Default
*input_args Union[ndarray, Tensor]

Variable length argument list of input tensors. Each argument should be either a numpy.ndarray or a torch.Tensor.

()
**input_kwargs Union[ndarray, Tensor]

Arbitrary keyword arguments of input tensors. Each argument should be either a numpy.ndarray or a torch.Tensor.

{}

Returns:

Type Description
Union[ndarray, Tensor, List[Union[ndarray, Tensor]]]

The output tensor(s) of the neural network. The return depends on the network's architecture and can be either a single tensor or a list of tensors. The tensor type (numpy.ndarray or torch.Tensor) is determined by the tensor_type provided during the Runtime object's initialization.

forward(*input_args, **input_kwargs)

An alias for the run method.

This method is provided for compatibility with PyTorch's naming convention.

Parameters:

Name Type Description Default
*input_args Union[ndarray, Tensor]

Variable length argument list of input tensors. Each argument should be either a numpy.ndarray or a torch.Tensor.

()
**input_kwargs Union[ndarray, Tensor]

Arbitrary keyword arguments of input tensors. Each argument should be either a numpy.ndarray or a torch.Tensor.

{}

Returns:

Type Description
Union[ndarray, Tensor, List[Union[ndarray, Tensor]]]

The output tensor(s) of the neural network, as returned by the run method.

__call__(*input_args, **input_kwargs)

Allows the Runtime object to be called as a function.

This method is provided for convenience and compatibility with common neural network frameworks.

Parameters:

Name Type Description Default
*input_args Union[ndarray, Tensor]

Variable length argument list of input tensors. Each argument should be either a numpy.ndarray or a torch.Tensor.

()
**input_kwargs Union[ndarray, Tensor]

Arbitrary keyword arguments of input tensors. Each argument should be either a numpy.ndarray or a torch.Tensor.

{}

Returns:

Type Description
Union[ndarray, Tensor, List[Union[ndarray, Tensor]]]

The output tensor(s) of the neural network, as returned by the run method.

AsyncRuntime

An AsyncRuntime object for executing a compiled neural network asynchronously on an NPU.

Functions
__init__(path, *, device=0, tensor_type='np')

Initializes an AsyncRuntime object for executing a compiled neural network asynchronously on an NPU.

Parameters:

Name Type Description Default
path str

The path to the compiled rbln neural network file (*.rbln).

required
device int

The device ID of the NPU to use for execution. Defaults to 0.

0
tensor_type str

The object type of the tensor used in the run function. Possible values are:

  • "np": Uses np.ndarray type.
  • "pt": Uses torch.Tensor type.

Defaults to "np".

'np'
run(*input_args, **input_kwargs)

Runs the compiled neural network asynchronously with the given input tensors.

Parameters:

Name Type Description Default
*input_args Union[ndarray, Tensor]

Variable length argument list of input tensors. Each argument should be either a numpy.ndarray or a torch.Tensor.

()
**input_kwargs Union[ndarray, Tensor]

Arbitrary keyword arguments of input tensors. Each argument should be either a numpy.ndarray or a torch.Tensor.

{}

Returns:

Type Description
AsyncTask

An asynchronous task object representing the neural network execution. The task object can be used to wait for the neural network execution to finish.

async_run(*input_args, **input_kwargs) async

Runs the compiled neural network asynchronously and returns the result awaitably.

This method is a coroutine that can be used with the await keyword to asynchronously run the neural network and retrieve the result.

Parameters:

Name Type Description Default
*input_args Union[ndarray, Tensor]

Variable length argument list of input tensors. Each argument should be either a numpy.ndarray or a torch.Tensor.

()
**input_kwargs Union[ndarray, Tensor]

Arbitrary keyword arguments of input tensors. Each argument should be either a numpy.ndarray or a torch.Tensor.

{}

Returns:

Type Description
Union[ndarray, Tensor, List[Union[ndarray, Tensor]]]

The output tensor(s) of the neural network. The return type depends on the network's architecture and can be either a single tensor or a list of tensors. The tensor type (numpy.ndarray or torch.Tensor) is determined by the tensor_type provided during the AsyncRuntime object's initialization.

AsyncTask

Functions
wait(timeout=None)

Waits for the asynchronous task to complete and returns the result.

This method blocks the calling thread until the task is completed or the specified timeout is reached.

Parameters:

Name Type Description Default
timeout Optional[float]

The maximum amount of time (in seconds) to wait for the task to complete. If None, the method will wait indefinitely until the task is completed. Defaults to None.

None

Returns:

Type Description
Union[ndarray, Tensor, List[Union[ndarray, Tensor]]]

The output tensor(s) of the neural network. The return depends on the network's architecture and can be either a single tensor or a list of tensors. The tensor type (numpy.ndarray or torch.Tensor) is determined by the tensor_type provided during the AsyncRuntime object's initialization.

utility API

Functions

npu_is_available(device=0)

Return a bool indicating whether the RBLN device is currently available.

Parameters:

Name Type Description Default
device int

Index of the npu. Defaults to 0.

0

Returns:

Type Description
bool

A bool indicating whether the RBLN device is currently available

get_npu_name(device=0)

Return the name of RBLN npu.

Parameters:

Name Type Description Default
device int

Index of the npu. Defaults to 0.

0

Returns:

Type Description
str

Corresponding name of the npu. e.g. "RBLN-CA12"