Time Series Transformer

Time Series Transformer is a vanilla encoder-decoder Transformer model designed for probabilistic time series forecasting. It takes past time series values as input and predicts future values by learning a distribution, making it suitable for datasets like M4, NN5, or the tourism-monthly dataset. RBLN NPUs can accelerate Time Series Transformer model inference using Optimum RBLN.

API Reference

Classes

RBLNTimeSeriesTransformerForPrediction

Bases: RBLNModel

The Time Series Transformer Model with a distribution head on top for time-series forecasting. e.g., for datasets like M4, NN5, or other time series forecasting benchmarks. This model inherits from [RBLNModel]. Check the superclass documentation for the generic methods the library implements for all its models.

A class to convert and run pre-trained transformer-based TimeSeriesTransformerForPrediction models on RBLN devices. It implements the methods to convert a pre-trained transformers TimeSeriesTransformerForPrediction model into a RBLN transformer model by:

• transferring the checkpoint weights of the original into an optimized RBLN graph,
• compiling the resulting graph using the RBLN Compiler.

Functions

from_model(model, config=None, rbln_config=None, model_save_dir=None, subfolder='', **kwargs) classmethod

Converts and compiles a pre-trained HuggingFace library model into a RBLN model. This method performs the actual model conversion and compilation process.

Parameters:

Name	Type	Description	Default
model	PreTrainedModel	The PyTorch model to be compiled. The object must be an instance of the HuggingFace transformers PreTrainedModel class.	required
config	Optional[PretrainedConfig]	The configuration object associated with the model.	None
rbln_config	Optional[Union[RBLNModelConfig, Dict]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

The method performs the following steps:

1. Compiles the PyTorch model into an optimized RBLN graph
2. Configures the model for the specified NPU device
3. Creates the necessary runtime objects if requested
4. Saves the compiled model and configurations

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

from_pretrained(model_id, export=None, rbln_config=None, **kwargs) classmethod

The from_pretrained() function is utilized in its standard form as in the HuggingFace transformers library. User can use this function to load a pre-trained model from the HuggingFace library and convert it to a RBLN model to be run on RBLN NPUs.

Parameters:

Name	Type	Description	Default
model_id	Union[str, Path]	The model id of the pre-trained model to be loaded. It can be downloaded from the HuggingFace model hub or a local path, or a model id of a compiled model using the RBLN Compiler.	required
export	Optional[bool]	A boolean flag to indicate whether the model should be compiled. If None, it will be determined based on the existence of the compiled model files in the model_id.	None
rbln_config	Optional[Union[Dict, RBLNModelConfig]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

save_pretrained(save_directory, push_to_hub=False, **kwargs)

Saves a model and its configuration file to a directory, so that it can be re-loaded using the [~optimum.rbln.modeling_base.RBLNBaseModel.from_pretrained] class method.

Parameters:

Name	Type	Description	Default
save_directory	Union[str, Path]	Directory where to save the model file.	required
push_to_hub	bool	Whether or not to push your model to the HuggingFace model hub after saving it.	False

Classes

RBLNTimeSeriesTransformerForPredictionConfig

Bases: RBLNModelConfig

Configuration class for RBLNTimeSeriesTransformerForPrediction.

This configuration class stores the configuration parameters specific to RBLN-optimized Time Series Transformer models for time series forecasting tasks.

Functions

__init__(batch_size=None, enc_max_seq_len=None, dec_max_seq_len=None, num_parallel_samples=None, **kwargs)

Parameters:

Name	Type	Description	Default
batch_size	Optional[int]	The batch size for inference. Defaults to 1.	None
enc_max_seq_len	Optional[int]	Maximum sequence length for the encoder.	None
dec_max_seq_len	Optional[int]	Maximum sequence length for the decoder.	None
num_parallel_samples	Optional[int]	Number of samples to generate in parallel during prediction.	None
kwargs	Any	Additional arguments passed to the parent RBLNModelConfig.	{}

Raises:

Type	Description
ValueError	If batch_size is not a positive integer.

load(path, **kwargs) classmethod

Load a RBLNModelConfig from a path.

Parameters:

Name	Type	Description	Default
path	str	Path to the RBLNModelConfig file or directory containing the config file.	required
kwargs	Any	Additional keyword arguments to override configuration values. Keys starting with 'rbln_' will have the prefix removed and be used to update the configuration.	{}

Returns:

Name	Type	Description
RBLNModelConfig	RBLNModelConfig	The loaded configuration instance.

Note

This method loads the configuration from the specified path and applies any provided overrides. If the loaded configuration class doesn't match the expected class, a warning will be logged.