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.
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_pretrained(model_id, export=False, 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
|
bool
|
A boolean flag to indicate whether the model should be compiled. |
False
|
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., |
None
|
kwargs
|
Dict[str, 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 |
---|---|
Self
|
A RBLN model instance ready for inference on RBLN NPU devices. |
from_model(model, *, rbln_config=None, **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 |
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., |
None
|
kwargs
|
Dict[str, 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:
- Compiles the PyTorch model into an optimized RBLN graph
- Configures the model for the specified NPU device
- Creates the necessary runtime objects if requested
- Saves the compiled model and configurations
Returns:
Type | Description |
---|---|
Self
|
A RBLN model instance ready for inference on RBLN NPU devices. |
save_pretrained(save_directory)
¶
Saves a model and its configuration file to a directory, so that it can be re-loaded using the
[from_pretrained
] class method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
save_directory
|
Union[str, PathLike]
|
The directory to save the model and its configuration files. Will be created if it doesn't exist. |
required |
Classes¶
RBLNTimeSeriesTransformerForPredictionConfig
¶
Bases: RBLNModelConfig
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
|
Dict[str, Any]
|
Additional arguments passed to the parent RBLNModelConfig. |
{}
|
Raises:
Type | Description |
---|---|
ValueError
|
If batch_size is not a positive integer. |