Transformers

You are viewing v4.45.2 version. A newer version v4.47.1 is available.

Join the Hugging Face community

and get access to the augmented documentation experience

Collaborate on models, datasets and Spaces

Faster examples with accelerated inference

Switch between documentation themes

to get started

ExecuTorch

ExecuTorch is an end-to-end solution for enabling on-device inference capabilities across mobile and edge devices including wearables, embedded devices and microcontrollers. It is part of the PyTorch ecosystem and supports the deployment of PyTorch models with a focus on portability, productivity, and performance.

ExecuTorch introduces well defined entry points to perform model, device, and/or use-case specific optimizations such as backend delegation, user-defined compiler transformations, memory planning, and more. The first step in preparing a PyTorch model for execution on an edge device using ExecuTorch is to export the model. This is achieved through the use of a PyTorch API called torch.export.

ExecuTorch Integration

An integration point is being developed to ensure that 🤗 Transformers can be exported using torch.export. The goal of this integration is not only to enable export but also to ensure that the exported artifact can be further lowered and optimized to run efficiently in ExecuTorch, particularly for mobile and edge use cases.

class transformers.TorchExportableModuleWithStaticCache

< source >

( model: PreTrainedModel )

A wrapper module designed to make a PreTrainedModel exportable with torch.export, specifically for use with static caching. This module ensures that the exported model is compatible with further lowering and execution in ExecuTorch.

Note: This class is specifically designed to support export process using torch.export in a way that ensures the model can be further lowered and run efficiently in ExecuTorch.

forward

< source >

( input_ids: Tensor cache_position: Tensor ) → torch.Tensor

Parameters

input_ids (torch.Tensor) — Tensor representing current input token id to the module.
cache_position (torch.Tensor) — Tensor representing current input position in the cache.

Returns

torch.Tensor

Logits output from the model.

Forward pass of the module, which is compatible with the ExecuTorch runtime.

This forward adapter serves two primary purposes:

Making the Model torch.export-Compatible: The adapter hides unsupported objects, such as the Cache, from the graph inputs and outputs, enabling the model to be exportable using torch.export without encountering issues.
Ensuring Compatibility with ExecuTorch runtime: The adapter matches the model’s forward signature with that in executorch/extension/llm/runner, ensuring that the exported model can be executed in ExecuTorch out-of-the-box.

transformers.convert_and_export_with_cache

< source >

( model: PreTrainedModel example_input_ids: Tensor = None example_cache_position: Tensor = None ) → Exported program (torch.export.ExportedProgram)

Parameters

model (PreTrainedModel) — The pretrained model to be exported.
example_input_ids (torch.Tensor) — Example input token id used by torch.export.
example_cache_position (torch.Tensor) — Example current cache position used by torch.export.

Returns

Exported program (torch.export.ExportedProgram)

The exported program generated via torch.export.

Convert a PreTrainedModel into an exportable module and export it using torch.export, ensuring the exported model is compatible with ExecuTorch.

< > Update on GitHub

←DeepSpeed Feature Extractor→