# Export to TorchScript

This is the very beginning of our experiments with TorchScript and we are still

exploring its capabilities with variable-input-size models. It is a focus of interest to

us and we will deepen our analysis in upcoming releases, with more code examples, a more

flexible implementation, and benchmarks comparing Python-based codes with compiled

TorchScript.

According to the [TorchScript documentation](https://pytorch.org/docs/stable/jit.html):

> TorchScript is a way to create serializable and optimizable models from PyTorch code.

There are two PyTorch modules, [JIT and

TRACE](https://pytorch.org/docs/stable/jit.html), that allow developers to export their

models to be reused in other programs like efficiency-oriented C++ programs.

We provide an interface that allows you to export 🤗 Transformers models to TorchScript

so they can be reused in a different environment than PyTorch-based Python programs.

Here, we explain how to export and use our models using TorchScript.

Exporting a model requires two things:

- model instantiation with the `torchscript` flag

- a forward pass with dummy inputs

These necessities imply several things developers should be careful about as detailed

below.

## TorchScript flag and tied weights

The `torchscript` flag is necessary because most of the 🤗 Transformers language models

have tied weights between their `Embedding` layer and their `Decoding` layer.

TorchScript does not allow you to export models that have tied weights, so it is

necessary to untie and clone the weights beforehand.

Models instantiated with the `torchscript` flag have their `Embedding` layer and

`Decoding` layer separated, which means that they should not be trained down the line.

Training would desynchronize the two layers, leading to unexpected results.

This is not the case for models that do not have a language model head, as those do not

have tied weights. These models can be safely exported without the `torchscript` flag.

## Dummy inputs and standard lengths

The dummy inputs are used for a models forward pass. While the inputs' values are

propagated through the layers, PyTorch keeps track of the different operations executed

on each tensor. These recorded operations are then used to create the *trace* of the

model.

The trace is created relative to the inputs' dimensions. It is therefore constrained by

the dimensions of the dummy input, and will not work for any other sequence length or

batch size. When trying with a different size, the following error is raised:

`The expanded size of the tensor (3) must match the existing size (7) at non-singleton dimension 2`

We recommended you trace the model with a dummy input size at least as large as the

largest input that will be fed to the model during inference. Padding can help fill the

missing values. However, since the model is traced with a larger input size, the

dimensions of the matrix will also be large, resulting in more calculations.

Be careful of the total number of operations done on each input and follow the

performance closely when exporting varying sequence-length models.

## Using TorchScript in Python

This section demonstrates how to save and load models as well as how to use the trace

for inference.

### Saving a model

To export a `BertModel` with TorchScript, instantiate `BertModel` from the `BertConfig`

class and then save it to disk under the filename `traced_bert.pt`:

thon

from transformers import BertModel, BertTokenizer, BertConfig

import torch

enc = BertTokenizer.from_pretrained("bert-base-uncased")

# Tokenizing input text

text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"

tokenized_text = enc.tokenize(text)

# Masking one of the input tokens

masked_index = 8

tokenized_text[masked_index] = "[MASK]"

indexed_tokens = enc.convert_tokens_to_ids(tokenized_text)

segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]

# Creating a dummy input

tokens_tensor = torch.tensor([indexed_tokens])

segments_tensors = torch.tensor([segments_ids])

dummy_input = [tokens_tensor, segments_tensors]

# Initializing the model with the torchscript flag

# Flag set to True even though it is not necessary as this model does not have an LM Head.

config = BertConfig(

    vocab_size_or_config_json_file=32000,

    hidden_size=768,

    num_hidden_layers=12,

    num_attention_heads=12,

    intermediate_size=3072,

    torchscript=True,

)

# Instantiating the model

model = BertModel(config)

# The model needs to be in evaluation mode

model.eval()

# If you are instantiating the model with *from_pretrained* you can also easily set the TorchScript flag

model = BertModel.from_pretrained("bert-base-uncased", torchscript=True)

# Creating the trace

traced_model = torch.jit.trace(model, [tokens_tensor, segments_tensors])

torch.jit.save(traced_model, "traced_bert.pt")

### Loading a model

Now you can load the previously saved `BertModel`, `traced_bert.pt`, from disk and use

it on the previously initialised `dummy_input`:

thon

loaded_model = torch.jit.load("traced_bert.pt")

loaded_model.eval()

all_encoder_layers, pooled_output = loaded_model(*dummy_input)

### Using a traced model for inference

Use the traced model for inference by using its `__call__` dunder method:

thon

traced_model(tokens_tensor, segments_tensors)

## Deploy Hugging Face TorchScript models to AWS with the Neuron SDK

AWS introduced the [Amazon EC2 Inf1](https://aws.amazon.com/ec2/instance-types/inf1/)

instance family for low cost, high performance machine learning inference in the cloud.

The Inf1 instances are powered by the AWS Inferentia chip, a custom-built hardware

accelerator, specializing in deep learning inferencing workloads. [AWS

Neuron](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/#) is the SDK for

Inferentia that supports tracing and optimizing transformers models for deployment on

Inf1. The Neuron SDK provides:

1. Easy-to-use API with one line of code change to trace and optimize a TorchScript

   model for inference in the cloud.

2. Out of the box performance optimizations for [improved

   cost-performance](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/neuron-guide/benchmark/>).

3. Support for Hugging Face transformers models built with either

   [PyTorch](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/src/examples/pytorch/bert_tutorial/tutorial_pretrained_bert.html)

   or

   [TensorFlow](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/src/examples/tensorflow/huggingface_bert/huggingface_bert.html).

### Implications

Transformers models based on the [BERT (Bidirectional Encoder Representations from

Transformers)](https://huggingface.co/docs/transformers/main/model_doc/bert)

architecture, or its variants such as

[distilBERT](https://huggingface.co/docs/transformers/main/model_doc/distilbert) and

[roBERTa](https://huggingface.co/docs/transformers/main/model_doc/roberta) run best on

Inf1 for non-generative tasks such as extractive question answering, sequence

classification, and token classification. However, text generation tasks can still be

adapted to run on Inf1 according to this [AWS Neuron MarianMT

tutorial](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/src/examples/pytorch/transformers-marianmt.html).

More information about models that can be converted out of the box on Inferentia can be

found in the [Model Architecture

Fit](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/neuron-guide/models/models-inferentia.html#models-inferentia)

section of the Neuron documentation.

### Dependencies

Using AWS Neuron to convert models requires a [Neuron SDK

environment](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/neuron-guide/neuron-frameworks/pytorch-neuron/index.html#installation-guide)

which comes preconfigured on [AWS Deep Learning

AMI](https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-inferentia-launching.html).

### Converting a model for AWS Neuron

Convert a model for AWS NEURON using the same code from [Using TorchScript in

Python](torchscript#using-torchscript-in-python) to trace a `BertModel`. Import the

`torch.neuron` framework extension to access the components of the Neuron SDK through a

Python API:

thon

from transformers import BertModel, BertTokenizer, BertConfig

import torch

import torch.neuron

You only need to modify the following line:

- torch.jit.trace(model, [tokens_tensor, segments_tensors])

+ torch.neuron.trace(model, [token_tensor, segments_tensors])

This enables the Neuron SDK to trace the model and optimize it for Inf1 instances.

To learn more about AWS Neuron SDK features, tools, example tutorials and latest

updates, please see the [AWS NeuronSDK

documentation](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/index.html).