Export a model to Inferentia

Summary

Exporting a PyTorch model to Neuron model is as simple as

optimum-cli export neuron \
  --model bert-base-uncased \
  --sequence_length 128 \
  --batch_size 1 \
  bert_neuron/

Check out the help for more options:

optimum-cli export neuron --help

Why compile to Neuron model?

AWS provides two generations of the Inferentia accelerator built for machine learning inference with higher throughput, lower latency but lower cost: inf2 (NeuronCore-v2) and inf1 (NeuronCore-v1).

In production environments, to deploy 🤗 Transformers models on Neuron devices, you need to compile your models and export them to a serialized format before inference. Through Ahead-Of-Time (AOT) compilation with Neuron Compiler( neuronx-cc or neuron-cc ), your models will be converted to serialized and optimized TorchScript modules.

Although pre-compilation avoids overhead during the inference, a compiled Neuron model has some limitations:

The input shapes and data types used during the compilation cannot be changed.
Neuron models are specialized for each hardware and SDK version, which means:
- Models compiled with Neuron can no longer be executed in non-Neuron environment.
- Models compiled for inf1 (NeuronCore-v1) are not compatible with inf2 (NeuronCore-v2), and vice versa.
- Models compiled for an SDK version are (generally) not compatible with another SDK version.

In this guide, we’ll show you how to export your models to serialized models optimized for Neuron devices.

🤗 Optimum provides support for the Neuron export by leveraging configuration objects. These configuration objects come ready made for a number of model architectures, and are designed to be easily extendable to other architectures.

To check the supported architectures, go to the configuration reference page.

Exporting a model to Neuron using the CLI

To export a 🤗 Transformers model to Neuron, you’ll first need to install some extra dependencies:

For Inf2

pip install optimum[neuronx]

For Inf1

pip install optimum[neuron]

The Optimum Neuron export can be used through Optimum command-line:

optimum-cli export neuron --help

usage: optimum-cli export neuron [-h] -m MODEL [--task TASK] [--atol ATOL] [--cache_dir CACHE_DIR] [--trust-remote-code]
                                 [--compiler_workdir COMPILER_WORKDIR] [--disable-validation] [--auto_cast {none,matmul,all}]
                                 [--auto_cast_type {bf16,fp16,tf32}] [--dynamic-batch-size] [--num_cores NUM_CORES] [--unet UNET]
                                 [--output_hidden_states] [--output_attentions] [--batch_size BATCH_SIZE]
                                 [--sequence_length SEQUENCE_LENGTH] [--num_beams NUM_BEAMS] [--num_choices NUM_CHOICES]
                                 [--num_channels NUM_CHANNELS] [--width WIDTH] [--height HEIGHT]
                                 [--num_images_per_prompt NUM_IMAGES_PER_PROMPT] [-O1 | -O2 | -O3]
                                 output

optional arguments:
  -h, --help            show this help message and exit
  -O1                   Enables the core performance optimizations in the compiler, while also minimizing compile time.
  -O2                   [Default] Provides the best balance between model performance and compile time.
  -O3                   May provide additional model execution performance but may incur longer compile times and higher host
                        memory usage during model compilation.

Required arguments:
  -m MODEL, --model MODEL
                        Model ID on huggingface.co or path on disk to load model from.
  output                Path indicating the directory where to store generated Neuronx compiled TorchScript model.

Optional arguments:
  --task TASK           The task to export the model for. If not specified, the task will be auto-inferred based on the model.
                        Available tasks depend on the model, but are among: ['audio-classification', 'audio-frame-
                        classification', 'audio-xvector', 'automatic-speech-recognition', 'conversational', 'depth-estimation',
                        'feature-extraction', 'fill-mask', 'image-classification', 'image-segmentation', 'image-to-image',
                        'image-to-text', 'mask-generation', 'masked-im', 'multiple-choice', 'object-detection', 'question-
                        answering', 'semantic-segmentation', 'text-to-audio', 'text-generation', 'text2text-generation', 'text-
                        classification', 'token-classification', 'zero-shot-image-classification', 'zero-shot-object-detection',
                        'stable-diffusion', 'stable-diffusion-xl'].
  --atol ATOL           If specified, the absolute difference tolerance when validating the model. Otherwise, the default atol
                        for the model will be used.
  --cache_dir CACHE_DIR
                        Path indicating where to store cache.
  --trust-remote-code   Allow to use custom code for the modeling hosted in the model repository. This option should only be set
                        for repositories you trust and in which you have read the code, as it will execute on your local machine
                        arbitrary code present in the model repository.
  --compiler_workdir COMPILER_WORKDIR
                        Path indicating the directory where to store intermediary files generated by Neuronx compiler.
  --disable-validation  Whether to disable the validation of inference on neuron device compared to the outputs of original
                        PyTorch model on CPU.
  --auto_cast {none,matmul,all}
                        Whether to cast operations from FP32 to lower precision to speed up the inference. Can be `"none"`,
                        `"matmul"` or `"all"`.
  --auto_cast_type {bf16,fp16,tf32}
                        The data type to cast FP32 operations to when auto-cast mode is enabled. Can be `"bf16"`, `"fp16"` or
                        `"tf32"`.
  --dynamic-batch-size  Enable dynamic batch size for neuron compiled model. If this option is enabled, the input batch size can
                        be a multiple of the batch size during the compilation, but it comes with a potential tradeoff in terms
                        of latency.
  --num_cores NUM_CORES
                        The number of cores on which the model should be deployed (text-generation only).
  --unet UNET           UNet model ID on huggingface.co or path on disk to load model from. This will replace the unet in the
                        original Stable Diffusion pipeline.
  --output_hidden_states
                        Whether or not for the traced model to return the hidden states of all layers.
  --output_attentions   Whether or not for the traced model to return the attentions tensors of all attention layers.

Input shapes:
  --batch_size BATCH_SIZE
                        Batch size that the Neuronx-cc compiler exported model will be able to take as input.
  --sequence_length SEQUENCE_LENGTH
                        Sequence length that the Neuronx-cc compiler exported model will be able to take as input.
  --num_beams NUM_BEAMS
                        Number of beams for beam search that the Neuronx-cc compiler exported model will be able to take as
                        input.
  --num_choices NUM_CHOICES
                        Only for the multiple-choice task. Num choices that the Neuronx-cc compiler exported model will be able
                        to take as input.
  --num_channels NUM_CHANNELS
                        Image tasks only. Number of channels that the Neuronx-cc compiler exported model will be able to take as
                        input.
  --width WIDTH         Image tasks only. Width that the Neuronx-cc compiler exported model will be able to take as input.
  --height HEIGHT       Image tasks only. Height that the Neuronx-cc compiler exported model will be able to take as input.
  --num_images_per_prompt NUM_IMAGES_PER_PROMPT
                        Stable diffusion only. Number of images per prompt that the Neuronx-cc compiler exported model will be
                        able to take as input.

Exporting standard (non-LLM) models

Most models present on the Hugging Face hub can be straightforwardly exported using torch trace, then converted to serialized and optimized TorchScript modules.

NEFF: Neuron Executable File Format which is a binary executable on Neuron devices.

When exporting a model, two sets of export arguments must be passed:

compiler_args are optional arguments for the compiler, these arguments usually control how the compiler makes tradeoff between the inference performance (latency and throughput) and the accuracy,
input_shapes are mandatory static shape information that you need to send to the neuron compiler.

Please type the following command to see all export parameters:

optimum-cli export neuron -h

Exporting a standard NLP model can be done as follows:

optimum-cli export neuron --model distilbert-base-uncased-distilled-squad \
                          --batch_size 1 --sequence_length 16 \
                          --auto_cast matmul --auto_cast_type fp16 \
                          distilbert_base_uncased_squad_neuron/

Here the model was exported with a static input shape of (1, 16), and with compiler arguments specifying that matmul operation must be performed with float16 precision for faster inference.

After export, you should see the following logs which validate the model on Neuron devices by comparing with PyTorch model on CPU:

Validating Neuron model...
        -[✓] Neuron model output names match reference model (last_hidden_state)
        - Validating Neuron Model output "last_hidden_state":
                -[✓] (1, 16, 32) matches (1, 16, 32)
                -[✓] all values close (atol: 0.0001)
The Neuronx export succeeded and the exported model was saved at: distilbert_base_uncased_squad_neuron/

This exports a neuron-compiled TorchScript module of the checkpoint defined by the --model argument.

As you can see, the task was automatically detected. This was possible because the model was on the Hub. For local models, providing the --task argument is needed or it will default to the model architecture without any task specific head:

optimum-cli export neuron --model local_path --task question-answering --batch_size 1 --sequence_length 16 --dynamic-batch-size distilbert_base_uncased_squad_neuron/

Note that providing the --task argument for a model on the Hub will disable the automatic task detection. The resulting model.neuron file, can then be loaded and run on Neuron devices.

For each model architecture, you can find the list of supported tasks via the ~exporters.tasks.TasksManager. For example, for DistilBERT, for the Neuron export, we have:

>>> from optimum.exporters.tasks import TasksManager
>>> from optimum.exporters.neuron.model_configs import *  # Register neuron specific configs to the TasksManager

>>> distilbert_tasks = list(TasksManager.get_supported_tasks_for_model_type("distilbert", "neuron").keys())
>>> print(distilbert_tasks)
['feature-extraction', 'fill-mask', 'multiple-choice', 'question-answering', 'text-classification', 'token-classification']

You can then pass one of these tasks to the --task argument in the optimum-cli export neuron command, as mentioned above.

Once exported, the neuron model can be used for inference directly with the NeuronModelForXXX class:

>>> from transformers import AutoTokenizer
>>> from optimum.neuron import NeuronModelForSequenceClassification

>>> tokenizer = AutoTokenizer.from_pretrained("./distilbert-base-uncased-finetuned-sst-2-english_neuron/")
>>> model = NeuronModelForSequenceClassification.from_pretrained("./distilbert-base-uncased-finetuned-sst-2-english_neuron/")

>>> inputs = tokenizer("Hamilton is considered to be the best musical of human history.", return_tensors="pt")
>>> logits = model(**inputs).logits
>>> print(model.config.id2label[logits.argmax().item()])
'POSITIVE'

As you see, there is no need to pass the neuron arguments used during the export as they are saved in a config.json file, and will be restored automatically by NeuronModelForXXX class.

Be careful, inputs are always padded to the shapes used for the compilation, and the padding brings computation overhead. Adjust the static shapes to be higher than the shape of the inputs that you will feed into the model during the inference, but not much more.

Exporting Stable Diffusion to Neuron

With the Optimum CLI you can compile components in the Stable Diffusion pipeline to gain acceleration on neuron devices during the inference.

So far, we support the export of following components in the pipeline:

CLIP text encoder
U-Net
VAE encoder
VAE decoder

“These blocks are chosen because they represent the bulk of the compute in the pipeline, and performance benchmarking has shown that running them on Neuron yields significant performance benefit.”

Besides, don’t hesitate to tweak the compilation configuration to find the best tradeoff between performance v.s accuracy in your use case. By default, we suggest casting FP32 matrix multiplication operations to BF16 which offers good performance with moderate sacrifice of the accuracy. Check out the guide from AWS Neuron documentation to better understand the options for your compilation.

Exporting a stable diffusion checkpoint can be done using the CLI:

optimum-cli export neuron --model stabilityai/stable-diffusion-2-1-base \
  --task stable-diffusion \
  --batch_size 1 \
  --height 512 `# height in pixels of generated image, eg. 512, 768` \
  --width 512 `# width in pixels of generated image, eg. 512, 768` \
  --num_images_per_prompt 4 `# number of images to generate per prompt, defaults to 1` \
  --auto_cast matmul `# cast only matrix multiplication operations` \
  --auto_cast_type bf16 `# cast operations from FP32 to BF16` \
  sd_neuron/

Exporting Stable Diffusion XL to Neuron

Similar to Stable Diffusion, you will be able to use Optimum CLI to compile components in the SDXL pipeline for inference on neuron devices.

We support the export of following components in the pipeline to boost the speed:

Text encoder
Second text encoder
U-Net (a three times larger UNet than the one in Stable Diffusion pipeline)
VAE encoder
VAE decoder

“Stable Diffusion XL works especially well with images between 768 and 1024.”

Exporting a SDXL checkpoint can be done using the CLI:

optimum-cli export neuron --model stabilityai/stable-diffusion-xl-base-1.0 \
  --task stable-diffusion-xl \
  --batch_size 1 \
  --height 1024 `# height in pixels of generated image, eg. 768, 1024` \
  --width 1024 `# width in pixels of generated image, eg. 768, 1024` \
  --num_images_per_prompt 4 `# number of images to generate per prompt, defaults to 1` \
  --auto_cast matmul `# cast only matrix multiplication operations` \
  --auto_cast_type bf16 `# cast operations from FP32 to BF16` \
  sd_neuron/

Exporting LLMs to Neuron

LLM models are not exported using Torch tracing, but converted directly to Neuron graphs into which the transformers checkpoint weights can be loaded.

Just like the standard NLP models, you need to specify static parameters when exporting an LLM model:

batch_size is the number of input sequences that the model will accept. Defaults to 1,
sequence_length is the maximum number of tokens in an input sequence. Defaults to max_position_embeddings (n_positions for older models).
auto_cast_type specifies the format to encode the weights. It can be one of fp32 (float32), fp16 (float16) or bf16 (bfloat16). Defaults to fp32.
num_cores is the number of neuron cores used when instantiating the model. Each neuron core has 16 Gb of memory, which means that bigger models need to be split on multiple cores. Defaults to 1,

optimum-cli export neuron --model meta-llama/Meta-Llama-3-8B \
  --batch_size 1 \
  --sequence_length 4096 \
  --auto_cast_type fp16 `# cast operations from BF16 to FP16` \
  --num_cores 2 \
  llama3_neuron/

An important restriction is that LLM models can only be exported on Neuron platforms, as they are tailored to fit on the actual devices during export.

The export of LLM models can take much longer than standard models (sometimes more than one hour).

As explained before, the neuron model parameters are static. This means in particular that during inference:

the batch_size of the inputs should be lower to the batch_size used during export,
the length of the input sequences should be lower than the sequence_length used during export,
the maximum number of tokens (input + generated) cannot exceed the sequence_length used during export.

Once exported, neuron mmodels can simply be reloaded using the NeuronModelForCausalLM class. As with the original transformers models, use generate() instead of forward() to generate text sequences.

from transformers import AutoTokenizer
-from transformers import AutoModelForCausalLM
+from optimum.neuron import NeuronModelForCausalLM

# Instantiate and convert to Neuron a PyTorch checkpoint
-model = AutoModelForCausalLM.from_pretrained("gpt2")
+model = NeuronModelForCausalLM.from_pretrained("./gpt2-neuron")

tokenizer = AutoTokenizer.from_pretrained("gpt2")
tokenizer.pad_token_id = tokenizer.eos_token_id

tokens = tokenizer("I really wish ", return_tensors="pt")
with torch.inference_mode():
    sample_output = model.generate(
        **tokens,
        do_sample=True,
        min_length=128,
        max_length=256,
        temperature=0.7,
    )
    outputs = [tokenizer.decode(tok) for tok in sample_output]
    print(outputs)

The generation is highly configurable. Please refer to https://huggingface.co/docs/transformers/generation_strategies for details.

Please be aware that:

for each model architecture, default values are provided for all parameters, but values passed to the generate method will take precedence,
the generation parameters can be stored in a generation_config.json file. When such a file is present in model directory, it will be parsed to set the default parameters (the values passed to the generate method still take precedence).

Exporting a model to Neuron programmatically via NeuronModel

As an alternative to the optimim-cli, you will also be able to export your models to Neuron inside your own python script or notebook with optimum.neuron.NeuronModelForXXX model classes.

Here is an example:

>>> from optimum.neuron import NeuronModelForSequenceClassification

>>> input_shapes = {"batch_size": 1, "sequence_length": 64}  # mandatory shapes
>>> model = NeuronModelForSequenceClassification.from_pretrained(
...   "distilbert-base-uncased-finetuned-sst-2-english", export=True, **input_shapes
... )

# Save the model
>>> model.save_pretrained("./distilbert-base-uncased-finetuned-sst-2-english_neuron/")

# Push the neuron model to HF Hub
>>> model.push_to_hub(
...     "a_local_path_for_compiled_neuron_model", repository_id="my-neuron-repo", use_auth_token=True
... )

This example can be adapted for other model types using the same export parameters as the optimum-cli.

Exporting neuron models using NeuronX TGI

The NeuronX TGI image includes not only NeuronX runtime, but also all packages and tools required to export Neuron models.

Use the following command to export a model to Neuron using a TGI image:

docker run --entrypoint optimum-cli \
       -v $(pwd)/data:/data \
       --privileged \
       ghcr.io/huggingface/neuronx-tgi:latest \
       export neuron \
       --model <organization>/<model> \
       --batch_size 1 \
       --sequence_length 4096 \
       --auto_cast_type fp16 \
       --num_cores 2 \
       /data/<neuron_model_path>

The exported model will be saved under ./data/<neuron_model_path>.

AWS Trainium & Inferentia