Neuron Model Inference

The APIs presented in the following documentation are relevant for the inference on inf2, trn1 and inf1.

NeuronModelForXXX classes help to load models from the Hugging Face Hub and compile them to a serialized format optimized for neuron devices. You will then be able to load the model and run inference with the acceleration powered by AWS Neuron devices.

Switching from Transformers to Optimum

The optimum.neuron.NeuronModelForXXX model classes are APIs compatible with Hugging Face Transformers models. This means seamless integration with Hugging Face’s ecosystem. You can just replace your AutoModelForXXX class with the corresponding NeuronModelForXXX class in optimum.neuron.

If you already use Transformers, you will be able to reuse your code just by replacing model classes:

from transformers import AutoTokenizer
-from transformers import AutoModelForSequenceClassification
+from optimum.neuron import NeuronModelForSequenceClassification

# PyTorch checkpoint
-model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")

+model = NeuronModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english",
+                                                             export=True, **neuron_kwargs)

As shown above, when you use NeuronModelForXXX for the first time, you will need to set export=True to compile your model from PyTorch to a neuron-compatible format.

You will also need to pass Neuron specific parameters to configure the export. Each model architecture has its own set of parameters, as detailed in the next paragraphs.

Once your model has been exported, you can save it either on your local or in the Hugging Face Model Hub:

# Save the neuron model
>>> model.save_pretrained("a_local_path_for_compiled_neuron_model")

# 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
... )

And the next time when you want to run inference, just load your compiled model which will save you the compilation time:

>>> from optimum.neuron import NeuronModelForSequenceClassification
>>> model = NeuronModelForSequenceClassification.from_pretrained("my-neuron-repo")

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.

Discriminative NLP models

As explained in the previous section, you will need only few modifications to your Transformers code to export and run NLP models:

from transformers import AutoTokenizer
-from transformers import AutoModelForSequenceClassification
+from optimum.neuron import NeuronModelForSequenceClassification

# PyTorch checkpoint
-model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")

# Compile your model during the first time
+compiler_args = {"auto_cast": "matmul", "auto_cast_type": "bf16"}
+input_shapes = {"batch_size": 1, "sequence_length": 64}
+model = NeuronModelForSequenceClassification.from_pretrained(
+    "distilbert-base-uncased-finetuned-sst-2-english", export=True, **compiler_args, **input_shapes,
+)

tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
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'

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. Here we cast FP32 operations to BF16 using the Neuron matrix-multiplication engine.

input_shapes are mandatory static shape information that you need to send to the neuron compiler. Wondering what shapes are mandatory for your model? Check it out with the following code:

>>> from transformers import AutoModelForSequenceClassification
>>> from optimum.exporters import TasksManager

>>> model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")

# Infer the task name if you don't know
>>> task = TasksManager.infer_task_from_model(model)  # 'text-classification'

>>> neuron_config_constructor = TasksManager.get_exporter_config_constructor(
...     model=model, exporter="neuron", task='text-classification'
... )
>>> print(neuron_config_constructor.func.get_mandatory_axes_for_task(task))
# ('batch_size', 'sequence_length')

Generative NLP models

As explained before, you will need only a few modifications to your Transformers code to export and run NLP models:

Configuring the export of a generative model

As for non-generative models, two sets of parameters can be passed to the from_pretrained() method to configure how a transformers checkpoint is exported to a neuron optimized model:

• compiler_args = { num_cores, auto_cast_type } are optional arguments for the compiler, these arguments usually control how the compiler makes tradeoff between the inference latency and throughput and the accuracy.

• input_shapes = { batch_size, sequence_length } correspond to the static shape of the model input and the KV-cache (attention keys and values for past tokens).

• 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,

• 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.

• 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).

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

# Instantiate and convert to Neuron a PyTorch checkpoint
+compiler_args = {"num_cores": 1, "auto_cast_type": 'fp32'}
+input_shapes = {"batch_size": 1, "sequence_length": 512}
-model = AutoModelForCausalLM.from_pretrained("gpt2")
+model = NeuronModelForCausalLM.from_pretrained("gpt2", export=True, **compiler_args, **input_shapes)

As explained before, these parameters can only be configured during export. This means in particular that during inference:

• the batch_size of the inputs should be equal 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.

Text generation inference

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", export=True)

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).

Happy inference with Neuron! 🚀