Accelerate Transformers on State of the Art Hardware
Hugging Face is partnering with leading AI Hardware accelerators to make state of the art production performance accessible
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Train Transformers faster with IPUsLearn more
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Optimum: the ML Optimization toolkit for production performance
Hardware-specific acceleration tools
Make models faster with minimal impact on accuracy, leveraging post-training quantization, quantization-aware training and dynamic quantization from Intel® Neural Compressor.
from transformers import AutoModelForQuestionAnswering from neural_compressor.config import PostTrainingQuantConfig from optimum.intel.neural_compressor import INCQuantizer, INCModelForQuestionAnswering model_name = "distilbert-base-cased-distilled-squad" model = AutoModelForQuestionAnswering.from_pretrained(model_name) # The directory where the quantized model will be saved save_dir = "quantized_model" # Load the quantization configuration detailing the quantization we wish to apply quantization_config = PostTrainingQuantConfig(approach="dynamic") quantizer = INCQuantizer.from_pretrained(model) # Apply dynamic quantization and save the resulting model quantizer.quantize(quantization_config=quantization_config, save_directory=save_dir) # Load the resulting quantized model, which can be hosted on the HF hub or locally loaded_model = INCModelForQuestionAnswering.from_pretrained(save_dir)
Make models smaller with minimal impact on accuracy, with easy to use configurations to remove model weights using Intel® Neural Compressor.
from transformers import AutoModelForSequenceClassification, AutoTokenizer from neural_compressor import QuantizationAwareTrainingConfig from optimum.intel.neural_compressor import INCTrainer model_id = "distilbert-base-uncased-finetuned-sst-2-english" model = AutoModelForSequenceClassification.from_pretrained(model_id) tokenizer = AutoTokenizer.from_pretrained(model_id) # Load the quantization configuration detailing the quantization we wish to apply quantization_config = QuantizationAwareTrainingConfig() trainer = INCTrainer(model, quantization_config=quantization_config, args=trainings_args) # Train the model while applying quantization trainer.train() # Save the model and/or push to hub trainer.save_model() trainer.push_to_hub()
Train models faster than ever before with Graphcore Intelligence Processing Units (IPUs), the latest generation of AI dedicated hardware, leveraging the built-in IPUTrainer API to train or finetune transformers models (coming soon)
from optimum.graphcore import IPUConfig, IPUTrainer from transformers import BertForPreTraining, BertTokenizer # Allocate model and tokenizer as usual tokenizer = BertTokenizer.from_pretrained("bert-base-cased") model = BertForPreTraining.from_pretrained("bert-base-cased") # IPU configuration + Trainer ipu_config = IPUConfig.from_pretrained("Graphcore/bert-base-ipu") trainer = IPUTrainer(model, ipu_config=ipu_config, args=trainings_args) # The Trainer takes care of compiling the model for the IPUs in the background # to perform training, the user does not have to deal with that trainer.train() # Save the model and/or push to hub model.save_pretrained("...") model.push_to_hub("...")