Accelerate Transformers on State of the Art Hardware
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Optimum: the ML Optimization toolkit for production performance
Hardware-specific acceleration tools
1. Quantize
Make models faster with minimal impact on accuracy, leveraging post-training quantization, quantization-aware training and dynamic quantization from Intel® Neural Compressor.
huggingface@hardware:~
from optimum.intel.neural_compressor import IncOptimizer, IncQuantizer, IncQuantizationConfig
# Load the quantization configuration detailing the quantization process to apply
quantization_config = IncQuantizationConfig.from_pretrained(
"echarlaix/distilbert-sst2-inc-dynamic-quantization-magnitude-pruning-0.1",
config_file_name="quantization.yml",
)
# Instantiate our IncQuantizer using the desired configuration
quantizer = IncQuantizer(quantization_config, eval_func=eval_func)
optimizer = IncOptimizer(model, quantizer=quantizer)
# Apply dynamic quantization
model = optimizer.fit()2. Prune
Make models smaller with minimal impact on accuracy, with easy to use configurations to remove model weights using Intel® Neural Compressor.
huggingface@hardware:~
from optimum.intel.neural_compressor import IncOptimizer, IncPruner, IncPruningConfig
# Load the pruning configuration detailing the pruning process to apply
pruning_config = IncPruningConfig.from_pretrained(
"echarlaix/distilbert-sst2-inc-dynamic-quantization-magnitude-pruning-0.1",
config_file_name="prune.yml",
)
# Instantiate our IncPruner using the desired configuration
pruner = IncPruner(pruning_config, eval_func=eval_func, train_func=train_func)
optimizer = IncOptimizer(model, pruner=pruner)
# Apply magnitude pruning
model = optimizer.fit()3. Train
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)
huggingface@hardware:~
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("...")