v1.6.0: INC API refactorization

Refactorization of the INC API for neural-compressor v2.0 (#118)

The INCQuantizer should be used to apply post-training (dynamic or static) quantization.

from transformers import AutoModelForQuestionAnswering
from neural_compressor.config import PostTrainingQuantConfig
from optimum.intel.neural_compressor import INCQuantizer

model_name = "distilbert-base-cased-distilled-squad"
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
# 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 in the given directory
quantizer.quantize(quantization_config=quantization_config, save_directory="quantized_model")

The INCTrainer should be used to apply and combine during training compression techniques such as pruning, quantization and distillation.

from transformers import TrainingArguments, default_data_collator
-from transformers import Trainer
+from optimum.intel.neural_compressor import INCTrainer
+from neural_compressor import QuantizationAwareTrainingConfig

# Load the quantization configuration detailing the quantization we wish to apply
+quantization_config = QuantizationAwareTrainingConfig()

-trainer = Trainer(
+trainer = INCTrainer(
    model=model,
+   quantization_config=quantization_config,
    args=TrainingArguments("quantized_model", num_train_epochs=3.0),
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    compute_metrics=compute_metrics,
    tokenizer=tokenizer,
    data_collator=default_data_collator,
)
trainer.save_model()

To load a quantized model, you can just replace your AutoModelForXxx class with the corresponding INCModelForXxx class.

from optimum.intel.neural_compressor import INCModelForSequenceClassification

loaded_model_from_hub = INCModelForSequenceClassification.from_pretrained(
    "Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic"
)