Cheat Sheet
Get started with Intel® Extension for PyTorch* using the following commands:
Description | Command |
---|---|
Basic CPU Installation | python -m pip install intel_extension_for_pytorch |
Import Intel® Extension for PyTorch* | import intel_extension_for_pytorch as ipex |
Capture a Verbose Log (Command Prompt) | export ONEDNN_VERBOSE=1 |
Optimization During Training | model = ... optimizer = ... model.train() model, optimizer = ipex.optimize(model, optimizer=optimizer) |
Optimization During Inference | model = ... model.eval() model = ipex.optimize(model) |
Optimization Using the Low-Precision Data Type bfloat16 During Training (Default FP32) |
model = ... optimizer = ... model.train() model, optimizer = ipex.optimize(model, optimizer=optimizer, dtype=torch.bfloat16) with torch.no_grad(): with torch.cpu.amp.autocast(): model(data) |
Optimization Using the Low-Precision Data Type bfloat16 During Inference (Default FP32) |
model = ... model.eval() model = ipex.optimize(model, dtype=torch.bfloat16) with torch.cpu.amp.autocast(): model(data) |
[Experimental] Fast BERT Optimization | from transformers import BertModel model = BertModel.from_pretrained("bert-base-uncased") model.eval() model = ipex.fast_bert(model, dtype=torch.bfloat16) |
Run CPU Launch Script (Command Prompt): Automate Configuration Settings for Performance |
ipexrun [knobs] <your_pytorch_script> [args] |
[Experimental] Run HyperTune to perform hyperparameter/execution configuration search | python -m intel_extension_for_pytorch.cpu.hypertune --conf-file <your_conf_file> <your_python_script> [args] |
[Experimental] Enable Graph capture | model = … model.eval() model = ipex.optimize(model, graph_mode=True) |
Post-Training INT8 Quantization (Static) | model = … model.eval() data = … qconfig = ipex.quantization.default_static_qconfig prepared_model = ipex.quantization.prepare(model, qconfig, example_inputs=data, anyplace=False) for d in calibration_data_loader(): prepared_model(d) converted_model = ipex.quantization.convert(prepared_model) |
Post-Training INT8 Quantization (Dynamic) | model = … model.eval() data = … qconfig = ipex.quantization.default_dynamic_qconfig prepared_model = ipex.quantization.prepare(model, qconfig, example_inputs=data) converted_model = ipex.quantization.convert(prepared_model) |
[Experimental] Post-Training INT8 Quantization (Tuning Recipe): | model = … model.eval() data = … qconfig = ipex.quantization.default_static_qconfig prepared_model = ipex.quantization.prepare(model, qconfig, example_inputs=data, inplace=False) tuned_model = ipex.quantization.autotune(prepared_model, calibration_data_loader, eval_function, sampling_sizes=[100], accuracy_criterion={'relative': .01}, tuning_time=0) convert_model = ipex.quantization.convert(tuned_model) |