ipex

Step-by-Step

This document is used to list steps of reproducing PyTorch DLRM tuning zoo result. and original DLRM README is in DLRM README

Note

Please ensure your PC have >370G memory to run DLRM IPEX version >= 1.11

Prerequisite

1. Environment

PyTorch 1.11 or higher version is needed with pytorch_fx backend.

# Install dependency
cd examples/pytorch/recommendation/dlrm/quantization/ptq/ipex
pip install -r requirements.txt

Note: Validated PyTorch Version.

2. Prepare Dataset

The code supports interface with the Criteo Terabyte Dataset

1. download the raw data files day_0.gz, ...,day_23.gz and unzip them.
2. Specify the location of the unzipped text files day_0, ...,day_23, using --raw-data-file=<path/day> (the day number will be appended automatically), please refer "Run" command.

3. Prepare pretrained model

Download the DLRM PyTorch weights (tb00_40M.pt, 90GB) from the MLPerf repo

Run

tune with INC

cd examples/pytorch/recommendation/dlrm/quantization/ptq/ipex
bash run_quant.sh --input_model="/path/of/pretrained/model" --dataset_location="/path/of/dataset"

benchmark

bash run_benchmark.sh --input_model="/path/of/pretrained/model" --dataset_location="/path/of/dataset" --mode=accuracy --int8=true

Examples of enabling Intel® Neural Compressor

This is a tutorial of how to enable DLRM model with Intel® Neural Compressor.

Code update

We need update dlrm_s_pytorch.py like below

class DLRM_DataLoader(object):
    def __init__(self, loader=None):
        self.loader = loader
        self.batch_size = loader.dataset.batch_size
    def __iter__(self):
        for X_test, lS_o_test, lS_i_test, T in self.loader:
            yield (X_test, lS_o_test, lS_i_test), T

	def eval_func(model):
		args.int8 = False if model.ipex_config_path is None else True
		args.int8_configure = "" \
			if model.ipex_config_path is None else model.ipex_config_path
		with torch.no_grad():
			return inference(
				args,
				model,
				best_acc_test,
				best_auc_test,
				test_ld,
				trace=args.int8
			)

	eval_dataloader = DLRM_DataLoader(train_ld)
	from neural_compressor import PostTrainingQuantConfig, quantization
	conf = PostTrainingQuantConfig(backend="ipex")
	q_model = quantization.fit(
						dlrm,
						conf=conf,
						eval_func=eval_func,
						calib_dataloader=eval_dataloader
						)
	q_model.save("saved_results")