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BERT Large (Hugging Face) inference

Description

This document has instructions for running BERT Large inference (using SQuAD dataset) from Hugging Face using TensorFlow.

The original Hugging Face model can be found here. This model has been slightly modified to print benchmarking information such as latency and throughput.

Dataset

The model script will automatically download and load SQuAD dataset from the datasets hub. No additional setup is needed from the user.

Quick Start Scripts

Script name Description
inference.sh Runs real-time inference using a default batch_size=1 (on whole socket) for the specified precision (fp32 fp16 bfloat16 or bfloat32). To run inference for throughtput, set BATCH_SIZE environment variable.
inference_realtime_multi_instance.sh Runs multi instance realtime inference using 4 cores per instance with batch size 1 (for precisions: fp32 fp16 bfloat16 or bfloat32) to compute latency. Waits for all instances to complete, then prints a summarized throughput value.
inference_throughput_multi_instance.sh Runs multi instance batch inference using 1 instance per socket with batch size 128 (for precisions: fp32 fp16 bfloat16 or bfloat32) to compute throughput. Waits for all instances to complete, then prints a summarized throughput value.
accuracy.sh Measures inference accuracy for the specified precision (fp32 fp16 bfloat16 or bfloat32).

Pre-Trained Model

Download the pretrained model from the link given below. Please set DATASET_DIR to point to the location of the pretrained model files on your local system.

  • Fp32 fp16 bfloat16 or bfloat32: 
  wget https://storage.googleapis.com/intel-optimized-tensorflow/models/3_2/bert_hf_pretrained_model.tar.gz
  tar -xzvf bert_hf_pretrained_model.tar.gz
  export DATASET_DIR=$(pwd)/bert_hf_pretrained_model
  • For benchmarking, DATASET_DIR can be empty (OR) set to the path of pre-trained model mentioned in the above link. If it is empty, the model scripts will automatically download the pre-trained model and SQuAD dataset from Hugging Face models and datasets respectively.
  • For accuracy measurement, DATASET_DIR cannot be empty since the pre-trained model from Hugging Face does not reach the published accuracy results. Hence, it needs to be set to the path of the pre-trained model mentioned in the above link.

Run the model

Install requirements

  1. Clone the Intel® AI Reference Models repository:

    git clone https://github.com/IntelAI/models.git

  2. Install tf-nightly

  3. Install model-specific requirements:

pip install -r benchmarks/language_modeling/tensorflow/bert_large_hf/inference/requirements.txt
  1. Set the required environment variables:
cd models
export PRECISION=<specify the precision to run: fp32 fp16 bfloat16 or bfloat32>
export OUTPUT_DIR=<directory where log files will be written>
export DATASET_NAME="squad"

Optional environment variables:
# For a custom batch size, set env var `BATCH_SIZE`. If not specified, model will run with a default value.
export BATCH_SIZE=<customized batch size value>

# For a custom dataset name, set env var `DATASET_NAME`. If not specified, model will use 'squad' dataset as the default.
export DATASET_NAME=<customized dataset name>

Accuracy

export DATASET_DIR=<path-to-pretrained-model>
./quickstart/language_modeling/tensorflow/bert_large_hf/inference/cpu/accuracy.sh

Real-time inference (latency) -- runs with batch size of 1

`DATASET_DIR` is an optional environment variable. If it is not set, the model scripts will automatically download the pretrained model from huggingface.co/models
  export DATASET_DIR=<path-to-pretrained-model>

./quickstart/language_modeling/tensorflow/bert_large_hf/inference/cpu/inference_realtime_multi_instance.sh

Throughput -- runs with batch size of 128

`DATASET_DIR` is an optional environment variable. If it is not set, the model scripts will automatically download the pretrained model from huggingface.co/models
  export DATASET_DIR=<path-to-pretrained-model>

./quickstart/language_modeling/tensorflow/bert_large_hf/inference/cpu/inference_throughput_multi_instance.sh

Real-time inference (latency) on whole socket -- runs with a batch size of 1

`DATASET_DIR` is an optional environment variable. If it is not set, the model scripts will automatically download the pretrained model from huggingface.co/models
  export DATASET_DIR=<path-to-pretrained-model>

The batch size can be varied using `BATCH_SIZE` env variable. This script differs from `inference_realtime_multi_instance.sh` by running the model on whole socket
instead of `CORES_PER_INSTANCE=4`.

./quickstart/language_modeling/tensorflow/bert_large_hf/inference/cpu/inference.sh

Output

Output from a successful run looks as follows:

05/07/2024 10:48:57 - INFO - __main__ - Batch size: 128
05/07/2024 10:48:57 - INFO - __main__ - Total examples: 51200, Warmup examples: 6400
05/07/2024 10:48:57 - INFO - __main__ - Benchmark examples: 44800, Benchmark time: 121.07 secs
05/07/2024 10:48:57 - INFO - __main__ - Throughput (examples/sec): 370.03