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RNN-T Inference

Description

This document has instructions for running RNN-T inference using Intel-optimized PyTorch.

Bare Metal

General setup

Follow link to install Conda and build Pytorch, IPEX, TorchVison and Jemalloc.

Model Specific Setup

  • Install dependencies

    export MODEL_DIR=<path to your clone of the model zoo>
bash ${MODEL_DIR}/quickstart/language_modeling/pytorch/rnnt/inference/cpu/install_dependency_baremetal.sh

  • Download and preprocess RNN-T dataset:

    export DATASET_DIR=#Where_to_save_Dataset
bash ${MODEL_DIR}/quickstart/language_modeling/pytorch/rnnt/inference/cpu/download_dataset.sh

  • Download pretrained model

    export CHECKPOINT_DIR=#Where_to_save_pretrained_model
bash ${MODEL_DIR}/quickstart/language_modeling/pytorch/rnnt/inference/cpu/download_model.sh

  • Set Jemalloc Preload for better performance

    The jemalloc should be built from the General setup section.

    export LD_PRELOAD="path/lib/libjemalloc.so":$LD_PRELOAD
export MALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000"

  • Set IOMP preload for better performance

    IOMP should be installed in your conda env from the General setup section.

    export LD_PRELOAD=path/lib/libiomp5.so:$LD_PRELOAD

  • Set ENV to use AMX if you are using SPR

    export DNNL_MAX_CPU_ISA=AVX512_CORE_AMX

Quick Start Scripts

DataType Throughput Latency Accuracy
FP32 bash inference_throughput.sh fp32 bash inference_realtime.sh fp32 bash accuracy.sh fp32
BF16 bash inference_throughput.sh bf16 bash inference_realtime.sh bf16 bash accuracy.sh bf16
BF32 bash inference_throughput.sh bf32 bash inference_realtime.sh bf32 bash accuracy.sh bf32

Run the model

Follow the instructions above to setup your bare metal environment, download and preprocess the dataset, and do the model specific setup. Once all the setup is done, the Model Zoo can be used to run a quickstart script. Ensure that you have enviornment variables set to point to the dataset directory, an output directory and the checkpoint directory.

# Clone the model zoo repo and set the MODEL_DIR
git clone https://github.com/IntelAI/models.git
cd models
export MODEL_DIR=$(pwd)

# Env vars
export OUTPUT_DIR=<path to an output directory>
export CHECKPOINT_DIR=<path to the pretrained model checkpoints>
export DATASET_DIR=<path to the dataset>

# Run a quickstart script (for example, FP32 batch inference)
cd ${MODEL_DIR}/quickstart/language_modeling/pytorch/rnnt/inference/cpu
bash inference_throughput.sh fp32

License

LICENSE