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PyTorch Latent Consistency Models inference

Description

This document has instructions for running Latent Consistency Models (LCMs). inference using Intel-optimized PyTorch.

Model Information

Use Case Framework Model Repository Branch/Commit Patch
Inference Pytorch https://github.com/huggingface/diffusers.git v0.23.1 diffusers.patch

Pre-Requisite

Follow to install and build Pytorch, IPEX, TorchVison, Jemalloc and TCMalloc.

  • Install Intel OpenMP 
    pip install packaging intel-openmp accelerate
  • Set IOMP, jemalloc and tcmalloc Preload for better performance 
    export LD_PRELOAD="<path to the jemalloc directory>/lib/libjemalloc.so":"<path_to>/tcmalloc/lib/libtcmalloc.so":"<path_to_iomp>/lib/libiomp5.so":$LD_PRELOAD
  • For distributed accuracy, you will need to install mpirun.

Datasets

Download the 2017 COCO dataset using the download_dataset.sh script. Export the DATASET_DIR environment variable to specify the directory where the dataset will be downloaded. This environment variable will be used again when running quickstart scripts.

export DATASET_DIR=<directory where the dataset will be saved>
bash download_dataset.sh

Inference

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

  2. cd models/models_v2/pytorch/LCM/inference/cpu

  3. Create virtual environment venv and activate it:

    python3 -m venv venv
. ./venv/bin/activate

  4. Install general model requirements

    ./setup.sh

  5. Install the latest CPU versions of torch, torchvision and intel_extension_for_pytorch.

  6. Setup required environment paramaters

Parameter export command
TEST_MODE (THROUGHPUT, ACCURACY, REALTIME) export TEST_MODE=THROUGHPUT (THROUGHPUT, ACCURACY, REALTIME)
RUN_MODE export RUN_MODE=ipex-jit (specify mode to run: eager, ipex-jit, compile-ipex, compile-inductor)
DATASET_DIR export DATASET_DIR=<path to the dataset>
PRECISION export PRECISION=fp32 <specify the precision to run: fp32, bf32, fp16, bf16, int8-bf16, int8-fp32>
OUTPUT_DIR export OUTPUT_DIR=<path to the directory where log files will be written>
MODEL_DIR export MODEL_DIR=$PWD (set the current path)
DISTRIBUTED(Only for Accuracy) export DISTRIBUTED=false (Set this to 'true' to run distributed accuracy)
BATCH_SIZE (optional) export BATCH_SIZE=<set a value for batch size, else it will run with default batch size>
TORCH_INDUCTOR (optional) export TORCH_INDUCTOR=< 0 or 1> (Compile model with PyTorch Inductor backend)
  • NOTE: For compile-inductor mode, please do calibration to get quantized model before running INT8-BF16 or INT8-FP32. 
    bash do_calibration.sh
  1. Run run_model.sh

Output

Output typically looks like this: Running benchmark ... 100%|██████████| 4/4 [00:17<00:00, 4.29s/it] time per prompt(s): 26.34 100%|██████████| 4/4 [00:17<00:00, 4.29s/it] time per prompt(s): 26.18 Latency: 26.18 s Throughput: 0.03820 samples/sec

Final results of the inference run can be found in results.yaml file.

results:
- key : throughput
  value: 0.03820
  unit: samples/sec
- key: latency
  value: 26.18
  unit: s
- key: accuracy
  value: 0.20004
  unit: percentage

License

LICENSE