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PyTorch LLaMA2 7B/13B inference (generation)

Description

This document has instructions for running LLaMA2 7B and LLaMA2 13B inference (generation) using Intel-optimized PyTorch.

Bare Metal

General setup

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

Model Specific Setup

  • Install Intel OpenMP

    pip install packaging intel-openmp accelerate

  • Set IOMP and tcmalloc Preload for better performance

    export LD_PRELOAD="<path_to>/tcmalloc/lib/libtcmalloc.so":"<path_to_iomp>/lib/libiomp5.so":$LD_PRELOAD

  • Set ENV to use fp16 AMX if you are using a supported platform

    export DNNL_MAX_CPU_ISA=AVX512_CORE_AMX_FP16

Inference

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

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

  3. Create virtual environment venv and activate it:

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

  4. Run setup.sh

    ./setup.sh

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

  6. Set INPUT_TOKEN before running the model

    export INPUT_TOKEN=32
(choice in [32 64 128 256 512 1024 2016], we prefer to benchmark on 32 and 2016)

    Set OUTPUT_TOKEN before running the model

    export OUTPUT_TOKEN=32
(32 is preferred, while you could set any other length)

    Set FINETUNED_MODEL to llama2 7b or llama2 13b before running

    #Test llama2 7b
export FINETUNED_MODEL="meta-llama/Llama-2-7b-hf"
#Test llama2 13b
export FINETUNED_MODEL="meta-llama/Llama-2-13b-hf"

    About the BATCH_SIZE in scripts

    using BATCH_SIZE=1 for realtime mode
using BATCH_SIZE=N for throughput mode (N could be further tuned according to the testing host, by default using 1);

    About the BEAM_SIZE in scripts

    using BEAM_SIZE=4 by default
  • Do calibration to get "qconfig.json" before running INT8. 
    #optional: qconfig.json is saved in this repo, you can also do calibration by yourself to re-generation it
bash do_quantization.sh calibration sq #using smooth quant as default

#unzip qconfig.zip to get qconfig.json, if you meet error to use this uploaded version of qconfig.zip, please re-generation it as above
unzip qconfig.zip
  1. Setup required environment paramaters
Parameter export command
TEST_MODE (THROUGHPUT, ACCURACY, REALTIME) export TEST_MODE=THROUGHPUT
OUTPUT_DIR export OUTPUT_DIR=<path to an output directory>
FINETUNED_MODEL #Test llama2 7b: export FINETUNED_MODEL="meta-llama/Llama-2-7b-hf"; #Test llama2 13b: export FINETUNED_MODEL="meta-llama/Llama-2-13b-hf"
PRECISION export PRECISION=bf16 (fp32, bf32, bf16, fp16, int8-fp32, int8-bf16)
INPUT_TOKEN export INPUT_TOKEN=32 (choice in [32 64 128 256 512 1024 2016], we prefer to benchmark on 32 and 2016)
OUTPUT_TOKEN export OUTPUT_TOKEN=32 (32 is preferred, while you could set any other length)
MODEL_DIR export MODEL_DIR=$(pwd)
BATCH_SIZE (optional) export BATCH_SIZE=256

Output

Single-tile output will typically looks like:

2024-05-17 22:35:31,097 - root - INFO - ---------- Summary: ----------
2024-05-17 22:35:31,097 - root - INFO - inference-latency: 18.211 sec.
2024-05-17 22:35:31,097 - root - INFO - first-token-latency: 4.227 sec.
2024-05-17 22:35:31,097 - root - INFO - rest-token-latency: 0.110 sec.
2024-05-17 22:35:31,097 - root - INFO - P90-rest-token-latency: 0.111 sec.
2024-05-17 22:35:36,648 - root - INFO - meta-llama/Llama-2-7b-hf;Input/Output Token;1024/128;latency;total-latency;bf16;1; 18.179000
2024-05-17 22:35:36,655 - root - INFO - meta-llama/Llama-2-7b-hf;Input/Output Token;1024/128;latency;first-token-latency;bf16;1; 4.238500
2024-05-17 22:35:36,664 - root - INFO - meta-llama/Llama-2-7b-hf;Input/Output Token;1024/128;latency;rest-token-latency;bf16;1; 0.110000
2024-05-17 22:35:36,671 - root - INFO - meta-llama/Llama-2-7b-hf;Input/Output Token;1024/128;latency;P90-rest-token-latency;bf16;1; 0.110500
2024-05-17 22:35:36,678 - root - INFO - meta-llama/Llama-2-7b-hf;Input/Output Token;1024/128;latency;token_per_sec;bf16;1; 9.110
2024-05-17 22:35:36,686 - root - INFO - meta-llama/Llama-2-7b-hf;Input/Output Token;1024/128;latency;first_token_thp;bf16;1; 0.236

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

results:
- key: first token throughput
  value: 15.648000
- key: rest token throughput
  value: 0.284250
- key: first token latency
  value: 4.238500
- key: rest_token_latency
  value: 0.110000
- key: accuracy
  value: 93.17

License

LICENSE