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Loading large Huggingface models with PiPPy (PyTorch Native Large inference solution)

This document briefs on serving large HF model with PiPPy.

PiPPy provides pipeline parallelism for serving large models that would not fit into one gpu. It takes your model and splits it into equal sizes (stages) partitioned over the number devices you specify. Then uses micro batching to run your batched input for inference ( its is more optimal for batch sizes >1). Micro-batching is the techniques in pipeline parallelism to maximize gpu utilization.

How to serve your large HuggingFace models with PiPPy in Torchserve?

We use a Torchserve custom handler that inherits from base_pippy_handler to load the model and define our logic for preprocess, inference and post processing. This is basically very similar to your evaluation process. Following settings has been tested on g5.12xlarge EC2 instance which has 4xA10 GPUs.

To run this example we need to have torchpippy installed. This has been added to the requirement.txt which can be bundled during model packaging.

Generally to install torchpippy you can run following

pip install torchpippy

Step 1: Download model

python ../utils/Download_model.py --model_name facebook/opt-30b

The script prints the path where the model is downloaded as below. This is an example and in your workload you want to use your actual trained model checkpoints.

model/models--facebook--opt-30b/snapshots/ceea0a90ac0f6fae7c2c34bcb40477438c152546/

The downloaded model is around 14GB.

Step 2: Create a model-config.yaml with that include following

minWorkers: 1
maxWorkers: 1
maxBatchDelay: 100
responseTimeout: 120
parallelLevel: 4
deviceType: "gpu"
parallelType: "pp" #PiPPy as the solution for distributed inference
torchrun:
    nproc-per-node: 4 # specifies the number of processes torchrun starts to serve your model, set to world_size or number of
                       # gpus you wish to split your model
pippy:
    chunks: 1 # This sets the microbatch sizes, microbatch = batch size/ chunks
    input_names: ['input_ids'] # input arg names to the model, this is required for FX tracing
    model_type: "HF" # set the model type to HF if you are using Huggingface model other wise leave it blank or any other model you use.
    rpc_timeout: 1800
    num_worker_threads: 512 #number of threads for rpc worker usually 512 is a good number

handler:
    max_length: 80 # max length of tokens for tokenizer in the handler
    model_name: "/home/ubuntu/serve/examples/large_models/Huggingface_pippy/model/models--facebook--opt-30b/snapshots/ceea0a90ac0f6fae7c2c34bcb40477438c152546" #the path to the checkpoints, in this example downloaded file. Please change to your model path.
    index_file_name: 'pytorch_model.bin.index.json' # index json file name in the model checkpoint folder, that keeps information of distributed checkpoints
    manual_seed: 40
    dtype: fp16 # data type to load your model checkpoint, supported fp32, fp16, bf16

Step 3: Generate Tar/ MAR file

Navigate up to largemodels directory. Here as bundling the large model checkpoints is very time consuming, we are passing model checkpoint path in the model_config.yaml as shown above. This let us make the packaging very fast, for production settings, the large models can be put in some shared location and used from there in the model-config.

torch-model-archiver --model-name opt --version 1.0 --handler pippy_handler.py  -r requirements.txt --config-file model-config.yaml --archive-format tgz

Step 4: Add the mar file to model store

mkdir model_store
mv opt.tar.gz model_store

Step 5: Start torchserve

Update config.properties and start torchserve

torchserve --ncs --start --model-store model_store --models opt.tar.gz

Step 6: Run inference

curl -v "http://localhost:8080/predictions/opt" -T sample_text.txt