Build OnnxRuntime Error

Hugging Face Transformer submillisecond inference️ and deployment to production: 🤗 → 🤯

Optimize and deploy in production 🤗 Hugging Face Transformer models in a single command line.

=> Up to 10X faster inference! <=

Why this tool?

At Lefebvre Dalloz we run in production semantic search engines in the legal domain, in non-marketing language it's a re-ranker, and we based ours on Transformer.
In those setup, latency is key to provide good user experience, and relevancy inference is done online for hundreds of snippets per user query.
We have tested many solutions, and below is what we found:

Pytorch + FastAPI = 🐢
Most tutorials on Transformer deployment in production are built over Pytorch and FastAPI. Both are great tools but not very performant in inference (actual measures below).

Microsoft ONNX Runtime + Nvidia Triton inference server = ️🏃💨
Then, if you spend some time, you can build something over ONNX Runtime and Triton inference server. You will usually get from 2X to 4X faster inference compared to vanilla Pytorch. It's cool!

Nvidia TensorRT + Nvidia Triton inference server = ⚡️🏃💨💨
However, if you want the best in class performances on GPU, there is only a single possible combination: Nvidia TensorRT and Triton. You will usually get 5X faster inference compared to vanilla Pytorch.
Sometimes it can rise up to 10X faster inference.
Buuuuttt... TensorRT can ask some efforts to master, it requires tricks not easy to come up with, we implemented them for you!

Detailed tool comparison table

Features

Heavily optimize transformer models for inference (CPU and GPU) -> between 5X and 10X speedup
deploy models on Nvidia Triton inference servers (enterprise grade), 6X faster than FastAPI
add quantization support for both CPU and GPU
simple to use: optimization done in a single command line!
supported model: any model that can be exported to ONNX (-> most of them)
supported tasks: document classification, token classification (NER), feature extraction (aka sentence-transformers dense embeddings), text generation

Want to understand how it works under the hood?
read 🤗 Hugging Face Transformer inference UNDER 1 millisecond latency 📖

Want to check by yourself in 3 minutes?

To have a raw idea of what kind of acceleration you will get on your own model, you can try the docker only run below. For GPU run, you need to have installed on your machine Nvidia drivers and NVIDIA Container Toolkit.

3 tasks are covered below:

Classification,
feature extraction (text to dense embeddings)
text generation (GPT-2 style).

Moreover, we have added a GPU quantization notebook to open directly on Docker to play with.

First, clone the repo as some commands below expect to find the demo folder:

git clone [email protected]:ELS-RD/transformer-deploy.git
cd transformer-deploy
# docker image may take a few minutes
docker pull ghcr.io/els-rd/transformer-deploy:0.6.0 


### Classification/reranking (encoder model)

Classification is a common task in NLP, and large language models have shown great results.  
This task is also used for search engines to provide Google like relevancy (cf. [arxiv](https://arxiv.org/abs/1901.04085))

#### Optimize existing model

This will optimize models, generate Triton configuration and Triton folder layout in a single command:

```shell
docker run -it --rm --gpus all \
  -v $PWD:/project ghcr.io/els-rd/transformer-deploy:0.6.0 \
  bash -c "cd /project && \
    convert_model -m \"philschmid/MiniLM-L6-H384-uncased-sst2\" \
    --backend tensorrt onnx \
    --seq-len 16 128 128"

# output:  
# ...
# Inference done on NVIDIA GeForce RTX 3090
# latencies:
# [Pytorch (FP32)] mean=5.43ms, sd=0.70ms, min=4.88ms, max=7.81ms, median=5.09ms, 95p=7.01ms, 99p=7.53ms
# [Pytorch (FP16)] mean=6.55ms, sd=1.00ms, min=5.75ms, max=10.38ms, median=6.01ms, 95p=8.57ms, 99p=9.21ms
# [TensorRT (FP16)] mean=0.53ms, sd=0.03ms, min=0.49ms, max=0.61ms, median=0.52ms, 95p=0.57ms, 99p=0.58ms
# [ONNX Runtime (FP32)] mean=1.57ms, sd=0.05ms, min=1.49ms, max=1.90ms, median=1.57ms, 95p=1.63ms, 99p=1.76ms
# [ONNX Runtime (optimized)] mean=0.90ms, sd=0.03ms, min=0.88ms, max=1.23ms, median=0.89ms, 95p=0.95ms, 99p=0.97ms
# Each infence engine output is within 0.3 tolerance compared to Pytorch output

It will output mean latency and other statistics.
Usually Nvidia TensorRT is the fastest option and ONNX Runtime is usually a strong second option.
On ONNX Runtime, optimized means that kernel fusion and mixed precision are enabled.
Pytorch is never competitive on transformer inference, including mixed precision, whatever the model size.

Run Nvidia Triton inference server

Note that we install transformers at run time.
For production, it's advised to build your own 3-line Docker image with transformers pre-installed.

docker run -it --rm --gpus all -p8000:8000 -p8001:8001 -p8002:8002 --shm-size 256m \
  -v $PWD/triton_models:/models nvcr.io/nvidia/tritonserver:22.07-py3 \
  bash -c "pip install transformers && tritonserver --model-repository=/models"

# output:
# ...
# I0207 09:58:32.738831 1 grpc_server.cc:4195] Started GRPCInferenceService at 0.0.0.0:8001
# I0207 09:58:32.739875 1 http_server.cc:2857] Started HTTPService at 0.0.0.0:8000
# I0207 09:58:32.782066 1 http_server.cc:167] Started Metrics Service at 0.0.0.0:8002

Query inference

Query ONNX models (replace transformer_onnx_inference by transformer_tensorrt_inference to query TensorRT engine):

curl -X POST  http://localhost:8000/v2/models/transformer_onnx_inference/versions/1/infer \
  --data-binary "@demo/infinity/query_body.bin" \
  --header "Inference-Header-Content-Length: 161"

# output:
# {"model_name":"transformer_onnx_inference","model_version":"1","parameters":{"sequence_id":0,"sequence_start":false,"sequence_end":false},"outputs":[{"name":"output","datatype":"FP32","shape":[1,2],"data":[-3.431640625,3.271484375]}]}

Model output is at the end of the Json (data field). More information about how to query the server from Python, and other languages.

To get very low latency inference in your Python code (no inference server): click here

Token-classification (NER) (encoder model)

Token classification assigns a label to individual tokens in a sentence. One of the most common token classification tasks is Named Entity Recognition (NER). NER attempts to find a label for each entity in a sentence, such as a person, location, or organization.

Optimize existing model

This will optimize models, generate Triton configuration and Triton folder layout in a single command:

docker run -it --rm --gpus all \
  -v $PWD:/project ghcr.io/els-rd/transformer-deploy:0.6.0 \
  bash -c "cd /project && \
    convert_model -m \"kamalkraj/bert-base-cased-ner-conll2003\" \
    --backend tensorrt onnx \
    --seq-len 16 128 128 \
    --task token-classification"

# output:  
# ...
# Inference done on Tesla T4
# latencies:
# [Pytorch (FP32)] mean=8.24ms, sd=0.46ms, min=7.66ms, max=13.91ms, median=8.20ms, 95p=8.38ms, 99p=10.01ms
# [Pytorch (FP16)] mean=6.87ms, sd=0.44ms, min=6.69ms, max=13.05ms, median=6.78ms, 95p=7.33ms, 99p=8.86ms
# [TensorRT (FP16)] mean=2.33ms, sd=0.32ms, min=2.19ms, max=4.18ms, median=2.24ms, 95p=3.00ms, 99p=4.04ms
# [ONNX Runtime (FP32)] mean=8.08ms, sd=0.33ms, min=7.78ms, max=10.61ms, median=8.06ms, 95p=8.18ms, 99p=10.55ms
# [ONNX Runtime (optimized)] mean=2.57ms, sd=0.04ms, min=2.38ms, max=2.83ms, median=2.56ms, 95p=2.68ms, 99p=2.73ms
# Each infence engine output is within 0.3 tolerance compared to Pytorch output

It will output mean latency and other statistics.
Usually Nvidia TensorRT is the fastest option and ONNX Runtime is usually a strong second option.
On ONNX Runtime, optimized means that kernel fusion and mixed precision are enabled.
Pytorch is never competitive on transformer inference, including mixed precision, whatever the model size.

Run Nvidia Triton inference server

Note that we install transformers at run time.
For production, it's advised to build your own 3-line Docker image with transformers pre-installed.

docker run -it --rm --gpus all -p8000:8000 -p8001:8001 -p8002:8002 --shm-size 256m \
  -v $PWD/triton_models:/models nvcr.io/nvidia/tritonserver:22.07-py3 \
  bash -c "pip install transformers torch==1.12.0 -f https://download.pytorch.org/whl/cu116/torch_stable.html && \
  tritonserver --model-repository=/models"

# output:
# ...
# I0207 09:58:32.738831 1 grpc_server.cc:4195] Started GRPCInferenceService at 0.0.0.0:8001
# I0207 09:58:32.739875 1 http_server.cc:2857] Started HTTPService at 0.0.0.0:8000
# I0207 09:58:32.782066 1 http_server.cc:167] Started Metrics Service at 0.0.0.0:8002

Query inference

Query ONNX models (replace transformer_onnx_inference by transformer_tensorrt_inference to query TensorRT engine):

curl -X POST  http://localhost:8000/v2/models/transformer_onnx_inference/versions/1/infer \
  --data-binary "@demo/infinity/query_body.bin" \
  --header "Inference-Header-Content-Length: 161"

# output:
# {"model_name":"transformer_onnx_inference","model_version":"1","outputs":[{"name":"output","datatype":"BYTES","shape":[],"data":["[{\"entity_group\": \"ORG\", \"score\": 0.9848777055740356, \"word\": \"Infinity\", \"start\": 45, \"end\": 53}]"]}]}

Question Answering (encoder model)

Question Answering models can retrieve the answer to a question from a given text, which is useful for searching for an answer in a document.

Optimize existing model

This will optimize models, generate Triton configuration and Triton folder layout in a single command:

docker run -it --rm --gpus all \
  -v $PWD:/project ghcr.io/els-rd/transformer-deploy:0.6.0 \
  bash -c "cd /project && \
    convert_model -m \"distilbert-base-cased-distilled-squad\" \
    --backend tensorrt onnx \
    --seq-len 16 128 384 \
    --task question-answering"

# output:  
# ...
# Inference done on Tesla T4
# latencies:
# [Pytorch (FP32)] mean=8.24ms, sd=0.46ms, min=7.66ms, max=13.91ms, median=8.20ms, 95p=8.38ms, 99p=10.01ms
# [Pytorch (FP16)] mean=6.87ms, sd=0.44ms, min=6.69ms, max=13.05ms, median=6.78ms, 95p=7.33ms, 99p=8.86ms
# [TensorRT (FP16)] mean=2.33ms, sd=0.32ms, min=2.19ms, max=4.18ms, median=2.24ms, 95p=3.00ms, 99p=4.04ms
# [ONNX Runtime (FP32)] mean=8.08ms, sd=0.33ms, min=7.78ms, max=10.61ms, median=8.06ms, 95p=8.18ms, 99p=10.55ms
# [ONNX Runtime (optimized)] mean=2.57ms, sd=0.04ms, min=2.38ms, max=2.83ms, median=2.56ms, 95p=2.68ms, 99p=2.73ms
# Each infence engine output is within 0.3 tolerance compared to Pytorch output

It will output mean latency and other statistics.
Usually Nvidia TensorRT is the fastest option and ONNX Runtime is usually a strong second option.
On ONNX Runtime, optimized means that kernel fusion and mixed precision are enabled.
Pytorch is never competitive on transformer inference, including mixed precision, whatever the model size.

Run Nvidia Triton inference server

Note that we install transformers at run time.
For production, it's advised to build your own 3-line Docker image with transformers pre-installed.

docker run -it --rm --gpus all -p8000:8000 -p8001:8001 -p8002:8002 --shm-size 1024m \
  -v $PWD/triton_models:/models nvcr.io/nvidia/tritonserver:22.07-py3 \
  bash -c "pip install transformers torch==1.12.0 -f https://download.pytorch.org/whl/cu116/torch_stable.html && \
  tritonserver --model-repository=/models"

# output:
# ...
# I0207 09:58:32.738831 1 grpc_server.cc:4195] Started GRPCInferenceService at 0.0.0.0:8001
# I0207 09:58:32.739875 1 http_server.cc:2857] Started HTTPService at 0.0.0.0:8000
# I0207 09:58:32.782066 1 http_server.cc:167] Started Metrics Service at 0.0.0.0:8002

Query inference

Query ONNX models (replace transformer_onnx_inference by transformer_tensorrt_inference to query TensorRT engine):

curl -X POST  http://localhost:8000/v2/models/transformer_onnx_inference/versions/1/infer \
  --data-binary "@demo/question-answering/query_body.bin" \
  --header "Inference-Header-Content-Length: 276"

# output:
# {"model_name":"transformer_onnx_inference","model_version":"1","outputs":[{"name":"output","datatype":"BYTES","shape":[],"data":["{\"score\": 0.9925152659416199, \"start\": 34, \"end\": 40, \"answer\": \"Berlin\"}"]}]}

Checkout demo/question-answering/query_bin_gen.ipynb for how to generate the query_body.bin file. More examples of inference can be found in demo/question-answering/

Feature extraction / dense embeddings

Feature extraction in NLP is the task to convert text to dense embeddings.
It has gained some traction as a robust way to improve search engine relevancy (increase recall).
This project supports models from sentence-transformers and it requires a version >= V2.2.0 of sentence-transformers library.

Optimize existing model

docker run -it --rm --gpus all \
  -v $PWD:/project ghcr.io/els-rd/transformer-deploy:0.6.0 \
  bash -c "cd /project && \
    convert_model -m \"sentence-transformers/msmarco-distilbert-cos-v5\" \
    --backend tensorrt onnx \
    --task embedding \
    --seq-len 16 128 128"

# output:
# ...
# Inference done on NVIDIA GeForce RTX 3090
# latencies:
# [Pytorch (FP32)] mean=5.19ms, sd=0.45ms, min=4.74ms, max=6.64ms, median=5.03ms, 95p=6.14ms, 99p=6.26ms
# [Pytorch (FP16)] mean=5.41ms, sd=0.18ms, min=5.26ms, max=8.15ms, median=5.36ms, 95p=5.62ms, 99p=5.72ms
# [TensorRT (FP16)] mean=0.72ms, sd=0.04ms, min=0.69ms, max=1.33ms, median=0.70ms, 95p=0.78ms, 99p=0.81ms
# [ONNX Runtime (FP32)] mean=1.69ms, sd=0.18ms, min=1.62ms, max=4.07ms, median=1.64ms, 95p=1.86ms, 99p=2.44ms
# [ONNX Runtime (optimized)] mean=1.03ms, sd=0.09ms, min=0.98ms, max=2.30ms, median=1.00ms, 95p=1.15ms, 99p=1.41ms
# Each infence engine output is within 0.3 tolerance compared to Pytorch output

Run Nvidia Triton inference server

docker run -it --rm --gpus all -p8000:8000 -p8001:8001 -p8002:8002 --shm-size 256m \
  -v $PWD/triton_models:/models nvcr.io/nvidia/tritonserver:22.07-py3 \
  bash -c "pip install transformers && tritonserver --model-repository=/models"

# output:
# ...
# I0207 11:04:33.761517 1 grpc_server.cc:4195] Started GRPCInferenceService at 0.0.0.0:8001
# I0207 11:04:33.761844 1 http_server.cc:2857] Started HTTPService at 0.0.0.0:8000
# I0207 11:04:33.803373 1 http_server.cc:167] Started Metrics Service at 0.0.0.0:8002

Query inference

curl -X POST  http://localhost:8000/v2/models/transformer_onnx_inference/versions/1/infer \
  --data-binary "@demo/infinity/query_body.bin" \
  --header "Inference-Header-Content-Length: 161"

# output:
# {"model_name":"transformer_onnx_inference","model_version":"1","parameters":{"sequence_id":0,"sequence_start":false,"sequence_end":false},"outputs":[{"name":"output","datatype":"FP32","shape":[1,768],"data":[0.06549072265625,-0.04327392578125,0.1103515625,-0.007320404052734375,...

Generate text (decoder model)

Text generation seems to be the way to go for NLP.
Unfortunately, they are slow to run, below we will accelerate the most famous of them: GPT-2.

GPT example

We will start with GPT-2 model example, then in the next section we will use T5-model.

Optimize existing model

Like before, command below will prepare Triton inference server stuff.
One point to have in mind is that Triton run:

inference engines (ONNX Runtime and TensorRT)
Python code in charge of the decoding part. Python code delegate to Triton server the model management.

Python code is in ./triton_models/transformer_tensorrt_generate/1/model.py

docker run -it --rm --gpus all \
  -v $PWD:/project ghcr.io/els-rd/transformer-deploy:0.6.0 \
  bash -c "cd /project && \
    convert_model -m gpt2 \
    --backend tensorrt onnx \
    --seq-len 6 256 256 \
    --task text-generation"

# output:
# ...
# Inference done on NVIDIA GeForce RTX 3090
# latencies:
# [Pytorch (FP32)] mean=9.43ms, sd=0.59ms, min=8.95ms, max=15.02ms, median=9.33ms, 95p=10.38ms, 99p=12.46ms
# [Pytorch (FP16)] mean=9.92ms, sd=0.55ms, min=9.50ms, max=15.06ms, median=9.74ms, 95p=10.96ms, 99p=12.26ms
# [TensorRT (FP16)] mean=2.19ms, sd=0.18ms, min=2.06ms, max=3.04ms, median=2.10ms, 95p=2.64ms, 99p=2.79ms
# [ONNX Runtime (FP32)] mean=4.99ms, sd=0.38ms, min=4.68ms, max=9.09ms, median=4.78ms, 95p=5.72ms, 99p=5.95ms
# [ONNX Runtime (optimized)] mean=3.93ms, sd=0.40ms, min=3.62ms, max=6.53ms, median=3.81ms, 95p=4.49ms, 99p=5.79ms
# Each infence engine output is within 0.3 tolerance compared to Pytorch output

Two detailed notebooks are available:

Optimize existing large model

To optimize models which typically don't fit twice onto a single GPU, run the script as follows:

docker run -it --rm --shm-size=24g --ulimit memlock=-1 --ulimit stack=67108864 --gpus all \
  -v $PWD:/project ghcr.io/els-rd/transformer-deploy:0.6.0 \
  bash -c "cd /project && \
    convert_model -m gpt2-medium \
    --backend tensorrt onnx \
    --seq-len 6 256 256 \
    --fast \
    --atol 3 \
    --task text-generation"

The larger the model gets, the more likely it is that you need to also increase the absolute tolerance of the script. Additionally, some models may return a message similar to: Converted FP32 value in weights (either FP32 infinity or FP32 value outside FP16 range) to corresponding FP16 infinity. It is best to test and evaluate the model afterwards to understand the implications of this conversion.

Depending on model size this may take really long. GPT Neo 2.7B can easily take 1 hour of conversion or more.

Run Nvidia Triton inference server

To run decoding algorithm server side, we need to install Pytorch on Triton docker image.

docker run -it --rm --gpus all -p8000:8000 -p8001:8001 -p8002:8002 --shm-size 8g \
  -v $PWD/triton_models:/models nvcr.io/nvidia/tritonserver:22.07-py3 \
  bash -c "pip install transformers torch==1.12.0 -f https://download.pytorch.org/whl/cu116/torch_stable.html && \
  tritonserver --model-repository=/models"

# output:
# ...
# I0207 10:29:19.091191 1 grpc_server.cc:4195] Started GRPCInferenceService at 0.0.0.0:8001
# I0207 10:29:19.091417 1 http_server.cc:2857] Started HTTPService at 0.0.0.0:8000
# I0207 10:29:19.132902 1 http_server.cc:167] Started Metrics Service at 0.0.0.0:8002

Query inference

Replace transformer_onnx_generate by transformer_tensorrt_generate to query TensorRT engine.

curl -X POST  http://localhost:8000/v2/models/transformer_onnx_generate/versions/1/infer \
  --data-binary "@demo/infinity/query_body.bin" \
  --header "Inference-Header-Content-Length: 161"

# output:
# {"model_name":"transformer_onnx_generate","model_version":"1","outputs":[{"name":"output","datatype":"BYTES","shape":[],"data":["This live event is great. I will sign-up for Infinity.\n\nI'm going to be doing a live stream of the event.\n\nI"]}]}

Ok, the output is not very interesting (💩 in -> 💩 out) but you get the idea.
Source code of the generative model is in ./triton_models/transformer_tensorrt_generate/1/model.py.
You may want to tweak it regarding your needs (default is set for greedy search and output 64 tokens).

Python code

You may be interested in running optimized text generation on Python directly, without using any inference server:

docker run -p 8888:8888 -v $PWD/demo/generative-model:/project ghcr.io/els-rd/transformer-deploy:0.6.0 \
  bash -c "cd /project && jupyter notebook --ip 0.0.0.0 --port 8888 --no-browser --allow-root"

T5-small example

In this section we will present the t5-small model conversion.

Optimize existing large model

To optimize model run the script as follows:

docker run -it --rm --shm-size=24g --ulimit memlock=-1 --ulimit stack=67108864 --gpus all \
  -v $PWD:/project ghcr.io/els-rd/transformer-deploy:0.6.0 \
  bash -c "cd /project && \
    convert_model -m t5-small \
    --backend onnx \
    --seq-len 16 256 256 \
    --task text-generation \
    --nb-measures 100 \
    --generative-model t5 \
    --output triton_models"

Run Nvidia Triton inference server

To run decoding algorithm server side, we need to install Pytorch on Triton docker image.

docker run -it --rm --gpus all -p8000:8000 -p8001:8001 -p8002:8002 --shm-size 8g \
  -v $PWD/triton_models/:/models nvcr.io/nvidia/tritonserver:22.07-py3 \
  bash -c "pip install onnx onnxruntime-gpu transformers==4.21.3 git+https://github.com/ELS-RD/transformer-deploy torch==1.12.0 -f https://download.pytorch.org/whl/cu116/torch_stable.html onnx onnxruntime-gpu && \
  tritonserver --model-repository=/models"

To test text generation, you can try this request:

curl -X POST http://localhost:8000/v2/models/t5_model_generate/versions/1/infer --data-binary "@demo/generative-model/t5_query_body.bin" --header "Inference-Header-Content-Length: 181"

# output:
# {"model_name":"t5_model_generate","model_version":"1","outputs":[{"name":"OUTPUT_TEXT","datatype":"BYTES","shape":[],"data":["Mein Name mein Wolfgang Wolfgang und ich wohne in Berlin."]}]}

Query inference

Replace transformer_onnx_generate by transformer_tensorrt_generate to query TensorRT engine.

curl -X POST  http://localhost:8000/v2/models/transformer_onnx_inference/versions/1/infer \
  --data-binary "@demo/infinity/seq2seq_query_body.bin" \
  --header "Inference-Header-Content-Length: 176"

Model quantization on GPU

Quantization is a generic method to get X2 speedup on top of other inference optimization.
GPU quantization on transformers is almost never used because it requires to modify model source code.

We have implemented in this library a mechanism which updates Hugging Face transformers library to support quantization.
It makes it easy to use.

To play with it, open this notebook:

docker run -p 8888:8888 -v $PWD/demo/quantization:/project ghcr.io/els-rd/transformer-deploy:0.6.0 \
  bash -c "cd /project && jupyter notebook --ip 0.0.0.0 --port 8888 --no-browser --allow-root"

See our documentation for detailed instructions on how to use the package, including setup, GPU quantization support and Nvidia Triton inference server deployment.

els-rd / transformer-deploy Goto Github PK

transformer-deploy's Introduction

Hugging Face Transformer submillisecond inference️ and deployment to production: 🤗 → 🤯

Optimize and deploy in production 🤗 Hugging Face Transformer models in a single command line.

Why this tool?

Features

Want to check by yourself in 3 minutes?

Run Nvidia Triton inference server

Query inference

Token-classification (NER) (encoder model)

Optimize existing model

Run Nvidia Triton inference server

Query inference

Question Answering (encoder model)

Optimize existing model

Run Nvidia Triton inference server

Query inference

Feature extraction / dense embeddings

Optimize existing model

Run Nvidia Triton inference server

Query inference

Generate text (decoder model)

GPT example

Optimize existing model

Optimize existing large model

Run Nvidia Triton inference server

Query inference

Python code

T5-small example

Optimize existing large model

Run Nvidia Triton inference server

Query inference

Model quantization on GPU

See our documentation for detailed instructions on how to use the package, including setup, GPU quantization support and Nvidia Triton inference server deployment.

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