From 1fe4ab011b8207b6b94ab30a0fb54005c8a0116f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Micha=C3=ABl=20Benesty?= Date: Tue, 23 Nov 2021 14:36:48 +0100 Subject: [PATCH] Switch from PoC to library (#3) * first python tensorrt commit * better tensorrt code * fix typing * change imports * better typing * fix fp16 issues * improve benchmarks * fix memory issue with Pytorch * generate configurations * fix tests * fix code * remove old file * fix tokenizer * move logic in folder creation * fix issue tokenizer + tensorrt * update code * delete * reformat code * small refactoring * add calibrator * fix multi profile bug * switch code to python package * add colored logs * update README.md * action * fix format * fix github action * removed pycuda * move dependency * add docker github action * fix * fix paths * update README.md add new demo README.md fix demo scripts update Docker image version * fix format * update documentation * add licence * add badge * add emoji * add image * reformat image * add measures * update README.md * update README.md * update README.md * update documentation * update documentation --- .dockerignore | 7 + .github/workflows/python-app.yml | 74 ++++ .gitignore | 1 + Dockerfile | 38 +- LICENCE | 202 ++++++++++ Makefile | 36 ++ README.md | 358 +++++++++++------- VERSION | 1 + convert_onnx.py | 199 ---------- demo/README.md | 178 +++++++++ demo/config_analyzer.yaml | 30 ++ demo/fast_api_server_onnx.py | 37 ++ query_body.bin => demo/query_body.bin | Bin demo/triton_client.py | 72 ++++ demo/triton_client_model.py | 51 +++ demo/triton_client_requests.py | 95 +++++ demo/triton_client_tokenizer_only.py | 49 +++ perf_data.json | 8 - requirements.txt | 10 +- requirements_gpu.txt | 10 + resources/infinity.png | Bin 0 -> 999896 bytes resources/infinity.xcf | Bin 0 -> 2053466 bytes resources/rabbit.jpg | Bin 0 -> 961302 bytes resources/social media.png | Bin 0 -> 67221 bytes resources/social media.xcf | Bin 0 -> 104630 bytes server_onnx.py | 23 -- setup.cfg | 30 ++ setup.py | 54 +++ src/__init__.py | 13 + src/transformer_deploy/__init__.py | 13 + src/transformer_deploy/backends/__init__.py | 13 + src/transformer_deploy/backends/ort_utils.py | 75 ++++ src/transformer_deploy/backends/trt_utils.py | 216 +++++++++++ src/transformer_deploy/benchmarks/__init__.py | 13 + src/transformer_deploy/benchmarks/utils.py | 67 ++++ src/transformer_deploy/convert.py | 257 +++++++++++++ src/transformer_deploy/templates/__init__.py | 13 + src/transformer_deploy/templates/triton.py | 232 ++++++++++++ src/transformer_deploy/utils/__init__.py | 13 + .../utils/python_tokenizer.py | 58 +++ tests/__init__.py | 13 + tests/test_triton.py | 195 ++++++++++ triton.py | 33 -- triton_models/sts/1/.gitignore | 3 - triton_models/sts/config.pbtxt | 29 -- triton_models/tokenize/1/model.py | 24 -- triton_models/tokenize/config.pbtxt | 30 -- triton_models/transformers/1/.gitignore | 3 - triton_models/transformers/config.pbtxt | 58 --- triton_python_backend_utils.py | 275 -------------- triton_requests.py | 61 --- triton_tokenizer.py | 33 -- triton_transformers.py | 38 -- utils.py | 35 -- 54 files changed, 2353 insertions(+), 1023 deletions(-) create mode 100644 .dockerignore create mode 100644 .github/workflows/python-app.yml create mode 100644 LICENCE create mode 100644 Makefile create mode 100644 VERSION delete mode 100644 convert_onnx.py create mode 100644 demo/README.md create mode 100644 demo/config_analyzer.yaml create mode 100644 demo/fast_api_server_onnx.py rename query_body.bin => demo/query_body.bin (100%) create mode 100644 demo/triton_client.py create mode 100644 demo/triton_client_model.py create mode 100644 demo/triton_client_requests.py create mode 100644 demo/triton_client_tokenizer_only.py delete mode 100644 perf_data.json create mode 100644 requirements_gpu.txt create mode 100644 resources/infinity.png create mode 100644 resources/infinity.xcf create mode 100644 resources/rabbit.jpg create mode 100644 resources/social media.png create mode 100644 resources/social media.xcf delete mode 100644 server_onnx.py create mode 100644 setup.cfg create mode 100644 setup.py create mode 100644 src/__init__.py create mode 100644 src/transformer_deploy/__init__.py create mode 100644 src/transformer_deploy/backends/__init__.py create mode 100644 src/transformer_deploy/backends/ort_utils.py create mode 100644 src/transformer_deploy/backends/trt_utils.py create mode 100644 src/transformer_deploy/benchmarks/__init__.py create mode 100644 src/transformer_deploy/benchmarks/utils.py create mode 100644 src/transformer_deploy/convert.py create mode 100644 src/transformer_deploy/templates/__init__.py create mode 100644 src/transformer_deploy/templates/triton.py create mode 100644 src/transformer_deploy/utils/__init__.py create mode 100644 src/transformer_deploy/utils/python_tokenizer.py create mode 100644 tests/__init__.py create mode 100644 tests/test_triton.py delete mode 100644 triton.py delete mode 100644 triton_models/sts/1/.gitignore delete mode 100644 triton_models/sts/config.pbtxt delete mode 100644 triton_models/tokenize/1/model.py delete mode 100644 triton_models/tokenize/config.pbtxt delete mode 100644 triton_models/transformers/1/.gitignore delete mode 100644 triton_models/transformers/config.pbtxt delete mode 100644 triton_python_backend_utils.py delete mode 100644 triton_requests.py delete mode 100644 triton_tokenizer.py delete mode 100644 triton_transformers.py delete mode 100644 utils.py diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 00000000..0b04f600 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,7 @@ +* +!src +!setup.py +!requirements.txt +!requirements_gpu.txt +!README.md +!VERSION diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml new file mode 100644 index 00000000..6506cb3e --- /dev/null +++ b/.github/workflows/python-app.yml @@ -0,0 +1,74 @@ +name: tests + +on: + push: + branches: [ main ] + pull_request: + branches: [ main ] + +jobs: + build: + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + + - name: Set up Python 3.10 + uses: actions/setup-python@v2 + with: + python-version: "3.10" + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install . + + - name: linter and tests + run: | + make test + + - name: read VERSION file + id: getversion + run: echo "::set-output name=version::$(cat VERSION)" + + - name: check version number pattern on main branch + if: github.ref == 'refs/heads/main' + shell: bash + run: echo ${{ steps.getversion.outputs.version }} | grep -P "^\d+\.\d+\.\d+$" + +# - name: Docker meta +# id: meta +# uses: docker/metadata-action@v3 +# with: +# images: | +# ghcr.io/els-rd/transformer-deploy +# tags: | +# type=ref,event=branch +# type=ref,event=pr +# type=semver,pattern={{version}} +# type=semver,pattern={{major}}.{{minor}} +# type=semver,pattern={{major}} +# type=sha +# +# - name: Set up QEMU +# uses: docker/setup-qemu-action@v1 +# +# - name: Set up Docker Buildx +# uses: docker/setup-buildx-action@v1 +# +# - name: Login to GHCR +# uses: docker/login-action@v1 +# with: +# registry: ghcr.io +# username: els-lab-ci +# password: ${{ secrets.XXX }} +# +# - name: Build and push +# uses: docker/build-push-action@v2 +# with: +# context: . +# # We push only for main and tags +# push: ${{ github.event_name == 'push' && (github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/tags/')) }} +# tags: ${{ steps.meta.outputs.tags }} +# labels: ${{ steps.meta.outputs.labels }} diff --git a/.gitignore b/.gitignore index 2401cfe3..5c719f5c 100644 --- a/.gitignore +++ b/.gitignore @@ -215,3 +215,4 @@ cython_debug/ *.onnx .idea/ TensorRT/ +triton_models/ diff --git a/Dockerfile b/Dockerfile index eb5146ce..f424dc45 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,34 +1,8 @@ -# -------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. -# -------------------------------------------------------------- -# Dockerfile to run ONNXRuntime with TensorRT integration +FROM nvcr.io/nvidia/tritonserver:21.10-py3 -# nVidia TensorRT Base Image -ARG TRT_VERSION=21.07 -FROM nvcr.io/nvidia/tensorrt:${TRT_VERSION}-py3 +# see .dockerignore to check what is transfered +COPY . ./ -ARG ONNXRUNTIME_REPO=https://github.com/Microsoft/onnxruntime -ARG ONNXRUNTIME_TAG="v1.9.1" -ARG CMAKE_CUDA_ARCHITECTURES=37;50;52;60;61;70;75;80;86 - -RUN apt-get update &&\ - apt-get install -y sudo git bash unattended-upgrades -RUN unattended-upgrade - -WORKDIR /code -ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:/code/cmake-3.21.0-linux-x86_64/bin:/opt/miniconda/bin:${PATH} - -# Prepare onnxruntime repository & build onnxruntime with TensorRT -RUN git clone --single-branch --branch ${ONNXRUNTIME_TAG} --recursive ${ONNXRUNTIME_REPO} onnxruntime &&\ - /bin/sh onnxruntime/dockerfiles/scripts/install_common_deps.sh &&\ - cp onnxruntime/docs/Privacy.md /code/Privacy.md &&\ - cp onnxruntime/dockerfiles/LICENSE-IMAGE.txt /code/LICENSE-IMAGE.txt &&\ - cp onnxruntime/ThirdPartyNotices.txt /code/ThirdPartyNotices.txt &&\ - cd onnxruntime &&\ - /bin/sh ./build.sh --parallel --skip_submodule_sync --cuda_home /usr/local/cuda --cudnn_home /usr/lib/x86_64-linux-gnu/ --use_tensorrt --tensorrt_home /workspace/tensorrt --config Release --build_wheel --update --build --cmake_extra_defines ONNXRUNTIME_VERSION=$(cat ./VERSION_NUMBER) '"CMAKE_CUDA_ARCHITECTURES='${CMAKE_CUDA_ARCHITECTURES}'"' &&\ - pip install /code/onnxruntime/build/Linux/Release/dist/*.whl &&\ - cd .. - -RUN pip install -U pip && \ - pip install transformers coloredlogs onnx sympy torch==1.10.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html +RUN pip3 install -U pip && \ + pip3 install nvidia-pyindex && \ + pip3 install -e .[GPU] -f https://download.pytorch.org/whl/cu113/torch_stable.html --no-cache-dir diff --git a/LICENCE b/LICENCE new file mode 100644 index 00000000..0f2b46dd --- /dev/null +++ b/LICENCE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2021, Lefebvre Sarrut Services + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/Makefile b/Makefile new file mode 100644 index 00000000..71eb1d9f --- /dev/null +++ b/Makefile @@ -0,0 +1,36 @@ +VERSION = $(shell cat VERSION) + +# makes all commands of each target exec in the same shell +# https://www.gnu.org/software/make/manual/html_node/One-Shell.html +.ONESHELL: + +.PHONY: source_code_format +source_code_format: + black --line-length 120 --target-version py37 . && \ + isort . + +.PHONY: source_code_check_format +source_code_check_format: + black --check --line-length 120 --target-version py37 . && \ + isort --check-only . && \ + flake8 . + +.PHONY: test +test: + ${MAKE} source_code_check_format || exit 1 + pytest + +.PHONY: build_docker +build_docker: + DOCKER_BUILDKIT=1 docker build \ + --rm \ + -t ghcr.io/els-rd/transformer-deploy:latest \ + -t ghcr.io/els-rd/transformer-deploy:$(VERSION) \ + -f Dockerfile . + +.PHONY: build_push_docker +build_push_docker: + ! docker manifest inspect ghcr.io/els-rd/transformer-deploy:$(shell cat VERSION) > /dev/null || exit 1 + ${MAKE} build_docker || exit 1 + docker push ghcr.io/els-rd/transformer-deploy:latest || exit 1 + docker push ghcr.io/els-rd/transformer-deploy:$(VERSION) || exit 1 diff --git a/README.md b/README.md index a8f35aff..7a1d969c 100644 --- a/README.md +++ b/README.md @@ -1,194 +1,284 @@ -# πŸ€— Hugging Face Transformer submillisecond inference 🀯 and deployment on Nvidia Triton server +# From πŸ€— to 🀯, Hugging Face Transformer submillisecond inference️ and deployment to production -Yes, you can perfom inference with transformer based model in less than 1ms on the cheapest GPU available on Amazon (T4)! +[![tests](https://github.com/ELS-RD/transformer-deploy/actions/workflows/python-app.yml/badge.svg)](https://github.com/ELS-RD/transformer-deploy/actions/workflows/python-app.yml) [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](./LICENCE) [![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/) -The commands below have been tested on a AWS G4.dnn with `Deep Learning Base AMI (Ubuntu 18.04) Version 44.0`. -They may require some small adaptations to be run on a another Linux distribution. +### Optimize and deploy in **production** Hugging Face Transformer models in a single command line. -You can find explanations on how it works in -[Hugging Face Transformer inference UNDER 1 millisecond latency](https://towardsdatascience.com/hugging-face-transformer-inference-under-1-millisecond-latency-e1be0057a51c?source=friends_link&sk=cd880e05c501c7880f2b9454830b8915) +=> Up to 10X faster inference! <= +#### Table of Contents -## Baseline set by Hugging Face Infinity demo +* [πŸ€” why this tool?](#why-this-tool) +* [πŸ€“ 1 command process](#single-command) +* [⏱ benchmarks](#benchmarks) +* [πŸ€— end to end reproduction of Infinity Hugging Face demo](./demo/README.md) +* [πŸ—οΈ build from sources](#install-from-sources) +* [🐍 TensorRT usage in Python script](#usage-in-python-script) -[Hugging Face infinity demo video](https://www.youtube.com/watch?v=jiftCAhOYQA) +#### Why this tool? -* AWS virtual machine: `g4dn.xlarge` (T4 GPU) -* model: `"philschmid/MiniLM-L6-H384-uncased-sst2"` (Hugging Face hub URL) -* experience 1 : batch size 1, seq len 16 tokens -> `1.7ms` -* experience 2 : batch size 1, seq len 128 tokens -> `2.5ms` +🐒 +Most tutorials on transformer deployment in production are built over [`Pytorch`](https://pytorch.org/) + [`FastAPI`](https://fastapi.tiangolo.com/). +Both are great tools but not very performant in inference. -## Install dependencies +οΈπŸƒπŸ’¨ +Then, if you spend some time, you can build something over [`Microsoft ONNX Runtime`](https://github.com/microsoft/onnxruntime/) + [`Nvidia Triton inference server`](https://github.com/triton-inference-server/server). +You will usually get from 2X to 4X faster inference compared to vanilla Pytorch. It's cool! -Those dependencies have to be installed on the remote machine directly (no container). +βš‘οΈπŸƒπŸ’¨πŸ’¨ +However, if you want the best in class performances on GPU, there is only a single choice: [`Nvidia TensorRT`](https://github.com/NVIDIA/TensorRT/) + [`Nvidia Triton inference server`](https://github.com/triton-inference-server/server). +You will usually get 5X faster inference compared to vanilla Pytorch. +Sometimes it can raises up to **10X faster inference**. +Buuuuttt... TensorRT is not easy to use, even less with Transformer models, it requires specific tricks not easy to come with. + + +> Want to understand how it works under the hood? +> read [πŸ“• Hugging Face Transformer inference UNDER 1 millisecond latency πŸ“–](https://towardsdatascience.com/hugging-face-transformer-inference-under-1-millisecond-latency-e1be0057a51c?source=friends_link&sk=cd880e05c501c7880f2b9454830b8915) +> + +## Single command + +With the single command below, you will: + +* **download** the model and its tokenizer from Hugging Face hub, +* **convert** the model to ONNX graph, +* **optimize** + * the model with ONNX Runtime and save artefact (`model.onnx`), + * the model with TensorRT and save artefact (`model.plan`), +* **benchmark** each backend (including Pytorch), +* **generate** configuration files for Triton inference server ```shell -git clone git@github.com:ELS-RD/triton_transformers.git -pip3 install -r requirements.txt +docker run -it --rm \ + --gpus all \ + -v $PWD:/project ghcr.io/els-rd/transformer-deploy:0.1.0 \ + bash -c "cd /project && \ + convert_model -m roberta-large-mnli \ + --backend tensorrt onnx pytorch \ + --seq-len 16 128 128 \ + --batch-size 1 32 32" ``` -## Generate optimized models +> **16 128 128** -> minimum, optimal, maximum sequence length, to help TensorRT better optimize your model +> **1 32 32** -> batch size, same as above -We generate the models from a Docker image so we can also get measures for `TensorRT + ONNX Runtime`. +* Launch Nvidia Triton inference server to play with both ONNX and TensorRT models: ```shell -cd triton_transformers -DOCKER_BUILDKIT=1 docker build --tag onnxruntime-trt:latest -f Dockerfile . -docker run -it --rm --gpus all -v $PWD:/project onnxruntime-trt bash -c "cd /project && python convert_onnx.py" +docker run -it --rm --gpus all -p8000:8000 -p8001:8001 -p8002:8002 --shm-size 256m \ + -v $PWD/triton_models:/models nvcr.io/nvidia/tritonserver:21.10-py3 \ + bash -c "pip install transformers && tritonserver --model-repository=/models" ``` -> ⚠️**WARNING**⚠️: if you run the conversion *outside* Docker container, you may have very different timings, and TensorRT won't work +> As you can see we install Transformers and then launch the server itself. This is of course a bad practice, you should make your own 2 lines Dockerfile with Transformers inside. -It should produce something like that: +* Query the inference server: -```log -10/31/2021 11:35:08 INFO inference done on Tesla T4 -10/31/2021 11:35:08 INFO timing [[TensorrtExecutionProvider] ./onnx_models/model-shape.onnx]: mean=0.61ms, sd=0.11ms, min=0.52ms, max=0.92ms, median=0.54ms, 95p=0.88ms, 99p=0.90ms -10/31/2021 11:35:08 INFO timing [[CUDAExecutionProvider] ./onnx_models/model.onnx]: mean=1.10ms, sd=0.10ms, min=1.04ms, max=3.44ms, median=1.07ms, 95p=1.29ms, 99p=1.36ms -10/31/2021 11:35:08 INFO timing [[CUDAExecutionProvider] ./onnx_models/model-optimized.onnx]: mean=0.63ms, sd=0.05ms, min=0.60ms, max=0.84ms, median=0.61ms, 95p=0.77ms, 99p=0.79ms -10/31/2021 11:35:08 INFO timing [Pytorch_32]: mean=5.09ms, sd=0.16ms, min=4.88ms, max=6.11ms, median=5.07ms, 95p=5.28ms, 99p=5.35ms -10/31/2021 11:35:08 INFO timing [Pytorch_FP16]: mean=6.04ms, sd=0.74ms, min=5.77ms, max=28.79ms, median=6.05ms, 95p=6.19ms, 99p=6.29ms +```shell +# @ means no data conversion (curl feature) +curl -X POST http://localhost:8000/v2/models/transformer_onnx_inference/versions/1/infer \ + --data-binary "@demo/query_body.bin" \ + --header "Inference-Header-Content-Length: 160" ``` -`TensorRT` and optimized `ONNX Runtime` provides very similar results on short sequences. -In the following steps, we will continue with ONNX Runtime model because the dynamic axis are easier to work with compared to TensorRT. +> check [`demo`](./demo) folder to discover more performant ways to query the server from Python or elsewhere. -> Docker build will is very slow on a G4, be patient... -> the docker image is only required for `TensorRT` support inside `ONNX Runtime` (and measure a difference, if any, with ONNX Runtime). +## Benchmarks -## FastAPI server +Most transformer encoder based models are supported like Bert, Roberta, miniLM, Camembert, Albert, XLM-R, Distilbert, etc. +Best results are obtained with TensorRT 8.2 (preview). +Below examples are representative of the performance gain to expect from this library. +Other improvements not shown here include GPU memory usage decrease, multi stream, etc. -This is our baseline, easy to run, but not very performant. +### Small architecture + +
batch 1, seq length 16 on T4/RTX 3090 GPUs (up to 10X faster with TensorRT vs Pytorch) + +command: ```shell -# launch server, disable logging for best performances -python3 -m uvicorn --log-level warning server_onnx:app --port 8000 --host 0.0.0.0 -# other variation, 1 worker per CPU for best latency (plus not a good idea to have several times the same model on a single GPU): -python3 -m gunicorn -w 1 -k uvicorn.workers.UvicornWorker --log-level warning server_onnx:app --bind 0.0.0.0:8000 +convert_model -m philschmid/MiniLM-L6-H384-uncased-sst2 --backend tensorrt onnx pytorch --seq-len 16 16 16 --batch-size 1 1 1 +``` + +#### GPU Nvidia T4 -# simple inference timing -time curl -G --data-urlencode query="This live event is great. I will sign-up for Infinity." localhost:8000/predict -# slightly more serious measure -sudo apt-get install linux-tools-common linux-tools-generic linux-tools-`uname -r` -sudo perf stat -r 50 -d curl -G --data-urlencode query="This live event is great. I will sign-up for Infinity." localhost:8000/predict -s > /dev/null +```log +Inference done on Tesla T4 +latencies: +[TensorRT (FP16)] mean=0.65ms, sd=0.11ms, min=0.57ms, max=0.96ms, median=0.59ms, 95p=0.93ms, 99p=0.94ms +[ONNX Runtime (vanilla)] mean=1.31ms, sd=0.05ms, min=1.27ms, max=1.48ms, median=1.30ms, 95p=1.44ms, 99p=1.45ms +[ONNX Runtime (optimized)] mean=0.71ms, sd=0.01ms, min=0.69ms, max=0.74ms, median=0.70ms, 95p=0.73ms, 99p=0.74ms +[Pytorch (FP32)] mean=5.01ms, sd=0.06ms, min=4.94ms, max=6.72ms, median=5.01ms, 95p=5.07ms, 99p=5.13ms +[Pytorch (FP16)] mean=5.44ms, sd=0.07ms, min=5.36ms, max=6.80ms, median=5.43ms, 95p=5.49ms, 99p=5.55ms +``` + +#### GPU Nvidia RTX 3090 + +```log +Inference done on NVIDIA GeForce RTX 3090 +latencies: +[TensorRT (FP16)] mean=0.45ms, sd=0.05ms, min=0.41ms, max=0.78ms, median=0.45ms, 95p=0.55ms, 99p=0.73ms +[ONNX Runtime (vanilla)] mean=1.32ms, sd=0.11ms, min=1.24ms, max=2.36ms, median=1.30ms, 95p=1.50ms, 99p=1.74ms +[ONNX Runtime (optimized)] mean=0.84ms, sd=0.11ms, min=0.76ms, max=2.03ms, median=0.81ms, 95p=1.10ms, 99p=1.25ms +[Pytorch (FP32)] mean=4.68ms, sd=0.28ms, min=4.38ms, max=7.83ms, median=4.65ms, 95p=4.97ms, 99p=6.16ms +[Pytorch (FP16)] mean=5.25ms, sd=0.60ms, min=4.83ms, max=8.54ms, median=5.03ms, 95p=6.54ms, 99p=7.77ms ``` -It should produce: +
+ +
batch 16, seq length 384 on T4/RTX 3090 GPUs (up to 5X faster with TensorRT vs Pytorch) + +command: ```shell -Performance counter stats for 'curl -G --data-urlencode query=This live event is great. I will sign-up for Infinity. localhost:8000/predict' (50 runs): +convert_model -m philschmid/MiniLM-L6-H384-uncased-sst2 --backend tensorrt onnx pytorch --seq-len 384 384 384 --batch-size 16 16 16 +``` - 6.14 msec task-clock # 0.494 CPUs utilized ( +- 0.59% ) - 3 context-switches # 0.462 K/sec ( +- 1.84% ) - 0 cpu-migrations # 0.000 K/sec - 577 page-faults # 0.094 M/sec ( +- 0.06% ) - cycles - instructions - branches - branch-misses - L1-dcache-loads - L1-dcache-load-misses - LLC-loads - LLC-load-misses +#### GPU Nvidia T4 - 0.0124429 +- 0.0000547 seconds time elapsed ( +- 0.44% ) +```log +Inference done on Tesla T4 +latencies: +[TensorRT (FP16)] mean=16.38ms, sd=0.30ms, min=15.45ms, max=17.42ms, median=16.42ms, 95p=16.83ms, 99p=17.09ms +[ONNX Runtime (vanilla)] mean=65.12ms, sd=1.53ms, min=61.74ms, max=68.51ms, median=65.21ms, 95p=67.46ms, 99p=67.90ms +[ONNX Runtime (optimized)] mean=26.75ms, sd=0.30ms, min=25.96ms, max=27.71ms, median=26.73ms, 95p=27.23ms, 99p=27.52ms +[Pytorch (FP32)] mean=82.22ms, sd=1.02ms, min=78.83ms, max=85.02ms, median=82.28ms, 95p=83.80ms, 99p=84.43ms +[Pytorch (FP16)] mean=46.29ms, sd=0.41ms, min=45.23ms, max=47.56ms, median=46.30ms, 95p=46.98ms, 99p=47.37ms +``` + +#### GPU Nvidia RTX 3090 + +```log +Inference done on NVIDIA GeForce RTX 3090 +latencies: +[TensorRT (FP16)] mean=5.44ms, sd=0.45ms, min=5.03ms, max=8.91ms, median=5.20ms, 95p=6.11ms, 99p=7.39ms +[ONNX Runtime (vanilla)] mean=16.87ms, sd=2.15ms, min=15.38ms, max=26.03ms, median=15.82ms, 95p=22.63ms, 99p=24.20ms +[ONNX Runtime (optimized)] mean=8.07ms, sd=0.58ms, min=7.59ms, max=13.63ms, median=7.93ms, 95p=8.71ms, 99p=11.45ms +[Pytorch (FP32)] mean=17.09ms, sd=0.21ms, min=16.87ms, max=18.99ms, median=17.04ms, 95p=17.49ms, 99p=18.08ms +[Pytorch (FP16)] mean=14.77ms, sd=1.83ms, min=13.50ms, max=20.97ms, median=13.87ms, 95p=19.15ms, 99p=20.01ms ``` -## Triton server +
+ +### Base architecture -We want to copy the ONNX model we have generated in the first step in this folder. -Then we launch the Triton image. As you can see we install Transformers and then launch the server itself. -This is of course a bad practice, you should make your own 2 lines Dockerfile with Transformers inside. +
batch 16, seq length 384 on T4/RTX 3090 GPUs (up to 5X faster with TensorRT vs Pytorch) + +command: ```shell -# copy the generated model to triton model folder -cp ./onnx_models/model-optimized.onnx ./triton_models/sts/1/model.onnx -# install transformers (and its tokenizer) and launch server in a single line, ugly but good enough for our demo -# --shm-size 256m -> to have several Python backend at the same time -docker run -it --rm --gpus all -p8000:8000 -p8001:8001 -p8002:8002 --shm-size 256m \ - -v $PWD/triton_models:/models nvcr.io/nvidia/tritonserver:21.10-py3 \ - bash -c "pip install transformers && tritonserver --model-repository=/models" +convert_model -m cardiffnlp/twitter-roberta-base-sentiment --backend tensorrt onnx pytorch --seq-len 384 384 384 --batch-size 16 16 16 ``` -## Triton server perf analysis +#### GPU Nvidia T4 + +```log +Inference done on Tesla T4 +latencies: +[TensorRT (FP16)] mean=80.57ms, sd=1.00ms, min=76.23ms, max=83.16ms, median=80.53ms, 95p=82.14ms, 99p=82.53ms +[ONNX Runtime (vanilla)] mean=353.81ms, sd=14.79ms, min=335.54ms, max=390.86ms, median=348.41ms, 95p=382.09ms, 99p=386.84ms +[ONNX Runtime (optimized)] mean=97.94ms, sd=1.66ms, min=93.83ms, max=102.11ms, median=97.84ms, 95p=100.73ms, 99p=101.57ms +[Pytorch (FP32)] mean=398.49ms, sd=25.76ms, min=369.81ms, max=454.55ms, median=387.17ms, 95p=445.52ms, 99p=450.81ms +[Pytorch (FP16)] mean=134.18ms, sd=1.16ms, min=131.60ms, max=138.48ms, median=133.80ms, 95p=136.57ms, 99p=137.39ms +``` -You need to edit the source code to load the 16 or 128 token sequence (the text is already included). +#### GPU Nvidia RTX 3090 -* 16 tokens: -```shell -ubuntu@ip-172-31-31-84:~/triton_transformers$ python3 triton_transformers.py -10/31/2021 12:09:34 INFO timing [triton transformers]: mean=1.53ms, sd=0.06ms, min=1.48ms, max=1.78ms, median=1.51ms, 95p=1.66ms, 99p=1.74ms -[[-3.4355469 3.2753906]] +```log +Inference done on NVIDIA GeForce RTX 3090 +latencies: +[TensorRT (FP16)] mean=27.52ms, sd=1.61ms, min=24.49ms, max=33.78ms, median=28.01ms, 95p=30.33ms, 99p=31.22ms +[ONNX Runtime (vanilla)] mean=65.95ms, sd=6.18ms, min=60.84ms, max=99.75ms, median=62.97ms, 95p=81.02ms, 99p=89.10ms +[ONNX Runtime (optimized)] mean=32.73ms, sd=4.80ms, min=28.84ms, max=48.84ms, median=30.15ms, 95p=43.03ms, 99p=44.78ms +[Pytorch (FP32)] mean=69.18ms, sd=4.79ms, min=65.97ms, max=97.74ms, median=67.16ms, 95p=77.88ms, 99p=92.43ms +[Pytorch (FP16)] mean=48.78ms, sd=2.02ms, min=47.02ms, max=61.37ms, median=47.67ms, 95p=52.34ms, 99p=55.56ms ``` -* 128 tokens: +
+ +### Large architecture + +
batch 16, seq length 384 on T4/RTX 3090 GPUs (up to 5X faster with TensorRT vs Pytorch) + +command: + ```shell -ubuntu@ip-XXX:~/triton_transformers$ python3 triton_transformers.py -10/31/2021 12:12:00 INFO timing [triton transformers]: mean=1.96ms, sd=0.08ms, min=1.88ms, max=2.24ms, median=1.93ms, 95p=2.17ms, 99p=2.23ms -[[-3.4589844 3.3027344]] +convert_model -m roberta-large-mnli --backend tensorrt onnx pytorch --seq-len 384 384 384 --batch-size 16 16 16 ``` -There is also a more serious performance analysis tool called perf_analyzer (it will take care to check that measures are stable, etc.). -[documentation](https://github.com/triton-inference-server/server/blob/main/docs/perf_analyzer.md) -The tool need to be run on Ubuntu >= 20.04 (and won't work on Ubuntu 18.04 used for the AWS official Ubuntu deep learning image): -It also make measures on torchserve and tensorflow. +#### GPU Nvidia T4 -```shell -# perf_analyzer needs this dependency -sudo apt install libb64-dev -# add -a for async measures, and -i grpc to use that protocol instead of http -~/.local/bin/perf_analyzer -m transformers --percentile=95 --input-data perf_data.json --shape TEXT:1 # -i grpc -a -# just test the model part (easier to get random input) -~/.local/bin/perf_analyzer --input-data zero -m sts --shape input_ids:1,16 --shape attention_mask:1,16 #-i grpc -a +```log +Inference done on Tesla T4 +latencies: +[TensorRT (FP16)] mean=240.39ms, sd=11.01ms, min=217.59ms, max=259.57ms, median=242.68ms, 95p=255.03ms, 99p=257.04ms +[ONNX Runtime (vanilla)] mean=1176.73ms, sd=63.51ms, min=1020.00ms, max=1225.03ms, median=1210.08ms, 95p=1217.54ms, 99p=1220.25ms +[ONNX Runtime (optimized)] mean=295.03ms, sd=19.69ms, min=255.74ms, max=314.78ms, median=307.07ms, 95p=311.20ms, 99p=312.47ms +[Pytorch (FP32)] mean=1220.41ms, sd=75.93ms, min=1119.93ms, max=1342.10ms, median=1216.23ms, 95p=1329.08ms, 99p=1336.47ms +[Pytorch (FP16)] mean=438.26ms, sd=13.71ms, min=398.29ms, max=459.97ms, median=442.36ms, 95p=453.96ms, 99p=457.57ms ``` -## Call Triton HTTP API directly +#### GPU Nvidia RTX 3090 -If you don't want to use the `tritonclient` API, you can call the Triton server those ways: +```log +Inference done on NVIDIA GeForce RTX 3090 +latencies: +[TensorRT (FP16)] mean=79.54ms, sd=5.99ms, min=74.47ms, max=113.25ms, median=76.87ms, 95p=88.02ms, 99p=104.48ms +[ONNX Runtime (vanilla)] mean=202.88ms, sd=16.21ms, min=187.91ms, max=277.85ms, median=194.80ms, 95p=239.58ms, 99p=261.44ms +[ONNX Runtime (optimized)] mean=97.04ms, sd=5.55ms, min=90.83ms, max=121.88ms, median=94.04ms, 95p=104.81ms, 99p=107.75ms +[Pytorch (FP32)] mean=202.80ms, sd=11.16ms, min=194.47ms, max=284.70ms, median=198.46ms, 95p=221.72ms, 99p=257.31ms +[Pytorch (FP16)] mean=142.63ms, sd=6.35ms, min=136.24ms, max=189.95ms, median=139.90ms, 95p=154.10ms, 99p=160.16ms +``` -```shell -# if you like Python requests library -python3 triton_requests.py +
-# if you want generic HTTP template, the @ means no data conversion -curl -X POST http://localhost:8000/v2/models/transformers/versions/1/infer \ - --data-binary "@query_body.bin" \ - --header "Inference-Header-Content-Length: 160" +## Install from sources + +```shell +git clone git@github.com:ELS-RD/triton_transformers.git +cd triton_transformers +pip3 install .[GPU] -f https://download.pytorch.org/whl/cu113/torch_stable.html ``` -## Use TensorRT model in Triton server (instead of ONNX) +### Prerequisites + +To run this package locally, you need: + +**TensorRT GA build** +* [TensorRT](https://developer.nvidia.com/nvidia-tensorrt-download) v8.2.0.6 + +**System Packages** +* [CUDA](https://developer.nvidia.com/cuda-toolkit) + * Recommended versions: + * cuda-11.4.x + cuDNN-8.2 + * cuda-10.2 + cuDNN-8.2 +* [GNU make](https://ftp.gnu.org/gnu/make/) >= v4.1 +* [cmake](https://github.com/Kitware/CMake/releases) >= v3.13 +* [python]() >= v3.6.9 +* [pip](https://pypi.org/project/pip/#history) >= v19.0 -To use TensorRT model instead of ONNX Runtime one: +### Docker build -* we need to convert the ONNX to TensorRT engine -* update the configuration, TensorRT takes `int32` as input instead of `int64` +You can also build your own version of the Docker image: ```shell -# we use Docker container to guarantee the use of the right trtexec version (otherwise you will have a deserialization error) -# it's a bacic conversion, IRL you want to provide minimum, optimimum and maximum shape at least -# it may take a few minutes... -docker run -it --rm --gpus all -v $PWD/onnx_models:/models nvcr.io/nvidia/tritonserver:21.10-py3 \ - /usr/src/tensorrt/bin/trtexec \ - --onnx=/models/model.onnx \ - --best \ - --minShapes=input_ids:1x16,attention_mask:1x16 \ - --optShapes=input_ids:1x16,attention_mask:1x16 \ - --maxShapes=input_ids:32x16,attention_mask:32x16 \ - --saveEngine="/models/model.plan" \ - --workspace=6000 \ - --useCudaGraph - -# move to triton model folder -cp ./onnx_models/model.plan ./triton_models/sts/1/model.plan -``` - -You then need to update you config.pbtxt in STS and tokenizer folders, replace all `TYPE_INT64` tensor type by `TYPE_INT32`. -In STS configuraiton file, replace `platform: "onnxruntime_onnx"` by `platform: "tensorrt_plan"` -Finally convert the numpy tensors to int32 in the tokenizer python code, like below (notice the `astype()`): - -```python -input_ids = pb_utils.Tensor("INPUT_IDS", tokens['input_ids'].astype(np.int32)) -attention = pb_utils.Tensor("ATTENTION", tokens['attention_mask'].astype(np.int32)) -``` - -And you are done! +make docker_build +``` + +### Usage in Python script + +If you just want to perform inference inside your Python script (without any server) and still get the best TensorRT performance, check: + +* [convert.py](./src/transformer_deploy/convert.py) +* [trt_utils.py](./src/transformer_deploy/backends/trt_utils.py) + +#### High level explanations + +* call `load_engine()` to parse an existing TensorRT engine +* setup a stream (for async call), a TensorRT runtime and a context +* load your profile(s) +* call `infer_tensorrt()` + +... and you are done! πŸŽ‰ + +> if you are looking for inspiration, check [onnx-tensorrt](https://github.com/onnx/onnx-tensorrt) diff --git a/VERSION b/VERSION new file mode 100644 index 00000000..6e8bf73a --- /dev/null +++ b/VERSION @@ -0,0 +1 @@ +0.1.0 diff --git a/convert_onnx.py b/convert_onnx.py deleted file mode 100644 index eb065d40..00000000 --- a/convert_onnx.py +++ /dev/null @@ -1,199 +0,0 @@ -import logging -import multiprocessing -import os -from pathlib import Path -from typing import Dict, List, Tuple - -import numpy as np -import torch -from onnxruntime import GraphOptimizationLevel, InferenceSession, SessionOptions -from onnxruntime.tools.symbolic_shape_infer import SymbolicShapeInference -from onnxruntime.transformers import optimizer -from onnxruntime.transformers.fusion_options import FusionOptions -from onnxruntime.transformers.onnx_model_bert import BertOnnxModel -from torch.cuda import get_device_name -from torch.cuda.amp import autocast -from transformers import ( - AutoModelForSequenceClassification, - AutoTokenizer, - PreTrainedModel, - PreTrainedTokenizer, - TensorType, -) -from transformers.file_utils import PaddingStrategy -from transformers.tokenization_utils_base import BatchEncoding, TruncationStrategy - -from utils import print_timings, setup_logging, track_infer_time - -onnx_folder = "./onnx_models" -Path(onnx_folder).mkdir(parents=True, exist_ok=True) -onnx_model_path = os.path.join(onnx_folder, "model.onnx") -infered_shape_model_onnx_path = os.path.join(onnx_folder, "model-shape.onnx") -onnx_optim_fp16_path_path = os.path.join(onnx_folder, "model-optimized.onnx") -huggingface_hub_path = "philschmid/MiniLM-L6-H384-uncased-sst2" -input_text = "This live event is great. I will sign-up for Infinity." - - -def create_model_for_provider(path: str, provider_to_use: str) -> InferenceSession: - options = SessionOptions() - options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL - if type(provider_to_use) == list and provider_to_use == "CPUExecutionProvider": - options.intra_op_num_threads = multiprocessing.cpu_count() - if type(provider_to_use) != list: - provider_to_use = [provider_to_use] - return InferenceSession(path, options, providers=provider_to_use) - - -def tokenize(dummy_text: List[str], tokenizer: PreTrainedTokenizer, type: TensorType) -> BatchEncoding: - return tokenizer( - text=dummy_text, - add_special_tokens=True, - max_length=16, - padding=PaddingStrategy.LONGEST, - pad_to_multiple_of=8, - return_token_type_ids=False, - return_attention_mask=True, - return_overflowing_tokens=False, - return_special_tokens_mask=False, - return_tensors=type, - truncation=TruncationStrategy.LONGEST_FIRST, - ) - - -def prepare_input( - dummy_text: str, tokenizer: PreTrainedTokenizer -) -> Tuple[Dict[str, torch.Tensor], Dict[str, np.ndarray]]: - inputs_pytorch = tokenize(dummy_text=[dummy_text] * 1, tokenizer=tokenizer, type=TensorType.PYTORCH) - inputs_pytorch = {k: v.to(device="cuda") for k, v in inputs_pytorch.items()} - inputs_onnx: Dict[str, np.ndarray] = { - k: np.ascontiguousarray(v.detach().cpu().numpy()) for k, v in inputs_pytorch.items() - } - return inputs_pytorch, inputs_onnx - - -def convert_to_onnx(model_pytorch: PreTrainedModel, output_path: str, inputs_pytorch: Dict[str, torch.Tensor]) -> None: - with torch.no_grad(): - torch.onnx.export( - model_pytorch, # model to optimize - args=(inputs_pytorch["input_ids"], inputs_pytorch["attention_mask"]), # tuple of multiple inputs - f=output_path, # output path / file object - opset_version=12, # the ONNX version to use - do_constant_folding=True, # simplify model (replace constant expressions) - input_names=["input_ids", "attention_mask"], # input names - output_names=["model_output"], # output name - dynamic_axes={ # declare dynamix axis for each input / output (dynamic axis == variable length axis) - "input_ids": {0: "batch_size", 1: "sequence"}, - "attention_mask": {0: "batch_size", 1: "sequence"}, - "model_output": {0: "batch_size"}, - }, - verbose=False, - ) - - -def optimize_onnx(onnx_path: str, onnx_optim_fp16_path: str, use_cuda: bool) -> None: - optimization_options = FusionOptions("bert") - optimization_options.enable_gelu_approximation = True # additional optimization - optimized_model: BertOnnxModel = optimizer.optimize_model( - input=onnx_path, - model_type="bert", - use_gpu=use_cuda, - opt_level=1, - num_heads=0, # automatic detection - hidden_size=0, # automatic detection - optimization_options=optimization_options, - ) - - optimized_model.convert_float_to_float16() # FP32 -> FP16 - logging.info(f"optimizations applied: {optimized_model.get_fused_operator_statistics()}") - optimized_model.save_model_to_file(onnx_optim_fp16_path) - - -setup_logging() -model_pytorch: PreTrainedModel = AutoModelForSequenceClassification.from_pretrained(huggingface_hub_path) -assert torch.cuda.is_available() -model_pytorch.cuda() -model_pytorch.eval() - -tokenizer: PreTrainedTokenizer = AutoTokenizer.from_pretrained(huggingface_hub_path) - -inputs_pytorch, inputs_onnx = prepare_input(dummy_text=input_text, tokenizer=tokenizer) - -with torch.no_grad(): - output = model_pytorch(**inputs_pytorch) - output = output.logits # extract the value of interest - output_pytorch: np.ndarray = output.detach().cpu().numpy() - -logging.info(f"[Pytorch] input shape {inputs_pytorch['input_ids'].shape}") -logging.info(f"[Pytorch] output shape: {output_pytorch.shape}") -# create onnx model and compare results -convert_to_onnx(model_pytorch=model_pytorch, output_path=onnx_model_path, inputs_pytorch=inputs_pytorch) -onnx_model = create_model_for_provider(path=onnx_model_path, provider_to_use="CPUExecutionProvider") -output_onnx = onnx_model.run(None, inputs_onnx) -del onnx_model -# del model_pytorch -assert np.allclose(a=output_onnx, b=output_pytorch, atol=1e-1) -# import after onnxruntime -import onnx -onnx.save( - SymbolicShapeInference.infer_shapes(onnx.load(onnx_model_path), auto_merge=True), infered_shape_model_onnx_path -) - -# create optimized onnx model and compare results -optimize_onnx( - onnx_path=onnx_model_path, - onnx_optim_fp16_path=onnx_optim_fp16_path_path, - use_cuda=True, -) -onnx_model = create_model_for_provider(path=onnx_optim_fp16_path_path, provider_to_use="CPUExecutionProvider") -# run the model (None = get all the outputs) -output_onnx_optimised = onnx_model.run(None, inputs_onnx) -del onnx_model -assert np.allclose(a=output_onnx_optimised, b=output_pytorch, atol=1e-1) -assert np.allclose(a=output_onnx_optimised, b=output_onnx, atol=1e-1) - -os.environ["ORT_TENSORRT_FP16_ENABLE"] = "1" -os.environ["ORT_TENSORRT_MAX_WORKSPACE_SIZE"] = f"{3 * 1024 * 1024 * 1024}" -# os.environ['ORT_TENSORRT_ENGINE_CACHE_ENABLE'] = '1' - -providers = [ - ("TensorrtExecutionProvider", infered_shape_model_onnx_path), - ("CUDAExecutionProvider", onnx_model_path), - ("CUDAExecutionProvider", onnx_optim_fp16_path_path), -] - -warm_up = 100 -nb_benchmark = 1000 -results = {} -for provider, model_path in providers: - model = create_model_for_provider(path=model_path, provider_to_use=provider) - time_buffer = [] - for _ in range(warm_up): - model.run(None, inputs_onnx) - for _ in range(nb_benchmark): - with track_infer_time(time_buffer): - model.run(None, inputs_onnx) - results[f"[{provider}] {model_path}"] = time_buffer -del model - -# Add PyTorch to the providers -for _ in range(warm_up): - res = model_pytorch(**inputs_pytorch) -time_buffer = [] -for _ in range(nb_benchmark): - with track_infer_time(time_buffer): - model_pytorch(**inputs_pytorch) -results["Pytorch_fp32"] = time_buffer - -with autocast(): - for _ in range(warm_up): - model_pytorch(**inputs_pytorch) - time_buffer = [] - for _ in range(nb_benchmark): - with track_infer_time(time_buffer): - model_pytorch(**inputs_pytorch) - results["Pytorch_fp16"] = time_buffer - -logging.info(f"inference done on {get_device_name(0)}") - -for name, time_buffer in results.items(): - print_timings(name=name, timings=time_buffer) diff --git a/demo/README.md b/demo/README.md new file mode 100644 index 00000000..02da6004 --- /dev/null +++ b/demo/README.md @@ -0,0 +1,178 @@ +# Demo scripts + +To understand why this demo, read [Hugging Face Transformer inference UNDER 1 millisecond latency](https://towardsdatascience.com/hugging-face-transformer-inference-under-1-millisecond-latency-e1be0057a51c?source=friends_link&sk=cd880e05c501c7880f2b9454830b8915) + +This folder contains scripts to run different benchmarks: + +* `triton_client.py`: query the model with a string +* `triton_client_model.py`: query the model directly (without using the tokenizer) with numpy arrays +* `triton_client_requests.py`: query the model directly (without using the tokenizer) with numpy arrays using only `requests` library +* `triton_client_tokenizer.py`: query the tokenizer only +* `fast_api_server_onnx.py`: FastAPI inference server to compare to Nvidia Triton + +Those examples may help you to implement the querying part in your own application. + +### Infinity demo information + +Hugging Face has announced a commercial product called [Infinity](https://huggingface.co/infinity) to perform enterprise scale inference solution. +There are very few information about its performances outside this video: +[demo video (Youtube)](https://www.youtube.com/watch?v=jiftCAhOYQA) + +Setup they used for their own demo: + +* AWS virtual machine: `g4dn.xlarge` (T4 GPU) +* model: `"philschmid/MiniLM-L6-H384-uncased-sst2"` (Hugging Face hub URL) +* experience 1 : batch size 1, seq len 16 tokens -> `1.7ms`/query +* experience 2 : batch size 1, seq len 128 tokens -> `2.5ms`/query + +![](./resources/infinity.png) + +### Model optimization + +We will optimize `philschmid/MiniLM-L6-H384-uncased-sst2` model from the Hugging Face hub. +We will use the 3 backends for that: ONNX Runtime, TensorRT and Pytorch. +Usually, ONNX Runtime provide a good trade-off between simplicity and performance, TensorRT the best performances +and Pytorch the simplest approach (at least it's the most well known tool). + +```shell +# add -v $PWD/src:/opt/tritonserver/src to apply source code modification to the container +docker run -it --rm --gpus all \ + -v $PWD:/project ghcr.io/els-rd/transformer-deploy:0.1.0 \ + bash -c "cd /project && \ + convert_model -m \"philschmid/MiniLM-L6-H384-uncased-sst2\" \ + --backend tensorrt onnx pytorch \ + --seq-len 16 128 128" +``` + +> 16 128 128 means that the TensorRT model will optimize for a sequence length between 16 and 128 tokens. +> ONNX Runtime don't use this information. + +After a few minutes, it should display something like this: + +```log +Inference done on Tesla T4 +latencies: +[TensorRT (FP16)] mean=1.00ms, sd=0.13ms, min=0.92ms, max=1.34ms, median=0.95ms, 95p=1.31ms, 99p=1.33ms +[ONNX Runtime (vanilla)] mean=1.67ms, sd=0.08ms, min=1.59ms, max=3.48ms, median=1.65ms, 95p=1.85ms, 99p=1.87ms +[ONNX Runtime (optimized)] mean=0.73ms, sd=0.01ms, min=0.71ms, max=0.87ms, median=0.73ms, 95p=0.75ms, 99p=0.76ms +[Pytorch (FP32)] mean=5.13ms, sd=0.06ms, min=5.06ms, max=6.85ms, median=5.13ms, 95p=5.18ms, 99p=5.22ms +[Pytorch (FP16)] mean=5.39ms, sd=0.10ms, min=5.31ms, max=8.39ms, median=5.39ms, 95p=5.45ms, 99p=5.48ms +``` + +> interesting to note that ONNX Runtime provide better performances than TensorRT for this setup, it's quite rare... + +Models are stored in newly generated `./triton_models/` folder. +Subfolders contain templates for Nvidia Triton server. + +### Launch Nvidia Triton inference server + +> ⚠️**WARNING**⚠️: if you have generated the models *outside* the Docker container, +> check that your TensorRT version is the same than the Triton backend one. + +Launch `Nvidia Triton inference server`: + +```shell +# add --shm-size 256m -> to have up to 4 Python backends (tokenizer) at the same time (64Mb per instance) +docker run -it --rm --gpus all -p8000:8000 -p8001:8001 -p8002:8002 --shm-size 256m \ + -v $PWD/triton_models:/models nvcr.io/nvidia/tritonserver:21.10-py3 \ + bash -c "pip install transformers && tritonserver --model-repository=/models" +``` + +> As you can see we install Transformers and then launch the server itself. +> This is of course a bad practice, you should make your own 2 lines Dockerfile with Transformers inside. + +### Performance analysis + +Measures: + +* 16 tokens + TensorRT: +```shell +# need a local installation of the package +# pip install .[GPU] +ubuntu@ip-XXX:~/triton_transformers$ python3 demo/triton_client.py --length 16 --model tensorrt +10/31/2021 12:09:34 INFO timing [triton transformers]: mean=1.53ms, sd=0.06ms, min=1.48ms, max=1.78ms, median=1.51ms, 95p=1.66ms, 99p=1.74ms +[[-3.4355469 3.2753906]] +``` + +* 128 tokens + TensorRT: +```shell +ubuntu@ip-XXX:~/triton_transformers$ python3 demo/triton_client.py --length 128 --model tensorrt +10/31/2021 12:12:00 INFO timing [triton transformers]: mean=1.96ms, sd=0.08ms, min=1.88ms, max=2.24ms, median=1.93ms, 95p=2.17ms, 99p=2.23ms +[[-3.4589844 3.3027344]] +``` + +There is also a performance analysis tool provided by Nvidia called [`perf_analyzer`](https://github.com/triton-inference-server/server/blob/main/docs/perf_analyzer.md) + +```shell +# perf_analyzer needs this dependency +sudo apt install libb64-dev +# add -a for async measures, and -i grpc to use that protocol instead of http +~/.local/bin/perf_analyzer -m transformer_tensorrt_inference \ + --percentile=95 \ + --string-data "This live event is great. I will sign-up for Infinity." \ + --shape TEXT:1 \ + --concurrency-range 1:4 \ + -i grpc \ + -a \ + -f perf.csv + +# just test the model part (easier to get random input) +~/.local/bin/perf_analyzer --input-data zero -m transformer_tensorrt_model \ + --shape input_ids:1,128 \ + --shape attention_mask:1,128 \ + --shape token_type_ids:1,128 \ + --concurrency-range 1:4 \ + -i grpc \ + -a \ + -f perf.csv +``` + +> The tool need to be run on Ubuntu >= 20.04 (and won't work on Ubuntu 18.04 used for the AWS official Ubuntu deep learning image) + +### Model analyzer + +Model analyzer is a powerful tool to adjust the Triton server configuration. +To run it: + +```shell +docker run -it --rm --gpus all -v $PWD:/project fast_transformer:0.1.0 \ + bash -c "model-analyzer profile -f /project/demo/config_analyzer.yaml" +``` + +### FastAPI server baseline + +This is our baseline, easy to run, but not very performant. + +```shell +# launch server, disable logging for best performances +python3 -m uvicorn --log-level warning demo.fast_api_server_onnx:app --port 8000 --host 0.0.0.0 +# other variation, 1 worker per CPU for best latency (plus not a good idea to have several times the same model on a single GPU): +python3 -m gunicorn -w 1 -k uvicorn.workers.UvicornWorker --log-level warning demo.fast_api_server_onnx --bind 0.0.0.0:8000 + +# simple inference timing +time curl -G --data-urlencode query="This live event is great. I will sign-up for Infinity." localhost:8000/predict +# slightly more serious measure +sudo apt-get install linux-tools-common linux-tools-generic linux-tools-`uname -r` +sudo perf stat -r 50 -d curl -G --data-urlencode query="This live event is great. I will sign-up for Infinity." localhost:8000/predict -s > /dev/null +``` + +It should produce: + +```shell +Performance counter stats for 'curl -G --data-urlencode query=This live event is great. I will sign-up for Infinity. localhost:8000/predict' (50 runs): + + 6.14 msec task-clock # 0.494 CPUs utilized ( +- 0.59% ) + 3 context-switches # 0.462 K/sec ( +- 1.84% ) + 0 cpu-migrations # 0.000 K/sec + 577 page-faults # 0.094 M/sec ( +- 0.06% ) + cycles + instructions + branches + branch-misses + L1-dcache-loads + L1-dcache-load-misses + LLC-loads + LLC-load-misses + + 0.0124429 +- 0.0000547 seconds time elapsed ( +- 0.44% ) +``` diff --git a/demo/config_analyzer.yaml b/demo/config_analyzer.yaml new file mode 100644 index 00000000..3859df4a --- /dev/null +++ b/demo/config_analyzer.yaml @@ -0,0 +1,30 @@ +model_repository: /project/triton_models/ +override_output_model_repository: true + +triton_launch_mode: 'local' +client_protocol: 'http' +triton_http_endpoint: 'localhost:8000' + +profile_models: + sts: + parameters: + batch_sizes: 0 + +concurrency: + start: 1 + stop: 2 + step: 1 + +batch_sizes: + start: 1 + stop: 32 + step: 8 + +run_config_search_max_instance_count: 1 + +perf_analyzer_flags: + percentile: 95 + measurement-mode: time_windows + input-data: zero +# shape: | +# input_ids:1,16 attention_mask:1,16 diff --git a/demo/fast_api_server_onnx.py b/demo/fast_api_server_onnx.py new file mode 100644 index 00000000..17927b29 --- /dev/null +++ b/demo/fast_api_server_onnx.py @@ -0,0 +1,37 @@ +# Copyright 2021, Lefebvre Sarrut Services +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +from fastapi import FastAPI +from onnxruntime import GraphOptimizationLevel, InferenceSession, SessionOptions +from transformers import AutoTokenizer, BatchEncoding, TensorType + + +app = FastAPI() +options = SessionOptions() +options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL +model = InferenceSession("triton_models/model.onnx", options, providers=["CUDAExecutionProvider"]) +tokenizer = AutoTokenizer.from_pretrained("philschmid/MiniLM-L6-H384-uncased-sst2") + + +@app.get("/predict") +def predict(query: str): + encode_dict: BatchEncoding = tokenizer( + text=query, + max_length=128, + truncation=True, + return_tensors=TensorType.NUMPY, + ) + result: np.ndarray = model.run(None, dict(encode_dict))[0] + return result.tolist() diff --git a/query_body.bin b/demo/query_body.bin similarity index 100% rename from query_body.bin rename to demo/query_body.bin diff --git a/demo/triton_client.py b/demo/triton_client.py new file mode 100644 index 00000000..a71fa70f --- /dev/null +++ b/demo/triton_client.py @@ -0,0 +1,72 @@ +# Copyright 2021, Lefebvre Sarrut Services +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse + +import numpy as np +import tritonclient.http + +from transformer_deploy.benchmarks.utils import print_timings, setup_logging, track_infer_time + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="require inference", formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + parser.add_argument("--length", required=True, help="sequence length", choices=(16, 128), type=int) + parser.add_argument("--model", required=True, help="sequence length", choices=("onnx", "tensorrt")) + args, _ = parser.parse_known_args() + + setup_logging() + model_name = f"transformer_{args.model}_inference" + url = "127.0.0.1:8000" + model_version = "1" + batch_size = 1 + + if args.length == 128: + # from https://venturebeat.com/2021/08/25/how-hugging-face-is-tackling-bias-in-nlp/, text used in the HF demo + text = """Today, Hugging Face has expanded to become a robust NLP startup, + known primarily for making open-source software such as Transformers and Datasets, + used for building NLP systems. β€œThe software Hugging Face develops can be used for + classification, question answering, translation, and many other NLP tasks,” Rush said. + Hugging Face also hosts a range of pretrained NLP models, on GitHub, that practitioners can download + and apply for their problems, Rush added.""" # noqa: W291 + else: + text = "This live event is great. I will sign-up for Infinity." + + triton_client = tritonclient.http.InferenceServerClient(url=url, verbose=False) + assert triton_client.is_model_ready( + model_name=model_name, model_version=model_version + ), f"model {model_name} not yet ready" + + model_metadata = triton_client.get_model_metadata(model_name=model_name, model_version=model_version) + model_config = triton_client.get_model_config(model_name=model_name, model_version=model_version) + + query = tritonclient.http.InferInput(name="TEXT", shape=(batch_size,), datatype="BYTES") + model_score = tritonclient.http.InferRequestedOutput(name="output", binary_data=False) + time_buffer = list() + for _ in range(10000): + query.set_data_from_numpy(np.asarray([text] * batch_size, dtype=object)) + _ = triton_client.infer( + model_name=model_name, model_version=model_version, inputs=[query], outputs=[model_score] + ) + for _ in range(1000): + with track_infer_time(time_buffer): + query.set_data_from_numpy(np.asarray([text] * batch_size, dtype=object)) + response = triton_client.infer( + model_name=model_name, model_version=model_version, inputs=[query], outputs=[model_score] + ) + + print_timings(name="triton transformers", timings=time_buffer) + print(response.as_numpy("output")) diff --git a/demo/triton_client_model.py b/demo/triton_client_model.py new file mode 100644 index 00000000..322480f9 --- /dev/null +++ b/demo/triton_client_model.py @@ -0,0 +1,51 @@ +# Copyright 2021, Lefebvre Sarrut Services +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import tritonclient.http + +from transformer_deploy.benchmarks.utils import print_timings, setup_logging, track_infer_time + + +model_name = "transformer_onnx_model" +url = "127.0.0.1:8000" +model_version = "1" +nb_tokens = 16 # edit to check longer sequence length + +setup_logging() +triton_client = tritonclient.http.InferenceServerClient(url=url, verbose=False) +time_buffer = list() + +input0 = tritonclient.http.InferInput("input_ids", (1, nb_tokens), "INT64") +input1 = tritonclient.http.InferInput("attention_mask", (1, nb_tokens), "INT64") +input1.set_data_from_numpy(np.ones((1, nb_tokens), dtype=np.int64), binary_data=False) +input2 = tritonclient.http.InferInput("token_type_ids", (1, nb_tokens), "INT64") +input2.set_data_from_numpy(np.ones((1, nb_tokens), dtype=np.int64), binary_data=False) +output = tritonclient.http.InferRequestedOutput("output", binary_data=False) + + +def perform_random_inference(): + input0.set_data_from_numpy(np.random.randint(10000, size=(1, nb_tokens), dtype=np.int64), binary_data=False) + triton_client.infer(model_name, model_version=model_version, inputs=[input0, input1, input2], outputs=[output]) + + +# warmup +for _ in range(100): + perform_random_inference() + +for _ in range(1000): + with track_infer_time(time_buffer): + perform_random_inference() + +print_timings(name=f"triton, # tokens: {nb_tokens}", timings=time_buffer) diff --git a/demo/triton_client_requests.py b/demo/triton_client_requests.py new file mode 100644 index 00000000..2530a3d4 --- /dev/null +++ b/demo/triton_client_requests.py @@ -0,0 +1,95 @@ +# Copyright 2021, Lefebvre Sarrut Services +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import zlib + +import requests +from transformers import AutoTokenizer, PreTrainedTokenizer, TensorType + +from transformer_deploy.benchmarks.utils import print_timings, setup_logging, track_infer_time + + +setup_logging() +tokenizer: PreTrainedTokenizer = AutoTokenizer.from_pretrained("philschmid/MiniLM-L6-H384-uncased-sst2") + +tokens = tokenizer( + text="This live event is great. I will sign-up for Infinity.", + max_length=16, + truncation=True, + return_tensors=TensorType.NUMPY, +) + +# https://github.com/triton-inference-server/server/blob/main/docs/protocol/extension_classification.md +url = "http://127.0.0.1:8000/v2/models/transformer_onnx_model/versions/1/infer" +message = { + "id": "42", + "inputs": [ + { + "name": "input_ids", + "shape": tokens["input_ids"].shape, + "datatype": "INT64", + "data": tokens["input_ids"].tolist(), + }, + { + "name": "token_type_ids", + "shape": tokens["token_type_ids"].shape, + "datatype": "INT64", + "data": tokens["token_type_ids"].tolist(), + }, + { + "name": "attention_mask", + "shape": tokens["attention_mask"].shape, + "datatype": "INT64", + "data": tokens["attention_mask"].tolist(), + }, + ], + "outputs": [ + { + "name": "output", + "parameters": {"binary_data": False}, + } + ], +} + +time_buffer = list() +session = requests.Session() +for _ in range(10000): + bytes_message = bytes(json.dumps(message), encoding="raw_unicode_escape") + request_body = zlib.compress(bytes_message) + _ = session.post( + url, + data=request_body, + headers={ + "Content-Encoding": "gzip", + "Accept-Encoding": "gzip", + "Inference-Header-Content-Length": str(len(bytes_message)), + }, + ) + +for _ in range(100): + with track_infer_time(time_buffer): + bytes_message = bytes(json.dumps(message), encoding="raw_unicode_escape") + request_body = zlib.compress(bytes_message) + _ = session.post( + url, + data=request_body, + headers={ + "Content-Encoding": "gzip", + "Accept-Encoding": "gzip", + "Inference-Header-Content-Length": str(len(bytes_message)), + }, + ) + +print_timings(name="triton (onnx backend) - requests", timings=time_buffer) diff --git a/demo/triton_client_tokenizer_only.py b/demo/triton_client_tokenizer_only.py new file mode 100644 index 00000000..0d4d9e65 --- /dev/null +++ b/demo/triton_client_tokenizer_only.py @@ -0,0 +1,49 @@ +# Copyright 2021, Lefebvre Sarrut Services +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import tritonclient.http + +from transformer_deploy.benchmarks.utils import print_timings, setup_logging, track_infer_time + + +model_name = "transformer_onnx_tokenize" +url = "127.0.0.1:8000" +model_version = "1" +text = "SOME TEXT" # edit to check longer sequence length +batch_size = 1 + +setup_logging() +triton_client = tritonclient.http.InferenceServerClient(url=url, verbose=False) +time_buffer = list() + +query = tritonclient.http.InferInput(name="TEXT", shape=(batch_size,), datatype="BYTES") +input_ids = tritonclient.http.InferRequestedOutput("input_ids", binary_data=False) +attention = tritonclient.http.InferRequestedOutput("attention_mask", binary_data=False) + + +def perform_inference(): + query.set_data_from_numpy(np.asarray([text] * batch_size, dtype=object)) + triton_client.infer(model_name, model_version=model_version, inputs=[query], outputs=[input_ids, attention]) + + +# warmup +for _ in range(100): + perform_inference() + +for _ in range(1000): + with track_infer_time(time_buffer): + perform_inference() + +print_timings(name=f"tokenize, # text len: {len(text)}", timings=time_buffer) diff --git a/perf_data.json b/perf_data.json deleted file mode 100644 index dcd9beea..00000000 --- a/perf_data.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "data" : - [ - { - "TEXT" : ["This live event is great. I will sign-up for Infinity."] - } - ] - } diff --git a/requirements.txt b/requirements.txt index 57f7e2ff..6d955a18 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,10 +5,10 @@ numpy requests transformers sentencepiece -tritonclient[all] -onnx -onnxruntime-gpu==1.9.0 sympy coloredlogs --f https://download.pytorch.org/whl/cu113/torch_stable.html -torch==1.10.0+cu113 +pytest +colored +black +isort +flake8 diff --git a/requirements_gpu.txt b/requirements_gpu.txt new file mode 100644 index 00000000..9fbf891f --- /dev/null +++ b/requirements_gpu.txt @@ -0,0 +1,10 @@ +onnx +onnxruntime-gpu +nvidia-pyindex +tritonclient[all] +pycuda +torch==1.10.0+cu113 +nvidia-tensorrt==8.0.3.4 +onnx_graphsurgeon +polygraphy==0.33.0 +triton-model-analyzer diff --git a/resources/infinity.png b/resources/infinity.png new file mode 100644 index 0000000000000000000000000000000000000000..164c00aab51c65ae31ff02fd7a29065843ddfbb2 GIT binary patch literal 999896 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z$03LjDUl)`liHz?eqaJ#~+g|}8HtW(%a;Q)mr6&|bbB!#CdJXhgG z3g;-iUg1K8OB60w_@u(s3SUvUSz)8Xy%ye9s<2jJ4~6{|4pVrv!Z8ZRD?CTx42824 z&Qo}+!aEf{pm3GKXB57m@C}7q74EWdL7~EGh5IY)qj0doQ3{V&I8Nc23NJ8_KZJJ6 z=@9$=@9Tqob70>b*f$6E&4GP$VBZ|rHwXSjbHG|X3O;A`Edv+Z-+{od>TiL4^|wH= HB?SKso InferenceSession: + options = SessionOptions() + options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL + if type(provider_to_use) == list and provider_to_use == "CPUExecutionProvider": + options.intra_op_num_threads = multiprocessing.cpu_count() + if type(provider_to_use) != list: + provider_to_use = [provider_to_use] + return InferenceSession(path, options, providers=provider_to_use) + + +def convert_to_onnx(model_pytorch: PreTrainedModel, output_path: str, inputs_pytorch: OD[str, torch.Tensor]) -> None: + # dynamic axis == variable length axis + dynamic_axis = OrderedDict() + for k in inputs_pytorch.keys(): + dynamic_axis[k] = {0: "batch_size", 1: "sequence"} + dynamic_axis["output"] = {0: "batch_size"} + with torch.no_grad(): + torch.onnx.export( + model_pytorch, # model to optimize + args=tuple(inputs_pytorch.values()), # tuple of multiple inputs + f=output_path, # output path / file object + opset_version=12, # the ONNX version to use + do_constant_folding=True, # simplify model (replace constant expressions) + input_names=list(inputs_pytorch.keys()), # input names + output_names=["output"], # output axis name + dynamic_axes=dynamic_axis, # declare dynamix axis for each input / output + training=TrainingMode.EVAL, # always put the model in evaluation mode + verbose=False, + ) + + +def optimize_onnx(onnx_path: str, onnx_optim_fp16_path: str, use_cuda: bool) -> None: + optimization_options = FusionOptions("bert") + optimization_options.enable_gelu_approximation = True # additional optimization + optimized_model: BertOnnxModel = optimizer.optimize_model( + input=onnx_path, + model_type="bert", + use_gpu=use_cuda, + opt_level=1, + num_heads=0, # automatic detection + hidden_size=0, # automatic detection + optimization_options=optimization_options, + ) + + optimized_model.convert_float_to_float16() # FP32 -> FP16 + logging.info(f"optimizations applied: {optimized_model.get_fused_operator_statistics()}") + optimized_model.save_model_to_file(onnx_optim_fp16_path) diff --git a/src/transformer_deploy/backends/trt_utils.py b/src/transformer_deploy/backends/trt_utils.py new file mode 100644 index 00000000..6034c601 --- /dev/null +++ b/src/transformer_deploy/backends/trt_utils.py @@ -0,0 +1,216 @@ +# Copyright 2021, Lefebvre Sarrut Services +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List, OrderedDict, Tuple + +import numpy as np +import pycuda.driver as cuda +import tensorrt as trt +from numpy import ndarray +from pycuda._driver import DeviceAllocation, Stream +from tensorrt import ICudaEngine, IExecutionContext +from tensorrt.tensorrt import ( + Builder, + IBuilderConfig, + IElementWiseLayer, + ILayer, + INetworkDefinition, + IOptimizationProfile, + IReduceLayer, + Logger, + OnnxParser, + Runtime, +) + + +class Calibrator(trt.IInt8Calibrator): + def __init__(self): + trt.IInt8Calibrator.__init__(self) + self.algorithm = trt.CalibrationAlgoType.MINMAX_CALIBRATION + self.batch_size = 32 + + input_list: List[ndarray] = [np.zeros((32, 512), dtype=np.int32) for _ in range(3)] + # allocate GPU memory for input tensors + self.device_inputs: List[DeviceAllocation] = [cuda.mem_alloc(tensor.nbytes) for tensor in input_list] + for h_input, d_input in zip(input_list, self.device_inputs): + cuda.memcpy_htod_async(d_input, h_input) # host to GPU + self.count = 0 + + def get_algorithm(self): + return trt.CalibrationAlgoType.MINMAX_CALIBRATION + + def get_batch_size(self): + return self.batch_size + + def get_batch(self, names, p_str=None): + self.count += 1 + if self.count > 20: + return [] + # return pointers to arrays + return [int(d) for d in self.device_inputs] + + def read_calibration_cache(self): + return None + + def write_calibration_cache(self, cache): + with open("calibration_cache.bin", "wb") as f: + f.write(cache) + + def free(self): + for dinput in self.device_inputs: + dinput.free() + + +def setup_binding_shapes( + context: trt.IExecutionContext, + host_inputs: List[np.ndarray], + input_binding_idxs: List[int], + output_binding_idxs: List[int], +): + # explicitly set dynamic input shapes, so dynamic output shapes can be computed internally + for host_input, binding_index in zip(host_inputs, input_binding_idxs): + context.set_binding_shape(binding_index, host_input.shape) + assert context.all_binding_shapes_specified + host_outputs = [] + device_outputs = [] + for binding_index in output_binding_idxs: + output_shape = context.get_binding_shape(binding_index) + # allocate buffers to hold output results after copying back to host + buffer = np.empty(output_shape, dtype=np.float32) + host_outputs.append(buffer) + # allocate output buffers on device + device_outputs.append(cuda.mem_alloc(buffer.nbytes)) + return host_outputs, device_outputs + + +def get_binding_idxs(engine: trt.ICudaEngine, profile_index: int): + # calculate start/end binding indices for current context's profile + # https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#opt_profiles_bindings + num_bindings_per_profile = engine.num_bindings // engine.num_optimization_profiles + start_binding = profile_index * num_bindings_per_profile + end_binding = start_binding + num_bindings_per_profile # Separate input and output binding indices for convenience + input_binding_idxs = [] + output_binding_idxs = [] + for binding_index in range(start_binding, end_binding): + if engine.binding_is_input(binding_index): + input_binding_idxs.append(binding_index) + else: + output_binding_idxs.append(binding_index) + return input_binding_idxs, output_binding_idxs + + +def fix_fp16_network(network_definition: INetworkDefinition) -> INetworkDefinition: + # search for patterns which may overflow in FP16 precision, we force FP32 precisions for those nodes + for layer_index in range(network_definition.num_layers - 1): + layer: ILayer = network_definition.get_layer(layer_index) + next_layer: ILayer = network_definition.get_layer(layer_index + 1) + # POW operation usually followed by mean reduce + if layer.type == trt.LayerType.ELEMENTWISE and next_layer.type == trt.LayerType.REDUCE: + # casting to get access to op attribute + layer.__class__ = IElementWiseLayer + next_layer.__class__ = IReduceLayer + if layer.op == trt.ElementWiseOperation.POW: + layer.precision = trt.DataType.FLOAT + next_layer.precision = trt.DataType.FLOAT + layer.set_output_type(index=0, dtype=trt.DataType.FLOAT) + next_layer.set_output_type(index=0, dtype=trt.DataType.FLOAT) + return network_definition + + +def build_engine( + runtime: Runtime, + onnx_file_path: str, + logger: Logger, + min_shape: Tuple[int, int], + optimal_shape: Tuple[int, int], + max_shape: Tuple[int, int], + workspace_size: int, +) -> ICudaEngine: + with trt.Builder(logger) as builder: # type: Builder + with builder.create_network( + flags=1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) + ) as network_definition: # type: INetworkDefinition + with trt.OnnxParser(network_definition, logger) as parser: # type: OnnxParser + builder.max_batch_size = max_shape[0] # max batch size + config: IBuilderConfig = builder.create_builder_config() + # config.min_timing_iterations = 1 + # config.avg_timing_iterations = 1 + config.max_workspace_size = workspace_size + # to enable complete trt inspector debugging, only for TensorRT >= 8.2 + # config.profiling_verbosity = trt.ProfilingVerbosity.DETAILED + # disable CUDNN optimizations + config.set_tactic_sources( + tactic_sources=1 << int(trt.TacticSource.CUBLAS) | 1 << int(trt.TacticSource.CUBLAS_LT) + ) + # config.set_flag(trt.BuilderFlag.INT8) + # config.set_quantization_flag(trt.QuantizationFlag.CALIBRATE_BEFORE_FUSION) + # config.int8_calibrator = Calibrator() + config.set_flag(trt.BuilderFlag.FP16) + config.set_flag(trt.BuilderFlag.DISABLE_TIMING_CACHE) + # https://github.com/NVIDIA/TensorRT/issues/1196 (sometimes big diff in output when using FP16) + config.set_flag(trt.BuilderFlag.STRICT_TYPES) + with open(onnx_file_path, "rb") as f: + parser.parse(f.read()) + profile: IOptimizationProfile = builder.create_optimization_profile() + for num_input in range(network_definition.num_inputs): + profile.set_shape( + input=network_definition.get_input(num_input).name, + min=min_shape, + opt=optimal_shape, + max=max_shape, + ) + config.add_optimization_profile(profile) + # for i in range(network.num_layers): + # layer: ILayer = network.get_layer(i) + # if "gemm" in str(layer.name).lower(): + # for g in range(layer.num_outputs): + # layer.precision = trt.DataType.FLOAT + network_definition = fix_fp16_network(network_definition) + trt_engine = builder.build_serialized_network(network_definition, config) + engine: ICudaEngine = runtime.deserialize_cuda_engine(trt_engine) + assert engine is not None, "error during engine generation, check error messages above :-(" + return engine + + +def save_engine(engine: ICudaEngine, engine_file_path: str): + with open(engine_file_path, "wb") as f: + f.write(engine.serialize()) + + +def load_engine(runtime: Runtime, engine_file_path: str) -> ICudaEngine: + with open(engine_file_path, "rb") as f: + return runtime.deserialize_cuda_engine(f.read()) + + +def infer_tensorrt( + context: IExecutionContext, + host_inputs: OrderedDict[str, np.ndarray], + input_binding_idxs: List[int], + output_binding_idxs: List[int], + stream: Stream, +) -> np.ndarray: + # warning: small change in output if int64 is used instead of int32 + input_list: List[ndarray] = [tensor.astype(np.int32) for tensor in host_inputs.values()] + # allocate GPU memory for input tensors + device_inputs = [cuda.mem_alloc(tensor.nbytes) for tensor in input_list] + for h_input, d_input in zip(input_list, device_inputs): + cuda.memcpy_htod_async(d_input, h_input) # host to GPU + # calculate input shape, bind it, allocate GPU memory for the output + host_outputs, device_outputs = setup_binding_shapes(context, input_list, input_binding_idxs, output_binding_idxs) + bindings = device_inputs + device_outputs + context.execute_async_v2(bindings, stream.handle) + for h_output, d_output in zip(host_outputs, device_outputs): + cuda.memcpy_dtoh_async(h_output, d_output) # GPU to host + stream.synchronize() # sync all CUDA ops + return host_outputs diff --git a/src/transformer_deploy/benchmarks/__init__.py b/src/transformer_deploy/benchmarks/__init__.py new file mode 100644 index 00000000..d754dd37 --- /dev/null +++ b/src/transformer_deploy/benchmarks/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2021, Lefebvre Sarrut Services +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/src/transformer_deploy/benchmarks/utils.py b/src/transformer_deploy/benchmarks/utils.py new file mode 100644 index 00000000..3bec209b --- /dev/null +++ b/src/transformer_deploy/benchmarks/utils.py @@ -0,0 +1,67 @@ +# Copyright 2021, Lefebvre Sarrut Services +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import time +from collections import OrderedDict +from contextlib import contextmanager +from typing import Dict, List, Tuple + +import numpy as np +import torch + + +def print_timings(name: str, timings: List[float]): + mean_time = 1e3 * np.mean(timings) + std_time = 1e3 * np.std(timings) + min_time = 1e3 * np.min(timings) + max_time = 1e3 * np.max(timings) + median, percent_95_time, percent_99_time = 1e3 * np.percentile(timings, [50, 95, 99]) + print( + f"[{name}] " + f"mean={mean_time:.2f}ms, " + f"sd={std_time:.2f}ms, " + f"min={min_time:.2f}ms, " + f"max={max_time:.2f}ms, " + f"median={median:.2f}ms, " + f"95p={percent_95_time:.2f}ms, " + f"99p={percent_99_time:.2f}ms" + ) + + +def setup_logging(level: int = logging.INFO): + logging.basicConfig(format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=level) + + +@contextmanager +def track_infer_time(buffer: [int]): + start = time.perf_counter() + yield + end = time.perf_counter() + buffer.append(end - start) + + +def prepare_input( + seq_len: int, batch_size: int, include_token_ids: bool +) -> Tuple[Dict[str, torch.Tensor], Dict[str, np.ndarray]]: + shape = (batch_size, seq_len) + inputs_pytorch: OrderedDict[str, torch.Tensor] = OrderedDict() + inputs_pytorch["input_ids"] = torch.randint(high=100, size=shape, dtype=torch.long, device="cuda") + if include_token_ids: + inputs_pytorch["token_type_ids"] = torch.ones(size=shape, dtype=torch.long, device="cuda") + inputs_pytorch["attention_mask"] = torch.ones(size=shape, dtype=torch.long, device="cuda") + inputs_onnx: Dict[str, np.ndarray] = { + k: np.ascontiguousarray(v.detach().cpu().numpy()) for k, v in inputs_pytorch.items() + } + return inputs_pytorch, inputs_onnx diff --git a/src/transformer_deploy/convert.py b/src/transformer_deploy/convert.py new file mode 100644 index 00000000..cf4ef983 --- /dev/null +++ b/src/transformer_deploy/convert.py @@ -0,0 +1,257 @@ +#!/usr/bin/env python3 + +# Copyright 2021, Lefebvre Sarrut Services +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import logging +import os +from pathlib import Path +from typing import List + +import numpy as np +import pycuda.autoinit +import tensorrt as trt +import torch +from pycuda._driver import Stream +from tensorrt.tensorrt import IExecutionContext, Logger, Runtime +from torch.cuda import get_device_name +from torch.cuda.amp import autocast +from transformers import AutoModelForSequenceClassification, AutoTokenizer, PreTrainedModel, PreTrainedTokenizer + +from transformer_deploy.backends.ort_utils import convert_to_onnx, create_model_for_provider, optimize_onnx +from transformer_deploy.backends.trt_utils import ( + build_engine, + get_binding_idxs, + infer_tensorrt, + load_engine, + save_engine, +) +from transformer_deploy.benchmarks.utils import prepare_input, print_timings, setup_logging, track_infer_time +from transformer_deploy.templates.triton import Configuration, ModelType + + +def main(): + + parser = argparse.ArgumentParser( + description="optimize and deploy transformers", formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + parser.add_argument("-m", "--model", required=True, help="path to model or URL to Hugging Face Hub") + parser.add_argument( + "-b", + "--batch-size", + default=[1, 1, 1], + help="batch sizes to optimize for (min, optimal, max)", + type=int, + nargs=3, + ) + parser.add_argument( + "-s", + "--seq-len", + default=[16, 16, 16], + help="sequence lengths to optimize for (min, opt, max)", + type=int, + nargs=3, + ) + parser.add_argument("-w", "--workspace-size", default=10000, help="workspace size in MiB (TensorRT)", type=int) + parser.add_argument("-o", "--output", default="triton_models", help="name to be used for ") + parser.add_argument("-n", "--name", default="transformer", help="model name to be used in triton server") + parser.add_argument("-v", "--verbose", action="store_true", help="display detailed information") + parser.add_argument( + "--backend", + default=["onnx"], + help="backend to use. One of [onnx,tensorrt, pytorch] or all", + nargs="*", + choices=["onnx", "tensorrt", "pytorch"], + ) + parser.add_argument("--nb-instances", default=1, help="# of model instances, may improve troughput", type=int) + parser.add_argument("--warmup", default=100, help="# of inferences to warm each model", type=int) + parser.add_argument("--nb-measures", default=1000, help="# of inferences for benchmarks", type=int) + parser.add_argument("--seed", default=123, help="seed for random inputs, etc.", type=int) + parser.add_argument("--atol", default=1e-1, help="tolerance when comparing outputs to Pytorch ones", type=float) + args, _ = parser.parse_known_args() + + setup_logging(level=logging.INFO if args.verbose else logging.WARNING) + + torch.manual_seed(args.seed) + + Path(args.output).mkdir(parents=True, exist_ok=True) + onnx_model_path = os.path.join(args.output, "model-original.onnx") + onnx_optim_fp16_path = os.path.join(args.output, "model.onnx") + tensorrt_path = os.path.join(args.output, "model.plan") + + assert torch.cuda.is_available(), "CUDA is not available. Please check your CUDA installation" + tokenizer: PreTrainedTokenizer = AutoTokenizer.from_pretrained(args.model) + input_names: List[str] = tokenizer.model_input_names + logging.info(f"axis: {input_names}") + include_token_ids = "token_type_ids" in input_names + model_pytorch: PreTrainedModel = AutoModelForSequenceClassification.from_pretrained(args.model) + model_pytorch.cuda() + model_pytorch.eval() + + tensor_shapes = list(zip(args.batch_size, args.seq_len)) + inputs_pytorch, inputs_onnx = prepare_input( + batch_size=tensor_shapes[-1][0], seq_len=tensor_shapes[-1][1], include_token_ids=include_token_ids + ) + + with torch.inference_mode(): + output = model_pytorch(**inputs_pytorch) + output = output.logits # extract the value of interest + output_pytorch: np.ndarray = output.detach().cpu().numpy() + + logging.info(f"[Pytorch] input shape {inputs_pytorch['input_ids'].shape}") + logging.info(f"[Pytorch] output shape: {output_pytorch.shape}") + # create onnx model and compare results + convert_to_onnx(model_pytorch=model_pytorch, output_path=onnx_model_path, inputs_pytorch=inputs_pytorch) + onnx_model = create_model_for_provider(path=onnx_model_path, provider_to_use="CUDAExecutionProvider") + output_onnx = onnx_model.run(None, inputs_onnx) + assert np.allclose(a=output_onnx, b=output_pytorch, atol=args.atol) + del onnx_model + if "pytorch" not in args.backend: + del model_pytorch + + timings = {} + + if "tensorrt" in args.backend: + trt_logger: Logger = trt.Logger(trt.Logger.INFO if args.verbose else trt.Logger.WARNING) + runtime: Runtime = trt.Runtime(trt_logger) + engine = build_engine( + runtime=runtime, + onnx_file_path=onnx_model_path, + logger=trt_logger, + min_shape=tensor_shapes[0], + optimal_shape=tensor_shapes[1], + max_shape=tensor_shapes[2], + workspace_size=args.workspace_size * 1024 * 1024, + ) + save_engine(engine=engine, engine_file_path=tensorrt_path) + # important to check the engine has been correctly serialized + engine = load_engine(runtime=runtime, engine_file_path=tensorrt_path) + stream: Stream = pycuda.driver.Stream() + context: IExecutionContext = engine.create_execution_context() + profile_index = 0 + context.set_optimization_profile_async(profile_index=profile_index, stream_handle=stream.handle) + # retrieve input/output IDs + input_binding_idxs, output_binding_idxs = get_binding_idxs(engine, profile_index) # type: List[int], List[int] + tensorrt_output = infer_tensorrt( + context=context, + host_inputs=inputs_onnx, + input_binding_idxs=input_binding_idxs, + output_binding_idxs=output_binding_idxs, + stream=stream, + ) + assert np.allclose(a=tensorrt_output, b=output_pytorch, atol=args.atol), ( + f"tensorrt accuracy is too low:\n" f"Pythorch:\n{output_pytorch}\n" f"VS\n" f"TensorRT:\n{tensorrt_output}" + ) + + for _ in range(args.warmup): + _ = infer_tensorrt( + context=context, + host_inputs=inputs_onnx, + input_binding_idxs=input_binding_idxs, + output_binding_idxs=output_binding_idxs, + stream=stream, + ) + time_buffer = list() + for _ in range(args.nb_measures): + with track_infer_time(time_buffer): + _ = infer_tensorrt( + context=context, + host_inputs=inputs_onnx, + input_binding_idxs=input_binding_idxs, + output_binding_idxs=output_binding_idxs, + stream=stream, + ) + timings["TensorRT (FP16)"] = time_buffer + del engine, context, runtime # delete all tensorrt objects + + conf = Configuration( + model_name=args.name, + model_type=ModelType.TensorRT, + batch_size=0, + nb_output=output_pytorch.shape[1], + nb_instance=args.nb_instances, + include_token_type=include_token_ids, + workind_directory=args.output, + ) + conf.create_folders(tokenizer=tokenizer, model_path=tensorrt_path) + + if "onnx" in args.backend: + # create optimized onnx model and compare results + optimize_onnx( + onnx_path=onnx_model_path, + onnx_optim_fp16_path=onnx_optim_fp16_path, + use_cuda=True, + ) + onnx_model = create_model_for_provider(path=onnx_optim_fp16_path, provider_to_use="CUDAExecutionProvider") + # run the model (None = get all the outputs) + output_onnx_optimised = onnx_model.run(None, inputs_onnx) + del onnx_model + assert np.allclose(a=output_onnx_optimised, b=output_pytorch, atol=args.atol) + + for provider, model_path, benchmar_name in [ + ("CUDAExecutionProvider", onnx_model_path, "ONNX Runtime (vanilla)"), + ("CUDAExecutionProvider", onnx_optim_fp16_path, "ONNX Runtime (optimized)"), + ]: + model = create_model_for_provider(path=model_path, provider_to_use=provider) + time_buffer = [] + for _ in range(args.warmup): + _ = model.run(None, inputs_onnx) + for _ in range(args.nb_measures): + with track_infer_time(time_buffer): + _ = model.run(None, inputs_onnx) + timings[benchmar_name] = time_buffer + del model + + conf = Configuration( + model_name=args.name, + model_type=ModelType.ONNX, + batch_size=0, + nb_output=output_pytorch.shape[1], + nb_instance=args.nb_instances, + include_token_type=include_token_ids, + workind_directory=args.output, + ) + conf.create_folders(tokenizer=tokenizer, model_path=onnx_optim_fp16_path) + + if "pytorch" in args.backend: + with torch.inference_mode(): + for _ in range(args.warmup): + _ = model_pytorch(**inputs_pytorch) + torch.cuda.synchronize() + time_buffer = [] + for _ in range(args.nb_measures): + with track_infer_time(time_buffer): + _ = model_pytorch(**inputs_pytorch) + torch.cuda.synchronize() + timings["Pytorch (FP32)"] = time_buffer + with autocast(): + for _ in range(args.warmup): + _ = model_pytorch(**inputs_pytorch) + torch.cuda.synchronize() + time_buffer = [] + for _ in range(args.nb_measures): + with track_infer_time(time_buffer): + _ = model_pytorch(**inputs_pytorch) + torch.cuda.synchronize() + timings["Pytorch (FP16)"] = time_buffer + + print(f"Inference done on {get_device_name(0)}") + print("latencies:") + for name, time_buffer in timings.items(): + print_timings(name=name, timings=time_buffer) + + +if __name__ == "__main__": + main() diff --git a/src/transformer_deploy/templates/__init__.py b/src/transformer_deploy/templates/__init__.py new file mode 100644 index 00000000..d754dd37 --- /dev/null +++ b/src/transformer_deploy/templates/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2021, Lefebvre Sarrut Services +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/src/transformer_deploy/templates/triton.py b/src/transformer_deploy/templates/triton.py new file mode 100644 index 00000000..39ee5a55 --- /dev/null +++ b/src/transformer_deploy/templates/triton.py @@ -0,0 +1,232 @@ +# Copyright 2021, Lefebvre Sarrut Services +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import shutil +from enum import Enum +from pathlib import Path + +from transformers import PreTrainedTokenizer + + +class ModelType(Enum): + ONNX = 1 + TensorRT = 2 + + +class Configuration: + def __init__( + self, + workind_directory: str, + model_name: str, + model_type: ModelType, + batch_size: int, + nb_output: int, + nb_instance: int, + include_token_type: bool, + ): + self.model_name = model_name + self.model_name += "_onnx" if model_type == ModelType.ONNX else "_tensorrt" + self.model_folder_name = f"{self.model_name}_model" + self.tokenizer_folder_name = f"{self.model_name}_tokenize" + self.inference_folder_name = f"{self.model_name}_inference" + self.batch_size = batch_size + self.nb_model_output = nb_output + assert nb_instance > 0, f"nb_instance=={nb_instance}: nb model instances should be positive" + self.nb_instance = nb_instance + self.include_token_type = include_token_type + self.workind_directory = workind_directory + if model_type == ModelType.ONNX: + self.input_type = "TYPE_INT64" + self.inference_platform = "onnxruntime_onnx" + elif model_type == ModelType.TensorRT: + self.input_type = "TYPE_INT32" + self.inference_platform = "tensorrt_plan" + else: + raise Exception(f"unknown model type: {model_type}") + + def __get_tokens(self): + token_type = "" + if self.include_token_type: + token_type = f""" {{ + name: "token_type_ids" + data_type: {self.input_type} + dims: [-1, -1] + }}, +""" + return f"""{{ + name: "input_ids" + data_type: {self.input_type} + dims: [-1, -1] + }}, + {token_type} + {{ + name: "attention_mask" + data_type: {self.input_type} + dims: [-1, -1] + }} +""" + + def __instance_group(self): + return f""" +instance_group [ + {{ + count: {self.nb_instance} + kind: KIND_GPU + }} +] +""".strip() + + def get_model_conf(self) -> str: + return f""" +name: "{self.model_folder_name}" +max_batch_size: {self.batch_size} +platform: "{self.inference_platform}" +default_model_filename: "model.bin" + +input [ + {self.__get_tokens()} +] + +output {{ + name: "output" + data_type: TYPE_FP32 + dims: [-1, {self.nb_model_output}] +}} + +{self.__instance_group()} +""".strip() + + def get_tokenize_conf(self): + return f""" +name: "{self.tokenizer_folder_name}" +max_batch_size: {self.batch_size} +backend: "python" + +input [ + {{ + name: "TEXT" + data_type: TYPE_STRING + dims: [ -1 ] + }} +] + +output [ + {self.__get_tokens()} +] + +{self.__instance_group()} +""".strip() + + def get_inference_conf(self): + input_token_type_ids = "" + if self.include_token_type: + input_token_type_ids = """ + { + key: "token_type_ids" + value: "token_type_ids" + }, + """.strip() + output_token_type_ids = "" + if self.include_token_type: + output_token_type_ids = """ + { + key: "token_type_ids" + value: "token_type_ids" + }, + """.strip() + return f""" +name: "{self.inference_folder_name}" +max_batch_size: {self.batch_size} +platform: "ensemble" + +input [ + {{ + name: "TEXT" + data_type: TYPE_STRING + dims: [ -1 ] + }} +] + +output {{ + name: "output" + data_type: TYPE_FP32 + dims: [-1, {self.nb_model_output}] +}} + +ensemble_scheduling {{ + step [ + {{ + model_name: "{self.tokenizer_folder_name}" + model_version: -1 + input_map {{ + key: "TEXT" + value: "TEXT" + }} + output_map [ + {{ + key: "input_ids" + value: "input_ids" + }}, + {input_token_type_ids} + {{ + key: "attention_mask" + value: "attention_mask" + }} + ] + }}, + {{ + model_name: "{self.model_folder_name}" + model_version: -1 + input_map [ + {{ + key: "input_ids" + value: "input_ids" + }}, + {output_token_type_ids} + {{ + key: "attention_mask" + value: "attention_mask" + }} + ] + output_map {{ + key: "output" + value: "output" + }} + }} + ] +}} +""".strip() + + def create_folders(self, tokenizer: PreTrainedTokenizer, model_path: str): + wd_path = Path(self.workind_directory) + wd_path.mkdir(parents=True, exist_ok=True) + for folder_name, conf_func in [ + (self.model_folder_name, self.get_model_conf), + (self.tokenizer_folder_name, self.get_tokenize_conf), + (self.inference_folder_name, self.get_inference_conf), + ]: + current_folder = wd_path.joinpath(folder_name) + current_folder.mkdir(exist_ok=True) + conf = conf_func() + current_folder.joinpath("config.pbtxt").write_text(conf) + version_folder = current_folder.joinpath("1") + version_folder.mkdir(exist_ok=True) + + tokenizer_model_folder_path = wd_path.joinpath(self.tokenizer_folder_name).joinpath("1") + tokenizer.save_pretrained(str(tokenizer_model_folder_path.absolute())) + tokenizer_model_path = Path(__file__).absolute().parent.parent.joinpath("utils").joinpath("python_tokenizer.py") + shutil.copy(str(tokenizer_model_path), str(Path(tokenizer_model_folder_path).joinpath("model.py"))) + model_folder_path = wd_path.joinpath(self.model_folder_name).joinpath("1") + shutil.copy(model_path, os.path.join(model_folder_path, "model.bin")) diff --git a/src/transformer_deploy/utils/__init__.py b/src/transformer_deploy/utils/__init__.py new file mode 100644 index 00000000..d754dd37 --- /dev/null +++ b/src/transformer_deploy/utils/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2021, Lefebvre Sarrut Services +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/src/transformer_deploy/utils/python_tokenizer.py b/src/transformer_deploy/utils/python_tokenizer.py new file mode 100644 index 00000000..22e84a58 --- /dev/null +++ b/src/transformer_deploy/utils/python_tokenizer.py @@ -0,0 +1,58 @@ +# Copyright 2021, Lefebvre Sarrut Services +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# noinspection DuplicatedCode +import os +from typing import Dict + +import numpy as np + +# noinspection PyUnresolvedReferences +import triton_python_backend_utils as pb_utils +from transformers import AutoTokenizer, PreTrainedTokenizer, TensorType + + +class TritonPythonModel: + is_tensorrt: bool + tokenizer: PreTrainedTokenizer + + def initialize(self, args: Dict[str, str]): + # more variables in https://github.com/triton-inference-server/python_backend/blob/main/src/python.cc + path: str = os.path.join(args["model_repository"], args["model_version"]) + model_name: str = args["model_name"] + self.is_tensorrt = "tensorrt" in model_name + self.tokenizer = AutoTokenizer.from_pretrained(path) + + def execute(self, requests): + responses = [] + # for loop for batch requests (disabled in our case) + for request in requests: + # binary data typed back to string + query = [t.decode("UTF-8") for t in pb_utils.get_input_tensor_by_name(request, "TEXT").as_numpy().tolist()] + tokens: Dict[str, np.ndarray] = self.tokenizer(text=query, return_tensors=TensorType.NUMPY) + if self.is_tensorrt: + # tensorrt uses int32 as input type, ort uses int64 + tokens = {k: v.astype(np.int32) for k, v in tokens.items()} + # communicate the tokenization results to Triton server + input_ids = pb_utils.Tensor("input_ids", tokens["input_ids"]) + attention = pb_utils.Tensor("attention_mask", tokens["attention_mask"]) + outputs = [input_ids, attention] + if "token_type_ids" in tokens.keys(): + token_type_ids = pb_utils.Tensor("token_type_ids", tokens["token_type_ids"]) + outputs.append(token_type_ids) + + inference_response = pb_utils.InferenceResponse(output_tensors=outputs) + responses.append(inference_response) + + return responses diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 00000000..d754dd37 --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2021, Lefebvre Sarrut Services +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/tests/test_triton.py b/tests/test_triton.py new file mode 100644 index 00000000..aa8c5cd1 --- /dev/null +++ b/tests/test_triton.py @@ -0,0 +1,195 @@ +# Copyright 2021, Lefebvre Sarrut Services +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import tempfile +from pathlib import Path + +import pytest +from transformers import AutoTokenizer, PreTrainedTokenizer + +from transformer_deploy.templates.triton import Configuration, ModelType + + +@pytest.fixture +def working_directory() -> tempfile.TemporaryDirectory: + return tempfile.TemporaryDirectory() + + +@pytest.fixture +def conf(working_directory: tempfile.TemporaryDirectory): + conf = Configuration( + model_name="test", + model_type=ModelType.ONNX, + batch_size=0, + nb_output=2, + nb_instance=1, + include_token_type=False, + workind_directory=working_directory.name, + ) + return conf + + +def test_model_conf(conf: Configuration): + expected = """ +name: "test_onnx_model" +max_batch_size: 0 +platform: "onnxruntime_onnx" +default_model_filename: "model.bin" + +input [ + { + name: "input_ids" + data_type: TYPE_INT64 + dims: [-1, -1] + }, + + { + name: "attention_mask" + data_type: TYPE_INT64 + dims: [-1, -1] + } + +] + +output { + name: "output" + data_type: TYPE_FP32 + dims: [-1, 2] +} + +instance_group [ + { + count: 1 + kind: KIND_GPU + } +] +""" # noqa: W293 + assert expected.strip() == conf.get_model_conf() + + +def test_tokenizer_conf(conf: Configuration): + expected = """ +name: "test_onnx_tokenize" +max_batch_size: 0 +backend: "python" + +input [ + { + name: "TEXT" + data_type: TYPE_STRING + dims: [ -1 ] + } +] + +output [ + { + name: "input_ids" + data_type: TYPE_INT64 + dims: [-1, -1] + }, + + { + name: "attention_mask" + data_type: TYPE_INT64 + dims: [-1, -1] + } + +] + +instance_group [ + { + count: 1 + kind: KIND_GPU + } +] +""" # noqa: W293 + assert expected.strip() == conf.get_tokenize_conf() + + +def test_inference_conf(conf: Configuration): + expected = """ +name: "test_onnx_inference" +max_batch_size: 0 +platform: "ensemble" + +input [ + { + name: "TEXT" + data_type: TYPE_STRING + dims: [ -1 ] + } +] + +output { + name: "output" + data_type: TYPE_FP32 + dims: [-1, 2] +} + +ensemble_scheduling { + step [ + { + model_name: "test_onnx_tokenize" + model_version: -1 + input_map { + key: "TEXT" + value: "TEXT" + } + output_map [ + { + key: "input_ids" + value: "input_ids" + }, + + { + key: "attention_mask" + value: "attention_mask" + } + ] + }, + { + model_name: "test_onnx_model" + model_version: -1 + input_map [ + { + key: "input_ids" + value: "input_ids" + }, + + { + key: "attention_mask" + value: "attention_mask" + } + ] + output_map { + key: "output" + value: "output" + } + } + ] +} +""" # noqa: W293 + assert expected.strip() == conf.get_inference_conf() + + +def test_create_folders(conf: Configuration, working_directory: tempfile.TemporaryDirectory): + fake_model_path = os.path.join(working_directory.name, "fake_model") + open(file=fake_model_path, mode="a").close() + tokenizer: PreTrainedTokenizer = AutoTokenizer.from_pretrained("philschmid/MiniLM-L6-H384-uncased-sst2") + conf.create_folders(model_path=fake_model_path, tokenizer=tokenizer) + for folder_name in [conf.model_folder_name, conf.tokenizer_folder_name, conf.inference_folder_name]: + path = Path(conf.workind_directory).joinpath(folder_name) + assert path.joinpath("config.pbtxt").exists() + assert path.joinpath("1").exists() diff --git a/triton.py b/triton.py deleted file mode 100644 index 219ece58..00000000 --- a/triton.py +++ /dev/null @@ -1,33 +0,0 @@ -import tritonclient.http -import numpy as np -from utils import print_timings, setup_logging, track_infer_time - -model_name = 'sts' -url = '127.0.0.1:8000' -model_version = '1' -nb_tokens = 16 # edit to check longer sequence length - -setup_logging() -triton_client = tritonclient.http.InferenceServerClient(url=url, verbose=False) -time_buffer = list() - -input0 = tritonclient.http.InferInput('input_ids', (1, nb_tokens), 'INT64') -input1 = tritonclient.http.InferInput('attention_mask', (1, nb_tokens), 'INT64') -input1.set_data_from_numpy(np.ones((1, nb_tokens), dtype=np.int64), binary_data=False) -output = tritonclient.http.InferRequestedOutput('model_output', binary_data=False) - - -def perform_random_inference(): - input0.set_data_from_numpy(np.random.randint(10000, size=(1, nb_tokens), dtype=np.int64), binary_data=False) - triton_client.infer(model_name, model_version=model_version, inputs=[input0, input1], outputs=[output]) - - -# warmup -for _ in range(10000): - perform_random_inference() - -for _ in range(1000): - with track_infer_time(time_buffer): - perform_random_inference() - -print_timings(name=f"triton, # tokens: {nb_tokens}", timings=time_buffer) diff --git a/triton_models/sts/1/.gitignore b/triton_models/sts/1/.gitignore deleted file mode 100644 index 94548af5..00000000 --- a/triton_models/sts/1/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -* -*/ -!.gitignore diff --git a/triton_models/sts/config.pbtxt b/triton_models/sts/config.pbtxt deleted file mode 100644 index 9fb79283..00000000 --- a/triton_models/sts/config.pbtxt +++ /dev/null @@ -1,29 +0,0 @@ -name: "sts" -platform: "onnxruntime_onnx" -max_batch_size: 0 - -input [ - { - name: "input_ids" - data_type: TYPE_INT64 - dims: [-1, -1] - }, - { - name: "attention_mask" - data_type: TYPE_INT64 - dims: [-1, -1] - } -] - -output { - name: "model_output" - data_type: TYPE_FP32 - dims: [-1, 2] -} - -instance_group [ - { - count: 1 - kind: KIND_GPU - } -] diff --git a/triton_models/tokenize/1/model.py b/triton_models/tokenize/1/model.py deleted file mode 100644 index 66cea727..00000000 --- a/triton_models/tokenize/1/model.py +++ /dev/null @@ -1,24 +0,0 @@ -from transformers import AutoTokenizer, TensorType -import triton_python_backend_utils as pb_utils - - -class TritonPythonModel: - - def initialize(self, _): - # hard coded path to simplify the code, IRL you serialize locally your tokenizer for stability - self.tokenizer = AutoTokenizer.from_pretrained("philschmid/MiniLM-L6-H384-uncased-sst2") - - def execute(self, requests): - responses = [] - # for loop for batch requests (disabled in our case) - for request in requests: - # binary data typed back to string - query = [t.decode('UTF-8') for t in pb_utils.get_input_tensor_by_name(request, "TEXT").as_numpy().tolist()] - tokens = self.tokenizer(text=query, return_token_type_ids=False, return_tensors=TensorType.NUMPY, max_length=128, truncation=True,) - # communicate the tokenization results to Triton server - input_ids = pb_utils.Tensor("INPUT_IDS", tokens['input_ids']) - attention = pb_utils.Tensor("ATTENTION", tokens['attention_mask']) - inference_response = pb_utils.InferenceResponse(output_tensors=[input_ids, attention]) - responses.append(inference_response) - - return responses diff --git a/triton_models/tokenize/config.pbtxt b/triton_models/tokenize/config.pbtxt deleted file mode 100644 index dcab860b..00000000 --- a/triton_models/tokenize/config.pbtxt +++ /dev/null @@ -1,30 +0,0 @@ -name: "tokenize" -backend: "python" -max_batch_size: 0 -input [ - { - name: "TEXT" - data_type: TYPE_STRING - dims: [ -1 ] - } -] - -output [ - { - name: "INPUT_IDS" - data_type: TYPE_INT64 - dims: [ -1, -1 ] - }, - { - name: "ATTENTION" - data_type: TYPE_INT64 - dims: [ -1, -1 ] - } -] - -instance_group [ - { - count: 1 - kind: KIND_CPU - } -] diff --git a/triton_models/transformers/1/.gitignore b/triton_models/transformers/1/.gitignore deleted file mode 100644 index 94548af5..00000000 --- a/triton_models/transformers/1/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -* -*/ -!.gitignore diff --git a/triton_models/transformers/config.pbtxt b/triton_models/transformers/config.pbtxt deleted file mode 100644 index 49439c21..00000000 --- a/triton_models/transformers/config.pbtxt +++ /dev/null @@ -1,58 +0,0 @@ -name: "transformers" -platform: "ensemble" -max_batch_size: 0 - -input [ - { - name: "TEXT" - data_type: TYPE_STRING - dims: [ -1 ] - } -] - -output { - name: "score" - data_type: TYPE_FP32 - dims: [-1, 2] -} - -ensemble_scheduling { - step [ - { - model_name: "tokenize" - model_version: -1 - input_map { - key: "TEXT" - value: "TEXT" - } - output_map [ - { - key: "INPUT_IDS" - value: "INPUT_IDS" - }, - { - key: "ATTENTION" - value: "ATTENTION" - } - ] - }, - { - model_name: "sts" - model_version: -1 - input_map [ - { - key: "input_ids" - value: "INPUT_IDS" - }, - { - key: "attention_mask" - value: "ATTENTION" - } - ] - output_map { - key: "model_output" - value: "score" - } - } - ] -} diff --git a/triton_python_backend_utils.py b/triton_python_backend_utils.py deleted file mode 100644 index 4789e9e8..00000000 --- a/triton_python_backend_utils.py +++ /dev/null @@ -1,275 +0,0 @@ -# Copyright (c) 2020-2021, NVIDIA CORPORATION. All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# * Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# * Neither the name of NVIDIA CORPORATION nor the names of its -# contributors may be used to endorse or promote products derived -# from this software without specific prior written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY -# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR -# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR -# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY -# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -import numpy as np -import struct - -TRITON_STRING_TO_NUMPY = { - 'TYPE_BOOL': bool, - 'TYPE_UINT8': np.uint8, - 'TYPE_UINT16': np.uint16, - 'TYPE_UINT32': np.uint32, - 'TYPE_UINT64': np.uint64, - 'TYPE_INT8': np.int8, - 'TYPE_INT16': np.int16, - 'TYPE_INT32': np.int32, - 'TYPE_INT64': np.int64, - 'TYPE_FP16': np.float16, - 'TYPE_FP32': np.float32, - 'TYPE_FP64': np.float64, - 'TYPE_STRING': np.object_ -} - - -def serialize_byte_tensor(input_tensor): - """ - Serializes a bytes tensor into a flat numpy array of length prepended - bytes. The numpy array should use dtype of np.object_. For np.bytes_, - numpy will remove trailing zeros at the end of byte sequence and because - of this it should be avoided. - Parameters - ---------- - input_tensor : np.array - The bytes tensor to serialize. - Returns - ------- - serialized_bytes_tensor : np.array - The 1-D numpy array of type uint8 containing the serialized bytes in 'C' order. - Raises - ------ - InferenceServerException - If unable to serialize the given tensor. - """ - - if input_tensor.size == 0: - return () - - # If the input is a tensor of string/bytes objects, then must flatten those - # into a 1-dimensional array containing the 4-byte byte size followed by the - # actual element bytes. All elements are concatenated together in "C" order. - if (input_tensor.dtype == np.object_) or (input_tensor.dtype.type - == np.bytes_): - flattened_ls = [] - for obj in np.nditer(input_tensor, flags=["refs_ok"], order='C'): - # If directly passing bytes to BYTES type, - # don't convert it to str as Python will encode the - # bytes which may distort the meaning - if input_tensor.dtype == np.object_: - if type(obj.item()) == bytes: - s = obj.item() - else: - s = str(obj.item()).encode('utf-8') - else: - s = obj.item() - flattened_ls.append(struct.pack("

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