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https://github.com/MachineLearningSystem/pipedream


https://github.com/MachineLearningSystem/pipedream

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README 

        
# PipeDream: Pipeline Parallelism for DNN Training



This repository contains the source code implementation of the following

papers:

- "[PipeDream: Generalized Pipeline Parallelism for DNN Training](https://www.microsoft.com/en-us/research/publication/pipedream-generalized-pipeline-parallelism-for-dnn-training/)",

  which appeared at SOSP 2019 (`pipedream` branch).

- "[Memory-Efficient Pipeline-Parallel DNN Training](https://www.microsoft.com/en-us/research/publication/memory-efficient-pipeline-parallel-dnn-training/)",

  which appeared at ICML 2021 (`pipedream_2bw` branch).



This work was one as part of Microsoft Research's

[Project Fiddle](https://aka.ms/msr-fiddle). This source code is available

under the [MIT License](LICENSE.txt).



## Directory Structure



### `graph`



This contains a Python implementation of a graph, used by the PipeDream profiler

and optimizer. Profiling scripts in `profiler` generate graph profiles, that can

then be ingested by the optimizer located in `optimizer` to generate a partitioned

model, that can then be fed to the PipeDream runtime.



### `profiler`



Instrumented PyTorch applications which return profiles that can be ingested by

the optimizer.



### `optimizer`



A Python implementation of PipeDream's optimizer.



### `runtime`



PipeDream's runtime, which implements model parallelism, as well as input

pipelining in PyTorch. This can be fused with data parallelism to give hybrid

model and data parallelism, and input pipelining.



## Setup



### Software Dependencies



To run PipeDream, you will need a NVIDIA GPU with CUDA 10.0, GPU driver version 418.56, nvidia-docker2,

and Python 3. On a Linux server with NVIDIA GPU(s) and Ubuntu 16.04, these dependencies can be installed

using,



```bash

bash setup.sh

```



All dependencies are in the nvcr.io/nvidia/pytorch:19.05-py3 container, which can be downloaded using,



```bash

nvidia-docker pull nvcr.io/nvidia/pytorch:19.05-py3

```



To run the PipeDream profiler, you will need to build a new Docker image, which can be done using the

Dockerfile in this directory. Note that the Dockerfile has a dependency on the `pre_hook.patch`  and

`requirements.txt` files in this directory. This container can be built using,



```bash

docker build --tag  .

```



The PyTorch Docker Container can then be run using,



```bash

nvidia-docker run -it -v /mnt:/mnt --ipc=host --net=host  /bin/bash

```



### Data



#### Image Classification

All image classification experiments are run using the ImageNet ILSVC 2012 dataset.

This can be downloaded using the following command (within the docker container above),



```bash

cd scripts; python download_imagenet.py --data_dir 

```



Note that the ImageNet dataset is about 145GB, so this download script can take some time.



#### Translation

All translation experiments are run using the WMT En-De dataset, also used for the MLPerf

translation (RNN) task. This can be downloaded using the instructions in [the MLPerf

repository](https://github.com/mlperf/training_results_v0.5/tree/master/v0.5.0/nvidia/submission/code/translation/pytorch#2-directions).



## End-to-end Workflow



To run a demo, run the following commands (the optimizer and runtime have been verified to work unchanged in `nvcr.io/nvidia/pytorch:19.05-py3`).

More detailed instructions for each of the individual components are in the corresponding directory READMEs,

and more detailed instructions on how to run the main experiments in the SOSP paper are in [`EXPERIMENTS.md`](EXPERIMENTS.md).



[from `pipedream/profiler/image_classification`; you will need to have the changes to PyTorch listed above]

Note that the profiling step must be run with only a single GPU (hence the `CUDA_VISIBLE_DEVICES=0` before the command).



```bash

CUDA_VISIBLE_DEVICES=0 python main.py -a vgg16 -b 64 --data_dir 

```



[from `pipedream/optimizer`]



```bash

python optimizer_graph_hierarchical.py -f ../profiler/image_classification/profiles/vgg16/graph.txt -n 4 --activation_compression_ratio 1 -o vgg16_partitioned

```



[from `pipedream/optimizer`]



```bash

python convert_graph_to_model.py -f vgg16_partitioned/gpus=4.txt -n VGG16Partitioned -a vgg16 -o ../runtime/image_classification/models/vgg16/gpus=4 --stage_to_num_ranks 0:3,1:1

```



[from `pipedream/runtime/image_classification`; run on 4 GPUs (including a single server with 4 GPUs)]



```bash

python main_with_runtime.py --module models.vgg16.gpus=4 -b 64 --data_dir  --rank 0 --local_rank 0 --master_addr  --config_path models/vgg16/gpus=4/hybrid_conf.json --distributed_backend gloo

python main_with_runtime.py --module models.vgg16.gpus=4 -b 64 --data_dir  --rank 1 --local_rank 1 --master_addr  --config_path models/vgg16/gpus=4/hybrid_conf.json --distributed_backend gloo

python main_with_runtime.py --module models.vgg16.gpus=4 -b 64 --data_dir  --rank 2 --local_rank 2 --master_addr  --config_path models/vgg16/gpus=4/hybrid_conf.json --distributed_backend gloo

python main_with_runtime.py --module models.vgg16.gpus=4 -b 64 --data_dir  --rank 3 --local_rank 3 --master_addr  --config_path models/vgg16/gpus=4/hybrid_conf.json --distributed_backend gloo

```



`master IP address` here is the IP address of the rank 0 process. On a server with 4 GPUs, `localhost` can be specified.



When running DP setups, please use the `nccl` backend for optimal performance. When running hybrid setups, please use

the `gloo` backend.



## Code of Conduct



This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [[email protected]](mailto:[email protected]) with any additional questions or comments.



## License



Copyright (c) Microsoft Corporation. All rights reserved.



Licensed under the [MIT](LICENSE.txt) license.