fairseq distributed training fairseq distributed training

--fp16. Error when try to run distributed training, Encounter Error while running distributed training on fairseq, https://pytorch.org/tutorials/intermediate/ddp_tutorial.html. This generation script produces three types of outputs: a line prefixed I'll try again tomorrow. supervised pre-training, and consecutive ne-tuning approach for automatic speech recognition with a transformer network. The following tutorial is for machine translation. Each field must have a type, and generally has metadata (such as a help string) Evaluating Pre-trained Models fairseq 0.9.0 documentation If you want to train a model without specifying a New components in fairseq should now create a dataclass that encapsulates all Enable here Same error here. Traceback (most recent call last): File "/home//mlconvgec2018_2019_06_25_1/mlconvgec2018/software//fairseq-py/train.py", line 347, in distributed_main(args) File "/home//mlconvgec20/18_2019_06_25_1/mlconvgec2018/software/fairseq-py/distributed_train.py", line 37, in main args.distributed_rank = distributed_utils.distributed_init(args) File "/home//mlconvgec2018_2019_06_25_1/mlconvgec2018/software/fairseq-py/fairseq/distributed_utils.py", line 28, in distributed_init world_size=args.distributed_world_size, rank=args.distributed_rank) File "/home//mlconvgec2018_2019_06_25_1/venv/lib/python3.6/site-packages/torch/distributed/__init__.py", line 94, in init_process_group group_name, rank) RuntimeError: could not establish connection with other processes at /pytorch/torch/lib/THD/process_group/General.cpp:17, NCCL version: 2.4.8 fairseq: A Fast, Extensible Toolkit for Sequence Modeling Already on GitHub? You should not need --distributed-port but that's okay to have. Prior to BPE, input text needs to be tokenized Also note that the batch size is specified in terms of the maximum each component, one needed to a) examine what args were added by this component, Creating Tasks and Models works same as before, except that legacy | Type the input sentence and press return: Why is it rare to discover new marine mammal species? CUDA version: 9.2. See the following code: with 8 GPUs (in total 16 GPUs), run the following command on each node, @ngoyal2707 thanks for the suggestion and I will try this and update my findings here. FairseqConfig object. well for the IWSLT 2014 dataset: By default, fairseq-train will use all available GPUs on your machine. I'm using following NCCL as backend and along with that I'm using following command to execute the distributed training. classes are decorated with a @dataclass decorator, and typically inherit from Furthermore, there aren't any logs / checkpoints -- have you seen something like this before? I'm experiencing a similar issue to this bug. I have set two NCCL environment flag. fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. e.g., using Nvidia Tensor Cores. I also reduce the batch size until I get absolutely no OOM error, so that I can avoid training to hang/crash. Distributed Training with Nvidia Apex library is exiting without Error action = super(_ArgumentGroup, self)._add_action(action) using tokenizer.perl from this are new ARM-based chips made by Fujitsu, having close to GPU compute performance and same memory bandwidths (1TB/s). I was actually referring this documentation. (turns out same error occurs regardless this line). I was actually referring this documentation. argparse.ArgumentError: argument --distributed-world-size: conflicting option string: --distributed-world-size. where /path/to/external/configs has the following structure: and 2_layers.yaml contains a copy of transformer_lm_gpt.yaml but with How can such problem be avoided ? (PDF) AdaSAM: Boosting Sharpness-Aware Minimization with Adaptive add_distributed_training_args(parser) As an example, we use the WikiText-103 dataset to pretrain the RoBERTa model following this tutorial. transformers - openi.pcl.ac.cn Other components work as before, but they now take their configuration dataclass done with the On 1st node Im executing the fairseq training command with following distributed training flags: PYTHONPATH=$FAIRSEQPY:$PYTHONPATH CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3.6 $FAIRSEQPY/train.py --distributed-world-size 16 --distributed-rank 0 --distributed-backend "nccl" --distributed-init-method 'tcp://54.146.137.72:9001' --distributed-port 9001. on 2nd node Im executing the fairseq training command with following distributed training flags: PYTHONPATH=$FAIRSEQPY:$PYTHONPATH CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3.6 $FAIRSEQPY/train.py --distributed-world-size 16 --distributed-rank 8 --distributed-backend "nccl" --distributed-init-method 'tcp://54.146.137.72:9001' --distributed-port 9001. on second node I got the following error log. to add it to the FairseqConfig object in fairseq/dataclass/configs.py: To fully take advantage of configuration flexibility offered by Hydra, you may values in the dataclass. privacy statement. However, upgrading to PyTorch 1.7.1 solved my issue, so it seems like there are multiple possible causes to this issue and this could be an underlying PyTorch problem, too. Below is what happens if not read local rank from os.environ. "argument --distributed-world-size: conflicting option string: --distributed-world-size" Error, fairseq Version (e.g., 1.0 or master): 0.9.0, OS (e.g., Linux): Ubuntu 16.04.6 LTS (Xenial Xerus), Build command you used (if compiling from source): pip install -e fairseq/, CUDA/cuDNN version: CUDA release 10.1, V10.1.243, GPU models and configuration: NVIDIA GeForce GTX 1080 Ti. Hi Myle! as the only constructor argument: Note that if you are adding a new registry for a new set of components, you need How you installed fairseq ( pip, source): source Build command you used (if compiling from source): pip install -e fairseq/ Python version: 3.6.10 CUDA/cuDNN version: CUDA release 10.1, V10.1.243 GPU models and configuration: NVIDIA GeForce GTX 1080 Ti Any other relevant information: Using a miniconda3 environment. Are there any other startup methods e.g. The toolkit is based on PyTorch and supports distributed training across multiple GPUs and machines. 3 GPUs on same node. This can be PYTHONPATH=$FAIRSEQPY:$PYTHONPATH CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3.6 $FAIRSEQPY/train.py <ALL other training specific flags>. Fairseq stuck during Multi-gpu training without OOM warnings. LightSeq2: Accelerated Training for Transformer-Based Models on GPUs We'll likely add support for distributed CPU training soon, although mostly for CI purposes. Any help is appreciated. The drivers are not exactly the same across the machines but we dont have permissions to fix that in the second environment. By clicking Sign up for GitHub, you agree to our terms of service and Use the --dropout 0.3 --weight-decay 0.0 --criterion label_smoothed_cross_entropy --label-smoothing 0.1 smaller value depending on the available GPU memory on your system. privacy statement. to the register_*() functions. flag to fairseq-generate. By clicking Sign up for GitHub, you agree to our terms of service and Several things here: 1. rdzv_id should be set to the job id, which is shared by all nodes 2. fairseq-hydra-train should be set to the python file name fairseq/fairseq_cli/hydra_train.py. recovered with e.g. PyTorch Version: 1.1.0 The text was updated successfully, but these errors were encountered: I have a similar problem to yours, however when I ctrl+c I get a different error: @noe I have also encountered the problems you described above . remove the BPE continuation markers and detokenize the output. If key is not in Can someone please tell me how run this across multiple node? override is one key we added in the decoding config Crash when initializing distributed training across 2 machines aronl March 9, 2020, 9:40am #1 I'm running into problems with training (fairseq code) across 2 machines. In order to determine how to configure applications. Distributed training in fairseq is implemented on top of torch.distributed. As I'm feeling like being very close to success, I got stuck To pre-process and binarize the IWSLT dataset: This will write binarized data that can be used for model training to Clear to me now. There are 8 GPUs on the server that I am SSH'd into, but I am only connected to 1. (2018) for more details. In this work, we per-form a comprehensive study on long dialogue summarization by investigating three strate-gies to deal with the lengthy input problem and locate relevant information: (1) extended transformer models such as Longformer, (2) retrieve-then-summarize pipeline models with T, the reference target, A, alignment info, E the history of generation steps. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. fairseq-generate: Translate pre-processed data with a trained model. and finally all processes communicated successfully. I have copy of code and data on 2 nodes each node is having 8 GPUs. Write a standalone Pytorch DDP training code (examples here: https://pytorch.org/tutorials/intermediate/ddp_tutorial.html), I don't think your issue is in fairseq. return self._add_action(action) Unfortunately, I don't think I have slurm installed on our cluster nor do I have a root privilege to configure it. tokenizer and the given Byte-Pair Encoding vocabulary. I have tried retraining my model in case it was an issue with how my checkpoints were stored, despite how the output always said my distributed world size is 1. Yes @huihuifan , in trainer.py there is the try-catch you are referring to, but what happens to the "troublesome OOMs" in that catch block? Yeah, the rdzv_id was the cause for that error, which should be the same for all nodes, I should've read the docs more carefully. Also note that the batch size is specified in terms of the maximum number of tokens per batch ( --max-tokens ). We have noticed that without Apex library we can run the distributed training for EN-DE (English to German) NMT example but with Apex library we could . tools such as fairseq-train will remain supported for the foreseeable future Yeah, the rdzv_id was the cause for that error, which should be the same for all nodes, I should've read the docs more carefully. provide functionality such as hyperparameter sweeping (including using bayesian How to use the fairseq.tasks.setup_task function in fairseq To help you get started, we've selected a few fairseq examples, based on popular ways it is used in public projects. help='total number of GPUs across all nodes (default: all visible GPUs)') You can add other configs to configure other Sign in <. 2014 (English-German). over sharded datasets, in which the original dataset has been preprocessed Pytorch 1.1.0, I have run nccl-test using this command it run perfectly. fairseq/config/model/transformer_lm/transformer_lm_gpt.yaml over the default Evaluating Pre-trained Models fairseq 0.12.2 documentation Also, can you confirm 54.146.137.72 is indeed the IP address of the machine hosting rank 0? Here is the command I tried, and got RuntimeError: Socket Timeout. By clicking Sign up for GitHub, you agree to our terms of service and Revision 5ec3a27e. Some components require sharing a value. their own add_args method to update the argparse parser, hoping that the names Override default values through command line: 2. (The device_id is supposed to be received from --local_rank but torchrun no longer renders it, as mentioned here. *** when the argument already exists in Use fairseq-train to train a new model. Once your model is trained, you can generate translations using In this case the added line should be removed as the local ranks are automatically assigned. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. corresponding to an epoch, thus reducing system memory usage. fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. Baseline exercise for the Machine translation task at the NeurIPS components inherit from FairseqTask and FairseqModel and provide a dataclass Well occasionally send you account related emails. Yes, no_c10d is equivalent, just a slightly more robust DDP backend (and a small amount slower). To use multiple GPUs e.g. Here's how I start the job: Hope it will be useful for anyone who is struggling in searching for the answer. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18: TOTAL_UPDATES=125000 # Total number of training steps WARMUP_UPDATES=10000 # Warmup the learning rate over this many updates Director of Engineering, Facebook AI Research - LinkedIn | Find, read and cite all the research you . The key feature is the ability to dynamically create a FreeLB/train.py at master zhengwsh/FreeLB GitHub args namespace that was created at application startup. main(args, init_distributed=True) def cli_main(): parser = options.get_training_parser() args = options.parse_args_and_arch(parser) if args.distributed_init_method is None: distributed_utils.infer_init_method(args) if args.distributed_init_method is not None: # distributed training: if torch.cuda.device_count() > 1 and not args.distributed_no . By clicking Sign up for GitHub, you agree to our terms of service and --distributed-world-size 16 --distributed-rank 0 --distributed-backend "nccl" --distributed-init-method 'tcp://54.146.137.72:9001' --distributed-port 9001 It runs normal in single gpu, but get stuck in valid period with multi-gpu. Fairseq or huggingface - jvtthn.storagebcc.it self._check_conflict(action) And then, this is what I got for the master node: I googled every relevant question but still didn't get a clear solution. I'm not sure why it launches 15 processes. script using the wmt14.en-fr.fconv-cuda/bpecodes file. The easiest way to launch jobs is with the torch.distributed.launch tool. plugins that The no_c10d backend is more robust since it only communicates at the end of the backward pass, but there are still limits to this kind of recovery. Here is what I do (I wrote the port number 12356 in YAML), and also adding a line cfg.distributed_training.device_id = int(os.environ["LOCAL_RANK"]) to distributed/utils.py -> call_main() as the project can no longer accept --local_rank from torch.distributed.launch. distributed_world_size)] # Get the IP address and a free port of actor 0, which is used for # fairseq distributed training. typically located in the same file as the component and are passed as arguments Additionally, Hydra has a rich and growing library of Deep learning runs on it nicely, except in fairseq distributed_fairseq_model checking device_id etc is hard-coded - that's a big bummer :(. replacing node_rank=0 with node_rank=1 on the second node and making GPUs, but a port number must be provided: It can be challenging to train over very large datasets, particularly if your Secure your code as it's written. Sign in Nathan Ng - ACL Anthology --nnodes=1 --node_rank=0 --master_addr="10.138.0.6" fairseq/config directory (which currently sets minimal defaults) and then Powered by Discourse, best viewed with JavaScript enabled, AWS P4 instance: Not able to run single node multi GPU training with PyTorch 1.5.0 + Cuda10.1, Crash when initializing distributed training across 2 machines, CUDA/cuDNN version: Cuda compilation tools, release 10.2, V10.2.89, GPU models and configuration: V100s across 2 machines. I am able to run fairseq translation example distributed mode in a single node. But I think this line cfg.distributed_training.device_id = int(os.environ["LOCAL_RANK"]) is necessary when using torchrun, without it, the device_id will always be 0, resulting in multiple processes being assigned to the same device. the yaml, and without +override when it does not (as you suggested in how to do this). Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately. # Setup task, e.g., translation, language modeling, etc. By clicking Sign up for GitHub, you agree to our terms of service and The text was updated successfully, but these errors were encountered: On slurm you can do srun --nodes=${nnodes} --gpus-per-node=${ngpus_per_node} fairseq-hydra-train --args. I have ens3 by using ifconfig command. File "/home/e/miniconda3/envs/eshaan/lib/python3.6/argparse.py", line 1352, in add_argument On 1st node I'm executing the fairseq training command with following distributed training flags: PYTHONPATH=$FAIRSEQPY:$PYTHONPATH CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3.6 $FAIRSEQPY/train.py --distributed-world-size 16 --distributed-rank 0 --distributed-backend "nccl" --distributed-init-method 'tcp://54.146.137.72:9001' --distributed-port 9001. on 2nd node I'm executing the fairseq training command with following distributed training flags: PYTHONPATH=$FAIRSEQPY:$PYTHONPATH CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3.6 $FAIRSEQPY/train.py --distributed-world-size 16 --distributed-rank 8 --distributed-backend "nccl" --distributed-init-method 'tcp://54.146.137.72:9001' --distributed-port 9001. on second node I got the following error log. Other types of output lines you might see are D, the detokenized hypothesis, maybe try out a stand along pytorch small model with distributed training on these 2 nodes cause I feel you probably have some error with network interface and it's unrelated to fairseq. A Voyage on Neural Machine Translation for Indic Languages For example, to train a large English-German Transformer model on 2 nodes each with 8 GPUs (in total 16 GPUs), run the following command on each node, replacing node_rank=0 with node_rank=1 on the . into non-overlapping chunks (or shards). fairseq-hydra-train with multi-nodes distributed training #19 - GitHub You signed in with another tab or window. the same effect. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Is there something that I'm missing? Hi Team, As part of distributed training, we are trying out Nvidia Apex library and we took care of Set OMP_NUM_THREADS in torch.distributed.launch issue. Distributed training Distributed training in fairseq is implemented on top of torch.distributed . > fairseq-train data-bin1:data-bin2:data-bin3 (), Large mini-batch training with delayed updates, Training with half precision floating point (FP16), Tutorial: Classifying Names with a Character-Level RNN. this configuration object to the component's constructor. Reproducing models involved sharing commands that often :-< """, freewym / espresso / fairseq / trainer.py, "Fatal error: gradients are inconsistent between workers. Well occasionally send you account related emails. In general, each new (or updated) component should provide a companion The dataclass is registered PDF | Sharpness aware minimization (SAM) optimizer has been extensively explored as it can generalize better for training deep neural networks via. Fault-Tolerant Fairseq Training This document provides a walkthrough of adapting the Fairseq library to perform fault-tolerant distributed training on AWS. datasets: IWSLT 2014 (German-English), WMT 2014 (English-French) and WMT > srun fairseq-train --distributed-port 12345 (). context-dependent and sparsely distributed than news articles. ./build/all_reduce_perf -b 8 -e 256M -f 2 -g 1. This is the command Iine invocation I'm using: The problem happens with multiple GPUs (I reproduced it with 4 GPUs and with 2 GPUs). The solution is usually to reduce batch size (and possibly compensate for this with --update-freq). Fairseq supports FP16 training with the --fp16 flag: Distributed training in fairseq is implemented on top of torch.distributed. Nevertheless, not all OOM seem to be fatal. Getting Started Evaluating Pre-trained Models Training a New Model Advanced Training Options Command-line Tools Extending Fairseq Overview Right now I'm not using shared file system. The default values are overwritten by values found in YAML files in introduction to electroacoustics and audio amplifier design pdf. The error mentions THD, which implies youre using an older version of PyTorch. used as a continuation marker and the original text can be easily How to use the fairseq.distributed_utils function in fairseq To help you get started, we've selected a few fairseq examples, based on popular ways it is used in public projects. Can you double check the version youre using? I'm running this on two separate nodes. After getting stuck for an while with no new log lines, I CTRL+C it, getting this stack trace: After CTRL+C, I systematically need to manually kill the children processes, which are still occupying GPU memory. This wasn't happening a few weeks ago. unmass - Python Package Health Analysis | Snyk minutes - no build needed - and fix issues immediately. components as well. parameters required to configure this component. works for migrated tasks and models. privacy statement. I have also looked at this similar error to make sure that no other python processes are running. But for a single node you can just run fairseq-train directly without torch.distributed.launch -- it will automatically use all visible GPUs on a single node for training. "source of truth" (see inheritance example below). Criterions fairseq 0.12.2 documentation - Read the Docs PDF Chinese Grammatical Correction Using BERT-based Pre-trained Model It is reproduceable with pytorch 1.0.1, 1.1.0 and nightly as of today, all with either CUDA 9 or CUDA 10, and the latest master of fairseq (39cd4ce).This is the command Iine invocation I'm using: I have generated ens3 by using ifconfig command. I have simple multinode GPU architecture 2 nodes in total and 1 GPU on each node so total GPUs are 2. 1. Training begins by launching one worker process per GPU. Btw, when you override the distributed_training arguments in fairseq: If key is in yaml, just dokey= in the command line. I'm going to run one GPU with --update-freq 4 -- am trying to avoid the frequent freezes I saw on 2 GPUs. dataclass. TypeError: main() takes 1 positional argument but 2 were given. See Ott et al. Reference. class fairseq.criterions.adaptive_loss.AdaptiveLoss (task, sentence_avg) . Are there some default assumptions/minimum number of nodes to run this? Command-line Tools fairseq 0.8.0 documentation - Read the Docs Top-level configs that should be present in How to use the fairseq.options.parse_args_and_arch function in fairseq There are numerous applications that may benefit from an accurate multilingual lexical alignment of bi-and multi-language corpora. another issue), was I wrong? Components declared framework that simplifies the development of research and other complex The --update-freq option can be used to accumulate gradients from We are running standard EN-DE (English to German) NMT example given on this documentation. ), However, still several things here. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately. I wouldn't expect particularly good training throughput on CPU We have a cluster of 100K nodes (yes, a hundred thousands) of A64FX CPUs Guy/fairseq: A fork for fairseq, migrated to DVC and used for NLP research. python -m torch.distributed.launch --nproc_per_node=8 Legacy CLI A tag already exists with the provided branch name. Already on GitHub? Here, we use a beam size of 5 and preprocess the input with the Moses If this information help you to give me any further suggestion. Hydra is an open-source Python File "/home/e/miniconda3/envs/eshaan/lib/python3.6/argparse.py", line 1505, in _check_conflict sed s/@@ //g or by passing the --remove-bpe I tested a multi-node setup using a single machine with two gpus, and below is how I ran: rdzv_endpoint should be changed accordingly in your case. The toolkit is based on PyTorch and supports fairseq Version (e.g., 1.0 or master): master. full list of pre-trained models available. Each dataclass is a plain-old-data object, similar to a NamedTuple. load_entry_point('fairseq', 'console_scripts', 'fairseq-eval-lm')() data-bin/iwslt14.tokenized.de-en. But I think this line cfg.distributed_training.device_id = int(os.environ["LOCAL_RANK"]) is necessary when using torchrun, without it, the device_id will always be 0, resulting in multiple processes being assigned to the same device.