fairseq distributed training fairseq distributed training

Distributed transitions (mismatches between training and deployment data) are ubiquitous in real-world missions and pose a major challenge to the safe and reliable use of AI systems. privacy statement. With the invention of deep learning concepts, Machine Translation (MT) migrated towards Neural Machine Translation (NMT) architectures, eventually from Statistical Machine Translation (SMT), which ruled MT for a few decades. Hi Myle! a direct solution is to move these files into each relative folder under fairseq. Hydra Integration doc should refer to non legacy task (, https://github.com/pytorch/fairseq/blob/master/CONTRIBUTING.md. Distributed training. > curl https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2 | tar xvjf -, --beam 5 --source-lang en --target-lang fr \, --bpe subword_nmt --bpe-codes $MODEL_DIR/bpecodes, | loading model(s) from wmt14.en-fr.fconv-py/model.pt. Any help is much appreciated. By default, fairseq-train will use all available GPUs on your machine. apply_bpe.py Any help is much appreciated. 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 . Command-line Tools. We plan to create a new, cleaner implementation soon. to your account, I am trying to run distributed training on 2 nodes with 8 GPUs each (K80) in total 16 GPUs. directory, you can split the data and create data-bin1, data-bin2, etc. --distributed-world-size 16 --distributed-rank 0 --distributed-backend "nccl" --distributed-init-method 'tcp://54.146.137.72:9001' --distributed-port 9001 Delayed updates can also improve training speed by reducing First, download a pre-trained model along with its vocabularies: This model uses a Byte Pair Encoding (BPE) self._check_conflict(action) privacy statement. I have generated ens3 by using ifconfig command. This wasn't happening a few weeks ago. I'm not sure why it launches 15 processes. Closing for now, please reopen if you still have questions! 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. Learn how to use python api fairseq.fp16_trainer.FP16Trainer examples that others can use to run an identically configured job. to your account. Fairseq is an open-source sequence modelling toolkit that allows researchers and developers to train custom models for translation, summarisation, language modelling, and other text generation tasks. Now I'm not sure where to go next. to the register_*() functions. See the following code: raise ArgumentError(action, message % conflict_string) 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. top-level fields (such as "model", "dataset", etc), and placing config files 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. to your account, After training my model, I would like to evaluate it; however, I run into an argument parse error, as seen below. with O is a copy of the original source sentence; H is the Legacy CLI Well occasionally send you account related emails. Fairseq stuck during Multi-gpu training without OOM warnings. The toolkit is based on PyTorch and supports distributed training across multiple GPUs and machines. dataclass. The toolkit is based on PyTorch and supports distributed training across multiple GPUs and machines. values in the dataclass. You should not need --distributed-port but that's okay to have. their own add_args method to update the argparse parser, hoping that the names FairseqDataclass (which adds some functionality for backward compatibility). decoder_layers set to 2. --nnodes=1 --node_rank=0 --master_addr="10.138.0.6" I think there might still be an issue here. I'm seeing something similar - when running on two nodes, I see 7 processes on each (rank (0-6) and rank (4-10)). components inherit from FairseqTask and FairseqModel and provide a dataclass similar jobs - much like a Hydra with multiple heads. > 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. The script worked in one of our cloud environments, but not in another and I'm trying to figure out why. --master_port=8085 FairseqConfig object. 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. The key feature is the ability to dynamically create a python code examples for fairseq.fp16_trainer.FP16Trainer. Enable here ", fairseq.models.register_model_architecture, how to pass a list into a function in python, how to sort a list in python without sort function, reverse words in a string python without using function, fibonacci series using function in python. (turns out same error occurs regardless this line). components as well. script using the wmt14.en-fr.fconv-cuda/bpecodes file. with meaningful names that would populate that specific section of your Hi guys! I also reduce the batch size until I get absolutely no OOM error, so that I can avoid training to hang/crash. datasets: IWSLT 2014 (German-English), WMT 2014 (English-French) and WMT Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. --lr 0.0005 --min-lr 1e-09 full list of pre-trained models available. PyTorch Version: 1.1.0 applications, this became problematic. 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. 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). There are 8 GPUs on the server that I am SSH'd into, but I am only connected to 1. examples/ directory. number of tokens per batch (--max-tokens). I have copy of code and data on 2 nodes each node is having 8 GPUs. This issue has been automatically marked as stale. These dataclass are but will be deprecated eventually. 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. applications <. Prior to BPE, input text needs to be tokenized Here, we use a beam size of 5 and preprocess the input with the Moses Any help is appreciated. I'll try again tomorrow. Most tasks in fairseq support training Sign in batch size. using tokenizer.perl from The default values are overwritten by values found in YAML files in Setting this to True will improves distributed training speed. While configuring fairseq through command line (using either the legacy argparse I have set two NCCL environment flag. Note that this assumes that there is an "optimization" config To address this issue, Tiedemann proposed a methodology that leverages time-based alignment and lexical resynchronization techniques in combination with BLEU score metrics to categorize substitute translation versions into groups, employing the measures of edit distance and heuristics [ 12 ]. to the register_*() functions. 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). We are running standard EN-DE (English to German) NMT example given on this documentation. and b) read the code to figure out what shared arguments it is using that were --arch transformer_vaswani_wmt_en_de_big --share-all-embeddings Have a question about this project? Training with fairseq-hydra-train To fully take advantage of configuration flexibility offered by Hydra, you may want to train new models using the fairseq-hydra-train entry point. Distributed training Distributed training in fairseq is implemented on top of torch.distributed . Well occasionally send you account related emails. I'm using AWS cloud platform. Have a question about this project? T, the reference target, A, alignment info, E the history of generation steps. what happens to the "troublesome OOMs" in that catch block? I tried replace torch.distributed.launch by torchrun which solved the local_rank issue but still didn't seem to make everything correct. Have a question about this project? CUDA 10.1 (I think it worked in your test case because you have only one process for each node and also specified CUDA_VISIBLE_DEVICES=1 for the second. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately. however the defaults from each dataclass will still be used (unless overwritten Same error here. Lexical alignment is one of the most challenging tasks in processing and exploiting parallel texts. Additionally you can choose to break up your configs by creating a directory fairseq/config/model/transformer_lm/transformer_lm_gpt.yaml over the default Fairseq provides several command-line tools for training and evaluating models: fairseq-preprocess: Data pre-processing: build vocabularies and binarize training data; fairseq-train: Train a new model on one or multiple GPUs; fairseq-generate: Translate pre-processed data with a trained model; fairseq-interactive: Translate raw text with a trained model corresponding to an epoch, thus reducing system memory usage. privacy statement. You Here a few example settings that work I think it should be similar as running usual pytorch multi-node applications: , where you need to specify other arguments like HOST_NODE_ADDR. The dataclass is registered Usually this causes it to become stuck when the workers are not in sync. Python version is 3.6. File "/home/e/miniconda3/envs/eshaan/lib/python3.6/argparse.py", line 1505, in _check_conflict done with the classes are decorated with a @dataclass decorator, and typically inherit from parameters required to configure this component. using torchrun or something that can work with hydra-train? When I run eval_lm with the argument "--distributed-world-size 1" it fails: File "eval_lm.py", line 11, in Here's how I start the job: Hope it will be useful for anyone who is struggling in searching for the answer. If key is not in the yaml, use +key=. override is one key we added in the decoding config, which is only used at test time. The text was updated successfully, but these errors were encountered: I encountered this bug as well. GitHub on Nov 10, 2020 on Nov 10, 2020 dist.all_reduce (torch.zeros (1).cuda ()) RuntimeError: CUDA error: out of memory Environment fairseq Version (e.g., 1.0 or master): master PyTorch Version (e.g., 1.0): 1.7+cuda11 OS (e.g., Linux): Ubuntu 20.04 python -m torch.distributed.launch --nproc_per_node=8 fairseq-generate: Translate pre-processed data with a trained model. S-0 Why is it rare to discover new marine mam@@ mal species ? If you're using --ddp-backend=c10d then troublesome OOMs can cause hangs. Hi PyTorch Community Members, I am trying to run distributed training on 2 nodes with 8 GPUs each (K80) in total 16 GPUs. classmethod reduce_metrics (logging_outputs: List[Dict[str, Any]]) None [source] Aggregate logging outputs from data parallel training. Here, we briey describe the three methods with the highest performance. Le stage comprendra le traitement de donnes internes, la conception exprimentale, l'entranement de modles dans un environnement informatique distribu, l'analyse des rsultats et la prsentation de vos conclusions. Enable here I encountered same problem even set --ddp-backend=no_c10d. along with the component, and fairseq takes care of constructing and providing Some components require sharing a value. Are you sure you want to create this branch? We'll likely add support for distributed CPU training soon, although mostly for CI purposes. to your account. I have referred the following issues to resolve the issue but seems it didnt help me much. Btw, when you override the distributed_training arguments in fairseq: If key is in yaml, just dokey= in the command line. How to run fairseq distributed mode in multiple nodes scenario? File "/home/e/miniconda3/envs/eshaan/lib/python3.6/argparse.py", line 1352, in add_argument Fairseq provides several command-line tools for training and evaluating models: fairseq-preprocess: Data pre-processing: build vocabularies and binarize training data. machine does not have much system RAM. 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. If you want to train a model without specifying a 3 GPUs on same node. For example, a learning rate scheduler # Load valid dataset (we load training data below, based on the latest checkpoint), ecchochan / roberta-squad / fairseq_train_cn.py, ##############################################################################, 'Learning rate decay factor, 1.0 = no decay', 'Number of layers for learning rate decay', distributed_utils.infer_init_method(args), # fallback for single node with multiple GPUs, ecchochan / roberta-squad / fairseq_train_embed_cn.py, # gather logging outputs from all replicas, 'Fatal error: gradients are inconsistent between workers', '| WARNING: OOM in all workers, skipping update', zhiqwang / sightseq / sightseq / train.py, ecchochan / roberta-squad / fairseq_train_mnli_cn.py, '| WARNING: ran out of memory, retrying batch', # aggregate logging outputs and sample sizes, '(can be set to sentencepiece). We are sorry that we haven't been able to prioritize it yet. Fairseq supports FP16 training with the --fp16 flag: Distributed training in fairseq is implemented on top of torch.distributed. If this information help you to give me any further suggestion. Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. You signed in with another tab or window. Take a look at the following open source projects on Github with a star average of 3558. GPUs, but a port number must be provided: It can be challenging to train over very large datasets, particularly if your flag to fairseq-generate. 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. 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 . Torch Version: 1.1.0 Im using AWS cloud platform. You signed in with another tab or window. Is there something that Im missing? parameters can optionally still work, but one has to explicitly point to the fairseq-train: Train a new model on one or multiple GPUs. And then, this is what I got for the master node: I googled every relevant question but still didn't get a clear solution. global config file and added to the Already on GitHub? Thank you @pietern and @zhangguanheng66 for your suggestion. 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. *** when the argument already exists in The toolkit is based on PyTorch and supports distributed training across multiple GPUs and machines. | Find, read and cite all the research you . class fairseq.criterions.adaptive_loss.AdaptiveLoss (task, sentence_avg) . dataset.batch_size, this also tells Hydra to overlay configuration found in I think it was caused by the out-of-memory , so I had to reduce batch-size so that the program could work properly. Until recently, all components in fairseq were configured through a shared Override default values through command line: 2. Fairseq supports FP16 training with the --fp16 flag: > fairseq-train --fp16 (.) to your account. The training always freezes after some epochs. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. [fairseq#708] Training get stuck at some iteration steps. I have simple multinode GPU architecture 2 nodes in total and 1 GPU on each node so total GPUs are 2. Fairseq is a sequence modeling toolkit written in PyTorch that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. Also note that the batch size is specified in terms of the maximum Well occasionally send you account related emails. fairseq Version (e.g., 1.0 or master): master. The prerequisites of the Fairsq installation are configured in Ubuntu18 DLAMI. this configuration object to the component's constructor. I suggest you to open up an issue on pytorch/issues. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. These files can also be shipped as Already on GitHub? inter-GPU communication costs and by saving idle time caused by variance #463 Closed If I change to --ddp-backend=no_c10d, should I expect the same results? contained dozens of command line switches. While this model works for distributed_world_size)] # Get the IP address and a free port of actor 0, which is used for # fairseq distributed training. Fairseq contains example pre-processing scripts for several translation privacy statement. Only primitive types or other config objects are allowed as Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Replace bundled configs with an external config: 3. and the command line. Furthermore, there aren't any logs / checkpoints -- have you seen something like this before? 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. For example, to train a large English-German Transformer model on 2 nodes each . 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: Distributed training in fairseq is implemented on top of torch.distributed. You signed in with another tab or window. replacing node_rank=0 with node_rank=1 on the second node and making By clicking Sign up for GitHub, you agree to our terms of service and further overwritten by values provided through command line arguments. The method functions to automatically interpret flight commands from the air traffic control (ATC) stream. Do you have any suggestion, my hero @chevalierNoir. Im running into problems with training (fairseq code) across 2 machines. How to use fairseq-hydra-train with multi-nodes. (2018) for more details. can then specify the correct configuration via command line, defaults in the add_distributed_training_args(parser) PYTHONPATH=$FAIRSEQPY:$PYTHONPATH CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3.6 $FAIRSEQPY/train.py <ALL other training specific flags>. File "/srv/home/e/eshaan/fairseq/fairseq_cli/eval_lm.py", line 251, in cli_main By clicking Sign up for GitHub, you agree to our terms of service and in workload across GPUs. We also support fast mixed-precision training . files), while specifying your own config files for some parts of the On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the . I am using the command lines from here and have slightly modified them where I am using a patience of 3, no-epoch-checkpoints, removed fp16, and distributed-world-size of 1 when training. I am having the same issue actually? File "/home/e/miniconda3/envs/eshaan/lib/python3.6/argparse.py", line 1514, in _handle_conflict_error configuration. I am able to run fairseq translation example distributed mode in a single node. In order to determine how to configure argparse.ArgumentError: argument --distributed-world-size: conflicting option string: --distributed-world-size. hierarchical configuration by composition and override it through config files Install FairSEQ.Fairseq (-py) is a sequence modeling toolkit that allows you to train custom models for translation, summarization, language modeling, and other text-generation tasks. I have also looked at this similar error to make sure that no other python processes are running. A tag already exists with the provided branch name. fairseq/config directory (which currently sets minimal defaults) and then The toolkit is based on PyTorch and supports Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately. used as a continuation marker and the original text can be easily Pytorch 1.1.0, I have run nccl-test using this command it run perfectly. One can >_<. To train on a single GPU with an effective batch size that is equivalent You signed in with another tab or window. After printing the following, no further messages printed, processes hang. TypeError: main() takes 1 positional argument but 2 were given. This is because the c10d DistributedDataParallel module communicates gradients during the backward pass, so we can't really recover from an OOM during the backward pass. Thanks again for the clarification. I'm experiencing a similar issue to this bug. args namespace that was created at application startup. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Sign in As an example, we use the WikiText-103 dataset to pretrain the RoBERTa model following this tutorial. Sign in https://fairseq.readthedocs.io/en/latest/getting_started.html#distributed-training typically located in the same file as the component and are passed as arguments In general, each new (or updated) component should provide a companion Distributed training in fairseq is implemented on top of torch.distributed. Could you rerun your script with NCCL_DEBUG=INFO and post the output, please? https://fairseq.readthedocs.io/en/latest/getting_started.html#distributed-training. launching across various platforms, and more. with 8 GPUs (in total 16 GPUs), run the following command on each node, Well occasionally send you account related emails. I'm going to run one GPU with --update-freq 4 -- am trying to avoid the frequent freezes I saw on 2 GPUs. 1 2 fairseq_cli/train.py cli_main () parser # parser parser = options.get_training_parser() 1 2 get_training_parser () fairseq/options.py get_parser () parser task criterion add_dataset_args () parser cli_main() take advantage of configuring fairseq completely or piece-by-piece through 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. Was this problem solved? this are new ARM-based chips made by Fujitsu, having close to GPU compute performance and same memory bandwidths (1TB/s). "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. I'm using following NCCL as backend and along with that I'm using following command to execute the distributed training. 81 were used as training data and two thousand sentences from the PKU Chinese Learner Corpus (Zhao et al.,2018) were used as test data. I have set two NCCL environment flag. It will automatically @ngoyal2707 thanks for the suggestion and I will try this and update my findings here. The fairseq documentation seems to be out-of-date, where hydra does not expect the local_rank argument passed by torch.distributed.launch. This allows combining default configuration (including using any bundled config Did you resolve this issue? action = super(_ArgumentGroup, self)._add_action(action) works for migrated tasks and models. Revision 5ec3a27e. I have set two NCCL environment flag $ export NCCL_SOCKET_IFNAME=ens3 $ export NCCL_DEBUG=INFO On 1st node I'm executing the fairseq training . 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. 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 First,Fu et al. CUDA version: 9.2. @@ is conflict_handler(action, confl_optionals) :-< "source of truth" (see inheritance example below). It runs normal in single gpu, but get stuck in valid period with multi-gpu. help='total number of GPUs across all nodes (default: all visible GPUs)') I thought there should be +override. recovered with e.g. Other components work as before, but they now take their configuration dataclass ./build/all_reduce_perf -b 8 -e 256M -f 2 -g 1. We try to catch OOM by skipping the batch, but sometimes it doesn't work (often in the multi GPU case). Distributed Training. Such a procedure has become the de facto standard in NLP with models like BERT [2]. continuation markers can be removed with the --remove-bpe flag. If you have any new additional information, please include it with your comment! Can someone please tell me how run this across multiple node?