training hangs with lightning ddp and cloud dir?

[rxqy]

🐛 Bug

Hi, we are using lightning with litdata on our local machine and aws s3 system. However, training would hang randomly during the very first iterations with ddp and remote cloud directory.

I tried several different configurations, but I'm not sure what I should check next.
GPU / Strategy / FileOn / results
1 / No DDP/ local ssd / OK
1 / No DDP/ remote(s3) / OK
8 / DDP/ local ssd / OK
8 / DDP/ remote(s3) / Stuck.

To Reproduce

I'm following the exact steps on the imagenet demo. And I write a trainer myself here.
Just run python train.py with different CUDA_VISIBLE_DEVICES is enough

Code sample

# train.py
import numpy as np
import lightning as L
import torch, torch.nn as nn, torch.utils.data as data, torchvision as tv, torch.nn.functional as F

class LitAutoEncoder(L.LightningModule):
    def __init__(self):
        super().__init__()
        self.decoder = nn.Sequential(nn.Linear(32, 128))

    def training_step(self, batch, batch_idx):
        loss = self.decoder(batch).mean()
        print(self.trainer.local_rank, loss)
        self.log("train_loss", loss)
        return loss

    def configure_optimizers(self):
        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
        return optimizer


from lightning.data import StreamingDataset, StreamingDataLoader

class ImageNetStreaming(StreamingDataset):
    def __init__(self, ):
        if 1:
            input_dir = "s3:// xxxxx"
            cache_dir = None
        else:
            input_dir = "val"
            cache_dir = None

        max_cache_size = "200GB"
        super().__init__(
            input_dir = input_dir,
            max_cache_size = max_cache_size,
            shuffle = True,
        )

    def __getitem__(self, idx):
        data = super().__getitem__(idx)
        return np.float32(123.)

dataset = ImageNetStreaming()
dataloader = StreamingDataLoader(
    dataset,
    batch_size = 32,
    num_workers = 2,
    pin_memory = True,
    shuffle = True,
    drop_last = True
)

autoencoder = LitAutoEncoder()
trainer = L.Trainer()
trainer.fit(autoencoder, dataloader)

Expected behavior

Training should finish

Additional context

Due to some regulations here we can not put we data or training scirpts on lightning-studio. I'm not sure if something's wrong with our s3 bucket or our our network configuration.
One thing I notice is that even if the training stucks at some iterations(<50), we can still observe large network throughputs on our machine (around 100mb/s), but the local chunk directory( ~/.lightning/chunks) stops growing.

Current environment

• CUDA:
  • GPU:
    • Tesla V100-SXM2-32GB
    • Tesla V100-SXM2-32GB
  • available: True
  • version: 12.1
• Lightning:
  • lightning: 2.3.0
  • lightning-utilities: 0.11.1
  • pytorch-lightning: 2.2.1
  • torch: 2.2.1
  • torchaudio: 2.2.1
  • torchmetrics: 1.3.2
  • torchvision: 0.17.1
• Packages:
  • absl-py: 2.1.0
  • accelerate: 0.30.1
  • aiofiles: 23.2.1
  • aiohttp: 3.9.3
  • aiosignal: 1.3.1
  • angle-emb: 0.3.10
  • annotated-types: 0.7.0
  • anyio: 4.4.0
  • async-timeout: 4.0.3
  • attrs: 23.2.0
  • auto-gptq: 0.7.1
  • av: 12.3.0
  • awscli: 1.32.70
  • backports-datetime-fromisoformat: 2.0.1
  • bitsandbytes: 0.43.1
  • blessed: 1.20.0
  • blinker: 1.7.0
  • boltons: 24.0.0
  • boto3: 1.34.143
  • botocore: 1.34.143
  • braceexpand: 0.1.7
  • brotli: 1.0.9
  • certifi: 2024.2.2
  • charset-normalizer: 2.0.4
  • click: 8.1.7
  • colorama: 0.4.4
  • coloredlogs: 15.0.1
  • contourpy: 1.2.1
  • cos-python-sdk-v5: 1.9.30
  • crcmod: 1.7
  • cycler: 0.12.1
  • datasets: 2.14.6
  • decord: 0.6.0
  • deepspeed: 0.14.0
  • dill: 0.3.7
  • dnspython: 2.6.1
  • docker-pycreds: 0.4.0
  • docstring-parser: 0.16
  • docutils: 0.16
  • einops: 0.7.0
  • email-validator: 2.2.0
  • et-xmlfile: 1.1.0
  • exceptiongroup: 1.2.2
  • faiss-gpu: 1.7.2
  • fastapi: 0.111.1
  • fastapi-cli: 0.0.4
  • ffmpy: 0.3.2
  • filelock: 3.13.1
  • fire: 0.6.0
  • flash-attn: 2.5.7
  • flask: 3.0.3
  • fonttools: 4.51.0
  • frozenlist: 1.4.1
  • fsspec: 2023.10.0
  • gekko: 1.2.1
  • gitdb: 4.0.11
  • gitpython: 3.1.43
  • gmpy2: 2.1.2
  • gpustat: 1.1.1
  • gradio: 4.39.0
  • gradio-client: 1.1.1
  • grpcio: 1.62.1
  • h11: 0.14.0
  • hide-warnings: 0.17
  • hjson: 3.1.0
  • httpcore: 1.0.5
  • httptools: 0.6.1
  • httpx: 0.27.0
  • huggingface-hub: 0.23.4
  • humanfriendly: 10.0
  • idna: 3.4
  • importlib-resources: 6.4.0
  • influxdb: 5.3.2
  • itsdangerous: 2.1.2
  • jinja2: 3.1.3
  • jmespath: 1.0.1
  • joblib: 1.4.0
  • jsonargparse: 4.27.7
  • kafka-python: 2.0.2
  • kiwisolver: 1.4.5
  • lightning: 2.3.0
  • lightning-utilities: 0.11.1
  • litdata: 0.2.29
  • llava: 1.7.0.dev0
  • llmtuner: 0.6.3.dev0
  • m3u8: 4.0.0
  • markdown: 3.6
  • markdown-it-py: 3.0.0
  • markupsafe: 2.1.3
  • matplotlib: 3.8.4
  • mdurl: 0.1.2
  • media-metric: 0.2.0.10
  • mkl-fft: 1.3.8
  • mkl-random: 1.2.4
  • mkl-service: 2.4.0
  • mmidls: 2.0.3
  • mpmath: 1.3.0
  • msgpack: 1.1.0
  • multidict: 6.0.5
  • multiprocess: 0.70.15
  • networkx: 3.1
  • ninja: 1.11.1.1
  • nssdk: 0.0.1
  • numpy: 1.26.4
  • nvidia-ml-py: 12.535.133
  • onnx: 1.16.0
  • onnxconverter-common: 1.14.0
  • opencv-python-headless: 4.9.0.80
  • openpyxl: 3.1.5
  • optimum: 1.21.1
  • orjson: 3.10.6
  • packaging: 24.0
  • pandas: 2.2.1
  • peft: 0.11.1
  • pillow: 10.2.0
  • pip: 23.3.1
  • platformdirs: 4.2.2
  • ply: 3.11
  • prettytable: 3.10.0
  • protobuf: 3.20.2
  • psutil: 5.9.8
  • py: 1.11.0
  • py-cpuinfo: 9.0.0
  • pyarrow: 15.0.2
  • pyarrow-hotfix: 0.6
  • pyasn1: 0.5.1
  • pycryptodome: 3.20.0
  • pydantic: 2.7.1
  • pydantic-core: 2.18.2
  • pydub: 0.25.1
  • pygments: 2.18.0
  • pynvml: 11.5.0
  • pyparsing: 3.1.2
  • pyrootutils: 1.0.4
  • pysocks: 1.7.1
  • python-dateutil: 2.9.0.post0
  • python-dotenv: 1.0.1
  • python-multipart: 0.0.9
  • pytorch-lightning: 2.2.1
  • pytz: 2024.1
  • pyyaml: 6.0.1
  • redis: 5.0.3
  • regex: 2023.12.25
  • requests: 2.31.0
  • rich: 13.7.1
  • rocketmq-client-python: 2.0.0
  • rouge: 1.0.1
  • rsa: 4.7.2
  • ruff: 0.5.4
  • s3transfer: 0.10.1
  • safetensors: 0.4.2
  • scikit-learn: 1.4.2
  • scipy: 1.13.0
  • seaborn: 0.13.2
  • semantic-version: 2.10.0
  • sentencepiece: 0.2.0
  • sentry-sdk: 2.5.1
  • setproctitle: 1.3.3
  • setuptools: 68.2.2
  • shellingham: 1.5.4
  • shtab: 1.7.1
  • six: 1.16.0
  • smmap: 5.0.1
  • sniffio: 1.3.1
  • sse-starlette: 2.1.2
  • starlette: 0.37.2
  • sympy: 1.12
  • tabulate: 0.9.0
  • taxonomy: 0.10.0
  • tensorboard: 2.16.2
  • tensorboard-data-server: 0.7.2
  • termcolor: 2.4.0
  • threadpoolctl: 3.4.0
  • thrift: 0.20.0
  • thriftpy2: 0.4.20
  • tiktoken: 0.7.0
  • timm: 1.0.3
  • tokenizers: 0.19.1
  • tomlkit: 0.12.0
  • torch: 2.2.1
  • torchaudio: 2.2.1
  • torchmetrics: 1.3.2
  • torchvision: 0.17.1
  • tqdm: 4.66.2
  • transformers: 4.42.4
  • transformers-stream-generator: 0.0.5
  • triton: 2.2.0
  • trl: 0.9.6
  • typer: 0.12.3
  • typeshed-client: 2.5.1
  • typing-extensions: 4.9.0
  • tyro: 0.8.5
  • tzdata: 2024.1
  • urllib3: 2.1.0
  • uvicorn: 0.30.3
  • uvloop: 0.19.0
  • videollama2: 1.0
  • wandb: 0.17.1
  • watchfiles: 0.22.0
  • wcwidth: 0.2.13
  • webdataset: 0.2.93
  • websockets: 11.0.3
  • werkzeug: 3.0.1
  • wheel: 0.41.2
  • xlrd: 2.0.1
  • xmltodict: 0.13.0
  • xxhash: 3.4.1
  • yarl: 1.9.4
• System:
  • OS: Linux
  • architecture:
    • 64bit
    • ELF
  • processor: x86_64
  • python: 3.10.13
  • release: 5.15.0-56-generic
  • version: ValueError with tree_unflatten when trying to read local cache #62-Ubuntu SMP Tue Nov 22 19:54:14 UTC 2022

[github-actions]

Hi! thanks for your contribution!, great first issue!

[deependujha]

Hi @rxqy, thanks for opening the issue. A similar issue is also open for Sagemaker.

We're looking into it and will try to fix it ASAP.

[rxqy]

@deependujha, Many thanks. BTW, the above code sometimes gives the FileNotFoundError (and the training loop continues for several iterations and hangs), and sometimes it just hangs. Not sure if it will help or not, but i'm still pasting it here.

Epoch 0:   0%|                                              | 3/10008 [00:04<4:05:42,  0.68it/s, v_num=3]1 tensor(-45.3273, device='cuda:1', grad_fn=<MeanBackward0>)
0 tensor(-45.3273, device='cuda:0', grad_fn=<MeanBackward0>)
Epoch 0:   0%|                                              | 4/10008 [00:05<3:41:55,  0.75it/s, v_num=3]1 tensor(-61.0723, device='cuda:1', grad_fn=<MeanBackward0>)
0 tensor(-61.0723, device='cuda:0', grad_fn=<MeanBackward0>)
Epoch 0:   0%|                                              | 5/10008 [00:05<2:57:38,  0.94it/s, v_num=3]Exception in thread Thread-3:
Traceback (most recent call last):
  File "/data/miniconda3/envs/pl/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
    self.run()
  File "/data/miniconda3/envs/pl/lib/python3.10/site-packages/litdata/streaming/reader.py", line 153, in run
    self._maybe_delete_chunks()
  File "/data/miniconda3/envs/pl/lib/python3.10/site-packages/litdata/streaming/reader.py", line 117, in _maybe_delete_chunks
    self._apply_delete(self._chunks_index_to_be_deleted.pop(0))
  File "/data/miniconda3/envs/pl/lib/python3.10/site-packages/litdata/streaming/reader.py", line 91, in _apply_delete
    os.remove(locak_chunk_path)
FileNotFoundError: [Errno 2] No such file or directory: '/data/.lightning/chunks/b515aeecb3a09f152677fce166405b10/1730182031.4955728/chunk-4-1309.bin.lock'
0 tensor(-76.8173, device='cuda:0', grad_fn=<MeanBackward0>)
Epoch 0:   0%|                                              | 6/10008 [00:05<2:45:20,  1.01it/s, v_num=3]1 tensor(-76.8173, device='cuda:1', grad_fn=<MeanBackward0>)
1 tensor(-92.5623, device='cuda:1', grad_fn=<MeanBackward0>)
0 tensor(-92.5623, device='cuda:0', grad_fn=<MeanBackward0>)
Epoch 0:   0%|                                              | 7/10008 [00:08<3:18:18,  0.84it/s, v_num=3]1 tensor(-108.3073, device='cuda:1', grad_fn=<MeanBackward0>)
0 tensor(-108.3073, device='cuda:0', grad_fn=<MeanBackward0>)
Epoch 0:   0%|                                              | 8/10008 [00:08<2:53:34,  0.96it/s, v_num=3]1 tensor(-124.0523, device='cuda:1', grad_fn=<MeanBackward0>)
0 tensor(-124.0523, device='cuda:0', grad_fn=<MeanBackward0>)
Epoch 0:   0%|                                              | 9/10008 [00:08<2:34:20,  1.08it/s, v_num=3]1 tensor(-139.7973, device='cuda:1', grad_fn=<MeanBackward0>)
0 tensor(-139.7973, device='cuda:0', grad_fn=<MeanBackward0>)
Epoch 0:   0%|                                             | 10/10008 [00:09<2:38:37,  1.05it/s, v_num=3]1 tensor(-155.5423, device='cuda:1', grad_fn=<MeanBackward0>)
0 tensor(-155.5423, device='cuda:0', grad_fn=<MeanBackward0>)
Epoch 0:   0%|                                             | 11/10008 [00:09<2:30:48,  1.10it/s, v_num=3]0 tensor(-171.2873, device='cuda:0', grad_fn=<MeanBackward0>)
Epoch 0:   0%|                                             | 12/10008 [00:09<2:18:17,  1.20it/s, v_num=3]1 tensor(-171.2873, device='cuda:1', grad_fn=<MeanBackward0>)
0 tensor(-187.0323, device='cuda:0', grad_fn=<MeanBackward0>)
1 tensor(-187.0323, device='cuda:1', grad_fn=<MeanBackward0>)
Epoch 0:   0%|                                             | 13/10008 [00:10<2:13:49,  1.24it/s, v_num=3]1 tensor(-202.7773, device='cuda:1', grad_fn=<MeanBackward0>)

[tchaton]

Hey @rxqy. Could you try to add a try / except around it in LitData and let us know if it helps ? There is a race condition on deleting the file but it is file to catch and skip it.

If it helps, would you mind making a PR with the fix ?

[rxqy]

Hi @tchaton. I think this should be on the lightning side?
Probablilty related: #411 . I'm observing a quite similar behavior, e.g., the exact same dataloader length with different num of gpus with lit trainer and lit ddp strategy.
From the issue above, we should init our streaming dataloader after ddp init, but with lightning's trainer, we are initializing dataloader before ddp init.

I wrote a pytorch ddp demo. With the exact same dataloader, we can finish training quite smoothly.

import os
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.optim as optim
from torch.nn.parallel import DistributedDataParallel as DDP

import numpy as np
import lightning
from lightning.data import StreamingDataset, StreamingDataLoader
from train import ImageNetStreaming
from tqdm import tqdm

def demo_basic():
    torch.cuda.set_device(int(os.environ["LOCAL_RANK"]))
    dist.init_process_group("nccl")
    rank = dist.get_rank()
    print(f"Start running basic DDP example on rank {rank}.")
    # create model and move it to GPU with id rank
    device_id = rank % torch.cuda.device_count()
    model = ToyModel().to(device_id)
    ddp_model = DDP(model, device_ids=[device_id])
    optimizer = optim.SGD(ddp_model.parameters(), lr=0.001)

    dataset = ImageNetStreaming()
    dataloader = StreamingDataLoader(
        dataset,
        batch_size = 32,
        num_workers = 2,
        pin_memory = True,
        shuffle = True,
        drop_last = True
    )
    print(len(dataset), len(dataloader))

    for _, data in enumerate(tqdm(dataloader, disable=(rank!=0))):
        optimizer.zero_grad()
        outputs = ddp_model(data)
        loss = outputs.mean()
        loss.backward()
        optimizer.step()
        print(rank, loss)


    dist.destroy_process_group()
    print(f"Finished running basic DDP example on rank {rank}.")

if __name__ == "__main__":
    demo_basic()

[rxqy]

Just to clarify, I made no code change to my litdata or lightning package. And we are not using fabric in our trainer.
You can launch the above pt ddp script with
torchrun --nnodes=1 --nproc_per_node=2 --rdzv_id=100 --rdzv_backend=c10d --rdzv_endpoint=127.0.0.1:29400 ddp_demo.py.
Not sure what's the correct way to init streaming dataloader with lightning trainer.

[tchaton]

You should instantiate the dataset in the setuo hook of the datamodule or directly within the dataloader hook

[rxqy]

Hi @tchaton , what's the dataloader hook you are mentioning here?
I'm looking at the datamodule's doc here, it seems that only the setup method (for spliting train/test datasets) is called on every device, but not the train_dataloader method.

[schopra8]

@tchaton We're running into an identical issue. We also are getting:

GPU / Strategy / FileOn / results
1 / No DDP/ local ssd / OK
1 / No DDP/ remote(s3) / OK
8 / DDP/ local ssd / OK
8 / DDP/ remote(s3) / Stuck.

We are initiating our dataset in setup of our CustomDataModule:

class TCDataModule(LightningDataModule):
    """
    Text Conditional Data Module wraps training streaming datasets.
    """

    def __init__(self,
                 train_dataset_configs: List[CustomStreamingDatasetConfig]):
        super().__init__()
        self.train_dataset_configs = train_dataset_configs

    def setup(self, stage=None):
        """
        Setup the module, by creating the underlying training datasets.
        """
        if stage in ['fit', None]:
            # Training
            train_datasets = []
            for config in self.train_dataset_configs:
                train_datasets.append(CustomStreamingDataset(config=config))
            self.train_dataset = CombinedStreamingDataset(train_datasets, iterate_over_all=True)

            # Validation
            ...

When using DDP with remote data, we get 1 iteration/second in terms of speed. After the 1st epoch, 15-16 steps run forward at 1 iteration/second and then training stalls for 3-5 minutes (no GPU utilization). Any ideas what the underlying issue could be?

[tchaton]

Hey @schopra8 @rxqy Could you try out this branch: #456. I am trying to add a fallback mechanism to force download the chunk if it got deleted. Not ideal but should unblock DDP training.

[JackUrb]

@tchaton a +1 to this, the issue we noted is that one of the dataloader worker threads ends up caught at exactly the loop you've added a timeout to in #456, however the zstd.bin exists, but the .bin does not, and it never seems to get unpacked (or was unpacked, then removed). I imagine #456 will cover us too, though agreed it doesn't hit the underlying issue.

For what it's worth, I was having a hard time tracking this bug down last week, and found it persists even when all of the StreamingDataloaders and underlying StreamingDatasets are initialized with per-gpu unique cache_dirs, implying that it's within a rank that the unzip fails to occur as expected.

[dawood95]

Training on 4 nodes, 8 gpu each, with DDP. Still see this problem with #456 in. Currently have training running by

1. Not using compression (@tchaton I believe there needs to be a timeout loop there as well, attest temporarily)
2. Using a really large cache size
3. Using a high max_pre_download number
4. Decreasing number of workers (although, not sure this is needed)
5. Using per training worker cache directory

[tchaton]

Hey @JackUrb. Thanks for the info. Yes, we need to spend more time finding the source of this bug.

I wonder if you could add more prints to see if you learn more. Happy to pair debug with you