RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method

[clam004]

Im using python3.8, pytorch 1.12.1, on Ubuntu 20.04.4 LTS (GNU/Linux 5.15.0-1029-azure x86_64) trying to use 2 V100 GPUs and CUDA Version: 11.6 from within my notebook using the notebook_launcher

notebook_launcher(training_loop, args, num_processes=2)

[muellerzr]

I need much more information than this, please provide if possible either the contents of each cell or the notebook itself. I can't do anything with what you've provided :)

[clam004]

Wow, what great response time! here are the cells

0

import numpy as np

import torch

from transformers import GPT2Tokenizer
from transformers import TrainingArguments

from accelerate import Accelerator

from myalgorithms import GPT2HeadWithValueModel # from https://github.com/lvwerra/trl 
from mydatasets import FineTuneDataset

1

train_dataset = torch.load('large_data/train_dataset.pth')

2

def training_loop(num_train_epoch, per_device_train_batch_size, gradient_accumulation_steps):
    
    default_args = {
        "output_dir": "tmp",
        "evaluation_strategy": "steps",
        "num_train_epochs": num_train_epoch,
        "log_level": "error",
        "report_to": "none",
    }
    
    model = GPT2HeadWithValueModel.from_pretrained("gpt2")

    training_args = TrainingArguments(
        per_device_train_batch_size=per_device_train_batch_size,
        gradient_accumulation_steps=gradient_accumulation_steps,
        gradient_checkpointing=True,
        fp16=True,
        **default_args,
    )

    train_dataloader = DataLoader(train_dataset, batch_size=training_args.per_device_train_batch_size)

    optimizer = torch.optim.SGD(
        model.parameters(), 
        lr=5e-5, 
        momentum=0.1,
        weight_decay=0.1,
        nesterov=True,
    )

    accelerator = Accelerator(fp16=training_args.fp16)

    model, optimizer, train_dataloader = accelerator.prepare(model, optimizer, train_dataloader)

    model.train()
    for step, batch in enumerate(tqdm(train_dataloader), start=1):

        inputs = torch.tensor(batch['input_ids'])
        inputs = inputs.to(model.device)

        outputs = model(inputs, return_dict=True)

        logits = outputs.logits

        shift_labels = inputs[..., 1:].contiguous()
        shift_logits = logits[..., :-1, :].contiguous()

        loss_func = torch.nn.CrossEntropyLoss()
        loss = loss_func(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
        loss = loss / training_args.gradient_accumulation_steps

        accelerator.backward(loss)

        if step % training_args.gradient_accumulation_steps == 0:
            optimizer.step()
            optimizer.zero_grad()

3

num_train_epoch, per_device_train_batch_size, gradient_accumulation_steps = 1,2,4
args = (num_train_epoch, per_device_train_batch_size, gradient_accumulation_steps)
notebook_launcher(training_loop, args, num_processes=2)

[lewtun]

FYI we're also seeing a similar error reported by participants in the DreamBooth Hackathon when using T4 x 2 GPUs on Kaggle.

Steps to reproduce

1. Open the Kaggle notebook. (I simplified it to the essential steps)
2. Select the T4 x 2 GPU accelerator and install the dependencies + restart notebook (Kaggle has an old version of torch preinstalled)
3. Run all remaining cells

Here's the output from accelerate env:

- `Accelerate` version: 0.15.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- Numpy version: 1.21.6
- PyTorch version (GPU?): 1.11.0 (True)
- `Accelerate` default config:
	Not found

[muellerzr]

As I'm on vacation and can't look at this thoroughly I'll give you both a TL;DR of how to investigate it :)

After every single cell, or at the end of it, include the following:

torch.cuda.is_initialized()

This must be False always until you do notebook_launcher(...)

If anything initializes CUDA then whatever triggers it must be in that training function you pass into notebook_launcher. HTH!

[muellerzr]

Another thing to watch out for @lewtun pointed out to me is making sure any of the libraries you are using @clam004 aren't trying to initialize cuda later on in their library code. If doing it cell by cell and finding that nothing is being done, that means they're initializing CUDA themselves and that's not an issue we can necessarily fix

[clam004]

Thanks @muellerzr , I did as you say and in every cell above

args = (model, tokenizer, config)
notebook_launcher(training_loop, args, num_processes=2)

I have verified that each has torch.cuda.is_initialized() returning False for me at the end of the cell.

unfortunately I am still getting the same Cannot re-initialize CUDA in forked subprocess. error in the title.

File ~/path/python3.8/site-packages/accelerate/launchers.py:122, in notebook_launcher(function, args, num_processes, use_fp16, mixed_precision, use_port)
    119         launcher = PrepareForLaunch(function, distributed_type="MULTI_GPU")
    121         print(f"Launching training on {num_processes} GPUs.")
--> 122         start_processes(launcher, args=args, nprocs=num_processes, start_method="fork")
    124 else:
    125     # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
    126     use_mps_device = "false"

File ~/path/python3.8/site-packages/torch/multiprocessing/spawn.py:198, in start_processes(fn, args, nprocs, join, daemon, start_method)
    195     return context
    197 # Loop on join until it returns True or raises an exception.
--> 198 while not context.join():
    199     pass

File ~/path/python3.8/site-packages/torch/multiprocessing/spawn.py:160, in ProcessContext.join(self, timeout)
    158 msg = "\n\n-- Process %d terminated with the following error:\n" % error_index
    159 msg += original_trace
--> 160 raise ProcessRaisedException(msg, error_index, failed_process.pid)

ProcessRaisedException: 

-- Process 0 terminated with the following error:
Traceback (most recent call last):
  File "/path/python3.8/site-packages/torch/multiprocessing/spawn.py", line 69, in _wrap
    fn(i, *args)
  File "/path/python3.8/site-packages/accelerate/utils/launch.py", line 97, in __call__
    self.launcher(*args)
  File "/tmp/ipykernel_49172/1813046040.py", line 15, in training_loop
    accelerator = Accelerator(mixed_precision='fp16')
  File "/path/python3.8/site-packages/accelerate/accelerator.py", line 308, in __init__
    self.state = AcceleratorState(
  File "python3.8/site-packages/accelerate/state.py", line 156, in __init__
    torch.cuda.set_device(self.device)
  File "python3.8/site-packages/torch/cuda/__init__.py", line 313, in set_device
    torch._C._cuda_setDevice(device)
  File "/path/python3.8/site-packages/torch/cuda/__init__.py", line 206, in _lazy_init
    raise RuntimeError(
RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method

OK thanks for your help and enjoy your vacation, ill check back here in Jan 2023

[leemengtw]

Facing the same error when already assert not torch.cuda.is_initialized() before notebook_launcher(..., num_processes=8)

I'm using accelerate==0.15.0 with python 3.10, any suggestion is highly appreciated! 🙏

Additional version Info:

torch          : 1.13.1
torchvision    : 0.14.1
diffusers      : 0.11.1
accelerate     : 0.15.0
xformers       : 0.0.16.dev432+git.bc08bbc

[rohan-gangaraju]

Was facing this issue due to a device variable initialized that was being done in the first cell

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

Commenting this line resolved the issue.

FYI: The torch.cuda.is_initialized() returned False despite the initialization that was happening. Probably best to manually check for initialization code like above.

[graldij]

Hi all. I get the same error using multiprocessing in this example notebook (run locally on SLURM server), i.e. with num_processes=2. Any suggestion is welcomed!

[willlllllio]

Had the same error, putting all my imports into my training_loop function fixed it, so my guess is some library that's being imported did something to trigger the cuda initialization or something like that.

[hikkilover]

same error. Try to launch your script with "accelerate launch" on CLI, do not use "notebook_launcher()" inside the script. (follow the official guideline: https://github.com/huggingface/diffusers/blob/3b641eabe9876e7c48977b35331fda54ce972b4a/examples/unconditional_image_generation/README.md) It works to me.

[the-waste-land]

I using T4 x 2 GPUs on Kaggle, face the same error.

[muellerzr]

@Liweixin22 ensure that you haven't called anything to CUDA before running notebook_launcher. If there are still issues, please let us know what notebook you are trying to run

[the-waste-land]

@muellerzr Here is the link of my notebook https://www.kaggle.com/code/liweixin23/ml2023spring-hw2

[stymbhrdwj]

I am experiencing the same issue in the official diffusers training example notebook here.

I downloaded the notebook and ran it on my system with dual-GPU setup and set num_processes=2. I had generated the accelerator config prior to this.

torch.cuda.is_initialized() returns False after each cell preceding the notebook_launcher call.

[RRaphaell]

I'm facing the same issue, any updates on this?

[mingrenbuke]

I'm facing the same issue, and I've encapsulated all the code within functions. The first line of code executed is notebook_launcher

[ghadiaravi13]

Hi @muellerzr I am able to reproduce the same error for a dummy case, where I make no others imports other than the accelerate library.

Cell:0

from accelerate import Accelerator

Cell:1

def dummy_func():
    accelerator = Accelerator()
    accelerator.print("Hello")

Cell:2

from accelerate import notebook_launcher
notebook_launcher(dummy_func,num_processes=2)

RuntimeError: CUDA has been initialized before the notebook_launcher could create a forked subprocess. This likely stems from an outside import causing issues once the notebook_launcher() is called. Please review your imports and test them when running the notebook_launcher() to identify which one is problematic.

Also, I tried looking at the GPU Memory before and after executing Cell:0, using nvidia-smi Before: GPU:0 - 0MB GPU:1 - 0MB

After executing Cell:0 GPU:0 - 2MB GPU:1 - 2MB

Could this mean that the import itself is leading to CUDA initialization, if so, how do we handle it?

[muellerzr]

Try installing from git via pip install git+https://github.com/huggingface/accelerate. I believe this is fixed on main

[Sewens]

Try installing from git via pip install git+https://github.com/huggingface/accelerate. I believe this is fixed on main

Work for me, I uninstall the old version which accelerate-0.21.0. And reinstall the package directly from github, to verison accelerate-0.22.0.dev0. Then it just work!

For those whoes default https github has been blocked, typing the following command for installing.

pip install git+ssh://[email protected]/huggingface/accelerate.git

[ghadiaravi13]

Works for me too, thanks a lot @muellerzr for the prompt response!

[muellerzr]

We'll have a release out this week with the non-braking version, thanks @Sewens @ghadiaravi13!

[github-actions[bot]]

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the contributing guidelines are likely to be ignored.

[infamix]

I am still getting the same error on Kaggle T4s. It doesn't matter whether I load the model outside the training function or inside, the outcome is the same.

[github-actions[bot]]

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the contributing guidelines are likely to be ignored.

[muellerzr]

@infamix i need a copy of your code to help

[csaroff]

@muellerzr the following code is breaking for me(First cell of the notebook):

from accelerate import notebook_launcher

def test_nb_launcher():
    from fastai.test_utils import synth_learner
    import fastai.distributed # Updated

    learn = synth_learner()
    with learn.distrib_ctx(in_notebook=True): # Updated
        learn.fit(3)

notebook_launcher(test_nb_launcher, num_processes=2)

Running accelerate v0.24.1

import accelerate
accelerate.__version__ # '0.24.1'

I'm also running this on a single node w/ 2x T4 GPUs. I'm guessing all the T4 reports are a coincidence because everyone's just trying to run on the two cheapest cloud gpu's they can get. 🤷

Edit: I forgot to include the distrib_ctx in the example. Unfortunately, it still fails with the same error even after I updated it.

[github-actions[bot]]

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the contributing guidelines are likely to be ignored.

[csaroff]

Bump @muellerzr

[muellerzr]

@csaroff ran just fine for me:

import os
import torch
from accelerate import notebook_launcher
from fastai.test_utils import synth_learner
import fastai.distributed

os.environ["NCCL_P2P_DISABLE"]="1"
os.environ["NCCL_IB_DISABLE"]="1"

def test_nb_launcher():
    learn = synth_learner()
    with learn.distrib_ctx(in_notebook=True): # Updated
        learn.fit(3)

notebook_launcher(test_nb_launcher, num_processes=2)

Note: I'm using 2x 4090's, hence why the OS setting is needed (this will be automatic soon)