Release NCCL distributed backend from experimental #4921

teng-li · 2018-01-29T23:43:43Z

After removing the hacky clear NCCL communicator cache and adding all the tests. I think we are in a good shape to release the NCCL backend from experimental.

torch/distributed/__init__.py

            contain correctly-sized tensors on each GPU to be used for output of
            the collective.
+
+            e.g. output_tensor_lists[i] contrains the all_gather


Yi-Li · 2018-02-26T19:24:28Z

Hi
I downloaded the most recent pytorch and tried to install it from the source so that I can make use of the new nccl APIs. I could run the torch/lib/nccl/test/single successfully.
I specified WITH_SYSTEM_NCCL=0 (along with WITH_NCCL=1 WITH_DISTRIBUTED=1, WITH_CUDA=1) when I invoke “python setup.py build develop". It seems the version of generated libnccl.so is 1.3.5, not version 2+, so THD was compiled without nccl support.
I also downloaded NCCL2 from Nvidia website, tried WITH_SYSTEM_NCCL=1, and specified NCCL_INCLUDE_DIR, NCCL_LIB_DIR, NCCL_ROOT_DIR. But THD was compiled without nccl support either. I have been stuck here for a few days. Could you help with this? Thanks a lot!

Best,
Lissa

apaszke · 2018-02-26T19:26:06Z

The NCCL library provided in the repo is version 1. Version 2 is closed source and you have to download it from NVIDIA and use WITH_SYSTEM_NCCL=1

Yi-Li · 2018-02-26T19:59:29Z

Hi Adam, I also downloaded NCCL2 from Nvidia website, tried WITH_SYSTEM_NCCL=1, and specified NCCL_INCLUDE_DIR, NCCL_LIB_DIR, NCCL_ROOT_DIR to install pytorch. The installed pytorch version is 0.4.0a0+7703670. When I run the following simple test example (toy.py), an error message was thrown: before init after init begin rank 1 Traceback (most recent call last): File "toy.py", line 32, in <module> init_processes(args.rank, size, run, 'nccl') File "toy.py", line 23, in init_processes fn(rank, size) File "toy.py", line 11, in run dist.all_reduce(tensor, op=dist.reduce_op.SUM, group=group) File "/home/liy/programs/pytorch/torch/distributed/__init__.py", line 326, in all_reduce return torch._C._dist_all_reduce(tensor, op, group) RuntimeError: NCCL error in: /home/liy/programs/pytorch/torch/lib/THD/base/data_channels/DataChannelNccl.cpp:324, unhandled system error Am I using something wrongly? Best Regards, Lissa cat toy.py: import torch import torch.distributed as dist import argparse def run(rank, size): """ Simple point-to-point communication. """ print('begin rank', rank) group = dist.new_group([0, 1]) tensor = torch.ones(1).cuda() dist.all_reduce(tensor, op=dist.reduce_op.SUM, group=group) print('Rank ', rank, ' has data ', tensor[0]) def init_processes(rank, size, fn, backend): """ Initialize the distributed environment. """ print('before init') init_method="tcp://10.6.48.150:13530" dist.init_process_group(backend,rank=rank,world_size=size,init_method=init_method) print('after init') fn(rank, size) if __name__ == "__main__": size = 2 parser = argparse.ArgumentParser() parser.add_argument('--rank', default=-1, type=int, help='rank') args = parser.parse_args() init_processes(args.rank, size, run, 'nccl') ==================================== From: Adam Paszke [mailto:notifications@github.com] Sent: Monday, February 26, 2018 2:27 PM To: pytorch/pytorch <pytorch@noreply.github.com> Cc: Yi Li <Yi.Li@jax.org>; Comment <comment@noreply.github.com> Subject: Re: [pytorch/pytorch] Release NCCL distributed backend from experimental (#4921) The NCCL library provided in the repo is version 1. Version 2 is closed source and you have to download it from NVIDIA and use WITH_SYSTEM_NCCL=1 — You are receiving this because you commented. Reply to this email directly, view it on GitHub<#4921 (comment)>, or mute the thread<https://github.com/notifications/unsubscribe-auth/AdgBrfU6YBGkrGJiYd4Fhvxh_qockX2wks5tYwVlgaJpZM4Rxif7>. --- The information in this email, including attachments, may be confidential and is intended solely for the addressee(s). If you believe you received this email by mistake, please notify the sender by return email as soon as possible.

Yi-Li · 2018-02-27T13:54:00Z

Hi Adam, My system info is as follows: CentOS Linux release 7.3.1611 (Core) kernel version: 3.10.0-693.11.6.el7.x86_64 Cuda8 and cudnn6 gcc 4.9.2 No IB devices, so I set WITH_GLOO_IBVERBS=0 Do you think cuda 8 causes the error? Best Regards, Lissa From: Adam Paszke [mailto:notifications@github.com] Sent: Monday, February 26, 2018 2:27 PM To: pytorch/pytorch <pytorch@noreply.github.com> Cc: Yi Li <Yi.Li@jax.org>; Comment <comment@noreply.github.com> Subject: Re: [pytorch/pytorch] Release NCCL distributed backend from experimental (#4921) The NCCL library provided in the repo is version 1. Version 2 is closed source and you have to download it from NVIDIA and use WITH_SYSTEM_NCCL=1 — You are receiving this because you commented. Reply to this email directly, view it on GitHub<#4921 (comment)>, or mute the thread<https://github.com/notifications/unsubscribe-auth/AdgBrfU6YBGkrGJiYd4Fhvxh_qockX2wks5tYwVlgaJpZM4Rxif7>. --- The information in this email, including attachments, may be confidential and is intended solely for the addressee(s). If you believe you received this email by mistake, please notify the sender by return email as soon as possible.

Yi-Li · 2018-02-27T13:58:29Z

Hi Adam,

Yes, I downloaded NCCL2 from Nvidia website, tried WITH_SYSTEM_NCCL=1, and specified NCCL_INCLUDE_DIR, NCCL_LIB_DIR, NCCL_ROOT_DIR to install pytorch. The installed pytorch version is 0.4.0a0+7703670. When I run the following simple test example (toy.py), an error message was thrown:

before init
after init
begin rank 1
Traceback (most recent call last):
File "toy.py", line 32, in
init_processes(args.rank, size, run, 'nccl')
File "toy.py", line 23, in init_processes
fn(rank, size)
File "toy.py", line 11, in run
dist.all_reduce(tensor, op=dist.reduce_op.SUM, group=group)
File "/home/liy/programs/pytorch/torch/distributed/init.py", line 326, in all_reduce
return torch._C._dist_all_reduce(tensor, op, group)
RuntimeError: NCCL error in: /home/liy/programs/pytorch/torch/lib/THD/base/data_channels/DataChannelNccl.cpp:324, unhandled system error

Am I using something wrongly?

==========================
cat toy.py:
import torch
import torch.distributed as dist
import argparse

def run(rank, size):
""" Simple point-to-point communication. """
print('begin rank', rank)
group = dist.new_group([0, 1])
tensor = torch.ones(1).cuda()
dist.all_reduce(tensor, op=dist.reduce_op.SUM, group=group)
print('Rank ', rank, ' has data ', tensor[0])

def init_processes(rank, size, fn, backend):
""" Initialize the distributed environment. """
print('before init')
init_method="tcp://10.6.48.150:13530"
dist.init_process_group(backend,rank=rank,world_size=size,init_method=init_method)
print('after init')
fn(rank, size)

if name == "main":
size = 2
parser = argparse.ArgumentParser()
parser.add_argument('--rank', default=-1, type=int,
help='rank')
args = parser.parse_args()
init_processes(args.rank, size, run, 'nccl')

apaszke · 2018-02-27T15:29:03Z

Hmm I don't know, it looks good at a first glance, and the error is coming somewhere from the inside of NCCL where we can't easily tell what's wrong 😕

Yi-Li · 2018-02-27T15:46:31Z

I am using Cuda8 and Nvidia driver version is 375.51. Will this cause NCCL error? Best Regards, Lissa From: Adam Paszke [mailto:notifications@github.com] Sent: Tuesday, February 27, 2018 10:29 AM To: pytorch/pytorch <pytorch@noreply.github.com> Cc: Yi Li <Yi.Li@jax.org>; Comment <comment@noreply.github.com> Subject: Re: [pytorch/pytorch] Release NCCL distributed backend from experimental (#4921) Hmm I don't know, it looks good at a first glance, and the error is coming somewhere from the inside of NCCL where we can't easily tell what's wrong 😕 — You are receiving this because you commented. Reply to this email directly, view it on GitHub<#4921 (comment)>, or mute the thread<https://github.com/notifications/unsubscribe-auth/AdgBrQ38mUr7RVJ0O3nkOWZy2QO3sP52ks5tZB9RgaJpZM4Rxif7>. --- The information in this email, including attachments, may be confidential and is intended solely for the addressee(s). If you believe you received this email by mistake, please notify the sender by return email as soon as possible.

apaszke · 2018-02-27T15:48:16Z

Sorry, I don't know that

teng-li · 2018-02-27T19:53:23Z

@Yi-Li this could possibly mean that NCCL is picking up the wrong interface. What does your ifconfig show?

teng-li · 2018-02-27T19:55:58Z

@Yi-Li I would first try to set NCCL_SOCKET_IFNAME to the interface you would like NCCL to communicate.

Also set NCCL_DEBUG=INFO and run your program will give more info on why your program is failing on NCCL

Yi-Li · 2018-02-27T20:43:34Z

Hi Teng Li, I dont know how to set it. Which choices do the interface have? NCCL2 seems need glibc2.19 or higher, but mine is glibc2.17, which may cause the error. Best Regards, Lissa On 27 Feb 2018, at 2:57 PM, Teng Li <notifications@github.com<mailto:notifications@github.com>> wrote: @Yi-Li<https://github.com/yi-li> I would first try to set NCCL_SOCKET_IFNAME to the interface you would like NCCL to communicate. Also set NCCL_DEBUG=INFO and run your program will give more info on why your program is failing on NCCL - You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub<#4921 (comment)>, or mute the thread<https://github.com/notifications/unsubscribe-auth/AdgBrb5MFa2BBp_RePCKtaKFHcpIyX54ks5tZF3hgaJpZM4Rxif7>. --- The information in this email, including attachments, may be confidential and is intended solely for the addressee(s). If you believe you received this email by mistake, please notify the sender by return email as soon as possible.

teng-li · 2018-02-27T22:02:11Z

yeah, I have seen similar issues with a mismatching glibc version.

Yi-Li · 2018-03-29T02:16:53Z

Hi Teng Li, My system administrator seems to get the glibc2.19 ready. I want to test the following small code (try.py) which you put on forum website. As you suggest, I will run "python try.py 1" on one terminal, and run "python try.py 2" on another terminal, and should get the output of "2". I want to check whether this code can be run on two gpu nodes. In init_processes, I set ? init_method="tcp://10.6.48.150:13530"?. My understanding is that 10.6.48.150 is the ip address of the master gpu node. Is it correct that we don't need to set the ip address of any slave gpu node? Thank you for your help!

cat try.py

import torch import torch.distributed as dist import argparse def run(rank, size): """ Simple point-to-point communication. """ print('begin rank', rank) group = dist.new_group([0, 1]) tensor = torch.ones(1).cuda() dist.all_reduce(tensor, op=dist.reduce_op.SUM, group=group) print('Rank ', rank, ' has data ', tensor[0]) def init_processes(rank, size, fn, backend): """ Initialize the distributed environment. """ print('before init') init_method="tcp://10.6.48.150:13530" dist.init_process_group(backend,rank=rank,world_size=size,init_method=init_method) print('after init') fn(rank, size) if __name__ == "__main__": size = 2 parser = argparse.ArgumentParser() parser.add_argument('--rank', default=-1, type=int,help='rank') args = parser.parse_args() init_processes(args.rank, size, run, 'nccl')

…

________________________________ From: Teng Li <notifications@github.com> Sent: Tuesday, February 27, 2018 5:03 PM To: pytorch/pytorch Cc: Yi Li; Mention Subject: Re: [pytorch/pytorch] Release NCCL distributed backend from experimental (#4921) yeah, I have seen similar issues with a mismatching glibc version. - You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub<#4921 (comment)>, or mute the thread<https://github.com/notifications/unsubscribe-auth/AdgBremmrKMGnITDdFBEQcLqRpWyMeGEks5tZHt0gaJpZM4Rxif7>. --- The information in this email, including attachments, may be confidential and is intended solely for the addressee(s). If you believe you received this email by mistake, please notify the sender by return email as soon as possible.

apaszke · 2018-03-29T09:40:24Z

Exactly. That address should be the IP and port that you gave the first process to listen at.

cyang49 · 2018-03-30T15:09:31Z

I'm having the same issue as @Yi-Li with the imagenet example. I tried the toy.py she posted and found I couldn't reproduce it. I realized that the difference is that I set CUDA_VISIBLE_DEVICES when I run imagenet. After adding this when I run toy.py, I can reproduce the same error.

The modified toy.py I used:

import torch
import torch.distributed as dist
import argparse

def run(rank, size):
    """ Simple point-to-point communication. """
    print('begin rank', rank)
    group = dist.new_group([0, 1])
    tensor = torch.ones(1).cuda()
    dist.all_reduce(tensor, op=dist.reduce_op.SUM, group=group)
    print('Rank ', rank, ' has data ', tensor[0])

def init_processes(rank, size, fn, backend):
    """ Initialize the distributed environment. """
    print('before init')
    init_method="file://./sync"
    #dist.init_process_group(backend,rank=rank,world_size=size,init_method=init_method)
    dist.init_process_group(backend,world_size=size,init_method=init_method)
    print('after init')
    fn(rank, size)


if __name__ == "__main__":
    size = 2
    parser = argparse.ArgumentParser()
    parser.add_argument('--rank', default=-1, type=int,
                        help='rank')
    args = parser.parse_args()
    init_processes(args.rank, size, run, 'nccl')

The commands I used on different terminal of the same machine to trigger the error:

# terminal 0
CUDA_VISIBLE_DEVICES=0 python toy.py --rank 0

# terminal 1
CUDA_VISIBLE_DEVICES=1 python toy.py --rank 1

I'm not exactly sure this is the same problem with @Yi-Li but could anyone help with using NCCL on different CUDA devices?

apaszke · 2018-03-30T18:19:47Z

CUDA_VISIBLE_DEVICES doesn't mix well with NCCL. Remove it and then use torch.cuda.set_device(X) at the top of your script

teng-li · 2018-03-30T18:42:37Z

@cyang49 Yeah, CUDA_VISIBLE_DEVICES is incompatible with CUDA IPCs, since each process only sees its own GPU, hence cannot even see others to use GPU Direct P2P. So please make sure that all processes that will use NCCL can see all the devices. Like Adam said, you can control which GPU the process is operating on by either using torch.cuda.set_device() or with torch.cuda.device()

cyang49 · 2018-03-30T19:49:27Z

@apaszke @teng-li Using torch.cuda.device() worked. However I put the call to fn(rank, size) in a loop and it has some error in rank 1 (the other one hangs) after a while:

1258th operation
begin rank 1
Rank  1  has data  
 2
[torch.cuda.FloatTensor of size () (GPU 0)]

1259th operation
begin rank 1
Traceback (most recent call last):
  File "toy.py", line 33, in <module>
    init_processes(args.rank, size, run, 'nccl')
  File "toy.py", line 24, in init_processes
    fn(rank, size)
  File "toy.py", line 10, in run
    dist.all_reduce(tensor, op=dist.reduce_op.SUM, group=group)
  File "/home/ccyang/anaconda3/lib/python3.6/site-packages/torch/distributed/__init__.py", line 326, in all_reduce
    return torch._C._dist_all_reduce(tensor, op, group)
RuntimeError: NCCL error in: /home/ccyang/gpfs/pytorch/torch/lib/THD/base/data_channels/DataChannelNccl.cpp:324, internal error

The above error is reproducible on my system and happens at the same 1259th operation on rank 1.

And I also got this other kind of error:

928th operation
begin rank 1
mlx5: c460login01: got completion with error:
00000000 00000000 00000000 00000000
00000000 00000000 00000000 00000000
00000001 00000000 00000000 00000000
00000000 9d00c311 00012ecb 0003b3e2
Traceback (most recent call last):
  File "toy.py", line 33, in <module>
    init_processes(args.rank, size, run, 'nccl')
  File "toy.py", line 24, in init_processes
    fn(rank, size)
  File "toy.py", line 10, in run
    dist.all_reduce(tensor, op=dist.reduce_op.SUM, group=group)
  File "/home/ccyang/anaconda3/lib/python3.6/site-packages/torch/distributed/__init__.py", line 326, in all_reduce
    return torch._C._dist_all_reduce(tensor, op, group)
RuntimeError: NCCL error in: /home/ccyang/gpfs/pytorch/torch/lib/THD/base/data_channels/DataChannelNccl.cpp:324, unhandled system error

The code is here

import torch
import torch.distributed as dist
import argparse

def run(rank, size):
    """ Simple point-to-point communication. """
    print('begin rank', rank)
    group = dist.new_group([0, 1])
    tensor = torch.FloatTensor(torch.ones(1)).cuda()
    dist.all_reduce(tensor, op=dist.reduce_op.SUM, group=group)
    print('Rank ', rank, ' has data ', tensor[0])

def init_processes(rank, size, fn, backend):
    """ Initialize the distributed environment. """
    print('before init')
    torch.cuda.device(rank)
    init_method="tcp://127.0.0.1:16543"
    dist.init_process_group(backend,rank=rank,world_size=size,init_method=init_method)
    print('after init')
    for i in range(100000):
      print('{}th operation'.format(i))
      fn(rank, size)

if __name__ == "__main__":
    size = 2
    parser = argparse.ArgumentParser()
    parser.add_argument('--rank', default=-1, type=int,
                        help='rank')
    args = parser.parse_args()
    init_processes(args.rank, size, run, 'nccl')

teng-li · 2018-03-30T20:05:09Z

This looks like NCCL issue to me, could you get the NCCL logs
by setting NCCL_DEBUG=INFO, like running: NCCL_DEBUG=INFO python YOUR_programs

cyang49 · 2018-03-30T20:13:37Z

Adding NCCL_DEBUG=INFO gives perhaps some hint to what happened. Maybe it's memory leak? I observed nvidia-smi and see that the device 0 memory ran out but device 1 memory usage stays constant. I'm not sure if the tensor is being correctly allocated on the corresponding devices?

c460c041:101211:106353 [0] INFO CUDA Dev 0, IB Ports : mlx5_2/1(SOC) mlx5_0/1(SOC)
c460c041:101211:106353 [0] init.cu:218 WARN Cuda failure 'out of memory'
c460c041:101211:106353 [0] transport/p2p.cu:404 WARN rank 1 failed to get CUDA IPC handle to device 0 : 11 invalid argument
c460c041:101211:106353 [0] INFO init.cu:191 -> 3
c460c041:101211:106353 [0] INFO init.cu:266 -> 3
c460c041:101211:106353 [0] INFO init.cu:460 -> 3
c460c041:101211:106353 [0] INFO init.cu:517 -> 3
c460c041:101211:106353 [0] INFO misc/group.cu:70 -> 3 [Async thread]
Traceback (most recent call last):
  File "toy.py", line 33, in <module>
    init_processes(args.rank, size, run, 'nccl')
  File "toy.py", line 24, in init_processes
    fn(rank, size)
  File "toy.py", line 10, in run
    dist.all_reduce(tensor, op=dist.reduce_op.SUM, group=group)
  File "/home/ccyang/anaconda3/lib/python3.6/site-packages/torch/distributed/__init__.py", line 326, in all_reduce
    return torch._C._dist_all_reduce(tensor, op, group)
RuntimeError: NCCL error in: /home/ccyang/gpfs/pytorch/torch/lib/THD/base/data_channels/DataChannelNccl.cpp:324, internal error

Yi-Li · 2018-03-30T20:20:47Z

Hi Cyang, Where you put torch.cuda.set_device(rank)?? inside if __name__ == "__main__":? ? Best Regards, Yi

…

________________________________ From: cyang <notifications@github.com> Sent: Friday, March 30, 2018 3:49 PM To: pytorch/pytorch Cc: Yi Li; Mention Subject: Re: [pytorch/pytorch] Release NCCL distributed backend from experimental (#4921) @apaszke<https://github.com/apaszke> @teng-li<https://github.com/teng-li> Using torch.cuda.device() worked. Thanks! - You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub<#4921 (comment)>, or mute the thread<https://github.com/notifications/unsubscribe-auth/AdgBrUKVspfBQEOIIb0aPVoNyjjEnxaVks5tjoxXgaJpZM4Rxif7>. --- The information in this email, including attachments, may be confidential and is intended solely for the addressee(s). If you believe you received this email by mistake, please notify the sender by return email as soon as possible.

cyang49 · 2018-03-30T20:23:29Z

@Yi-Li

def init_processes(rank, size, fn, backend):
    """ Initialize the distributed environment. """
    print('before init')
    torch.cuda.set_device(rank)
    init_method="tcp://127.0.0.1:16543"
    dist.init_process_group(backend,rank=rank,world_size=size,init_method=init_method)
    print('after init')
    for i in range(100000):
      print('{}th operation'.format(i))
      fn(rank, size)

Edit:
I think the torch.cuda.device() didn't really take effect. I used torch.cuda.set_device() instead and nvidia-smi shows both devices are being used now. The same out of memory error still happens.

Yi-Li · 2018-03-30T21:25:18Z

Thanks. Best Regards, Yi-Li On 30 Mar 2018, at 4:24 PM, cyang <notifications@github.com<mailto:notifications@github.com>> wrote: @Yi-Li<https://github.com/Yi-Li> def init_processes(rank, size, fn, backend): """ Initialize the distributed environment. """ print('before init') torch.cuda.device(rank) init_method="tcp://127.0.0.1:16543" dist.init_process_group(backend,rank=rank,world_size=size,init_method=init_method) print('after init') for i in range(100000): print('{}th operation'.format(i)) fn(rank, size) - You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub<#4921 (comment)>, or mute the thread<https://github.com/notifications/unsubscribe-auth/AdgBrRyMC5kzSM3-h2VfkQkL19heoxvdks5tjpRYgaJpZM4Rxif7>. --- The information in this email, including attachments, may be confidential and is intended solely for the addressee(s). If you believe you received this email by mistake, please notify the sender by return email as soon as possible.

zsk423200 · 2018-12-08T06:27:25Z

@cyang49 I'm having the same issue as you, 2 docker in one node, and use nccl
1st docker : export CUDA_VISIBLE_DEVICES=0,1
2nd docker: export CUDA_VISIBLE_DEVICES=2,3

get error:
DataChannelNccl.cpp:324, unhandled cuda error

use NCCL_DEBUG=INFO, info:
host1:1567:1583 [0] transport/p2p.cu:515 WARN failed to open CUDA IPC handle : 11 invalid argument
host1:1567:1583 [0] INFO init.cu:485 -> 1
host1:1567:1583 [0] INFO init.cu:542 -> 1
host1:1567:1583 [0] INFO misc/group.cu:70 -> 1 [Async thread]

if i set the same CUDA_VISIBLE_DEVICES, it works fine. and gloo is also ok.

have you settled the problem

stdacore · 2018-12-16T18:00:09Z

I have the same problem, but i find out the main reason is that the sample code does distributed.new_group every iteration. It seems to allocate new memory when calling. So i just do it one time when initialization, then the problem solved.

Release NCCL distributed backend from experimental

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torch/distributed/__init__.py Outdated

contain correctly-sized tensors on each GPU to be used for output of

the collective.

e.g. output_tensor_lists[i] contrains the all_gather

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fix typo

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soumith merged commit 5c65466 into pytorch:master Jan 30, 2018

teng-li deleted the ncc2_release branch February 27, 2018 19:58

Release NCCL distributed backend from experimental #4921

Release NCCL distributed backend from experimental #4921

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teng-li commented Mar 30, 2018 •

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cyang49 commented Mar 30, 2018 •

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cyang49 commented Mar 30, 2018 •

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cyang49 commented Mar 30, 2018 •

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