Error related to magma during training

[Cdk29]

@henry090 : I am trying to train a xse_resnet50.

During training I got the following error :

R: /opt/conda/conda-bld/magma-cuda101_1583546950098/work/interface_cuda/interface.cpp:901: void magma_queue_create_from_cuda_internal(magma_device_t, cudaStream_t, cublasHandle_t, cusparseHandle_t, magma_queue**, const char*, const char*, int): Assertion `queue->dCarray__ != __null' failed.

It is not a simple out of memory error, it seems to be some kind of memory leak related to magma, similar to related here.

But : I did not find any mention of this bug occurring with fastai, which I would have expect if this thing occurred recurrently, except for this message on this thread : https://forums.fast.ai/t/a-walk-with-fastai2-vision-study-group-and-online-lectures-megathread/59929/1293 :

The only “new” thing I am doing is that I am encapsulating most of my code for training the model in a try/except block in a while loop.

I wonder if the memory leak is not somehow due to using a function as a wrapper or reticulate.
Link towards the code and error : https://www.kaggle.com/cdk292/magma-error-xse-resnext50-with-r?scriptVersionId=50229515

The last version is still compiling but you can see in the log of execution of version 4 the error, and will probably shown up again in V6.

PS : merry Christmas.

[Cdk29]

I am trying without the $to_fp16() to see if the error came from there.

[turgut090]

I get the following with to_fp16:

Error in py_call_impl(callable, dots$args, dots$keywords) : 
  AssertionError: Mixed-precision training requires a GPU, remove the call `to_fp16`

Detailed traceback: 
  File "/home/turgut/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/fastcore/logargs.py", line 56, in _f
    return inst if to_return else f(*args, **kwargs)
  File "/home/turgut/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/fastai/callback/schedule.py", line 113, in fit_one_cycle
    self.fit(n_epoch, cbs=ParamScheduler(scheds)+L(cbs), reset_opt=reset_opt, wd=wd)
  File "/home/turgut/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/fastcore/logargs.py", line 56, in _f
    return inst if to_return else f(*args, **kwargs)
  File "/home/turgut/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/fastai/learner.py", line 207, in fit
    self._with_events(self._do_fit, 'fit', CancelFitException, self._end_cleanup)
  File "/home/turgut/.local/share/r-miniconda/envs/r-reticulate/lib/python3.

[Cdk29]

Weird. You don't have a GPU ?

I tried without the $to_fp16() but I just got a classic out of memory error. A first glance it does seems to came from that.

With $to_fp16() the full error message looks something like this :

/usr/local/share/.virtualenvs/r-reticulate/lib/python3.7/site-packages/fastai/learner.
py:53: UserWarning: Could not load the optimizer state. 
 if with_opt: warn("Could not load the optimizer state.")
█0         1.302142    1.065968    0.646413  12:58    
█1         1.160781    1.065510    0.620005  12:47  
█2         1.134573    1.001534    0.633092  12:51
R: /opt/conda/conda-bld/magma-cuda101_1583546950098/work/interface_cuda/interface.cpp:901: void 
magma_queue_create_from_cuda_internal(magma_device_t, cudaStream_t, cublasHandle_t, cusparseHandle_t, 
magma_queue**, const char*, const char*, int): Assertion `queue->dCarray__ != __null' failed.

Any idea on what could happen ?

[Cdk29]

@henry090 : I wonder if it is not a more general memory leak issue as the GitHub issue I linked in the first post suggest. My technical background is light on that matter. (/pytorch/issues/26120)

If so, I am not sure what are the models affected. My guess is that happen on every models but got visible only on the one that require a lot of GPU computation such as the XSE_resnext. Or maybe a specific unit such as the SE but I am just guessing.

[turgut090]

I probably installed the cpu version. Regarding your question I advise to run similar code from python side and see if it works. Btw, did it work without fp16?

…

On Fri, Dec 25, 2020, 9:19 PM Cdk29 ***@***.***> wrote: Weird. You don't have a GPU ? I tried without the $to_fp16() but I just got a classic out of memory error. A first glance it does seems to came from that. With $to_fp16() the full error message looks something like this : /usr/local/share/.virtualenvs/r-reticulate/lib/python3.7/site-packages/fastai/learner. py:53: UserWarning: Could not load the optimizer state. if with_opt: warn("Could not load the optimizer state.") █0 1.302142 1.065968 0.646413 12:58 █1 1.160781 1.065510 0.620005 12:47 █2 1.134573 1.001534 0.633092 12:51 R: /opt/conda/conda-bld/magma-cuda101_1583546950098/work/interface_cuda/interface.cpp:901: void magma_queue_create_from_cuda_internal(magma_device_t, cudaStream_t, cublasHandle_t, cusparseHandle_t, magma_queue**, const char*, const char*, int): Assertion `queue->dCarray__ != __null' failed. Any idea on what could happen ? — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#101 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AJF5GM7LNLTDWHTD7U4QI2DSWTCQ7ANCNFSM4VJDGBUQ> .

[Cdk29]

Same without fp16. Memory error but only after few epoch (!) and I got the same traceback/error if I look into the log.

Looks like a memory leak. I will try with a smaller batch. Or do a really quick and dirty training on 3 epochs before saving the model and restarting in an other kernel.

[turgut090]

I reintalled fastai and now it works fine with batch size == 10. This is the result for 1 epoch:

epoch   train_loss   valid_loss   accuracy   time  
------  -----------  -----------  ---------  ------
0       3.050841     1.718752     0.580000   00:07 

system('nvidia-smi',intern = T)
 [1] "Fri Dec 25 21:46:02 2020       "                                                
 [2] "+-----------------------------------------------------------------------------+"
 [3] "| NVIDIA-SMI 450.36.06    Driver Version: 450.36.06    CUDA Version: 11.0     |"
 [4] "|-------------------------------+----------------------+----------------------+"
 [5] "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |"
 [6] "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |"
 [7] "|                               |                      |               MIG M. |"
 [8] "|===============================+======================+======================|"
 [9] "|   0  GeForce RTX 2060    Off  | 00000000:01:00.0  On |                  N/A |"
[10] "|  0%   42C    P8    10W / 170W |   3226MiB /  5931MiB |      4%      Default |"
[11] "|                               |                      |                  N/A |"
[12] "+-------------------------------+----------------------+----------------------+"
[13] "                                                                               "
[14] "+-----------------------------------------------------------------------------+"
[15] "| Processes:                                                                  |"
[16] "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |"
[17] "|        ID   ID                                                   Usage      |"
[18] "|=============================================================================|"
[19] "|    0   N/A  N/A      1298      G   /usr/lib/xorg/Xorg                 85MiB |"
[20] "|    0   N/A  N/A      2205      G   compiz                             31MiB |"
[21] "|    0   N/A  N/A      4977      G   ...AAAAAAAAA= --shared-files       63MiB |"
[22] "|    0   N/A  N/A      6185      G   /usr/lib/rstudio/bin/rstudio       40MiB |"
[23] "|    0   N/A  N/A      6239      C   .../lib/rstudio/bin/rsession     2999MiB |"
[24] "+-----------------------------------------------------------------------------+"

[turgut090]

This is my full code:

library(fastai)
library(magrittr)



df = data.table::fread("~/Downloads/plant/train.csv")
df[['label']] = as.character(df[['label']])
smp = data.table::fread("~/Downloads/plant/sample_submission.csv")

df = dplyr::sample_n(df,250)

batch_tfms = list(RandomResizedCrop(256),
                  #DeterministicDihedral(),
                  #Warp(), Hue(), Saturation(),
                  Hue(0.2, 0.5),
                  aug_transforms(size = 300, max_rotate = 180.,
                                 max_lighting = 0.2, max_warp = 0.4,
                                 flip_vert = TRUE, max_zoom = 2.),
                  Normalize_from_stats( imagenet_stats() ))

plant = DataBlock(blocks = list(ImageBlock(), CategoryBlock()),
                         get_x = function(x) {paste('plant/train_images', x[[0]], sep = '/')},
                         get_y = ColReader('label'),
                         item_tfms = Resize(300),
                         splitter = RandomSplitter(),
                         batch_tfms = batch_tfms) 

dls = plant %>% dataloaders(df, bs = 10)

dls %>% show_batch(max_n = 10)


learn <- dls %>% cnn_learner(xse_resnext50(), metrics = accuracy) #prettier
learn$to_fp16()
cyc = learn %>% fit_one_cycle(1)

[Cdk29]

Yes. To me it happen only after some epochs, whether it is on my local computer or kaggle.

[turgut090]

I think it is a CUDA error. So, it is not related to the wrapper, IMO. Kaggle output also tells that:

Error in py_call_impl(callable, dots$args, dots$keywords): RuntimeError: CUDA out of memory. 
Tried to allocate 20.00 MiB (GPU 0; 15.90 GiB total capacity; 13.80 GiB already allocated; 
9.75 MiB free; 15.08 GiB reserved in total by PyTorch)

You could try to skip lr_find and see if it might work without that.

With sample data it works fine:

cyc = learn %>% fit_one_cycle(10)
epoch   train_loss   valid_loss   accuracy   time 
------  -----------  -----------  ---------  -----
0       2.654885     1.186562     0.580000   00:07 
1       2.604904     1.621742     0.600000   00:06 
2       2.524296     2.486575     0.260000   00:07 
3       2.336369     1.181870     0.500000   00:07 
4       2.164644     1.734055     0.480000   00:07 
5       2.027067     1.226135     0.580000   00:07 
6       1.933184     1.205350     0.620000   00:07 
7       1.812553     1.124996     0.580000   00:07 
8       1.736114     1.102648     0.600000   00:07 
9       1.677608     1.133445     0.620000   00:07 

[Cdk29]

Yes. I just got suspicious because of the only report saying that it was because the guy was making everything into a function. Actually it might have triggered the leak quicker since report that they have to reboot their computer to clear the memory.

I let you close the issue, maybe people will come around if they have this error and see it is a maybe memory leak.

[turgut090]

Hi. Did you manage to solve this issue?

[Cdk29]

@henry090 Hi! Yes and no. The issue cannot be solved per se directly. But I managed to train such a big network by splitting it across a lot of notebooks on kaggle (probably works the same for a computer). It is a version of starting and stopping a computer to clean the memory solve the memory leak on the GPU. (you just need to save the model every time). Actually it made me think that I did not have the time to open an issue related to export(). I will do it know).

[turgut090]

Were you able to train the model with TPUs on Colab?