Install torch memory error



on new install got out of memory or access violations torch.cuda.empty_cache() and others #13778

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tyoc213 commented Nov 9, 2018 •

On a new install with this spec

I get memory errors almost all times.

To Reproduce

Steps to reproduce the behavior:

Install fastai library as https://github.com/fastai/fastai/blob/master/README.md#installation and on a new jupyter nootebook get the cuda available = True and then try to call empty (to see if it frees something because the out of memory).

I have some problems running the examples provided in fastai lib so I posted on their forum. But after searching here for a solution , I found torch.cuda.empty_cache() but still I get the memory error. so that is why Im comming here

/anaconda3/lib/python3.7/site-packages/torch/cuda/__init__.py in empty_cache() 372 «»» 373 if _initialized: —> 374 torch._C._cuda_emptyCache() 375 376 RuntimeError: CUDA error: an illegal memory access was encountered»>

Or try to run the different examples provided there collab.ipynb works OK but stepping on cyfar on fastai/examples I an error executing this line

I get this output

/fastai/fastai/basic_train.py in __post_init__(self) 136 self.path = Path(ifnone(self.path, self.data.path)) 137 (self.path/self.model_dir).mkdir(parents=True, exist_ok=True) —> 138 self.model = self.model.to(self.data.device) 139 self.loss_func = ifnone(self.loss_func, self.data.loss_func) 140 self.metrics=listify(self.metrics)

/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in to(self, *args, **kwargs) 377 return t.to(device, dtype if t.is_floating_point() else None, non_blocking) 378 —> 379 return self._apply(convert) 380 381 def register_backward_hook(self, hook):

/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in _apply(self, fn) 183 def _apply(self, fn): 184 for module in self.children(): —> 185 module._apply(fn) 186 187 for param in self._parameters.values():

/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in _apply(self, fn) 183 def _apply(self, fn): 184 for module in self.children(): —> 185 module._apply(fn) 186 187 for param in self._parameters.values():

/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in _apply(self, fn) 189 # Tensors stored in modules are graph leaves, and we don’t 190 # want to create copy nodes, so we have to unpack the data. —> 191 param.data = fn(param.data) 192 if param._grad is not None: 193 param._grad.data = fn(param._grad.data)

/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in convert(t) 375 376 def convert(t): —> 377 return t.to(device, dtype if t.is_floating_point() else None, non_blocking) 378 379 return self._apply(convert) RuntimeError: cuda runtime error (77) : an illegal memory access was encountered at /opt/conda/conda-bld/pytorch-nightly_1541411195070/work/aten/src/THC/generic/THCTensorCopy.cpp:20″>

torch.cuda.is_available() return True .

Im also running out of memory in dogs_cats.ipynb .

/fastai/fastai/vision/learner.py in create_cnn(data, arch, cut, pretrained, lin_ftrs, ps, custom_head, split_on, classification, **kwargs) 67 learn.split(ifnone(split_on,meta[‘split’])) 68 if pretrained: learn.freeze() —> 69 apply_init(model[1], nn.init.kaiming_normal_) 70 return learn 71

/fastai/fastai/torch_core.py in apply_init(m, init_func) 193 def apply_init(m, init_func:LayerFunc): 194 «Initialize all non-batchnorm layers of `m` with `init_func`.» —> 195 apply_leaf(m, partial(cond_init, init_func=init_func)) 196 197 def in_channels(m:nn.Module) -> List[int]:

/fastai/fastai/torch_core.py in apply_leaf(m, f) 189 c = children(m) 190 if isinstance(m, nn.Module): f(m) —> 191 for l in c: apply_leaf(l,f) 192 193 def apply_init(m, init_func:LayerFunc):

/fastai/fastai/torch_core.py in apply_leaf(m, f) 188 «Apply `f` to children of `m`.» 189 c = children(m) —> 190 if isinstance(m, nn.Module): f(m) 191 for l in c: apply_leaf(l,f) 192

/fastai/fastai/torch_core.py in cond_init(m, init_func) 183 if (not isinstance(m, bn_types)) and requires_grad(m): 184 if hasattr(m, ‘weight’): init_func(m.weight) —> 185 if hasattr(m, ‘bias’) and hasattr(m.bias, ‘data’): m.bias.data.fill_(0.) 186 187 def apply_leaf(m:nn.Module, f:LayerFunc): RuntimeError: cuda runtime error (2) : out of memory at /opt/conda/conda-bld/pytorch-nightly_1541411195070/work/aten/src/THC/generic/THCTensorMath.cu:14″>

I get the cuda memory error also in tabular

/fastai/fastai/tabular/data.py in get_tabular_learner(data, layers, emb_szs, metrics, ps, emb_drop, y_range, use_bn, **kwargs) 93 model = TabularModel(emb_szs, len(data.cont_names), out_sz=data.c, layers=layers, ps=ps, emb_drop=emb_drop, 94 y_range=y_range, use_bn=use_bn) —> 95 return Learner(data, model, metrics=metrics, **kwargs) 96 in __init__(self, data, model, opt_func, loss_func, metrics, true_wd, bn_wd, wd, train_bn, path, model_dir, callback_fns, callbacks, layer_groups)

/fastai/fastai/basic_train.py in __post_init__(self) 136 self.path = Path(ifnone(self.path, self.data.path)) 137 (self.path/self.model_dir).mkdir(parents=True, exist_ok=True) —> 138 self.model = self.model.to(self.data.device) 139 self.loss_func = ifnone(self.loss_func, self.data.loss_func) 140 self.metrics=listify(self.metrics)

/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in to(self, *args, **kwargs) 377 return t.to(device, dtype if t.is_floating_point() else None, non_blocking) 378 —> 379 return self._apply(convert) 380 381 def register_backward_hook(self, hook):

/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in _apply(self, fn) 183 def _apply(self, fn): 184 for module in self.children(): —> 185 module._apply(fn) 186 187 for param in self._parameters.values():

/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in _apply(self, fn) 183 def _apply(self, fn): 184 for module in self.children(): —> 185 module._apply(fn) 186 187 for param in self._parameters.values():

/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in _apply(self, fn) 189 # Tensors stored in modules are graph leaves, and we don’t 190 # want to create copy nodes, so we have to unpack the data. —> 191 param.data = fn(param.data) 192 if param._grad is not None: 193 param._grad.data = fn(param._grad.data)

/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in convert(t) 375 376 def convert(t): —> 377 return t.to(device, dtype if t.is_floating_point() else None, non_blocking) 378 379 return self._apply(convert) RuntimeError: CUDA error: out of memory»>

Expected behavior

Examples to work

Environment

python collect_env.py
Collecting environment information.
PyTorch version: 0.4.1
Is debug build: No
CUDA used to build PyTorch: 9.2.148

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OS: Ubuntu 18.10
GCC version: (Ubuntu 8.2.0-7ubuntu1) 8.2.0
CMake version: version 3.12.2

Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: GeForce RTX 2080
Nvidia driver version: 410.73
cuDNN version: Could not collect

Versions of relevant libraries:
[pip] Could not collect
[conda] cuda92 1.0 0 pytorch
[conda] pytorch 0.4.1 py37_cuda9.2.148_cudnn7.1.4_1 [cuda92] pytorch
[conda] torchvision 0.2.1 py37_1 pytorch
[conda] torchvision-nightly 0.2.1

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Pytorch cannot allocate enough memory #913

Comments

craftpag commented Nov 28, 2021

I am trying to run encoder_train.py
I have preprocessed Train_other_500, but when I try to start encoder_train.py I get this message
CUDA out of memory. Tried to allocate 4.98 GiB (GPU 0; 8.00 GiB total capacity; 1.64 GiB already allocated; 4.51 GiB free; 1.67 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

if I have read it correctly, i most add/change max_split_size_mb = to one of the codes. I have tried to search around, and everyone has a solution but none of them says where to change the code.

Where do i add/change the code, to add max_split_size_mb = ?

this may be a stupid question, but I am lost.

Specs:
Windows 11 PRO 21H2
RTX3070
AMD Rysen 7 5800x
32Gb DDR4 3200MH/z
Pytorch 1.10, CUDA 11.3
Python 3.7.9

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sveneschlbeck commented Nov 28, 2021

@craftpag This is not a parameter to be found in the code here but a PyTorch command that (if I’m not wrong) needs to be set as an environment variable.
Try setting PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb: .

Doc Quote: » max_split_size_mb prevents the allocator from splitting blocks larger than this size (in MB). This can help prevent fragmentation and may allow some borderline workloads to complete without running out of memory.»

Checkout this link to see the full documentation for PyTorch’s memory management:
https://pytorch.org/docs/stable/notes/cuda.html

craftpag commented Nov 30, 2021

Hello
Thank you for replying @sveneschlbeck

I have tried to add those environment variables, with no luck.
I have tried to add it in different ways, but I still get the same error.
Do you think it can be other solutions out there?

sveneschlbeck commented Nov 30, 2021 •

There’s a couple of things remaining until I am out of answers, too:

1. Are you running any other scripts/games/programs that might be taking up GPU memory? If so, do the following:

Type nvidia-smi into the terminal and find the PID of the process using most GPU memory (apart from PyTorch of course), then kill it by typing taskkill /F /PID

2. Try to reduce memory-intensive (hyper)parameters, e.g. train/test size, batch size, etc.

3. Run the following

My guess is that it’s the batch_size since that is where you specify how much data is loaded into the memory at once. See #914 to get an idea on where you can decrease the batch size. I’d do it file after file to see where the error is caused. Alternatively, you can change it in all files at once. But keep in mind that a lower batch size results in a longer training/testing duration.

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Frequently Asked Questions¶

My model reports “cuda runtime error(2): out of memory”¶

As the error message suggests, you have run out of memory on your GPU. Since we often deal with large amounts of data in PyTorch, small mistakes can rapidly cause your program to use up all of your GPU; fortunately, the fixes in these cases are often simple. Here are a few common things to check:

Don’t accumulate history across your training loop. By default, computations involving variables that require gradients will keep history. This means that you should avoid using such variables in computations which will live beyond your training loops, e.g., when tracking statistics. Instead, you should detach the variable or access its underlying data.

Sometimes, it can be non-obvious when differentiable variables can occur. Consider the following training loop (abridged from source):

Here, total_loss is accumulating history across your training loop, since loss is a differentiable variable with autograd history. You can fix this by writing total_loss += float(loss) instead.

Other instances of this problem: 1.

Don’t hold onto tensors and variables you don’t need. If you assign a Tensor or Variable to a local, Python will not deallocate until the local goes out of scope. You can free this reference by using del x . Similarly, if you assign a Tensor or Variable to a member variable of an object, it will not deallocate until the object goes out of scope. You will get the best memory usage if you don’t hold onto temporaries you don’t need.

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The scopes of locals can be larger than you expect. For example:

Here, intermediate remains live even while h is executing, because its scope extrudes past the end of the loop. To free it earlier, you should del intermediate when you are done with it.

Avoid running RNNs on sequences that are too large. The amount of memory required to backpropagate through an RNN scales linearly with the length of the RNN input; thus, you will run out of memory if you try to feed an RNN a sequence that is too long.

The technical term for this phenomenon is backpropagation through time, and there are plenty of references for how to implement truncated BPTT, including in the word language model example; truncation is handled by the repackage function as described in this forum post.

Don’t use linear layers that are too large. A linear layer nn.Linear(m, n) uses O ( n m ) O(nm) O ( nm ) memory: that is to say, the memory requirements of the weights scales quadratically with the number of features. It is very easy to blow through your memory this way (and remember that you will need at least twice the size of the weights, since you also need to store the gradients.)

Consider checkpointing. You can trade-off memory for compute by using checkpoint.

My GPU memory isn’t freed properly¶

PyTorch uses a caching memory allocator to speed up memory allocations. As a result, the values shown in nvidia-smi usually don’t reflect the true memory usage. See Memory management for more details about GPU memory management.

If your GPU memory isn’t freed even after Python quits, it is very likely that some Python subprocesses are still alive. You may find them via ps -elf | grep python and manually kill them with kill -9 [pid] .

My out of memory exception handler can’t allocate memory¶

You may have some code that tries to recover from out of memory errors.

But find that when you do run out of memory, your recovery code can’t allocate either. That’s because the python exception object holds a reference to the stack frame where the error was raised. Which prevents the original tensor objects from being freed. The solution is to move you OOM recovery code outside of the except clause.

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RuntimeError: $ Torch: not enough memory: you tried to allocate 72GB. Buy new RAM! #5434

Comments

ponomarevsy commented Feb 27, 2018

Red Hat Enterprise Linux Server release 7.2 (Maipo)

pytorch 0.3.1 py35_cuda8.0.61_cudnn7.0.5_2 pytorch
torchvision 0.2.0 py35heaa392f_1 pytorch

  • How you installed PyTorch (conda, pip, source):

$ module load anaconda3/4.3.1
$ source activate pytorchenv
$ conda install pytorch torchvision -c pytorch

$ python -V
Python 3.5.5

  • GPU models and configuration:
  • GCC version (if compiling from source):

In addition, including the following information will also be very helpful for us to diagnose the problem:

  • A script to reproduce the bug. Please try to provide as minimal of a test case as possible.
  • Error messages and/or stack traces of the bug
  • Context around what you are trying to do

Training a model with:

I’ve tried batch sizes from 128 to 8, and using GPUs from just one to all 8. GPU node has plenty of RAM (124G):

$ free
total used free shared buff/cache available
Mem: 131930696 6299776 124697204 16968 933716 124718092
Swap: 16777212 441996 16335216

Do you have a maximum RAM allocation limit hardcoded in PyTorch (file «THGeneral.c»)? Thank you in advance!

Complete error message:

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apaszke commented Feb 27, 2018

72GB is a lot, it might be the OS that rejects such a request. We don’t have any allocation limits, this error is raised when malloc fails to claim more memory.

zou3519 commented Feb 27, 2018

@ponomarevsy do you think your code should be allocating 72GB?

I’m currently investigating large memory usage with convolutions on the CPU: #5285, if you’re using any convolutions in your model this could be related.

ponomarevsy commented Feb 27, 2018 •

Thank you for your feedback, @apaszke and @zou3519. @apaszke, are you sure this is not a memory leakage issue similar to @zou3519? Also, I see no reason why PyTorch wouldn’t allocate more than 72G (if needed), knowing that there is 124G available on the node. Still this sounds like too much RAM to me. I remember having similar issues using DIGITS (with Caffe). Do these training sets require that much RAM?

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colesbury commented Feb 27, 2018

You probably don’t have 124 GB available at the point where you try to allocate 72GB. It looks like the triggering call is torch.stack . Unless you have repeated inputs, there’s a good chance that the inputs use up 72 GB as well.

yeladlouni commented Aug 24, 2018

It could be a problem of memory alignment.

lisiyaoATbnu commented Dec 3, 2018 •

I think I’ve met a similar situation.

I tested my small (totally forward) network on one

1000×1000 image but it seemed to allocate

45G memory. Hence it broke.

Very interestingly, when I tried Python 2 (different env) on the same computer, it ran and I got a good result.

I’m still confused by that.

linchart commented Dec 24, 2018 •

I also met a similar situation with a small network and data, when my code run after 5 epochs, it raised: RuntimeError: $ Torch: not enough memory: you tried to allocate 5GB . very strange

ronalddas commented Apr 5, 2020 •

Even I am facing a simillar issue, am trying run evaluation on a 28MB model file and am getting issue, RuntimeError: [enforce fail at CPUAllocator.cpp:64] . DefaultCPUAllocator: can’t allocate memory: you tried to allocate 377216000 bytes. Error code 12 (Cannot allocate memory) , why is it trying to allocate in PetaBytes ??(Correction, 377mb)

zou3519 commented Apr 6, 2020

@ronalddas 377216000 is 377 mb

GraphGrailAi commented Jul 1, 2020

Even I am facing a simillar issue, am trying run evaluation on a 28MB model file and am getting issue, RuntimeError: [enforce fail at CPUAllocator.cpp:64] . DefaultCPUAllocator: can’t allocate memory: you tried to allocate 377216000 bytes. Error code 12 (Cannot allocate memory) , why is it trying to allocate in PetaBytes ??(Correction, 377mb)

Similar problem: RAM is 4GB, GPU with 12 GB memory, my model i try to load is 5GB. But i see error «you tried to allocate 108216000 bytes» — that is 108mb — that is strange

dzungarian commented Aug 20, 2020 •

When I save my model using torch.save and load again using torch.load and run embedding layer in the model, I encounter the same error. But if I don’t save the model, it runs without errors.

nusherjk commented Sep 13, 2020

Even I am facing a simillar issue, am trying run evaluation on a 28MB model file and am getting issue, RuntimeError: [enforce fail at CPUAllocator.cpp:64] . DefaultCPUAllocator: can’t allocate memory: you tried to allocate 377216000 bytes. Error code 12 (Cannot allocate memory) , why is it trying to allocate in PetaBytes ??(Correction, 377mb)

Similar problem: RAM is 4GB, GPU with 12 GB memory, my model i try to load is 5GB. But i see error «you tried to allocate 108216000 bytes» — that is 108mb — that is strange

having same issue with 196 mb of RAM. any solution?

nusherjk commented Sep 27, 2020

Even I am facing a simillar issue, am trying run evaluation on a 28MB model file and am getting issue, RuntimeError: [enforce fail at CPUAllocator.cpp:64] . DefaultCPUAllocator: can’t allocate memory: you tried to allocate 377216000 bytes. Error code 12 (Cannot allocate memory) , why is it trying to allocate in PetaBytes ??(Correction, 377mb)

Similar problem: RAM is 4GB, GPU with 12 GB memory, my model i try to load is 5GB. But i see error «you tried to allocate 108216000 bytes» — that is 108mb — that is strange

having same issue with 196 mb of RAM. any solution?

Fixed it with a garbage collector.
add import gc
and add gc.collect() after each end of epoch or wherever you please

afogarty85 commented Mar 22, 2021

Fixed it with a garbage collector.
add import gc
and add gc.collect() after each end of epoch or wherever you please

This fixed a similar issue for me with repeated hyperopt trials. Thank you.

Mehmaam99 commented Jun 6, 2022

Same issue occur, Open Task Manager and End Task all files related to this code, (ex: Python, VS Code etc.) and restart your IDE.
These steps solve my issue.

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