How to free CPU RAM after `module.to(cuda_device)`?

1

I am trying to optimize memory consumption of a model and profiled it using memory_profiler. It appears to me that calling module.to(cuda_device) copies to GPU RAM, but doesn’t release memory of CPU RAM.
Is there a way to reclaim some/most of CPU RAM that was originally allocated for loading/initialization after moving my modules to GPU?

Some more info:

Line 214, uses about 2GB to initialize my model.
Line 221 or later, I no longer need this CPU RAM stuff, and I am trying to empty it (even with forced GC in line 224 didn’t help!)

Line #    Mem usage    Increment   Line Contents
================================================
   209     88.7 MiB     88.7 MiB       @profile
   210                                 def __init__(self, exp: Experiment, model=None, lr=0.0001):
   211     88.7 MiB      0.0 MiB           self.exp = exp
   212     88.7 MiB      0.0 MiB           self.start_epoch = 0
   213     88.7 MiB      0.0 MiB           if model is None:
   214   2159.7 MiB   2071.0 MiB               model = Seq2Seq(**exp.get_model_args()).to(device)
   215   2159.7 MiB      0.0 MiB               last_check_pt, last_epoch = self.exp.get_last_saved_model()
   216   2159.7 MiB      0.0 MiB               if last_check_pt:
   217                                             log.info(f"Resuming training from epoch:{self.start_epoch}, model={last_check_pt}")
   218                                             self.start_epoch = last_epoch + 1
   219                                             model.load_state_dict(torch.load(last_check_pt))
   220   2159.7 MiB      0.0 MiB           log.info(f"Moving model to device = {device}")
   221   2159.7 MiB      0.0 MiB           self.model = model.to(device=device)
   222   2159.7 MiB      0.0 MiB           self.model.train()
   223   2159.7 MiB      0.0 MiB           del model           # this was on CPU, free that memory
   224   2159.7 MiB      0.0 MiB           gc.collect()        # should the GC cleanup CPU buffers after moving to GPU ?
   225   2159.7 MiB      0.0 MiB           self.optimizer = optim.Adam(self.model.parameters(), lr=lr)

Edit:
version pytorch 0.4.0 on linux-64

2

You could try Python garbage collection (import gc ; gc.collect()).

Best regards

Thomas

3

That didn’t help. (see last but one line in code snippet of original post)

4

I will be thankful if somebody who has seen the implementation of .to() functionality also comment on this matter

5

Update: I found what I was looking for!
Torch (in version 0.4.0) takes about 2GB CPU RAM upfront when the first cuda/GPU tensor is allocated. That was not a memory leak.

Line #    Mem usage    Increment   Line Contents
================================================
     4     29.2 MiB     29.2 MiB   @profile
     5                             def main():
     6     95.6 MiB     66.4 MiB       import torch
     7     95.6 MiB      0.0 MiB       size = (1024, 1024, 10)
     8     95.6 MiB      0.0 MiB       cpu, gpu = torch.device('cpu'), torch.device('cuda:0')
     9                                 # couple of CPU tensors
    10    135.6 MiB     40.0 MiB       t_cpu1 = torch.zeros(size, device=cpu)
    11    175.6 MiB     40.0 MiB       t_cpu2 = torch.zeros(size, device=cpu)
    12                                 # couple of GPU tensors
    13   2120.5 MiB   1944.9 MiB       t_gpu1 = torch.zeros(size, device=gpu)
    14   2120.5 MiB      0.0 MiB       t_gpu2 = torch.zeros(size, device=gpu)
    15   2120.6 MiB      0.1 MiB       t_gpu3 = t_cpu1.to(gpu)
    16   2080.6 MiB    -40.0 MiB       del t_cpu1
    17   2080.6 MiB      0.0 MiB       gc.collect()  # does this free memory of t_cpu1 ?
    18   2040.6 MiB    -40.0 MiB       del t_cpu2
    19   2040.6 MiB      0.0 MiB       gc.collect()  # does this free memory of t_cpu2?
    20   2040.6 MiB      0.0 MiB       del t_gpu1, t_gpu2, t_gpu3
    21   2040.6 MiB      0.0 MiB       gc.collect()  # these doesnt take any CPU RAM !!
    22   2040.6 MiB      0.0 MiB       del torch    # remove torch module from context
    23   2040.6 MiB      0.0 MiB       gc.collect()  # does it free everything ?

P.S.
the memory leak was somewhere else on my code, I was misled by the initial 2GB unaccounted memory.

Thanks, to anyone who attempted to answer my question!

1 Like
6

Hello t.g,
I am facing the same problem now, i.e., “Torch (in version 0.4.0) takes about 2GB CPU RAM upfront when the first cuda/GPU tensor is allocated”.
Therefore, I am kindly asking you how you manage to solve this problem? b.t.w, what do you mean by “misled by the initial 2GB unaccounted memory”?

image
image758×310 23.1 KB

Looking forward to your reply.
Best,
Changgong

7

I wasnt able to fix it. I understood that its the way pytorch works and moved on.

1 Like
8

hey how did you get memory usage at each line

1 Like
9

this can be done with memory_profiler

10

Any ideas why it works this way?

I also faced with this behaviour

import torch
from memory_profiler import profile
import gc

import resource
def mem_usage():
    usage=resource.getrusage(resource.RUSAGE_SELF)
    return f'mem usage={usage[2]/1024.0} mb'

@profile
def load_func(path):
    m = torch.load(path, map_location="cuda:0")
    return m

if __name__ == "__main__":
    big_model_path ='pytorch_model.bin'
    m = load_func(big_model_path)
    gc.collect()
    print(f'cuda memory: {torch.cuda.memory_allocated()  /1024/1024}')
    print(mem_usage())

Output:

$python3 memory_test.py
Filename: memory_test.py

Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
    10    212.5 MiB    212.5 MiB           1   @profile
    11                                         def load_func(path):
    12   2767.9 MiB   2555.3 MiB           1       m = torch.load(path, map_location="cuda:0")
    13   2767.9 MiB      0.0 MiB           1       return m


cuda memory: 516.2373046875
mem usage=3007.8828125 mb