Research on dynamic allocation of video memory in Python

itread01 2020-11-17 01:30:18
research dynamic allocation video memory


The following is an experiment to explore Pytorch How video memory is allocated .

Experiment

Video memory to main memory

I use VSCode Of jupyter To experiment , First, only import pytorch, The code is as follows :

import torch

Open the work manager to view the main memory and video memory . The situation is as follows :

Set up in video memory 1GB Tensor of , Assign to a, The code is as follows :

a = torch.zeros([256,1024,1024],device= 'cpu') 

View main memory and video memory :

You can see that both main memory and video memory are getting bigger , And video memory is not only getting bigger 1G, The extra memory is pytorch Some configuration variables required for execution , We ignore .

Once again, create a... In the video memory 1GB Tensor of , Assign to b, The code is as follows :

b = torch.zeros([256,1024,1024],device= 'cpu') 

View the main video memory :

This time the main memory size has not changed , Video memory is getting higher 1GB, It's reasonable . Then we will b Move to main memory , The code is as follows :

b = b.to('cpu') 

View the main video memory :

 

Found that the main memory is getting higher 1GB, Video memory is only getting smaller 0.1GB, It's like copying video memory tensor to main memory . Actually ,pytorch It's a copy of the tensor into main memory , But it also records the movement of this tensor in video memory . We then run the following code , Re establishment 1GB The tensor of is assigned to c:

c = torch.zeros([256,1024,1024],device= 'cuda') 

View the main video memory :

Found that only the video memory size increased 0.1GB, This shows that ,Pytorch It does record the movement of tensors in video memory , It's just that there's no immediate release of video memory space , It chooses to override this position the next time a new variable is created . Next , We repeat the above line of code :

c = torch.zeros([256,1024,1024],device= 'cuda') 

The main video memory is as follows :

Clearly we measure the tensor c It's covered , The video memory is getting bigger , Why is that ? Actually ,Pytorch When running this code , Is to find the available video memory location first , Build this 1GB Tensor of , And then assign it to c. But because when this tensor is newly created , The original c Still possess 1GB Video memory ,pytorch You can only retrieve the other one first 1GB Video memory to create this tensor , Then assign this tensor to c. In this way , The original one c The video memory is empty , But as I said before ,pytorch It doesn't immediately release the video memory here , And waiting for the next coverage , So the video memory doesn't decrease .

Let's build 1GB Of d Tensors , We can verify the above conjecture , The code is as follows :

d = torch.zeros([256,1024,1024],device= 'cuda') 

The main video memory is as follows :

Video memory size has not changed , Because pytorch The new tensor is built on the previous step c Empty space , And then assign it to d. in addition , Deleting variables also does not immediately free video memory :

del d

Main video memory condition :

Video memory has not changed , It's also waiting for the next coverage .

Main memory to video memory

And then the experiment above , We build directly in main memory 1GB And assign it to e, The code is as follows :

e = torch.zeros([256,1024,1024],device= 'cpu') 

The main video memory is as follows :

Main memory gets bigger 1GB, be perfectly logical and reasonable . Then the e Move to video memory , The code is as follows :

e = e.to('cuda')

The main video memory is as follows :

The main memory becomes smaller 1GB, The video memory doesn't change because of the tensor above d Deleted, not covered , be perfectly logical and reasonable . The release of main memory is executed immediately .

Summary  

Through the above experiment , We learned that ,pytorch Memory of failed variables in video memory is not immediately released , It uses the available space in video memory in an overlay way . in addition , If you want to reset a large tensor in video memory , It's better to move it to main memory first , Or delete it directly , Create a new value , Otherwise, it takes twice as much memory to do this , There may be a shortage of video memory . 

The experimental code is summarized as follows :

#%%
import torch
#%%
a = torch.zeros([256,1024,1024],device= 'cuda')
#%%
b = torch.zeros([256,1024,1024],device= 'cuda')
#%%
b = b.to('cpu')
#%%
c = torch.zeros([256,1024,1024],device= 'cuda')
#%%
c = torch.zeros([256,1024,1024],device= 'cuda')
#%%
d = torch.zeros([256,1024,1024],device= 'cuda')
#%%
del d
#%%
e = torch.zeros([256,1024,1024],device= 'cpu')
#%%
e = e.to('cuda')
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