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Pytorch_Analyzer

This tool for Pytorch can analyze your model.

Information include layer memory usage, max memory usage, cumulative memory usage and execution time.

Requirement

pytorch

matplotlib (python -m pip install -U matplotlib)

Analysis

Print the analysis.

No.    Layer_memory    Max_memory        Memory      Exec_time    Layer
Initial---------------------------------------------------------------------------------------------------
0          61.18 MB     118.39 MB      61.18 MB     3971.82 us    Input, label etc.                  
Forward---------------------------------------------------------------------------------------------------
1        6400.00 kB      87.13 MB      67.43 MB       97.75 us    Conv2d(3, 32, kernel_size=(3,      
2        6400.00 kB      73.68 MB      73.68 MB       29.56 us    LeakyReLU(negative_slope=0.01)     
3        6400.00 kB      79.93 MB      79.93 MB       52.21 us    BatchNorm2d(32, eps=1e-05, mom     
4        4800.00 kB      84.62 MB      84.62 MB       34.57 us    MaxPool2d(kernel_size=2, strid     
5        3200.00 kB     101.52 MB      87.74 MB       77.01 us    Conv2d(32, 64, kernel_size=(3,     
6        3200.00 kB      90.87 MB      90.87 MB       25.99 us    LeakyReLU(negative_slope=0.01)     
7        3200.00 kB      93.99 MB      93.99 MB       44.82 us    BatchNorm2d(64, eps=1e-05, mom     
8        2400.00 kB      96.34 MB      96.34 MB       30.04 us    MaxPool2d(kernel_size=2, strid     
9        1600.00 kB     123.90 MB      97.90 MB       87.50 us    Conv2d(64, 128, kernel_size=(3     
10       1600.00 kB      99.46 MB      99.46 MB       25.27 us    LeakyReLU(negative_slope=0.01)     
11       1600.00 kB     101.02 MB     101.02 MB       43.15 us    BatchNorm2d(128, eps=1e-05, mo     
12        800.00 kB     101.81 MB     101.81 MB       67.95 us    Conv2d(128, 64, kernel_size=(1     
13        800.00 kB     102.59 MB     102.59 MB       25.27 us    LeakyReLU(negative_slope=0.01)     
14        800.00 kB     103.37 MB     103.37 MB       42.92 us    BatchNorm2d(64, eps=1e-05, mom     
15       1600.00 kB     130.93 MB     104.93 MB       75.82 us    Conv2d(64, 128, kernel_size=(3     
16       1600.00 kB     106.49 MB     106.49 MB       25.03 us    LeakyReLU(negative_slope=0.01)     
17       1600.00 kB     108.06 MB     108.06 MB       42.20 us    BatchNorm2d(128, eps=1e-05, mo     
18       1200.00 kB     109.23 MB     109.23 MB       29.80 us    MaxPool2d(kernel_size=2, strid     
19       3125.00 kB     112.28 MB     112.28 MB       72.24 us    Conv2d(128, 1000, kernel_size=     
20       3125.00 kB     115.33 MB     115.33 MB       24.80 us    LeakyReLU(negative_slope=0.01)     
21       3125.00 kB     118.38 MB     118.38 MB       43.39 us    BatchNorm2d(1000, eps=1e-05, m     
22          0.00 kB     118.38 MB     118.38 MB       18.60 us    Flatten()                          
23          2.00 kB     118.39 MB     118.39 MB       66.52 us    Linear(in_features=16000, out_     
24          2.00 kB     118.39 MB     118.39 MB       25.27 us    LeakyReLU(negative_slope=0.01)

Plot the result

  • Every layer memory usage

png

  • Total memory usage (cumulative memory usage)

png

  • Every layer execution time

png

How to use

  1. Import Pytorch_Analyzer in your code
from pytorch_analyzer import Pytorch_Analyzer
  1. Construct Pytorch_Analyzer and input your model.
analyzer = Pytorch_Analyzer(Your_model)
  1. Print the analysis.
analyzer.analysis()
  1. Plot the analysis
analyzer.analysis_plot()

Reference

https://pytorch.org/docs/stable/cuda.html
https://pytorch.org/tutorials/beginner/former_torchies/nnft_tutorial.html