MegEngine / MegEngine/NBNet

why in linux loss blow up?

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Dominant language
Python
Stars
155
Forks
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Description

It is a strange issue that the loss will blow up to Nan in linux ubuntu 20.04 after I install the cuda, without cuda the loss decrease normally.

environment:
megengine == 1.3.0
torch == 1.9.1

more strange is the loss will fall back :

Epoch 0 Step 0, Speed=1.7 mb/s, dp_cost=0.0098, Loss=4.02e-02, lr=2.00e-04
Epoch 0 Step 10, Speed=14 mb/s, dp_cost=0.25, Loss=1.56e+00, lr=2.00e-04
Epoch 0 Step 20, Speed=19 mb/s, dp_cost=0.034, Loss= nan, lr=2.00e-04
Epoch 0 Step 30, Speed=17 mb/s, dp_cost=0.054, Loss=3.62e-02, lr=2.00e-04
Epoch 0 Step 40, Speed=20 mb/s, dp_cost=0.037, Loss=2.22e-01, lr=2.00e-04
Epoch 0 Step 50, Speed=15 mb/s, dp_cost=0.16, Loss= nan, lr=2.00e-04
Epoch 0 Step 60, Speed=16 mb/s, dp_cost=0.21, Loss= nan, lr=2.00e-04
Epoch 0 Step 70, Speed=18 mb/s, dp_cost=0.033, Loss= nan, lr=2.00e-04
Epoch 0 Step 80, Speed=18 mb/s, dp_cost=0.11, Loss= nan, lr=2.00e-04
Epoch 0 Step 90, Speed=15 mb/s, dp_cost=0.2, Loss= nan, lr=2.00e-04
Epoch 0 Step 100, Speed=12 mb/s, dp_cost=0.037, Loss= nan, lr=2.00e-04
Epoch 0 Step 110, Speed=15 mb/s, dp_cost=0.22, Loss= nan, lr=2.00e-04
Epoch 0 Step 120, Speed=17 mb/s, dp_cost=0.035, Loss= nan, lr=2.00e-04
Epoch 0 Step 130, Speed=17 mb/s, dp_cost=0.028, Loss= nan, lr=2.00e-04
Epoch 0 Step 140, Speed=13 mb/s, dp_cost=0.1, Loss= nan, lr=2.00e-04
Epoch 0 Step 150, Speed=17 mb/s, dp_cost=0.24, Loss= nan, lr=2.00e-04
Epoch 0 Step 160, Speed=15 mb/s, dp_cost=0.23, Loss= nan, lr=2.00e-04
Epoch 0 Step 170, Speed=8.5 mb/s, dp_cost=0.18, Loss= nan, lr=2.00e-04
Epoch 0 Step 180, Speed=16 mb/s, dp_cost=0.078, Loss= nan, lr=2.00e-04
Epoch 0 Step 190, Speed=15 mb/s, dp_cost=0.21, Loss= nan, lr=2.00e-04
Epoch 0 Step 200, Speed=15 mb/s, dp_cost=0.06, Loss= nan, lr=2.00e-04
Epoch 0 Step 210, Speed=19 mb/s, dp_cost=0.034, Loss=6.01e-02, lr=2.00e-04
Epoch 0 Step 220, Speed=16 mb/s, dp_cost=0.058, Loss= nan, lr=2.00e-04
Epoch 0 Step 230, Speed=19 mb/s, dp_cost=0.037, Loss= nan, lr=2.00e-04
Epoch 0 Step 240, Speed=17 mb/s, dp_cost=0.11, Loss= nan, lr=2.00e-04
Epoch 0 Step 250, Speed=10 mb/s, dp_cost=0.016, Loss= nan, lr=2.00e-04
Epoch 0 Step 260, Speed=16 mb/s, dp_cost=0.028, Loss= nan, lr=2.00e-04
Epoch 0 Step 270, Speed=16 mb/s, dp_cost=0.031, Loss= nan, lr=2.00e-04
Epoch 0 Step 280, Speed=10 mb/s, dp_cost=0.14, Loss= nan, lr=2.00e-04
Epoch 0 Step 290, Speed=17 mb/s, dp_cost=0.034, Loss= nan, lr=2.00e-04
Epoch 0 Step 300, Speed=15 mb/s, dp_cost=0.029, Loss= nan, lr=2.00e-04
Epoch 0 Step 310, Speed=19 mb/s, dp_cost=0.032, Loss=2.82e-01, lr=2.00e-04
Epoch 0 Step 320, Speed=17 mb/s, dp_cost=0.036, Loss=6.47e-02, lr=2.00e-04
Epoch 0 Step 330, Speed=15 mb/s, dp_cost=0.031, Loss= nan, lr=2.00e-04
Epoch 0 Step 340, Speed=13 mb/s, dp_cost=0.082, Loss= nan, lr=2.00e-04
Epoch 0 Step 350, Speed=13 mb/s, dp_cost=0.021, Loss=3.08e-01, lr=2.00e-04
Epoch 0 Step 360, Speed=14 mb/s, dp_cost=0.022, Loss= nan, lr=2.00e-04
Epoch 0 Step 370, Speed=16 mb/s, dp_cost=0.051, Loss= nan, lr=2.00e-04
Epoch 0 Step 380, Speed=11 mb/s, dp_cost=0.19, Loss= nan, lr=2.00e-04
Epoch 0 Step 390, Speed=15 mb/s, dp_cost=0.069, Loss= nan, lr=2.00e-04
Epoch 0 Step 400, Speed=16 mb/s, dp_cost=0.037, Loss= nan, lr=2.00e-04
Epoch 0 Step 410, Speed=17 mb/s, dp_cost=0.032, Loss= nan, lr=2.00e-04
Epoch 0 Step 420, Speed=19 mb/s, dp_cost=0.038, Loss= nan, lr=2.00e-04
Epoch 0 Step 430, Speed=17 mb/s, dp_cost=0.035, Loss= nan, lr=2.00e-04
Epoch 0 Step 440, Speed=11 mb/s, dp_cost=0.021, Loss=9.56e-02, lr=2.00e-04
Epoch 0 Step 450, Speed=16 mb/s, dp_cost=0.057, Loss= nan, lr=2.00e-04
Epoch 0 Step 460, Speed=15 mb/s, dp_cost=0.22, Loss= nan, lr=2.00e-04

Contributor guide

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Research direction

Reproduce the training run on Ubuntu 20.04 with CUDA installed, using MegEngine 1.3.0 and torch 1.9.1, then compare it with the non-CUDA run. Inspect the steps where the logged loss changes to NaN and verify that the completed run keeps the loss finite and decreases normally.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
machine-learning
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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