shuimu / word2vec

Automatically exported from code.google.com/p/word2vec
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Segfault in script demo-phrase-accuracy.sh #4

Closed GoogleCodeExporter closed 9 years ago

GoogleCodeExporter commented 9 years ago
$ ./demo-phrase-accuracy.sh 
make: Nothing to be done for `all'.
Starting training using file text8
Words processed: 17000K     Vocab size: 4399K  
Vocab size (unigrams + bigrams): 2586139
Words in train file: 17005206
Words written: 17000K
real    0m21.130s
user    0m20.062s
sys 0m1.054s
Starting training using file text8-phrase
Vocab size: 123636
Words in train file: 16337523
Alpha: 0.000119  Progress: 99.59%  Words/thread/sec: 22.70k  
real    1m38.617s
user    12m0.795s
sys 0m1.501s
newspapers:
./demo-phrase-accuracy.sh: line 12: 36538 Segmentation fault: 11  
./compute-accuracy vectors-phrase.bin < questions-phrases.txt

I'm on OSX (latest non-beta), and had to switch out "#include <stdlib.h>" to 
get it to compile, but no other changes.

Original issue reported on code.google.com by benjamin...@gmail.com on 19 Aug 2013 at 7:41

GoogleCodeExporter commented 9 years ago
demo-word-accuracy.sh also crashes.
The other demos run great.

Original comment by benjamin...@gmail.com on 19 Aug 2013 at 7:45

GoogleCodeExporter commented 9 years ago
Im on OSX Lion compiled with clang.

Using valgrind the issue appears to be on line 102 of compute-accuracy.c

vec[a] = M[a + b2 * size] - M[a + b1 * size] + m[a + b3 * size];

With 30k as the input on the command line for words the size of M is 24,000,000 
bytes or 6M float array, but from putting in an if statement the program 
regularly accesses memory outside of this range.

Putting the if statement with a printf msg stops the seg fault.

I have:
  if (a + b3 * size > 6000000) printf("Memory overflow\n"); 

Putting this statement in there outputs a bunch of memory overflow messages but 
aside from that it seems as the though the program keeps trucking along and I 
get a final output of

ACCURACY TOP1: 18.77 % (122 / 650)
Total accuracy 26.19%  Semantic accuracy: 24.76% Syntactic accuracy: 26.91%
Questions seen / total: 12268 19544 62.77%

This is obviously not a fix, something to do with buffers but I'm not a C 
expert by any means.

Original comment by dluna...@gmail.com on 22 Aug 2013 at 5:55

GoogleCodeExporter commented 9 years ago
Thanks for reporting this bug, it should be fixed now.

Original comment by tmiko...@google.com on 23 Aug 2013 at 6:08

GoogleCodeExporter commented 9 years ago
Seems still broken.
deleted all data files.  Updated to latest.  Re-applied the OSX fix (#include 
<malloc.h> becomes stdlib.h)
make clean
make
re-ran the script.

Starting training using file text8
Words processed: 17000K     Vocab size: 4399K  
Vocab size (unigrams + bigrams): 2586139
Words in train file: 17005206
Words written: 17000K
real    0m20.452s
user    0m19.601s
sys 0m0.816s
Starting training using file text8-phrase
Vocab size: 123636
Words in train file: 16337523
Alpha: 0.000119  Progress: 99.59%  Words/thread/sec: 22.46k  
real    1m37.069s
user    12m8.130s
sys 0m1.240s
newspapers:
./demo-phrase-accuracy.sh: line 12:  1189 Segmentation fault: 11  
./compute-accuracy vectors-phrase.bin < questions-phrases.txt

Original comment by benjamin...@gmail.com on 23 Aug 2013 at 6:30

GoogleCodeExporter commented 9 years ago
No idea what I'm doing, but if it helps:

(gdb) run vectors-phrase.bin <questions-phrases.txt
Starting program: /Users/benjamin/Documents/code/word2vec/compute-accuracy 
vectors-phrase.bin <questions-phrases.txt
Reading symbols for shared libraries +.............................. done
newspapers:
Program received signal EXC_BAD_ACCESS, Could not access memory.
Reason: KERN_INVALID_ADDRESS at address: 0x000004b4b1e6d740
0x00000001000019fd in main ()

Original comment by benjamin...@gmail.com on 23 Aug 2013 at 6:37

GoogleCodeExporter commented 9 years ago
Removing -Ofast from the make file seems to have helped.  But wow is it slower, 
maybe a 90% speed reduction?

output: 

newspapers:
ACCURACY TOP1: 8.33 %  (1 / 12)
Total accuracy: 8.33 %   Semantic accuracy: 8.33 %   Syntactic accuracy: nan % 
ice_hockey:
ACCURACY TOP1: 0.00 %  (0 / 56)
Total accuracy: 1.47 %   Semantic accuracy: 1.47 %   Syntactic accuracy: nan % 
basketball:
ACCURACY TOP1: 0.00 %  (0 / 30)
Total accuracy: 1.02 %   Semantic accuracy: 1.02 %   Syntactic accuracy: nan % 
airlines:
ACCURACY TOP1: 14.29 %  (6 / 42)
Total accuracy: 5.00 %   Semantic accuracy: 5.00 %   Syntactic accuracy: nan % 
people-companies:
ACCURACY TOP1: 25.00 %  (1 / 4)
Total accuracy: 5.56 %   Semantic accuracy: 5.56 %   Syntactic accuracy: nan % 
Questions seen / total: 144 3218   4.47 % 

Original comment by benjamin...@gmail.com on 23 Aug 2013 at 7:05