mrdrozdov

"""
I have two tensors, a "reference" tensor of size (8192,) and a "query"
tensor of size (10000, 500). I want an binary mask of size (10000, 8192)
with 1s in all indices (i, j) where reference[ j ] exists in query[ i , : ].
Also, all values are integers and upper bounded by another integer.
"""
import torch

seed = 10
vocabsize = 300 # value isn't important, but it's more than refsize

mrdrozdov

"""
How to find intersection of big tensor and all small tensors?
"""

import torch

bigsize = 30
smallsize = 8
smallcount = 12
vocabsize = 100

mrdrozdov

import os

try:
    import psutil
except:
    psutil = None


def get_memory_used():
    if psutil is None:

mrdrozdov

import os

candidates = [
'from allennlp.common.file_utils import cached_path',
'from allennlp.common.checks import ConfigurationError',
'from allennlp.common import Params',
'from allennlp.common.util import lazy_groups_of',
'from allennlp.modules.elmo_lstm import ElmoLstm',
'from allennlp.modules.highway import Highway',
'from allennlp.modules.scalar_mix import ScalarMix',

mrdrozdov

nw/wsj/16/wsj_1657      0       0       Recovering      VBG     VBG     4       csubj   _       recover 02      3       -       O       B-V     B-ARG1  O       O       -
nw/wsj/16/wsj_1657      0       1       radiophonic     JJ      JJ      3       amod    _       -       -       -       -       O       B-ARG1  I-ARG1  O       O       -
nw/wsj/16/wsj_1657      0       2       sovereignty     NN      NN      1       dobj    _       -       -       -       -       O       I-ARG1  I-ARG1  O       O       -
nw/wsj/16/wsj_1657      0       3       was     VBD     VBD     0       root    _       be      01      -       -       O       O       B-V     O       O       -
nw/wsj/16/wsj_1657      0       4       the     DT      DT      6       det     _       -       -       -       -       O       O       B-ARG2  O       O       -
nw/wsj/16/wsj_1657      0       5       purpose NN      NN      4       xcomp   _       -       -       -       -       O       O       I-ARG2  O       O       -
nw/wsj/16/wsj_1657      0   

mrdrozdov

less baseline-l_3-lr_002-d_800-dist-1gpu-b4/experiment.log.0

27s for 100 batches of 4 (1x speedup)

2019-01-22 22:22:09,913 [INFO] Epoch/Step/Batch=0/1300/1300 reconstruction_mse_loss=0.382 total_loss=0.382
2019-01-22 22:22:11,924 [INFO] Average-Length=10.29
2019-01-22 22:22:11,924 [INFO] Words-Per-Second=1
2019-01-22 22:22:36,047 [INFO] Epoch/Step/Batch=0/1400/1400 reconstruction_mse_loss=0.380 total_loss=0.380
2019-01-22 22:22:38,032 [INFO] Average-Length=10.410000000000007
2019-01-22 22:22:38,033 [INFO] Words-Per-Second=1

mrdrozdov

class Trainer(self):
    def __init__(self, net, optimizer, ngpus=1):
        self.net = net
        self.optimizer = optimizer
        self.ngpus = ngpus

    def step(self, batch, train, ...):
        """
        Alternatively, you can avoid replicating your model each batch.
        This is particularly useful if your model has any type of state.

mrdrozdov

In [1]: print('hello world')
hello world

In [2]: print('hello world!')
hello world!

In [3]: import torch

In [4]: print(torch.__version__)
0.4.1

mrdrozdov

verbs = [
    ['agree', 'with'],
    ['bog', 'down'],
    ['break', 'away'],
    ['bring', 'about'],
    ['bring', 'along'],
    ['bring', 'back'],
    ['bring', 'forward'],
    ['bring', 'in'],
    ['bring', 'off'],

mrdrozdov

2018-11-20 12:25:06,998 [INFO] Accuracy=0.926402290907022
2018-11-20 12:25:07,011 [INFO] Confusion Matrix:
        t/p        CARDINAL        DATE       EVENT         FAC         GPE    LANGUAGE         LAW         LOC       MONEY        NORP     ORDINAL         ORG     PERCENT      PERSON     PRODUCT    QUANTITY        TIME WORK_OF_ART
       CARDINAL       477.0         4.0         0.0         0.0         1.0         0.0         0.0         0.0        18.0         0.0         1.0         1.0         0.0         1.0         0.0         3.0         1.0         0.0
           DATE         6.0       795.0         3.0         0.0         2.0         0.0         0.0         0.0         1.0         0.0         2.0         0.0         0.0         1.0         0.0         1.0         5.0         0.0
          EVENT         0.0         2.0        31.0         1.0         0.0         0.0         0.0         0.0         0.0         0.0         0.0         3.0         0.0         2.0         1.0         0.0         0.0