myleott

#!/usr/bin/env python

import argparse
import gzip
import json
import requests
from io import BytesIO, StringIO
from urllib.parse import urlencode

myleott

#!/bin/bash

GEN=$1

SYS=$GEN.sys
REF=$GEN.ref

grep ^H $GEN | cut -f3- | perl -ple 's{(\S)-(\S)}{$1 ##AT##-##AT## $2}g' > $SYS
grep ^T $GEN | cut -f2- | perl -ple 's{(\S)-(\S)}{$1 ##AT##-##AT## $2}g' > $REF
python score.py --sys $SYS --ref $REF

myleott

#!/bin/bash
source activate fairseq
fairseq-train --no-save --disable-validation --task dummy_masked_lm --masked-lm-only --arch bert_large --num-segment 0 --optimizer adam --lr 1e-4 --max-sentences 8 --update-freq 1 --fp16 --ddp-backend $DDP_BACKEND --bucket-cap-mb $BUCKET_CAP_MB --tokens-per-sample 512 --dataset-size $DATASET_SIZE --criterion masked_lm_loss --log-format json --log-interval 10 --max-epoch 1

myleott

# maybe change "fairseq-benchmark" below to your own tag
BENCHMARK=4node aws ec2 run-instances \
    --image-id ami-0707a23a1930bb11c \
    --count 4 \
    --instance-type p3dn.24xlarge \
    --key-name fair-$USER \
    --tag-specifications "ResourceType=instance,Tags=[{Key=fair-user,Value=$USER},{Key=fairseq-benchmark,Value=''}]" \
    --placement "GroupName = ${PLACEMENT_GROUP_NAME}" \
    --network-interfaces "DeviceIndex=0,InterfaceType=efa,Groups=${SECURITY_GROUP_ID},SubnetId=${SUBNET_ID}" \
    --block-device-mappings file://block_device_mappings.gp2.json

myleott

# maybe change "fairseq-benchmark" below to your own tag
BENCHMARK=4node aws ec2 run-instances \
    --image-id ami-0707a23a1930bb11c \
    --count 4 \
    --instance-type p3dn.24xlarge \
    --key-name fair-$USER \
    --tag-specifications "ResourceType=instance,Tags=[{Key=fair-user,Value=$USER},{Key=fairseq-benchmark,Value=''}]" \
    --placement "GroupName = ${PLACEMENT_GROUP_NAME}" \
    --network-interfaces "DeviceIndex=0,InterfaceType=efa,Groups=${SECURITY_GROUP_ID},SubnetId=${SUBNET_ID}" \
    --block-device-mappings file://block_device_mappings.gp2.json

myleott

import sys
import time

import torch
import torch.nn as nn
import torch.optim as optim

#platform = "tpu"
platform = "gpu"

myleott

Metric: CompileTime
  TotalSamples: 2
  Accumulator: 226ms137.620us
  ValueRate: 439ms863.608us / second
  Rate: 3.88139 / second
  Percentiles: 1%=109ms635.741us; 5%=109ms635.741us; 10%=109ms635.741us; 20%=109ms635.741us; 50%=118ms501.879us; 80%=118ms501.879us; 90%=118ms501.879us; 95%=118ms501.879us; 99%=118ms501.879us
Metric: DeviceLockWait
  TotalSamples: 101
  Accumulator: 31s573ms487.494us
  ValueRate: 754ms252.918us / second

myleott

import sys
import time

import torch
import torch.nn as nn
import torch.optim as optim


platform = "gpu" if torch.cuda.is_available() else "tpu"

myleott

text: a b c </s> d e f g </s>

Suppose the model is trained with a context length of 4.
Then the most favorable way to evaluate your model's perplexity is:

batch 1:  a b c </s>
         |----------|    <-- count perplexity of this
batch 2:  b c </s> d
                  |-|    <-- count perplexity of this
batch 3:  c </s> d e

myleott

#!/usr/bin/env python3

import argparse
import time

import torch

import torch_xla
import torch_xla.core.xla_model as xm
import torch_xla.distributed.xla_multiprocessing as xmp