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# Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
This repository contains the code in both **PyTorch** and **TensorFlow** for our paper
>[Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](http://arxiv.org/abs/1901.02860)
>Zihang Dai\*, Zhilin Yang\*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov (*: equal contribution)
>Preprint 2018
## TensorFlow
- The source code is in the `tf/` folder, supporting (1) single-node multi-gpu training, and (2) multi-host TPU training.
- Besides the source code, we also provide pretrained "TensorFlow" models with state-of-the-art (SoTA) performances reported in the paper.
- Please refer to `tf/README.md` for details.
## PyTorch
- The source code is in the `pytorch/` folder, supporting single-node multi-gpu training via the module `nn.DataParallel`.
- Please refer to `pytorch/README.md` for details.
## Results
Transformer-XL achieves new state-of-the-art results on multiple language modeling benchmarks. Transformer-XL is also the first to break through the 1.0 barrier on char-level language modeling. Below is a summary.
Method | enwiki8 | text8 | One Billion Word | WT-103 | PTB (w/o finetuning)
-- | -- | -- | -- | -- | --
Previous Best | 1.06 | 1.13 | 23.7 | 20.5 | 55.5
Transformer-XL | **0.99** | **1.08** | **21.8** | **18.3** | **54.5**
## Acknowledgement
A large portion of the `getdata.sh` script comes from the [awd-lstm](https://github.com/salesforce/awd-lstm-lm/) repo. Happy Language Modeling :)

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echo "=== Acquiring datasets ==="
echo "---"
mkdir -p data
cd data
if [[ ! -d 'wikitext-2' ]]; then
echo "- Downloading WikiText-2 (WT2)"
wget --quiet --continue https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip
unzip -q wikitext-2-v1.zip
cd wikitext-2
mv wiki.train.tokens train.txt
mv wiki.valid.tokens valid.txt
mv wiki.test.tokens test.txt
cd ..
fi
echo "- Downloading WikiText-103 (WT2)"
if [[ ! -d 'wikitext-103' ]]; then
wget --continue https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip
unzip -q wikitext-103-v1.zip
cd wikitext-103
mv wiki.train.tokens train.txt
mv wiki.valid.tokens valid.txt
mv wiki.test.tokens test.txt
cd ..
fi
echo "- Downloading enwik8 (Character)"
if [[ ! -d 'enwik8' ]]; then
mkdir -p enwik8
cd enwik8
wget --continue http://mattmahoney.net/dc/enwik8.zip
wget https://raw.githubusercontent.com/salesforce/awd-lstm-lm/master/data/enwik8/prep_enwik8.py
python3 prep_enwik8.py
cd ..
fi
echo "- Downloading text8 (Character)"
if [[ ! -d 'text8' ]]; then
mkdir -p text8
cd text8
wget --continue http://mattmahoney.net/dc/text8.zip
python ../../prep_text8.py
cd ..
fi
echo "- Downloading Penn Treebank (PTB)"
if [[ ! -d 'penn' ]]; then
wget --quiet --continue http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz
tar -xzf simple-examples.tgz
mkdir -p penn
cd penn
mv ../simple-examples/data/ptb.train.txt train.txt
mv ../simple-examples/data/ptb.test.txt test.txt
mv ../simple-examples/data/ptb.valid.txt valid.txt
cd ..
echo "- Downloading Penn Treebank (Character)"
mkdir -p pennchar
cd pennchar
mv ../simple-examples/data/ptb.char.train.txt train.txt
mv ../simple-examples/data/ptb.char.test.txt test.txt
mv ../simple-examples/data/ptb.char.valid.txt valid.txt
cd ..
rm -rf simple-examples/
fi
echo "- Downloading 1B words"
if [[ ! -d 'one-billion-words' ]]; then
mkdir -p one-billion-words
cd one-billion-words
wget --no-proxy http://www.statmt.org/lm-benchmark/1-billion-word-language-modeling-benchmark-r13output.tar.gz
tar xzvf 1-billion-word-language-modeling-benchmark-r13output.tar.gz
path="1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/"
cat ${path}/news.en.heldout-00000-of-00050 > valid.txt
cat ${path}/news.en.heldout-00000-of-00050 > test.txt
wget https://github.com/rafaljozefowicz/lm/raw/master/1b_word_vocab.txt
cd ..
fi
echo "---"
echo "Happy language modeling :)"

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#!/usr/bin/env python
# coding=utf-8
import os
import sys
import zipfile
from io import open
if os.path.exists('train.txt'):
print('Tokenized text8 already exists - skipping processing')
sys.exit()
data = zipfile.ZipFile('text8.zip').extractall()
data = open('text8', 'r', encoding='utf-8').read()
print('Length of text8: {}'.format(len(data)))
num_test_chars = 5000000
train_data = data[: -2 * num_test_chars]
valid_data = data[-2 * num_test_chars: -num_test_chars]
test_data = data[-num_test_chars:]
for fn, part in [('train.txt', train_data), ('valid.txt', valid_data), ('test.txt', test_data)]:
print('{} will have {} bytes'.format(fn, len(part)))
print('- Tokenizing...')
# Change space ' ' to underscore '_'
part_str = ' '.join(['_' if c == ' ' else c for c in part.strip()])
print('- Writing...')
f = open(fn, 'w').write(part_str)
f = open(fn + '.raw', 'w', encoding='utf-8').write(part)

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## Introduction
This directory contains our pytorch implementation of Transformer-XL. Note that our state-of-the-art results reported in the paper were obtained by training the model on a large-scale TPU cluster, and our pytorch codebase currently does not support distributed training. Here we provide two sets of hyperparameters and scripts:
- `*large.sh` are for the SoTA setting with large models which might not be directly runnable on a local GPU machine.
- `*base.sh` are for the base models which can be run on a few GPUs.
The pytorch implementation produces similar results to the TF codebase under the same settings in our preliminary experiments.
## Prerequisite
- Pytorch 0.4: `conda install pytorch torchvision -c pytorch`
## Data Prepration
`bash getdata.sh`
## Training and Evaluation
#### Replicate the "bpc = 1.06" result on `enwik8` with a 12-layer Transformer-XL
- Make sure the machine have **4 GPUs**, each with **at least 11G memory**
- Training
`bash run_enwik8_base.sh train --work_dir PATH_TO_WORK_DIR`
- Evaluation
`bash run_enwik8_base.sh eval --work_dir PATH_TO_WORK_DIR`
#### Replicate the "PPL = 24.03" result on `wikitext-103` with Transformer-XL
- Make sure the machine have **4 GPUs**, each with **at least 11G memory**
- Training
`bash run_wt103_base.sh train --work_dir PATH_TO_WORK_DIR`
- Evaluation
`bash run_wt103_base.sh eval --work_dir PATH_TO_WORK_DIR`
#### Other options:
- `--batch_chunk`: this option allows one to trade speed for memory. For `batch_chunk > 1`, the program will split each training batch into `batch_chunk` sub-batches and perform forward and backward on each sub-batch sequentially, with the gradient accumulated and divided by `batch_chunk`. Hence, the memory usage will propertionally lower while the computation time will inversely higher.
- `--div_val`: when using adaptive softmax and embedding, the embedding dimension is divided by `div_val` from bin $i$ to bin $i+1$. This saves both GPU memory and the parameter budget.
- `--fp16` and `--dynamic-loss-scale`: Run in pseudo-fp16 mode (fp16 storage fp32 math) with dynamic loss scaling.
- Note: to explore the `--fp16` option, please make sure the `apex` package is installed (https://github.com/NVIDIA/apex/).
- To see performance without the recurrence mechanism, simply use `mem_len=0` in all your scripts.
- To see performance of a standard Transformer without relative positional encodings or recurrence mechanisms, use `attn_type=2` and `mem_len=0`.
#### Other datasets:
- `Text8` character-level language modeling: check out `run_text8_base.sh`
- `lm1b` word-level language modeling: check out `run_lm1b_base.sh`

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import os, sys
import glob
from collections import Counter, OrderedDict
import numpy as np
import torch
from utils.vocabulary import Vocab
class LMOrderedIterator(object):
def __init__(self, data, bsz, bptt, device='cpu', ext_len=None):
"""
data -- LongTensor -- the LongTensor is strictly ordered
"""
self.bsz = bsz
self.bptt = bptt
self.ext_len = ext_len if ext_len is not None else 0
self.device = device
# Work out how cleanly we can divide the dataset into bsz parts.
self.n_step = data.size(0) // bsz
# Trim off any extra elements that wouldn't cleanly fit (remainders).
data = data.narrow(0, 0, self.n_step * bsz)
# Evenly divide the data across the bsz batches.
self.data = data.view(bsz, -1).t().contiguous().to(device)
# Number of mini-batches
self.n_batch = (self.n_step + self.bptt - 1) // self.bptt
def get_batch(self, i, bptt=None):
if bptt is None: bptt = self.bptt
seq_len = min(bptt, self.data.size(0) - 1 - i)
end_idx = i + seq_len
beg_idx = max(0, i - self.ext_len)
data = self.data[beg_idx:end_idx]
target = self.data[i+1:i+1+seq_len]
return data, target, seq_len
def get_fixlen_iter(self, start=0):
for i in range(start, self.data.size(0) - 1, self.bptt):
yield self.get_batch(i)
def get_varlen_iter(self, start=0, std=5, min_len=5, max_deviation=3):
max_len = self.bptt + max_deviation * std
i = start
while True:
bptt = self.bptt if np.random.random() < 0.95 else self.bptt / 2.
bptt = min(max_len, max(min_len, int(np.random.normal(bptt, std))))
data, target, seq_len = self.get_batch(i, bptt)
i += seq_len
yield data, target, seq_len
if i >= self.data.size(0) - 2:
break
def __iter__(self):
return self.get_fixlen_iter()
class LMShuffledIterator(object):
def __init__(self, data, bsz, bptt, device='cpu', ext_len=None, shuffle=False):
"""
data -- list[LongTensor] -- there is no order among the LongTensors
"""
self.data = data
self.bsz = bsz
self.bptt = bptt
self.ext_len = ext_len if ext_len is not None else 0
self.device = device
self.shuffle = shuffle
def get_sent_stream(self):
# index iterator
epoch_indices = np.random.permutation(len(self.data)) if self.shuffle \
else np.array(range(len(self.data)))
# sentence iterator
for idx in epoch_indices:
yield self.data[idx]
def stream_iterator(self, sent_stream):
# streams for each data in the batch
streams = [None] * self.bsz
data = torch.LongTensor(self.bptt, self.bsz)
target = torch.LongTensor(self.bptt, self.bsz)
n_retain = 0
while True:
# data : [n_retain+bptt x bsz]
# target : [bptt x bsz]
data[n_retain:].fill_(-1)
target.fill_(-1)
valid_batch = True
for i in range(self.bsz):
n_filled = 0
try:
while n_filled < self.bptt:
if streams[i] is None or len(streams[i]) <= 1:
streams[i] = next(sent_stream)
# number of new tokens to fill in
n_new = min(len(streams[i]) - 1, self.bptt - n_filled)
# first n_retain tokens are retained from last batch
data[n_retain+n_filled:n_retain+n_filled+n_new, i] = \
streams[i][:n_new]
target[n_filled:n_filled+n_new, i] = \
streams[i][1:n_new+1]
streams[i] = streams[i][n_new:]
n_filled += n_new
except StopIteration:
valid_batch = False
break
if not valid_batch:
return
data = data.to(self.device)
target = target.to(self.device)
yield data, target, self.bptt
n_retain = min(data.size(0), self.ext_len)
if n_retain > 0:
data[:n_retain] = data[-n_retain:]
data.resize_(n_retain + self.bptt, data.size(1))
def __iter__(self):
# sent_stream is an iterator
sent_stream = self.get_sent_stream()
for batch in self.stream_iterator(sent_stream):
yield batch
class LMMultiFileIterator(LMShuffledIterator):
def __init__(self, paths, vocab, bsz, bptt, device='cpu', ext_len=None,
shuffle=False):
self.paths = paths
self.vocab = vocab
self.bsz = bsz
self.bptt = bptt
self.ext_len = ext_len if ext_len is not None else 0
self.device = device
self.shuffle = shuffle
def get_sent_stream(self, path):
sents = self.vocab.encode_file(path, add_double_eos=True)
if self.shuffle:
np.random.shuffle(sents)
sent_stream = iter(sents)
return sent_stream
def __iter__(self):
if self.shuffle:
np.random.shuffle(self.paths)
for path in self.paths:
# sent_stream is an iterator
sent_stream = self.get_sent_stream(path)
for batch in self.stream_iterator(sent_stream):
yield batch
class Corpus(object):
def __init__(self, path, dataset, *args, **kwargs):
self.dataset = dataset
self.vocab = Vocab(*args, **kwargs)
if self.dataset in ['ptb', 'wt2', 'enwik8', 'text8']:
self.vocab.count_file(os.path.join(path, 'train.txt'))
self.vocab.count_file(os.path.join(path, 'valid.txt'))
self.vocab.count_file(os.path.join(path, 'test.txt'))
elif self.dataset == 'wt103':
self.vocab.count_file(os.path.join(path, 'train.txt'))
elif self.dataset == 'lm1b':
train_path_pattern = os.path.join(
path, '1-billion-word-language-modeling-benchmark-r13output',
'training-monolingual.tokenized.shuffled', 'news.en-*')
train_paths = glob.glob(train_path_pattern)
# the vocab will load from file when build_vocab() is called
self.vocab.build_vocab()
if self.dataset in ['ptb', 'wt2', 'wt103']:
self.train = self.vocab.encode_file(
os.path.join(path, 'train.txt'), ordered=True)
self.valid = self.vocab.encode_file(
os.path.join(path, 'valid.txt'), ordered=True)
self.test = self.vocab.encode_file(
os.path.join(path, 'test.txt'), ordered=True)
elif self.dataset in ['enwik8', 'text8']:
self.train = self.vocab.encode_file(
os.path.join(path, 'train.txt'), ordered=True, add_eos=False)
self.valid = self.vocab.encode_file(
os.path.join(path, 'valid.txt'), ordered=True, add_eos=False)
self.test = self.vocab.encode_file(
os.path.join(path, 'test.txt'), ordered=True, add_eos=False)
elif self.dataset == 'lm1b':
self.train = train_paths
self.valid = self.vocab.encode_file(
os.path.join(path, 'valid.txt'), ordered=False, add_double_eos=True)
self.test = self.vocab.encode_file(
os.path.join(path, 'test.txt'), ordered=False, add_double_eos=True)
def get_iterator(self, split, *args, **kwargs):
if split == 'train':
if self.dataset in ['ptb', 'wt2', 'wt103', 'enwik8', 'text8']:
data_iter = LMOrderedIterator(self.train, *args, **kwargs)
elif self.dataset == 'lm1b':
kwargs['shuffle'] = True
data_iter = LMMultiFileIterator(self.train, self.vocab, *args, **kwargs)
elif split in ['valid', 'test']:
data = self.valid if split == 'valid' else self.test
if self.dataset in ['ptb', 'wt2', 'wt103', 'enwik8', 'text8']:
data_iter = LMOrderedIterator(data, *args, **kwargs)
elif self.dataset == 'lm1b':
data_iter = LMShuffledIterator(data, *args, **kwargs)
return data_iter
def get_lm_corpus(datadir, dataset):
fn = os.path.join(datadir, 'cache.pt')
if os.path.exists(fn):
print('Loading cached dataset...')
corpus = torch.load(fn)
else:
print('Producing dataset {}...'.format(dataset))
kwargs = {}
if dataset in ['wt103', 'wt2']:
kwargs['special'] = ['<eos>']
kwargs['lower_case'] = False
elif dataset == 'ptb':
kwargs['special'] = ['<eos>']
kwargs['lower_case'] = True
elif dataset == 'lm1b':
kwargs['special'] = []
kwargs['lower_case'] = False
kwargs['vocab_file'] = os.path.join(datadir, '1b_word_vocab.txt')
elif dataset in ['enwik8', 'text8']:
pass
corpus = Corpus(datadir, dataset, **kwargs)
torch.save(corpus, fn)
return corpus
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='unit test')
parser.add_argument('--datadir', type=str, default='../data/text8',
help='location of the data corpus')
parser.add_argument('--dataset', type=str, default='text8',
choices=['ptb', 'wt2', 'wt103', 'lm1b', 'enwik8', 'text8'],
help='dataset name')
args = parser.parse_args()
corpus = get_lm_corpus(args.datadir, args.dataset)
print('Vocab size : {}'.format(len(corpus.vocab.idx2sym)))

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# coding: utf-8
import argparse
import time
import math
import os, sys
import torch
from data_utils import get_lm_corpus
from mem_transformer import MemTransformerLM
from utils.exp_utils import get_logger
parser = argparse.ArgumentParser(description='PyTorch Transformer Language Model')
parser.add_argument('--data', type=str, default='../data/wikitext-103',
help='location of the data corpus')
parser.add_argument('--dataset', type=str, default='wt103',
choices=['wt103', 'lm1b', 'enwik8', 'text8'],
help='dataset name')
parser.add_argument('--split', type=str, default='all',
choices=['all', 'valid', 'test'],
help='which split to evaluate')
parser.add_argument('--batch_size', type=int, default=10,
help='batch size')
parser.add_argument('--tgt_len', type=int, default=5,
help='number of tokens to predict')
parser.add_argument('--ext_len', type=int, default=0,
help='length of the extended context')
parser.add_argument('--mem_len', type=int, default=0,
help='length of the retained previous heads')
parser.add_argument('--clamp_len', type=int, default=-1,
help='max positional embedding index')
parser.add_argument('--cuda', action='store_true',
help='use CUDA')
parser.add_argument('--work_dir', type=str, required=True,
help='path to the work_dir')
parser.add_argument('--no_log', action='store_true',
help='do not log the eval result')
parser.add_argument('--same_length', action='store_true',
help='set same length attention with masking')
args = parser.parse_args()
assert args.ext_len >= 0, 'extended context length must be non-negative'
device = torch.device("cuda" if args.cuda else "cpu")
# Get logger
logging = get_logger(os.path.join(args.work_dir, 'log.txt'),
log_=not args.no_log)
# Load dataset
corpus = get_lm_corpus(args.data, args.dataset)
ntokens = len(corpus.vocab)
va_iter = corpus.get_iterator('valid', args.batch_size, args.tgt_len,
device=device, ext_len=args.ext_len)
te_iter = corpus.get_iterator('test', args.batch_size, args.tgt_len,
device=device, ext_len=args.ext_len)
# Load the best saved model.
with open(os.path.join(args.work_dir, 'model.pt'), 'rb') as f:
model = torch.load(f)
model.backward_compatible()
model = model.to(device)
logging('Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
args.batch_size, args.tgt_len, args.ext_len, args.mem_len, args.clamp_len))
model.reset_length(args.tgt_len, args.ext_len, args.mem_len)
if args.clamp_len > 0:
model.clamp_len = args.clamp_len
if args.same_length:
model.same_length = True
###############################################################################
# Evaluation code
###############################################################################
def evaluate(eval_iter):
# Turn on evaluation mode which disables dropout.
model.eval()
total_len, total_loss = 0, 0.
start_time = time.time()
with torch.no_grad():
mems = tuple()
for idx, (data, target, seq_len) in enumerate(eval_iter):
ret = model(data, target, *mems)
loss, mems = ret[0], ret[1:]
loss = loss.mean()
total_loss += seq_len * loss.item()
total_len += seq_len
total_time = time.time() - start_time
logging('Time : {:.2f}s, {:.2f}ms/segment'.format(
total_time, 1000 * total_time / (idx+1)))
return total_loss / total_len
# Run on test data.
if args.split == 'all':
test_loss = evaluate(te_iter)
valid_loss = evaluate(va_iter)
elif args.split == 'valid':
valid_loss = evaluate(va_iter)
test_loss = None
elif args.split == 'test':
test_loss = evaluate(te_iter)
valid_loss = None
def format_log(loss, split):
if args.dataset in ['enwik8', 'text8']:
log_str = '| {0} loss {1:5.2f} | {0} bpc {2:9.5f} '.format(
split, loss, loss / math.log(2))
else:
log_str = '| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(
split, loss, math.exp(loss))
return log_str
log_str = ''
if valid_loss is not None:
log_str += format_log(valid_loss, 'valid')
if test_loss is not None:
log_str += format_log(test_loss, 'test')
logging('=' * 100)
logging(log_str)
logging('=' * 100)

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import sys
import math
import functools
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
sys.path.append('utils')
from proj_adaptive_softmax import ProjectedAdaptiveLogSoftmax
from log_uniform_sampler import LogUniformSampler, sample_logits
class PositionalEmbedding(nn.Module):
def __init__(self, demb):
super(PositionalEmbedding, self).__init__()
self.demb = demb
inv_freq = 1 / (10000 ** (torch.arange(0.0, demb, 2.0) / demb))
self.register_buffer('inv_freq', inv_freq)
def forward(self, pos_seq, bsz=None):
sinusoid_inp = torch.ger(pos_seq, self.inv_freq)
pos_emb = torch.cat([sinusoid_inp.sin(), sinusoid_inp.cos()], dim=-1)
if bsz is not None:
return pos_emb[:,None,:].expand(-1, bsz, -1)
else:
return pos_emb[:,None,:]
class PositionwiseFF(nn.Module):
def __init__(self, d_model, d_inner, dropout, pre_lnorm=False):
super(PositionwiseFF, self).__init__()
self.d_model = d_model
self.d_inner = d_inner
self.dropout = dropout
self.CoreNet = nn.Sequential(
nn.Linear(d_model, d_inner), nn.ReLU(inplace=True),
nn.Dropout(dropout),
nn.Linear(d_inner, d_model),
nn.Dropout(dropout),
)
self.layer_norm = nn.LayerNorm(d_model)
self.pre_lnorm = pre_lnorm
def forward(self, inp):
if self.pre_lnorm:
##### layer normalization + positionwise feed-forward
core_out = self.CoreNet(self.layer_norm(inp))
##### residual connection
output = core_out + inp
else:
##### positionwise feed-forward
core_out = self.CoreNet(inp)
##### residual connection + layer normalization
output = self.layer_norm(inp + core_out)
return output
class MultiHeadAttn(nn.Module):
def __init__(self, n_head, d_model, d_head, dropout, dropatt=0,
pre_lnorm=False):
super(MultiHeadAttn, self).__init__()
self.n_head = n_head
self.d_model = d_model
self.d_head = d_head
self.dropout = dropout
self.q_net = nn.Linear(d_model, n_head * d_head, bias=False)
self.kv_net = nn.Linear(d_model, 2 * n_head * d_head, bias=False)
self.drop = nn.Dropout(dropout)
self.dropatt = nn.Dropout(dropatt)
self.o_net = nn.Linear(n_head * d_head, d_model, bias=False)
self.layer_norm = nn.LayerNorm(d_model)
self.scale = 1 / (d_head ** 0.5)
self.pre_lnorm = pre_lnorm
def forward(self, h, attn_mask=None, mems=None):
##### multihead attention
# [hlen x bsz x n_head x d_head]
if mems is not None:
c = torch.cat([mems, h], 0)
else:
c = h
if self.pre_lnorm:
##### layer normalization
c = self.layer_norm(c)
head_q = self.q_net(h)
head_k, head_v = torch.chunk(self.kv_net(c), 2, -1)
head_q = head_q.view(h.size(0), h.size(1), self.n_head, self.d_head)
head_k = head_k.view(c.size(0), c.size(1), self.n_head, self.d_head)
head_v = head_v.view(c.size(0), c.size(1), self.n_head, self.d_head)
# [qlen x klen x bsz x n_head]
attn_score = torch.einsum('ibnd,jbnd->ijbn', (head_q, head_k))
attn_score.mul_(self.scale)
if attn_mask is not None and attn_mask.any().item():
if attn_mask.dim() == 2:
attn_score.masked_fill_(attn_mask[None,:,:,None], -float('inf'))
elif attn_mask.dim() == 3:
attn_score.masked_fill_(attn_mask[:,:,:,None], -float('inf'))
# [qlen x klen x bsz x n_head]
attn_prob = F.softmax(attn_score, dim=1)
attn_prob = self.dropatt(attn_prob)
# [qlen x klen x bsz x n_head] + [klen x bsz x n_head x d_head] -> [qlen x bsz x n_head x d_head]
attn_vec = torch.einsum('ijbn,jbnd->ibnd', (attn_prob, head_v))
attn_vec = attn_vec.contiguous().view(
attn_vec.size(0), attn_vec.size(1), self.n_head * self.d_head)
##### linear projection
attn_out = self.o_net(attn_vec)
attn_out = self.drop(attn_out)
if self.pre_lnorm:
##### residual connection
output = h + attn_out
else:
##### residual connection + layer normalization
output = self.layer_norm(h + attn_out)
return output
class RelMultiHeadAttn(nn.Module):
def __init__(self, n_head, d_model, d_head, dropout, dropatt=0,
tgt_len=None, ext_len=None, mem_len=None, pre_lnorm=False):
super(RelMultiHeadAttn, self).__init__()
self.n_head = n_head
self.d_model = d_model
self.d_head = d_head
self.dropout = dropout
self.qkv_net = nn.Linear(d_model, 3 * n_head * d_head, bias=False)
self.drop = nn.Dropout(dropout)
self.dropatt = nn.Dropout(dropatt)
self.o_net = nn.Linear(n_head * d_head, d_model, bias=False)
self.layer_norm = nn.LayerNorm(d_model)
self.scale = 1 / (d_head ** 0.5)
self.pre_lnorm = pre_lnorm
def _parallelogram_mask(self, h, w, left=False):
mask = torch.ones((h, w)).byte()
m = min(h, w)
mask[:m,:m] = torch.triu(mask[:m,:m])
mask[-m:,-m:] = torch.tril(mask[-m:,-m:])
if left:
return mask
else:
return mask.flip(0)
def _shift(self, x, qlen, klen, mask, left=False):
if qlen > 1:
zero_pad = torch.zeros((x.size(0), qlen-1, x.size(2), x.size(3)),
device=x.device, dtype=x.dtype)
else:
zero_pad = torch.zeros(0, device=x.device, dtype=x.dtype)
if left:
mask = mask.flip(1)
x_padded = torch.cat([zero_pad, x], dim=1).expand(qlen, -1, -1, -1)
else:
x_padded = torch.cat([x, zero_pad], dim=1).expand(qlen, -1, -1, -1)
x = x_padded.masked_select(mask[:,:,None,None]) \
.view(qlen, klen, x.size(2), x.size(3))
return x
def _rel_shift(self, x, zero_triu=False):
zero_pad = torch.zeros((x.size(0), 1, *x.size()[2:]),
device=x.device, dtype=x.dtype)
x_padded = torch.cat([zero_pad, x], dim=1)
x_padded = x_padded.view(x.size(1) + 1, x.size(0), *x.size()[2:])
x = x_padded[1:].view_as(x)
if zero_triu:
ones = torch.ones((x.size(0), x.size(1)))
x = x * torch.tril(ones, x.size(1) - x.size(0))[:,:,None,None]
return x
def forward(self, w, r, attn_mask=None, mems=None):
raise NotImplementedError
class RelPartialLearnableMultiHeadAttn(RelMultiHeadAttn):
def __init__(self, *args, **kwargs):
super(RelPartialLearnableMultiHeadAttn, self).__init__(*args, **kwargs)
self.r_net = nn.Linear(self.d_model, self.n_head * self.d_head, bias=False)
def forward(self, w, r, r_w_bias, r_r_bias, attn_mask=None, mems=None):
qlen, rlen, bsz = w.size(0), r.size(0), w.size(1)
if mems is not None:
cat = torch.cat([mems, w], 0)
if self.pre_lnorm:
w_heads = self.qkv_net(self.layer_norm(cat))
else:
w_heads = self.qkv_net(cat)
r_head_k = self.r_net(r)
w_head_q, w_head_k, w_head_v = torch.chunk(w_heads, 3, dim=-1)
w_head_q = w_head_q[-qlen:]
else:
if self.pre_lnorm:
w_heads = self.qkv_net(self.layer_norm(w))
else:
w_heads = self.qkv_net(w)
r_head_k = self.r_net(r)
w_head_q, w_head_k, w_head_v = torch.chunk(w_heads, 3, dim=-1)
klen = w_head_k.size(0)
w_head_q = w_head_q.view(qlen, bsz, self.n_head, self.d_head) # qlen x bsz x n_head x d_head
w_head_k = w_head_k.view(klen, bsz, self.n_head, self.d_head) # qlen x bsz x n_head x d_head
w_head_v = w_head_v.view(klen, bsz, self.n_head, self.d_head) # qlen x bsz x n_head x d_head
r_head_k = r_head_k.view(rlen, self.n_head, self.d_head) # qlen x n_head x d_head
#### compute attention score
rw_head_q = w_head_q + r_w_bias # qlen x bsz x n_head x d_head
AC = torch.einsum('ibnd,jbnd->ijbn', (rw_head_q, w_head_k)) # qlen x klen x bsz x n_head
rr_head_q = w_head_q + r_r_bias
BD = torch.einsum('ibnd,jnd->ijbn', (rr_head_q, r_head_k)) # qlen x klen x bsz x n_head
BD = self._rel_shift(BD)
# [qlen x klen x bsz x n_head]
attn_score = AC + BD
attn_score.mul_(self.scale)
#### compute attention probability
if attn_mask is not None and attn_mask.any().item():
if attn_mask.dim() == 2:
attn_score = attn_score.float().masked_fill(
attn_mask[None,:,:,None], -float('inf')).type_as(attn_score)
elif attn_mask.dim() == 3:
attn_score = attn_score.float().masked_fill(
attn_mask[:,:,:,None], -float('inf')).type_as(attn_score)
# [qlen x klen x bsz x n_head]
attn_prob = F.softmax(attn_score, dim=1)
attn_prob = self.dropatt(attn_prob)
#### compute attention vector
attn_vec = torch.einsum('ijbn,jbnd->ibnd', (attn_prob, w_head_v))
# [qlen x bsz x n_head x d_head]
attn_vec = attn_vec.contiguous().view(
attn_vec.size(0), attn_vec.size(1), self.n_head * self.d_head)
##### linear projection
attn_out = self.o_net(attn_vec)
attn_out = self.drop(attn_out)
if self.pre_lnorm:
##### residual connection
output = w + attn_out
else:
##### residual connection + layer normalization
output = self.layer_norm(w + attn_out)
return output
class RelLearnableMultiHeadAttn(RelMultiHeadAttn):
def __init__(self, *args, **kwargs):
super(RelLearnableMultiHeadAttn, self).__init__(*args, **kwargs)
def forward(self, w, r_emb, r_w_bias, r_bias, attn_mask=None, mems=None):
# r_emb: [klen, n_head, d_head], used for term B
# r_w_bias: [n_head, d_head], used for term C
# r_bias: [klen, n_head], used for term D
qlen, bsz = w.size(0), w.size(1)
if mems is not None:
cat = torch.cat([mems, w], 0)
if self.pre_lnorm:
w_heads = self.qkv_net(self.layer_norm(cat))
else:
w_heads = self.qkv_net(cat)
w_head_q, w_head_k, w_head_v = torch.chunk(w_heads, 3, dim=-1)
w_head_q = w_head_q[-qlen:]
else:
if self.pre_lnorm:
w_heads = self.qkv_net(self.layer_norm(w))
else:
w_heads = self.qkv_net(w)
w_head_q, w_head_k, w_head_v = torch.chunk(w_heads, 3, dim=-1)
klen = w_head_k.size(0)
w_head_q = w_head_q.view(qlen, bsz, self.n_head, self.d_head)
w_head_k = w_head_k.view(klen, bsz, self.n_head, self.d_head)
w_head_v = w_head_v.view(klen, bsz, self.n_head, self.d_head)
if klen > r_emb.size(0):
r_emb_pad = r_emb[0:1].expand(klen-r_emb.size(0), -1, -1)
r_emb = torch.cat([r_emb_pad, r_emb], 0)
r_bias_pad = r_bias[0:1].expand(klen-r_bias.size(0), -1)
r_bias = torch.cat([r_bias_pad, r_bias], 0)
else:
r_emb = r_emb[-klen:]
r_bias = r_bias[-klen:]
#### compute attention score
rw_head_q = w_head_q + r_w_bias[None] # qlen x bsz x n_head x d_head
AC = torch.einsum('ibnd,jbnd->ijbn', (rw_head_q, w_head_k)) # qlen x klen x bsz x n_head
B_ = torch.einsum('ibnd,jnd->ijbn', (w_head_q, r_emb)) # qlen x klen x bsz x n_head
D_ = r_bias[None, :, None] # 1 x klen x 1 x n_head
BD = self._rel_shift(B_ + D_)
# [qlen x klen x bsz x n_head]
attn_score = AC + BD
attn_score.mul_(self.scale)
#### compute attention probability
if attn_mask is not None and attn_mask.any().item():
if attn_mask.dim() == 2:
attn_score.masked_fill_(attn_mask[None,:,:,None], -float('inf'))
elif attn_mask.dim() == 3:
attn_score.masked_fill_(attn_mask[:,:,:,None], -float('inf'))
# [qlen x klen x bsz x n_head]
attn_prob = F.softmax(attn_score, dim=1)
attn_prob = self.dropatt(attn_prob)
#### compute attention vector
attn_vec = torch.einsum('ijbn,jbnd->ibnd', (attn_prob, w_head_v))
# [qlen x bsz x n_head x d_head]
attn_vec = attn_vec.contiguous().view(
attn_vec.size(0), attn_vec.size(1), self.n_head * self.d_head)
##### linear projection
attn_out = self.o_net(attn_vec)
attn_out = self.drop(attn_out)
if self.pre_lnorm:
##### residual connection
output = w + attn_out
else:
##### residual connection + layer normalization
output = self.layer_norm(w + attn_out)
return output
class DecoderLayer(nn.Module):
def __init__(self, n_head, d_model, d_head, d_inner, dropout, **kwargs):
super(DecoderLayer, self).__init__()
self.dec_attn = MultiHeadAttn(n_head, d_model, d_head, dropout, **kwargs)
self.pos_ff = PositionwiseFF(d_model, d_inner, dropout,
pre_lnorm=kwargs.get('pre_lnorm'))
def forward(self, dec_inp, dec_attn_mask=None, mems=None):
output = self.dec_attn(dec_inp, attn_mask=dec_attn_mask,
mems=mems)
output = self.pos_ff(output)
return output
class RelLearnableDecoderLayer(nn.Module):
def __init__(self, n_head, d_model, d_head, d_inner, dropout,
**kwargs):
super(RelLearnableDecoderLayer, self).__init__()
self.dec_attn = RelLearnableMultiHeadAttn(n_head, d_model, d_head, dropout,
**kwargs)
self.pos_ff = PositionwiseFF(d_model, d_inner, dropout,
pre_lnorm=kwargs.get('pre_lnorm'))
def forward(self, dec_inp, r_emb, r_w_bias, r_bias, dec_attn_mask=None, mems=None):
output = self.dec_attn(dec_inp, r_emb, r_w_bias, r_bias,
attn_mask=dec_attn_mask,
mems=mems)
output = self.pos_ff(output)
return output
class RelPartialLearnableDecoderLayer(nn.Module):
def __init__(self, n_head, d_model, d_head, d_inner, dropout,
**kwargs):
super(RelPartialLearnableDecoderLayer, self).__init__()
self.dec_attn = RelPartialLearnableMultiHeadAttn(n_head, d_model,
d_head, dropout, **kwargs)
self.pos_ff = PositionwiseFF(d_model, d_inner, dropout,
pre_lnorm=kwargs.get('pre_lnorm'))
def forward(self, dec_inp, r, r_w_bias, r_r_bias, dec_attn_mask=None, mems=None):
output = self.dec_attn(dec_inp, r, r_w_bias, r_r_bias,
attn_mask=dec_attn_mask,
mems=mems)
output = self.pos_ff(output)
return output
class AdaptiveEmbedding(nn.Module):
def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1,
sample_softmax=False):
super(AdaptiveEmbedding, self).__init__()
self.n_token = n_token
self.d_embed = d_embed
self.cutoffs = cutoffs + [n_token]
self.div_val = div_val
self.d_proj = d_proj
self.emb_scale = d_proj ** 0.5
self.cutoff_ends = [0] + self.cutoffs
self.emb_layers = nn.ModuleList()
self.emb_projs = nn.ParameterList()
if div_val == 1:
self.emb_layers.append(
nn.Embedding(n_token, d_embed, sparse=sample_softmax>0)
)
if d_proj != d_embed:
self.emb_projs.append(nn.Parameter(torch.Tensor(d_proj, d_embed)))
else:
for i in range(len(self.cutoffs)):
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i+1]
d_emb_i = d_embed // (div_val ** i)
self.emb_layers.append(nn.Embedding(r_idx-l_idx, d_emb_i))
self.emb_projs.append(nn.Parameter(torch.Tensor(d_proj, d_emb_i)))
def forward(self, inp):
if self.div_val == 1:
embed = self.emb_layers[0](inp)
if self.d_proj != self.d_embed:
embed = F.linear(embed, self.emb_projs[0])
else:
param = next(self.parameters())
inp_flat = inp.view(-1)
emb_flat = torch.zeros([inp_flat.size(0), self.d_proj],
dtype=param.dtype, device=param.device)
for i in range(len(self.cutoffs)):
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
mask_i = (inp_flat >= l_idx) & (inp_flat < r_idx)
indices_i = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
inp_i = inp_flat.index_select(0, indices_i) - l_idx
emb_i = self.emb_layers[i](inp_i)
emb_i = F.linear(emb_i, self.emb_projs[i])
emb_flat.index_copy_(0, indices_i, emb_i)
embed = emb_flat.view(*inp.size(), self.d_proj)
embed.mul_(self.emb_scale)
return embed
class MemTransformerLM(nn.Module):
def __init__(self, n_token, n_layer, n_head, d_model, d_head, d_inner,
dropout, dropatt, tie_weight=True, d_embed=None,
div_val=1, tie_projs=[False], pre_lnorm=False,
tgt_len=None, ext_len=None, mem_len=None,
cutoffs=[], adapt_inp=False,
same_length=False, attn_type=0, clamp_len=-1,
sample_softmax=-1):
super(MemTransformerLM, self).__init__()
self.n_token = n_token
d_embed = d_model if d_embed is None else d_embed
self.d_embed = d_embed
self.d_model = d_model
self.n_head = n_head
self.d_head = d_head
self.word_emb = AdaptiveEmbedding(n_token, d_embed, d_model, cutoffs,
div_val=div_val)
self.drop = nn.Dropout(dropout)
self.n_layer = n_layer
self.tgt_len = tgt_len
self.mem_len = mem_len
self.ext_len = ext_len
self.max_klen = tgt_len + ext_len + mem_len
self.attn_type = attn_type
self.layers = nn.ModuleList()
if attn_type == 0: # the default attention
for i in range(n_layer):
self.layers.append(
RelPartialLearnableDecoderLayer(
n_head, d_model, d_head, d_inner, dropout,
tgt_len=tgt_len, ext_len=ext_len, mem_len=mem_len,
dropatt=dropatt, pre_lnorm=pre_lnorm)
)
elif attn_type == 1: # learnable embeddings
for i in range(n_layer):
self.layers.append(
RelLearnableDecoderLayer(
n_head, d_model, d_head, d_inner, dropout,
tgt_len=tgt_len, ext_len=ext_len, mem_len=mem_len,
dropatt=dropatt, pre_lnorm=pre_lnorm)
)
elif attn_type in [2, 3]: # absolute embeddings
for i in range(n_layer):
self.layers.append(
DecoderLayer(
n_head, d_model, d_head, d_inner, dropout,
dropatt=dropatt, pre_lnorm=pre_lnorm)
)
self.sample_softmax = sample_softmax
# use sampled softmax
if sample_softmax > 0:
self.out_layer = nn.Linear(d_model, n_token)
if tie_weight:
self.out_layer.weight = self.word_emb.weight
self.tie_weight = tie_weight
self.sampler = LogUniformSampler(n_token, sample_softmax)
# use adaptive softmax (including standard softmax)
else:
self.crit = ProjectedAdaptiveLogSoftmax(n_token, d_embed, d_model,
cutoffs, div_val=div_val)
if tie_weight:
for i in range(len(self.crit.out_layers)):
self.crit.out_layers[i].weight = self.word_emb.emb_layers[i].weight
if tie_projs:
for i, tie_proj in enumerate(tie_projs):
if tie_proj and div_val == 1 and d_model != d_embed:
self.crit.out_projs[i] = self.word_emb.emb_projs[0]
elif tie_proj and div_val != 1:
self.crit.out_projs[i] = self.word_emb.emb_projs[i]
self.same_length = same_length
self.clamp_len = clamp_len
self._create_params()
def backward_compatible(self):
self.sample_softmax = -1
def _create_params(self):
if self.attn_type == 0: # default attention
self.pos_emb = PositionalEmbedding(self.d_model)
self.r_w_bias = nn.Parameter(torch.Tensor(self.n_head, self.d_head))
self.r_r_bias = nn.Parameter(torch.Tensor(self.n_head, self.d_head))
elif self.attn_type == 1: # learnable
self.r_emb = nn.Parameter(torch.Tensor(
self.n_layer, self.max_klen, self.n_head, self.d_head))
self.r_w_bias = nn.Parameter(torch.Tensor(
self.n_layer, self.n_head, self.d_head))
self.r_bias = nn.Parameter(torch.Tensor(
self.n_layer, self.max_klen, self.n_head))
elif self.attn_type == 2: # absolute standard
self.pos_emb = PositionalEmbedding(self.d_model)
elif self.attn_type == 3: # absolute deeper SA
self.r_emb = nn.Parameter(torch.Tensor(
self.n_layer, self.max_klen, self.n_head, self.d_head))
def reset_length(self, tgt_len, ext_len, mem_len):
self.tgt_len = tgt_len
self.mem_len = mem_len
self.ext_len = ext_len
def init_mems(self):
if self.mem_len > 0:
mems = []
param = next(self.parameters())
for i in range(self.n_layer+1):
empty = torch.empty(0, dtype=param.dtype, device=param.device)
mems.append(empty)
return mems
else:
return None
def _update_mems(self, hids, mems, qlen, mlen):
# does not deal with None
if mems is None: return None
# mems is not None
assert len(hids) == len(mems), 'len(hids) != len(mems)'
# There are `mlen + qlen` steps that can be cached into mems
# For the next step, the last `ext_len` of the `qlen` tokens
# will be used as the extended context. Hence, we only cache
# the tokens from `mlen + qlen - self.ext_len - self.mem_len`
# to `mlen + qlen - self.ext_len`.
with torch.no_grad():
new_mems = []
end_idx = mlen + max(0, qlen - 0 - self.ext_len)
beg_idx = max(0, end_idx - self.mem_len)
for i in range(len(hids)):
cat = torch.cat([mems[i], hids[i]], dim=0)
new_mems.append(cat[beg_idx:end_idx].detach())
return new_mems
def _forward(self, dec_inp, mems=None):
qlen, bsz = dec_inp.size()
word_emb = self.word_emb(dec_inp)
mlen = mems[0].size(0) if mems is not None else 0
klen = mlen + qlen
if self.same_length:
all_ones = word_emb.new_ones(qlen, klen)
mask_len = klen - self.mem_len
if mask_len > 0:
mask_shift_len = qlen - mask_len
else:
mask_shift_len = qlen
dec_attn_mask = (torch.triu(all_ones, 1+mlen)
+ torch.tril(all_ones, -mask_shift_len)).byte()[:, :, None] # -1
else:
dec_attn_mask = torch.triu(
word_emb.new_ones(qlen, klen), diagonal=1+mlen).byte()[:,:,None]
hids = []
if self.attn_type == 0: # default
pos_seq = torch.arange(klen-1, -1, -1.0, device=word_emb.device,
dtype=word_emb.dtype)
if self.clamp_len > 0:
pos_seq.clamp_(max=self.clamp_len)
pos_emb = self.pos_emb(pos_seq)
core_out = self.drop(word_emb)
pos_emb = self.drop(pos_emb)
hids.append(core_out)
for i, layer in enumerate(self.layers):
mems_i = None if mems is None else mems[i]
core_out = layer(core_out, pos_emb, self.r_w_bias,
self.r_r_bias, dec_attn_mask=dec_attn_mask, mems=mems_i)
hids.append(core_out)
elif self.attn_type == 1: # learnable
core_out = self.drop(word_emb)
hids.append(core_out)
for i, layer in enumerate(self.layers):
if self.clamp_len > 0:
r_emb = self.r_emb[i][-self.clamp_len :]
r_bias = self.r_bias[i][-self.clamp_len :]
else:
r_emb, r_bias = self.r_emb[i], self.r_bias[i]
mems_i = None if mems is None else mems[i]
core_out = layer(core_out, r_emb, self.r_w_bias[i],
r_bias, dec_attn_mask=dec_attn_mask, mems=mems_i)
hids.append(core_out)
elif self.attn_type == 2: # absolute
pos_seq = torch.arange(klen - 1, -1, -1.0, device=word_emb.device,
dtype=word_emb.dtype)
if self.clamp_len > 0:
pos_seq.clamp_(max=self.clamp_len)
pos_emb = self.pos_emb(pos_seq)
core_out = self.drop(word_emb + pos_emb[-qlen:])
hids.append(core_out)
for i, layer in enumerate(self.layers):
mems_i = None if mems is None else mems[i]
if mems_i is not None and i == 0:
mems_i += pos_emb[:mlen]
core_out = layer(core_out, dec_attn_mask=dec_attn_mask,
mems=mems_i)
hids.append(core_out)
elif self.attn_type == 3:
core_out = self.drop(word_emb)
hids.append(core_out)
for i, layer in enumerate(self.layers):
mems_i = None if mems is None else mems[i]
if mems_i is not None and mlen > 0:
cur_emb = self.r_emb[i][:-qlen]
cur_size = cur_emb.size(0)
if cur_size < mlen:
cur_emb_pad = cur_emb[0:1].expand(mlen-cur_size, -1, -1)
cur_emb = torch.cat([cur_emb_pad, cur_emb], 0)
else:
cur_emb = cur_emb[-mlen:]
mems_i += cur_emb.view(mlen, 1, -1)
core_out += self.r_emb[i][-qlen:].view(qlen, 1, -1)
core_out = layer(core_out, dec_attn_mask=dec_attn_mask,
mems=mems_i)
hids.append(core_out)
core_out = self.drop(core_out)
new_mems = self._update_mems(hids, mems, mlen, qlen)
return core_out, new_mems
def forward(self, data, target, *mems):
# nn.DataParallel does not allow size(0) tensors to be broadcasted.
# So, have to initialize size(0) mems inside the model forward.
# Moreover, have to return new_mems to allow nn.DataParallel to piece
# them together.
if not mems: mems = self.init_mems()
tgt_len = target.size(0)
hidden, new_mems = self._forward(data, mems=mems)
pred_hid = hidden[-tgt_len:]
if self.sample_softmax > 0 and self.training:
assert self.tie_weight
logit = sample_logits(self.word_emb,
self.out_layer.bias, target, pred_hid, self.sampler)
loss = -F.log_softmax(logit, -1)[:, :, 0]
else:
loss = self.crit(pred_hid.view(-1, pred_hid.size(-1)), target.view(-1))
loss = loss.view(tgt_len, -1)
if new_mems is None:
return [loss]
else:
return [loss] + new_mems
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='unit test')
parser.add_argument('--n_layer', type=int, default=4, help='')
parser.add_argument('--n_rel_layer', type=int, default=4, help='')
parser.add_argument('--n_head', type=int, default=2, help='')
parser.add_argument('--d_head', type=int, default=2, help='')
parser.add_argument('--d_model', type=int, default=200, help='')
parser.add_argument('--d_embed', type=int, default=200, help='')
parser.add_argument('--d_inner', type=int, default=200, help='')
parser.add_argument('--dropout', type=float, default=0.0, help='')
parser.add_argument('--cuda', action='store_true', help='')
parser.add_argument('--seed', type=int, default=1111, help='')
parser.add_argument('--multi_gpu', action='store_true', help='')
args = parser.parse_args()
device = torch.device("cuda" if args.cuda else "cpu")
B = 4
tgt_len, mem_len, ext_len = 36, 36, 0
data_len = tgt_len * 20
args.n_token = 10000
import data_utils
data = torch.LongTensor(data_len*B).random_(0, args.n_token).to(device)
diter = data_utils.LMOrderedIterator(data, B, tgt_len, device=device, ext_len=ext_len)
cutoffs = [args.n_token // 2]
tie_projs = [False] + [True] * len(cutoffs)
for div_val in [1, 2]:
for d_embed in [200, 100]:
model = MemTransformerLM(args.n_token, args.n_layer, args.n_head,
args.d_model, args.d_head, args.d_inner, args.dropout,
dropatt=args.dropout, tie_weight=True,
d_embed=d_embed, div_val=div_val,
tie_projs=tie_projs, pre_lnorm=True,
tgt_len=tgt_len, ext_len=ext_len, mem_len=mem_len,
cutoffs=cutoffs, attn_type=0).to(device)
print(sum(p.numel() for p in model.parameters()))
mems = tuple()
for idx, (inp, tgt, seqlen) in enumerate(diter):
print('batch {}'.format(idx))
out = model(inp, tgt, *mems)
mems = out[1:]

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transformer-xl/pytorch/run_enwik8_base.sh Normal file
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#!/bin/bash
if [[ $1 == 'train' ]]; then
echo 'Run training...'
python train.py \
--cuda \
--data ../data/enwik8/ \
--dataset enwik8 \
--n_layer 12 \
--d_model 512 \
--n_head 8 \
--d_head 64 \
--d_inner 2048 \
--dropout 0.1 \
--dropatt 0.0 \
--optim adam \
--lr 0.00025 \
--warmup_step 0 \
--max_step 400000 \
--tgt_len 512 \
--mem_len 512 \
--eval_tgt_len 128 \
--batch_size 22 \
--multi_gpu \
--gpu0_bsz 4 \
${@:2}
elif [[ $1 == 'eval' ]]; then
echo 'Run evaluation...'
python eval.py \
--cuda \
--data ../data/enwik8/ \
--dataset enwik8 \
--tgt_len 80 \
--mem_len 2100 \
--clamp_len 820 \
--same_length \
--split test \
${@:2}
else
echo 'unknown argment 1'
fi

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transformer-xl/pytorch/run_enwik8_large.sh Normal file
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#!/bin/bash
if [[ $1 == 'train' ]]; then
echo 'Run training...'
python train.py \
--cuda \
--data ../data/enwik8/ \
--dataset enwik8 \
--n_layer 24 \
--d_model 1024 \
--n_head 8 \
--d_head 128 \
--d_inner 3072 \
--dropout 0.15 \
--dropatt 0.15 \
--optim adam \
--lr 0.00025 \
--warmup_step 4000 \
--max_step 400000 \
--tgt_len 768 \
--mem_len 768 \
--eval_tgt_len 128 \
--batch_size 64 \
--multi_gpu \
--gpu0_bsz 0 \
${@:2}
elif [[ $1 == 'eval' ]]; then
echo 'Run evaluation...'
python eval.py \
--cuda \
--data ../data/enwik8/ \
--dataset enwik8 \
--tgt_len 128 \
--mem_len 3800 \
--clamp_len 1000 \
--same_length \
--split test \
${@:2}
else
echo 'unknown argment 1'
fi

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transformer-xl/pytorch/run_lm1b_base.sh Normal file
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#!/bin/bash
if [[ $1 == 'train' ]]; then
echo 'Run training...'
python train.py \
--cuda \
--data ../data/one-billion-words/ \
--dataset lm1b \
--adaptive \
--n_layer 18 \
--d_model 1024 \
--div_val 4 \
--n_head 8 \
--d_head 128 \
--d_inner 4096 \
--dropout 0.0 \
--dropatt 0.0 \
--optim adam \
--warmup_step 20000 \
--max_step 500000 \
--lr 0.00025 \
--tgt_len 32 \
--mem_len 32 \
--eval_tgt_len 32 \
--batch_size 224 \
--multi_gpu \
--gpu0_bsz 32 \
${@:2}
elif [[ $1 == 'eval' ]]; then
echo 'Run evaluation...'
python eval.py \
--cuda \
--data ../data/one-billion-words/ \
--dataset lm1b \
--batch_size 64 \
--tgt_len 32 \
--mem_len 128 \
--split test \
--same_length \
${@:2}
else
echo 'unknown argment 1'
fi

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transformer-xl/pytorch/run_lm1b_large.sh Normal file
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#!/bin/bash
if [[ $1 == 'train' ]]; then
echo 'Run training...'
python train.py \
--cuda \
--data ../data/one-billion-words/ \
--dataset lm1b \
--adaptive \
--div_val 4 \
--n_layer 24 \
--d_model 1280 \
--n_head 16 \
--d_head 80 \
--d_inner 8192 \
--dropout 0.05 \
--dropatt 0.05 \
--optim adam \
--warmup_step 30000 \
--max_step 1200000 \
--lr 0.00025 \
--tgt_len 32 \
--mem_len 32 \
--eval_tgt_len 32 \
--batch_size 512 \
--multi_gpu \
--gpu0_bsz 0 \
${@:2}
elif [[ $1 == 'eval' ]]; then
echo 'Run evaluation...'
python eval.py \
--cuda \
--data ../data/one-billion-words/ \
--dataset lm1b \
--batch_size 8 \
--tgt_len 32 \
--mem_len 128 \
--split test \
--same_length \
${@:2}
else
echo 'unknown argment 1'
fi

41
transformer-xl/pytorch/run_text8_base.sh Normal file
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#!/bin/bash
if [[ $1 == 'train' ]]; then
echo 'Run training...'
python train.py \
--cuda \
--data ../data/text8/ \
--dataset text8 \
--n_layer 12 \
--d_model 512 \
--n_head 8 \
--d_head 64 \
--d_inner 2048 \
--dropout 0.1 \
--dropatt 0.0 \
--optim adam \
--lr 0.00025 \
--warmup_step 0 \
--max_step 400000 \
--tgt_len 512 \
--mem_len 512 \
--eval_tgt_len 128 \
--batch_size 22 \
--multi_gpu \
--gpu0_bsz 4 \
${@:2}
elif [[ $1 == 'eval' ]]; then
echo 'Run evaluation...'
python eval.py \
--cuda \
--data ../data/text8/ \
--dataset text8 \
--tgt_len 80 \
--mem_len 2100 \
--clamp_len 820 \
--same_length \
--split test \
${@:2}
else
echo 'unknown argment 1'
fi

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transformer-xl/pytorch/run_text8_large.sh Normal file
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#!/bin/bash
if [[ $1 == 'train' ]]; then
echo 'Run training...'
python train.py \
--cuda \
--data ../data/text8/ \
--dataset text8 \
--n_layer 24 \
--d_model 1024 \
--n_head 8 \
--d_head 128 \
--d_inner 3072 \
--dropout 0.15 \
--dropatt 0.15 \
--optim adam \
--lr 0.00025 \
--tgt_len 768 \
--mem_len 768 \
--eval_tgt_len 128 \
--batch_size 64 \
--max_step 400000 \
${@:2}
elif [[ $1 == 'eval' ]]; then
echo 'Run evaluation...'
python eval.py \
--cuda \
--data ../data/text8/ \
--dataset text8 \
--tgt_len 128 \
--mem_len 3800 \
--clamp_len 1000 \
--same_length \
--split test \
${@:2}
else
echo 'unknown argment 1'
fi

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transformer-xl/pytorch/run_wt103_base.sh Normal file
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#!/bin/bash
if [[ $1 == 'train' ]]; then
echo 'Run training...'
python train.py \
--cuda \
--data ../data/wikitext-103/ \
--dataset wt103 \
--adaptive \
--n_layer 16 \
--d_model 410 \
--n_head 10 \
--d_head 41 \
--d_inner 2100 \
--dropout 0.1 \
--dropatt 0.0 \
--optim adam \
--lr 0.00025 \
--warmup_step 0 \
--max_step 200000 \
--tgt_len 150 \
--mem_len 150 \
--eval_tgt_len 150 \
--batch_size 60 \
--multi_gpu \
--gpu0_bsz 4 \
${@:2}
elif [[ $1 == 'eval' ]]; then
echo 'Run evaluation...'
python eval.py \
--cuda \
--data ../data/wikitext-103/ \
--dataset wt103 \
--tgt_len 64 \
--mem_len 640 \
--clamp_len 400 \
--same_length \
--split test \
${@:2}
else
echo 'unknown argment 1'
fi

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transformer-xl/pytorch/run_wt103_large.sh Normal file
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#!/bin/bash
if [[ $1 == 'train' ]]; then
echo 'Run training...'
python train.py \
--cuda \
--data ../data/wikitext-103/ \
--dataset wt103 \
--adaptive \
--div_val 4 \
--n_layer 18 \
--d_model 1024 \
--n_head 16 \
--d_head 64 \
--d_inner 4096 \
--dropout 0.2 \
--dropatt 0.2 \
--optim adam \
--lr 0.00025 \
--warmup_step 16000 \
--max_step 4000000 \
--tgt_len 384 \
--mem_len 384 \
--eval_tgt_len 128 \
--batch_size 128 \
--multi_gpu \
--gpu0_bsz 0 \
${@:2}
elif [[ $1 == 'eval' ]]; then
echo 'Run evaluation...'
python eval.py \
--cuda \
--data ../data/wikitext-103/ \
--dataset wt103 \
--tgt_len 128 \
--mem_len 1600 \
--clamp_len 1000 \
--same_length \
--split test \
${@:2}
else
echo 'unknown argment 1'
fi

562
transformer-xl/pytorch/train.py Normal file
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# coding: utf-8
import argparse
import time
import math
import os, sys
import itertools
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from data_utils import get_lm_corpus
from mem_transformer import MemTransformerLM
from utils.exp_utils import create_exp_dir
from utils.data_parallel import BalancedDataParallel
parser = argparse.ArgumentParser(description='PyTorch Transformer Language Model')
parser.add_argument('--data', type=str, default='../data/wikitext-103',
help='location of the data corpus')
parser.add_argument('--dataset', type=str, default='wt103',
choices=['wt103', 'lm1b', 'enwik8', 'text8'],
help='dataset name')
parser.add_argument('--n_layer', type=int, default=12,
help='number of total layers')
parser.add_argument('--n_head', type=int, default=10,
help='number of heads')
parser.add_argument('--d_head', type=int, default=50,
help='head dimension')
parser.add_argument('--d_embed', type=int, default=-1,
help='embedding dimension')
parser.add_argument('--d_model', type=int, default=500,
help='model dimension')
parser.add_argument('--d_inner', type=int, default=1000,
help='inner dimension in FF')
parser.add_argument('--dropout', type=float, default=0.0,
help='global dropout rate')
parser.add_argument('--dropatt', type=float, default=0.0,
help='attention probability dropout rate')
parser.add_argument('--init', default='normal', type=str,
help='parameter initializer to use.')
parser.add_argument('--emb_init', default='normal', type=str,
help='parameter initializer to use.')
parser.add_argument('--init_range', type=float, default=0.1,
help='parameters initialized by U(-init_range, init_range)')
parser.add_argument('--emb_init_range', type=float, default=0.01,
help='parameters initialized by U(-init_range, init_range)')
parser.add_argument('--init_std', type=float, default=0.02,
help='parameters initialized by N(0, init_std)')
parser.add_argument('--proj_init_std', type=float, default=0.01,
help='parameters initialized by N(0, init_std)')
parser.add_argument('--optim', default='adam', type=str,
choices=['adam', 'sgd', 'adagrad'],
help='optimizer to use.')
parser.add_argument('--lr', type=float, default=0.00025,
help='initial learning rate (0.00025|5 for adam|sgd)')
parser.add_argument('--mom', type=float, default=0.0,
help='momentum for sgd')
parser.add_argument('--scheduler', default='cosine', type=str,
choices=['cosine', 'inv_sqrt', 'dev_perf', 'constant'],
help='lr scheduler to use.')
parser.add_argument('--warmup_step', type=int, default=0,
help='upper epoch limit')
parser.add_argument('--decay_rate', type=float, default=0.5,
help='decay factor when ReduceLROnPlateau is used')
parser.add_argument('--lr_min', type=float, default=0.0,
help='minimum learning rate during annealing')
parser.add_argument('--clip', type=float, default=0.25,
help='gradient clipping')
parser.add_argument('--clip_nonemb', action='store_true',
help='only clip the gradient of non-embedding params')
parser.add_argument('--max_step', type=int, default=100000,
help='upper epoch limit')
parser.add_argument('--batch_size', type=int, default=60,
help='batch size')
parser.add_argument('--batch_chunk', type=int, default=1,
help='split batch into chunks to save memory')
parser.add_argument('--tgt_len', type=int, default=70,
help='number of tokens to predict')
parser.add_argument('--eval_tgt_len', type=int, default=50,
help='number of tokens to predict for evaluation')
parser.add_argument('--ext_len', type=int, default=0,
help='length of the extended context')
parser.add_argument('--mem_len', type=int, default=0,
help='length of the retained previous heads')
parser.add_argument('--not_tied', action='store_true',
help='do not tie the word embedding and softmax weights')
parser.add_argument('--seed', type=int, default=1111,
help='random seed')
parser.add_argument('--cuda', action='store_true',
help='use CUDA')
parser.add_argument('--adaptive', action='store_true',
help='use adaptive softmax')
parser.add_argument('--div_val', type=int, default=1,
help='divident value for adapative input and softmax')
parser.add_argument('--pre_lnorm', action='store_true',
help='apply LayerNorm to the input instead of the output')
parser.add_argument('--varlen', action='store_true',
help='use variable length')
parser.add_argument('--multi_gpu', action='store_true',
help='use multiple GPU')
parser.add_argument('--log-interval', type=int, default=200,
help='report interval')
parser.add_argument('--eval-interval', type=int, default=4000,
help='evaluation interval')
parser.add_argument('--work_dir', default='LM-TFM', type=str,
help='experiment directory.')
parser.add_argument('--restart', action='store_true',
help='restart training from the saved checkpoint')
parser.add_argument('--restart_dir', type=str, default='',
help='restart dir')
parser.add_argument('--debug', action='store_true',
help='run in debug mode (do not create exp dir)')
parser.add_argument('--same_length', action='store_true',
help='use the same attn length for all tokens')
parser.add_argument('--attn_type', type=int, default=0,
help='attention type. 0 for ours, 1 for Shaw et al,'
'2 for Vaswani et al, 3 for Al Rfou et al.')
parser.add_argument('--clamp_len', type=int, default=-1,
help='use the same pos embeddings after clamp_len')
parser.add_argument('--eta_min', type=float, default=0.0,
help='min learning rate for cosine scheduler')
parser.add_argument('--gpu0_bsz', type=int, default=-1,
help='batch size on gpu 0')
parser.add_argument('--max_eval_steps', type=int, default=-1,
help='max eval steps')
parser.add_argument('--sample_softmax', type=int, default=-1,
help='number of samples in sampled softmax')
parser.add_argument('--patience', type=int, default=0,
help='patience')
parser.add_argument('--finetune_v2', action='store_true',
help='finetune v2')
parser.add_argument('--finetune_v3', action='store_true',
help='finetune v3')
parser.add_argument('--fp16', action='store_true',
help='Run in pseudo-fp16 mode (fp16 storage fp32 math).')
parser.add_argument('--static-loss-scale', type=float, default=1,
help='Static loss scale, positive power of 2 values can '
'improve fp16 convergence.')
parser.add_argument('--dynamic-loss-scale', action='store_true',
help='Use dynamic loss scaling. If supplied, this argument'
' supersedes --static-loss-scale.')
args = parser.parse_args()
args.tied = not args.not_tied
if args.d_embed < 0:
args.d_embed = args.d_model
assert args.ext_len >= 0, 'extended context length must be non-negative'
assert args.batch_size % args.batch_chunk == 0
args.work_dir = '{}-{}'.format(args.work_dir, args.dataset)
args.work_dir = os.path.join(args.work_dir, time.strftime('%Y%m%d-%H%M%S'))
logging = create_exp_dir(args.work_dir,
scripts_to_save=['train.py', 'mem_transformer.py'], debug=args.debug)
# Set the random seed manually for reproducibility.
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
if not args.cuda:
print('WARNING: You have a CUDA device, so you should probably run with --cuda')
else:
torch.cuda.manual_seed_all(args.seed)
# Validate `--fp16` option
if args.fp16:
if not args.cuda:
print('WARNING: --fp16 requires --cuda, ignoring --fp16 option')
args.fp16 = False
else:
try:
from apex.fp16_utils import FP16_Optimizer
except:
print('WARNING: apex not installed, ignoring --fp16 option')
args.fp16 = False
device = torch.device('cuda' if args.cuda else 'cpu')
###############################################################################
# Load data
###############################################################################
corpus = get_lm_corpus(args.data, args.dataset)
ntokens = len(corpus.vocab)
args.n_token = ntokens
eval_batch_size = 10
tr_iter = corpus.get_iterator('train', args.batch_size, args.tgt_len,
device=device, ext_len=args.ext_len)
va_iter = corpus.get_iterator('valid', eval_batch_size, args.eval_tgt_len,
device=device, ext_len=args.ext_len)
te_iter = corpus.get_iterator('test', eval_batch_size, args.eval_tgt_len,
device=device, ext_len=args.ext_len)
# adaptive softmax / embedding
cutoffs, tie_projs = [], [False]
if args.adaptive:
assert args.dataset in ['wt103', 'lm1b']
if args.dataset == 'wt103':
cutoffs = [20000, 40000, 200000]
tie_projs += [True] * len(cutoffs)
elif args.dataset == 'lm1b':
cutoffs = [60000, 100000, 640000]
tie_projs += [False] * len(cutoffs)
###############################################################################
# Build the model
###############################################################################
def init_weight(weight):
if args.init == 'uniform':
nn.init.uniform_(weight, -args.init_range, args.init_range)
elif args.init == 'normal':
nn.init.normal_(weight, 0.0, args.init_std)
def init_bias(bias):
nn.init.constant_(bias, 0.0)
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
if hasattr(m, 'weight') and m.weight is not None:
init_weight(m.weight)
if hasattr(m, 'bias') and m.bias is not None:
init_bias(m.bias)
elif classname.find('AdaptiveEmbedding') != -1:
if hasattr(m, 'emb_projs'):
for i in range(len(m.emb_projs)):
if m.emb_projs[i] is not None:
nn.init.normal_(m.emb_projs[i], 0.0, args.proj_init_std)
elif classname.find('Embedding') != -1:
if hasattr(m, 'weight'):
init_weight(m.weight)
elif classname.find('ProjectedAdaptiveLogSoftmax') != -1:
if hasattr(m, 'cluster_weight') and m.cluster_weight is not None:
init_weight(m.cluster_weight)
if hasattr(m, 'cluster_bias') and m.cluster_bias is not None:
init_bias(m.cluster_bias)
if hasattr(m, 'out_projs'):
for i in range(len(m.out_projs)):
if m.out_projs[i] is not None:
nn.init.normal_(m.out_projs[i], 0.0, args.proj_init_std)
elif classname.find('LayerNorm') != -1:
if hasattr(m, 'weight'):
nn.init.normal_(m.weight, 1.0, args.init_std)
if hasattr(m, 'bias') and m.bias is not None:
init_bias(m.bias)
elif classname.find('TransformerLM') != -1:
if hasattr(m, 'r_emb'):
init_weight(m.r_emb)
if hasattr(m, 'r_w_bias'):
init_weight(m.r_w_bias)
if hasattr(m, 'r_r_bias'):
init_weight(m.r_r_bias)
if hasattr(m, 'r_bias'):
init_bias(m.r_bias)
def update_dropout(m):
classname = m.__class__.__name__
if classname.find('Dropout') != -1:
if hasattr(m, 'p'):
m.p = args.dropout
def update_dropatt(m):
if hasattr(m, 'dropatt'):
m.dropatt.p = args.dropatt
if args.restart:
with open(os.path.join(args.restart_dir, 'model.pt'), 'rb') as f:
model = torch.load(f)
if not args.fp16:
model = model.float()
model.apply(update_dropout)
model.apply(update_dropatt)
else:
model = MemTransformerLM(ntokens, args.n_layer, args.n_head, args.d_model,
args.d_head, args.d_inner, args.dropout, args.dropatt,
tie_weight=args.tied, d_embed=args.d_embed, div_val=args.div_val,
tie_projs=tie_projs, pre_lnorm=args.pre_lnorm, tgt_len=args.tgt_len,
ext_len=args.ext_len, mem_len=args.mem_len, cutoffs=cutoffs,
same_length=args.same_length, attn_type=args.attn_type,
clamp_len=args.clamp_len, sample_softmax=args.sample_softmax)
model.apply(weights_init)
model.word_emb.apply(weights_init) # ensure embedding init is not overridden by out_layer in case of weight sharing
args.n_all_param = sum([p.nelement() for p in model.parameters()])
args.n_nonemb_param = sum([p.nelement() for p in model.layers.parameters()])
if args.fp16:
model = model.half()
if args.multi_gpu:
model = model.to(device)
if args.gpu0_bsz >= 0:
para_model = BalancedDataParallel(args.gpu0_bsz // args.batch_chunk,
model, dim=1).to(device)
else:
para_model = nn.DataParallel(model, dim=1).to(device)
else:
para_model = model.to(device)
#### optimizer
if args.optim.lower() == 'sgd':
if args.sample_softmax > 0:
dense_params, sparse_params = [], []
for param in model.parameters():
if param.size() == model.word_emb.weight.size():
sparse_params.append(param)
else:
dense_params.append(param)
optimizer_sparse = optim.SGD(sparse_params, lr=args.lr * 2)
optimizer = optim.SGD(dense_params, lr=args.lr, momentum=args.mom)
else:
optimizer = optim.SGD(model.parameters(), lr=args.lr,
momentum=args.mom)
elif args.optim.lower() == 'adam':
if args.sample_softmax > 0:
dense_params, sparse_params = [], []
for param in model.parameters():
if param.size() == model.word_emb.weight.size():
sparse_params.append(param)
else:
dense_params.append(param)
optimizer_sparse = optim.SparseAdam(sparse_params, lr=args.lr)
optimizer = optim.Adam(dense_params, lr=args.lr)
else:
optimizer = optim.Adam(model.parameters(), lr=args.lr)
elif args.optim.lower() == 'adagrad':
optimizer = optim.Adagrad(model.parameters(), lr=args.lr)
#### scheduler
if args.scheduler == 'cosine':
# here we do not set eta_min to lr_min to be backward compatible
# because in previous versions eta_min is default to 0
# rather than the default value of lr_min 1e-6
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer,
args.max_step, eta_min=args.eta_min) # should use eta_min arg
if args.sample_softmax > 0:
scheduler_sparse = optim.lr_scheduler.CosineAnnealingLR(optimizer_sparse,
args.max_step, eta_min=args.eta_min) # should use eta_min arg
elif args.scheduler == 'inv_sqrt':
# originally used for Transformer (in Attention is all you need)
def lr_lambda(step):
# return a multiplier instead of a learning rate
if step == 0 and args.warmup_step == 0:
return 1.
else:
return 1. / (step ** 0.5) if step > args.warmup_step \
else step / (args.warmup_step ** 1.5)
scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lr_lambda)
elif args.scheduler == 'dev_perf':
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
factor=args.decay_rate, patience=args.patience, min_lr=args.lr_min)
if args.sample_softmax > 0:
scheduler_sparse = optim.lr_scheduler.ReduceLROnPlateau(optimizer_sparse,
factor=args.decay_rate, patience=args.patience, min_lr=args.lr_min)
elif args.scheduler == 'constant':
pass
if args.cuda and args.fp16:
# If args.dynamic_loss_scale is False, static_loss_scale will be used.
# If args.dynamic_loss_scale is True, it will take precedence over static_loss_scale.
optimizer = FP16_Optimizer(optimizer,
static_loss_scale = args.static_loss_scale,
dynamic_loss_scale = args.dynamic_loss_scale,
dynamic_loss_args = {'init_scale': 2 ** 16})
if args.restart:
if os.path.exists(os.path.join(args.restart_dir, 'optimizer.pt')):
with open(os.path.join(args.restart_dir, 'optimizer.pt'), 'rb') as f:
opt_state_dict = torch.load(f)
optimizer.load_state_dict(opt_state_dict)
else:
print('Optimizer was not saved. Start from scratch.')
logging('=' * 100)
for k, v in args.__dict__.items():
logging(' - {} : {}'.format(k, v))
logging('=' * 100)
logging('#params = {}'.format(args.n_all_param))
logging('#non emb params = {}'.format(args.n_nonemb_param))
###############################################################################
# Training code
###############################################################################
def evaluate(eval_iter):
# Turn on evaluation mode which disables dropout.
model.eval()
# If the model does not use memory at all, make the ext_len longer.
# Otherwise, make the mem_len longer and keep the ext_len the same.
if args.mem_len == 0:
model.reset_length(args.eval_tgt_len,
args.ext_len+args.tgt_len-args.eval_tgt_len, args.mem_len)
else:
model.reset_length(args.eval_tgt_len,
args.ext_len, args.mem_len+args.tgt_len-args.eval_tgt_len)
# Evaluation
total_len, total_loss = 0, 0.
with torch.no_grad():
mems = tuple()
for i, (data, target, seq_len) in enumerate(eval_iter):
if args.max_eval_steps > 0 and i >= args.max_eval_steps:
break
ret = model(data, target, *mems)
loss, mems = ret[0], ret[1:]
loss = loss.mean()
total_loss += seq_len * loss.float().item()
total_len += seq_len
# Switch back to the training mode
model.reset_length(args.tgt_len, args.ext_len, args.mem_len)
model.train()
return total_loss / total_len
def train():
# Turn on training mode which enables dropout.
global train_step, train_loss, best_val_loss, eval_start_time, log_start_time
model.train()
if args.batch_chunk > 1:
mems = [tuple() for _ in range(args.batch_chunk)]
else:
mems = tuple()
train_iter = tr_iter.get_varlen_iter() if args.varlen else tr_iter
for batch, (data, target, seq_len) in enumerate(train_iter):
model.zero_grad()
if args.batch_chunk > 1:
data_chunks = torch.chunk(data, args.batch_chunk, 1)
target_chunks = torch.chunk(target, args.batch_chunk, 1)
for i in range(args.batch_chunk):
data_i = data_chunks[i].contiguous()
target_i = target_chunks[i].contiguous()
ret = para_model(data_i, target_i, *mems[i])
loss, mems[i] = ret[0], ret[1:]
loss = loss.float().mean().type_as(loss) / args.batch_chunk
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
train_loss += loss.float().item()
else:
ret = para_model(data, target, *mems)
loss, mems = ret[0], ret[1:]
loss = loss.float().mean().type_as(loss)
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
train_loss += loss.float().item()
if args.fp16:
optimizer.clip_master_grads(args.clip)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()
if args.sample_softmax > 0:
optimizer_sparse.step()
# step-wise learning rate annealing
train_step += 1
if args.scheduler in ['cosine', 'constant', 'dev_perf']:
# linear warmup stage
if train_step < args.warmup_step:
curr_lr = args.lr * train_step / args.warmup_step
optimizer.param_groups[0]['lr'] = curr_lr
if args.sample_softmax > 0:
optimizer_sparse.param_groups[0]['lr'] = curr_lr * 2
else:
if args.scheduler == 'cosine':
scheduler.step(train_step)
if args.sample_softmax > 0:
scheduler_sparse.step(train_step)
elif args.scheduler == 'inv_sqrt':
scheduler.step(train_step)
if train_step % args.log_interval == 0:
cur_loss = train_loss / args.log_interval
elapsed = time.time() - log_start_time
log_str = '| epoch {:3d} step {:>8d} | {:>6d} batches | lr {:.3g} ' \
'| ms/batch {:5.2f} | loss {:5.2f}'.format(
epoch, train_step, batch+1, optimizer.param_groups[0]['lr'],
elapsed * 1000 / args.log_interval, cur_loss)
if args.dataset in ['enwik8', 'text8']:
log_str += ' | bpc {:9.5f}'.format(cur_loss / math.log(2))
else:
log_str += ' | ppl {:9.3f}'.format(math.exp(cur_loss))
logging(log_str)
train_loss = 0
log_start_time = time.time()
if train_step % args.eval_interval == 0:
val_loss = evaluate(va_iter)
logging('-' * 100)
log_str = '| Eval {:3d} at step {:>8d} | time: {:5.2f}s ' \
'| valid loss {:5.2f}'.format(
train_step // args.eval_interval, train_step,
(time.time() - eval_start_time), val_loss)
if args.dataset in ['enwik8', 'text8']:
log_str += ' | bpc {:9.5f}'.format(val_loss / math.log(2))
else:
log_str += ' | valid ppl {:9.3f}'.format(math.exp(val_loss))
logging(log_str)
logging('-' * 100)
# Save the model if the validation loss is the best we've seen so far.
if not best_val_loss or val_loss < best_val_loss:
if not args.debug:
with open(os.path.join(args.work_dir, 'model.pt'), 'wb') as f:
torch.save(model, f)
with open(os.path.join(args.work_dir, 'optimizer.pt'), 'wb') as f:
torch.save(optimizer.state_dict(), f)
best_val_loss = val_loss
# dev-performance based learning rate annealing
if args.scheduler == 'dev_perf':
scheduler.step(val_loss)
if args.sample_softmax > 0:
scheduler_sparse.step(val_loss)
eval_start_time = time.time()
if train_step == args.max_step:
break
# Loop over epochs.
train_step = 0
train_loss = 0
best_val_loss = None
log_start_time = time.time()
eval_start_time = time.time()
# At any point you can hit Ctrl + C to break out of training early.
try:
for epoch in itertools.count(start=1):
train()
if train_step == args.max_step:
logging('-' * 100)
logging('End of training')
break
except KeyboardInterrupt:
logging('-' * 100)
logging('Exiting from training early')
# Load the best saved model.
with open(os.path.join(args.work_dir, 'model.pt'), 'rb') as f:
model = torch.load(f)
para_model = model.to(device)
# Run on test data.
test_loss = evaluate(te_iter)
logging('=' * 100)
if args.dataset in ['enwik8', 'text8']:
logging('| End of training | test loss {:5.2f} | test bpc {:9.5f}'.format(
test_loss, test_loss / math.log(2)))
else:
logging('| End of training | test loss {:5.2f} | test ppl {:9.3f}'.format(
test_loss, math.exp(test_loss)))
logging('=' * 100)

90
transformer-xl/pytorch/utils/adaptive_softmax.py Normal file
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from collections import defaultdict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class AdaptiveLogSoftmax(nn.Module):
def __init__(self, in_features, n_classes, cutoffs, keep_order=False):
super(AdaptiveLogSoftmax, self).__init__()
cutoffs = list(cutoffs)
if (cutoffs != sorted(cutoffs)) \
or (min(cutoffs) <= 0) \
or (max(cutoffs) >= (n_classes - 1)) \
or (len(set(cutoffs)) != len(cutoffs)) \
or any([int(c) != c for c in cutoffs]):
raise ValueError("cutoffs should be a sequence of unique, positive "
"integers sorted in an increasing order, where "
"each value is between 1 and n_classes-1")
self.in_features = in_features
self.n_classes = n_classes
self.cutoffs = cutoffs + [n_classes]
self.shortlist_size = self.cutoffs[0]
self.n_clusters = len(self.cutoffs) - 1
self.head_size = self.shortlist_size + self.n_clusters
self.cluster_weight = nn.Parameter(torch.zeros(self.n_clusters, self.in_features))
self.cluster_bias = nn.Parameter(torch.zeros(self.n_clusters))
self.keep_order = keep_order
def forward(self, hidden, target, weight, bias, keep_order=False):
if hidden.size(0) != target.size(0):
raise RuntimeError('Input and target should have the same size '
'in the batch dimension.')
head_weight = torch.cat(
[weight[:self.shortlist_size], self.cluster_weight], dim=0)
head_bias = torch.cat(
[bias[:self.shortlist_size], self.cluster_bias], dim=0)
head_logit = F.linear(hidden, head_weight, bias=head_bias)
head_logprob = F.log_softmax(head_logit, dim=1)
nll = torch.zeros_like(target,
dtype=hidden.dtype, device=hidden.device)
offset = 0
cutoff_values = [0] + self.cutoffs
for i in range(len(cutoff_values) - 1):
l_idx, h_idx = cutoff_values[i], cutoff_values[i + 1]
mask_i = (target >= l_idx) & (target < h_idx)
indices_i = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
target_i = target.index_select(0, indices_i) - l_idx
head_logprob_i = head_logprob.index_select(0, indices_i)
if i == 0:
logprob_i = head_logprob_i.gather(1, target_i[:,None]).squeeze(1)
else:
weight_i = weight[l_idx:h_idx]
bias_i = bias[l_idx:h_idx]
hidden_i = hidden.index_select(0, indices_i)
tail_logit_i = F.linear(hidden_i, weight_i, bias=bias_i)
tail_logprob_i = F.log_softmax(tail_logit_i, dim=1)
logprob_i = head_logprob_i[:, -i] \
+ tail_logprob_i.gather(1, target_i[:,None]).squeeze(1)
if (hasattr(self, 'keep_order') and self.keep_order) or keep_order:
nll.index_copy_(0, indices_i, -logprob_i)
else:
nll[offset:offset+logprob_i.size(0)].copy_(-logprob_i)
offset += logprob_i.size(0)
return nll

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from torch.nn.parallel import DataParallel
import torch
from torch.nn.parallel._functions import Scatter
from torch.nn.parallel.parallel_apply import parallel_apply
def scatter(inputs, target_gpus, chunk_sizes, dim=0):
r"""
Slices tensors into approximately equal chunks and
distributes them across given GPUs. Duplicates
references to objects that are not tensors.
"""
def scatter_map(obj):
if isinstance(obj, torch.Tensor):
try:
return Scatter.apply(target_gpus, chunk_sizes, dim, obj)
except:
print('obj', obj.size())
print('dim', dim)
print('chunk_sizes', chunk_sizes)
quit()
if isinstance(obj, tuple) and len(obj) > 0:
return list(zip(*map(scatter_map, obj)))
if isinstance(obj, list) and len(obj) > 0:
return list(map(list, zip(*map(scatter_map, obj))))
if isinstance(obj, dict) and len(obj) > 0:
return list(map(type(obj), zip(*map(scatter_map, obj.items()))))
return [obj for targets in target_gpus]
# After scatter_map is called, a scatter_map cell will exist. This cell
# has a reference to the actual function scatter_map, which has references
# to a closure that has a reference to the scatter_map cell (because the
# fn is recursive). To avoid this reference cycle, we set the function to
# None, clearing the cell
try:
return scatter_map(inputs)
finally:
scatter_map = None
def scatter_kwargs(inputs, kwargs, target_gpus, chunk_sizes, dim=0):
r"""Scatter with support for kwargs dictionary"""
inputs = scatter(inputs, target_gpus, chunk_sizes, dim) if inputs else []
kwargs = scatter(kwargs, target_gpus, chunk_sizes, dim) if kwargs else []
if len(inputs) < len(kwargs):
inputs.extend([() for _ in range(len(kwargs) - len(inputs))])
elif len(kwargs) < len(inputs):
kwargs.extend([{} for _ in range(len(inputs) - len(kwargs))])
inputs = tuple(inputs)
kwargs = tuple(kwargs)
return inputs, kwargs
class BalancedDataParallel(DataParallel):
def __init__(self, gpu0_bsz, *args, **kwargs):
self.gpu0_bsz = gpu0_bsz
super().__init__(*args, **kwargs)
def forward(self, *inputs, **kwargs):
if not self.device_ids:
return self.module(*inputs, **kwargs)
if self.gpu0_bsz == 0:
device_ids = self.device_ids[1:]
else:
device_ids = self.device_ids
inputs, kwargs = self.scatter(inputs, kwargs, device_ids)
if len(self.device_ids) == 1:
return self.module(*inputs[0], **kwargs[0])
replicas = self.replicate(self.module, self.device_ids)
if self.gpu0_bsz == 0:
replicas = replicas[1:]
outputs = self.parallel_apply(replicas, device_ids, inputs, kwargs)
return self.gather(outputs, self.output_device)
def parallel_apply(self, replicas, device_ids, inputs, kwargs):
return parallel_apply(replicas, inputs, kwargs, device_ids)
def scatter(self, inputs, kwargs, device_ids):
bsz = inputs[0].size(self.dim)
num_dev = len(self.device_ids)
gpu0_bsz = self.gpu0_bsz
bsz_unit = (bsz - gpu0_bsz) // (num_dev - 1)
if gpu0_bsz < bsz_unit:
chunk_sizes = [gpu0_bsz] + [bsz_unit] * (num_dev - 1)
delta = bsz - sum(chunk_sizes)
for i in range(delta):
chunk_sizes[i + 1] += 1
if gpu0_bsz == 0:
chunk_sizes = chunk_sizes[1:]
else:
return super().scatter(inputs, kwargs, device_ids)
return scatter_kwargs(inputs, kwargs, device_ids, chunk_sizes, dim=self.dim)

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import functools
import os, shutil
import numpy as np
import torch
def logging(s, log_path, print_=True, log_=True):
if print_:
print(s)
if log_:
with open(log_path, 'a+') as f_log:
f_log.write(s + '\n')
def get_logger(log_path, **kwargs):
return functools.partial(logging, log_path=log_path, **kwargs)
def create_exp_dir(dir_path, scripts_to_save=None, debug=False):
if debug:
print('Debug Mode : no experiment dir created')
return functools.partial(logging, log_path=None, log_=False)
if not os.path.exists(dir_path):
os.makedirs(dir_path)
print('Experiment dir : {}'.format(dir_path))
if scripts_to_save is not None:
script_path = os.path.join(dir_path, 'scripts')
if not os.path.exists(script_path):
os.makedirs(script_path)
for script in scripts_to_save:
dst_file = os.path.join(dir_path, 'scripts', os.path.basename(script))
shutil.copyfile(script, dst_file)
return get_logger(log_path=os.path.join(dir_path, 'log.txt'))
def save_checkpoint(model, optimizer, path, epoch):
torch.save(model, os.path.join(path, 'model_{}.pt'.format(epoch)))
torch.save(optimizer.state_dict(), os.path.join(path, 'optimizer_{}.pt'.format(epoch)))

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import torch
from torch import nn
import numpy as np
class LogUniformSampler(object):
def __init__(self, range_max, n_sample):
"""
Reference : https://github.com/tensorflow/tensorflow/blob/r1.10/tensorflow/python/ops/candidate_sampling_ops.py
`P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1)`
expected count can be approximated by 1 - (1 - p)^n
and we use a numerically stable version -expm1(num_tries * log1p(-p))
Our implementation fixes num_tries at 2 * n_sample, and the actual #samples will vary from run to run
"""
with torch.no_grad():
self.range_max = range_max
log_indices = torch.arange(1., range_max+2., 1.).log_()
self.dist = (log_indices[1:] - log_indices[:-1]) / log_indices[-1]
# print('P', self.dist.numpy().tolist()[-30:])
self.log_q = (- (-self.dist.double().log1p_() * 2 * n_sample).expm1_()).log_().float()
self.n_sample = n_sample
def sample(self, labels):
"""
labels: [b1, b2]
Return
true_log_probs: [b1, b2]
samp_log_probs: [n_sample]
neg_samples: [n_sample]
"""
# neg_samples = torch.empty(0).long()
n_sample = self.n_sample
n_tries = 2 * n_sample
with torch.no_grad():
neg_samples = torch.multinomial(self.dist, n_tries, replacement=True).unique()
device = labels.device
neg_samples = neg_samples.to(device)
true_log_probs = self.log_q[labels].to(device)
samp_log_probs = self.log_q[neg_samples].to(device)
return true_log_probs, samp_log_probs, neg_samples
def sample_logits(embedding, bias, labels, inputs, sampler):
"""
embedding: an nn.Embedding layer
bias: [n_vocab]
labels: [b1, b2]
inputs: [b1, b2, n_emb]
sampler: you may use a LogUniformSampler
Return
logits: [b1, b2, 1 + n_sample]
"""
true_log_probs, samp_log_probs, neg_samples = sampler.sample(labels)
n_sample = neg_samples.size(0)
b1, b2 = labels.size(0), labels.size(1)
all_ids = torch.cat([labels.view(-1), neg_samples])
all_w = embedding(all_ids)
true_w = all_w[: -n_sample].view(b1, b2, -1)
sample_w = all_w[- n_sample:].view(n_sample, -1)
all_b = bias[all_ids]
true_b = all_b[: -n_sample].view(b1, b2)
sample_b = all_b[- n_sample:]
hit = (labels[:, :, None] == neg_samples).detach()
true_logits = torch.einsum('ijk,ijk->ij',
[true_w, inputs]) + true_b - true_log_probs
sample_logits = torch.einsum('lk,ijk->ijl',
[sample_w, inputs]) + sample_b - samp_log_probs
sample_logits.masked_fill_(hit, -1e30)
logits = torch.cat([true_logits[:, :, None], sample_logits], -1)
return logits
# class LogUniformSampler(object):
# def __init__(self, range_max, unique=False):
# """
# Reference : https://github.com/tensorflow/tensorflow/blob/r1.10/tensorflow/python/ops/candidate_sampling_ops.py
# `P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1)`
# """
# self.range_max = range_max
# log_indices = torch.arange(1., range_max+2., 1.).log_()
# self.dist = (log_indices[1:] - log_indices[:-1]) / log_indices[-1]
# self.unique = unique
# if self.unique:
# self.exclude_mask = torch.ByteTensor(range_max).fill_(0)
# def sample(self, n_sample, labels):
# pos_sample, new_labels = labels.unique(return_inverse=True)
# n_pos_sample = pos_sample.size(0)
# n_neg_sample = n_sample - n_pos_sample
# if self.unique:
# self.exclude_mask.index_fill_(0, pos_sample, 1)
# sample_dist = self.dist.clone().masked_fill_(self.exclude_mask, 0)
# self.exclude_mask.index_fill_(0, pos_sample, 0)
# else:
# sample_dist = self.dist
# neg_sample = torch.multinomial(sample_dist, n_neg_sample)
# sample = torch.cat([pos_sample, neg_sample])
# sample_prob = self.dist[sample]
# return new_labels, sample, sample_prob
if __name__ == '__main__':
S, B = 3, 4
n_vocab = 10000
n_sample = 5
H = 32
labels = torch.LongTensor(S, B).random_(0, n_vocab)
# sampler = LogUniformSampler(n_vocab, unique=False)
# new_labels, sample, sample_prob = sampler.sample(n_sample, labels)
sampler = LogUniformSampler(n_vocab, unique=True)
# true_probs, samp_probs, neg_samples = sampler.sample(n_sample, labels)
# print('true_probs', true_probs.numpy().tolist())
# print('samp_probs', samp_probs.numpy().tolist())
# print('neg_samples', neg_samples.numpy().tolist())
# print('sum', torch.sum(sampler.dist).item())
# assert torch.all(torch.sort(sample.unique())[0].eq(torch.sort(sample)[0])).item()
embedding = nn.Embedding(n_vocab, H)
bias = torch.zeros(n_vocab)
inputs = torch.Tensor(S, B, H).normal_()
logits, out_labels = sample_logits(embedding, bias, labels, inputs, sampler, n_sample)
print('logits', logits.detach().numpy().tolist())
print('logits shape', logits.size())
print('out_labels', out_labels.detach().numpy().tolist())
print('out_labels shape', out_labels.size())

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from collections import defaultdict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
CUDA_MAJOR = int(torch.version.cuda.split('.')[0])
CUDA_MINOR = int(torch.version.cuda.split('.')[1])
class ProjectedAdaptiveLogSoftmax(nn.Module):
def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1,
keep_order=False):
super(ProjectedAdaptiveLogSoftmax, self).__init__()
self.n_token = n_token
self.d_embed = d_embed
self.d_proj = d_proj
self.cutoffs = cutoffs + [n_token]
self.cutoff_ends = [0] + self.cutoffs
self.div_val = div_val
self.shortlist_size = self.cutoffs[0]
self.n_clusters = len(self.cutoffs) - 1
self.head_size = self.shortlist_size + self.n_clusters
if self.n_clusters > 0:
self.cluster_weight = nn.Parameter(torch.zeros(self.n_clusters, self.d_embed))
self.cluster_bias = nn.Parameter(torch.zeros(self.n_clusters))
self.out_layers = nn.ModuleList()
self.out_projs = nn.ParameterList()
if div_val == 1:
for i in range(len(self.cutoffs)):
if d_proj != d_embed:
self.out_projs.append(
nn.Parameter(torch.Tensor(d_proj, d_embed))
)
else:
self.out_projs.append(None)
self.out_layers.append(nn.Linear(d_embed, n_token))
else:
for i in range(len(self.cutoffs)):
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i+1]
d_emb_i = d_embed // (div_val ** i)
self.out_projs.append(
nn.Parameter(torch.Tensor(d_proj, d_emb_i))
)
self.out_layers.append(nn.Linear(d_emb_i, r_idx-l_idx))
self.keep_order = keep_order
def _compute_logit(self, hidden, weight, bias, proj):
if proj is None:
logit = F.linear(hidden, weight, bias=bias)
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
proj_hid = F.linear(hidden, proj.t().contiguous())
logit = F.linear(proj_hid, weight, bias=bias)
# else:
# logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
# if bias is not None:
# logit = logit + bias
return logit
def forward(self, hidden, target, keep_order=False):
'''
hidden :: [len*bsz x d_proj]
target :: [len*bsz]
'''
if hidden.size(0) != target.size(0):
raise RuntimeError('Input and target should have the same size '
'in the batch dimension.')
if self.n_clusters == 0:
logit = self._compute_logit(hidden, self.out_layers[0].weight,
self.out_layers[0].bias, self.out_projs[0])
nll = -F.log_softmax(logit, dim=-1) \
.gather(1, target.unsqueeze(1)).squeeze(1)
else:
# construct weights and biases
weights, biases = [], []
for i in range(len(self.cutoffs)):
if self.div_val == 1:
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
weight_i = self.out_layers[0].weight[l_idx:r_idx]
bias_i = self.out_layers[0].bias[l_idx:r_idx]
else:
weight_i = self.out_layers[i].weight
bias_i = self.out_layers[i].bias
if i == 0:
weight_i = torch.cat(
[weight_i, self.cluster_weight], dim=0)
bias_i = torch.cat(
[bias_i, self.cluster_bias], dim=0)
weights.append(weight_i)
biases.append(bias_i)
head_weight, head_bias, head_proj = weights[0], biases[0], self.out_projs[0]
head_logit = self._compute_logit(hidden, head_weight, head_bias, head_proj)
head_logprob = F.log_softmax(head_logit, dim=1)
nll = torch.zeros_like(target,
dtype=hidden.dtype, device=hidden.device)
offset = 0
cutoff_values = [0] + self.cutoffs
for i in range(len(cutoff_values) - 1):
l_idx, r_idx = cutoff_values[i], cutoff_values[i + 1]
mask_i = (target >= l_idx) & (target < r_idx)
indices_i = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
target_i = target.index_select(0, indices_i) - l_idx
head_logprob_i = head_logprob.index_select(0, indices_i)
if i == 0:
logprob_i = head_logprob_i.gather(1, target_i[:,None]).squeeze(1)
else:
weight_i, bias_i, proj_i = weights[i], biases[i], self.out_projs[i]
hidden_i = hidden.index_select(0, indices_i)
tail_logit_i = self._compute_logit(hidden_i, weight_i, bias_i, proj_i)
tail_logprob_i = F.log_softmax(tail_logit_i, dim=1)
logprob_i = head_logprob_i[:, -i] \
+ tail_logprob_i.gather(1, target_i[:,None]).squeeze(1)
if (hasattr(self, 'keep_order') and self.keep_order) or keep_order:
nll.index_copy_(0, indices_i, -logprob_i)
else:
nll[offset:offset+logprob_i.size(0)].copy_(-logprob_i)
offset += logprob_i.size(0)
return nll

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import os
from collections import Counter, OrderedDict
import torch
class Vocab(object):
def __init__(self, special=[], min_freq=0, max_size=None, lower_case=True,
delimiter=None, vocab_file=None):
self.counter = Counter()
self.special = special
self.min_freq = min_freq
self.max_size = max_size
self.lower_case = lower_case
self.delimiter = delimiter
self.vocab_file = vocab_file
def tokenize(self, line, add_eos=False, add_double_eos=False):
line = line.strip()
# convert to lower case
if self.lower_case:
line = line.lower()
# empty delimiter '' will evaluate False
if self.delimiter == '':
symbols = line
else:
symbols = line.split(self.delimiter)
if add_double_eos: # lm1b
return ['<S>'] + symbols + ['<S>']
elif add_eos:
return symbols + ['<eos>']
else:
return symbols
def count_file(self, path, verbose=False, add_eos=False):
if verbose: print('counting file {} ...'.format(path))
assert os.path.exists(path)
sents = []
with open(path, 'r', encoding='utf-8') as f:
for idx, line in enumerate(f):
if verbose and idx > 0 and idx % 500000 == 0:
print(' line {}'.format(idx))
symbols = self.tokenize(line, add_eos=add_eos)
self.counter.update(symbols)
sents.append(symbols)
return sents
def count_sents(self, sents, verbose=False):
"""
sents : a list of sentences, each a list of tokenized symbols
"""
if verbose: print('counting {} sents ...'.format(len(sents)))
for idx, symbols in enumerate(sents):
if verbose and idx > 0 and idx % 500000 == 0:
print(' line {}'.format(idx))
self.counter.update(symbols)
def _build_from_file(self, vocab_file):
self.idx2sym = []
self.sym2idx = OrderedDict()
with open(vocab_file, 'r', encoding='utf-8') as f:
for line in f:
symb = line.strip().split()[0]
self.add_symbol(symb)
self.unk_idx = self.sym2idx['<UNK>']
def build_vocab(self):
if self.vocab_file:
print('building vocab from {}'.format(self.vocab_file))
self._build_from_file(self.vocab_file)
print('final vocab size {}'.format(len(self)))
else:
print('building vocab with min_freq={}, max_size={}'.format(
self.min_freq, self.max_size))
self.idx2sym = []
self.sym2idx = OrderedDict()
for sym in self.special:
self.add_special(sym)
for sym, cnt in self.counter.most_common(self.max_size):
if cnt < self.min_freq: break
self.add_symbol(sym)
print('final vocab size {} from {} unique tokens'.format(
len(self), len(self.counter)))
def encode_file(self, path, ordered=False, verbose=False, add_eos=True,
add_double_eos=False):
if verbose: print('encoding file {} ...'.format(path))
assert os.path.exists(path)
encoded = []
with open(path, 'r', encoding='utf-8') as f:
for idx, line in enumerate(f):
if verbose and idx > 0 and idx % 500000 == 0:
print(' line {}'.format(idx))
symbols = self.tokenize(line, add_eos=add_eos,
add_double_eos=add_double_eos)
encoded.append(self.convert_to_tensor(symbols))
if ordered:
encoded = torch.cat(encoded)
return encoded
def encode_sents(self, sents, ordered=False, verbose=False):
if verbose: print('encoding {} sents ...'.format(len(sents)))
encoded = []
for idx, symbols in enumerate(sents):
if verbose and idx > 0 and idx % 500000 == 0:
print(' line {}'.format(idx))
encoded.append(self.convert_to_tensor(symbols))
if ordered:
encoded = torch.cat(encoded)
return encoded
def add_special(self, sym):
if sym not in self.sym2idx:
self.idx2sym.append(sym)
self.sym2idx[sym] = len(self.idx2sym) - 1
setattr(self, '{}_idx'.format(sym.strip('<>')), self.sym2idx[sym])
def add_symbol(self, sym):
if sym not in self.sym2idx:
self.idx2sym.append(sym)
self.sym2idx[sym] = len(self.idx2sym) - 1
def get_sym(self, idx):
assert 0 <= idx < len(self), 'Index {} out of range'.format(idx)
return self.idx2sym[idx]
def get_idx(self, sym):
if sym in self.sym2idx:
return self.sym2idx[sym]
else:
# print('encounter unk {}'.format(sym))
assert '<eos>' not in sym
assert hasattr(self, 'unk_idx')
return self.sym2idx.get(sym, self.unk_idx)
def get_symbols(self, indices):
return [self.get_sym(idx) for idx in indices]
def get_indices(self, symbols):
return [self.get_idx(sym) for sym in symbols]
def convert_to_tensor(self, symbols):
return torch.LongTensor(self.get_indices(symbols))
def convert_to_sent(self, indices, exclude=None):
if exclude is None:
return ' '.join([self.get_sym(idx) for idx in indices])
else:
return ' '.join([self.get_sym(idx) for idx in indices if idx not in exclude])
def __len__(self):
return len(self.idx2sym)

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## Introduction
This directory contains our TF implementation of Transformer-XL. Note that our state-of-the-art results reported in the paper were obtained by training the model on a large-scale TPU cluster, and our gpu codebase currently does not support distributed training. Here we provide two sets of hyperparameters and scripts:
- `*large_tpu.sh` are for the SoTA setting on TPUs. These are exactly the commands we used to obtained our best results.
- `*base_gpu.sh` are for the base models which can be run on a few GPUs.
## Prerequisite
- Python 2.7
- Tensorflow [1.12.0](https://github.com/tensorflow/tensorflow/releases/tag/v1.12.0)
## Obtain and evaluate pretrained SoTA models
#### 1. Download preprocessed data (vocab) & pretrained models
(a) Set your own `DATA_ROOT` in `sota/download.sh` (default to `./`), which will be the root diretory of downloaded model.
(b) Then, download the model & data by `bash sota/download.sh`. After downloading, the expected directory structure is as follows
```markdown
pretrained_xl
tf_enwik8/
data/
cache.pkl
corpus-info.json
model/
checkpoint
model.ckpt*
tf_wt103/
...
...
```
**Note**: we include preprocessed data in the download files to make sure the **same vocabulary** is used. Please see the code `tf/data_utils.py` to understand the data structure.
#### 2. Run evaluation scripts to replicate SoTA results on GPUs
- **enwik8**: modify the script `sota/enwik8.sh` accordingly (see below)
- set `DATA_ROOT` to the same folder used in the download step (default to `./`)
- set `TEST_NUM_CORE ` (number of GPUs to use): we recommend 2 GPUs => about 60 mins
- run the script: `bash sota/enwik8.sh`
- **lm1b**: modify the script `sota/lm1b.sh` accordingly (see below)
- set `DATA_ROOT` to the same folder used in the download step (default to `./`)
- set `TEST_NUM_CORE ` (number of GPUs to use): we recommend 1 GPUs => less than 5 mins
- run the script: `bash sota/lm1b.sh`
- **wt103**: modify the script `sota/wt103.sh` accordingly (see below)
- set `DATA_ROOT` to the same folder used in the download step (default to `./`)
- set `TEST_NUM_CORE ` (number of GPUs to use): we recommend 1 GPUs => less than 5 mins
- run the script: `bash sota/wt103.sh`
- **text8**: modify the script `sota/text8.sh` accordingly (see below)
- set `DATA_ROOT` to the same folder used in the download step (default to `./`)
- set `TEST_NUM_CORE ` (number of GPUs to use): we recommend 2 GPUs => about 60 mins
- run the script: `bash sota/text8.sh`
#### 3. Resources Needed for SoTA Model Training
We used 32, 32, 64, and 512 TPU cores for training our best models on enwik8, text8, wt103, and lm1b respectively. The training time for each model ranges from 2 to 5 days.
## Train "Transformer-XL" from scratch with GPUs or TPUs
### 1. Download raw data
`bash getdata.sh`
### 2. Preprocess, training and evaluation
For `dataset` in `[enwik8, lm1b, wt103, text8]`:
- check out `scripts/dataset_base_gpu.sh` for GPU training and evaluation
- check out `scripts/dataset_large_tpu.sh` for TPU training and evaluation
#### (1) Preprocess raw data and create tfrecords
**NOTE**: The preprocessing for GPU and TPU are different. So, you have to run them separately.
GPU:
- create training and validation data: `bash scripts/dataset_bas_gpu.sh train_data`
- create test data: `bash scripts/dataset_base_gpu.sh test_data`
TPU:
- Set the Google storage URL in `scripts/dataset_large_tpu.sh`:
- `GSDATA`: data URL
- `GSEXP`: experiment URL
- create training and validation data: `bash scripts/dataset_large_tpu.sh train_data`
- create test data: `bash scripts/dataset_large_tpu.sh test_data`
#### (2) Run training
Base models on GPUs:
- Modify the configurations in `scripts/dataset_base_gpu.sh` according to your needs.
- `bash scripts/dataset_base_gpu.sh train`
- If enough resources are available, increasing the model sizes (e.g., `N_LAYER`, `D_MODEL`, `D_EMBED`, `D_HEAD`, `D_INNER`) so that they are closer to the values defined in `scripts/dataset_large_tpu.sh`. Likewise, when resources are limited, decrease the model sizes. It is recommended to ensure that `D_MODEL == D_EMBED` and `D_MODEL == N_HEAD x D_HEAD`. When the model sizes increase, remember to increase `warmup_steps` accordingly to alleviate optimization difficulties.
- Adjust the `NUM_CORE` parameter to reflect the number of GPUs to use.
Larger models on TPUs:
- Modify the configurations in `scripts/dataset_large_tpu.sh` according to your needs.
- `bash scripts/dataset_large_tpu.sh train`
#### (3) Run evaluation
Base models on GPUs:
- `bash scripts/dataset_base_gpu.sh eval --eval_ckpt_path PATH_TO_CKPT`
Larger models on TPUs:
- `bash scripts/dataset_base_tpu.sh eval --eval_ckpt_path PATH_TO_CKPT`

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# coding=utf-8
# Copyright 2018 The Tensor2Tensor Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Script to average values of variables in a list of checkpoint files."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import six
from six.moves import zip # pylint: disable=redefined-builtin
import tensorflow as tf
flags = tf.flags
FLAGS = flags.FLAGS
flags.DEFINE_string("checkpoints", "",
"Comma-separated list of checkpoints to average.")
flags.DEFINE_integer("num_last_checkpoints", 0,
"Averages the last N saved checkpoints."
" If the checkpoints flag is set, this is ignored.")
flags.DEFINE_string("prefix", "",
"Prefix (e.g., directory) to append to each checkpoint.")
flags.DEFINE_string("output_path", "/tmp/averaged.ckpt",
"Path to output the averaged checkpoint to.")
def checkpoint_exists(path):
return (tf.gfile.Exists(path) or tf.gfile.Exists(path + ".meta") or
tf.gfile.Exists(path + ".index"))
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
if FLAGS.checkpoints:
# Get the checkpoints list from flags and run some basic checks.
checkpoints = [c.strip() for c in FLAGS.checkpoints.split(",")]
checkpoints = [c for c in checkpoints if c]
if not checkpoints:
raise ValueError("No checkpoints provided for averaging.")
if FLAGS.prefix:
checkpoints = [FLAGS.prefix + c for c in checkpoints]
else:
assert FLAGS.num_last_checkpoints >= 1, "Must average at least one model"
assert FLAGS.prefix, ("Prefix must be provided when averaging last"
" N checkpoints")
checkpoint_state = tf.train.get_checkpoint_state(
os.path.dirname(FLAGS.prefix))
# Checkpoints are ordered from oldest to newest.
checkpoints = checkpoint_state.all_model_checkpoint_paths[
-FLAGS.num_last_checkpoints:]
checkpoints = [c for c in checkpoints if checkpoint_exists(c)]
if not checkpoints:
if FLAGS.checkpoints:
raise ValueError(
"None of the provided checkpoints exist. %s" % FLAGS.checkpoints)
else:
raise ValueError("Could not find checkpoints at %s" %
os.path.dirname(FLAGS.prefix))
# Read variables from all checkpoints and average them.
tf.logging.info("Reading variables and averaging checkpoints:")
for c in checkpoints:
tf.logging.info("%s ", c)
var_list = tf.contrib.framework.list_variables(checkpoints[0])
var_values, var_dtypes = {}, {}
for (name, shape) in var_list:
if not name.startswith("global_step"):
var_values[name] = np.zeros(shape)
for checkpoint in checkpoints:
reader = tf.contrib.framework.load_checkpoint(checkpoint)
for name in var_values:
tensor = reader.get_tensor(name)
var_dtypes[name] = tensor.dtype
var_values[name] += tensor
tf.logging.info("Read from checkpoint %s", checkpoint)
for name in var_values: # Average.
var_values[name] /= len(checkpoints)
with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
tf_vars = [
tf.get_variable(v, shape=var_values[v].shape, dtype=var_dtypes[v])
for v in var_values
]
placeholders = [tf.placeholder(v.dtype, shape=v.shape) for v in tf_vars]
assign_ops = [tf.assign(v, p) for (v, p) in zip(tf_vars, placeholders)]
global_step = tf.Variable(
0, name="global_step", trainable=False, dtype=tf.int64)
saver = tf.train.Saver(tf.all_variables())
# Build a model consisting only of variables, set them to the average values.
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for p, assign_op, (name, value) in zip(placeholders, assign_ops,
six.iteritems(var_values)):
sess.run(assign_op, {p: value})
# Use the built saver to save the averaged checkpoint.
saver.save(sess, FLAGS.output_path, global_step=global_step)
tf.logging.info("Averaged checkpoints saved in %s", FLAGS.output_path)
if __name__ == "__main__":
tf.app.run()

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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import os
from functools import partial
from collections import Counter, OrderedDict
import pickle
import json
import multiprocessing as mp
import numpy as np
from absl import flags
import tensorflow as tf
from vocabulary import Vocab
from tensorflow.gfile import Exists as exists
from tensorflow.gfile import MakeDirs as makedirs
from tensorflow.gfile import Glob as glob
def _preprocess(shard, train, vocab, save_dir, cutoffs, bin_sizes, bsz, tgt_len,
num_core_per_host, use_tpu, num_shuffle):
file_names = []
num_batch = 0
path = train[shard]
data_shard = vocab.encode_file(path, ordered=False, add_double_eos=True)
for shuffle in range(num_shuffle):
basename = "train-{:03d}-{:02d}".format(shard, shuffle)
print("Processing shard {} shuffle {}".format(shard, shuffle))
np.random.shuffle(data_shard)
file_name, num_batch_shuffle = create_ordered_tfrecords(
save_dir, basename, np.concatenate(data_shard), bsz, tgt_len,
num_core_per_host, cutoffs, bin_sizes, use_tpu=use_tpu)
file_names.append(file_name)
num_batch += num_batch_shuffle
return file_names, num_batch
class Corpus(object):
def __init__(self, path, dataset, *args, **kwargs):
self.dataset = dataset
self.vocab = Vocab(*args, **kwargs)
if self.dataset in ["ptb", "wt2", "enwik8", "text8"]:
self.vocab.count_file(os.path.join(path, "train.txt"))
self.vocab.count_file(os.path.join(path, "valid.txt"))
self.vocab.count_file(os.path.join(path, "test.txt"))
elif self.dataset == "wt103":
self.vocab.count_file(os.path.join(path, "train.txt"))
elif self.dataset == "lm1b":
train_path_pattern = os.path.join(
path, "1-billion-word-language-modeling-benchmark-r13output",
"training-monolingual.tokenized.shuffled", "news.en-*")
train_paths = glob(train_path_pattern)
# the vocab will load from file when build_vocab() is called
# for train_path in sorted(train_paths):
# self.vocab.count_file(train_path, verbose=True)
self.vocab.build_vocab()
if self.dataset in ["ptb", "wt2", "wt103"]:
self.train = self.vocab.encode_file(
os.path.join(path, "train.txt"), ordered=True)
self.valid = self.vocab.encode_file(
os.path.join(path, "valid.txt"), ordered=True)
self.test = self.vocab.encode_file(
os.path.join(path, "test.txt"), ordered=True)
elif self.dataset in ["enwik8", "text8"]:
self.train = self.vocab.encode_file(
os.path.join(path, "train.txt"), ordered=True, add_eos=False)
self.valid = self.vocab.encode_file(
os.path.join(path, "valid.txt"), ordered=True, add_eos=False)
self.test = self.vocab.encode_file(
os.path.join(path, "test.txt"), ordered=True, add_eos=False)
elif self.dataset == "lm1b":
self.train = train_paths
valid_path = os.path.join(path, "valid.txt")
test_path = valid_path
self.valid = self.vocab.encode_file(
valid_path, ordered=True, add_double_eos=True)
self.test = self.vocab.encode_file(
test_path, ordered=True, add_double_eos=True)
if self.dataset == "wt103":
self.cutoffs = [0, 20000, 40000, 200000] + [len(self.vocab)]
elif self.dataset == "lm1b":
self.cutoffs = [0, 60000, 100000, 640000] + [len(self.vocab)]
else:
self.cutoffs = []
def convert_to_tfrecords(self, split, save_dir, bsz, tgt_len,
num_core_per_host, **kwargs):
FLAGS = kwargs.get('FLAGS')
file_names = []
use_tpu = FLAGS.use_tpu and not (split == "test" and num_core_per_host == 1)
if use_tpu:
record_name = "record_info-{}.bsz-{}.tlen-{}.core-{}.json".format(
split, bsz, tgt_len, num_core_per_host)
else:
record_name = "record_info-{}.bsz-{}.tlen-{}.json".format(
split, bsz, tgt_len)
record_info_path = os.path.join(save_dir, record_name)
if self.dataset in ["ptb", "wt2", "wt103", "enwik8", "text8"]:
data = getattr(self, split)
bin_sizes = get_bin_sizes(
data, bsz // num_core_per_host, tgt_len, self.cutoffs)
file_name, num_batch = create_ordered_tfrecords(
save_dir, split, data, bsz, tgt_len, num_core_per_host,
self.cutoffs, bin_sizes,
num_passes=FLAGS.num_passes if split == 'train' and use_tpu else 1,
use_tpu=use_tpu)
file_names.append(file_name)
elif self.dataset == "lm1b":
bin_sizes = get_bin_sizes(
self.valid, bsz // num_core_per_host, tgt_len, self.cutoffs)
if split == "train":
np.random.seed(123456)
num_batch = 0
if FLAGS.num_procs > 1:
_preprocess_wrapper = partial(_preprocess,
train=self.train, vocab=self.vocab, save_dir=save_dir,
cutoffs=self.cutoffs, bin_sizes=bin_sizes, bsz=bsz,
tgt_len=tgt_len, num_core_per_host=num_core_per_host,
use_tpu=use_tpu, num_shuffle=FLAGS.num_shuffle)
pool = mp.Pool(processes=FLAGS.num_procs)
results = pool.map(_preprocess_wrapper, range(len(self.train)))
for res in results:
file_names.extend(res[0])
num_batch += res[1]
else:
for shard, path in enumerate(self.train):
data_shard = self.vocab.encode_file(path, ordered=False,
add_double_eos=True)
num_shuffle = FLAGS.num_shuffle
for shuffle in range(num_shuffle):
print("Processing shard {} shuffle {}".format(shard, shuffle))
basename = "train-{:03d}-{:02d}".format(shard, shuffle)
np.random.shuffle(data_shard)
file_name, num_batch_ = create_ordered_tfrecords(
save_dir, basename, np.concatenate(data_shard), bsz, tgt_len,
num_core_per_host,
self.cutoffs, bin_sizes, use_tpu=use_tpu)
file_names.append(file_name)
num_batch += num_batch_
else:
file_name, num_batch = create_ordered_tfrecords(
save_dir, split, getattr(self, split), bsz, tgt_len,
num_core_per_host,
self.cutoffs, bin_sizes, use_tpu=use_tpu)
file_names.append(file_name)
with open(record_info_path, "w") as fp:
record_info = {
"filenames": file_names,
"bin_sizes": bin_sizes,
"num_batch": num_batch
}
json.dump(record_info, fp)
def get_bin_sizes(data, batch_size, tgt_len, cutoffs, std_mult=[2.5, 2.5, 2.5]):
"""
Note: the `batch_size` here should be per-core batch size
"""
bin_sizes = []
def _nearest_to_eight(x): # so that it's faster on TPUs
y = x - x % 8
return y + 8 if x % 8 >= 4 else max(8, y)
if cutoffs:
num_batch = len(data) // batch_size // tgt_len
data = data[:batch_size * num_batch * tgt_len]
data = data.reshape(batch_size, num_batch, tgt_len)
tot = batch_size * tgt_len
for b, (left, right) in enumerate(zip(cutoffs[1:-1], cutoffs[2:])):
mask = (data >= left) * (data < right)
percents = mask.astype(np.float64).sum(2).sum(0) / tot
mean = np.mean(percents)
std = np.std(percents)
bin_size = int(math.ceil(tgt_len * batch_size * (mean + std_mult[b] * std)))
bin_size = _nearest_to_eight(bin_size)
bin_sizes.append(bin_size)
return bin_sizes
def _int64_feature(values):
return tf.train.Feature(int64_list=tf.train.Int64List(value=values))
def _float_feature(values):
return tf.train.Feature(float_list=tf.train.FloatList(value=values))
def batchify(data, batch_size, num_passes):
"""
if use_tpu = True: num_passes > 1
Since TPU training requires entire [bsz x tgt_len] chunks, it can discard
as many as `bsz * tgt_len` tokens in training. When `bsz` and `tgt_len` are
both large, as in the case of TPU training for Transformer-XL, the problem
may lead to detectable performance drop.
Here, we use multiple randomly shifted copies to deal with this problem.
"""
if num_passes > 1:
data_len = len(data)
double_data = np.concatenate([data, data])
data_list = []
for i in range(num_passes):
start = np.random.randint(0, data_len)
data_list.append(double_data[start:start+data_len])
data = np.concatenate(data_list)
num_step = len(data) // batch_size
data = data[:batch_size * num_step]
data = data.reshape(batch_size, num_step)
return data
def create_ordered_tfrecords(save_dir, basename, data, batch_size, tgt_len,
num_core_per_host, cutoffs=[], bin_sizes=[],
num_passes=1, use_tpu=False):
if use_tpu:
file_name = "{}.bsz-{}.tlen-{}.core-{}.tfrecords".format(
basename, batch_size, tgt_len, num_core_per_host)
else:
file_name = "{}.bsz-{}.tlen-{}.tfrecords".format(
basename, batch_size, tgt_len)
save_path = os.path.join(save_dir, file_name)
record_writer = tf.python_io.TFRecordWriter(save_path)
batched_data = batchify(data, batch_size, num_passes)
num_batch = 0
# for t in range(0, batched_data.shape[1] - tgt_len - 1, tgt_len):
for t in range(0, batched_data.shape[1] - 1, tgt_len):
cur_tgt_len = min(batched_data.shape[1] - 1 - t, tgt_len)
# drop the remainder if use tpu
if use_tpu and cur_tgt_len < tgt_len:
break
if num_batch % 500 == 0:
print(" processing batch {}".format(num_batch))
for idx in range(batch_size):
inputs = batched_data[idx, t:t + cur_tgt_len]
labels = batched_data[idx, t + 1:t + cur_tgt_len + 1]
# features dict
feature = {
"inputs": _int64_feature(inputs),
"labels": _int64_feature(labels),
}
if len(cutoffs) > 0 and use_tpu:
# validate `bin_sizes` and `cutoffs`
assert len(cutoffs) - len(bin_sizes) == 2, \
"len(cutoffs) - len(bin_sizes) != 2"
# mask for bin 0
left, right = cutoffs[:2]
inp_mask = ((inputs >= left) * (inputs < right)).astype(np.float32)
tgt_mask = ((labels >= left) * (labels < right)).astype(np.float32)
feature["inp_mask"] = _float_feature(inp_mask)
feature["tgt_mask"] = _float_feature(tgt_mask)
# refresh `inp_cnts` and `tgt_cnts` for each TPU core
if idx % (batch_size // num_core_per_host) == 0:
inp_cnts = [0] * len(bin_sizes)
tgt_cnts = [0] * len(bin_sizes)
head_labels = np.copy(labels)
inp_pos_per_bin, tgt_pos_per_bin = [], []
for b, (left, right) in enumerate(zip(cutoffs[1:-1], cutoffs[2:])):
inp_pos = np.where((inputs >= left) * (inputs < right))[0]
tgt_pos = np.where((labels >= left) * (labels < right))[0]
inp_pos_per_bin.append(inp_pos)
tgt_pos_per_bin.append(tgt_pos)
head_labels[tgt_pos] = cutoffs[1] + b
feature["head_labels"] = _int64_feature(head_labels)
# permutation feature
def _add_perm_feature(feature, pos_per_bin, cnts, prefix):
for b, pos in enumerate(pos_per_bin):
idx_tuple = []
for p in pos:
if cnts[b] < bin_sizes[b]:
idx_tuple.append([p, cnts[b]])
cnts[b] += 1
else:
break
n_tup = len(idx_tuple)
tup = np.array(idx_tuple).reshape(n_tup * 2)
feature["{}_cnt_{}".format(prefix, b)] = _int64_feature([n_tup])
feature["{}_tup_{}".format(prefix, b)] = _int64_feature(tup)
_add_perm_feature(feature, inp_pos_per_bin, inp_cnts, "inp")
_add_perm_feature(feature, tgt_pos_per_bin, tgt_cnts, "tgt")
example = tf.train.Example(features=tf.train.Features(feature=feature))
record_writer.write(example.SerializeToString())
num_batch += 1
record_writer.close()
print("Done writing {}. batches: {}".format(file_name, num_batch))
return file_name, num_batch
def get_lm_corpus(data_dir, dataset):
fn = os.path.join(data_dir, "cache.pkl")
if exists(fn):
print("Loading cached dataset...")
with open(fn, "rb") as fp:
corpus = pickle.load(fp)
else:
print("Producing dataset...")
kwargs = {}
if dataset in ["wt103", "wt2"]:
kwargs["special"] = ["<eos>"]
kwargs["lower_case"] = False
elif dataset == "ptb":
kwargs["special"] = ["<eos>"]
kwargs["lower_case"] = True
elif dataset == "lm1b":
kwargs["special"] = []
kwargs["lower_case"] = False
kwargs["vocab_file"] = os.path.join(data_dir, "1b_word_vocab.txt")
elif dataset in ["enwik8", "text8"]:
pass
corpus = Corpus(data_dir, dataset, **kwargs)
print("Saving dataset...")
with open(fn, "wb") as fp:
pickle.dump(corpus, fp, protocol=2)
corpus_info = {
"vocab_size" : len(corpus.vocab),
"cutoffs" : corpus.cutoffs,
"dataset" : corpus.dataset
}
with open(os.path.join(data_dir, "corpus-info.json"), "w") as fp:
json.dump(corpus_info, fp)
return corpus
def main(unused_argv):
del unused_argv # Unused
corpus = get_lm_corpus(FLAGS.data_dir, FLAGS.dataset)
save_dir = os.path.join(FLAGS.data_dir, "tfrecords")
if not exists(save_dir):
makedirs(save_dir)
# test mode
if FLAGS.per_host_test_bsz > 0:
corpus.convert_to_tfrecords("test", save_dir, FLAGS.per_host_test_bsz,
FLAGS.tgt_len, FLAGS.num_core_per_host,
FLAGS=FLAGS)
return
for split, batch_size in zip(
["train", "valid"],
[FLAGS.per_host_train_bsz, FLAGS.per_host_valid_bsz]):
if batch_size <= 0: continue
print("Converting {} set...".format(split))
corpus.convert_to_tfrecords(split, save_dir, batch_size, FLAGS.tgt_len,
FLAGS.num_core_per_host, FLAGS=FLAGS)
def load_record_info(record_info_dir, split, per_host_bsz, tgt_len,
num_core_per_host, use_tpu):
if use_tpu:
record_name = "record_info-{}.bsz-{}.tlen-{}.core-{}.json".format(
split, per_host_bsz, tgt_len, num_core_per_host)
else:
record_name = "record_info-{}.bsz-{}.tlen-{}.json".format(
split, per_host_bsz, tgt_len)
record_info_path = os.path.join(record_info_dir, record_name)
with open(record_info_path, "r") as fp:
record_info = json.load(fp)
return record_info
def get_input_fn(record_info_dir, split, per_host_bsz, tgt_len,
num_core_per_host, num_hosts=1, use_tpu=False):
"""Creates input function."""
record_info = load_record_info(record_info_dir, split, per_host_bsz, tgt_len,
num_core_per_host, use_tpu=use_tpu)
file_names = record_info["filenames"]
bin_sizes = record_info["bin_sizes"]
num_batch = record_info["num_batch"]
tf.logging.info("[{}] File names {}".format(split, file_names))
def input_fn(params):
# per-core batch size
per_core_bsz = params["batch_size"]
# data_dir could be a remote path, e.g., a google storage url
data_dir = params["data_dir"]
def parser(record):
# preprocess "inp_perm" and "tgt_perm"
def _process_perm_feature(example, prefix):
for b in range(len(bin_sizes)):
cnt = example.pop("{}_cnt_{}".format(prefix, b))[0]
tup = example.pop("{}_tup_{}".format(prefix, b))
tup = tf.reshape(
tf.sparse_tensor_to_dense(tup),
shape=[cnt, 2])
# tf.float32
perm = tf.sparse_to_dense(
sparse_indices=tup,
output_shape=[tgt_len, bin_sizes[b]],
sparse_values=1.0,
default_value=0.0)
example["{}_perm_{}".format(prefix, b)] = perm
# whether allow the last batch with a potentially shorter length
if use_tpu:
record_spec = {
"inputs": tf.FixedLenFeature([tgt_len], tf.int64),
"labels": tf.FixedLenFeature([tgt_len], tf.int64),
}
else:
record_spec = {
"inputs": tf.VarLenFeature(tf.int64),
"labels": tf.VarLenFeature(tf.int64),
}
# permutation related features
if bin_sizes and use_tpu:
# tf.float32
record_spec["inp_mask"] = tf.FixedLenFeature([tgt_len], tf.float32)
record_spec["tgt_mask"] = tf.FixedLenFeature([tgt_len], tf.float32)
record_spec["head_labels"] = tf.FixedLenFeature([tgt_len], tf.int64)
for b in range(len(bin_sizes)):
record_spec["inp_cnt_{}".format(b)] = tf.FixedLenFeature([1], tf.int64)
record_spec["inp_tup_{}".format(b)] = tf.VarLenFeature(tf.int64)
record_spec["tgt_cnt_{}".format(b)] = tf.FixedLenFeature([1], tf.int64)
record_spec["tgt_tup_{}".format(b)] = tf.VarLenFeature(tf.int64)
# retrieve serialized example
example = tf.parse_single_example(
serialized=record,
features=record_spec)
# transform permutation tuples to permutation matrices
if bin_sizes and use_tpu:
_process_perm_feature(example, "inp")
_process_perm_feature(example, "tgt")
# cast int64 into int32
# cast sparse to dense
for key in list(example.keys()):
val = example[key]
if tf.keras.backend.is_sparse(val):
val = tf.sparse.to_dense(val)
if val.dtype == tf.int64:
val = tf.to_int32(val)
example[key] = val
if use_tpu:
return example
else:
return example["inputs"], example["labels"]
file_paths = []
for file_name in file_names:
file_path = os.path.join(data_dir, file_name)
file_paths.append(file_path)
if split == "train":
dataset = tf.data.Dataset.from_tensor_slices(file_paths)
if len(file_paths) > 1:
dataset = dataset.shuffle(len(file_paths)).repeat()
dataset = tf.data.TFRecordDataset(dataset)
elif num_hosts > 1:
host_id = params["context"].current_host
# drop the remaining batches
num_batch_per_host = num_batch // num_hosts
my_start_sample_id = (host_id * num_batch_per_host * num_core_per_host *
per_core_bsz)
my_sample_num = num_batch_per_host * num_core_per_host * per_core_bsz
dataset = tf.data.TFRecordDataset(dataset).skip(
my_start_sample_id).take(my_sample_num)
else:
dataset = tf.data.TFRecordDataset(dataset)
dataset = dataset.map(parser).cache().repeat()
dataset = dataset.batch(per_core_bsz, drop_remainder=True)
dataset = dataset.prefetch(num_core_per_host * per_core_bsz)
else:
# do not shuffle, repeat or cache in evaluation
dataset = tf.data.Dataset.from_tensor_slices(file_paths)
dataset = tf.data.TFRecordDataset(dataset)
dataset = dataset.map(parser)
dataset = dataset.batch(per_core_bsz, drop_remainder=True)
return dataset
if split == "train" and num_hosts > 1:
record_info["num_batch"] = num_batch // num_hosts
return input_fn, record_info
def get_corpus_info(corpus_info_path):
with open(corpus_info_path, "r") as fp:
corpus_info = json.load(fp)
return corpus_info
if __name__ == "__main__":
FLAGS = flags.FLAGS
flags.DEFINE_string("data_dir", None,
help="Location of the data corpus")
flags.DEFINE_enum("dataset", "wt103",
["ptb", "wt2", "wt103", "lm1b", "enwik8", "text8"],
help="Dataset name.")
flags.DEFINE_integer("per_host_train_bsz", 60,
help="train batch size each host")
flags.DEFINE_integer("per_host_valid_bsz", 60,
help="valid batch size each host")
flags.DEFINE_integer("per_host_test_bsz", 0,
help="If > 0, enter test mode and process test set only."
"Otherwise, process train and dev sets only.")
flags.DEFINE_integer("tgt_len", 70,
help="number of tokens to predict")
flags.DEFINE_integer("max_batch", -1,
help="run in debug mode")
flags.DEFINE_integer("num_core_per_host", 8,
help="8 for TPU v2.")
flags.DEFINE_bool("debug", default=False,
help="Process only the first batch without shuffle for lm1b.")
flags.DEFINE_integer("num_procs", 1,
help="number of processes")
flags.DEFINE_integer("num_passes", 10,
help="number of passes when use_tpu=True")
flags.DEFINE_integer("num_shuffle", 4,
help="number of shuffles for lm1b")
flags.DEFINE_bool("use_tpu", True,
help="use tpu")
tf.app.run(main)

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import os
import tensorflow as tf
def assign_to_gpu(gpu=0, ps_dev="/device:CPU:0"):
def _assign(op):
node_def = op if isinstance(op, tf.NodeDef) else op.node_def
if node_def.op == "Variable":
return ps_dev
else:
return "/gpu:%d" % gpu
return _assign
def average_grads_and_vars(tower_grads_and_vars):
def average_dense(grad_and_vars):
if len(grad_and_vars) == 1:
return grad_and_vars[0][0]
grad = grad_and_vars[0][0]
for g, _ in grad_and_vars[1:]:
grad += g
return grad / len(grad_and_vars)
def average_sparse(grad_and_vars):
if len(grad_and_vars) == 1:
return grad_and_vars[0][0]
indices = []
values = []
for g, _ in grad_and_vars:
indices += [g.indices]
values += [g.values]
indices = tf.concat(indices, 0)
values = tf.concat(values, 0) / len(grad_and_vars)
return tf.IndexedSlices(values, indices, grad_and_vars[0][0].dense_shape)
average_grads_and_vars = []
for grad_and_vars in zip(*tower_grads_and_vars):
if grad_and_vars[0][0] is None:
grad = None
elif isinstance(grad_and_vars[0][0], tf.IndexedSlices):
grad = average_sparse(grad_and_vars)
else:
grad = average_dense(grad_and_vars)
# Keep in mind that the Variables are redundant because they are shared
# across towers. So .. we will just return the first tower's pointer to
# the Variable.
v = grad_and_vars[0][1]
grad_and_var = (grad, v)
average_grads_and_vars.append(grad_and_var)
return average_grads_and_vars
def load_from_checkpoint(saver, logdir):
sess = tf.get_default_session()
ckpt = tf.train.get_checkpoint_state(logdir)
if ckpt and ckpt.model_checkpoint_path:
if os.path.isabs(ckpt.model_checkpoint_path):
# Restores from checkpoint with absolute path.
saver.restore(sess, ckpt.model_checkpoint_path)
else:
# Restores from checkpoint with relative path.
saver.restore(sess, os.path.join(logdir, ckpt.model_checkpoint_path))
return True
return False

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import tensorflow as tf
def positional_embedding(pos_seq, inv_freq, bsz=None):
sinusoid_inp = tf.einsum('i,j->ij', pos_seq, inv_freq)
pos_emb = tf.concat([tf.sin(sinusoid_inp), tf.cos(sinusoid_inp)], -1)
if bsz is not None:
return tf.tile(pos_emb[:, None, :], [1, bsz, 1])
else:
return pos_emb[:, None, :]
def positionwise_FF(inp, d_model, d_inner, dropout, kernel_initializer,
scope='ff', is_training=True):
output = inp
with tf.variable_scope(scope):
output = tf.layers.dense(inp, d_inner, activation=tf.nn.relu,
kernel_initializer=kernel_initializer,
name='layer_1')
output = tf.layers.dropout(output, dropout, training=is_training,
name='drop_1')
output = tf.layers.dense(output, d_model,
kernel_initializer=kernel_initializer,
name='layer_2')
output = tf.layers.dropout(output, dropout, training=is_training,
name='drop_2')
output = tf.contrib.layers.layer_norm(output + inp, begin_norm_axis=-1)
return output
def rel_shift(x):
x_size = tf.shape(x)
x = tf.pad(x, [[0, 0], [1, 0], [0, 0], [0, 0]])
x = tf.reshape(x, [x_size[1] + 1, x_size[0], x_size[2], x_size[3]])
x = tf.slice(x, [1, 0, 0, 0], [-1, -1, -1, -1])
x = tf.reshape(x, x_size)
return x
def rel_multihead_attn(w, r, r_w_bias, r_r_bias, attn_mask, mems, d_model,
n_head, d_head, dropout, dropatt, is_training,
kernel_initializer, scope='rel_attn'):
scale = 1 / (d_head ** 0.5)
with tf.variable_scope(scope):
qlen = tf.shape(w)[0]
rlen = tf.shape(r)[0]
bsz = tf.shape(w)[1]
cat = tf.concat([mems, w],
0) if mems is not None and mems.shape.ndims > 1 else w
w_heads = tf.layers.dense(cat, 3 * n_head * d_head, use_bias=False,
kernel_initializer=kernel_initializer, name='qkv')
r_head_k = tf.layers.dense(r, n_head * d_head, use_bias=False,
kernel_initializer=kernel_initializer, name='r')
w_head_q, w_head_k, w_head_v = tf.split(w_heads, 3, -1)
w_head_q = w_head_q[-qlen:]
klen = tf.shape(w_head_k)[0]
w_head_q = tf.reshape(w_head_q, [qlen, bsz, n_head, d_head])
w_head_k = tf.reshape(w_head_k, [klen, bsz, n_head, d_head])
w_head_v = tf.reshape(w_head_v, [klen, bsz, n_head, d_head])
r_head_k = tf.reshape(r_head_k, [rlen, n_head, d_head])
rw_head_q = w_head_q + r_w_bias
rr_head_q = w_head_q + r_r_bias
AC = tf.einsum('ibnd,jbnd->ijbn', rw_head_q, w_head_k)
BD = tf.einsum('ibnd,jnd->ijbn', rr_head_q, r_head_k)
BD = rel_shift(BD)
attn_score = (AC + BD) * scale
attn_mask_t = attn_mask[:, :, None, None]
attn_score = attn_score * (1 - attn_mask_t) - 1e30 * attn_mask_t
attn_prob = tf.nn.softmax(attn_score, 1)
attn_prob = tf.layers.dropout(attn_prob, dropatt, training=is_training)
attn_vec = tf.einsum('ijbn,jbnd->ibnd', attn_prob, w_head_v)
size_t = tf.shape(attn_vec)
attn_vec = tf.reshape(attn_vec, [size_t[0], size_t[1], n_head * d_head])
attn_out = tf.layers.dense(attn_vec, d_model, use_bias=False,
kernel_initializer=kernel_initializer, name='o')
attn_out = tf.layers.dropout(attn_out, dropout, training=is_training)
output = tf.contrib.layers.layer_norm(attn_out + w, begin_norm_axis=-1)
return output
def embedding_lookup(lookup_table, x, use_tpu=True):
if use_tpu:
n_token = tf.shape(lookup_table)[0]
one_hot_idx = tf.one_hot(x, n_token)
if one_hot_idx.shape.ndims == 2:
return tf.einsum('nd,in->id', lookup_table, one_hot_idx)
else:
return tf.einsum('nd,ibn->ibd', lookup_table, one_hot_idx)
else:
return tf.nn.embedding_lookup(lookup_table, x)
def mask_adaptive_embedding_lookup(x, n_token, d_embed, d_proj, cutoffs, initializer,
proj_initializer, div_val=1,
proj_same_dim=True,
scope='adaptive_embed', **kwargs):
emb_scale = d_proj ** 0.5
with tf.variable_scope(scope):
if div_val == 1:
lookup_table = tf.get_variable('lookup_table', [n_token, d_embed],
initializer=initializer)
y = embedding_lookup(lookup_table, x, use_tpu=False)
if d_proj != d_embed:
proj_W = tf.get_variable('proj_W', [d_embed, d_proj],
initializer=proj_initializer)
y = tf.einsum('ibe,ed->ibd', y, proj_W)
else:
proj_W = None
ret_params = [lookup_table, proj_W]
else:
tables, projs = [], []
cutoff_ends = [0] + cutoffs + [n_token]
x_size = tf.shape(x)
y = tf.zeros([x_size[0], x_size[1], d_proj])
for i in range(len(cutoff_ends) - 1):
with tf.variable_scope('cutoff_{}'.format(i)):
l_idx, r_idx = cutoff_ends[i], cutoff_ends[i + 1]
mask = (x >= l_idx) & (x < r_idx)
cur_x = tf.boolean_mask(x, mask) - l_idx
cur_d_embed = d_embed // (div_val ** i)
lookup_table = tf.get_variable('lookup_table',
[r_idx - l_idx, cur_d_embed],
initializer=initializer)
cur_y = embedding_lookup(lookup_table, cur_x, use_tpu=False)
if d_proj == cur_d_embed and not proj_same_dim:
proj_W = None
else:
proj_W = tf.get_variable('proj_W', [cur_d_embed, d_proj],
initializer=proj_initializer)
cur_y = tf.einsum('id,de->ie', cur_y, proj_W)
mask_idx = tf.to_int64(tf.where(mask))
y += tf.scatter_nd(mask_idx, cur_y, tf.to_int64(tf.shape(y)))
tables.append(lookup_table)
projs.append(proj_W)
ret_params = [tables, projs]
y *= emb_scale
return y, ret_params
def mul_adaptive_embedding_lookup(x, n_token, d_embed, d_proj, cutoffs, initializer,
proj_initializer, div_val=1, perms=None,
proj_same_dim=True,
scope='adaptive_embed'):
"""
perms: If None, first compute W = W1 x W2 (projection for each bin),
and then compute X x W (embedding lookup). If not None,
use bin-based embedding lookup with max_bin_size defined by
the shape of perms.
"""
emb_scale = d_proj ** 0.5
with tf.variable_scope(scope):
if div_val == 1:
lookup_table = tf.get_variable('lookup_table', [n_token, d_embed],
initializer=initializer)
y = embedding_lookup(lookup_table, x)
if d_proj != d_embed:
proj_W = tf.get_variable('proj_W', [d_embed, d_proj],
initializer=proj_initializer)
y = tf.einsum('ibe,ed->ibd', y, proj_W)
else:
proj_W = None
ret_params = [lookup_table, proj_W]
else:
tables, projs = [], []
cutoff_ends = [0] + cutoffs + [n_token]
x_size = tf.shape(x)
if perms is None:
cat_lookup = []
else:
cat_lookup = tf.zeros([x_size[0], x_size[1], d_proj])
for i in range(len(cutoff_ends) - 1):
with tf.variable_scope('cutoff_{}'.format(i)):
l_idx, r_idx = cutoff_ends[i], cutoff_ends[i + 1]
cur_d_embed = d_embed // (div_val ** i)
lookup_table = tf.get_variable('lookup_table',
[r_idx - l_idx, cur_d_embed],
initializer=initializer)
if cur_d_embed == d_proj and not proj_same_dim:
proj_W = None
else:
proj_W = tf.get_variable('proj_W', [cur_d_embed, d_proj],
initializer=proj_initializer)
if perms is None:
cat_lookup.append(tf.einsum('ie,ed->id', lookup_table, proj_W))
else:
# speed up the computation of the first bin
# also save some meory
if i == 0:
cur_y = embedding_lookup(lookup_table, tf.minimum(x, r_idx - 1))
if proj_W is not None:
cur_y = tf.einsum('ibe,ed->ibd', cur_y, proj_W)
cur_y *= perms[i][:, :, None]
cat_lookup += cur_y
else:
cur_x = tf.einsum('ib,ibk->k', tf.to_float(x - l_idx), perms[i])
cur_x = tf.to_int32(cur_x)
cur_y = embedding_lookup(lookup_table, cur_x)
if proj_W is not None:
cur_y = tf.einsum('ke,ed->kd', cur_y, proj_W)
cat_lookup += tf.einsum('kd,ibk->ibd', cur_y, perms[i])
tables.append(lookup_table)
projs.append(proj_W)
if perms is None:
cat_lookup = tf.concat(cat_lookup, 0)
y = embedding_lookup(cat_lookup, x)
else:
y = cat_lookup
ret_params = [tables, projs]
y *= emb_scale
return y, ret_params
def mask_adaptive_logsoftmax(hidden, target, n_token, d_embed, d_proj, cutoffs,
params, tie_projs,
initializer=None, proj_initializer=None,
div_val=1, scope='adaptive_softmax',
proj_same_dim=True,
return_mean=True, **kwargs):
def _logit(x, W, b, proj):
y = x
if proj is not None:
y = tf.einsum('ibd,ed->ibe', y, proj)
return tf.einsum('ibd,nd->ibn', y, W) + b
params_W, params_projs = params[0], params[1]
def _gather_logprob(logprob, target):
lp_size = tf.shape(logprob)
r = tf.range(lp_size[0])
idx = tf.stack([r, target], 1)
return tf.gather_nd(logprob, idx)
with tf.variable_scope(scope):
if len(cutoffs) == 0:
softmax_b = tf.get_variable('bias', [n_token],
initializer=tf.zeros_initializer())
output = _logit(hidden, params_W, softmax_b, params_projs)
nll = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=target,
logits=output)
else:
cutoff_ends = [0] + cutoffs + [n_token]
nll = tf.zeros_like(target, dtype=tf.float32)
for i in range(len(cutoff_ends) - 1):
with tf.variable_scope('cutoff_{}'.format(i)):
l_idx, r_idx = cutoff_ends[i], cutoff_ends[i + 1]
mask = (target >= l_idx) & (target < r_idx)
mask_idx = tf.where(mask)
cur_target = tf.boolean_mask(target, mask) - l_idx
cur_d_embed = d_embed // (div_val ** i)
if div_val == 1:
cur_W = params_W[l_idx: r_idx]
else:
cur_W = params_W[i]
cur_b = tf.get_variable('b', [r_idx - l_idx],
initializer=tf.zeros_initializer())
if tie_projs[i]:
if div_val == 1:
cur_proj = params_projs
else:
cur_proj = params_projs[i]
else:
if (div_val == 1 or not proj_same_dim) and d_proj == cur_d_embed:
cur_proj = None
else:
cur_proj = tf.get_variable('proj', [cur_d_embed, d_proj],
initializer=proj_initializer)
if i == 0:
cluster_W = tf.get_variable('cluster_W', [len(cutoffs), d_embed],
initializer=tf.zeros_initializer())
cluster_b = tf.get_variable('cluster_b', [len(cutoffs)],
initializer=tf.zeros_initializer())
cur_W = tf.concat([cur_W, cluster_W], 0)
cur_b = tf.concat([cur_b, cluster_b], 0)
head_logit = _logit(hidden, cur_W, cur_b, cur_proj)
head_logprob = tf.nn.log_softmax(head_logit)
cur_head_logprob = tf.boolean_mask(head_logprob, mask)
cur_logprob = _gather_logprob(cur_head_logprob, cur_target)
else:
cur_head_logprob = tf.boolean_mask(head_logprob, mask)
cur_hidden = tf.boolean_mask(hidden, mask)
tail_logit = tf.squeeze(_logit(
cur_hidden[None], cur_W, cur_b, cur_proj), 0)
tail_logprob = tf.nn.log_softmax(tail_logit)
cur_logprob = (cur_head_logprob[:, cutoff_ends[1] + i - 1] +
_gather_logprob(tail_logprob, cur_target))
nll += tf.scatter_nd(mask_idx, -cur_logprob,
tf.to_int64(tf.shape(nll)))
if return_mean:
nll = tf.reduce_mean(nll)
return nll
def mul_adaptive_logsoftmax(hidden, target, n_token, d_embed, d_proj, cutoffs,
params, tie_projs,
initializer=None, proj_initializer=None,
div_val=1, perms=None, proj_same_dim=True,
scope='adaptive_softmax',
**kwargs):
def _logit(x, W, b, proj):
y = x
if x.shape.ndims == 3:
if proj is not None:
y = tf.einsum('ibd,ed->ibe', y, proj)
return tf.einsum('ibd,nd->ibn', y, W) + b
else:
if proj is not None:
y = tf.einsum('id,ed->ie', y, proj)
return tf.einsum('id,nd->in', y, W) + b
params_W, params_projs = params[0], params[1]
with tf.variable_scope(scope):
if len(cutoffs) == 0:
softmax_b = tf.get_variable('bias', [n_token],
initializer=tf.zeros_initializer())
output = _logit(hidden, params_W, softmax_b, params_projs)
nll = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=target,
logits=output)
nll = tf.reduce_mean(nll)
else:
total_loss, total_cnt = 0, 0
cutoff_ends = [0] + cutoffs + [n_token]
for i in range(len(cutoff_ends) - 1):
with tf.variable_scope('cutoff_{}'.format(i)):
l_idx, r_idx = cutoff_ends[i], cutoff_ends[i + 1]
cur_d_embed = d_embed // (div_val ** i)
if div_val == 1:
cur_W = params_W[l_idx: r_idx]
else:
cur_W = params_W[i]
cur_b = tf.get_variable('b', [r_idx - l_idx],
initializer=tf.zeros_initializer())
if tie_projs[i]:
if div_val == 1:
cur_proj = params_projs
else:
cur_proj = params_projs[i]
else:
if (div_val == 1 or not proj_same_dim) and d_proj == cur_d_embed:
cur_proj = None
else:
cur_proj = tf.get_variable('proj', [cur_d_embed, d_proj],
initializer=proj_initializer)
if i == 0:
cluster_W = tf.get_variable('cluster_W', [len(cutoffs), d_embed],
initializer=tf.zeros_initializer())
cluster_b = tf.get_variable('cluster_b', [len(cutoffs)],
initializer=tf.zeros_initializer())
cur_W = tf.concat([cur_W, cluster_W], 0)
cur_b = tf.concat([cur_b, cluster_b], 0)
head_logit = _logit(hidden, cur_W, cur_b, cur_proj)
head_target = kwargs.get("head_target")
head_nll = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=head_target,
logits=head_logit)
masked_loss = head_nll * perms[i]
total_loss += tf.reduce_sum(masked_loss)
total_cnt += tf.reduce_sum(perms[i])
# head_logprob = tf.nn.log_softmax(head_logit)
# final_logprob = head_logprob * perms[i][:, :, None]
# final_target = tf.one_hot(target, tf.shape(head_logprob)[2])
# total_loss -= tf.einsum('ibn,ibn->', final_logprob, final_target)
# total_cnt += tf.reduce_sum(perms[i])
else:
cur_head_nll = tf.einsum('ib,ibk->k', head_nll, perms[i])
cur_hidden = tf.einsum('ibd,ibk->kd', hidden, perms[i])
tail_logit = _logit(cur_hidden, cur_W, cur_b, cur_proj)
tail_target = tf.einsum('ib,ibk->k', tf.to_float(target - l_idx),
perms[i])
tail_nll = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=tf.to_int32(tail_target),
logits=tail_logit)
sum_nll = cur_head_nll + tail_nll
mask = tf.reduce_sum(perms[i], [0, 1])
masked_loss = sum_nll * mask
total_loss += tf.reduce_sum(masked_loss)
total_cnt += tf.reduce_sum(mask)
nll = total_loss / total_cnt
return nll
def _create_mask(qlen, mlen, same_length=False):
attn_mask = tf.ones([qlen, qlen])
mask_u = tf.matrix_band_part(attn_mask, 0, -1)
mask_dia = tf.matrix_band_part(attn_mask, 0, 0)
attn_mask_pad = tf.zeros([qlen, mlen])
ret = tf.concat([attn_mask_pad, mask_u - mask_dia], 1)
if same_length:
mask_l = tf.matrix_band_part(attn_mask, -1, 0)
ret = tf.concat([ret[:, :qlen] + mask_l - mask_dia, ret[:, qlen:]], 1)
return ret
def _cache_mem(curr_out, prev_mem, mem_len=None):
if mem_len is None or prev_mem is None:
new_mem = curr_out
elif mem_len == 0:
return prev_mem
else:
new_mem = tf.concat([prev_mem, curr_out], 0)[- mem_len:]
return tf.stop_gradient(new_mem)
def transformer(dec_inp, target, mems, n_token, n_layer, d_model, d_embed,
n_head, d_head, d_inner, dropout, dropatt,
initializer, is_training, proj_initializer=None,
mem_len=None, cutoffs=[], div_val=1, tie_projs=[],
same_length=False, clamp_len=-1, use_tpu=True,
input_perms=None, target_perms=None, head_target=None,
untie_r=False, proj_same_dim=True,
scope='transformer'):
"""
cutoffs: a list of python int. Cutoffs for adaptive softmax.
tie_projs: a list of python bools. Whether to tie the projections.
use_tpu: if True, use one_hot in embedding lookup and bin-based implementation
of adaptive softmax.
perms: a list of tensors. Each tensor should of size [len, bsz, bin_size].
Only used in the adaptive setting.
"""
new_mems = []
with tf.variable_scope(scope):
if untie_r:
r_w_bias = tf.get_variable('r_w_bias', [n_layer, n_head, d_head],
initializer=initializer)
r_r_bias = tf.get_variable('r_r_bias', [n_layer, n_head, d_head],
initializer=initializer)
else:
r_w_bias = tf.get_variable('r_w_bias', [n_head, d_head],
initializer=initializer)
r_r_bias = tf.get_variable('r_r_bias', [n_head, d_head],
initializer=initializer)
qlen = tf.shape(dec_inp)[0]
mlen = tf.shape(mems[0])[0] if mems is not None else 0
klen = mlen + qlen
if proj_initializer is None:
proj_initializer = initializer
lookup_fn = (mul_adaptive_embedding_lookup if use_tpu else
mask_adaptive_embedding_lookup)
embeddings, shared_params = lookup_fn(
x=dec_inp,
n_token=n_token,
d_embed=d_embed,
d_proj=d_model,
cutoffs=cutoffs,
initializer=initializer,
proj_initializer=proj_initializer,
div_val= div_val,
perms=input_perms,
proj_same_dim=proj_same_dim)
attn_mask = _create_mask(qlen, mlen, same_length)
pos_seq = tf.range(klen - 1, -1, -1.0)
if clamp_len > 0:
pos_seq = tf.minimum(pos_seq, clamp_len)
inv_freq = 1 / (10000 ** (tf.range(0, d_model, 2.0) / d_model))
pos_emb = positional_embedding(pos_seq, inv_freq)
output = tf.layers.dropout(embeddings, dropout, training=is_training)
pos_emb = tf.layers.dropout(pos_emb, dropout, training=is_training)
if mems is None:
mems = [None] * n_layer
for i in range(n_layer):
# cache new mems
new_mems.append(_cache_mem(output, mems[i], mem_len))
with tf.variable_scope('layer_{}'.format(i)):
output = rel_multihead_attn(
w=output,
r=pos_emb,
r_w_bias=r_w_bias if not untie_r else r_w_bias[i],
r_r_bias=r_r_bias if not untie_r else r_r_bias[i],
attn_mask=attn_mask,
mems=mems[i],
d_model=d_model,
n_head=n_head,
d_head=d_head,
dropout=dropout,
dropatt=dropatt,
is_training=is_training,
kernel_initializer=initializer)
output = positionwise_FF(
inp=output,
d_model=d_model,
d_inner=d_inner,
dropout=dropout,
kernel_initializer=initializer,
is_training=is_training)
output = tf.layers.dropout(output, dropout, training=is_training)
logsoftmax_fn = (mul_adaptive_logsoftmax if use_tpu else
mask_adaptive_logsoftmax)
loss = logsoftmax_fn(
hidden=output,
target=target,
n_token=n_token,
d_embed=d_embed,
d_proj=d_model,
cutoffs=cutoffs,
params=shared_params,
tie_projs=tie_projs,
initializer=initializer,
proj_initializer=proj_initializer,
div_val=div_val,
perms=target_perms,
head_target=head_target,
proj_same_dim=proj_same_dim)
return loss, new_mems

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#!/bin/bash
# Data
DATA_ROOT=../data/enwik8/
# Model
N_LAYER=12
D_MODEL=512
D_EMBED=512
N_HEAD=8
D_HEAD=64
D_INNER=2048
# Training
TGT_LEN=512
MEM_LEN=512
BSZ=24
NUM_CORE=4
# Testing
TEST_TGT_LEN=80
TEST_MEM_LEN=2100
TEST_CLAMP_LEN=820
TEST_BSZ=10
TEST_NUM_CORE=1
if [[ $1 == 'train_data' ]]; then
python data_utils.py \
--data_dir=${DATA_ROOT}/ \
--dataset=enwik8 \
--tgt_len=${TGT_LEN} \
--per_host_train_bsz=${BSZ} \
--per_host_valid_bsz=${BSZ} \
--num_passes=1 \
--use_tpu=False \
${@:2}
elif [[ $1 == 'test_data' ]]; then
python data_utils.py \
--data_dir=${DATA_ROOT}/ \
--dataset=enwik8 \
--tgt_len=${TEST_TGT_LEN} \
--per_host_test_bsz=${TEST_BSZ} \
--num_passes=1 \
--use_tpu=False \
${@:2}
elif [[ $1 == 'train' ]]; then
echo 'Run training...'
python train_gpu.py \
--data_dir=${DATA_ROOT}/tfrecords \
--record_info_dir=${DATA_ROOT}/tfrecords/ \
--corpus_info_path=${DATA_ROOT}/corpus-info.json \
--model_dir=EXP-enwik8 \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--dropout=0.1 \
--dropatt=0.0 \
--learning_rate=0.00025 \
--warmup_steps=0 \
--train_steps=400000 \
--tgt_len=${TGT_LEN} \
--mem_len=${MEM_LEN} \
--train_batch_size=${BSZ} \
--num_core_per_host=${NUM_CORE} \
--iterations=200 \
--save_steps=4000 \
--do_train=True \
--do_eval=False \
${@:2}
elif [[ $1 == 'eval' ]]; then
echo 'Run evaluation...'
python train_gpu.py \
--data_dir=${DATA_ROOT}/tfrecords \
--record_info_dir=${DATA_ROOT}/tfrecords/ \
--corpus_info_path=${DATA_ROOT}/corpus-info.json \
--model_dir=EXP-enwik8 \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--dropout=0.0 \
--dropatt=0.0 \
--tgt_len=${TEST_TGT_LEN} \
--mem_len=${TEST_MEM_LEN} \
--clamp_len=${TEST_CLAMP_LEN} \
--same_length=True \
--eval_batch_size=${TEST_BSZ} \
--num_core_per_host=${TEST_NUM_CORE} \
--do_train=False \
--do_eval=True \
--eval_split=test \
${@:2}
else
echo 'unknown argment 1'
fi

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#!/bin/bash
# Path
LOCAL_DIR=../data/enwik8/
GSDATA=
GSEXP=
# TPU setting
NUM_HOST=2
NUM_CORE=16 # TPUv2 -> 8 | TPUv3 -> 16
TEST_NUM_HOST=1
TEST_NUM_CORE=8 # TPUv2 -> 8 | TPUv3 -> 16
# Model
N_LAYER=24
D_MODEL=1024
D_EMBED=1024
N_HEAD=8
D_HEAD=128
D_INNER=3072
# Training
TGT_LEN=768
MEM_LEN=768
TRAIN_BSZ=64
VALID_BSZ=64
# Testing
TEST_TGT_LEN=128
TEST_MEM_LEN=3800
TEST_CLAMP_LEN=1000
TEST_BSZ=16
if [[ $1 == 'train_data' ]]; then
python data_utils.py \
--data_dir=${LOCAL_DIR}/ \
--dataset=enwik8 \
--tgt_len=${TGT_LEN} \
--per_host_train_bsz=${TRAIN_BSZ} \
--per_host_valid_bsz=${VALID_BSZ} \
--num_core_per_host=${NUM_CORE} \
--num_passes=10 \
--use_tpu=True \
${@:2}
SRC_PATTERN=train.bsz-${TRAIN_BSZ}.tlen-${TGT_LEN}.core-${NUM_CORE}*
gsutil cp ${LOCAL_DIR}/tfrecords/${SRC_PATTERN} ${GSDATA}/enwik8-tfrecords/
SRC_PATTERN=valid.bsz-${VALID_BSZ}.tlen-${TGT_LEN}.core-${NUM_CORE}*
gsutil cp ${LOCAL_DIR}/tfrecords/${SRC_PATTERN} ${GSDATA}/enwik8-tfrecords/
elif [[ $1 == 'test_data' ]]; then
python data_utils.py \
--data_dir=${LOCAL_DIR}/ \
--dataset=enwik8 \
--tgt_len=${TEST_TGT_LEN} \
--per_host_test_bsz=${TEST_BSZ} \
--num_core_per_host=${TEST_NUM_CORE} \
--num_passes=1 \
--use_tpu=True \
${@:2}
SRC_PATTERN=test.bsz-${TEST_BSZ}.tlen-${TEST_TGT_LEN}.core-${TEST_NUM_CORE}*
gsutil cp ${LOCAL_DIR}/tfrecords/${SRC_PATTERN} ${GSDATA}/enwik8-tfrecords/
elif [[ $1 == 'train' ]]; then
echo 'Run training...'
python train.py \
--data_dir=${GSDATA}/enwik8-tfrecords \
--record_info_dir=${LOCAL_DIR}/tfrecords/ \
--corpus_info_path=${LOCAL_DIR}/corpus-info.json \
--model_dir=${GSEXP}/enwik8 \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--dropout=0.15 \
--dropatt=0.15 \
--learning_rate=0.00025 \
--warmup_steps=4000 \
--train_steps=400000 \
--tgt_len=${TGT_LEN} \
--mem_len=${MEM_LEN} \
--train_batch_size=${TRAIN_BSZ} \
--use_tpu=True \
--num_host=${NUM_HOST} \
--num_core_per_host=${NUM_CORE} \
--iterations=1000 \
--save_steps=10000 \
--do_train=True \
--do_eval=False \
${@:2}
elif [[ $1 == 'eval' ]]; then
echo 'Run evaluation...'
python train.py \
--data_dir=${GSDATA}/enwik8-tfrecords \
--record_info_dir=${LOCAL_DIR}/tfrecords/ \
--corpus_info_path=${LOCAL_DIR}/corpus-info.json \
--model_dir=${GSEXP}/enwik8 \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--tgt_len=${TEST_TGT_LEN} \
--mem_len=${TEST_MEM_LEN} \
--eval_batch_size=${TEST_BSZ} \
--num_host=${TEST_NUM_HOST} \
--num_core_per_host=${TEST_NUM_CORE} \
--use_tpu=True \
--do_train=False \
--do_eval_only=True \
--eval_split=test \
${@:2}
else
echo 'unknown argment 1'
fi

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#!/bin/bash
# Data
DATA_ROOT=../data/one-billion-words/
# Model
DIV_VAL=4
N_LAYER=18
D_MODEL=1024
D_EMBED=1024
N_HEAD=8
D_HEAD=128
D_INNER=4096
# Training
TGT_LEN=256
MEM_LEN=256
BSZ=256
NUM_CORE=4
# Testing
TEST_TGT_LEN=32
TEST_MEM_LEN=128
TEST_CLAMP_LEN=-1
TEST_BSZ=16
TEST_NUM_CORE=1
if [[ $1 == 'train_data' ]]; then
python data_utils.py \
--data_dir=${DATA_ROOT}/ \
--dataset=lm1b \
--tgt_len=${TGT_LEN} \
--per_host_train_bsz=${BSZ} \
--per_host_valid_bsz=${BSZ} \
--num_passes=1 \
--use_tpu=False \
${@:2}
elif [[ $1 == 'test_data' ]]; then
python data_utils.py \
--data_dir=${DATA_ROOT}/ \
--dataset=lm1b \
--tgt_len=${TEST_TGT_LEN} \
--per_host_test_bsz=${TEST_BSZ} \
--num_passes=1 \
--use_tpu=False \
${@:2}
elif [[ $1 == 'train' ]]; then
echo 'Run training...'
python train_gpu.py \
--data_dir=${DATA_ROOT}/tfrecords \
--record_info_dir=${DATA_ROOT}/tfrecords/ \
--corpus_info_path=${DATA_ROOT}/corpus-info.json \
--model_dir=EXP-lm1b \
--div_val=${DIV_VAL} \
--untie_r=True \
--proj_share_all_but_first=False \
--proj_same_dim=False \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--dropout=0.1 \
--dropatt=0.0 \
--learning_rate=0.00025 \
--warmup_steps=0 \
--train_steps=400000 \
--tgt_len=${TGT_LEN} \
--mem_len=${MEM_LEN} \
--train_batch_size=${BSZ} \
--num_core_per_host=${NUM_CORE} \
--iterations=200 \
--save_steps=4000 \
${@:2}
elif [[ $1 == 'eval' ]]; then
echo 'Run evaluation...'
python train_gpu.py \
--data_dir=${DATA_ROOT}/tfrecords \
--record_info_dir=${DATA_ROOT}/tfrecords/ \
--corpus_info_path=${DATA_ROOT}/corpus-info.json \
--model_dir=EXP-lm1b \
--div_val=${DIV_VAL} \
--untie_r=True \
--proj_share_all_but_first=False \
--proj_same_dim=False \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--dropout=0.0 \
--dropatt=0.0 \
--tgt_len=${TEST_TGT_LEN} \
--mem_len=${TEST_MEM_LEN} \
--clamp_len=${TEST_CLAMP_LEN} \
--same_length=True \
--eval_batch_size=${TEST_BSZ} \
--num_core_per_host=${TEST_NUM_CORE} \
--do_train=False \
--do_eval=True \
--eval_split=test \
${@:2}
else
echo 'unknown argment 1'
fi

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#!/bin/bash
# Path
LOCAL_DIR=../data/one-billion-words/
GSDATA=
GSEXP=
# TPU setting
NUM_HOST=32
NUM_CORE=16 # TPUv2 -> 8 | TPUv3 -> 16
TEST_NUM_HOST=1
TEST_NUM_CORE=8 # TPUv2 -> 8 | TPUv3 -> 16
# Model
DIV_VAL=4
N_LAYER=24
D_MODEL=1280
D_EMBED=1280
N_HEAD=16
D_HEAD=80
D_INNER=8192
# Training
TGT_LEN=32
MEM_LEN=32
TRAIN_BSZ=512
VALID_BSZ=512
TRAIN_BSZ_PER_HOST=$((TRAIN_BSZ / NUM_HOST))
VALID_BSZ_PER_HOST=$((VALID_BSZ / NUM_HOST))
# Testing
TEST_TGT_LEN=32
TEST_MEM_LEN=128
TEST_CLAMP_LEN=-1
TEST_BSZ=8
if [[ $1 == 'train_data' ]]; then
python data_utils.py \
--data_dir=${LOCAL_DIR}/ \
--dataset=lm1b \
--tgt_len=${TGT_LEN} \
--per_host_train_bsz=${TRAIN_BSZ_PER_HOST} \
--per_host_valid_bsz=${VALID_BSZ_PER_HOST} \
--num_core_per_host=${NUM_CORE} \
--num_passes=10 \
--use_tpu=True \
${@:2}
SRC_PATTERN=train.bsz-${TRAIN_BSZ}.tlen-${TGT_LEN}.core-${NUM_CORE}*
gsutil cp ${LOCAL_DIR}/tfrecords/${SRC_PATTERN} ${GSDATA}/lm1b-tfrecords/
SRC_PATTERN=valid.bsz-${VALID_BSZ}.tlen-${TGT_LEN}.core-${NUM_CORE}*
gsutil cp ${LOCAL_DIR}/tfrecords/${SRC_PATTERN} ${GSDATA}/lm1b-tfrecords/
elif [[ $1 == 'test_data' ]]; then
python data_utils.py \
--data_dir=${LOCAL_DIR}/ \
--dataset=lm1b \
--tgt_len=${TEST_TGT_LEN} \
--per_host_test_bsz=${TEST_BSZ} \
--num_core_per_host=${TEST_NUM_CORE} \
--num_passes=1 \
--use_tpu=True \
${@:2}
SRC_PATTERN=test.bsz-${TEST_BSZ}.tlen-${TEST_TGT_LEN}.core-${TEST_NUM_CORE}*
gsutil cp ${LOCAL_DIR}/tfrecords/${SRC_PATTERN} ${GSDATA}/lm1b-tfrecords/
elif [[ $1 == 'train' ]]; then
echo 'Run training...'
python train.py \
--data_dir=${GSDATA}/lm1b-tfrecords \
--record_info_dir=${LOCAL_DIR}/tfrecords/ \
--corpus_info_path=${LOCAL_DIR}/corpus-info.json \
--model_dir=${GSEXP}/lm1b \
--div_val=${DIV_VAL} \
--untie_r=True \
--proj_share_all_but_first=False \
--proj_same_dim=False \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--dropout=0.05 \
--dropatt=0.05 \
--init_std=0.005 \
--learning_rate=0.0001 \
--warmup_steps=30000 \
--train_steps=1200000 \
--tgt_len=${TGT_LEN} \
--mem_len=${MEM_LEN} \
--train_batch_size=${TRAIN_BSZ} \
--num_hosts=${NUM_HOST} \
--num_core_per_host=${NUM_CORE} \
--iterations=1000 \
--save_steps=10000 \
--use_tpu=True \
--do_eval=False \
${@:2}
elif [[ $1 == 'eval' ]]; then
echo 'Run evaluation...'
python train.py \
--data_dir=${GSDATA}/lm1b-tfrecords \
--record_info_dir=${LOCAL_DIR}/tfrecords/ \
--corpus_info_path=${LOCAL_DIR}/corpus-info.json \
--model_dir=${GSEXP}/lm1b \
--div_val=${DIV_VAL} \
--untie_r=True \
--proj_share_all_but_first=False \
--proj_same_dim=False \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--tgt_len=${TEST_TGT_LEN} \
--mem_len=${TEST_MEM_LEN} \
--clamp_len=${TEST_CLAMP_LEN} \
--same_length=True \
--eval_batch_size=${TEST_BSZ} \
--num_host=${TEST_NUM_HOST} \
--num_core_per_host=${TEST_NUM_CORE} \
--use_tpu=True \
--do_train=False \
--do_eval_only=True \
--eval_split=test \
${@:2}
else
echo 'unknown argment 1'
fi

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#!/bin/bash
# Data
DATA_ROOT=../data/text8/
# Model
N_LAYER=12
D_MODEL=512
D_EMBED=512
N_HEAD=8
D_HEAD=64
D_INNER=2048
# Training
TGT_LEN=512
MEM_LEN=512
BSZ=24
NUM_CORE=4
# Testing
TEST_TGT_LEN=80
TEST_MEM_LEN=2100
TEST_CLAMP_LEN=820
TEST_BSZ=10
TEST_NUM_CORE=1
if [[ $1 == 'train_data' ]]; then
python data_utils.py \
--data_dir=${DATA_ROOT}/ \
--dataset=text8 \
--tgt_len=${TGT_LEN} \
--per_host_train_bsz=${BSZ} \
--per_host_valid_bsz=${BSZ} \
--num_passes=1 \
--use_tpu=False \
${@:2}
elif [[ $1 == 'test_data' ]]; then
python data_utils.py \
--data_dir=${DATA_ROOT}/ \
--dataset=text8 \
--tgt_len=${TEST_TGT_LEN} \
--per_host_test_bsz=${TEST_BSZ} \
--num_passes=1 \
--use_tpu=False \
${@:2}
elif [[ $1 == 'train' ]]; then
echo 'Run training...'
python train_gpu.py \
--data_dir=${DATA_ROOT}/tfrecords \
--record_info_dir=${DATA_ROOT}/tfrecords/ \
--corpus_info_path=${DATA_ROOT}/corpus-info.json \
--model_dir=EXP-text8 \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--dropout=0.1 \
--dropatt=0.0 \
--learning_rate=0.00025 \
--warmup_steps=0 \
--train_steps=400000 \
--tgt_len=${TGT_LEN} \
--mem_len=${MEM_LEN} \
--train_batch_size=${BSZ} \
--num_core_per_host=${NUM_CORE} \
--iterations=200 \
--save_steps=4000 \
--do_train=True \
--do_eval=False \
${@:2}
elif [[ $1 == 'eval' ]]; then
echo 'Run evaluation...'
python train_gpu.py \
--data_dir=${DATA_ROOT}/tfrecords \
--record_info_dir=${DATA_ROOT}/tfrecords/ \
--corpus_info_path=${DATA_ROOT}/corpus-info.json \
--model_dir=EXP-text8 \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--dropout=0.0 \
--dropatt=0.0 \
--tgt_len=${TEST_TGT_LEN} \
--mem_len=${TEST_MEM_LEN} \
--clamp_len=${TEST_CLAMP_LEN} \
--same_length=True \
--eval_batch_size=${TEST_BSZ} \
--num_core_per_host=${TEST_NUM_CORE} \
--do_train=False \
--do_eval=True \
--eval_split=test \
${@:2}
else
echo 'unknown argment 1'
fi

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#!/bin/bash
# Path
LOCAL_DIR=../data/text8/
GSDATA=
GSEXP=
# TPU setting
NUM_HOST=2
NUM_CORE=16 # TPUv2 -> 8 | TPUv3 -> 16
TEST_NUM_HOST=1
TEST_NUM_CORE=8 # TPUv2 -> 8 | TPUv3 -> 16
# Model
N_LAYER=24
D_MODEL=1024
D_EMBED=1024
N_HEAD=8
D_HEAD=128
D_INNER=3072
# Training
TGT_LEN=768
MEM_LEN=768
TRAIN_BSZ=64
VALID_BSZ=64
# Testing
TEST_TGT_LEN=128
TEST_MEM_LEN=3800
TEST_CLAMP_LEN=1000
TEST_BSZ=16
if [[ $1 == 'train_data' ]]; then
python data_utils.py \
--data_dir=${LOCAL_DIR}/ \
--dataset=text8 \
--tgt_len=${TGT_LEN} \
--per_host_train_bsz=${TRAIN_BSZ} \
--per_host_valid_bsz=${VALID_BSZ} \
--num_core_per_host=${NUM_CORE} \
--num_passes=10 \
--use_tpu=True \
${@:2}
SRC_PATTERN=train.bsz-${TRAIN_BSZ}.tlen-${TGT_LEN}.core-${NUM_CORE}*
gsutil cp ${LOCAL_DIR}/tfrecords/${SRC_PATTERN} ${GSDATA}/text8-tfrecords/
SRC_PATTERN=valid.bsz-${VALID_BSZ}.tlen-${TGT_LEN}.core-${NUM_CORE}*
gsutil cp ${LOCAL_DIR}/tfrecords/${SRC_PATTERN} ${GSDATA}/text8-tfrecords/
elif [[ $1 == 'test_data' ]]; then
python data_utils.py \
--data_dir=${LOCAL_DIR}/ \
--dataset=text8 \
--tgt_len=${TEST_TGT_LEN} \
--per_host_test_bsz=${TEST_BSZ} \
--num_core_per_host=${TEST_NUM_CORE} \
--num_passes=1 \
--use_tpu=True \
${@:2}
SRC_PATTERN=test.bsz-${TEST_BSZ}.tlen-${TEST_TGT_LEN}.core-${TEST_NUM_CORE}*
gsutil cp ${LOCAL_DIR}/tfrecords/${SRC_PATTERN} ${GSDATA}/text8-tfrecords/
elif [[ $1 == 'train' ]]; then
echo 'Run training...'
python train.py \
--data_dir=${GSDATA}/text8-tfrecords \
--record_info_dir=${LOCAL_DIR}/tfrecords/ \
--corpus_info_path=${LOCAL_DIR}/corpus-info.json \
--model_dir=${GSEXP}/text8 \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--dropout=0.15 \
--dropatt=0.15 \
--learning_rate=0.00025 \
--warmup_steps=4000 \
--train_steps=400000 \
--tgt_len=${TGT_LEN} \
--mem_len=${MEM_LEN} \
--train_batch_size=${TRAIN_BSZ} \
--use_tpu=True \
--num_host=${NUM_HOST} \
--num_core_per_host=${NUM_CORE} \
--iterations=1000 \
--save_steps=10000 \
--do_train=True \
--do_eval=False \
${@:2}
elif [[ $1 == 'eval' ]]; then
echo 'Run evaluation...'
python train.py \
--data_dir=${GSDATA}/text8-tfrecords \
--record_info_dir=${LOCAL_DIR}/tfrecords/ \
--corpus_info_path=${LOCAL_DIR}/corpus-info.json \
--model_dir=${GSEXP}/text8 \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--tgt_len=${TEST_TGT_LEN} \
--mem_len=${TEST_MEM_LEN} \
--eval_batch_size=${TEST_BSZ} \
--num_host=${TEST_NUM_HOST} \
--num_core_per_host=${TEST_NUM_CORE} \
--use_tpu=True \
--do_train=False \
--do_eval_only=True \
--eval_split=test \
${@:2}
else
echo 'unknown argment 1'
fi

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#!/bin/bash
# Data
DATA_ROOT=../data/wikitext-103/
# Model
DIV_VAL=1
N_LAYER=16
D_MODEL=410
D_EMBED=410
N_HEAD=10
D_HEAD=41
D_INNER=2100
# Training
TGT_LEN=150
MEM_LEN=150
BSZ=60
NUM_CORE=4
# Testing
TEST_TGT_LEN=64
TEST_MEM_LEN=640
TEST_CLAMP_LEN=400
TEST_BSZ=10
TEST_NUM_CORE=1
if [[ $1 == 'train_data' ]]; then
python data_utils.py \
--data_dir=${DATA_ROOT}/ \
--dataset=wt103 \
--tgt_len=${TGT_LEN} \
--per_host_train_bsz=${BSZ} \
--per_host_valid_bsz=${BSZ} \
--num_passes=1 \
--use_tpu=False \
${@:2}
elif [[ $1 == 'test_data' ]]; then
python data_utils.py \
--data_dir=${DATA_ROOT}/ \
--dataset=enwik8 \
--tgt_len=${TEST_TGT_LEN} \
--per_host_test_bsz=${TEST_BSZ} \
--num_passes=1 \
--use_tpu=False \
${@:2}
elif [[ $1 == 'train' ]]; then
echo 'Run training...'
python train_gpu.py \
--data_dir=${DATA_ROOT}/tfrecords \
--record_info_dir=${DATA_ROOT}/tfrecords/ \
--corpus_info_path=${DATA_ROOT}/corpus-info.json \
--model_dir=EXP-wt103 \
--div_val=${DIV_VAL} \
--untie_r=True \
--proj_share_all_but_first=True \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--dropout=0.1 \
--dropatt=0.0 \
--learning_rate=0.00025 \
--warmup_steps=0 \
--train_steps=400000 \
--tgt_len=${TGT_LEN} \
--mem_len=${MEM_LEN} \
--train_batch_size=${BSZ} \
--num_core_per_host=${NUM_CORE} \
--iterations=200 \
--save_steps=4000 \
${@:2}
elif [[ $1 == 'eval' ]]; then
echo 'Run evaluation...'
python train_gpu.py \
--data_dir=${DATA_ROOT}/tfrecords \
--record_info_dir=${DATA_ROOT}/tfrecords/ \
--corpus_info_path=${DATA_ROOT}/corpus-info.json \
--model_dir=EXP-wt103 \
--div_val=${DIV_VAL} \
--untie_r=True \
--proj_share_all_but_first=True \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--dropout=0.0 \
--dropatt=0.0 \
--tgt_len=${TEST_TGT_LEN} \
--mem_len=${TEST_MEM_LEN} \
--clamp_len=${TEST_CLAMP_LEN} \
--same_length=True \
--eval_batch_size=${TEST_BSZ} \
--num_core_per_host=${TEST_NUM_CORE} \
--do_train=False \
--do_eval=True \
--eval_split=test \
${@:2}
else
echo 'unknown argment 1'
fi

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#!/bin/bash
# Path
LOCAL_DIR=../data/wikitext-103/
GSDATA=
GSEXP=
# TPU setting
NUM_HOST=4
NUM_CORE=16 # TPUv2 -> 8 | TPUv3 -> 16
TEST_NUM_HOST=1
TEST_NUM_CORE=8 # TPUv2 -> 8 | TPUv3 -> 16
# Model
DIV_VAL=4
N_LAYER=18
D_MODEL=1024
D_EMBED=1024
N_HEAD=16
D_HEAD=64
D_INNER=4096
# Training
TGT_LEN=384
MEM_LEN=384
TRAIN_BSZ=128
VALID_BSZ=128
# Testing
TEST_TGT_LEN=128
TEST_MEM_LEN=1600
TEST_CLAMP_LEN=1000
TEST_BSZ=8
if [[ $1 == 'train_data' ]]; then
python data_utils.py \
--data_dir=${LOCAL_DIR}/ \
--dataset=wt103 \
--tgt_len=${TGT_LEN} \
--per_host_train_bsz=${TRAIN_BSZ} \
--per_host_valid_bsz=${VALID_BSZ} \
--num_core_per_host=${NUM_CORE} \
--num_passes=10 \
--use_tpu=True \
${@:2}
SRC_PATTERN=train.bsz-${TRAIN_BSZ}.tlen-${TGT_LEN}.core-${NUM_CORE}*
gsutil cp ${LOCAL_DIR}/tfrecords/${SRC_PATTERN} ${GSDATA}/wt103-tfrecords/
SRC_PATTERN=valid.bsz-${VALID_BSZ}.tlen-${TGT_LEN}.core-${NUM_CORE}*
gsutil cp ${LOCAL_DIR}/tfrecords/${SRC_PATTERN} ${GSDATA}/wt103-tfrecords/
elif [[ $1 == 'test_data' ]]; then
python data_utils.py \
--data_dir=${LOCAL_DIR}/ \
--dataset=wt103 \
--tgt_len=${TEST_TGT_LEN} \
--per_host_test_bsz=${TEST_BSZ} \
--num_core_per_host=${TEST_NUM_CORE} \
--num_passes=1 \
--use_tpu=True \
${@:2}
SRC_PATTERN=test.bsz-${TEST_BSZ}.tlen-${TEST_TGT_LEN}.core-${TEST_NUM_CORE}*
gsutil cp ${LOCAL_DIR}/tfrecords/${SRC_PATTERN} ${GSDATA}/wt103-tfrecords/
elif [[ $1 == 'train' ]]; then
echo 'Run training...'
python train.py \
--data_dir=${GSDATA}/wt103-tfrecords \
--record_info_dir=${LOCAL_DIR}/tfrecords/ \
--corpus_info_path=${LOCAL_DIR}/corpus-info.json \
--model_dir=${GSEXP}/wt103 \
--div_val=${DIV_VAL} \
--untie_r=True \
--proj_share_all_but_first=True \
--proj_same_dim=True \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--dropout=0.2 \
--dropatt=0.2 \
--init_std=0.005 \
--learning_rate=0.00025 \
--warmup_steps=16000 \
--train_steps=4000000 \
--tgt_len=${TGT_LEN} \
--mem_len=${MEM_LEN} \
--train_batch_size=${TRAIN_BSZ} \
--num_hosts=${NUM_HOST} \
--num_core_per_host=${NUM_CORE} \
--iterations=1000 \
--save_steps=10000 \
--use_tpu=True \
--do_eval=False \
${@:2}
elif [[ $1 == 'eval' ]]; then
echo 'Run evaluation...'
python train.py \
--data_dir=${GSDATA}/wt103-tfrecords \
--record_info_dir=${LOCAL_DIR}/tfrecords/ \
--corpus_info_path=${LOCAL_DIR}/corpus-info.json \
--model_dir=${GSEXP}/wt103 \
--div_val=${DIV_VAL} \
--untie_r=True \
--proj_share_all_but_first=True \
--proj_same_dim=True \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--tgt_len=${TEST_TGT_LEN} \
--mem_len=${TEST_MEM_LEN} \
--clamp_len=${TEST_CLAMP_LEN} \
--same_length=True \
--eval_batch_size=${TEST_BSZ} \
--num_host=${TEST_NUM_HOST} \
--num_core_per_host=${TEST_NUM_CORE} \
--use_tpu=True \
--do_train=False \
--do_eval_only=True \
--eval_split=test \
${@:2}
else
echo 'unknown argment 1'
fi

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#!/bin/bash
URL=http://curtis.ml.cmu.edu/datasets/pretrained_xl
DATA_ROOT=./
function download () {
fileurl=${1}
filename=${fileurl##*/}
if [ ! -f ${filename} ]; then
echo ">>> Download '${filename}' from '${fileurl}'."
wget --quiet ${fileurl}
else
echo "*** File '${filename}' exists. Skip."
fi
}
cd $DATA_ROOT
mkdir -p pretrained_xl && cd pretrained_xl
# enwik8
mkdir -p tf_enwik8 && cd tf_enwik8
mkdir -p data && cd data
download ${URL}/tf_enwiki8/data/cache.pkl
download ${URL}/tf_enwiki8/data/corpus-info.json
cd ..
mkdir -p model && cd model
download ${URL}/tf_enwiki8/model/checkpoint
download ${URL}/tf_enwiki8/model/model.ckpt-0.data-00000-of-00001
download ${URL}/tf_enwiki8/model/model.ckpt-0.index
download ${URL}/tf_enwiki8/model/model.ckpt-0.meta
cd ..
cd ..
# text8
mkdir -p tf_text8 && cd tf_text8
mkdir -p data && cd data
download ${URL}/tf_text8/data/cache.pkl
download ${URL}/tf_text8/data/corpus-info.json
cd ..
mkdir -p model && cd model
download ${URL}/tf_text8/model/checkpoint
download ${URL}/tf_text8/model/model.ckpt-0.data-00000-of-00001
download ${URL}/tf_text8/model/model.ckpt-0.index
download ${URL}/tf_text8/model/model.ckpt-0.meta
cd ..
cd ..
# wt103
mkdir -p tf_wt103 && cd tf_wt103
mkdir -p data && cd data
download ${URL}/tf_wt103/data/cache.pkl
download ${URL}/tf_wt103/data/corpus-info.json
cd ..
mkdir -p model && cd model
download ${URL}/tf_wt103/model/checkpoint
download ${URL}/tf_wt103/model/model.ckpt-0.data-00000-of-00001
download ${URL}/tf_wt103/model/model.ckpt-0.index
download ${URL}/tf_wt103/model/model.ckpt-0.meta
cd ..
cd ..
# lm1b
mkdir -p tf_lm1b && cd tf_lm1b
mkdir -p data && cd data
download ${URL}/tf_lm1b/data/cache.pkl
download ${URL}/tf_lm1b/data/corpus-info.json
cd ..
mkdir -p model && cd model
download ${URL}/tf_lm1b/model/checkpoint
download ${URL}/tf_lm1b/model/model.ckpt-1191000.data-00000-of-00001
download ${URL}/tf_lm1b/model/model.ckpt-1191000.index
download ${URL}/tf_lm1b/model/model.ckpt-1191000.meta
cd ..
cd ..

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#!/bin/bash
# Data
DATA_ROOT=./
DATA_DIR=${DATA_ROOT}/pretrained_xl/tf_enwik8/data
MODEL_DIR=${DATA_ROOT}/pretrained_xl/tf_enwik8/model
# Model
N_LAYER=24
D_MODEL=1024
D_EMBED=1024
N_HEAD=8
D_HEAD=128
D_INNER=3072
# Testing
TEST_TGT_LEN=128
TEST_MEM_LEN=3800
TEST_CLAMP_LEN=1000
TEST_CKPT_PATH=${MODEL_DIR}/model.ckpt-0
TEST_BSZ=16
TEST_NUM_CORE=2
echo 'Preprocess test set...'
python data_utils.py \
--data_dir=${DATA_DIR}/ \
--dataset=enwik8 \
--tgt_len=${TEST_TGT_LEN} \
--per_host_test_bsz=${TEST_BSZ} \
--num_passes=1 \
--use_tpu=False
echo 'Run evaluation on test set...'
python train_gpu.py \
--data_dir=${DATA_DIR}/tfrecords \
--record_info_dir=${DATA_DIR}/tfrecords/ \
--corpus_info_path=${DATA_DIR}/corpus-info.json \
--eval_ckpt_path=${TEST_CKPT_PATH} \
--model_dir=EXP-enwik8 \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--dropout=0.0 \
--dropatt=0.0 \
--tgt_len=${TEST_TGT_LEN} \
--mem_len=${TEST_MEM_LEN} \
--clamp_len=${TEST_CLAMP_LEN} \
--same_length=True \
--eval_batch_size=${TEST_BSZ} \
--num_core_per_host=${TEST_NUM_CORE} \
--do_train=False \
--do_eval=True \
--eval_split=test

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#!/bin/bash
# Data
DATA_ROOT=./
DATA_DIR=${DATA_ROOT}/pretrained_xl/tf_lm1b/data
MODEL_DIR=${DATA_ROOT}/pretrained_xl/tf_lm1b/model
# Model
DIV_VAL=4
N_LAYER=24
D_MODEL=1280
D_EMBED=1280
N_HEAD=16
D_HEAD=80
D_INNER=8192
# Testing
TEST_TGT_LEN=32
TEST_MEM_LEN=128
TEST_CLAMP_LEN=-1
TEST_CKPT_PATH=${MODEL_DIR}/model.ckpt-1191000
TEST_BSZ=16
TEST_NUM_CORE=1
echo 'Preprocess test set...'
python data_utils.py \
--data_dir=${DATA_DIR}/ \
--dataset=lm1b \
--tgt_len=${TEST_TGT_LEN} \
--per_host_test_bsz=${TEST_BSZ} \
--num_passes=1 \
--use_tpu=False
echo 'Run evaluation on test set...'
python train_gpu.py \
--data_dir=${DATA_DIR}/tfrecords \
--record_info_dir=${DATA_DIR}/tfrecords/ \
--corpus_info_path=${DATA_DIR}/corpus-info.json \
--eval_ckpt_path=${TEST_CKPT_PATH} \
--model_dir=EXP-lm1b \
--div_val=${DIV_VAL} \
--untie_r=True \
--proj_share_all_but_first=False \
--proj_same_dim=False \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--dropout=0.0 \
--dropatt=0.0 \
--tgt_len=${TEST_TGT_LEN} \
--mem_len=${TEST_MEM_LEN} \
--clamp_len=${TEST_CLAMP_LEN} \
--same_length=True \
--eval_batch_size=${TEST_BSZ} \
--num_core_per_host=${TEST_NUM_CORE} \
--do_train=False \
--do_eval=True \
--eval_split=test

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#!/bin/bash
# Data
DATA_ROOT=./
DATA_DIR=${DATA_ROOT}/pretrained_xl/tf_text8/data
MODEL_DIR=${DATA_ROOT}/pretrained_xl/tf_text8/model
# Model
N_LAYER=24
D_MODEL=1024
D_EMBED=1024
N_HEAD=8
D_HEAD=128
D_INNER=3072
# Testing
TEST_TGT_LEN=128
TEST_MEM_LEN=3800
TEST_CLAMP_LEN=1000
TEST_CKPT_PATH=${MODEL_DIR}/model.ckpt-0
TEST_BSZ=16
TEST_NUM_CORE=2
echo 'Preprocess test set...'
python data_utils.py \
--data_dir=${DATA_DIR}/ \
--dataset=text8 \
--tgt_len=${TEST_TGT_LEN} \
--per_host_test_bsz=${TEST_BSZ} \
--num_passes=1 \
--use_tpu=False
echo 'Run evaluation on test set...'
python train_gpu.py \
--data_dir=${DATA_DIR}/tfrecords \
--record_info_dir=${DATA_DIR}/tfrecords/ \
--corpus_info_path=${DATA_DIR}/corpus-info.json \
--eval_ckpt_path=${TEST_CKPT_PATH} \
--model_dir=EXP-text8 \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--dropout=0.0 \
--dropatt=0.0 \
--tgt_len=${TEST_TGT_LEN} \
--mem_len=${TEST_MEM_LEN} \
--clamp_len=${TEST_CLAMP_LEN} \
--same_length=True \
--eval_batch_size=${TEST_BSZ} \
--num_core_per_host=${TEST_NUM_CORE} \
--do_train=False \
--do_eval=True \
--eval_split=test

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#!/bin/bash
# Data
DATA_ROOT=./
DATA_DIR=${DATA_ROOT}/pretrained_xl/tf_wt103/data
MODEL_DIR=${DATA_ROOT}/pretrained_xl/tf_wt103/model
# Model
DIV_VAL=4
N_LAYER=18
D_MODEL=1024
D_EMBED=1024
N_HEAD=16
D_HEAD=64
D_INNER=4096
# Training
TGT_LEN=256
MEM_LEN=256
BSZ=16
NUM_CORE=2
# Testing
TEST_TGT_LEN=128
TEST_MEM_LEN=1600
TEST_CLAMP_LEN=1000
TEST_CKPT_PATH=${MODEL_DIR}/model.ckpt-0
TEST_BSZ=16
TEST_NUM_CORE=1
echo 'Preprocess test set...'
python data_utils.py \
--data_dir=${DATA_DIR}/ \
--dataset=enwik8 \
--tgt_len=${TEST_TGT_LEN} \
--per_host_test_bsz=${TEST_BSZ} \
--num_passes=1 \
--use_tpu=False
echo 'Run evaluation on test set...'
python train_gpu.py \
--data_dir=${DATA_DIR}/tfrecords \
--record_info_dir=${DATA_DIR}/tfrecords/ \
--corpus_info_path=${DATA_DIR}/corpus-info.json \
--eval_ckpt_path=${TEST_CKPT_PATH} \
--model_dir=EXP-wt103 \
--div_val=${DIV_VAL} \
--untie_r=True \
--proj_share_all_but_first=True \
--n_layer=${N_LAYER} \
--d_model=${D_MODEL} \
--d_embed=${D_EMBED} \
--n_head=${N_HEAD} \
--d_head=${D_HEAD} \
--d_inner=${D_INNER} \
--dropout=0.0 \
--dropatt=0.0 \
--tgt_len=${TEST_TGT_LEN} \
--mem_len=${TEST_MEM_LEN} \
--clamp_len=${TEST_CLAMP_LEN} \
--same_length=True \
--eval_batch_size=${TEST_BSZ} \
--num_core_per_host=${TEST_NUM_CORE} \
--do_train=False \
--do_eval=True \
--eval_split=test

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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import time
from absl import flags
import absl.logging as _logging # pylint: disable=unused-import
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
from tensorflow.gfile import Exists as exists
import model
import data_utils
import tpu_estimator
import numpy as np
from time import sleep
# TPU parameters
flags.DEFINE_string("master", default=None,
help="master")
flags.DEFINE_string("tpu", default=None,
help="The Cloud TPU to use for training. This should be either the name "
"used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 url.")
flags.DEFINE_string("gcp_project", default=None,
help="Project name for the Cloud TPU-enabled project. If not specified, "
"we will attempt to automatically detect the GCE project from metadata.")
flags.DEFINE_string("tpu_zone",default=None,
help="GCE zone where the Cloud TPU is located in. If not specified, we "
"will attempt to automatically detect the GCE project from metadata.")
flags.DEFINE_bool("use_tpu", default=True,
help="Use TPUs rather than plain CPUs.")
flags.DEFINE_integer("num_hosts", default=1,
help="number of TPU hosts")
flags.DEFINE_integer("num_core_per_host", default=8,
help="number of cores per host")
# Experiment (data/checkpoint/directory) parameters
flags.DEFINE_string("data_dir", default="",
help="Path to tf-records directory.")
flags.DEFINE_string("record_info_dir", default="",
help="Path to local directory containing filenames.txt.")
flags.DEFINE_string("corpus_info_path", default="",
help="Path to corpus-info.json file.")
flags.DEFINE_string("model_dir", default=None,
help="Estimator model_dir.")
flags.DEFINE_bool("do_eval", default=False,
help="Whether to run eval on the dev set.")
flags.DEFINE_bool("track_mean", default=True,
help="Trace mean loss during training.")
flags.DEFINE_string("eval_ckpt_path", None,
help="Checkpoint path for evaluation."
"If set, model_dir will be ignored."
"If unset, will use the latest ckpt in model_dir.")
flags.DEFINE_string("warm_start_path", None,
help="Checkpoint path for warm start."
"If set, will clear Adam states."
"Note that the new model_dir should be different"
" from warm_start_path.")
# Optimization paramenters
flags.DEFINE_float("learning_rate", default=2.5e-4,
help="Maximum learning rate.")
flags.DEFINE_float("clip", default=0.25,
help="Gradient clipping value.")
# for cosine decay
flags.DEFINE_float("min_lr_ratio", default=0.01,
help="Minimum ratio learning rate.")
flags.DEFINE_integer("warmup_steps", default=0,
help="Number of steps for linear lr warmup.")
# Training parameters
flags.DEFINE_integer("train_batch_size", default=60,
help="Size of train batch.")
flags.DEFINE_integer("eval_batch_size", default=60,
help="Size of valid batch.")
flags.DEFINE_integer("train_steps", default=100000,
help="Total number of training steps.")
flags.DEFINE_integer("iterations", default=500,
help="Number of iterations per repeat loop.")
flags.DEFINE_integer("save_steps", default=10000,
help="number of steps for model checkpointing.")
# Evaluation parameters
flags.DEFINE_integer("max_eval_batch", default=-1,
help="Set -1 to turn off. Only used in test mode.")
flags.DEFINE_bool("do_eval_only", default=False,
help="Run evaluation only.")
flags.DEFINE_integer("start_eval_steps", default=10000,
help="Which checkpoint to start with in `do_eval_only` mode.")
flags.DEFINE_string("eval_split", "valid",
help="Which data split to evaluate.")
# Model paramenters
flags.DEFINE_integer("tgt_len", default=70,
help="Number of steps to predict")
flags.DEFINE_integer("mem_len", default=70,
help="Number of steps to cache")
flags.DEFINE_bool("same_length", default=False,
help="Same length attention")
flags.DEFINE_integer("clamp_len", default=-1,
help="Clamp length")
flags.DEFINE_integer("n_layer", default=6,
help="Number of layers.")
flags.DEFINE_integer("d_model", default=500,
help="Dimension of the model.")
flags.DEFINE_integer("d_embed", default=500,
help="Dimension of the embeddings.")
flags.DEFINE_integer("n_head", default=10,
help="Number of attention heads.")
flags.DEFINE_integer("d_head", default=50,
help="Dimension of each attention head.")
flags.DEFINE_integer("d_inner", default=1000,
help="Dimension of inner hidden size in positionwise feed-forward.")
flags.DEFINE_float("dropout", default=0.1,
help="Dropout rate.")
flags.DEFINE_float("dropatt", default=0.1,
help="Attention dropout rate.")
flags.DEFINE_bool("untie_r", default=False,
help="untie r_w_bias and r_r_bias")
# Adaptive Softmax / Embedding
flags.DEFINE_bool("tie_weight", default=True,
help="Tie embedding and softmax weight.")
flags.DEFINE_integer("div_val", default=1,
help="Divide the embedding size by this val for each bin")
flags.DEFINE_bool("proj_share_all_but_first", default=False,
help="True to share all but first projs, False not to share.")
flags.DEFINE_bool("proj_same_dim", default=True,
help="Project the bin with the same dimension.")
# Parameter initialization
flags.DEFINE_enum("init", default="normal",
enum_values=["normal", "uniform"],
help="Initialization method.")
flags.DEFINE_float("init_std", default=0.02,
help="Initialization std when init is normal.")
flags.DEFINE_float("proj_init_std", default=0.01,
help="Initialization std for embedding projection.")
flags.DEFINE_float("init_range", default=0.1,
help="Initialization std when init is uniform.")
FLAGS = flags.FLAGS
def metric_fn(loss):
"""Evaluation metric Fn which runs on CPU."""
perplexity = tf.exp(tf.reduce_mean(loss))
bpc = tf.reduce_mean(loss) / tf.constant(math.log(2))
return {
"perplexity": tf.metrics.mean(perplexity),
"bpc": tf.metrics.mean(bpc),
}
def get_model_fn(n_token, cutoffs, train_bin_sizes, eval_bin_sizes):
def model_fn(features, labels, mode, params):
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
batch_size = params["batch_size"]
mems = params["cache"]
inp = tf.transpose(features["inputs"], [1, 0])
tgt = tf.transpose(features["labels"], [1, 0])
bin_sizes = train_bin_sizes if is_training else eval_bin_sizes
if bin_sizes:
inp_perms = [tf.transpose(features["inp_mask"], [1, 0])]
tgt_perms = [tf.transpose(features["tgt_mask"], [1, 0])]
head_tgt = tf.transpose(features["head_labels"], [1, 0])
for b in range(len(bin_sizes)):
inp_perm = tf.transpose(features["inp_perm_{}".format(b)], [1, 0, 2])
tgt_perm = tf.transpose(features["tgt_perm_{}".format(b)], [1, 0, 2])
inp_perms.append(inp_perm)
tgt_perms.append(tgt_perm)
else:
inp_perms, tgt_perms, head_tgt = None, None, None
if FLAGS.init == "uniform":
initializer = tf.initializers.random_uniform(
minval=-FLAGS.init_range,
maxval=FLAGS.init_range,
seed=None)
elif FLAGS.init == "normal":
initializer = tf.initializers.random_normal(
stddev=FLAGS.init_std,
seed=None)
proj_initializer = tf.initializers.random_normal(
stddev=FLAGS.proj_init_std,
seed=None)
tie_projs = [False for _ in range(len(cutoffs) + 1)]
if FLAGS.proj_share_all_but_first:
for i in range(1, len(tie_projs)):
tie_projs[i] = True
tf.logging.info("Vocab size : {}".format(n_token))
tf.logging.info("Batch size : {}".format(batch_size))
loss, new_mems = model.transformer(
dec_inp=inp,
target=tgt,
mems=mems,
n_token=n_token,
n_layer=FLAGS.n_layer,
d_model=FLAGS.d_model,
d_embed=FLAGS.d_embed,
n_head=FLAGS.n_head,
d_head=FLAGS.d_head,
d_inner=FLAGS.d_inner,
dropout=FLAGS.dropout,
dropatt=FLAGS.dropatt,
initializer=initializer,
is_training=is_training,
mem_len=FLAGS.mem_len,
cutoffs=cutoffs,
div_val=FLAGS.div_val,
tie_projs=tie_projs,
input_perms=inp_perms,
target_perms=tgt_perms,
head_target=head_tgt,
same_length=FLAGS.same_length,
clamp_len=FLAGS.clamp_len,
use_tpu=FLAGS.use_tpu,
untie_r=FLAGS.untie_r,
proj_same_dim=FLAGS.proj_same_dim)
total_loss = tf.reduce_mean(loss)
if mode == tf.estimator.ModeKeys.EVAL:
if FLAGS.use_tpu:
with tf.colocate_with(total_loss):
total_loss = tf.contrib.tpu.cross_replica_sum(total_loss) \
/ FLAGS.num_hosts / FLAGS.num_core_per_host
metric_loss = tf.tile(tf.reshape(total_loss, [1, 1]), [batch_size, 1])
eval_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
eval_metrics=(metric_fn, [metric_loss]))
eval_spec.cache = new_mems
return eval_spec
# Configuring the optimization step.
global_step = tf.train.get_global_step()
# increase the learning rate linearly
if FLAGS.warmup_steps > 0:
warmup_lr = tf.to_float(global_step) / tf.to_float(FLAGS.warmup_steps) \
* FLAGS.learning_rate
else:
warmup_lr = 0.0
# number of parameters
num_params = np.sum([np.prod(v.shape) for v in tf.trainable_variables()])
tf.logging.info("#params: {}".format(num_params))
# format_str = '{{:<{0}s}}\t{{}}'.format(
# max([len(v.name) for v in tf.trainable_variables()]))
# for v in tf.trainable_variables():
# tf.logging.info(format_str.format(v.name, v.get_shape()))
# decay the learning rate using the cosine schedule
decay_lr = tf.train.cosine_decay(
FLAGS.learning_rate,
global_step=global_step-FLAGS.warmup_steps,
decay_steps=FLAGS.train_steps-FLAGS.warmup_steps,
alpha=FLAGS.min_lr_ratio)
learning_rate = tf.where(global_step < FLAGS.warmup_steps,
warmup_lr, decay_lr)
if FLAGS.use_tpu:
optimizer = tf.contrib.tpu.CrossShardOptimizer(
tf.train.AdamOptimizer(learning_rate=learning_rate))
#GradientDescentOptimizer
else:
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
grads_and_vars = optimizer.compute_gradients(total_loss)
gradients, variables = zip(*grads_and_vars)
clipped, _ = tf.clip_by_global_norm(gradients, FLAGS.clip)
train_op = optimizer.apply_gradients(
zip(clipped, variables), global_step=tf.train.get_global_step())
# Constucting TPUEstimatorSpec with cache.
train_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode, loss=total_loss, train_op=train_op)
if FLAGS.mem_len < FLAGS.tgt_len:
new_mems = [new_mems[: FLAGS.mem_len] for mem_t in new_mems]
train_spec.cache = new_mems
return train_spec
return model_fn
def get_cache_fn(mem_len):
def cache_fn(batch_size):
mems = []
for l in xrange(FLAGS.n_layer):
if mem_len > 0:
mems.append(
tf.zeros([mem_len, batch_size, FLAGS.d_model], dtype=tf.float32))
else:
mems.append(tf.zeros([mem_len], dtype=tf.float32))
return mems
return cache_fn
def main(unused_argv):
del unused_argv # Unused
tf.logging.set_verbosity(tf.logging.INFO)
# Get corpus info
corpus_info = data_utils.get_corpus_info(FLAGS.corpus_info_path)
n_token = corpus_info["vocab_size"]
cutoffs = corpus_info["cutoffs"][1:-1]
if FLAGS.save_steps == 0:
FLAGS.save_steps = None
if not FLAGS.do_eval_only:
# Get train input function
train_input_fn, train_record_info = data_utils.get_input_fn(
record_info_dir=FLAGS.record_info_dir,
split="train",
per_host_bsz=FLAGS.train_batch_size // FLAGS.num_hosts,
tgt_len=FLAGS.tgt_len,
num_core_per_host=FLAGS.num_core_per_host,
num_hosts=FLAGS.num_hosts,
use_tpu=FLAGS.use_tpu)
train_bin_sizes = train_record_info["bin_sizes"]
num_train_batch = train_record_info["num_batch"]
# Get train cache function
train_cache_fn = get_cache_fn(FLAGS.mem_len)
else:
train_bin_sizes = []
num_train_batch = None
train_cache_fn = None
if FLAGS.do_eval or FLAGS.do_eval_only:
assert FLAGS.num_hosts == 1
# Get eval input function
eval_input_fn, eval_record_info = data_utils.get_input_fn(
record_info_dir=FLAGS.record_info_dir,
split=FLAGS.eval_split,
per_host_bsz=FLAGS.eval_batch_size // FLAGS.num_hosts,
tgt_len=FLAGS.tgt_len,
num_core_per_host=FLAGS.num_core_per_host,
num_hosts=FLAGS.num_hosts,
use_tpu=FLAGS.use_tpu)
eval_bin_sizes = eval_record_info["bin_sizes"]
num_eval_batch = eval_record_info["num_batch"]
if FLAGS.max_eval_batch > 0:
num_eval_batch = min(FLAGS.max_eval_batch, num_eval_batch)
# Get eval cache function
eval_cache_fn = get_cache_fn(FLAGS.mem_len)
model_fn = get_model_fn(n_token, cutoffs, train_bin_sizes, eval_bin_sizes)
else:
eval_cache_fn = None
model_fn = get_model_fn(n_token, cutoffs, train_bin_sizes, [])
##### Create estimator
# TPU Configuration
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
per_host_input = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
run_config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
model_dir=FLAGS.model_dir,
session_config=tf.ConfigProto(
allow_soft_placement=True, log_device_placement=True),
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations,
num_shards=FLAGS.num_core_per_host * FLAGS.num_hosts,
per_host_input_for_training=per_host_input),
keep_checkpoint_max=100000, # effectively save all checkpoints
save_checkpoints_secs=None,
save_checkpoints_steps=FLAGS.save_steps
)
# warm start
warm_start_from = None
if FLAGS.warm_start_path is not None:
warm_start_from = tf.estimator.WarmStartSettings(
ckpt_to_initialize_from=FLAGS.warm_start_path)
# TPU Estimator
estimator = tpu_estimator.TPUEstimator(
model_fn=model_fn,
train_cache_fn=train_cache_fn,
eval_cache_fn=eval_cache_fn,
use_tpu=FLAGS.use_tpu,
config=run_config,
params={"data_dir":FLAGS.data_dir, "track_mean":FLAGS.track_mean},
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
warm_start_from=warm_start_from)
if FLAGS.do_eval_only:
if FLAGS.eval_ckpt_path is not None:
ret = estimator.evaluate(input_fn=eval_input_fn, steps=num_eval_batch,
checkpoint_path=FLAGS.eval_ckpt_path)
tf.logging.info("=" * 200)
log_str = "Eval results | "
for key, val in ret.items():
log_str += "{} {} | ".format(key, val)
tf.logging.info(log_str)
tf.logging.info("=" * 200)
else:
ckpt_state = tf.train.get_checkpoint_state(FLAGS.model_dir)
eval_results = []
for eval_checkpoint in ckpt_state.all_model_checkpoint_paths:
if not exists(eval_checkpoint + ".index"): continue
global_step = int(eval_checkpoint.split("-")[-1])
if global_step < FLAGS.start_eval_steps or global_step > FLAGS.train_steps:
continue
ret = estimator.evaluate(input_fn=eval_input_fn, steps=num_eval_batch,
checkpoint_path=eval_checkpoint)
eval_results.append(ret)
eval_results.sort(key = lambda x: x["perplexity"])
tf.logging.info("=" * 200)
log_str = "Best results | "
for key, val in eval_results[0].items():
log_str += "{} {} | ".format(key, val)
tf.logging.info(log_str)
tf.logging.info("=" * 200)
else:
if not FLAGS.do_eval:
estimator.train(input_fn=train_input_fn, steps=FLAGS.train_steps)
else:
for step in range(0, FLAGS.train_steps, num_train_batch):
train_steps = min(FLAGS.train_steps - step, num_train_batch)
estimator.train(input_fn=train_input_fn, steps=train_steps)
estimator.evaluate(input_fn=eval_input_fn, steps=num_eval_batch)
if __name__ == "__main__":
tf.app.run()

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transformer-xl/tf/train_gpu.py Normal file
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import math
import time
from absl import flags
import absl.logging as _logging # pylint: disable=unused-import
import tensorflow as tf
import model
import data_utils
from gpu_utils import assign_to_gpu, average_grads_and_vars
import numpy as np
# GPU config
flags.DEFINE_integer("num_hosts", default=1,
help="Number of TPU hosts")
flags.DEFINE_integer("num_core_per_host", default=8,
help="Number of cores per host")
# Experiment (data/checkpoint/directory) config
flags.DEFINE_string("data_dir", default="",
help="Path to tf-records directory.")
flags.DEFINE_string("record_info_dir", default="",
help="Path to local directory containing filenames.txt.")
flags.DEFINE_string("corpus_info_path", default="",
help="Path to corpus-info.json file.")
flags.DEFINE_string("model_dir", default=None,
help="Estimator model_dir.")
flags.DEFINE_bool("do_train", default=True,
help="Whether to run training.")
flags.DEFINE_bool("do_eval", default=False,
help="Whether to run eval on the dev set.")
flags.DEFINE_string("eval_ckpt_path", None,
help="Checkpoint path for do_test evaluation."
"If set, model_dir will be ignored."
"If unset, will use the latest ckpt in model_dir.")
flags.DEFINE_string("warm_start_path", None,
help="Checkpoint path for warm start."
"If set, will clear Adam states."
"Note that the new model_dir should be different"
" from warm_start_path.")
# Optimization config
flags.DEFINE_float("learning_rate", default=2.5e-4,
help="Maximum learning rate.")
flags.DEFINE_float("clip", default=0.25,
help="Gradient clipping value.")
# for cosine decay
flags.DEFINE_float("min_lr_ratio", default=0.004,
help="Minimum ratio learning rate.")
flags.DEFINE_integer("warmup_steps", default=0,
help="Number of steps for linear lr warmup.")
# Training config
flags.DEFINE_integer("train_batch_size", default=60,
help="Size of train batch.")
flags.DEFINE_integer("eval_batch_size", default=60,
help="Size of valid batch.")
flags.DEFINE_integer("train_steps", default=100000,
help="Total number of training steps.")
flags.DEFINE_integer("iterations", default=500,
help="Number of iterations per repeat loop.")
flags.DEFINE_integer("save_steps", default=10000,
help="number of steps for model checkpointing.")
# Evaluation config
flags.DEFINE_bool("do_test", default=False,
help="Run on the test set.")
flags.DEFINE_integer("max_eval_batch", default=-1,
help="Set -1 to turn off. Only used in test mode.")
flags.DEFINE_bool("do_eval_only", default=False,
help="Run evaluation only.")
flags.DEFINE_integer("start_eval_steps", default=10000,
help="Which checkpoint to start with in `do_eval_only` mode.")
flags.DEFINE_string("eval_split", "valid",
help="Which data split to evaluate.")
# Model config
flags.DEFINE_integer("tgt_len", default=70,
help="Number of steps to predict")
flags.DEFINE_integer("mem_len", default=70,
help="Number of steps to cache")
flags.DEFINE_bool("same_length", default=False,
help="Same length attention")
flags.DEFINE_integer("clamp_len", default=-1,
help="Clamp length")
flags.DEFINE_integer("n_layer", default=6,
help="Number of layers.")
flags.DEFINE_integer("d_model", default=500,
help="Dimension of the model.")
flags.DEFINE_integer("d_embed", default=500,
help="Dimension of the embeddings.")
flags.DEFINE_integer("n_head", default=10,
help="Number of attention heads.")
flags.DEFINE_integer("d_head", default=50,
help="Dimension of each attention head.")
flags.DEFINE_integer("d_inner", default=1000,
help="Dimension of inner hidden size in positionwise feed-forward.")
flags.DEFINE_float("dropout", default=0.1,
help="Dropout rate.")
flags.DEFINE_float("dropatt", default=0.1,
help="Attention dropout rate.")
flags.DEFINE_bool("untie_r", default=False,
help="untie r_w_bias and r_r_bias")
# Adaptive Softmax / Embedding
flags.DEFINE_bool("tie_weight", default=True,
help="Tie embedding and softmax weight.")
flags.DEFINE_integer("div_val", default=1,
help="Divide the embedding size by this val for each bin")
flags.DEFINE_bool("proj_share_all_but_first", default=False,
help="True to share all but first projs, False not to share.")
flags.DEFINE_bool("proj_same_dim", default=True,
help="Project the bin with the same dimension.")
# Parameter initialization
flags.DEFINE_enum("init", default="normal",
enum_values=["normal", "uniform"],
help="Initialization method.")
flags.DEFINE_float("init_std", default=0.02,
help="Initialization std when init is normal.")
flags.DEFINE_float("proj_init_std", default=0.01,
help="Initialization std for embedding projection.")
flags.DEFINE_float("init_range", default=0.1,
help="Initialization std when init is uniform.")
FLAGS = flags.FLAGS
def get_model_fn(n_token, cutoffs):
def model_fn(inp, tgt, mems, is_training):
inp = tf.transpose(inp, [1, 0])
tgt = tf.transpose(tgt, [1, 0])
if FLAGS.init == "uniform":
initializer = tf.initializers.random_uniform(
minval=-FLAGS.init_range,
maxval=FLAGS.init_range,
seed=None)
elif FLAGS.init == "normal":
initializer = tf.initializers.random_normal(
stddev=FLAGS.init_std,
seed=None)
proj_initializer = tf.initializers.random_normal(
stddev=FLAGS.proj_init_std,
seed=None)
tie_projs = [False for _ in range(len(cutoffs) + 1)]
if FLAGS.proj_share_all_but_first:
for i in range(1, len(tie_projs)):
tie_projs[i] = True
loss, new_mems = model.transformer(
dec_inp=inp,
target=tgt,
mems=mems,
n_token=n_token,
n_layer=FLAGS.n_layer,
d_model=FLAGS.d_model,
d_embed=FLAGS.d_embed,
n_head=FLAGS.n_head,
d_head=FLAGS.d_head,
d_inner=FLAGS.d_inner,
dropout=FLAGS.dropout,
dropatt=FLAGS.dropatt,
initializer=initializer,
proj_initializer=proj_initializer,
is_training=is_training,
mem_len=FLAGS.mem_len,
cutoffs=cutoffs,
div_val=FLAGS.div_val,
tie_projs=tie_projs,
input_perms=None,
target_perms=None,
head_target=None,
same_length=FLAGS.same_length,
clamp_len=FLAGS.clamp_len,
use_tpu=False,
untie_r=FLAGS.untie_r,
proj_same_dim=FLAGS.proj_same_dim)
# number of parameters
num_params = sum([np.prod(v.shape) for v in tf.trainable_variables()])
tf.logging.info('#params: {}'.format(num_params))
# format_str = '{{:<{0}s}}\t{{}}'.format(
# max([len(v.name) for v in tf.trainable_variables()]))
# for v in tf.trainable_variables():
# tf.logging.info(format_str.format(v.name, v.get_shape()))
if is_training:
all_vars = tf.trainable_variables()
grads = tf.gradients(loss, all_vars)
grads_and_vars = list(zip(grads, all_vars))
return loss, new_mems, grads_and_vars
else:
return loss, new_mems
return model_fn
def single_core_graph(n_token, cutoffs, is_training, inp, tgt, mems):
model_fn = get_model_fn(
n_token=n_token,
cutoffs=cutoffs)
model_ret = model_fn(
inp=inp,
tgt=tgt,
mems=mems,
is_training=is_training)
return model_ret
def train(n_token, cutoffs, ps_device):
##### Get input function and model function
train_input_fn, train_record_info = data_utils.get_input_fn(
record_info_dir=FLAGS.record_info_dir,
split="train",
per_host_bsz=FLAGS.train_batch_size,
tgt_len=FLAGS.tgt_len,
num_core_per_host=FLAGS.num_core_per_host,
num_hosts=1,
use_tpu=False)
tf.logging.info("num of batches {}".format(train_record_info["num_batch"]))
##### Create computational graph
train_set = train_input_fn({
"batch_size": FLAGS.train_batch_size,
"data_dir": FLAGS.data_dir})
input_feed, label_feed = train_set.make_one_shot_iterator().get_next()
inputs = tf.split(input_feed, FLAGS.num_core_per_host, 0)
labels = tf.split(label_feed, FLAGS.num_core_per_host, 0)
per_core_bsz = FLAGS.train_batch_size // FLAGS.num_core_per_host
tower_mems, tower_losses, tower_new_mems, tower_grads_and_vars = [], [], [], []
for i in range(FLAGS.num_core_per_host):
reuse = True if i > 0 else None
with tf.device(assign_to_gpu(i, ps_device)), \
tf.variable_scope(tf.get_variable_scope(), reuse=reuse):
mems_i = [tf.placeholder(tf.float32,
[FLAGS.mem_len, per_core_bsz, FLAGS.d_model])
for _ in range(FLAGS.n_layer)]
loss_i, new_mems_i, grads_and_vars_i = single_core_graph(
n_token=n_token,
cutoffs=cutoffs,
is_training=True,
inp=inputs[i],
tgt=labels[i],
mems=mems_i)
tower_mems.append(mems_i)
tower_losses.append(loss_i)
tower_new_mems.append(new_mems_i)
tower_grads_and_vars.append(grads_and_vars_i)
## average losses and gradients across towers
if len(tower_losses) > 1:
loss = tf.add_n(tower_losses) / len(tower_losses)
grads_and_vars = average_grads_and_vars(tower_grads_and_vars)
else:
loss = tower_losses[0]
grads_and_vars = tower_grads_and_vars[0]
grads, all_vars = zip(*grads_and_vars)
## clip gradient
clipped, gnorm = tf.clip_by_global_norm(grads, FLAGS.clip)
grads_and_vars = list(zip(clipped, all_vars))
## configure the optimizer
global_step = tf.train.get_or_create_global_step()
# warmup stage: increase the learning rate linearly
if FLAGS.warmup_steps > 0:
warmup_lr = tf.to_float(global_step) / tf.to_float(FLAGS.warmup_steps) \
* FLAGS.learning_rate
else:
warmup_lr = 0.0
# decay stage: decay the learning rate using the cosine schedule
decay_lr = tf.train.cosine_decay(
FLAGS.learning_rate,
global_step=global_step-FLAGS.warmup_steps,
decay_steps=FLAGS.train_steps-FLAGS.warmup_steps,
alpha=FLAGS.min_lr_ratio)
# choose warmup or decay
learning_rate = tf.where(global_step < FLAGS.warmup_steps,
warmup_lr, decay_lr)
# get the train op
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.apply_gradients(grads_and_vars, global_step)
##### Training loop
tower_mems_np = [
[np.zeros([FLAGS.mem_len, per_core_bsz, FLAGS.d_model], dtype=np.float32)
for layer in range(FLAGS.n_layer)]
for core in range(FLAGS.num_core_per_host)
]
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
sess.run(tf.global_variables_initializer())
if FLAGS.warm_start_path is not None:
tf.logging.info("warm start from {}".format(FLAGS.warm_start_path))
saver.restore(sess, FLAGS.warm_start_path)
fetches = [loss, tower_new_mems, global_step, gnorm, learning_rate, train_op]
total_loss, prev_step = 0., -1
while True:
feed_dict = {}
for i in range(FLAGS.num_core_per_host):
for m, m_np in zip(tower_mems[i], tower_mems_np[i]):
feed_dict[m] = m_np
fetched = sess.run(fetches, feed_dict=feed_dict)
loss_np, tower_mems_np, curr_step = fetched[:3]
total_loss += loss_np
if curr_step > 0 and curr_step % FLAGS.iterations == 0:
curr_loss = total_loss / (curr_step - prev_step)
tf.logging.info("[{}] | gnorm {:.2f} lr {:8.6f} "
"| loss {:.2f} | pplx {:>7.2f}, bpc {:>7.4f}".format(
curr_step, fetched[-3], fetched[-2],
curr_loss, math.exp(curr_loss), curr_loss / math.log(2)))
total_loss, prev_step = 0., curr_step
if curr_step > 0 and curr_step % FLAGS.save_steps == 0:
save_path = os.path.join(FLAGS.model_dir, "model.ckpt")
saver.save(sess, save_path)
tf.logging.info("Model saved in path: {}".format(save_path))
if curr_step == FLAGS.train_steps:
break
def evaluate(n_token, cutoffs, ps_device):
##### Get input function and model function
eval_input_fn, eval_record_info = data_utils.get_input_fn(
record_info_dir=FLAGS.record_info_dir,
split=FLAGS.eval_split,
per_host_bsz=FLAGS.eval_batch_size,
tgt_len=FLAGS.tgt_len,
num_core_per_host=FLAGS.num_core_per_host,
num_hosts=1,
use_tpu=False)
num_batch = eval_record_info["num_batch"]
if FLAGS.max_eval_batch > 0:
num_batch = FLAGS.max_eval_batch
tf.logging.info("num of batches {}".format(num_batch))
##### Create computational graph
eval_set = eval_input_fn({
"batch_size": FLAGS.eval_batch_size,
"data_dir": FLAGS.data_dir})
input_feed, label_feed = eval_set.make_one_shot_iterator().get_next()
inputs = tf.split(input_feed, FLAGS.num_core_per_host, 0)
labels = tf.split(label_feed, FLAGS.num_core_per_host, 0)
per_core_bsz = FLAGS.eval_batch_size // FLAGS.num_core_per_host
tower_mems, tower_losses, tower_new_mems = [], [], []
for i in range(FLAGS.num_core_per_host):
with tf.device(assign_to_gpu(i, ps_device)), \
tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
mems_i = [tf.placeholder(tf.float32,
[FLAGS.mem_len, per_core_bsz, FLAGS.d_model])
for _ in range(FLAGS.n_layer)]
loss_i, new_mems_i = single_core_graph(
n_token=n_token,
cutoffs=cutoffs,
is_training=False,
inp=inputs[i],
tgt=labels[i],
mems=mems_i)
tower_mems.append(mems_i)
tower_losses.append(loss_i)
tower_new_mems.append(new_mems_i)
## sum losses across towers
if len(tower_losses) > 1:
loss = tf.add_n(tower_losses) / len(tower_losses)
else:
loss = tower_losses[0]
##### Evaluation loop
tower_mems_np = [
[np.zeros([FLAGS.mem_len, per_core_bsz, FLAGS.d_model], dtype=np.float32)
for layer in range(FLAGS.n_layer)]
for core in range(FLAGS.num_core_per_host)
]
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
sess.run(tf.global_variables_initializer())
if FLAGS.eval_ckpt_path is None:
eval_ckpt_path = tf.train.latest_checkpoint(FLAGS.model_dir)
else:
eval_ckpt_path = FLAGS.eval_ckpt_path
tf.logging.info("Evaluate {}".format(eval_ckpt_path))
saver.restore(sess, eval_ckpt_path)
fetches = [loss, tower_new_mems, tf.size(label_feed)]
format_str = " >> processing batch {{:{0}d}}/{{:{0}d}} ..".format(
len(str(num_batch)))
total_loss, total_cnt = 0, 0
for step in range(num_batch):
if step % (num_batch // 10) == 0:
tf.logging.info(format_str.format(step, num_batch))
feed_dict = {}
for i in range(FLAGS.num_core_per_host):
for m, m_np in zip(tower_mems[i], tower_mems_np[i]):
feed_dict[m] = m_np
fetched = sess.run(fetches, feed_dict=feed_dict)
loss_np, tower_mems_np, cnt_np = fetched[:3]
total_loss += loss_np * cnt_np
total_cnt += cnt_np
avg_loss = total_loss / total_cnt
tf.logging.info("| loss {:.2f} | pplx {:>7.2f}, bpc {:>7.4f}".format(
avg_loss, math.exp(avg_loss), avg_loss / math.log(2)))
def main(unused_argv):
del unused_argv # Unused
tf.logging.set_verbosity(tf.logging.INFO)
# Get corpus info
corpus_info = data_utils.get_corpus_info(FLAGS.corpus_info_path)
n_token = corpus_info["vocab_size"]
cutoffs = corpus_info["cutoffs"][1:-1]
tf.logging.info("n_token {}".format(n_token))
if FLAGS.do_train:
train(n_token, cutoffs, "/gpu:0")
if FLAGS.do_eval:
evaluate(n_token, cutoffs, "/gpu:0")
if __name__ == "__main__":
tf.app.run()

170
transformer-xl/tf/vocabulary.py Normal file
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import Counter, OrderedDict
import numpy as np
import tensorflow as tf
from tensorflow.gfile import Open as open
from tensorflow.gfile import Exists as exists
class Vocab(object):
def __init__(self, special=[], min_freq=0, max_size=None, lower_case=True,
delimiter=None, vocab_file=None):
self.counter = Counter()
self.special = special
self.min_freq = min_freq
self.max_size = max_size
self.lower_case = lower_case
self.delimiter = delimiter
self.vocab_file = vocab_file
def tokenize(self, line, add_eos=False, add_double_eos=False):
line = line.strip()
# convert to lower case
if self.lower_case:
line = line.lower()
# empty delimiter '' will evaluate False
if self.delimiter == '':
symbols = line
else:
symbols = line.split(self.delimiter)
if add_double_eos: # lm1b
return ['<S>'] + symbols + ['<S>']
elif add_eos:
return symbols + ['<eos>']
else:
return symbols
def count_file(self, path, verbose=False, add_eos=False):
if verbose: print('counting file {} ...'.format(path))
assert exists(path)
sents = []
with open(path, 'r') as f:
for idx, line in enumerate(f):
if verbose and idx > 0 and idx % 500000 == 0:
print(' line {}'.format(idx))
symbols = self.tokenize(line, add_eos=add_eos)
self.counter.update(symbols)
sents.append(symbols)
return sents
def count_sents(self, sents, verbose=False):
"""
sents : a list of sentences, each a list of tokenized symbols
"""
if verbose: print('counting {} sents ...'.format(len(sents)))
for idx, symbols in enumerate(sents):
if verbose and idx > 0 and idx % 500000 == 0:
print(' line {}'.format(idx))
self.counter.update(symbols)
def _build_from_file(self, vocab_file):
self.idx2sym = []
self.sym2idx = OrderedDict()
with open(vocab_file, 'r') as f:
for line in f:
symb = line.strip().split()[0]
self.add_symbol(symb)
self.unk_idx = self.sym2idx['<UNK>']
def build_vocab(self):
if self.vocab_file:
print('building vocab from {}'.format(self.vocab_file))
self._build_from_file(self.vocab_file)
print('final vocab size {}'.format(len(self)))
else:
print('building vocab with min_freq={}, max_size={}'.format(
self.min_freq, self.max_size))
self.idx2sym = []
self.sym2idx = OrderedDict()
for sym in self.special:
self.add_special(sym)
for sym, cnt in self.counter.most_common(self.max_size):
if cnt < self.min_freq: break
self.add_symbol(sym)
print('final vocab size {} from {} unique tokens'.format(
len(self), len(self.counter)))
def encode_file(self, path, ordered=False, verbose=False, add_eos=True,
add_double_eos=False):
if verbose: print('encoding file {} ...'.format(path))
assert exists(path)
encoded = []
with open(path, 'r') as f:
for idx, line in enumerate(f):
if verbose and idx > 0 and idx % 500000 == 0:
print(' line {}'.format(idx))
symbols = self.tokenize(line, add_eos=add_eos,
add_double_eos=add_double_eos)
encoded.append(self.convert_to_nparray(symbols))
if ordered:
encoded = np.concatenate(encoded)
return encoded
def encode_sents(self, sents, ordered=False, verbose=False):
if verbose: print('encoding {} sents ...'.format(len(sents)))
encoded = []
for idx, symbols in enumerate(sents):
if verbose and idx > 0 and idx % 500000 == 0:
print(' line {}'.format(idx))
encoded.append(self.convert_to_nparray(symbols))
if ordered:
encoded = np.concatenate(encoded)
return encoded
def add_special(self, sym):
if sym not in self.sym2idx:
self.idx2sym.append(sym)
self.sym2idx[sym] = len(self.idx2sym) - 1
setattr(self, '{}_idx'.format(sym.strip('<>')), self.sym2idx[sym])
def add_symbol(self, sym):
if sym not in self.sym2idx:
self.idx2sym.append(sym)
self.sym2idx[sym] = len(self.idx2sym) - 1
def get_sym(self, idx):
assert 0 <= idx < len(self), 'Index {} out of range'.format(idx)
return self.idx2sym[idx]
def get_idx(self, sym):
if sym in self.sym2idx:
return self.sym2idx[sym]
else:
assert hasattr(self, 'unk_idx')
return self.sym2idx.get(sym, self.unk_idx)
def get_symbols(self, indices):
return [self.get_sym(idx) for idx in indices]
def get_indices(self, symbols):
return [self.get_idx(sym) for sym in symbols]
def convert_to_nparray(self, symbols):
nparray = np.array(self.get_indices(symbols), dtype=np.int64)
return nparray
def convert_to_sent(self, indices, exclude=None):
if exclude is None:
return ' '.join([self.get_sym(idx) for idx in indices])
else:
return ' '.join([self.get_sym(idx) for idx in indices if idx not in exclude])
def __len__(self):
return len(self.idx2sym)