chore(transformer-xl): Initial commit

transformer-xl/pytorch/README.md

@@ -0,0 +1,62 @@
+## 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`

transformer-xl/pytorch/data_utils.py

@@ -0,0 +1,273 @@
+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)))

transformer-xl/pytorch/eval.py

@@ -0,0 +1,122 @@
+# 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)

transformer-xl/pytorch/mem_transformer.py

@@ -0,0 +1,812 @@
+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:]

transformer-xl/pytorch/run_enwik8_base.sh

@@ -0,0 +1,41 @@
+#!/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

transformer-xl/pytorch/run_enwik8_large.sh

@@ -0,0 +1,41 @@
+#!/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

transformer-xl/pytorch/run_lm1b_base.sh

@@ -0,0 +1,43 @@
+#!/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

transformer-xl/pytorch/run_lm1b_large.sh

@@ -0,0 +1,43 @@
+#!/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

transformer-xl/pytorch/run_text8_base.sh

@@ -0,0 +1,41 @@
+#!/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

transformer-xl/pytorch/run_text8_large.sh

@@ -0,0 +1,38 @@
+#!/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

transformer-xl/pytorch/run_wt103_base.sh

@@ -0,0 +1,42 @@
+#!/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

transformer-xl/pytorch/run_wt103_large.sh

@@ -0,0 +1,43 @@
+#!/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

transformer-xl/pytorch/train.py

@@ -0,0 +1,562 @@
+# 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)

transformer-xl/pytorch/utils/adaptive_softmax.py

@@ -0,0 +1,90 @@
+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

transformer-xl/pytorch/utils/data_parallel.py

@@ -0,0 +1,91 @@
+
+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)
+

transformer-xl/pytorch/utils/exp_utils.py

@@ -0,0 +1,40 @@
+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)))

transformer-xl/pytorch/utils/log_uniform_sampler.py

@@ -0,0 +1,147 @@
+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())
+

transformer-xl/pytorch/utils/proj_adaptive_softmax.py

@@ -0,0 +1,151 @@
+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

transformer-xl/pytorch/utils/vocabulary.py

@@ -0,0 +1,163 @@
+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)