Make the code compatible with PyTorch 0.4.1

.gitignore

@@ -99,3 +99,5 @@ ENV/
 
 # mypy
 .mypy_cache/
+
+data/*

dynamiceval.py

@@ -76,9 +76,9 @@ def repackage_hidden(h):
         return tuple(repackage_hidden(v) for v in h)
 
 
-def get_batch(source, i, evaluation=False):
+def get_batch(source, i):
     seq_len = min(args.bptt, len(source) - 1 - i)
-    data = Variable(source[i:i+seq_len], volatile=evaluation)
+    data = Variable(source[i:i+seq_len])
     target = Variable(source[i+1:i+1+seq_len].view(-1))
     return data, target
 
@@ -95,7 +95,8 @@ def gradstat():
 
     while i < train_data.size(0) - 1 - 1:
         seq_len = args.bptt
-        model.eval()
+        model.train()
+        model.use_dropout = False
 
         data, targets = get_batch(train_data, i)
         hidden = repackage_hidden(hidden)
@@ -161,7 +162,8 @@ def evaluate():
     #loops through data
     while i < eval_data.size(0) - 1 - 1:
 
-        model.eval()
+        model.train()
+        model.use_dropout = False
         #gets last chunk of seqlence if seqlen doesn't divide full sequence cleanly
         if (i+seq_len)>=eval_data.size(0):
             if last:

embed_regularize.py

@@ -1,6 +1,7 @@
 import numpy as np
 
 import torch
+import torch.nn.functional as F
 from torch.autograd import Variable
 
 def embedded_dropout(embed, words, dropout=0.1, scale=None):
@@ -16,7 +17,7 @@ def embedded_dropout(embed, words, dropout=0.1, scale=None):
   padding_idx = embed.padding_idx
   if padding_idx is None:
       padding_idx = -1
-  X = embed._backend.Embedding.apply(words, masked_embed_weight,
+  X = F.embedding(words, masked_embed_weight,
     padding_idx, embed.max_norm, embed.norm_type,
     embed.scale_grad_by_freq, embed.sparse
   )

finetune.py

@@ -151,16 +151,17 @@ def evaluate(data_source, batch_size=10):
     total_loss = 0
     ntokens = len(corpus.dictionary)
     hidden = model.init_hidden(batch_size)
-    for i in range(0, data_source.size(0) - 1, args.bptt):
-        data, targets = get_batch(data_source, i, args, evaluation=True)
-        targets = targets.view(-1)
-
-        log_prob, hidden = parallel_model(data, hidden)
-        loss = nn.functional.nll_loss(log_prob.view(-1, log_prob.size(2)), targets).data
-
-        total_loss += len(data) * loss
-        hidden = repackage_hidden(hidden)
-    return total_loss[0] / len(data_source)
+    with torch.no_grad():
+        for i in range(0, data_source.size(0) - 1, args.bptt):
+            data, targets = get_batch(data_source, i, args)
+            targets = targets.view(-1)
+
+            log_prob, hidden = parallel_model(data, hidden)
+            loss = nn.functional.nll_loss(log_prob.view(-1, log_prob.size(2)), targets).data
+
+            total_loss += len(data) * loss
+            hidden = repackage_hidden(hidden)
+    return total_loss.item() / len(data_source)
 
 def train():
     assert args.batch_size % args.small_batch_size == 0, 'batch_size must be divisible by small_batch_size'
@@ -210,13 +211,13 @@ def train():
             end = start + args.small_batch_size
 
         # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
-        torch.nn.utils.clip_grad_norm(model.parameters(), args.clip)
+        torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
         optimizer.step()
 
         # total_loss += raw_loss.data
         optimizer.param_groups[0]['lr'] = lr2
         if batch % args.log_interval == 0 and batch > 0:
-            cur_loss = total_loss[0] / args.log_interval
+            cur_loss = total_loss.item() / args.log_interval
             elapsed = time.time() - start_time
             logging('| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.2f} | ms/batch {:5.2f} | '
                     'loss {:5.2f} | ppl {:8.2f}'.format(

main.py

@@ -157,16 +157,17 @@ def evaluate(data_source, batch_size=10):
     total_loss = 0
     ntokens = len(corpus.dictionary)
     hidden = model.init_hidden(batch_size)
-    for i in range(0, data_source.size(0) - 1, args.bptt):
-        data, targets = get_batch(data_source, i, args, evaluation=True)
-        targets = targets.view(-1)
+    with torch.no_grad():
+        for i in range(0, data_source.size(0) - 1, args.bptt):
+            data, targets = get_batch(data_source, i, args)
+            targets = targets.view(-1)
 
-        log_prob, hidden = parallel_model(data, hidden)
-        loss = nn.functional.nll_loss(log_prob.view(-1, log_prob.size(2)), targets).data
+            log_prob, hidden = parallel_model(data, hidden)
+            loss = nn.functional.nll_loss(log_prob.view(-1, log_prob.size(2)), targets).data
 
-        total_loss += loss * len(data)
+            total_loss += loss * len(data)
 
-        hidden = repackage_hidden(hidden)
+            hidden = repackage_hidden(hidden)
     return total_loss[0] / len(data_source)
 
 
@@ -220,7 +221,7 @@ def train():
             gc.collect()
 
         # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
-        torch.nn.utils.clip_grad_norm(model.parameters(), args.clip)
+        torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
         optimizer.step()
 
         # total_loss += raw_loss.data

model.py

@@ -15,12 +15,13 @@ def __init__(self, rnn_type, ntoken, ninp, nhid, nhidlast, nlayers,
                  dropout=0.5, dropouth=0.5, dropouti=0.5, dropoute=0.1, wdrop=0, 
                  tie_weights=False, ldropout=0.5, n_experts=10):
         super(RNNModel, self).__init__()
+        self.use_dropout = True
         self.lockdrop = LockedDropout()
         self.encoder = nn.Embedding(ntoken, ninp)
 
         self.rnns = [torch.nn.LSTM(ninp if l == 0 else nhid, nhid if l != nlayers - 1 else nhidlast, 1, dropout=0) for l in range(nlayers)]
         if wdrop:
-            self.rnns = [WeightDrop(rnn, ['weight_hh_l0'], dropout=wdrop) for rnn in self.rnns]
+            self.rnns = [WeightDrop(rnn, ['weight_hh_l0'], dropout=wdrop if self.use_dropout else 0) for rnn in self.rnns]
         self.rnns = torch.nn.ModuleList(self.rnns)
 
         self.prior = nn.Linear(nhidlast, n_experts, bias=False)
@@ -68,10 +69,10 @@ def init_weights(self):
     def forward(self, input, hidden, return_h=False, return_prob=False):
         batch_size = input.size(1)
 
-        emb = embedded_dropout(self.encoder, input, dropout=self.dropoute if self.training else 0)
+        emb = embedded_dropout(self.encoder, input, dropout=self.dropoute if (self.training and self.use_dropout) else 0)
         #emb = self.idrop(emb)
 
-        emb = self.lockdrop(emb, self.dropouti)
+        emb = self.lockdrop(emb, self.dropouti if self.use_dropout else 0)
 
         raw_output = emb
         new_hidden = []
@@ -85,21 +86,21 @@ def forward(self, input, hidden, return_h=False, return_prob=False):
             raw_outputs.append(raw_output)
             if l != self.nlayers - 1:
                 #self.hdrop(raw_output)
-                raw_output = self.lockdrop(raw_output, self.dropouth)
+                raw_output = self.lockdrop(raw_output, self.dropouth if self.use_dropout else 0)
                 outputs.append(raw_output)
         hidden = new_hidden
 
-        output = self.lockdrop(raw_output, self.dropout)
+        output = self.lockdrop(raw_output, self.dropout if self.use_dropout else 0)
         outputs.append(output)
 
         latent = self.latent(output)
-        latent = self.lockdrop(latent, self.dropoutl)
+        latent = self.lockdrop(latent, self.dropoutl if self.use_dropout else 0)
         logit = self.decoder(latent.view(-1, self.ninp))
 
         prior_logit = self.prior(output).contiguous().view(-1, self.n_experts)
-        prior = nn.functional.softmax(prior_logit)
+        prior = nn.functional.softmax(prior_logit, -1)
 
-        prob = nn.functional.softmax(logit.view(-1, self.ntoken)).view(-1, self.n_experts, self.ntoken)
+        prob = nn.functional.softmax(logit.view(-1, self.ntoken), -1).view(-1, self.n_experts, self.ntoken)
         prob = (prob * prior.unsqueeze(2).expand_as(prob)).sum(1)
 
         if return_prob:
@@ -129,4 +130,3 @@ def init_hidden(self, bsz):
     # input = Variable(torch.LongTensor(13, 9).random_(0, 10))
     # hidden = model.init_hidden(9)
     # print(model.sample(input, hidden, 5, 6, 1, 2, sample_latent=True).size())
-

utils.py

@@ -4,10 +4,10 @@
 
 def repackage_hidden(h):
     """Wraps hidden states in new Variables, to detach them from their history."""
-    if type(h) == Variable:
-        return Variable(h.data)
-    else:
+    if isinstance(h, tuple) or isinstance(h, list):
         return tuple(repackage_hidden(v) for v in h)
+    else:
+        return h.detach()
 
 def batchify(data, bsz, args):
     # Work out how cleanly we can divide the dataset into bsz parts.
@@ -21,9 +21,9 @@ def batchify(data, bsz, args):
         data = data.cuda()
     return data
 
-def get_batch(source, i, args, seq_len=None, evaluation=False):
+def get_batch(source, i, args, seq_len=None):
     seq_len = min(seq_len if seq_len else args.bptt, len(source) - 1 - i)
-    data = Variable(source[i:i+seq_len], volatile=evaluation)
+    data = Variable(source[i:i+seq_len])
     # target = Variable(source[i+1:i+1+seq_len].view(-1))
     target = Variable(source[i+1:i+1+seq_len])
     return data, target