Merge pull request #89 from ptrblck/update_examples

Update examples to PyTorch >=0.4.0

examples/FP16_Optimizer_simple/closure.py

 import torch
-from torch.autograd import Variable
 from apex.fp16_utils import FP16_Optimizer
 
 torch.backends.cudnn.benchmark = True
 
 N, D_in, D_out = 64, 1024, 16
 
-x = Variable(torch.cuda.FloatTensor(N, D_in ).normal_()).half()
-y = Variable(torch.cuda.FloatTensor(N, D_out).normal_()).half()
+x = torch.randn(N, D_in, device='cuda', dtype=torch.half)
+y = torch.randn(N, D_out, device='cuda', dtype=torch.half)
 
 model = torch.nn.Linear(D_in, D_out).cuda().half()
 

examples/FP16_Optimizer_simple/distributed_apex/distributed_data_parallel.py

 import torch
-from torch.autograd import Variable
 import argparse
 from apex.parallel import DistributedDataParallel as DDP
 from apex.fp16_utils import FP16_Optimizer
@@ -16,8 +15,8 @@ torch.backends.cudnn.benchmark = True
 
 N, D_in, D_out = 64, 1024, 16
 
-x = Variable(torch.cuda.FloatTensor(N, D_in ).normal_()).half()
-y = Variable(torch.cuda.FloatTensor(N, D_out).normal_()).half()
+x = torch.randn(N, D_in, device='cuda', dtype=torch.half)
+y = torch.randn(N, D_out, device='cuda', dtype=torch.half)
 
 model = torch.nn.Linear(D_in, D_out).cuda().half()
 model = DDP(model)

examples/FP16_Optimizer_simple/distributed_apex_legacy_launcher/distributed_data_parallel.py

 import torch
-from torch.autograd import Variable
 import argparse
 from apex.parallel import DistributedDataParallel as DDP
 from apex.fp16_utils import FP16_Optimizer
@@ -24,8 +23,8 @@ torch.backends.cudnn.benchmark = True
 
 N, D_in, D_out = 64, 1024, 16
 
-x = Variable(torch.cuda.FloatTensor(N, D_in ).normal_()).half()
-y = Variable(torch.cuda.FloatTensor(N, D_out).normal_()).half()
+x = torch.randn(N, D_in, device='cuda', dtype=torch.half)
+y = torch.randn(N, D_out, device='cuda', dtype=torch.half)
 
 model = torch.nn.Linear(D_in, D_out).cuda().half()
 model = DDP(model)

examples/FP16_Optimizer_simple/distributed_pytorch/distributed_data_parallel.py

 import torch
-from torch.autograd import Variable
 import argparse
 from apex.fp16_utils import FP16_Optimizer
 
@@ -15,8 +14,8 @@ torch.backends.cudnn.benchmark = True
 
 N, D_in, D_out = 64, 1024, 16
 
-x = Variable(torch.cuda.FloatTensor(N, D_in ).normal_()).half()
-y = Variable(torch.cuda.FloatTensor(N, D_out).normal_()).half()
+x = torch.randn(N, D_in, device='cuda', dtype=torch.half)
+y = torch.randn(N, D_out, device='cuda', dtype=torch.half)
 
 model = torch.nn.Linear(D_in, D_out).cuda().half()
 model = torch.nn.parallel.DistributedDataParallel(model,

examples/FP16_Optimizer_simple/minimal.py

 import torch
-from torch.autograd import Variable
 from apex.fp16_utils import FP16_Optimizer
 
 torch.backends.cudnn.benchmark = True
 
 N, D_in, D_out = 64, 1024, 16
 
-x = Variable(torch.cuda.FloatTensor(N, D_in ).normal_()).half()
-y = Variable(torch.cuda.FloatTensor(N, D_out).normal_()).half()
+x = torch.randn(N, D_in, device='cuda', dtype=torch.half)
+y = torch.randn(N, D_out, device='cuda', dtype=torch.half)
 
 model = torch.nn.Linear(D_in, D_out).cuda().half()
 

examples/FP16_Optimizer_simple/save_load.py

 import torch
-from torch.autograd import Variable
 from apex.fp16_utils import FP16_Optimizer
 
 torch.backends.cudnn.benchmark = True
 
 N, D_in, D_out = 64, 1024, 16
 
-x = Variable(torch.cuda.FloatTensor(N, D_in ).normal_()).half()
-y = Variable(torch.cuda.FloatTensor(N, D_out).normal_()).half()
+x = torch.randn(N, D_in, device='cuda', dtype=torch.half)
+y = torch.randn(N, D_out, device='cuda', dtype=torch.half)
 
 model = torch.nn.Linear(D_in, D_out).cuda().half()
 

examples/distributed/main.py

@@ -6,7 +6,6 @@ import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 from torchvision import datasets, transforms
-from torch.autograd import Variable
 from apex.fp16_utils import to_python_float
 
 #=====START: ADDED FOR DISTRIBUTED======
@@ -82,9 +81,6 @@ if args.distributed:
 #=====END:   ADDED FOR DISTRIBUTED======
 
 torch.manual_seed(args.seed)
-if args.cuda:
-    torch.cuda.manual_seed(args.seed)
-
 
 kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
 
@@ -158,7 +154,6 @@ def train(epoch):
     for batch_idx, (data, target) in enumerate(train_loader):
         if args.cuda:
             data, target = data.cuda(), target.cuda()
-        data, target = Variable(data), Variable(target)
         optimizer.zero_grad()
         output = model(data)
         loss = F.nll_loss(output, target)
@@ -177,11 +172,10 @@ def test():
         with torch.no_grad():
             if args.cuda:
                 data, target = data.cuda(), target.cuda()
-            data, target = Variable(data), Variable(target)
             output = model(data)
             test_loss += to_python_float(F.nll_loss(output, target, size_average=False).data) # sum up batch loss
             pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
-            correct += pred.eq(target.data.view_as(pred)).cpu().sum()
+            correct += pred.eq(target.data.view_as(pred)).float().cpu().sum()
 
     test_loss /= len(test_loader.dataset)
 

examples/imagenet/main.py

@@ -4,7 +4,6 @@ import shutil
 import time
 
 import torch
-from torch.autograd import Variable
 import torch.nn as nn
 import torch.nn.parallel
 import torch.backends.cudnn as cudnn
@@ -315,12 +314,9 @@ def train(train_loader, model, criterion, optimizer, epoch):
         # measure data loading time
         data_time.update(time.time() - end)
 
-        input_var = Variable(input)
-        target_var = Variable(target)
-
         # compute output
-        output = model(input_var)
-        loss = criterion(output, target_var)
+        output = model(input)
+        loss = criterion(output, target)
 
         # measure accuracy and record loss
         prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
@@ -392,13 +388,11 @@ def validate(val_loader, model, criterion):
         i += 1
 
         target = target.cuda(async=True)
-        input_var = Variable(input)
-        target_var = Variable(target)
 
         # compute output
         with torch.no_grad():
-            output = model(input_var)
-            loss = criterion(output, target_var)
+            output = model(input)
+            loss = criterion(output, target)
 
         # measure accuracy and record loss
         prec1, prec5 = accuracy(output.data, target, topk=(1, 5))

examples/imagenet/main_fp16_optimizer.py

@@ -4,7 +4,6 @@ import shutil
 import time
 
 import torch
-from torch.autograd import Variable
 import torch.nn as nn
 import torch.nn.parallel
 import torch.backends.cudnn as cudnn
@@ -307,12 +306,9 @@ def train(train_loader, model, criterion, optimizer, epoch):
         # measure data loading time
         data_time.update(time.time() - end)
 
-        input_var = Variable(input)
-        target_var = Variable(target)
-
         # compute output
-        output = model(input_var)
-        loss = criterion(output, target_var)
+        output = model(input)
+        loss = criterion(output, target)
 
         # measure accuracy and record loss
         prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
@@ -375,14 +371,10 @@ def validate(val_loader, model, criterion):
     while input is not None:
         i += 1
 
-        target = target.cuda(async=True)
-        input_var = Variable(input)
-        target_var = Variable(target)
-
         # compute output
         with torch.no_grad():
-            output = model(input_var)
-            loss = criterion(output, target_var)
+            output = model(input)
+            loss = criterion(output, target)
 
         # measure accuracy and record loss
         prec1, prec5 = accuracy(output.data, target, topk=(1, 5))

examples/imagenet/main_reducer.py

@@ -4,7 +4,6 @@ import shutil
 import time
 
 import torch
-from torch.autograd import Variable
 import torch.nn as nn
 import torch.nn.parallel
 import torch.backends.cudnn as cudnn
@@ -301,12 +300,9 @@ def train(train_loader, model, criterion, optimizer, epoch):
         # measure data loading time
         data_time.update(time.time() - end)
 
-        input_var = Variable(input)
-        target_var = Variable(target)
-
         # compute output
-        output = model(input_var)
-        loss = criterion(output, target_var)
+        output = model(input)
+        loss = criterion(output, target)
 
         # measure accuracy and record loss
         prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
@@ -382,13 +378,11 @@ def validate(val_loader, model, criterion):
         i += 1
 
         target = target.cuda(async=True)
-        input_var = Variable(input)
-        target_var = Variable(target)
 
         # compute output
         with torch.no_grad():
-            output = model(input_var)
-            loss = criterion(output, target_var)
+            output = model(input)
+            loss = criterion(output, target)
 
         # measure accuracy and record loss
         prec1, prec5 = accuracy(output.data, target, topk=(1, 5))

examples/word_language_model/generate.py

@@ -8,7 +8,6 @@
 import argparse
 
 import torch
-from torch.autograd import Variable
 
 import data
 
@@ -38,8 +37,6 @@ 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(args.seed)
 
 if args.temperature < 1e-3:
     parser.error("--temperature has to be greater or equal 1e-3")
@@ -56,14 +53,15 @@ else:
 corpus = data.Corpus(args.data)
 ntokens = len(corpus.dictionary)
 hidden = model.init_hidden(1)
-input = Variable(torch.rand(1, 1).mul(ntokens).long(), volatile=True)
-if args.cuda:
-    input.data = input.data.cuda()
+with torch.no_grad():
+    input = torch.rand(1, 1).mul(ntokens).long()
+    if args.cuda:
+        input = input.cuda()
 
-with open(args.outf, 'w') as outf:
+    with open(args.outf, 'w') as outf:
         for i in range(args.words):
             output, hidden = model(input, hidden)
-        word_weights = output.squeeze().data.div(args.temperature).exp().cpu()
+            word_weights = output.squeeze().float().data.div(args.temperature).exp().cpu()
             word_idx = torch.multinomial(word_weights, 1)[0]
             input.data.fill_(word_idx)
             word = corpus.dictionary.idx2word[word_idx]

examples/word_language_model/main.py

@@ -4,7 +4,6 @@ import time
 import math
 import torch
 import torch.nn as nn
-from torch.autograd import Variable
 import data
 import model
 
@@ -58,8 +57,6 @@ 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(args.seed)
 if args.fp16 and not args.cuda:
     print("WARNING: --fp16 requires --cuda, ignoring --fp16 option")
 
@@ -117,7 +114,7 @@ criterion = nn.CrossEntropyLoss()
 
 
 def repackage_hidden(h):
-    """Wraps hidden states in new Variables, to detach them from their history."""
+    """Detaches hidden states from their history."""
     if torch.is_tensor(h):
         return h.detach()
     else:
@@ -136,8 +133,8 @@ def repackage_hidden(h):
 
 def get_batch(source, i):
     seq_len = min(args.bptt, len(source) - 1 - i)
-    data = Variable(source[i:i+seq_len])
-    target = Variable(source[i+1:i+1+seq_len].view(-1))
+    data = source[i:i+seq_len]
+    target = source[i+1:i+1+seq_len].view(-1)
     return data, target
 
 

examples/word_language_model/main_fp16_optimizer.py

@@ -4,7 +4,6 @@ import time
 import math
 import torch
 import torch.nn as nn
-from torch.autograd import Variable
 import data
 import model
 
@@ -61,8 +60,6 @@ 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(args.seed)
 if args.fp16 and not args.cuda:
     print("WARNING: --fp16 requires --cuda, ignoring --fp16 option")
 
@@ -132,7 +129,7 @@ if args.cuda and args.fp16:
 
 
 def repackage_hidden(h):
-    """Wraps hidden states in new Variables, to detach them from their history."""
+    """Detaches hidden states from their history."""
     if torch.is_tensor(h):
         return h.detach()
     else:
@@ -151,8 +148,8 @@ def repackage_hidden(h):
 
 def get_batch(source, i):
     seq_len = min(args.bptt, len(source) - 1 - i)
-    data = Variable(source[i:i+seq_len])
-    target = Variable(source[i+1:i+1+seq_len].view(-1))
+    data = source[i:i+seq_len]
+    target = source[i+1:i+1+seq_len].view(-1)
     return data, target
 
 

examples/word_language_model/model.py

 import torch.nn as nn
-from torch.autograd import Variable
 
 
 class RNNModel(nn.Module):
@@ -53,7 +52,7 @@ class RNNModel(nn.Module):
     def init_hidden(self, bsz):
         weight = next(self.parameters()).data
         if self.rnn_type == 'LSTM':
-            return (Variable(weight.new(self.nlayers, bsz, self.nhid).zero_()),
-                    Variable(weight.new(self.nlayers, bsz, self.nhid).zero_()))
+            return (weight.new(self.nlayers, bsz, self.nhid).zero_(),
+                    weight.new(self.nlayers, bsz, self.nhid).zero_())
         else:
-            return Variable(weight.new(self.nlayers, bsz, self.nhid).zero_())
+            return weight.new(self.nlayers, bsz, self.nhid).zero_()