Some modifications to improve readability (#31352)

Summary:
In the long string, formalstring thinks it is good to have a name.

When using dict, literal is better for readability and faster than dict constructor.

I always appreciate your efforts in creating the world's best frameworks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31352

Differential Revision: D19191967

Pulled By: ngimel

fbshipit-source-id: 21f063b163b67de8cf9761a4db5991f74318e991

benchmarks/fastrnns/bench.py

@@ -85,9 +85,12 @@ def trainbench(name, rnn_creator, nloops=100, warmup=10,
         return fwd_time, bwd_time
 
     assert device == 'cuda'
-    creator_args = dict(seqLength=seqLength, numLayers=numLayers,
-                        inputSize=inputSize, hiddenSize=hiddenSize,
-                        miniBatch=miniBatch, device=device, seed=seed)
+    creator_args = creator_args = {
+        'seqLength': seqLength, 'numLayers': numLayers,
+        'inputSize': inputSize, 'hiddenSize': hiddenSize,
+        'miniBatch': miniBatch, 'device': device, 'seed': seed
+    }
+
     modeldef = rnn_creator(**creator_args)
 
     [train_batch(modeldef) for _ in range(warmup)]
@@ -217,7 +220,7 @@ if __name__ == '__main__':
     del bench_args['cnns']
     del bench_args['variable_lstms']
 
-    results = dict()
+    results = {}
     if should_bench_varlen_lstms:
         if args.nloops + args.warmup > 30:
             print_stderr(

torch/serialization.py

@@ -161,10 +161,10 @@ def validate_cuda_device(location):
                            'to map your storages to the CPU.')
     if device >= torch.cuda.device_count():
         raise RuntimeError('Attempting to deserialize object on CUDA device '
-                           '{} but torch.cuda.device_count() is {}. Please use '
+                           '{device} but torch.cuda.device_count() is {device_count}. Please use '
                            'torch.load with map_location to map your storages '
                            'to an existing device.'.format(
-                               device, torch.cuda.device_count()))
+                               device=device, device_count=torch.cuda.device_count()))
     return device
 
 
@@ -188,8 +188,8 @@ def location_tag(storage):
         location = tagger(storage)
         if location:
             return location
-    raise RuntimeError("don't know how to determine data location of " +
-                       torch.typename(storage))
+    raise RuntimeError("don't know how to determine data location of "
+                       + torch.typename(storage))
 
 
 def default_restore_location(storage, location):
@@ -197,9 +197,9 @@ def default_restore_location(storage, location):
         result = fn(storage, location)
         if result is not None:
             return result
-    raise RuntimeError("don't know how to restore data location of " +
-                       torch.typename(storage) + " (tagged with " +
-                       location + ")")
+    raise RuntimeError("don't know how to restore data location of "
+                       + torch.typename(storage) + " (tagged with "
+                       + location + ")")
 
 
 def normalize_storage_type(storage_type):
@@ -320,9 +320,9 @@ def _check_seekable(f):
     def raise_err_msg(patterns, e):
         for p in patterns:
             if p in str(e):
-                msg = (str(e) + ". You can only torch.load from a file that is seekable." +
-                                " Please pre-load the data into a buffer like io.BytesIO and" +
-                                " try to load from it instead.")
+                msg = (str(e) + ". You can only torch.load from a file that is seekable."
+                                + " Please pre-load the data into a buffer like io.BytesIO and"
+                                + " try to load from it instead.")
                 raise type(e)(msg)
         raise e
 
@@ -656,8 +656,8 @@ def _legacy_load(f, map_location, pickle_module, **pickle_load_args):
                        "accessing the object's source attribute or set "
                        "`torch.nn.Module.dump_patches = True` and use the "
                        "patch tool to revert the changes.")
-            msg = ("source code of class '{}' has changed. {}"
-                   .format(torch.typename(container_type), msg))
+            msg = ("source code of class '{container_type}' has changed. {msg}"
+                   .format(container_type=torch.typename(container_type), msg=msg))
             warnings.warn(msg, SourceChangeWarning)
 
     def legacy_load(f):
@@ -753,7 +753,8 @@ def _legacy_load(f, map_location, pickle_module, **pickle_load_args):
         except tarfile.TarError:
             if _is_zipfile(f):
                 # .zip is used for torch.jit.save and will throw an un-pickling error here
-                raise RuntimeError("{} is a zip archive (did you mean to use torch.jit.load()?)".format(f.name))
+                raise RuntimeError(
+                    "{filename} is a zip archive (did you mean to use torch.jit.load()?)".format(filename=f.name))
             # if not a tarfile, reset file offset and proceed
             f.seek(0)
 

torch/utils/hooks.py

@@ -1,5 +1,5 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
-import collections
+from collections import OrderedDict
 import weakref
 import warnings
 
@@ -25,7 +25,7 @@ class RemovableHandle(object):
     def __setstate__(self, state):
         if state[0] is None:
             # create a dead reference
-            self.hooks_dict_ref = weakref.ref(collections.OrderedDict())
+            self.hooks_dict_ref = weakref.ref(OrderedDict())
         else:
             self.hooks_dict_ref = weakref.ref(state[0])
         self.id = state[1]