fix init

byteps/torch/__init__.py

@@ -55,7 +55,8 @@ def __init__(self, params, named_parameters, compression,
                              'tuples (name, parameter), usually produced by '
                              'model.named_parameters().')
 
-        dups = _DistributedOptimizer.find_duplicates([k for k, _ in named_parameters])
+        dups = _DistributedOptimizer.find_duplicates(
+            [k for k, _ in named_parameters])
         if len(dups) > 0:
             raise ValueError('Parameter names in named_parameters must be unique. '
                              'Found duplicates: %s' % ', '.join(dups))
@@ -70,7 +71,8 @@ def __init__(self, params, named_parameters, compression,
                 # https://github.com/pytorch/pytorch/issues/7733
                 self._parameter_names = {v.__hash__(): k for k, v
                                          in sorted(named_parameters)}
-                self._tensor_list = [tensor for name, tensor in named_parameters]
+                self._tensor_list = [tensor for name,
+                                     tensor in named_parameters]
             else:
                 self._is_tensor_instance = False
                 self._parameter_names = {v: k for k, v
@@ -134,7 +136,8 @@ def _push_pull_grad_async(self, p):
         else:
             tensor = p.grad
             tensor_compressed, ctx = self._compression.compress(tensor)
-            handle = byteps_push_pull(tensor_compressed, average=True, name="Gradient."+name)
+            handle = byteps_push_pull(
+                tensor_compressed, average=True, name="Gradient."+name)
         return handle, ctx
 
     def _make_hook(self, p):
@@ -176,7 +179,8 @@ def synchronize(self):
     @contextmanager
     def skip_synchronize(self):
         if self._enable_async:
-            raise AssertionError("skip_synchronize cannot be used in async training")
+            raise AssertionError(
+                "skip_synchronize cannot be used in async training")
         self._should_sync = False
         try:
             yield
@@ -201,7 +205,8 @@ def step(self, closure=None):
                         name = self._parameter_names.get(p.__hash__())
                     else:
                         name = self._parameter_names.get(p)
-                    handle = byteps_push_pull(p, average=False, name="AsyncParam."+name)
+                    handle = byteps_push_pull(
+                        p, average=False, name="Parameter."+name)
                     _, ctx = self._compression.compress(p)
                     self._handles[p] = (handle, ctx)
 
@@ -378,7 +383,8 @@ def _from_tensor():
             key = '%s.%d' % (option_key, index)
             dtypes = _get_types(option_value)
             option_tensor = torch.Tensor([option_value]).cuda()
-            callbacks[key] = _create_option_callback(index, option_key, option_tensor, dtypes)
+            callbacks[key] = _create_option_callback(
+                index, option_key, option_tensor, dtypes)
             params.append((key, option_tensor))
 
         # The params list here is ordered by the layers in the model