Fixes flake8 --select W605 test warnings (#829)

asears · web-flow · commit 9165b27fdf24 · 2020-05-13T17:55:40.000-06:00
Signed-off-by: asears &lt;asears@users.noreply.github.com&gt;
diff --git a/apex/contrib/optimizers/fused_adam.py b/apex/contrib/optimizers/fused_adam.py
@@ -29,7 +29,7 @@ class FusedAdam(torch.optim.Optimizer):
         use_mt (boolean, optional): use multi tensor apply for lower launch
             latency. (default: False)
 
-    .. _Adam\: A Method for Stochastic Optimization:
+    .. _Adam - A Method for Stochastic Optimization:
         https://arxiv.org/abs/1412.6980
     .. _On the Convergence of Adam and Beyond:
         https://openreview.net/forum?id=ryQu7f-RZ
diff --git a/apex/contrib/optimizers/fused_lamb.py b/apex/contrib/optimizers/fused_lamb.py
@@ -54,7 +54,7 @@ class FusedLAMB(torch.optim.Optimizer):
         max_grad_norm (float, optional): value used to clip global grad norm
             (default: 1.0)
 
-    .. _Large Batch Optimization for Deep Learning\: Training BERT in 76 minutes:
+    .. _Large Batch Optimization for Deep Learning - Training BERT in 76 minutes:
         https://arxiv.org/abs/1904.00962
     .. _On the Convergence of Adam and Beyond:
         https://openreview.net/forum?id=ryQu7f-RZ
diff --git a/apex/optimizers/fused_adam.py b/apex/optimizers/fused_adam.py
@@ -53,7 +53,7 @@ class FusedAdam(torch.optim.Optimizer):
         set_grad_none (bool, optional): whether set grad to None when zero_grad()
             method is called. (default: True)
 
-    .. _Adam\: A Method for Stochastic Optimization:
+    .. _Adam - A Method for Stochastic Optimization:
         https://arxiv.org/abs/1412.6980
     .. _On the Convergence of Adam and Beyond:
         https://openreview.net/forum?id=ryQu7f-RZ
diff --git a/apex/optimizers/fused_lamb.py b/apex/optimizers/fused_lamb.py
@@ -52,7 +52,7 @@ class FusedLAMB(torch.optim.Optimizer):
         max_grad_norm (float, optional): value used to clip global grad norm
             (default: 1.0)
 
-    .. _Large Batch Optimization for Deep Learning\: Training BERT in 76 minutes:
+    .. _Large Batch Optimization for Deep Learning - Training BERT in 76 minutes:
         https://arxiv.org/abs/1904.00962
     .. _On the Convergence of Adam and Beyond:
         https://openreview.net/forum?id=ryQu7f-RZ
diff --git a/apex/optimizers/fused_novograd.py b/apex/optimizers/fused_novograd.py
@@ -58,7 +58,7 @@ class FusedNovoGrad(torch.optim.Optimizer):
         set_grad_none (bool, optional): whether set grad to None when zero_grad()
             method is called. (default: True)
 
-    .. _Jasper\: An End-to-End Convolutional Neural Acoustic Model:
+    .. _Jasper - An End-to-End Convolutional Neural Acoustic Model:
         https://arxiv.org/abs/1904.03288
     .. _On the Convergence of Adam and Beyond:
         https://openreview.net/forum?id=ryQu7f-RZ
diff --git a/apex/reparameterization/__init__.py b/apex/reparameterization/__init__.py
@@ -2,7 +2,7 @@
 from .reparameterization import Reparameterization
 
 def apply_weight_norm(module, name='', dim=0, hook_child=True):
-    """
+    r"""
     Applies weight normalization to a parameter in the given module.
     If no parameter is provided, applies weight normalization to all
     parameters in model (except 1-d vectors and scalars).
diff --git a/apex/reparameterization/weight_norm.py b/apex/reparameterization/weight_norm.py
@@ -20,7 +20,7 @@ def _norm(p, dim):
 HALF_TYPES = (torch.cuda.HalfTensor, torch.HalfTensor)
 
 class WeightNorm(Reparameterization):
-    """
+    r"""
     Weight normalization is a reparameterization that decouples the magnitude
     of a weight tensor from its direction. This replaces the parameter specified
     by `name` (e.g. "weight") with two parameters: one specifying the magnitude
diff --git a/requirements_dev.txt b/requirements_dev.txt
@@ -0,0 +1,3 @@
+-r requirements.txt
+flake8>=3.7.9
+Sphinx>=3.0.3

Original file line number	Diff line number	Diff line change
`@@ -0,0 +1,3 @@`
	`1`	`+-r requirements.txt`
	`2`	`+flake8>=3.7.9`
	`3`	`+Sphinx>=3.0.3`