Add type annotations to conv-relu (pytorch#47680)

Summary:
Fixes pytorch#47679

Pull Request resolved: pytorch#47680

Reviewed By: zhangguanheng66

Differential Revision: D25416628

Pulled By: malfet

fbshipit-source-id: 103bea1e8c300990f74689787a71b1cfe916cfef

mypy.ini

@@ -101,15 +101,15 @@ ignore_errors = True
 [mypy-torch.nn.quantized.modules.conv]
 ignore_errors = True
 
-[mypy-torch._lobpcg]
-ignore_errors = True
-
 [mypy-torch._appdirs]
 ignore_errors = True
 
 [mypy-torch._utils]
 ignore_errors = True
 
+[mypy-torch._overrides]
+ignore_errors = True
+
 [mypy-torch.utils.tensorboard._caffe2_graph]
 ignore_errors = True
 
@@ -131,15 +131,6 @@ ignore_errors = True
 [mypy-torch.nn.quantized.modules.batchnorm]
 ignore_errors = True
 
-[mypy-torch.nn.intrinsic.quantized.modules.conv_relu]
-ignore_errors = True
-
-[mypy-torch.nn.intrinsic.quantized.modules.bn_relu]
-ignore_errors = True
-
-[mypy-torch.nn.intrinsic.quantized.modules.linear_relu]
-ignore_errors = True
-
 [mypy-torch.nn.intrinsic.qat.modules.conv_fused]
 ignore_errors = True
 

torch/_lobpcg.py

@@ -262,7 +262,7 @@ def _symeig_backward(D_grad, U_grad, A, D, U, largest):
 class LOBPCGAutogradFunction(torch.autograd.Function):
 
     @staticmethod
-    def forward(ctx,
+    def forward(ctx,  # type: ignore[override]
                 A: Tensor,
                 k: Optional[int] = None,
                 B: Optional[Tensor] = None,
@@ -606,7 +606,7 @@ def _lobpcg(A: Tensor,
         bparams['ortho_use_drop'] = bparams.get('ortho_use_drop', False)
 
     if not torch.jit.is_scripting():
-        LOBPCG.call_tracker = LOBPCG_call_tracker
+        LOBPCG.call_tracker = LOBPCG_call_tracker  # type: ignore
 
     if len(A.shape) > 2:
         N = int(torch.prod(torch.tensor(A.shape[:-2])))
@@ -628,7 +628,7 @@ def _lobpcg(A: Tensor,
             bXret[i] = worker.X[:, :k]
 
         if not torch.jit.is_scripting():
-            LOBPCG.call_tracker = LOBPCG_call_tracker_orig
+            LOBPCG.call_tracker = LOBPCG_call_tracker_orig  # type: ignore
 
         return bE.reshape(A.shape[:-2] + (k,)), bXret.reshape(A.shape[:-2] + (m, k))
 
@@ -640,7 +640,7 @@ def _lobpcg(A: Tensor,
     worker.run()
 
     if not torch.jit.is_scripting():
-        LOBPCG.call_tracker = LOBPCG_call_tracker_orig
+        LOBPCG.call_tracker = LOBPCG_call_tracker_orig  # type: ignore
 
     return worker.E[:k], worker.X[:, :k]
 

torch/nn/intrinsic/quantized/modules/conv_relu.py

@@ -16,7 +16,7 @@ class ConvReLU1d(nnq.Conv1d):
         Same as torch.nn.quantized.Conv1d
 
     """
-    _FLOAT_MODULE = torch.nn.intrinsic.ConvReLU1d
+    _FLOAT_MODULE = torch.nn.intrinsic.ConvReLU1d  # type: ignore[assignment]
 
     def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                  padding=0, dilation=1, groups=1, bias=True,
@@ -55,7 +55,7 @@ class ConvReLU2d(nnq.Conv2d):
         Same as torch.nn.quantized.Conv2d
 
     """
-    _FLOAT_MODULE = torch.nn.intrinsic.ConvReLU2d
+    _FLOAT_MODULE = torch.nn.intrinsic.ConvReLU2d  # type: ignore[assignment]
 
     def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                  padding=0, dilation=1, groups=1, bias=True,
@@ -94,7 +94,7 @@ class ConvReLU3d(nnq.Conv3d):
     Attributes: Same as torch.nn.quantized.Conv3d
 
     """
-    _FLOAT_MODULE = torch.nn.intrinsic.ConvReLU3d
+    _FLOAT_MODULE = torch.nn.intrinsic.ConvReLU3d  # type: ignore[assignment]
 
     def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                  padding=0, dilation=1, groups=1, bias=True,