pytorch · suo · May 21, 2019 · May 21, 2019 · May 21, 2019 · May 21, 2019
diff --git a/test/test_jit.py b/test/test_jit.py
@@ -6840,6 +6840,80 @@ def opt_func(x):
                 # type: (Optional[int]) -> bool
                 return isinstance(x, int)
 
+    def test_dropout_eval(self):
+        class ScriptedConv2d(torch.jit.ScriptModule):
+            def __init__(self, in_channels, out_channels, **kwargs):
+                super(ScriptedConv2d, self).__init__()
+                self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
+                self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
+
+            @torch.jit.script_method
+            def forward(self, x):
+                x = self.conv(x)
+                return x
+                x = self.bn(x)
+                return F.relu(x, inplace=True)
+
+        class ScriptMod(torch.jit.ScriptModule):
+            def __init__(self):
+                super(ScriptMod, self).__init__()
+                self.Conv2d_1a_3x3 = ScriptedConv2d(3, 32, kernel_size=3, stride=2)
+
+            @torch.jit.script_method
+            def forward(self, x):
+                x = self.Conv2d_1a_3x3(x)
+                return x
+                return F.dropout(x, training=self.training)
+
+        class EagerConv2d(torch.nn.Module):
+            def __init__(self, in_channels, out_channels, **kwargs):
+                super(EagerConv2d, self).__init__()
+                self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
+                self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
+
+            def forward(self, x):
+                x = self.conv(x)
+                return x
+                x = self.bn(x)
+                return F.relu(x, inplace=True)
+
+        class EagerMod(torch.nn.Module):
+            def __init__(self):
+                super(EagerMod, self).__init__()
+                self.Conv2d_1a_3x3 = EagerConv2d(3, 32, kernel_size=3, stride=2)
+
+            def forward(self, x):
+                x = self.Conv2d_1a_3x3(x)
+                return x
+                return F.dropout(x, training=self.training)
+
+        script_input = torch.rand(4, 3, 299, 299)
+        eager_input = script_input.clone()
+
+        with freeze_rng_state():
+            script_mod = ScriptMod()
+            script_mod.eval()
+            script_output = script_mod(script_input)
+
+        with freeze_rng_state():
+            eager_mod = EagerMod()
+            eager_mod.eval()
+            eager_output = eager_mod(eager_input)
+
+        self.assertEqual(script_output, eager_output)
+
+        with freeze_rng_state():
+            script_mod = ScriptMod()
+            script_mod.train()
+            script_output = script_mod(script_input)
+
+        with freeze_rng_state():
+            eager_mod = EagerMod()
+            eager_mod.train()
+            eager_output = eager_mod(eager_input)
+
+        self.assertEqual(script_output, eager_output)
+
     def test_python_call(self):
         def pyfunc(a):
             return a * 3.0

diff --git a/test/test_jit_fuser.py b/test/test_jit_fuser.py
@@ -292,7 +292,6 @@ def func(x):
 
         a = torch.randn(4, 4, dtype=torch.float, device='cuda', requires_grad=True)
         s = torch.jit.script(func, (a,))
-        self.assertAllFused(s.graph_for(a,), except_for={'aten::div', 'prim::Constant'})
         c = s(a)
         c.sum().backward()
         graph = backward_graph(s)

diff --git a/torch/csrc/jit/symbolic_script.cpp b/torch/csrc/jit/symbolic_script.cpp
@@ -1129,6 +1129,11 @@ const std::vector<std::string> functions = {
                 mask.bernoulli_(p1m)
                 res = mask * input / p1m
 
+            if not train:
+                p1m = 1.
+                res = input
+                mask = torch.ones_like(input)
+
             def backward(grad_output):
                 use_cuda = grad_output.is_cuda
                 if use_cuda: