Fix undefined bias for conv batching rule

Fixes part of #338

Author: zou3519

functorch/csrc/BatchRulesModules.cpp

@@ -23,8 +23,9 @@ convolution_batch_rule(const Tensor& lhs, optional<int64_t> lhs_bdim, const Tens
   // If we have a batched bias or weight, we need to perform the computation separately.
   optional<Tensor> unbatched_bias;
   bool separate_bias;
-  if ((rhs_bdim && bias) || bias_bdim) {
+  if ((rhs_bdim && bias && bias->defined()) || bias_bdim) {
     TORCH_INTERNAL_ASSERT(bias.has_value());
+    TORCH_INTERNAL_ASSERT(bias->defined());
     unbatched_bias = nullopt;
     separate_bias = true;
   } else {

test/test_ops.py

@@ -469,7 +469,6 @@ def vjp_of_vjp(*args_and_cotangents):
 
         # PyTorch changed its convolution recently.
         # Maybe it is responsible for all of the following changes.
-        xfail('nn.functional.conv1d'),
         xfail('nn.functional.conv_transpose1d'),
         xfail('nn.functional.conv_transpose2d'),
         xfail('nn.functional.conv_transpose3d'),
@@ -479,7 +478,7 @@ def vjp_of_vjp(*args_and_cotangents):
     @skipOps('TestOperators', 'test_vmapvjp', vmapvjp_fail)
     def test_vmapvjp(self, device, dtype, op):
         # These are too annoying to put into the list above
-        if op.name in {'nn.functional.linear', 'nn.functional.conv2d'}:
+        if op.name in {'nn.functional.linear'}:
             self.skipTest("Skipped! ExpectedF failures")
         if not op.supports_autograd:
             self.skipTest("Skipped! Autograd not supported.")