also supported the latest master (1.7)

python/tvm/relay/frontend/pytorch.py

@@ -535,6 +535,9 @@ def _impl(inputs, input_types):
 
 def _where():
     def _impl(inputs, input_types):
+        if len(inputs) == 1:
+            return _nonzero(False)([inputs[0], True], input_types)
+
         cond = inputs[0]
         x, y = _pytorch_promote_types(inputs[1:3], input_types[1:3])
         return _op.where(cond, x, y)
@@ -2278,9 +2281,8 @@ def _impl(inputs, input_types):
         ret = _op.transform.argwhere(data)
 
         if is_numpy_style or (len(inputs) > 1 and inputs[1]):
-            # TODO(kevinthesun): Support this by adding unbind op
-            # ret = _unbind()([ret, 0], None)
-            raise RuntimeError("as_tuple is not supported yet for nonzero.")
+            return _unbind()([ret, 1], None)
+
         return ret
 
     return _impl

tests/python/frontend/pytorch/test_forward.py

@@ -2865,10 +2865,19 @@ class Where2(Module):
         def forward(self, *args):
             return torch.where(args[0] > 0, args[0], args[1])
 
+    class Where3(Module):
+        def forward(self, *args):
+            return torch.where(args[0])[0]
+
     x = torch.rand([3, 2]).float()
-    verify_model(Where1().float().eval(), input_data=[x])
+    verify_model(Where1(), input_data=[x])
     y = torch.rand([3, 2])
-    verify_model(Where2().float().eval(), input_data=[x, y])
+    verify_model(Where2(), input_data=[x, y])
+
+    # a single argument variant, equivalent to torch.nonzero(..., as_tuple=True)
+    inp = torch.rand([10])
+    inp[3:8] = 0
+    verify_trace_model(Where3(), [inp], ["llvm"])
 
 
 @tvm.testing.uses_gpu