Broadcast minimum input

Author: swimdi

tests/lowering/eltwise/binary/test_minimum.py

@@ -16,18 +16,10 @@ def forward(self, x, y):
     "input_shapes",
     (
         ((32, 32), (32, 32)),
-        pytest.param(
-            ((64,), (32, 64)),
-            marks=pytest.mark.xfail(reason="broadcasting issues (#64)"),
-        ),
-        pytest.param(
-            ((64, 32), (64, 1)),
-            marks=pytest.mark.xfail(reason="broadcasting issues (#64)"),
-        ),
-        pytest.param(
-            ((64, 1), (1, 64)),
-            marks=pytest.mark.xfail(reason="broadcasting issues (#64)"),
-        ),
+        ((64,), (32, 64)),
+        ((64, 32), (64, 1)),
+        ((64, 1), (1, 64)),
+        ((1, 16, 59, 59), ()),
     ),
 )
 def test_minimum(device, input_shapes):

torch_ttnn/passes/lowering/to_tt_pass.py

@@ -1170,7 +1170,7 @@ def rewrite_node(node):
             kwargs = node.kwargs
 
             # workaround for issue #64
-            if node.target == torch.ops.aten.maximum.default:
+            if node.target in [torch.ops.aten.maximum.default, torch.ops.aten.minimum.default]:
                 self_tensor = args[0]
                 if len(args) > 1:
                     other_tensor = args[1]
@@ -1179,7 +1179,7 @@ def rewrite_node(node):
                 if get_shape(self_tensor) is None or get_shape(other_tensor) is None:
                     return None
                 broadcasted_shape, broadcasted_tensors = broadcast_tensors(g, [self_tensor, other_tensor])
-                return g.call_function(torch.ops.aten.maximum.default, tuple(broadcasted_tensors))
+                return g.call_function(node.target, tuple(broadcasted_tensors))
 
         with g.inserting_before(node):
             new_node = rewrite_node(node)