fix(imperative): update region restricted conv testcase for bias shape

GitOrigin-RevId: 8bf66e7312d2649ae91dde54f7e8218bbdeea7f9

imperative/python/test/unit/functional/test_functional.py

@@ -1008,7 +1008,7 @@ def test_region_restricted_conv_forward_backward_cuda(bias):
         rin = tensor(np.ones(rin_shape).astype(np.int32), device="cpu0")
         rout = tensor(np.ones(rout_shape).astype(np.int32), device="cpu0")
         bias_cpu = (
-            tensor(np.ones(diff_shape).astype(np.float32), device="cpu0")
+            tensor(np.ones((1, GROUP * OCPG, 1, 1)).astype(np.float32), device="cpu0")
             if bias
             else None
         )
@@ -1033,7 +1033,9 @@ def test_region_restricted_conv_forward_backward_cuda(bias):
     rin = tensor(np.ones(rin_shape).astype(np.int32), device=handle)
     rout = tensor(np.ones(rout_shape).astype(np.int32), device=handle)
     bias_gpu = (
-        tensor(np.ones(diff_shape).astype(np.float32), device=handle) if bias else None
+        tensor(np.ones((1, GROUP * OCPG, 1, 1)).astype(np.float32), device=handle)
+        if bias
+        else None
     )
     gm = GradManager().attach([src, filter])
     with gm:
@@ -1085,7 +1087,7 @@ def test_region_restricted_conv_forward_backward_uint8(bias):
         rin = tensor(np.ones(rin_shape).astype(np.int32), device="cpu0")
         rout = tensor(np.ones(rout_shape).astype(np.int32), device="cpu0")
         bias_cpu = (
-            tensor(np.ones(diff_shape).astype(np.float32), device="cpu0")
+            tensor(np.ones((1, GROUP * OCPG, 1, 1)).astype(np.float32), device="cpu0")
             if bias
             else None
         )
@@ -1111,7 +1113,9 @@ def test_region_restricted_conv_forward_backward_uint8(bias):
     rin = tensor(np.ones(rin_shape).astype(np.uint8), device=handle)
     rout = tensor(np.ones(rout_shape).astype(np.uint8), device=handle)
     bias_gpu = (
-        tensor(np.ones(diff_shape).astype(np.float32), device=handle) if bias else None
+        tensor(np.ones((1, GROUP * OCPG, 1, 1)).astype(np.float32), device=handle)
+        if bias
+        else None
     )
 
     gm = GradManager().attach([src, filter])