replace fill_zeros_like op with fill_any_like op (#45657)

* relace fill_zeros_like op with fill_any_like op in backward.py and tensor.py

* Remove unnecessary comments

* modify create op_desc param

python/paddle/fluid/backward.py

@@ -673,7 +673,7 @@ def _remove_no_grad_branch_(op_descs,
         op_desc for op_desc in op_descs
         if not _op_can_be_removed_(op_desc, no_grad_set)
     ]
-    # Insert fill_zeros_like_op
+    # Insert fill_any_like_op with value 0
     to_insert = []
     for idx, op_desc in enumerate(op_descs):
         for arg in op_desc.input_arg_names():
@@ -682,8 +682,11 @@ def _remove_no_grad_branch_(op_descs,
                 x_in = _strip_grad_suffix_(arg)
                 # the reason should be: arg can be input of another grad op
                 # and the op is a not-to-remove op
-                new_op_desc = _create_op_desc_("fill_zeros_like", {"X": [x_in]},
-                                               {"Out": [arg]}, {})
+                new_op_desc = _create_op_desc_("fill_any_like", {"X": [x_in]},
+                                               {"Out": [arg]}, {
+                                                   'value': 0,
+                                                   'dtype': -1
+                                               })
                 # update the mapping between fwd and bwd
                 if grad_op_id_to_fwd_op is not None and grad_op_id_to_fwd_op.get(
                         op_desc.original_id(), None) is not None:

python/paddle/fluid/layers/tensor.py

@@ -1680,10 +1680,9 @@ def zeros_like(x, out=None):
           data = fluid.layers.zeros_like(x) # [0.0, 0.0, 0.0]
 
     """
-
     check_variable_and_dtype(x, "x",
                              ['bool', 'float32', 'float64', 'int32', 'int64'],
-                             'ones_like')
+                             'zeros_like')
     helper = LayerHelper("zeros_like", **locals())
     if out is None:
         out = helper.create_variable_for_type_inference(dtype=x.dtype)
@@ -1691,9 +1690,12 @@ def zeros_like(x, out=None):
         check_variable_and_dtype(
             out, "out", ['bool', 'float32', 'float64', 'int32', 'int64'],
             'zeros_like')
-
-    helper.append_op(type='fill_zeros_like',
+    helper.append_op(type='fill_any_like',
                      inputs={'X': [x]},
+                     attrs={
+                         'value': 0,
+                         "dtype": x.dtype
+                     },
                      outputs={'Out': [out]})
     out.stop_gradient = True
     return out