【prim】modify split vjp Concat can't get one input "split_0_tmp_1@GRAD" (#54132)

* modify gradOpMaker

* modify concat bug

* modify concat bug

* delete unnecessary block

* modify fill_any_like value from 1 to 0

* modify fill_any_like dtype from other to -1

* ci_bug

python/paddle/fluid/backward.py

@@ -751,29 +751,32 @@ def _remove_no_grad_branch_(
     ]
     # 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():
-            # arg is a gradient var name and arg should not have gradient
-            if core.grad_var_suffix() in arg and arg in no_grad_set:
-                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_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
-                ):
-                    grad_op_id_to_fwd_op[
-                        new_op_desc.original_id()
-                    ] = grad_op_id_to_fwd_op[op_desc.original_id()]
-                to_insert.append((new_op_desc, idx))
+    if not core._is_bwd_prim_enabled():
+        for idx, op_desc in enumerate(op_descs):
+            for arg in op_desc.input_arg_names():
+                # arg is a gradient var name and arg should not have gradient
+                if core.grad_var_suffix() in arg and arg in no_grad_set:
+                    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_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
+                    ):
+                        grad_op_id_to_fwd_op[
+                            new_op_desc.original_id()
+                        ] = grad_op_id_to_fwd_op[op_desc.original_id()]
+                    to_insert.append((new_op_desc, idx))
 
     list([op_descs.insert(p[1], p[0]) for p in reversed(to_insert)])
 
@@ -1349,6 +1352,28 @@ def _append_backward_ops_(
     assert isinstance(rename_var_map, dict)
 
     if core._is_bwd_prim_enabled():
+        grad_name_set = set()
+        for target in target_vars:
+            grad_name_set.add(_append_grad_suffix_(target.name))
+
+        for op in reversed(block.ops):
+            if op.type == "fill_any_like":
+                for out_name in op.desc.output_arg_names():
+                    grad_name_set.add(out_name)
+                continue
+            for var_name in op.desc.output_arg_names():
+                grad_var_name = _append_grad_suffix_(var_name)
+                if grad_var_name not in grad_name_set:
+                    op_desc = _create_op_desc_(
+                        "fill_any_like",
+                        {"X": [var_name]},
+                        {"Out": [grad_var_name]},
+                        {'value': 0, 'dtype': target_vars[0].dtype},
+                    )
+                    block.desc.append_op().copy_from(op_desc)
+            break
+        block.program._sync_with_cpp()
+
         composite_block = program.clone().current_block()
         # Create output and infer shape for operators whose output haven't
         # been created.

test/prim/composite_ops/test_composite_batch_norm.py

@@ -489,7 +489,7 @@ class TestPrimEvalBranch(unittest.TestCase):
     def train(self, use_prim):
         core._set_prim_all_enabled(use_prim)
         paddle.seed(2022)
-        net = BatchNorm(2, is_test=True)
+        net = BatchNorm(2, act="relu", is_test=True)
         net = apply_to_static(net, False)
         out = net(self.x)
         loss = paddle.mean(out)

test/prim/test_comp_custom_vjp.py

@@ -42,6 +42,7 @@ class TestCustomVJP(unittest.TestCase):
             'uniform_random',
             'dropout',
             'fill_any_like',
+            'fill_any_like',
             'cast',
             'elementwise_mul',
             'scale',
@@ -56,6 +57,7 @@ class TestCustomVJP(unittest.TestCase):
             'scale',
             'cast',
             'fill_constant',
+            'fill_constant',
             'cast',
             'elementwise_mul',
             'scale',