diff --git a/python/paddle/distributed/fleet/meta_optimizers/sharding/offload_helper.py b/python/paddle/distributed/fleet/meta_optimizers/sharding/offload_helper.py
index 3816e9b3051abfc026dd426d6988537d64185de0..3ad6e320316c61ed1b74829b6074685874eb61fc 100755
--- a/python/paddle/distributed/fleet/meta_optimizers/sharding/offload_helper.py
+++ b/python/paddle/distributed/fleet/meta_optimizers/sharding/offload_helper.py
@@ -12,8 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import copy
 from ..common import is_optimizer_op, OP_ROLE_KEY, OpRole, is_update_op
 from paddle.fluid import core, unique_name
+from .shard import Shard
 
 __all__ = []
 
@@ -23,11 +25,8 @@ class OffloadHelper(object):
     cuda_place_type = 1
     cuda_pinned_place_type = 2
 
-    def __init__(self):
-        pass
-        "0: dst is on CPUPlace. "
-        "1: dst is on CUDAPlace. "
-        "2: dst is on CUDAPinnedPlace. "
+    def __init__(self, ring_id=None):
+        self.ring_id = ring_id
 
     def _insert_cast_op(self, block, idx, src_name, dst_name):
         src_var = block.var(src_name)
@@ -50,6 +49,21 @@ class OffloadHelper(object):
                 OP_ROLE_KEY: OpRole.Optimize
             })
 
+    def _insert_broadcast_op(self, block, idx, param):
+        if self.ring_id is None:
+            return
+        block._insert_op_without_sync(
+            idx,
+            type="c_broadcast",
+            inputs={'X': param},
+            outputs={'Out': param},
+            attrs={
+                'ring_id': self.ring_id,
+                'root': 0,
+                'use_calc_stream': True,
+                OP_ROLE_KEY: OpRole.Forward,
+            })
+
     def _insert_memcpy_op(self, block, idx, src_name, dst_name, dst_place_type):
         src_var = block.var(src_name)
         dst_var = block.var(dst_name)
@@ -206,6 +220,8 @@ class OffloadHelper(object):
 
         # step5: startup_block add offload
         visited_vars = set()
+        # FIXME(wangxi): should insert in idx, need move comm init to the head.
+        insert_idx = len(startup_block.ops)
         for idx, op in reversed(list(enumerate(startup_block.ops))):
             for out_name in op.output_arg_names:
                 if out_name in visited_vars:
@@ -213,13 +229,16 @@ class OffloadHelper(object):
 
                 if out_name in param_name_to_offload_name:
                     var_name = out_name
-                    # FIXME(wangxi): offload should insert after broadcast param
                     if offload:
                         offload_var_name = param_name_to_offload_name[var_name]
-                        self._insert_offload_op(startup_block, idx + 1,
+                        self._insert_offload_op(startup_block, insert_idx,
                                                 var_name, offload_var_name)
-                    self._insert_cast_op(startup_block, idx + 1, var_name,
+                    self._insert_cast_op(startup_block, insert_idx, var_name,
                                          param_to_fp16[var_name])
+                    # NOTE(wangxi): cast and offload should insert after broadcast param.
+                    # the insert op order is: broadcast, cast, offload
+                    self._insert_broadcast_op(startup_block, insert_idx,
+                                              var_name)
 
                 visited_vars.add(out_name)
 
@@ -303,3 +322,181 @@ class OffloadHelper(object):
 
         block._sync_with_cpp()
         startup_block._sync_with_cpp()
+
+    def opt_sharding_cast_fp32param(self,
+                                    block,
+                                    startup_block,
+                                    params,
+                                    offload=False):
+        """
+        (p_fp16) = cast(p)
+        (p_fp16_recompute) = cast(p)
+        (pout,) = adam(p)
+        ===========================>
+        rename(p_fp16_recompute, p_fp16)
+
+        (pout,) = adam(p)
+        (p_fp16) = cast(p)
+        broadcast(p_fp16)
+        """
+        global_params = set()
+        local_params = set()
+        param_to_fp16 = dict()
+        # recompute_var which need rename to fp16_param
+        fp16_param_to_recompute = dict()
+        recompute_to_fp16 = dict()
+
+        def remove_param(input_name):
+            global_params.remove(input_name)
+            if input_name in local_params:
+                local_params.remove(input_name)
+            if input_name in param_to_fp16:
+                fp16_param = param_to_fp16.pop(input_name)
+                if fp16_param in fp16_param_to_recompute:
+                    recompute = fp16_param_to_recompute.pop(fp16_param)
+                    recompute_to_fp16.pop(recompute)
+
+        # step1: record param
+        global_params = set(params)
+        for idx, op in reversed(list(enumerate(block.ops))):
+            if is_update_op(op):
+                param = op.desc.input("Param")[0]
+                local_params.add(param)
+
+        # step2: remove param which can't offload and
+        #        record param->fp16param, fp16param->recompute_var
+        for idx, op in enumerate(block.ops):
+            if is_optimizer_op(op):
+                break
+            # TODO (Yuang Liu): tmp solution for fuse_grad_merge + optimize_cast
+            if op.type == 'coalesce_tensor':
+                continue
+            for input_name in op.desc.input_arg_names():
+                if input_name not in global_params:
+                    continue
+
+                # param which will be used by fp32 op
+                if op.type != 'cast':
+                    remove_param(input_name)
+                    continue
+
+                # param is only used by cast op,
+                # which to cast fp32_param to fp16_param
+                output_name = op.output_arg_names[0]
+                if 'cast_fp16' not in output_name:
+                    remove_param(input_name)
+                    continue
+
+                if 'subprog' not in output_name:
+                    assert output_name == input_name + '.cast_fp16'
+                    assert input_name not in param_to_fp16, \
+                        "There must be only one cast op from fp32 param to fp16 param."
+                    param_to_fp16[input_name] = output_name
+                else:
+                    # fp16-->recompute_var
+                    assert input_name in param_to_fp16, \
+                        "param must first be cast to fp16"
+                    fp16_param = param_to_fp16[input_name]
+                    fp16_param_to_recompute[fp16_param] = output_name
+                    recompute_to_fp16[output_name] = fp16_param
+
+        param_name_to_offload_name = dict()
+        # step3: main_block add offload, cast op
+        # change recompute to fp16, remove cast(param) to fp16
+        for idx, op in reversed(list(enumerate(block.ops))):
+            if is_update_op(op):
+                param = op.desc.input("Param")[0]
+                if param not in global_params:
+                    continue
+                # step3.1: create offload_var
+                offload_var_name = self._get_offload_var_name(param)
+                param_name_to_offload_name[param] = offload_var_name
+                if offload:
+                    self._create_offload_var(param, offload_var_name,
+                                             [block, startup_block])
+
+                    # step3.2: insert cast op and offload op
+                    self._insert_offload_op(block, idx + 1, param,
+                                            offload_var_name)
+
+                assert param in param_to_fp16
+                fp16_param_name = param_to_fp16[param]
+                fp16_param_var = block.var(fp16_param_name)
+                fp16_param_var.persistable = True
+                self._insert_cast_op(block, idx + 1, param,
+                                     param_to_fp16[param])
+
+                if offload:
+                    # step3.3: insert fetch op
+                    self._insert_fetch_op(block, idx, offload_var_name, param)
+
+                continue
+
+            # step3.4: remove cast op
+            if op.type == 'cast':
+                input_name = op.desc.input_arg_names()[0]
+                if input_name in global_params:
+                    block._remove_op(idx, sync=False)
+                    continue
+
+            # step3.5: change recompute_param to fp16_param
+            for input_name in op.desc.input_arg_names():
+                if input_name in recompute_to_fp16:
+                    op._rename_input(input_name, recompute_to_fp16[input_name])
+            for output_name in op.desc.output_arg_names():
+                if output_name in recompute_to_fp16:
+                    op._rename_output(output_name,
+                                      recompute_to_fp16[output_name])
+
+        # step4: remove recompute_param
+        for name in recompute_to_fp16.keys():
+            block._remove_var(name, sync=False)
+
+        # step5: remove fp32 param which not need
+        for idx, op in enumerate(block.ops):
+            if op.type not in ['coalesce_tensor', 'c_broadcast']:
+                continue
+            for input_name in op.desc.input_arg_names():
+                if input_name in param_to_fp16:
+                    op._rename_input(input_name, param_to_fp16[input_name])
+            for output_name in op.desc.output_arg_names():
+                if output_name in param_to_fp16:
+                    op._rename_output(output_name, param_to_fp16[output_name])
+
+        for param in global_params:
+            assert param in param_to_fp16
+            fp16_param_name = param_to_fp16[param]
+            fp16_param_var = block.var(fp16_param_name)
+            fp16_param_var.persistable = True
+
+            if param not in local_params:
+                block._remove_var(param, sync=False)
+
+        # step6: startup_block add offload
+        visited_vars = set()
+        insert_idx = len(startup_block.ops)
+        for idx, op in reversed(list(enumerate(startup_block.ops))):
+            for out_name in op.output_arg_names:
+                if out_name in visited_vars: continue
+
+                if out_name in param_to_fp16:
+                    var_name = out_name
+                    if offload:
+                        self._insert_offload_op(
+                            startup_block, idx + 1, var_name,
+                            param_name_to_offload_name[var_name])
+
+                    self._insert_cast_op(startup_block, insert_idx, var_name,
+                                         param_to_fp16[var_name])
+
+                    self._insert_broadcast_op(startup_block, insert_idx,
+                                              var_name)
+
+                    if var_name not in local_params:
+                        param = startup_block.var(out_name)
+                        param.persistable = False
+
+                visited_vars.add(out_name)
+
+        block._sync_with_cpp()
+        startup_block._sync_with_cpp()
diff --git a/python/paddle/distributed/fleet/meta_optimizers/sharding/utils.py b/python/paddle/distributed/fleet/meta_optimizers/sharding/utils.py
index 0b8f67a0a7cd9f141fcbaf9c447d8f64e8451e69..447b52ace697878cf2a4d3425e5e2c99d79c073d 100755
--- a/python/paddle/distributed/fleet/meta_optimizers/sharding/utils.py
+++ b/python/paddle/distributed/fleet/meta_optimizers/sharding/utils.py
@@ -14,7 +14,7 @@
 import paddle
 from paddle.fluid import core, unique_name
 from functools import reduce
-from paddle.distributed.fleet.meta_optimizers.common import is_loss_grad_op, is_backward_op
+from paddle.distributed.fleet.meta_optimizers.common import is_loss_grad_op, is_backward_op, is_optimizer_op
 from paddle.distributed.fleet.meta_optimizers.common import OpRole, OP_ROLE_KEY, OP_ROLE_VAR_KEY
 
 import re
@@ -366,6 +366,24 @@ def insert_allreduce_ops(block,
 
 
 class FuseHelper(object):
+    @staticmethod
+    def sort_vars_by_dtype(block, vars_name):
+        fp32_vars = []
+        fp16_vars = []
+        other_vars = []
+        for var in vars_name:
+            dtype = block.var(var).dtype
+            if dtype == paddle.float32:
+                fp32_vars.append(var)
+            elif dtype == paddle.float16:
+                fp16_vars.append(var)
+            else:
+                other_vars.append(var)
+        assert len(other_vars) == 0, "only support fp32/fp16 vars for fuse"
+
+        fp32_vars.extend(fp16_vars)
+        return fp32_vars
+
     @staticmethod
     def get_fused_groups(block, vars_name, fuse_size=32.):
         """ coalesce tensor, get fused group """
@@ -639,6 +657,54 @@ def insert_broadcast_param_ops(block,
     return param_in_this_device
 
 
+def fuse_opt_broadcast_param_ops(block,
+                                 ring_id,
+                                 shard,
+                                 op_role=OpRole.Optimize,
+                                 strategy=None):
+    """
+    fuse optimizer sharding broadcast param ops
+    """
+    if strategy is None or not strategy.fuse_all_reduce_ops:
+        return
+
+    fuse_size = strategy.fuse_grad_size_in_MB
+
+    nranks = shard.worker_num
+    device_to_vars = [[] for _ in range(nranks)]
+
+    for idx, op in reversed(list(enumerate(block.ops))):
+        if not is_optimizer_op(op) or op.type != 'c_broadcast':
+            break
+        var = op.input_arg_names[0]
+        root_id = op.attr('root')
+        device_to_vars[root_id].insert(0, var)
+        block._remove_op(idx, sync=False)
+
+    insert_idx = idx + 1
+    for root_id, vars_name in enumerate(device_to_vars):
+        vars_name = FuseHelper.sort_vars_by_dtype(block, vars_name)
+        groups = FuseHelper.get_fused_groups(block, vars_name, fuse_size)
+
+        fused_vars, insert_num = FuseHelper.insert_coalesce_tensor(
+            block, insert_idx, groups, op_role, prefix="Param")
+
+        for fused_var in fused_vars:
+            block._insert_op_without_sync(
+                insert_idx + insert_num,
+                type='c_broadcast',
+                inputs={'X': fused_var},
+                outputs={'Out': fused_var},
+                attrs={
+                    'ring_id': ring_id,
+                    'root': root_id,
+                    'use_calc_stream': True,
+                    OP_ROLE_KEY: op_role
+                })
+
+    block._sync_with_cpp()
+
+
 def get_grad_device(grad_name, shard):
     assert "@GRAD" in grad_name, "[{}] should be a grad variable.".format(
         grad_name)
diff --git a/python/paddle/distributed/fleet/meta_optimizers/sharding_optimizer.py b/python/paddle/distributed/fleet/meta_optimizers/sharding_optimizer.py
index 1af646b3959e014a99713ccb984e1fc12a320c7e..75a69e5527bc18e71eb9286ce1bda60c0aeaaf1d 100755
--- a/python/paddle/distributed/fleet/meta_optimizers/sharding_optimizer.py
+++ b/python/paddle/distributed/fleet/meta_optimizers/sharding_optimizer.py
@@ -329,6 +329,7 @@ class ShardingOptimizer(MetaOptimizerBase):
         if self.pp_degree == 1: return
 
         strategy = self.user_defined_strategy
+        sharding_configs = strategy.sharding_configs
 
         main_block = self._main_program.global_block()
         startup_block = self._startup_program.global_block()
@@ -399,6 +400,8 @@ class ShardingOptimizer(MetaOptimizerBase):
             first_optimize_op_index += (len(main_block.ops) - len_of_ops)
             len_of_ops = len(main_block.ops)
 
+            # NOTE(wangxi): we fused after optimize_cast
+            optimize_cast = sharding_configs['optimize_cast']
             optimizer_param = utils.insert_broadcast_param_ops(
                 main_block,
                 len_of_ops,
@@ -407,10 +410,10 @@ class ShardingOptimizer(MetaOptimizerBase):
                 OpRole.Optimize,
                 use_calc_stream=True,
                 rank=self.dp_rank,
-                strategy=strategy)
+                strategy=None if optimize_cast else strategy)
             logger.info("Optimizer param in this rank {}".format(
                 optimizer_param))
-            if not strategy.fuse_grad_merge:
+            if not strategy.fuse_grad_merge and not optimize_cast:
                 assert len(accumulated_grad_names) == len(optimizer_param)
         elif self.hybrid_dp and self.hybrid_dp_mode == "pp_hybrid_dp":
             insert_allreduce_ops(
@@ -458,18 +461,20 @@ class ShardingOptimizer(MetaOptimizerBase):
 
         main_block._sync_with_cpp()
 
-    def _apply_optimize_offload_pass(self):
+    def _apply_optimize_offload_pass(self, params_grads):
         strategy = self.user_defined_strategy
         sharding_configs = strategy.sharding_configs
         main_block = self._main_program.global_block()
         startup_block = self._startup_program.global_block()
 
+        dp_ring_id = self.dp_ring_id if self.dp_degree > 1 else None
+
         # optimize offload should be enable while gradient merge is enable and
         # acc_step is quite large (e.g. >> 100). Since its memcpy could not be
         # overlap with calc, otherwise it will slower down training severely.
         if sharding_configs["optimize_offload"]:
             logger.info("Sharding with optimize offload !")
-            offload_helper = OffloadHelper()
+            offload_helper = OffloadHelper(ring_id=dp_ring_id)
             offload_helper.offload(main_block, startup_block)
             # The optimize_cast is already included in offload_fp32param
             offload_helper.offload_fp32param(main_block, startup_block)
@@ -477,8 +482,17 @@ class ShardingOptimizer(MetaOptimizerBase):
             logger.info("Sharding with optimize cast !")
             # NOTE(wangxi): optimize_cast will persist fp16 param, it
             # will take more memory, but will be faster. Trade space for time.
-            offload_helper = OffloadHelper()
-            offload_helper.cast_fp32param_in_optimize(main_block, startup_block)
+            offload_helper = OffloadHelper(ring_id=dp_ring_id)
+            if self._optimizer_sharding:
+                offload_helper.opt_sharding_cast_fp32param(
+                    main_block, startup_block,
+                    [x[0].name for x in params_grads])
+                # NOTE(wangxi): fused after optimize_cast
+                utils.fuse_opt_broadcast_param_ops(
+                    main_block, dp_ring_id, self._shard, strategy=strategy)
+            else:
+                offload_helper.cast_fp32param_in_optimize(main_block,
+                                                          startup_block)
 
     def _dump_program_for_debug(self):
         main_block = self._main_program.global_block()
@@ -525,7 +539,7 @@ class ShardingOptimizer(MetaOptimizerBase):
         self._insert_loss_grad_scale_op()
 
         # apply optimize offload or optimize cast
-        self._apply_optimize_offload_pass()
+        self._apply_optimize_offload_pass(params_grads)
 
         # step6: (optional) sharding gradient merge
         self._sharding_gradient_merge()
@@ -1381,17 +1395,50 @@ class ShardingOptimizer(MetaOptimizerBase):
 
         startup_block = self._startup_program.global_block()
         params = startup_block.all_parameters()
+        params_name = []
 
-        broadcast_params = []
+        # NOTE(wangxi): if param is not persistable, program.clone will
+        #  failed, so we remove no persistable param, re add param as a var
         for param in params:
-            broadcast_params.append(param)
-            # optimize_cast need broadcast fp16 param
-            fp16_param_name = param.name + '.cast_fp16'
-            if startup_block.has_var(fp16_param_name):
-                fp16_param = startup_block.var(fp16_param_name)
-                broadcast_params.append(fp16_param)
-
-        for param in broadcast_params:
+            params_name.append(param.name)
+            if not param.persistable:
+                name = param.name
+                shape = param.shape
+                dtype = param.dtype
+                type = param.type
+                lod_level = param.lod_level
+                stop_gradient = param.stop_gradient
+                trainable = param.trainable
+                optimize_attr = param.optimize_attr
+                regularizer = param.regularizer
+
+                have_dist_attr = False
+                is_distributed = False
+                if hasattr(param, 'is_distributed'):
+                    have_dist_attr = True
+                    is_distributed = param.is_distributed
+
+                startup_block._remove_var(name, sync=False)
+                var = startup_block.create_var(
+                    name=name,
+                    shape=shape,
+                    dtype=dtype,
+                    type=type,
+                    lod_level=lod_level,
+                    stop_gradient=stop_gradient,
+                    trainable=trainable,
+                    persistable=False)
+                if have_dist_attr:
+                    var.is_distributed = is_distributed
+
+        # offload and optimize_cast will insert broadcast op
+        broadcast_params = set()
+        for op in startup_block.ops:
+            if op.type == 'c_broadcast':
+                broadcast_params.add(op.desc.output_arg_names()[0])
+
+        for param in params_name:
+            if param in broadcast_params: continue
             startup_block.append_op(
                 type='c_broadcast',
                 inputs={'X': param},
@@ -1399,15 +1446,19 @@ class ShardingOptimizer(MetaOptimizerBase):
                 attrs={
                     'ring_id': self.dp_ring_id,
                     'root': 0,
+                    'use_calc_stream': True,
                     OP_ROLE_KEY: OpRole.Forward
                 })
+
         startup_block.append_op(
             type='c_sync_comm_stream',
-            inputs={'X': broadcast_params},
-            outputs={'Out': broadcast_params},
+            inputs={'X': params_name},
+            outputs={'Out': params_name},
             attrs={'ring_id': self.dp_ring_id,
                    OP_ROLE_KEY: OpRole.Forward})
 
+        startup_block._sync_with_cpp()
+
     # sharding gradient merge
     def create_persistable_gradients_and_insert_merge_ops(
             self, main_block, startup_block, insert_idx, grad_names, shard):
diff --git a/python/paddle/fluid/tests/unittests/test_fleet_hybrid_meta_optimizer.py b/python/paddle/fluid/tests/unittests/test_fleet_hybrid_meta_optimizer.py
index db8689c14c30f3785a1e38593acbb09756f7692f..6eb566935d9d52ced1444bad96dd16df94832fc0 100755
--- a/python/paddle/fluid/tests/unittests/test_fleet_hybrid_meta_optimizer.py
+++ b/python/paddle/fluid/tests/unittests/test_fleet_hybrid_meta_optimizer.py
@@ -321,6 +321,82 @@ class TestFleetHybridOptimizer(TestFleetMetaOptimizer):
             'c_broadcast'
         ])
 
+    def test_opt_sharding_with_pp_amp_ckp_fuse_gm_optcast(self):
+        train_prog, startup_prog = static.Program(), static.Program()
+        avg_cost, strategy = self.pp_net(train_prog, startup_prog)
+
+        self.set_strategy(strategy, 'pipeline')
+        self.set_strategy(strategy, 'amp')
+        strategy.amp_configs = {'custom_black_varnames': ['fc_6.b_0'], }
+        strategy.recompute = True
+        strategy.recompute_configs = {
+            "checkpoints":
+            ["fc_0.tmp_2", "fc_1.tmp_2", "fc_2.tmp_2", "fc_3.tmp_2"]
+        }
+
+        strategy.sharding = True
+        strategy.sharding_configs = {
+            "sharding_degree": 1,
+            "pp_degree": 2,
+            "dp_degree": 2,
+            "_dp_as_optimizer_sharding": True,
+            'optimize_cast': True,
+        }
+        strategy.fuse_all_reduce_ops = True
+        strategy.fuse_grad_size_in_MB = 32
+        strategy.fuse_grad_merge = True
+
+        self.optimizer(avg_cost, strategy, train_prog, startup_prog)
+        train_prog = train_prog._pipeline_opt['section_program']
+        startup_prog = startup_prog._pipeline_opt['startup_program']
+
+        # self._debug = True
+        self.debug_program(train_prog, startup_prog)
+
+        startup_prog_ops = startup_prog.global_block().ops
+        main_prog_ops = train_prog.global_block().ops
+
+        # check program
+        startup_prog_op_types = [op.type for op in startup_prog_ops]
+        main_prog_op_types = [op.type for op in main_prog_ops]
+
+        # global, sharding, pp_send, pp_recv
+        self.assertEqual(startup_prog_op_types, [
+            'uniform_random', 'fill_constant', 'uniform_random',
+            'fill_constant', 'uniform_random', 'fill_constant',
+            'uniform_random', 'fill_constant', 'fill_constant', 'fill_constant',
+            'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
+            'fill_constant', 'fill_constant', 'fill_constant', 'c_gen_nccl_id',
+            'c_comm_init', 'c_gen_nccl_id', 'c_comm_init', 'c_gen_nccl_id',
+            'c_comm_init', 'c_gen_nccl_id', 'c_comm_init', 'c_broadcast',
+            'cast', 'c_broadcast', 'cast', 'c_broadcast', 'cast', 'c_broadcast',
+            'cast', 'c_broadcast', 'cast', 'c_broadcast', 'cast', 'c_broadcast',
+            'cast', 'c_broadcast', 'c_sync_comm_stream'
+        ])
+
+        self.assertEqual(main_prog_op_types, [
+            'recv_v2', 'cast', 'mul', 'elementwise_add', 'cast', 'tanh', 'cast',
+            'mul', 'elementwise_add', 'cast', 'tanh', 'cast', 'mul',
+            'elementwise_add', 'cast', 'tanh', 'cast', 'mul', 'cast',
+            'elementwise_add', 'cast', 'softmax', 'cast', 'cross_entropy2',
+            'mean', 'elementwise_mul', 'coalesce_tensor', 'coalesce_tensor',
+            'coalesce_tensor', 'coalesce_tensor', 'coalesce_tensor',
+            'coalesce_tensor', 'fill_constant', 'elementwise_mul_grad',
+            'mean_grad', 'cross_entropy_grad2', 'cast', 'softmax_grad', 'cast',
+            'elementwise_add_grad', 'cast', 'mul_grad', 'cast', 'tanh_grad',
+            'cast', 'elementwise_add_grad', 'mul_grad', 'cast', 'tanh_grad',
+            'cast', 'elementwise_add_grad', 'mul_grad', 'cast', 'cast', 'mul',
+            'elementwise_add', 'cast', 'tanh_grad', 'cast',
+            'elementwise_add_grad', 'mul_grad', 'cast', 'c_sync_calc_stream',
+            'send_v2', 'cast', 'sum', 'sum', 'cast', 'sum', 'c_reduce_sum',
+            'c_reduce_sum', 'c_reduce_sum', 'c_sync_comm_stream',
+            'check_finite_and_unscale', 'cast', 'c_allreduce_max',
+            'c_allreduce_max', 'cast', 'update_loss_scaling', 'momentum',
+            'cast', 'momentum', 'cast', 'momentum', 'cast', 'momentum',
+            'momentum', 'cast', 'coalesce_tensor', 'c_broadcast', 'c_broadcast',
+            'coalesce_tensor', 'c_broadcast'
+        ])
+
 
 class TestFleetHybridOptimizerBoundary(TestFleetMetaOptimizer):
     def setUp(self):
diff --git a/python/paddle/fluid/tests/unittests/test_fleet_sharding_meta_optimizer.py b/python/paddle/fluid/tests/unittests/test_fleet_sharding_meta_optimizer.py
index 61d98d32ec5fd7f2512a54ef998ae7b8ef392f2e..73eacd118ecad506aa993e65d76f70f3177b3d26 100755
--- a/python/paddle/fluid/tests/unittests/test_fleet_sharding_meta_optimizer.py
+++ b/python/paddle/fluid/tests/unittests/test_fleet_sharding_meta_optimizer.py
@@ -922,18 +922,17 @@ class TestFleetShardingHybridOptimizer(TestFleetMetaOptimizer):
 
         # ring: mp, pp_group, pp_pair, pp_pair
         self.assertEqual(startup_prog_op_types, [
-            'uniform_random', 'cast', 'fill_constant', 'cast', 'uniform_random',
-            'cast', 'fill_constant', 'cast', 'uniform_random', 'cast',
-            'fill_constant', 'cast', 'uniform_random', 'cast', 'fill_constant',
-            'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
+            'uniform_random', 'fill_constant', 'uniform_random',
+            'fill_constant', 'uniform_random', 'fill_constant',
+            'uniform_random', 'fill_constant', 'fill_constant', 'fill_constant',
             'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
             'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
+            'fill_constant', 'fill_constant', 'c_gen_nccl_id', 'c_comm_init',
             'c_gen_nccl_id', 'c_comm_init', 'c_gen_nccl_id', 'c_comm_init',
-            'c_gen_nccl_id', 'c_comm_init', 'c_gen_nccl_id', 'c_comm_init',
-            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_broadcast',
-            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_broadcast',
-            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_broadcast',
-            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_sync_comm_stream'
+            'c_gen_nccl_id', 'c_comm_init', 'c_broadcast', 'cast',
+            'c_broadcast', 'cast', 'c_broadcast', 'cast', 'c_broadcast', 'cast',
+            'c_broadcast', 'cast', 'c_broadcast', 'cast', 'c_broadcast', 'cast',
+            'c_broadcast', 'c_sync_comm_stream'
         ])
 
         self.assertEqual(main_prog_op_types, [
@@ -1019,19 +1018,17 @@ class TestFleetShardingHybridOptimizer(TestFleetMetaOptimizer):
 
         # ring: mp, pp_group, pp_pair, pp_pair
         self.assertEqual(startup_prog_op_types, [
-            'uniform_random', 'cast', 'memcpy', 'fill_constant', 'cast',
-            'memcpy', 'uniform_random', 'cast', 'memcpy', 'fill_constant',
-            'cast', 'memcpy', 'uniform_random', 'cast', 'memcpy',
-            'fill_constant', 'cast', 'memcpy', 'uniform_random', 'cast',
-            'memcpy', 'fill_constant', 'fill_constant', 'fill_constant',
+            'uniform_random', 'fill_constant', 'uniform_random',
+            'fill_constant', 'uniform_random', 'fill_constant',
+            'uniform_random', 'fill_constant', 'fill_constant', 'fill_constant',
             'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
             'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
             'fill_constant', 'fill_constant', 'c_gen_nccl_id', 'c_comm_init',
             'c_gen_nccl_id', 'c_comm_init', 'c_gen_nccl_id', 'c_comm_init',
-            'c_gen_nccl_id', 'c_comm_init', 'c_broadcast', 'c_broadcast',
-            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_broadcast',
-            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_broadcast',
-            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_broadcast',
+            'c_gen_nccl_id', 'c_comm_init', 'c_broadcast', 'cast', 'memcpy',
+            'c_broadcast', 'cast', 'memcpy', 'c_broadcast', 'cast', 'memcpy',
+            'c_broadcast', 'cast', 'memcpy', 'c_broadcast', 'cast', 'memcpy',
+            'c_broadcast', 'cast', 'memcpy', 'c_broadcast', 'cast', 'memcpy',
             'c_broadcast', 'c_sync_comm_stream'
         ])
 
@@ -1122,18 +1119,17 @@ class TestFleetShardingHybridOptimizer(TestFleetMetaOptimizer):
 
         # ring: mp, pp_group, pp_pair, pp_pair
         self.assertEqual(startup_prog_op_types, [
-            'uniform_random', 'cast', 'fill_constant', 'cast', 'uniform_random',
-            'cast', 'fill_constant', 'cast', 'uniform_random', 'cast',
-            'fill_constant', 'cast', 'uniform_random', 'cast', 'fill_constant',
-            'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
+            'uniform_random', 'fill_constant', 'uniform_random',
+            'fill_constant', 'uniform_random', 'fill_constant',
+            'uniform_random', 'fill_constant', 'fill_constant', 'fill_constant',
             'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
             'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
+            'fill_constant', 'fill_constant', 'c_gen_nccl_id', 'c_comm_init',
             'c_gen_nccl_id', 'c_comm_init', 'c_gen_nccl_id', 'c_comm_init',
-            'c_gen_nccl_id', 'c_comm_init', 'c_gen_nccl_id', 'c_comm_init',
-            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_broadcast',
-            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_broadcast',
-            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_broadcast',
-            'c_broadcast', 'c_broadcast', 'c_broadcast', 'c_sync_comm_stream'
+            'c_gen_nccl_id', 'c_comm_init', 'c_broadcast', 'cast',
+            'c_broadcast', 'cast', 'c_broadcast', 'cast', 'c_broadcast', 'cast',
+            'c_broadcast', 'cast', 'c_broadcast', 'cast', 'c_broadcast', 'cast',
+            'c_broadcast', 'c_sync_comm_stream'
         ])
 
         self.assertEqual(main_prog_op_types, [