diff --git a/python/paddle/distributed/fleet/recompute/recompute.py b/python/paddle/distributed/fleet/recompute/recompute.py
index f20bbdb3834c822abee6c9bc8549d804b608edd5..70a38ff3b9aa663f8b6b21c85ba7e1677310f109 100755
--- a/python/paddle/distributed/fleet/recompute/recompute.py
+++ b/python/paddle/distributed/fleet/recompute/recompute.py
@@ -18,7 +18,6 @@ import weakref
 import paddle
 from paddle import framework
 from paddle.autograd import PyLayer
-from paddle.autograd.py_layer import LegacyPyLayer
 from paddle.distributed.fleet.meta_parallel.parallel_layers.random import (
     get_rng_state_tracker,
 )
@@ -67,147 +66,6 @@ def swith_rng_state_tracker(rng_state, tracker):
         get_rng_state_tracker().set_states_tracker(orig_rng_tracker)
 
 
-class LegacyRecomputeFunction(LegacyPyLayer):
-    @staticmethod
-    def forward(ctx, run_function, preserve_rng_state, *args):
-        # store for recomputing
-        ctx.run_function = run_function
-        ctx.preserve_rng_state = preserve_rng_state
-
-        # NOTE the number of outputs of backward() should be equal to the number of tensors in forward()'s input
-        # the order of tensors in backward()'s output should be the same as tensors in forward()'s input
-        # None tensor inputs will be filtered in backward inputs.
-
-        # save input for backward
-        ctx.inputs = []
-        ctx.tensor_indices = []
-        tensor_inputs = []
-        for i, arg in enumerate(args):
-            if paddle.is_tensor(arg):
-                tensor_inputs.append(arg)
-                ctx.tensor_indices.append(i)
-                ctx.inputs.append(None)
-            else:
-                ctx.inputs.append(arg)
-        ctx.save_for_backward(*tensor_inputs)
-
-        # NOTE recompute with restore RNG only support one senario where one process for one cuda gpu.
-        # one process with multiple gpu and mix-gpu-cpu senarios are not support
-        if ctx.preserve_rng_state:
-            ctx.fw_rng_state = paddle.get_rng_state()
-            ctx.fwd_rng_state_tracker = (
-                get_rng_state_tracker().get_states_tracker()
-            )
-
-        # TODO support AMP
-        tracer = framework._dygraph_tracer()
-        ctx.is_fw_autocast = (
-            False if tracer._amp_level == core.AmpLevel.O0 else True
-        )
-        if tracer._amp_level == core.AmpLevel.O2:
-            ctx.amp_level = 'O2'
-        elif tracer._amp_level in (core.AmpLevel.O1, core.AmpLevel.O0):
-            ctx.amp_level = 'O1'
-        else:
-            raise ValueError(
-                "unsupported amp level: {}".format(tracer._amp_level)
-            )
-
-        if tracer._amp_dtype == 'float16':
-            ctx.amp_dtype = 'float16'
-        elif tracer._amp_dtype in ('bfloat16', 'float32'):
-            ctx.amp_dtype = 'bfloat16'
-        else:
-            raise ValueError(
-                "unsupported amp dtype: {}".format(tracer._amp_dtype)
-            )
-
-        ctx.amp_white_list, ctx.amp_black_list = tracer._get_amp_op_list()
-
-        with paddle.no_grad():
-            outputs = run_function(*args)
-        return outputs
-
-    @staticmethod
-    def backward(ctx, *args):
-        with paddle.fluid.dygraph.guard():
-            # TODO need to check the recompute calling is vaild or not
-
-            # Restore inputs
-            inputs = list(ctx.inputs)
-            tensor_indices = ctx.tensor_indices
-            tensors = ctx.saved_tensor()
-            for i, idx in enumerate(tensor_indices):
-                inputs[idx] = tensors[i]
-
-            # paddle.enable_grad()
-            tracer = framework._dygraph_tracer()
-            tracer._has_grad = True
-
-            # NOTE support AMP
-            # need restore auto_cast state as well as w/b list
-            if ctx.preserve_rng_state:
-                with swith_rng_state_tracker(
-                    ctx.fw_rng_state, ctx.fwd_rng_state_tracker
-                ):
-                    with paddle.amp.auto_cast(
-                        enable=ctx.is_fw_autocast,
-                        custom_white_list=ctx.amp_white_list,
-                        custom_black_list=ctx.amp_black_list,
-                        level=ctx.amp_level,
-                        dtype=ctx.amp_dtype,
-                    ):
-                        detached_inputs = detach_variable(tuple(inputs))
-                        outputs = ctx.run_function(*detached_inputs)
-            else:
-                with paddle.amp.auto_cast(
-                    enable=ctx.is_fw_autocast,
-                    custom_white_list=ctx.amp_white_list,
-                    custom_black_list=ctx.amp_black_list,
-                    level=ctx.amp_level,
-                    dtype=ctx.amp_dtype,
-                ):
-                    detached_inputs = detach_variable(tuple(inputs))
-                    outputs = ctx.run_function(*detached_inputs)
-
-            if isinstance(outputs, core.VarBase):
-                outputs = (outputs,)
-            assert len(outputs) == len(args)
-
-            # run backward() with only tensor that requires grad
-            forward_outputs_with_grad = []
-            # NOTE In Transformer-like network, if user put the attention mask into the recompute segment output,
-            # pylayer will force the stop_gradient of attention mask to be False, which will make the number of
-            # tensor that need grad does not match.
-            # the following backward_inputs_with_grad is used to avoid this case.
-            backward_inputs_with_grad = []
-            for i in range(len(outputs)):
-                if (
-                    isinstance(outputs[i], core.VarBase)
-                    and not outputs[i].stop_gradient
-                ):
-                    forward_outputs_with_grad.append(outputs[i])
-                    backward_inputs_with_grad.append(args[i])
-
-            if len(forward_outputs_with_grad) == 0:
-                raise RuntimeError(
-                    "none of output has requires_grad=True, this recompute() is not necessary"
-                )
-
-            # actually backward
-            with paddle.amp.auto_cast(enable=False):
-                paddle.autograd.backward(
-                    forward_outputs_with_grad, backward_inputs_with_grad
-                )
-
-            grads = list(
-                inp._grad_ivar()
-                for inp in detached_inputs
-                if isinstance(inp, core.VarBase)
-            )
-            return grads
-
-
 class RecomputeFunction(PyLayer):
     @staticmethod
     def forward(ctx, run_function, preserve_rng_state, *args, **kwargs):