diff --git a/paddle/fluid/pybind/eager_functions.cc b/paddle/fluid/pybind/eager_functions.cc
index db0da96493c20949800865c494cf8857ee4f88bc..b1ace9c76417c8cbb2a0f7e022da18f6de8543f8 100644
--- a/paddle/fluid/pybind/eager_functions.cc
+++ b/paddle/fluid/pybind/eager_functions.cc
@@ -190,6 +190,41 @@ static PyObject* eager_api_tensor_copy(PyObject* self,
   EAGER_CATCH_AND_THROW_RETURN_NULL
 }
 
+PyObject* eager_api_get_grads_lists(PyObject* self,
+                                    PyObject* args,
+                                    PyObject* kwargs) {
+  EAGER_TRY
+  auto tensor_list = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 0), 0);
+
+  std::vector<std::vector<paddle::experimental::Tensor>> ret(3);
+
+  for (auto& tensor : tensor_list) {
+    VLOG(6) << "Get grad for tensor: " << tensor.name();
+    auto meta = egr::EagerUtils::nullable_autograd_meta(tensor);
+    VLOG(6) << meta << " initialized: " << meta->Grad().initialized();
+    if (meta && meta->Grad().initialized()) {
+      auto& grad = meta->Grad();
+      switch (grad.dtype()) {
+        case paddle::experimental::DataType::FLOAT16:
+          ret[0].emplace_back(grad);
+          break;
+        case paddle::experimental::DataType::BFLOAT16:
+          ret[1].emplace_back(grad);
+          break;
+        case paddle::experimental::DataType::FLOAT32:
+          ret[2].emplace_back(grad);
+          break;
+        default:
+          break;
+      }
+    }
+  }
+
+  return ToPyObject(ret);
+
+  EAGER_CATCH_AND_THROW_RETURN_NULL
+}
+
 static PyObject* eager_api_read_next_tensor_list(PyObject* self,
                                                  PyObject* args,
                                                  PyObject* kwargs) {
@@ -1001,6 +1036,10 @@ PyMethodDef variable_functions[] = {
      (PyCFunction)(void (*)(void))eager_api_tensor_copy,
      METH_VARARGS | METH_KEYWORDS,
      NULL},
+    {"get_grads_lists",
+     (PyCFunction)(void (*)(void))eager_api_get_grads_lists,
+     METH_VARARGS | METH_KEYWORDS,
+     NULL},
     {"read_next_tensor_list",
      (PyCFunction)(void (*)(void))eager_api_read_next_tensor_list,
      METH_VARARGS | METH_KEYWORDS,
diff --git a/paddle/fluid/pybind/eager_utils.cc b/paddle/fluid/pybind/eager_utils.cc
index 895a715c8bad82c9490bede76f2b97a8f62838f3..1237e4092f02f5c1a326f614e38833643d8a63a5 100644
--- a/paddle/fluid/pybind/eager_utils.cc
+++ b/paddle/fluid/pybind/eager_utils.cc
@@ -720,6 +720,17 @@ PyObject* ToPyObject(const std::vector<paddle::experimental::Tensor>& value,
   return result;
 }
 
+PyObject* ToPyObject(
+    const std::vector<std::vector<paddle::experimental::Tensor>>& value) {
+  PyObject* result = PyList_New((Py_ssize_t)value.size());
+
+  for (size_t i = 0; i < value.size(); i++) {
+    PyList_SET_ITEM(result, static_cast<Py_ssize_t>(i), ToPyObject(value[i]));
+  }
+
+  return result;
+}
+
 PyObject* ToPyObject(const platform::Place& value) {
   auto obj = ::pybind11::cast(value);
   obj.inc_ref();
diff --git a/paddle/fluid/pybind/eager_utils.h b/paddle/fluid/pybind/eager_utils.h
index 845b681163e0d55c48651de5ffdebcff6060c163..145eeacc049f91f3e2c6eee88e585382f96183e9 100644
--- a/paddle/fluid/pybind/eager_utils.h
+++ b/paddle/fluid/pybind/eager_utils.h
@@ -103,6 +103,8 @@ PyObject* ToPyObject(const std::vector<double>& value);
 PyObject* ToPyObject(const std::vector<std::vector<size_t>>& value);
 PyObject* ToPyObject(const std::vector<paddle::experimental::Tensor>& value,
                      bool return_py_none_if_not_initialize = false);
+PyObject* ToPyObject(
+    const std::vector<std::vector<paddle::experimental::Tensor>>& value);
 PyObject* ToPyObject(const platform::Place& value);
 PyObject* ToPyObject(const phi::DenseTensor* value);
 PyObject* ToPyObject(const phi::SelectedRows* value);
diff --git a/python/paddle/fluid/dygraph/amp/loss_scaler.py b/python/paddle/fluid/dygraph/amp/loss_scaler.py
index 6ab153e4a2e9a07cf4ed5ed50f211566300b5de0..c59588e9d03b611fe3240f6edca52b42df9d4e3f 100644
--- a/python/paddle/fluid/dygraph/amp/loss_scaler.py
+++ b/python/paddle/fluid/dygraph/amp/loss_scaler.py
@@ -26,6 +26,7 @@ import numpy as np
 from paddle import _C_ops, _legacy_C_ops
 from collections import defaultdict
 from enum import Enum
+from paddle.fluid import in_dygraph_mode
 
 __all__ = ['AmpScaler', 'OptimizerState']
 
@@ -297,26 +298,33 @@ class AmpScaler(object):
                         else:
                             param_grads_fp32.append(param._grad_ivar())
         else:
-            param_grads = [
-                param._grad_ivar()
-                for param in optimizer._parameter_list
-                if param._grad_ivar() is not None
-            ]
-            param_grads_fp16 = [
-                param
-                for param in param_grads
-                if param.dtype == core.VarDesc.VarType.FP16
-            ]
-            param_grads_bf16 = [
-                param
-                for param in param_grads
-                if param.dtype == core.VarDesc.VarType.BF16
-            ]
-            param_grads_fp32 = [
-                param
-                for param in param_grads
-                if param.dtype == core.VarDesc.VarType.FP32
-            ]
+            if in_dygraph_mode():
+                (
+                    param_grads_fp16,
+                    param_grads_bf16,
+                    param_grads_fp32,
+                ) = core.eager.get_grads_lists(optimizer._parameter_list)
+            else:
+                param_grads = [
+                    param._grad_ivar()
+                    for param in optimizer._parameter_list
+                    if param._grad_ivar() is not None
+                ]
+                param_grads_fp16 = [
+                    param
+                    for param in param_grads
+                    if param.dtype == core.VarDesc.VarType.FP16
+                ]
+                param_grads_bf16 = [
+                    param
+                    for param in param_grads
+                    if param.dtype == core.VarDesc.VarType.BF16
+                ]
+                param_grads_fp32 = [
+                    param
+                    for param in param_grads
+                    if param.dtype == core.VarDesc.VarType.FP32
+                ]
         if core.is_compiled_with_npu():
             float_status = _legacy_C_ops.alloc_float_status()
             _legacy_C_ops.clear_float_status(float_status, float_status)