diff --git a/paddle/fluid/operators/conv_op_npu.cc b/paddle/fluid/operators/conv_op_npu.cc
index 86a6ec2c3a1603c64c14d03ffdbd9821f5719657..3ace825e7b80df6032183505c048a6c0e796aaca 100644
--- a/paddle/fluid/operators/conv_op_npu.cc
+++ b/paddle/fluid/operators/conv_op_npu.cc
@@ -20,6 +20,29 @@ namespace operators {
 
 using Tensor = framework::Tensor;
 using NPUDeviceContext = platform::NPUDeviceContext;
+static void CastToFP16(const framework::ExecutionContext& ctx,
+                       const aclrtStream& stream, const Tensor& in,
+                       Tensor* out) {
+  out->mutable_data<paddle::platform::float16>(ctx.GetPlace());
+  NpuOpRunner runner;
+  runner.SetType("Cast")
+      .AddInput(in)
+      .AddOutput(*out)
+      .AddAttr("dst_type", ACL_FLOAT16)
+      .Run(stream);
+}
+
+static void CastToFP32(const framework::ExecutionContext& ctx,
+                       const aclrtStream& stream, const Tensor& in,
+                       Tensor* out) {
+  out->mutable_data<float>(ctx.GetPlace());
+  NpuOpRunner runner;
+  runner.SetType("Cast")
+      .AddInput(in)
+      .AddOutput(*out)
+      .AddAttr("dst_type", ACL_FLOAT)
+      .Run(stream);
+}
 
 template <typename T>
 class DepthwiseConvNPUKernel : public framework::OpKernel<T> {
@@ -356,18 +379,33 @@ class NPUConvGradOpKernel : public framework::OpKernel<T> {
 
     auto stream = ctx.template device_context<NPUDeviceContext>().stream();
     if (filter_grad) {
-      filter_grad->mutable_data<float>(ctx.GetPlace());
+      filter_grad->mutable_data<T>(ctx.GetPlace());
       std::vector<int> filter_shape_vec = phi::vectorize<int>(filter->dims());
 
+      Tensor filter_grad_fp32(experimental::DataType::FLOAT32);
+      filter_grad_fp32.Resize(filter_grad->dims());
+
+      if (framework::TransToProtoVarType(input->dtype()) ==
+          framework::proto::VarType::FP16) {
+        CastToFP32(ctx, stream, *filter_grad, &filter_grad_fp32);
+      } else {
+        filter_grad_fp32.ShareDataWith(*filter_grad);
+      }
+
       const auto& runner = NpuOpRunner(
           "Conv2DBackpropFilterD", {input_tensor, output_grad_tensor},
-          {*filter_grad}, {{"filter_size", filter_shape_vec},
-                           {"strides", strides_vec},
-                           {"pads", paddings},
-                           {"dilations", dilations_vec},
-                           {"groups", groups},
-                           {"data_format", data_format}});
+          {filter_grad_fp32}, {{"filter_size", filter_shape_vec},
+                               {"strides", strides_vec},
+                               {"pads", paddings},
+                               {"dilations", dilations_vec},
+                               {"groups", groups},
+                               {"data_format", data_format}});
       runner.Run(stream);
+
+      if (framework::TransToProtoVarType(input->dtype()) ==
+          framework::proto::VarType::FP16) {
+        CastToFP16(ctx, stream, filter_grad_fp32, filter_grad);
+      }
     }
     if (input_grad) {
       input_grad->mutable_data<T>(ctx.GetPlace());
diff --git a/paddle/fluid/operators/top_k_v2_op_npu.cc b/paddle/fluid/operators/top_k_v2_op_npu.cc
index dff5c2d3f39378486bb5d2f8010d005d57b20550..04e4d88b008e0c293cf206c63badb12121cca30a 100644
--- a/paddle/fluid/operators/top_k_v2_op_npu.cc
+++ b/paddle/fluid/operators/top_k_v2_op_npu.cc
@@ -89,7 +89,9 @@ class TopkV2NPUKernel : public framework::OpKernel<T> {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
+namespace plat = paddle::platform;
 REGISTER_OP_NPU_KERNEL(top_k_v2, ops::TopkV2NPUKernel<float>,
+                       ops::TopkV2NPUKernel<plat::float16>,
                        ops::TopkV2NPUKernel<double>,
                        ops::TopkV2NPUKernel<int32_t>,
                        ops::TopkV2NPUKernel<int64_t>);