diff --git a/paddle/phi/backends/xpu/xpu2_op_list.cc b/paddle/phi/backends/xpu/xpu2_op_list.cc
index fef881ffd249d398664e507dc4a390bff2c20c77..d0ba2083b750c25297262e14a6ad13585b10213b 100644
--- a/paddle/phi/backends/xpu/xpu2_op_list.cc
+++ b/paddle/phi/backends/xpu/xpu2_op_list.cc
@@ -29,6 +29,8 @@ XPUOpMap& get_kl2_ops() {
       {"adadelta", XPUKernelSet({phi::DataType::FLOAT32})},
       {"adamw", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
       {"adam", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
+      {"adam_dense_param_sparse_grad",
+       XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
       {"arg_max", XPUKernelSet({phi::DataType::FLOAT32})},
       {"argsort_grad",
        XPUKernelSet({phi::DataType::INT32,
@@ -152,7 +154,10 @@ XPUOpMap& get_kl2_ops() {
       {"elementwise_mul_grad",
        XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
       {"elementwise_mul",
-       XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
+       XPUKernelSet({phi::DataType::FLOAT32,
+                     phi::DataType::FLOAT16,
+                     phi::DataType::INT32,
+                     phi::DataType::INT64})},
       {"elementwise_pow",
        XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
       {"elementwise_sub_grad",
@@ -176,6 +181,7 @@ XPUOpMap& get_kl2_ops() {
                      phi::DataType::FLOAT16,
                      phi::DataType::FLOAT32,
                      phi::DataType::FLOAT64})},
+      {"embedding_grad", XPUKernelSet({phi::DataType::FLOAT32})},
       {"embedding_sparse_grad", XPUKernelSet({phi::DataType::FLOAT32})},
       {"equal",
        XPUKernelSet({phi::DataType::INT64,
@@ -356,6 +362,11 @@ XPUOpMap& get_kl2_ops() {
       {"mul", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
       {"mul_grad",
        XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
+      {"multiply",
+       XPUKernelSet({phi::DataType::FLOAT32,
+                     phi::DataType::FLOAT16,
+                     phi::DataType::INT32,
+                     phi::DataType::INT64})},
       {"nearest_interp_v2", XPUKernelSet({phi::DataType::FLOAT32})},
       {"nearest_interp_v2_grad", XPUKernelSet({phi::DataType::FLOAT32})},
       {"not_equal",
@@ -549,8 +560,14 @@ XPUOpMap& get_kl2_ops() {
       {"temporal_shift_grad", XPUKernelSet({phi::DataType::FLOAT32})},
       {"tril_triu",
        XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::INT32})},
+      {"tril", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::INT32})},
+      {"triu", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::INT32})},
       {"tril_triu_grad",
        XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::INT32})},
+      {"tril_grad",
+       XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::INT32})},
+      {"triu_grad",
+       XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::INT32})},
       {"tile",
        XPUKernelSet({phi::DataType::INT32,
                      phi::DataType::INT64,
diff --git a/paddle/phi/kernels/xpu/embedding_grad_kernel.cc b/paddle/phi/kernels/xpu/embedding_grad_kernel.cc
index cd3b920feffa85b476a5e1eb64680be4b8c3e428..19e2e380aa1e4ffadeb1dc46bab093537b9b2fcc 100644
--- a/paddle/phi/kernels/xpu/embedding_grad_kernel.cc
+++ b/paddle/phi/kernels/xpu/embedding_grad_kernel.cc
@@ -17,6 +17,7 @@
 #include "paddle/fluid/memory/memcpy.h"
 #include "paddle/phi/backends/xpu/enforce_xpu.h"
 #include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/embedding_util.h"
 
 namespace phi {
 
@@ -80,15 +81,18 @@ void EmbeddingSparseGradKernel(const Context& ctx,
                                int64_t padding_idx,
                                SelectedRows* weight_grad) {
   DDim table_dim = weight.dims();
+  auto xpu_place = ctx.GetPlace();
 
   xpu::ctx_guard RAII_GUARD(ctx.x_context());
-  std::vector<int64_t> ids(input.numel());
+  std::vector<int64_t> ids;
+  DenseTensor ids_cpu;
+  ids_cpu.Resize(input.dims());
+  ctx.template HostAlloc(
+      &ids_cpu, input.dtype(), input.numel() * sizeof(int64_t));
   if (input.dtype() == phi::DataType::INT64) {
-    paddle::memory::Copy(CPUPlace(),
-                         ids.data(),
-                         input.place(),
-                         input.data<int64_t>(),
-                         sizeof(int64_t) * input.numel());
+    phi::Copy(ctx, input, CPUPlace(), false, &ids_cpu);
+
+    ids = CopyIdsToVector<int64_t, int64_t>(ids_cpu);
 
   } else if (input.dtype() == phi::DataType::INT32) {
     int64_t* id_t = RAII_GUARD.alloc_l3_or_gm<int64_t>(input.numel());
@@ -96,10 +100,11 @@ void EmbeddingSparseGradKernel(const Context& ctx,
         ctx.x_context(), input.data<int>(), id_t, input.numel());
     PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
     paddle::memory::Copy(CPUPlace(),
-                         ids.data(),
+                         ids_cpu.data(),
                          input.place(),
                          id_t,
                          sizeof(int64_t) * input.numel());
+    ids = CopyIdsToVector<int, int64_t>(ids_cpu);
   } else {
     PADDLE_THROW(phi::errors::Unimplemented(
         "emebdding input only support int32 and int64"));
@@ -115,7 +120,7 @@ void EmbeddingSparseGradKernel(const Context& ctx,
   auto* d_table_value = d_table->mutable_value();
   d_table_value->Resize({ids_num, table_dim[1]});
 
-  ctx.template Alloc<T>(d_table_value);
+  ctx.template HostAlloc<T>(d_table_value);
 
   d_table->set_height(table_dim[0]);
 
@@ -134,9 +139,12 @@ void EmbeddingSparseGradKernel(const Context& ctx,
                         "output@Grad's shape = [%s].",
                         d_table_value->dims(),
                         d_output_dims_2d));
-  int r = xpu::copy<T>(
-      ctx.x_context(), d_output_data, d_table_data, d_output->numel());
-  PADDLE_ENFORCE_XDNN_SUCCESS(r, "copy");
+
+  paddle::memory::Copy(CPUPlace(),
+                       d_table_data,
+                       xpu_place,
+                       d_output_data,
+                       d_output->numel() * sizeof(T));
 }
 }  // namespace phi
 
diff --git a/paddle/phi/kernels/xpu/log_softmax_grad_kernel.cc b/paddle/phi/kernels/xpu/log_softmax_grad_kernel.cc
index 1f5d95c50f8e2a234f774014eee304471dcc763c..946daf5d7104a57c5b10141a393c1e2277c99770 100644
--- a/paddle/phi/kernels/xpu/log_softmax_grad_kernel.cc
+++ b/paddle/phi/kernels/xpu/log_softmax_grad_kernel.cc
@@ -37,42 +37,16 @@ void LogSoftmaxGradKernel(const Context& dev_ctx,
     return;
   }
 
-  if (out.numel() != 0) {
-    auto out_shape = phi::vectorize<int>(out.dims());
-    dev_ctx.template Alloc<T>(x_grad);
-    xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
-    T* tmp_ptr = RAII_GUARD.alloc_l3_or_gm<T>(out_grad.numel());
-    T* tmp2_ptr = RAII_GUARD.alloc_l3_or_gm<T>(out_grad.numel());
-    PADDLE_ENFORCE_NE(
-        tmp_ptr, nullptr, phi::errors::External("no enough memory in xpu"));
-    PADDLE_ENFORCE_NE(
-        tmp2_ptr, nullptr, phi::errors::External("no enough memory in xpu"));
-
-    int r = xpu::exp<XPUType>(dev_ctx.x_context(),
-                              reinterpret_cast<const XPUType*>(out.data<T>()),
-                              reinterpret_cast<XPUType*>(tmp_ptr),
-                              out_grad.numel());
-    PADDLE_ENFORCE_XDNN_SUCCESS(r, "exp");
-    r = xpu::reciprocal<XPUType>(dev_ctx.x_context(),
-                                 reinterpret_cast<const XPUType*>(tmp_ptr),
-                                 reinterpret_cast<XPUType*>(tmp2_ptr),
-                                 out_grad.numel());
-    PADDLE_ENFORCE_XDNN_SUCCESS(r, "reciprocal");
-    r = xpu::mul<XPUType>(dev_ctx.x_context(),
-                          reinterpret_cast<const XPUType*>(tmp2_ptr),
-                          reinterpret_cast<const XPUType*>(out_grad.data<T>()),
-                          reinterpret_cast<XPUType*>(tmp2_ptr),
-                          out_grad.numel());
-    PADDLE_ENFORCE_XDNN_SUCCESS(r, "mul");
-    r = xpu::softmax_grad<XPUType>(
-        dev_ctx.x_context(),
-        reinterpret_cast<const XPUType*>(tmp_ptr),
-        reinterpret_cast<const XPUType*>(tmp2_ptr),
-        reinterpret_cast<XPUType*>(x_grad->data<T>()),
-        out_shape,
-        axis);
-    PADDLE_ENFORCE_XDNN_SUCCESS(r, "softmax_grad");
-  }
+  auto out_shape = phi::vectorize<int>(out.dims());
+  dev_ctx.template Alloc<T>(x_grad);
+  int r = xpu::log_softmax_grad(
+      dev_ctx.x_context(),
+      reinterpret_cast<const XPUType*>(out.data<T>()),
+      reinterpret_cast<const XPUType*>(out_grad.data<T>()),
+      reinterpret_cast<XPUType*>(x_grad->data<T>()),
+      out_shape,
+      axis);
+  PADDLE_ENFORCE_XDNN_SUCCESS(r, "log_softmax_grad");
 }
 
 }  // namespace phi
diff --git a/paddle/phi/kernels/xpu/log_softmax_kernel.cc b/paddle/phi/kernels/xpu/log_softmax_kernel.cc
index a4feac7b2330716770ec97ec7147e27e2b12a22b..059157db63a3149743aed6359dfd28ee70e50818 100644
--- a/paddle/phi/kernels/xpu/log_softmax_kernel.cc
+++ b/paddle/phi/kernels/xpu/log_softmax_kernel.cc
@@ -39,17 +39,13 @@ void LogSoftmaxKernel(const Context& dev_ctx,
     auto x_shape = phi::vectorize<int>(x.dims());
     dev_ctx.template Alloc<T>(out);
     if (axis < 0) axis += rank;
-    int r = xpu::softmax<XPUType>(dev_ctx.x_context(),
+    int r =
+        xpu::log_softmax<XPUType>(dev_ctx.x_context(),
                                   reinterpret_cast<const XPUType*>(x.data<T>()),
                                   reinterpret_cast<XPUType*>(out->data<T>()),
                                   x_shape,
                                   axis);
-    PADDLE_ENFORCE_XDNN_SUCCESS(r, "softmax");
-    r = xpu::log<XPUType>(dev_ctx.x_context(),
-                          reinterpret_cast<const XPUType*>(out->data<T>()),
-                          reinterpret_cast<XPUType*>(out->data<T>()),
-                          out->numel());
-    PADDLE_ENFORCE_XDNN_SUCCESS(r, "log");
+    PADDLE_ENFORCE_XDNN_SUCCESS(r, "log_softmax");
   }
 }