add aarch64 and sunway kunlun lib (#30027)

* add aarch64 and sunway kunlun lib

* minor

* optimize elementwise_add for kunlun

* update kunlun dependence

* minor

* minor

cmake/external/xpu.cmake

@@ -4,7 +4,15 @@ endif()
 
 INCLUDE(ExternalProject)
 SET(XPU_PROJECT                 "extern_xpu")
-SET(XPU_URL    "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/xpu_2020_12_15.tar.gz" CACHE STRING "" FORCE)
+
+if (WITH_AARCH64)
+    SET(XPU_URL    "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/aarch64/xpu_2020_1229.tar.gz" CACHE STRING "" FORCE)
+elseif(WITH_SUNWAY)
+    SET(XPU_URL    "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/sunway/xpu_2020_1227.tar.gz" CACHE STRING "" FORCE)
+else()
+    SET(XPU_URL    "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/xpu_2021_0105.tar.gz" CACHE STRING "" FORCE)
+endif()
+
 SET(XPU_SOURCE_DIR              "${THIRD_PARTY_PATH}/xpu")
 SET(XPU_DOWNLOAD_DIR            "${XPU_SOURCE_DIR}/src/${XPU_PROJECT}")
 SET(XPU_INSTALL_DIR             "${THIRD_PARTY_PATH}/install/xpu")

paddle/fluid/operators/elementwise/elementwise_add_op_xpu.cc

@@ -26,17 +26,140 @@ namespace operators {
 template <typename DeviceContext, typename T>
 class ElementwiseAddXPUKernel : public framework::OpKernel<T> {
  public:
-  void Compute(const framework::ExecutionContext &ctx) const override {
-    XPUElementwise<T>(ctx, xpu::add<T>);
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    // XPUElementwise<T>(ctx, xpu::add<T>);
+    // ToDo(QingshuChen): update this optimization to elementwise_xpu.h
+    auto x_var = ctx.InputVar("X");
+    PADDLE_ENFORCE_NE(x_var, nullptr, platform::errors::InvalidArgument(
+                                          "Cannot get input Variable X"));
+    PADDLE_ENFORCE_EQ(
+        x_var->IsType<framework::LoDTensor>(), true,
+        platform::errors::InvalidArgument(
+            "XPU only support LoDTensor, Input(X) is not LoDTensor"));
+
+    auto x = x_var->Get<framework::LoDTensor>();
+    auto* y = ctx.Input<framework::LoDTensor>("Y");
+    auto* z = ctx.Output<framework::LoDTensor>("Out");
+    z->mutable_data<T>(ctx.GetPlace());
+    auto x_dims = x.dims();
+    auto y_dims = y->dims();
+    int max_dim = std::max(x_dims.size(), y_dims.size());
+    int axis = ctx.Attr<int>("axis");
+    axis = (axis == -1 ? std::abs(x_dims.size() - y_dims.size()) : axis);
+
+    PADDLE_ENFORCE_GE(
+        axis, 0,
+        platform::errors::InvalidArgument(
+            "Axis should be great than or equal to 0, but received axis is %d.",
+            axis));
+    PADDLE_ENFORCE_LT(
+        axis, max_dim,
+        platform::errors::InvalidArgument(
+            "Axis should be less than %d, but received axis is %d.", max_dim,
+            axis));
+    std::vector<int> x_dims_vec(max_dim, 1);
+    std::vector<int> y_dims_vec(max_dim, 1);
+    if (x_dims.size() == max_dim) {
+      for (int i = 0; i < max_dim; i++) {
+        x_dims_vec[i] = x_dims[i];
+      }
+    } else {
+      for (int i = 0; i < x_dims.size(); i++) {
+        x_dims_vec[i + axis] = x_dims[i];
+      }
+    }
+    if (y_dims.size() == max_dim) {
+      for (int i = 0; i < max_dim; i++) {
+        y_dims_vec[i] = y_dims[i];
+      }
+    } else {
+      for (int i = 0; i < y_dims.size(); i++) {
+        y_dims_vec[i + axis] = y_dims[i];
+      }
+    }
+    const T* x_data = x.data<T>();
+    const T* y_data = y->data<T>();
+    T* z_data = z->data<T>();
+
+    auto& dev_ctx =
+        ctx.template device_context<paddle::platform::XPUDeviceContext>();
+    int ret = xpu::SUCCESS;
+    ret = xpu::broadcast_add<T>(dev_ctx.x_context(), x_data, y_data, z_data,
+                                x_dims_vec, y_dims_vec);
+    PADDLE_ENFORCE_EQ(
+        ret, xpu::SUCCESS,
+        platform::errors::External(
+            "XPU kernel Elementwise occur error in XPUElementwise error code ",
+            ret, XPUAPIErrorMsg[ret]));
   }
 };
 
 template <typename DeviceContext, typename T>
 class ElementwiseAddGradXPUKernel : public ElemwiseGradKernel<T> {
  public:
-  void Compute(const framework::ExecutionContext &ctx) const override {
+  void Compute(const framework::ExecutionContext& ctx) const override {
     ElemwiseGradKernel<T>::Compute(ctx);
-    XPUElementwiseGrad<T>(ctx, xpu::add_grad<T>, false);
+    // XPUElementwiseGrad<T>(ctx, xpu::add_grad<T>, false);
+    auto* x = ctx.Input<framework::Tensor>("X");
+    auto* y = ctx.Input<framework::Tensor>("Y");
+    auto* dz = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto* dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+    auto* dy = ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
+    int axis = ctx.Attr<int>("axis");
+    const framework::DDim& x_dims = x->dims();
+    const framework::DDim& y_dims = y->dims();
+    int max_dim = std::max(x_dims.size(), y_dims.size());
+    axis = (axis == -1 ? std::abs(x_dims.size() - y_dims.size()) : axis);
+    PADDLE_ENFORCE_GE(
+        axis, 0,
+        platform::errors::InvalidArgument(
+            "Axis should be great than or equal to 0, but received axis is %d.",
+            axis));
+    PADDLE_ENFORCE_LT(
+        axis, max_dim,
+        platform::errors::InvalidArgument(
+            "Axis should be less than %d, but received axis is %d.", max_dim,
+            axis));
+    std::vector<int> x_dims_vec(max_dim, 1);
+    std::vector<int> y_dims_vec(max_dim, 1);
+    if (x_dims.size() == max_dim) {
+      for (int i = 0; i < max_dim; i++) {
+        x_dims_vec[i] = x_dims[i];
+      }
+    } else {
+      for (int i = 0; i < x_dims.size(); i++) {
+        x_dims_vec[i + axis] = x_dims[i];
+      }
+    }
+    if (y_dims.size() == max_dim) {
+      for (int i = 0; i < max_dim; i++) {
+        y_dims_vec[i] = y_dims[i];
+      }
+    } else {
+      for (int i = 0; i < y_dims.size(); i++) {
+        y_dims_vec[i + axis] = y_dims[i];
+      }
+    }
+
+    T* dx_data = nullptr;
+    T* dy_data = nullptr;
+    if (dx) {
+      dx_data = dx->mutable_data<T>(ctx.GetPlace());
+    }
+    if (dy) {
+      dy_data = dy->mutable_data<T>(ctx.GetPlace());
+    }
+
+    auto& dev_ctx =
+        ctx.template device_context<paddle::platform::XPUDeviceContext>();
+    int ret = xpu::broadcast_add_grad<T>(dev_ctx.x_context(), dx_data, dx_data,
+                                         dx_data, dz->data<T>(), dy_data,
+                                         dx_data, x_dims_vec, y_dims_vec);
+    PADDLE_ENFORCE_EQ(
+        ret, xpu::SUCCESS,
+        platform::errors::External(
+            "XPU kernel Elementwise occur error in XPUElementwise error code ",
+            ret, XPUAPIErrorMsg[ret]));
   }
 };