move clip XPU kernel to PHI,test=kunlun (#45568)

* move clip XPU kernel to PHI,test=kunlun

* delete clip_op_xpu.cc,test=kunlun

paddle/fluid/operators/clip_op_xpu.cc

-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#ifdef PADDLE_WITH_XPU
-
-#include "paddle/fluid/framework/op_registry.h"
-
-namespace paddle {
-namespace operators {
-
-using Tensor = framework::Tensor;
-
-template <typename DeviceContext, typename T>
-class ClipXPUKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* x = ctx.Input<Tensor>("X");
-    auto* out = ctx.Output<Tensor>("Out");
-    out->mutable_data<T>(ctx.GetPlace());
-
-    auto max = static_cast<T>(ctx.Attr<float>("max"));
-    if (ctx.HasInput("Max")) {
-      Tensor max_cpu;
-      auto* max_t = ctx.Input<Tensor>("Max");
-      auto* max_data = max_t->data<T>();
-      if (platform::is_xpu_place(max_t->place())) {
-        paddle::framework::TensorCopySync(
-            *max_t, platform::CPUPlace(), &max_cpu);
-        max_data = max_cpu.data<T>();
-      }
-      max = max_data[0];
-    }
-
-    auto min = ctx.Attr<float>("min");
-    if (ctx.HasInput("Min")) {
-      Tensor min_cpu;
-      auto* min_t = ctx.Input<Tensor>("Min");
-      auto* min_data = min_t->data<T>();
-      if (platform::is_xpu_place(min_t->place())) {
-        paddle::framework::TensorCopySync(
-            *min_t, platform::CPUPlace(), &min_cpu);
-        min_data = min_cpu.data<T>();
-      }
-      min = min_data[0];
-    }
-
-    using XPUDataType = typename XPUTypeTrait<T>::Type;
-    auto& dev_ctx = ctx.template device_context<DeviceContext>();
-    auto x_data = reinterpret_cast<const XPUDataType*>(x->data<T>());
-    auto out_data = reinterpret_cast<XPUDataType*>(out->data<T>());
-    int r = xpu::clip_v2(
-        dev_ctx.x_context(), x_data, out_data, x->numel(), min, max);
-    PADDLE_ENFORCE_EQ(
-        r,
-        XPU_SUCCESS,
-        platform::errors::External("XPU API(clip_v2) return wrong "
-                                   "value[%d %s]",
-                                   r,
-                                   XPUAPIErrorMsg[r]));
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-namespace ops = paddle::operators;
-namespace plat = paddle::platform;
-
-REGISTER_OP_XPU_KERNEL(clip, ops::ClipXPUKernel<plat::XPUDeviceContext, float>);
-
-#endif

paddle/phi/kernels/xpu/clip_kernel.cc

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/phi/kernels/clip_kernel.h"
+#include "paddle/phi/backends/xpu/xpu_context.h"
+#include "paddle/phi/backends/xpu/xpu_header.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void ClipKernel(const Context& dev_ctx,
+                const DenseTensor& x,
+                const Scalar& min,
+                const Scalar& max,
+                DenseTensor* out) {
+  dev_ctx.template Alloc<T>(out);
+  using XPUDataType = typename XPUTypeTrait<T>::Type;
+  auto x_data = reinterpret_cast<const XPUDataType*>(x.data<T>());
+  auto out_data = reinterpret_cast<XPUDataType*>(out->data<T>());
+  int r = xpu::clip_v2(dev_ctx.x_context(),
+                       x_data,
+                       out_data,
+                       x.numel(),
+                       min.to<float>(),
+                       max.to<float>());
+
+  PADDLE_ENFORCE_EQ(r,
+                    XPU_SUCCESS,
+                    phi::errors::External("XPU API(clip_v2) return wrong "
+                                          "value[%d %s]",
+                                          r,
+                                          XPUAPIErrorMsg[r]));
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(clip, XPU, ALL_LAYOUT, phi::ClipKernel, float) {}