move clip_by_norm kernel to phi for xpu (#52183)

paddle/fluid/operators/clip_by_norm_op_xpu.cc

-/* Copyright (c) 2016 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 <vector>
-
-#include "paddle/fluid/operators/clip_by_norm_op.h"
-
-namespace paddle {
-namespace operators {
-
-template <typename DeviceContext, typename T>
-class XPUClipByNormKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto max_norm = context.Attr<T>("max_norm");
-    auto in_var = context.InputVar("X");
-
-    phi::DenseTensor* output = nullptr;
-    const phi::DenseTensor* input = nullptr;
-    if (in_var->IsType<phi::DenseTensor>()) {
-      input = context.Input<phi::DenseTensor>("X");
-
-      output = context.Output<phi::DenseTensor>("Out");
-      output->mutable_data<T>(context.GetPlace());
-    } else {
-      PADDLE_THROW(platform::errors::InvalidArgument(
-          "Invalid input variable type, only support LodTensor"
-          "type, but got type is %s.",
-          framework::ToTypeName(in_var->Type())));
-    }
-
-    PADDLE_ENFORCE_NOT_NULL(input,
-                            platform::errors::InvalidArgument(
-                                "Input(X) of ClipByNormOp should not be null. "
-                                "Please check if it is created correctly."));
-    auto& dev_ctx = context.template device_context<DeviceContext>();
-    const auto& x_dims = input->dims();
-    std::vector<int> xshape(x_dims.size());
-    std::vector<int> rdims(x_dims.size());
-    for (int i = 0; i < x_dims.size(); i++) {
-      xshape[i] = x_dims[i];
-      rdims[i] = i;
-    }
-    int r = xpu::clip_by_norm<T>(dev_ctx.x_context(),
-                                 input->data<T>(),
-                                 output->data<T>(),
-                                 max_norm,
-                                 xshape,
-                                 rdims);
-    PADDLE_ENFORCE_EQ(
-        r,
-        XPU_SUCCESS,
-        platform::errors::External("XPU API(clip_by_norm) return "
-                                   "wrong value[%d], please check whether "
-                                   "Baidu Kunlun Card is properly installed.",
-                                   r));
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-namespace ops = paddle::operators;
-REGISTER_OP_XPU_KERNEL(
-    clip_by_norm,
-    ops::XPUClipByNormKernel<paddle::platform::XPUDeviceContext, float>);
-
-#endif  // PADDLE_WITH_XPU

paddle/phi/kernels/xpu/clip_by_norm_kernel.cc

+// Copyright (c) 2023 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_by_norm_kernel.h"
+
+#include "paddle/phi/backends/xpu/enforce_xpu.h"
+#include "paddle/phi/backends/xpu/xpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void ClipByNormKernel(const Context& dev_ctx,
+                      const DenseTensor& in,
+                      float max_norm,
+                      DenseTensor* output) {
+  auto input = &in;
+  dev_ctx.template Alloc<T>(output);
+
+  PADDLE_ENFORCE_NOT_NULL(input,
+                          phi::errors::InvalidArgument(
+                              "Input(X) of ClipByNormOp should not be null. "
+                              "Please check if it is created correctly."));
+
+  const auto& x_dims = input->dims();
+  std::vector<int> xshape(x_dims.size());
+  std::vector<int> rdims(x_dims.size());
+  for (int i = 0; i < x_dims.size(); i++) {
+    xshape[i] = x_dims[i];
+    rdims[i] = i;
+  }
+  int r = xpu::clip_by_norm<T>(dev_ctx.x_context(),
+                               input->data<T>(),
+                               output->data<T>(),
+                               max_norm,
+                               xshape,
+                               rdims);
+  PADDLE_ENFORCE_XDNN_SUCCESS(r, "clip_by_norm");
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(
+    clip_by_norm, XPU, ALL_LAYOUT, phi::ClipByNormKernel, float) {}