[Phi] Migrate squared_l2_norm_op to phi (#44492)

paddle/fluid/framework/new_executor/standalone_executor_test.cc

@@ -57,7 +57,7 @@ USE_OP_ITSELF(sqrt);
 USE_OP_ITSELF(elementwise_max);
 USE_OP_ITSELF(elementwise_div);
 USE_OP_ITSELF(sgd);
-USE_OP(squared_l2_norm);
+USE_OP_ITSELF(squared_l2_norm);
 USE_OP_ITSELF(memcpy_h2d);
 USE_OP_ITSELF(memcpy_d2h);
 USE_OP_ITSELF(fetch_v2);
@@ -87,6 +87,7 @@ PD_DECLARE_KERNEL(mean, GPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(mean_grad, GPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(sigmoid, GPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(sigmoid_grad, GPU, ALL_LAYOUT);
+PD_DECLARE_KERNEL(squared_l2_norm, GPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(reshape_grad, GPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(add_grad, GPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(matmul_grad, GPU, ALL_LAYOUT);

paddle/fluid/operators/inplace_abn_op.cu

@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/inplace_abn_op.h"
-#include <iostream>
 #include "paddle/fluid/operators/batch_norm_op.h"
 #include "paddle/phi/kernels/batch_norm_grad_kernel.h"
 #include "paddle/phi/kernels/batch_norm_kernel.h"

paddle/fluid/operators/optimizers/lamb_op.h

@@ -22,11 +22,11 @@ limitations under the License. */
 #include "paddle/fluid/memory/buffer.h"
 #include "paddle/fluid/operators/amp/fp16_type_traits.h"
 #include "paddle/fluid/operators/math/selected_rows_functor.h"
-#include "paddle/fluid/operators/math/squared_l2_norm.h"
 #include "paddle/fluid/operators/tensor_to_string.h"
 #include "paddle/fluid/platform/for_range.h"
 #include "paddle/phi/kernels/funcs/algorithm.h"
 #include "paddle/phi/kernels/funcs/eigen/extensions.h"
+#include "paddle/phi/kernels/funcs/squared_l2_norm.h"
 
 namespace paddle {
 namespace operators {
@@ -756,13 +756,14 @@ class LambOpKernel : public framework::OpKernel<T> {
     // TODO(zengjinle): remove the following Eigen operations when
     // *skip_update == true.
     memory::Buffer buffer(dev_ctx.GetPlace());
-    math::SquaredL2Norm(dev_ctx,
-                        reinterpret_cast<const MT*>(
-                            IsMultiPrecision ? master_param_ptr : param_ptr),
-                        p_norm_ptr,
-                        numel,
-                        &buffer);
-    math::SquaredL2Norm(
+    phi::funcs::SquaredL2Norm(
+        dev_ctx,
+        reinterpret_cast<const MT*>(IsMultiPrecision ? master_param_ptr
+                                                     : param_ptr),
+        p_norm_ptr,
+        numel,
+        &buffer);
+    phi::funcs::SquaredL2Norm(
         dev_ctx, trust_ratio_div_ptr, trust_ratio_div_norm_ptr, numel, &buffer);
 
     if (VLOG_IS_ON(1)) {

paddle/fluid/operators/squared_l2_norm_op.cc

@@ -12,9 +12,10 @@ 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/fluid/operators/squared_l2_norm_op.h"
-
-#include <memory>
+#include "paddle/fluid/framework/infershape_utils.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/phi/core/infermeta_utils.h"
+#include "paddle/phi/infermeta/unary.h"
 
 namespace paddle {
 namespace operators {
@@ -24,13 +25,6 @@ using framework::Tensor;
 class SquaredL2NormOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
-
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SquaredL2NormOp");
-    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "SquaredL2NormOp");
-
-    ctx->SetOutputDim("Out", {1});
-  }
 };
 
 template <typename T>
@@ -54,20 +48,6 @@ class SquaredL2NormGradOpMaker : public framework::SingleGradOpMaker<T> {
 class SquaredL2NormGradOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
-
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SquaredL2NormGradOp");
-    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")),
-                   "Input",
-                   "Out@GRAD",
-                   "SquaredL2NormGradOp");
-    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")),
-                   "Output",
-                   "X@GRAD",
-                   "SquaredL2NormGradOp");
-
-    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
-  }
 };
 
 class SquaredL2NormOpMaker : public framework::OpProtoAndCheckerMaker {
@@ -90,15 +70,22 @@ $$Out = \sum_{i} X_{i}^2$$
 }  // namespace paddle
 
 namespace ops = paddle::operators;
+
+DECLARE_INFER_SHAPE_FUNCTOR(squared_l2_norm,
+                            SquaredL2NormInferShapeFunctor,
+                            PD_INFER_META(phi::SquaredL2NormInferMeta));
+
+DECLARE_INFER_SHAPE_FUNCTOR(squared_l2_norm_grad,
+                            SquaredL2NormGradInferShapeFunctor,
+                            PD_INFER_META(phi::UnchangedInferMeta));
+
 REGISTER_OPERATOR(squared_l2_norm,
                   ops::SquaredL2NormOp,
                   ops::SquaredL2NormOpMaker,
                   ops::SquaredL2NormGradOpMaker<paddle::framework::OpDesc>,
-                  ops::SquaredL2NormGradOpMaker<paddle::imperative::OpBase>);
-REGISTER_OPERATOR(squared_l2_norm_grad, ops::SquaredL2NormGradOp);
-REGISTER_OP_CPU_KERNEL(squared_l2_norm,
-                       ops::SquaredL2NormKernel<phi::CPUContext, float>,
-                       ops::SquaredL2NormKernel<phi::CPUContext, double>);
-REGISTER_OP_CPU_KERNEL(squared_l2_norm_grad,
-                       ops::SquaredL2NormGradKernel<phi::CPUContext, float>,
-                       ops::SquaredL2NormGradKernel<phi::CPUContext, double>);
+                  ops::SquaredL2NormGradOpMaker<paddle::imperative::OpBase>,
+                  SquaredL2NormInferShapeFunctor);
+
+REGISTER_OPERATOR(squared_l2_norm_grad,
+                  ops::SquaredL2NormGradOp,
+                  SquaredL2NormGradInferShapeFunctor);

paddle/fluid/operators/squared_l2_norm_op.cu

-/* 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. */
-#include "paddle/fluid/operators/squared_l2_norm_op.h"
-
-namespace ops = paddle::operators;
-REGISTER_OP_CUDA_KERNEL(
-    squared_l2_norm,
-    ops::SquaredL2NormKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::SquaredL2NormKernel<paddle::platform::CUDADeviceContext, double>);
-REGISTER_OP_CUDA_KERNEL(
-    squared_l2_norm_grad,
-    ops::SquaredL2NormGradKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::SquaredL2NormGradKernel<paddle::platform::CUDADeviceContext, double>);

paddle/fluid/operators/squared_l2_norm_op.h

-/* 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. */
-
-#pragma once
-#include "paddle/fluid/framework/eigen.h"
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/operators/math/squared_l2_norm.h"
-
-namespace paddle {
-namespace operators {
-
-// Out = sum(square(X))
-template <typename DeviceContext, typename T>
-class SquaredL2NormKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext &context) const override {
-    const framework::Tensor *x = context.Input<framework::Tensor>("X");
-    const auto *x_ptr = x->data<T>();
-    auto numel = x->numel();
-
-    framework::Tensor *out = context.Output<framework::Tensor>("Out");
-    auto *out_ptr = out->mutable_data<T>(context.GetPlace());
-
-    math::SquaredL2Norm(context.template device_context<DeviceContext>(),
-                        x_ptr,
-                        out_ptr,
-                        numel);
-  }
-};
-
-// dX = X
-template <typename DeviceContext, typename T>
-class SquaredL2NormGradKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext &context) const override {
-    const framework::Tensor *X = context.Input<framework::Tensor>("X");
-    const framework::Tensor *dOut =
-        context.Input<framework::Tensor>(framework::GradVarName("Out"));
-    PADDLE_ENFORCE_EQ(
-        dOut->numel(),
-        1,
-        platform::errors::InvalidArgument(
-            "Input(GRAD@Out) of SquaredL2NormGradOP should be a scalar."));
-    framework::Tensor *dX =
-        context.Output<framework::Tensor>(framework::GradVarName("X"));
-    dX->mutable_data<T>(context.GetPlace());
-
-    auto x = framework::EigenVector<T>::Flatten(*X);
-    auto dout = framework::EigenVector<T>::Flatten(*dOut);
-    auto dx = framework::EigenVector<T>::Flatten(*dX);
-    auto *place =
-        context.template device_context<DeviceContext>().eigen_device();
-
-    Eigen::DSizes<int, 1> x_dsize(X->numel());
-    dx.device(*place) = (dout.broadcast(x_dsize) * x) * static_cast<T>(2.0);
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle

paddle/fluid/operators/squared_l2_norm_op_mlu.cc

@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/operators/squared_l2_norm_op.h"
 // #include "paddle/fluid/platform/device/npu/npu_op_runner.h"
 #include "paddle/fluid/operators/mlu/mlu_baseop.h"
 

paddle/fluid/operators/squared_l2_norm_op_npu.cc

@@ -12,7 +12,7 @@ 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/fluid/operators/squared_l2_norm_op.h"
+#include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/platform/device/npu/npu_op_runner.h"
 
 namespace paddle {

paddle/phi/api/yaml/legacy_api.yaml

@@ -2062,6 +2062,15 @@
     func : square
   backward : square_grad
 
+- api : squared_l2_norm
+  args : (Tensor x)
+  output : Tensor
+  infer_meta :
+    func : SquaredL2NormInferMeta
+  kernel :
+    func : squared_l2_norm
+  backward : squared_l2_norm_grad
+
 - api : squeeze
   args : (Tensor x, int[] axes)
   output : Tensor(out), Tensor(xshape)

paddle/phi/api/yaml/legacy_backward.yaml

@@ -2009,6 +2009,16 @@
   backward : square_double_grad
   inplace : (out_grad -> x_grad)
 
+- backward_api : squared_l2_norm_grad
+  forward : squared_l2_norm(Tensor x) -> Tensor(out)
+  args : (Tensor x, Tensor out_grad)
+  output : Tensor(x_grad)
+  infer_meta :
+    func : UnchangedInferMeta
+    param: [x]
+  kernel :
+    func : squared_l2_norm_grad
+
 - backward_api : squeeze_double_grad
   forward : squeeze_grad(Tensor xshape, Tensor grad_out, int[] axes) -> Tensor(grad_x)
   args : (Tensor grad_x_grad, int[] axes)

paddle/phi/infermeta/unary.cc

@@ -2489,6 +2489,10 @@ void SplitInferMeta(const MetaTensor& x,
   }
 }
 
+void SquaredL2NormInferMeta(const MetaTensor& x, MetaTensor* out) {
+  out->set_dims({1});
+}
+
 void SqueezeInferMeta(const MetaTensor& x,
                       const std::vector<int>& axes,
                       MetaTensor* out) {

paddle/phi/infermeta/unary.h

@@ -345,6 +345,8 @@ void SplitInferMeta(const MetaTensor& x_meta,
                     std::vector<MetaTensor*> out,
                     MetaConfig config = MetaConfig());
 
+void SquaredL2NormInferMeta(const MetaTensor& x, MetaTensor* out);
+
 void SqueezeInferMeta(const MetaTensor& x,
                       const std::vector<int>& axes,
                       MetaTensor* out);

paddle/phi/kernels/cpu/squared_l2_norm_grad_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/squared_l2_norm_grad_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/impl/squared_l2_norm_grad_kernel_impl.h"
+
+PD_REGISTER_KERNEL(squared_l2_norm_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::SquaredL2NormGradKernel,
+                   float,
+                   double) {}

paddle/phi/kernels/cpu/squared_l2_norm_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/squared_l2_norm_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/impl/squared_l2_norm_kernel_impl.h"
+
+PD_REGISTER_KERNEL(
+    squared_l2_norm, CPU, ALL_LAYOUT, phi::SquaredL2NormKernel, float, double) {
+}

paddle/fluid/operators/math/squared_l2_norm.h → paddle/phi/kernels/funcs/squared_l2_norm.h

@@ -14,13 +14,12 @@
 
 #pragma once
 
-#include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/memory/buffer.h"
-#include "paddle/fluid/platform/device_context.h"
-#include "paddle/fluid/platform/enforce.h"
+#include "paddle/phi/core/device_context.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
 
 #if defined(__NVCC__) || defined(__HIPCC__)
-#include "paddle/fluid/operators/kernel_primitives/functor_primitives.h"
+#include "paddle/phi/kernels/primitive/functor_primitives.h"
 #ifdef __NVCC__
 #include "cub/cub.cuh"
 #else
@@ -29,20 +28,19 @@ namespace cub = hipcub;
 #endif
 #endif
 
-namespace paddle {
-namespace operators {
-namespace math {
+namespace phi {
+namespace funcs {
 
 template <typename T1, typename T2 = T1>
 void SquaredL2Norm(const phi::CPUContext& ctx,
                    const T1* x,
                    T2* y,
                    size_t numel,
-                   memory::Buffer* buffer = nullptr) {
+                   paddle::memory::Buffer* buffer = nullptr) {
   if (std::is_same<T1, T2>::value) {
-    using EigenT = typename framework::EigenTensor<T1, 1>::Type;
-    using ConstEigenT = typename framework::EigenTensor<T1, 1>::ConstType;
-    using EigenDim = typename framework::EigenDim<1>::Type;
+    using EigenT = typename phi::EigenTensor<T1, 1>::Type;
+    using ConstEigenT = typename phi::EigenTensor<T1, 1>::ConstType;
+    using EigenDim = typename phi::EigenDim<1>::Type;
     ConstEigenT input(x, EigenDim(numel));
     EigenT output(reinterpret_cast<T1*>(y), EigenDim(1));
     output.device(*ctx.eigen_device()) = input.square().sum();
@@ -58,17 +56,17 @@ void SquaredL2Norm(const phi::CPUContext& ctx,
 
 #if defined(__NVCC__) || defined(__HIPCC__)
 template <typename T1, typename T2 = T1>
-void SquaredL2Norm(const platform::CUDADeviceContext& ctx,
+void SquaredL2Norm(const phi::GPUContext& ctx,
                    const T1* x,
                    T2* y,
                    size_t numel,
-                   memory::Buffer* buffer = nullptr) {
+                   paddle::memory::Buffer* buffer = nullptr) {
   if (UNLIKELY(buffer == nullptr)) {
-    memory::Buffer tmp_buffer(ctx.GetPlace());
+    paddle::memory::Buffer tmp_buffer(ctx.GetPlace());
     return SquaredL2Norm(ctx, x, y, numel, &tmp_buffer);
   }
 
-  using FunctorT = kernel_primitives::SquareFunctor<T1, T2>;
+  using FunctorT = phi::kps::SquareFunctor<T1, T2>;
   cub::TransformInputIterator<T2, FunctorT, const T1*> iter(x, FunctorT());
   size_t temp_storage_bytes = 0;
   void* d_temp_storage = nullptr;
@@ -89,6 +87,5 @@ void SquaredL2Norm(const platform::CUDADeviceContext& ctx,
 }
 #endif
 
-}  // namespace math
-}  // namespace operators
-}  // namespace paddle
+}  // namespace funcs
+}  // namespace phi

paddle/phi/kernels/gpu/squared_l2_norm_grad_kernel.cu

+// 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/squared_l2_norm_grad_kernel.h"
+
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/impl/squared_l2_norm_grad_kernel_impl.h"
+
+PD_REGISTER_KERNEL(squared_l2_norm_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::SquaredL2NormGradKernel,
+                   float,
+                   double) {}

paddle/phi/kernels/gpu/squared_l2_norm_kernel.cu

+// 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/squared_l2_norm_kernel.h"
+
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/impl/squared_l2_norm_kernel_impl.h"
+
+PD_REGISTER_KERNEL(
+    squared_l2_norm, GPU, ALL_LAYOUT, phi::SquaredL2NormKernel, float, double) {
+}

paddle/phi/kernels/impl/squared_l2_norm_grad_kernel_impl.h

+// 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.
+
+#pragma once
+
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+
+namespace phi {
+template <typename T, typename Context>
+void SquaredL2NormGradKernel(const Context& dev_ctx,
+                             const DenseTensor& x,
+                             const DenseTensor& dout,
+                             DenseTensor* dx) {
+  dev_ctx.template Alloc<T>(dx);
+
+  PADDLE_ENFORCE_EQ(
+      dout.numel(),
+      1,
+      phi::errors::InvalidArgument(
+          "Input(GRAD@Out) of SquaredL2NormGradOP should be a scalar."));
+
+  auto input = phi::EigenVector<T>::Flatten(x);
+  auto d_out = phi::EigenVector<T>::Flatten(dout);
+  auto d_x = phi::EigenVector<T>::Flatten(*dx);
+  auto* place = dev_ctx.eigen_device();
+  Eigen::DSizes<int, 1> x_dsize(x.numel());
+  d_x.device(*place) = (d_out.broadcast(x_dsize) * input) * static_cast<T>(2.0);
+}
+}  // namespace phi

paddle/phi/kernels/impl/squared_l2_norm_kernel_impl.h

+// 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.
+
+#pragma once
+
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/kernels/funcs/squared_l2_norm.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void SquaredL2NormKernel(const Context& dev_ctx,
+                         const DenseTensor& x,
+                         DenseTensor* out) {
+  dev_ctx.template Alloc<T>(out);
+  auto x_ptr = x.template data<T>();
+  auto numel = x.numel();
+  return phi::funcs::SquaredL2Norm(dev_ctx, x_ptr, out->data<T>(), numel);
+}
+
+}  // namespace phi

paddle/phi/kernels/squared_l2_norm_grad_kernel.h

+// 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.
+
+#pragma once
+
+#include "paddle/phi/core/dense_tensor.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void SquaredL2NormGradKernel(const Context& dev_ctx,
+                             const DenseTensor& x,
+                             const DenseTensor& dout,
+                             DenseTensor* dx);
+}  // namespace phi

paddle/phi/kernels/squared_l2_norm_kernel.h

+// 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.
+
+#pragma once
+
+#include "paddle/phi/core/dense_tensor.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void SquaredL2NormKernel(const Context& dev_ctx,
+                         const DenseTensor& x,
+                         DenseTensor* out);
+}  // namespace phi

paddle/phi/ops/compat/squared_l2_norm_sig.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/core/compat/op_utils.h"
+
+namespace phi {
+
+KernelSignature SquaredL2NormOpArgumentMapping(
+    const ArgumentMappingContext& ctx) {
+  return KernelSignature("squared_l2_norm", {"X"}, {}, {"Out"});
+}
+
+KernelSignature SquaredL2NormGradOpArgumentMapping(
+    const ArgumentMappingContext& ctx) {
+  return KernelSignature(
+      "squared_l2_norm_grad", {"X", "Out@GRAD"}, {}, {"X@GRAD"});
+}
+
+}  // namespace phi
+
+PD_REGISTER_ARG_MAPPING_FN(squared_l2_norm,
+                           phi::SquaredL2NormOpArgumentMapping);
+PD_REGISTER_ARG_MAPPING_FN(squared_l2_norm_grad,
+                           phi::SquaredL2NormGradOpArgumentMapping);

python/paddle/fluid/clip.py

@@ -73,8 +73,8 @@ def _squared_l2_norm(x):
     if in_dygraph_mode():
         if x.is_selected_rows():
             new_x = paddle.to_tensor(x.numpy())
-            return _C_ops.squared_l2_norm(new_x)
-        return _C_ops.squared_l2_norm(x)
+            return _C_ops.final_state_squared_l2_norm(new_x)
+        return _C_ops.final_state_squared_l2_norm(x)
     else:
         if _in_legacy_dygraph():
             return _C_ops.squared_l2_norm(x)

python/paddle/fluid/tests/unittests/test_squared_l2_norm_op.py

@@ -20,6 +20,14 @@ from numpy import linalg as LA
 from op_test import OpTest
 import paddle
 from paddle import _C_ops
+from paddle.framework import in_dygraph_mode
+
+
+def test_squared_l2_norm(x):
+    if in_dygraph_mode():
+        return _C_ops.final_state_squared_l2_norm(x)
+    else:
+        return _C_ops.squared_l2_norm(x)
 
 
 class TestL2LossOp(OpTest):
@@ -27,6 +35,7 @@ class TestL2LossOp(OpTest):
     """
 
     def setUp(self):
+        self.python_api = test_squared_l2_norm
         self.op_type = "squared_l2_norm"
         self.max_relative_error = 0.05
 
@@ -36,7 +45,7 @@ class TestL2LossOp(OpTest):
         self.outputs = {'Out': np.square(LA.norm(X))}
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(check_eager=True)
 
     def test_check_grad(self):
         self.check_grad(['X'],