【Hackathon No.21】为 Paddle 新增 SoftMarginLoss

paddle/fluid/operators/soft_margin_loss_op.cc

@@ -0,0 +1,147 @@
+/* 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 <memory>
+#include <string>
+#include <vector>
+
+#include "paddle/fluid/framework/infershape_utils.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/phi/infermeta/binary.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class SoftMarginLossOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        OperatorWithKernel::IndicateVarDataType(ctx, "X"),
+        ctx.device_context());
+  }
+};
+
+class SoftMarginLossGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SoftMarginLossGrad");
+    OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label", "SoftMarginLossGrad");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
+                   framework::GradVarName("Out"), "SoftMarginLossGrad");
+    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
+                   framework::GradVarName("X"), "SoftMarginLossGrad");
+
+    auto x_dims = ctx->GetInputDim("X");
+    auto labels_dims = ctx->GetInputDim("Label");
+    auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
+
+    bool check = true;
+    if ((!ctx->IsRuntime()) &&
+        (phi::product(x_dims) <= 0 || phi::product(labels_dims) <= 0)) {
+      check = false;
+    }
+
+    if (check) {
+      PADDLE_ENFORCE_EQ(x_dims, labels_dims,
+                        platform::errors::InvalidArgument(
+                            "Input(X) and Input(Label) shall have the same "
+                            "shape. But received: the shape of Input(X) is "
+                            "[%s], the shape of Input(Label) is [%s].",
+                            x_dims, labels_dims));
+
+      PADDLE_ENFORCE_EQ(x_dims, dout_dims,
+                        platform::errors::InvalidArgument(
+                            "Input(X) and Input(Out@Grad) shall have the same "
+                            "shape. But received: the shape of Input(X) is "
+                            "[%s], the shape of Input(Out@Grad) is [%s].",
+                            x_dims, dout_dims));
+    }
+
+    ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
+    ctx->ShareLoD("X", framework::GradVarName("X"));
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        OperatorWithKernel::IndicateVarDataType(ctx, "X"),
+        ctx.device_context());
+  }
+};
+
+class SoftMarginLossOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X",
+             "(Tensor, default Tensor<float>), the input is a tensor of logits"
+             "computed by the previous operator. ");
+    AddInput("Label",
+             "(Tensor, default Tensor<float>), have same shape with input"
+             "label should between in 0 and 1.");
+    AddOutput("Out",
+              "(Tensor, default Tensor<float>), have same shape with"
+              "input");
+    AddComment(R"DOC(
+SoftMarginLoss operator.
+This measures the element-wise probability error in classification tasks
+in which each class is independent.
+The logitstic loss is given as follows:
+      $$loss = log(1+exp(-Label * X))$$
+)DOC");
+  }
+};
+
+template <typename T>
+class SoftMarginLossGradOpMaker : public framework::SingleGradOpMaker<T> {
+ public:
+  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+ protected:
+  void Apply(GradOpPtr<T> op) const override {
+    op->SetType("soft_margin_loss_grad");
+    op->SetInput("X", this->Input("X"));
+    op->SetInput("Label", this->Input("Label"));
+    op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
+    op->SetAttrMap(this->Attrs());
+  }
+};
+
+//DECLARE_INPLACE_OP_INFERER(SoftMarginLossInplaceInferer, {"X", "Out"});
+/*DECLARE_INPLACE_OP_INFERER(SoftMarginLossGradInplaceInferer,
+                           {framework::GradVarName("Out"),
+                            framework::GradVarName("X")});*/
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+DECLARE_INFER_SHAPE_FUNCTOR(soft_margin_loss,
+                            SoftMarginLossInferShapeFunctor,
+                            PD_INFER_META(phi::SoftMarginLossInferMeta));
+
+REGISTER_OPERATOR(soft_margin_loss, ops::SoftMarginLossOp,
+                  ops::SoftMarginLossOpMaker,
+                  ops::SoftMarginLossGradOpMaker<paddle::framework::OpDesc>,
+                  ops::SoftMarginLossGradOpMaker<paddle::imperative::OpBase>,
+                  //ops::SoftMarginLossInplaceInferer,
+                  SoftMarginLossInferShapeFunctor);
+REGISTER_OPERATOR(soft_margin_loss_grad, ops::SoftMarginLossGradOp);
+                  //ops::SoftMarginLossGradInplaceInferer);

paddle/phi/infermeta/binary.cc

@@ -1757,6 +1757,45 @@ void SigmoidCrossEntropyWithLogitsInferMeta(const MetaTensor& x,
   out->share_lod(x);
 }
 
+void SoftMarginLossInferMeta(const MetaTensor& input,
+                             const MetaTensor& label,
+                             MetaTensor* out,
+                             MetaConfig config) {
+  auto input_dims = input.dims();
+  auto label_dims = label.dims();
+
+  int rank = input_dims.size();
+  PADDLE_ENFORCE_EQ(rank,
+                    label_dims.size(),
+                    phi::errors::InvalidArgument(
+                        "Input(X) and Input(Label) shall have the same rank."
+                        "But received: the rank of Input(X) is [%d], "
+                        "the rank of Input(Label) is [%d].",
+                        rank,
+                        label_dims.size()));
+
+  bool check = true;
+  if ((!config.is_runtime) &&
+      (phi::product(input_dims) <= 0 || phi::product(label_dims) <= 0)) {
+    check = false;
+  }
+
+  if (check) {
+    PADDLE_ENFORCE_EQ(input_dims,
+                      label_dims,
+                      phi::errors::InvalidArgument(
+                          "Input(X) and Input(Label) shall have the same "
+                          "shape. But received: the shape of Input(X) is "
+                          "[%s], the shape of Input(Label) is [%s].",
+                          input_dims,
+                          label_dims));
+  }
+
+  out->set_dims(input_dims);
+  out->set_dtype(input.dtype());
+  out->share_lod(input);
+}
+
 void TakeAlongAxisInferMeta(const MetaTensor& x,
                             const MetaTensor& index,
                             int axis,

paddle/phi/infermeta/binary.h

@@ -253,6 +253,11 @@ void SigmoidCrossEntropyWithLogitsInferMeta(const MetaTensor& x,
                                             MetaTensor* out,
                                             MetaConfig config = MetaConfig());
 
+void SoftMarginLossInferMeta(const MetaTensor& input,
+                             const MetaTensor& label,
+                             MetaTensor* out,
+                             MetaConfig config = MetaConfig());
+
 void TakeAlongAxisInferMeta(const MetaTensor& x,
                             const MetaTensor& index,
                             int axis,

paddle/phi/kernels/cpu/soft_margin_loss_grad_kernel.cc

@@ -0,0 +1,47 @@
+// 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/soft_margin_loss_grad_kernel.h"
+
+#include <algorithm>
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+
+namespace phi {
+
+template <typename T, typename Context>
+void SoftMarginLossGradKernel(const Context& dev_ctx,
+                       const DenseTensor& input,
+                       const DenseTensor& label,
+                       const DenseTensor& out_grad,
+                       DenseTensor* input_grad) {
+  auto dx_data = dev_ctx.template Alloc<T>(input_grad);
+  auto dout_data = out_grad.data<T>();
+  auto x_data = input.data<T>();
+  auto label_data = label.data<T>();
+
+  int x_numel = input.numel();
+
+  // dx = dout * (-label * exp(-label * x))/(1 + exp(-label * x ))
+  for (int i = 0; i < x_numel; ++i) {
+    dx_data[i] =
+        dout_data[i] * ((- label_data[i]* std::exp(-label_data[i]*x_data[i] )) /
+                        (static_cast<T>(1) + std::exp(-label_data[i]*x_data[i])));
+  }
+}
+}  // namespace phi
+
+PD_REGISTER_KERNEL(
+    soft_margin_loss_grad, CPU, ALL_LAYOUT, phi::SoftMarginLossGradKernel, float, double) {}

paddle/phi/kernels/cpu/soft_margin_loss_kernel.cc

@@ -0,0 +1,40 @@
+// 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/soft_margin_loss_kernel.h"
+
+#include <algorithm>
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void SoftMarginLossKernel(const Context& dev_ctx,
+                   const DenseTensor& input,
+                   const DenseTensor& label,
+                   DenseTensor* out) {
+  auto x_data = input.data<T>();
+  auto label_data = label.data<T>();
+  auto out_data = dev_ctx.template Alloc<T>(out);
+  auto x_numel = input.numel();
+
+  // out = ln(1+exp(-label * x)/(x_numel)
+  for (int64_t i = 0; i < x_numel; ++i) {
+    out_data[i] =std::log(static_cast<T>(1) + std::exp(-label_data[i]* x_data[i]));
+  }
+}
+}  // namespace phi
+PD_REGISTER_KERNEL(
+    soft_margin_loss, CPU, ALL_LAYOUT, phi::SoftMarginLossKernel, float, double) {}

paddle/phi/kernels/gpu/soft_margin_loss_grad_kernel.cu

@@ -0,0 +1,59 @@
+// 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/soft_margin_loss_grad_kernel.h"
+
+#include <algorithm>
+#include <vector>
+
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/core/hostdevice.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/elementwise_base.h"
+
+namespace phi {
+
+template <typename T>
+struct SoftMarginLossGradFunctor {
+  T one;
+  T eps;
+
+  HOSTDEVICE inline SoftMarginLossGradFunctor() {
+    one = static_cast<T>(1.0f);
+    eps = static_cast<T>(1e-12);
+  }
+
+  HOSTDEVICE inline T operator()(const T x, const T label, const T dout) const {
+    T term1 = (one + std::exp(-label * x));
+    return (dout * (-label * std::exp(-label * x)) / term1);
+  }
+};
+
+template <typename T, typename Context>
+void SoftMarginLossGradKernel(const Context& dev_ctx,
+                       const DenseTensor& input,
+                       const DenseTensor& label,
+                       const DenseTensor& out_grad,
+                       DenseTensor* input_grad) {
+  dev_ctx.template Alloc<T>(input_grad);
+  std::vector<const DenseTensor*> ins = {&input, &label, &out_grad};
+  std::vector<DenseTensor*> outs = {input_grad};
+  auto functor = SoftMarginLossGradFunctor<T>();
+  phi::funcs::ElementwiseKernel<T>(dev_ctx, ins, &outs, functor);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(
+    soft_margin_loss_grad, GPU, ALL_LAYOUT, phi::SoftMarginLossGradKernel, float, double) {}