【PaddlePaddle Hackathon 2】15 新增 API Nanmedian

paddle/fluid/operators/nanmedian_op.cc

@@ -0,0 +1,121 @@
+/*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 "paddle/fluid/framework/infershape_utils.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/op_version_registry.h"
+#include "paddle/phi/core/infermeta_utils.h"
+#include "paddle/phi/infermeta/unary.h"
+
+namespace paddle {
+namespace operators {
+
+class NanmedianOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace());
+  }
+};
+
+class NanmedianOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X",
+             "(Tensor), "
+             "the input feature data of NanmedianOp, dtype should be"
+             "int32, int64, float16, float32 or float64.");
+    AddOutput(
+        "MedianIndex",
+        "Store the index position of median values, The calculation differs "
+        "in the odd or even valid elements numbers."
+        "Along the axis, two elements contributed to the median value in "
+        "each row."
+        "If the amount of valid elements were even, both were the same.")
+        .AsIntermediate()
+        .AsExtra();
+    AddOutput("Out",
+              "(Tensor),"
+              " the output of  NanmedianOp, whose dtype is the same as X");
+    AddAttr<bool>("keepdim",
+                  "(bool, default true) "
+                  "If true, retain the reduced axis with length 1.")
+        .SetDefault(true);
+    AddAttr<std::vector<int>>("axis",
+                              "(std::vector<int>). List of integers,"
+                              " indicating the dimensions to calculate medians")
+        .SetDefault({});
+    AddComment(R"DOC(
+                Nanmedian operator
+
+                This operator is considered as an extention of median operation,
+                which supports specifically the case of NaN values in the input.
+
+                If all the elements in input are NaN it will also return NaN.
+                If no elements in input are Nan, this op is identical to thie median op.
+
+                If the valid count of elements is a even number, the average value of
+                the elements in the middle is calculated as the median.
+
+                This operator can also supports multiple axis.
+        )DOC");
+  }
+};
+
+template <typename T>
+class NanmedianGradMaker : public framework::SingleGradOpMaker<T> {
+ public:
+  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+  void Apply(GradOpPtr<T> op) const override {
+    op->SetType("nanmedian_grad");
+    op->SetInput("X", this->Input("X"));
+    op->SetInput("MedianIndex", this->Output("MedianIndex"));
+    op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
+    op->SetAttrMap(this->Attrs());
+  }
+};
+
+class NanmedianGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType(
+                                       ctx, framework::GradVarName("Out")),
+                                   ctx.GetPlace());
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+DECLARE_INFER_SHAPE_FUNCTOR(nanmedian, NanmedianInferShapeFunctor,
+                            PD_INFER_META(phi::NanmedianInferMeta));
+
+REGISTER_OPERATOR(nanmedian, ops::NanmedianOp, ops::NanmedianOpMaker,
+                  ops::NanmedianGradMaker<paddle::framework::OpDesc>,
+                  ops::NanmedianGradMaker<paddle::imperative::OpBase>,
+                  NanmedianInferShapeFunctor);
+
+DECLARE_INFER_SHAPE_FUNCTOR(nanmedian_grad, NanmedianGradInferShapeFunctor,
+                            PD_INFER_META(phi::NanmedianGradInferMeta));
+
+REGISTER_OPERATOR(nanmedian_grad, ops::NanmedianGradOp,
+                  NanmedianGradInferShapeFunctor);

paddle/phi/infermeta/unary.cc

@@ -1245,6 +1245,76 @@ void MultinomialInferMeta(const MetaTensor& x,
   out->set_dtype(DataType::INT64);
 }
 
+void NanmedianInferMeta(const MetaTensor& x,
+                        const IntArray& axes,
+                        bool keep_dim,
+                        MetaTensor* out,
+                        MetaTensor* median_index) {
+  std::vector<int64_t> axis_list = axes.GetData();
+  auto x_dim = x.dims();
+  int64_t x_rank = x_dim.size();
+  out->set_dtype(x.dtype());
+  median_index->set_dtype(DataType::INT64);
+  median_index->set_dims(make_ddim({x.numel() * 2}));
+
+  std::vector<int32_t> out_dim;
+  if (axis_list.empty()) {
+    if (keep_dim) {
+      for (int64_t i = 0; i < x_rank; i++) {
+        out_dim.push_back(1);
+      }
+    } else {
+      out_dim.push_back(1);
+    }
+  } else {
+    std::vector<int64_t> cleaned_axis;
+    for (auto& axis : axis_list) {
+      if (axis < 0) axis += x_rank;
+
+      PADDLE_ENFORCE_LT(
+          axis,
+          x_rank,
+          errors::InvalidArgument(
+              "Attr(axis) value should be in range [-R, R-1], R is "
+              "the rank of Input(X). But received axis: %d, R: %d. "
+              "Current Input(X)'s shape is=[%s].",
+              axis,
+              x_rank,
+              x_dim));
+
+      PADDLE_ENFORCE_EQ(
+          std::find(cleaned_axis.begin(), cleaned_axis.end(), axis),
+          cleaned_axis.end(),
+          errors::InvalidArgument("Attr(axes) has duplicated elements: %d.",
+                                  static_cast<int>(axis)));
+
+      cleaned_axis.push_back(axis);
+    }
+
+    for (int64_t i = 0; i < x_rank; i++) {
+      if (std::find(cleaned_axis.begin(), cleaned_axis.end(), i) ==
+          cleaned_axis.end()) {
+        out_dim.push_back(x_dim[i]);
+      } else if (keep_dim) {
+        out_dim.push_back(1);
+      }
+    }
+  }
+
+  out->set_dims(make_ddim(out_dim));
+}
+
+void NanmedianGradInferMeta(const MetaTensor& x,
+                            const MetaTensor& median_index,
+                            const MetaTensor& out_grad,
+                            const IntArray& axes,
+                            bool keep_dim,
+                            MetaTensor* x_grad) {
+  auto x_dims = x.dims();
+  x_grad->set_dims(x_dims);
+  x_grad->set_dtype(x.dtype());
+}
+
 void NormInferMeta(const MetaTensor& x,
                    int axis,
                    float epsilon,

paddle/phi/infermeta/unary.h

@@ -177,6 +177,20 @@ void MultinomialInferMeta(const MetaTensor& x,
                           int num_samples,
                           bool replacement,
                           MetaTensor* out);
+
+void NanmedianInferMeta(const MetaTensor& x,
+                        const IntArray& axes,
+                        bool keep_dim,
+                        MetaTensor* out,
+                        MetaTensor* median_index);
+
+void NanmedianGradInferMeta(const MetaTensor& x,
+                            const MetaTensor& median_index,
+                            const MetaTensor& out_grad,
+                            const IntArray& axes,
+                            bool keep_dim,
+                            MetaTensor* x_grad);
+
 void NormInferMeta(const MetaTensor& x,
                    int axis,
                    float epsilon,

paddle/phi/kernels/cpu/nanmedian_grad_kernel.cc

@@ -0,0 +1,99 @@
+// 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/nanmedian_grad_kernel.h"
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void CalcMedianGradKernel(const Context& dev_ctx,
+                          const DenseTensor& x,
+                          const DenseTensor& median_index,
+                          const DenseTensor& out_grad,
+                          const IntArray& axes,
+                          DenseTensor* x_grad,
+                          T* x_grad_ptr) {
+  phi::funcs::SetConstant<Context, T> set_zero;
+  set_zero(dev_ctx, x_grad, static_cast<T>(0));
+  if (!x_grad_ptr) return;
+
+  const int64_t* m_ptr = median_index.data<int64_t>();
+  const T* out_grad_ptr = out_grad.data<T>();
+  int64_t numel = x.numel();
+  auto x_dim = x.dims();
+  int64_t rank = x_dim.size();
+  int64_t stride = x_dim[rank - 1];
+
+  int64_t pre_dim = numel / stride;
+  int64_t i = 0;
+  int64_t offset = 0;
+  T div_factor = static_cast<T>(2.0);
+  for (i = 0; i < pre_dim; i++) {
+    if (m_ptr[2 * i] >= 0) {
+      if (m_ptr[2 * i] == m_ptr[2 * i + 1]) {
+        x_grad_ptr[offset + m_ptr[2 * i]] = out_grad_ptr[i];
+      } else {
+        x_grad_ptr[offset + m_ptr[2 * i]] = out_grad_ptr[i] / div_factor;
+        x_grad_ptr[offset + m_ptr[2 * i + 1]] = out_grad_ptr[i] / div_factor;
+      }
+    }
+    offset += stride;
+  }
+}
+
+template <typename T, typename Context>
+void BaseMedianGradKernel(const Context& dev_ctx,
+                          const DenseTensor& x,
+                          const DenseTensor& median_index,
+                          const DenseTensor& out_grad,
+                          const IntArray& axes,
+                          DenseTensor* x_grad) {
+  auto rank = x.dims().size();
+  T* x_grad_ptr = dev_ctx.template Alloc<T>(x_grad);
+  if (axes.size() && (rank > 1)) {
+    DenseTensor tmp_x_grad(*x_grad);
+    CalcMedianGradKernel<T, Context>(
+        dev_ctx, x, median_index, out_grad, axes, &tmp_x_grad, x_grad_ptr);
+    PostprocessMedianGradKernel<T, Context>(dev_ctx, &tmp_x_grad, axes, x_grad);
+  } else {
+    CalcMedianGradKernel<T, Context>(
+        dev_ctx, x, median_index, out_grad, axes, x_grad, x_grad_ptr);
+  }
+}
+
+template <typename T, typename Context>
+void NanmedianGradKernel(const Context& dev_ctx,
+                         const DenseTensor& input,
+                         const DenseTensor& median_index,
+                         const DenseTensor& out_grad,
+                         const IntArray& axes,
+                         bool keep_dim,
+                         DenseTensor* x_grad) {
+  BaseMedianGradKernel<T, Context>(
+      dev_ctx, input, median_index, out_grad, axes, x_grad);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(nanmedian_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::NanmedianGradKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t) {}