chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/argsort_kernel.cc

@@ -0,0 +1,197 @@
+// 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/argsort_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+#include "paddle/phi/kernels/transpose_kernel.h"
+
+namespace phi {
+
+template <typename T, typename Type>
+static void FullSort(Type input_height,
+                     Type input_width,
+                     int input_dim,
+                     const DenseTensor* input,
+                     T* t_out,
+                     Type* t_indices,
+                     bool descending,
+                     bool stable) {
+#ifdef PADDLE_WITH_MKLML
+#pragma omp parallel for
+#endif
+  for (Type i = 0; i < input_height; ++i) {
+    std::vector<std::pair<T, Type>> col_vec;
+    col_vec.reserve(input_width);
+    if (input_dim == 1) {
+      auto e_input = EigenVector<T>::Flatten(*input);
+      for (Type j = 0; j < input_width; ++j) {
+        col_vec.push_back(std::pair<T, Type>(e_input(j), j));
+      }
+    } else {
+      auto e_input = EigenMatrix<T>::Reshape(*input, input_dim - 1);
+      for (Type j = 0; j < input_width; ++j) {
+        col_vec.push_back(std::pair<T, Type>(e_input(i, j), j));
+      }
+    }
+    if (stable) {
+      std::stable_sort(
+          col_vec.begin(),
+          col_vec.end(),
+          [&](const std::pair<T, Type>& l, const std::pair<T, Type>& r) {
+            if (descending)
+              return (std::isnan(static_cast<double>(l.first)) &&
+                      !std::isnan(static_cast<double>(r.first))) ||
+                     (l.first > r.first);
+            else
+              return (!std::isnan(static_cast<double>(l.first)) &&
+                      std::isnan(static_cast<double>(r.first))) ||
+                     (l.first < r.first);
+          });
+    } else {
+      std::sort(col_vec.begin(),
+                col_vec.end(),
+                [&](const std::pair<T, Type>& l, const std::pair<T, Type>& r) {
+                  if (descending)
+                    return (std::isnan(static_cast<double>(l.first)) &&
+                            !std::isnan(static_cast<double>(r.first))) ||
+                           (l.first > r.first);
+                  else
+                    return (!std::isnan(static_cast<double>(l.first)) &&
+                            std::isnan(static_cast<double>(r.first))) ||
+                           (l.first < r.first);
+                });
+    }
+
+    for (Type j = 0; j < input_width; ++j) {
+      t_out[i * input_width + j] = col_vec[j].first;
+      t_indices[i * input_width + j] = col_vec[j].second;
+    }
+  }
+}
+
+template <typename T, typename Context>
+void ArgsortKernel(const Context& dev_ctx,
+                   const DenseTensor& input,
+                   int axis,
+                   bool descending,
+                   bool stable,
+                   DenseTensor* output,
+                   DenseTensor* indices) {
+  auto in_dims = input.dims();
+  auto rank = in_dims.size();
+
+  if (input.numel() == 0) {
+    output->Resize(in_dims);
+    indices->Resize(in_dims);
+    dev_ctx.template Alloc<T>(output);
+    dev_ctx.template Alloc<int64_t>(indices);
+    return;
+  }
+
+  axis = (axis < 0) ? (in_dims.size() + axis) : axis;
+  T* out_data = dev_ctx.template Alloc<T>(output);
+
+  // For 0D Tensor
+  if (rank == 0) {
+    Copy<Context>(dev_ctx, input, dev_ctx.GetPlace(), false, output);
+    dev_ctx.template Alloc<int64_t>(indices);
+    funcs::set_constant(dev_ctx, indices, static_cast<int64_t>(0));
+    return;
+  }
+
+  // Do full sort
+  if (axis == -1 || axis + 1 == in_dims.size()) {
+    const int64_t input_height =
+        common::product(slice_ddim(in_dims, 0, in_dims.size() - 1));
+    const int64_t input_width = in_dims[in_dims.size() - 1];
+    int64_t* ids_data = dev_ctx.template Alloc<int64_t>(indices);
+    FullSort<T, int64_t>(input_height,
+                         input_width,
+                         in_dims.size(),
+                         &input,
+                         out_data,
+                         ids_data,
+                         descending,
+                         stable);
+  } else {
+    // If not full sort do transpose
+    std::vector<int> trans;
+    for (int i = 0; i < axis; i++) {
+      trans.push_back(i);
+    }
+    trans.push_back(in_dims.size() - 1);
+    for (int i = axis + 1; i < in_dims.size() - 1; i++) {
+      trans.push_back(i);
+    }
+    trans.push_back(axis);
+    DDim trans_dims(in_dims);
+    for (size_t i = 0; i < trans.size(); i++) {
+      trans_dims[static_cast<int>(i)] = in_dims[trans[i]];
+    }
+
+    DenseTensor trans_inp;
+    trans_inp.Resize(trans_dims);
+    dev_ctx.template Alloc<T>(&trans_inp);
+    // Do transpose
+    TransposeKernel<T, Context>(dev_ctx, input, trans, &trans_inp);
+
+    const int64_t input_height =
+        common::product(slice_ddim(trans_dims, 0, trans_dims.size() - 1));
+    const int64_t input_width = trans_dims[trans_dims.size() - 1];
+
+    DenseTensor tmp_out;
+    tmp_out.Resize(trans_dims);
+    T* t_out = dev_ctx.template Alloc<T>(&tmp_out);
+
+    DenseTensor tmp_indices;
+    tmp_indices.Resize(trans_dims);
+    auto* t_ind = dev_ctx.template Alloc<int64_t>(&tmp_indices);
+
+    FullSort<T, int64_t>(input_height,
+                         input_width,
+                         in_dims.size(),
+                         &trans_inp,
+                         t_out,
+                         t_ind,
+                         descending,
+                         stable);
+
+    dev_ctx.template Alloc<int64_t>(indices);
+    TransposeKernel<int64_t, Context>(dev_ctx, tmp_indices, trans, indices);
+    // transpose back
+    TransposeKernel<T, Context>(dev_ctx, tmp_out, trans, output);
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(argsort,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::ArgsortKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t,
+                   int16_t,
+                   uint8_t,
+                   phi::float16,
+                   phi::bfloat16) {
+  kernel->OutputAt(1).SetDataType(phi::DataType::INT64);
+}