chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/mode_kernel.cc

@@ -0,0 +1,149 @@
+// 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/mode_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/full_kernel.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+#include "paddle/phi/kernels/funcs/mode.h"
+namespace phi {
+
+template <typename T, typename Context>
+void ModeKernel(const Context& dev_ctx,
+                const DenseTensor& x,
+                int axis,
+                bool keepdim,
+                DenseTensor* out,
+                DenseTensor* indices) {
+  const auto& in_dims = x.dims();
+  for (int i = 0; i < in_dims.size(); i++) {
+    PADDLE_ENFORCE_LE(
+        0,
+        in_dims[i],
+        errors::InvalidArgument(
+            "The dims of Input(X) should be greater than or equal to 0."));
+  }
+  auto out_dims = out->dims();
+  // axis < 0, calculate the real axis
+  if (axis < 0) axis += in_dims.size();
+  if (keepdim) {
+    PADDLE_ENFORCE_GT(
+        in_dims[axis],
+        0,
+        errors::InvalidArgument(
+            "If keepdim is True, in_dims[axis] should be greater than 0."));
+  }
+
+  T* output_data = dev_ctx.template Alloc<T>(out);
+  int64_t* indices_data = dev_ctx.template Alloc<int64_t>(indices);
+  // out and indices have the same numel.
+  if (out->numel() == 0) {
+    return;
+  }
+
+  if (in_dims.size() == 0) {
+    Copy<Context>(dev_ctx, x, dev_ctx.GetPlace(), false, out);
+    funcs::set_constant(dev_ctx, indices, static_cast<int64_t>(0));
+    return;
+  }
+
+  // if axis is not the last dim, transpose it to the last dim, do the
+  // calculation, then transpose it back to original axis.
+  if (axis == in_dims.size() - 1) {
+    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];
+    funcs::GetMode<T, int64_t>(input_height,
+                               input_width,
+                               in_dims.size(),
+                               &x,
+                               output_data,
+                               indices_data);
+  } else {
+    std::vector<int> trans_axis;
+    for (int i = 0; i < axis; i++) {
+      trans_axis.emplace_back(i);
+    }
+    trans_axis.push_back(in_dims.size() - 1);
+    for (int i = axis + 1; i < in_dims.size() - 1; i++) {
+      trans_axis.emplace_back(i);
+    }
+    trans_axis.emplace_back(axis);
+
+    if (!keepdim) {
+      std::vector<int> tmp_out_shape;
+      for (int i = 0; i < axis; i++) {
+        tmp_out_shape.emplace_back(in_dims[i]);
+      }
+      tmp_out_shape.emplace_back(1);
+      for (int i = axis + 1; i < in_dims.size(); i++) {
+        tmp_out_shape.emplace_back(in_dims[i]);
+      }
+      DDim tmp_out_dim = make_ddim(tmp_out_shape);
+      out->Resize(tmp_out_dim);
+      indices->Resize(tmp_out_dim);
+    }
+
+    // get the trans input_dims, out_dims
+    DDim trans_shape(in_dims);
+    DDim trans_out_shape(in_dims);
+
+    for (int i = 0; i < static_cast<int>(trans_axis.size()); i++) {
+      trans_shape[i] = in_dims[trans_axis[i]];
+      trans_out_shape[i] = in_dims[trans_axis[i]];
+    }
+    trans_out_shape[in_dims.size() - 1] = 1;
+
+    DenseTensor trans_input;
+    trans_input.Resize(trans_shape);
+    dev_ctx.template Alloc<T>(&trans_input);
+    int ndims = static_cast<int>(trans_axis.size());
+
+    // transpose the input value
+    funcs::TransCompute<CPUContext, T>(
+        ndims, dev_ctx, x, &trans_input, trans_axis);
+
+    const int64_t input_height =
+        common::product(slice_ddim(trans_shape, 0, trans_shape.size() - 1));
+    const int64_t input_width = trans_shape[trans_shape.size() - 1];
+    DenseTensor tmp_out;
+    tmp_out.Resize(trans_out_shape);
+    T* t_out = dev_ctx.template Alloc<T>(&tmp_out);
+
+    DenseTensor tmp_indices;
+    tmp_indices.Resize(trans_out_shape);
+    int64_t* t_ind = dev_ctx.template Alloc<int64_t>(&tmp_indices);
+
+    funcs::GetMode<T, int64_t>(
+        input_height, input_width, in_dims.size(), &trans_input, t_out, t_ind);
+    // transpose back
+    funcs::TransCompute<CPUContext, int64_t>(
+        ndims, dev_ctx, tmp_indices, indices, trans_axis);
+    funcs::TransCompute<CPUContext, T>(
+        ndims, dev_ctx, tmp_out, out, trans_axis);
+    if (!keepdim) {
+      out->Resize(out_dims);
+      indices->Resize(out_dims);
+    }
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(
+    mode, CPU, ALL_LAYOUT, phi::ModeKernel, float, double, int32_t, int64_t) {
+  kernel->OutputAt(1).SetDataType(phi::DataType::INT64);
+}