chore: import upstream snapshot with attribution

paddle/phi/kernels/xpu/argsort_kernel.cc

@@ -0,0 +1,153 @@
+// 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/xpu/enforce_xpu.h"
+#include "paddle/phi/backends/xpu/xpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/full_kernel.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+
+namespace phi {
+
+template <typename T, typename TID>
+static inline void xpu_argsort(xpu::Context* xpu_ctx,
+                               const T* input_data,
+                               T* output_data,
+                               TID* indices_data,
+                               int64_t m,
+                               int64_t n,
+                               bool descending,
+                               bool stable) {
+  int ret;
+  if (stable) {
+    ret = xpu::stable_sort(
+        xpu_ctx, input_data, output_data, indices_data, m, n, descending);
+    PADDLE_ENFORCE_XDNN_SUCCESS(ret, "stable_sort");
+  } else {
+    ret = xpu::sort(
+        xpu_ctx, input_data, output_data, indices_data, m, n, descending);
+    PADDLE_ENFORCE_XDNN_SUCCESS(ret, "sort");
+  }
+}
+
+template <typename T>
+static inline void xpu_transpose(xpu::Context* xpu_ctx,
+                                 const T* x,
+                                 T* y,
+                                 const std::vector<int64_t>& xshape,
+                                 const std::vector<int64_t>& permute) {
+  int ret = xpu::transpose(xpu_ctx, x, y, xshape, permute);
+  PADDLE_ENFORCE_XDNN_SUCCESS(ret, "transpose");
+}
+
+template <typename T>
+struct XPUArgsort {
+  void operator()(xpu::Context* xpu_ctx,
+                  const T* input_data,
+                  T* output_data,
+                  int64_t* indices_data,
+                  const std::vector<int64_t>& data_shape,
+                  const std::vector<int64_t>& permute,
+                  bool descending,
+                  bool stable) {
+    xpu::ctx_guard RAII_GUARD(xpu_ctx);
+    int64_t m = data_shape[0] * data_shape[2];
+    int64_t n = data_shape[1];
+    int64_t len = data_shape[0] * data_shape[1] * data_shape[2];
+    std::vector<int64_t> trans_data_shape{
+        data_shape[0], data_shape[2], data_shape[1]};
+
+    T* input_data_trans = RAII_GUARD.alloc_l3_or_gm<T>(len);
+    T* output_data_trans = RAII_GUARD.alloc_l3_or_gm<T>(len);
+    int64_t* indices_data_trans = RAII_GUARD.alloc_l3_or_gm<int64_t>(len);
+
+    xpu_transpose(xpu_ctx, input_data, input_data_trans, data_shape, permute);
+    xpu_argsort(xpu_ctx,
+                input_data_trans,
+                output_data_trans,
+                indices_data_trans,
+                m,
+                n,
+                descending,
+                stable);
+    xpu_transpose(
+        xpu_ctx, output_data_trans, output_data, trans_data_shape, permute);
+    xpu_transpose(
+        xpu_ctx, indices_data_trans, indices_data, trans_data_shape, permute);
+  }
+};
+
+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;
+  int64_t n = in_dims[axis];
+
+  auto input_data = input.data<T>();
+  auto output_data = dev_ctx.template Alloc<T>(output);
+  auto indices_data = dev_ctx.template Alloc<int64_t>(indices);
+
+  if (rank == 0) {
+    Copy<Context>(dev_ctx, input, dev_ctx.GetPlace(), false, output);
+    funcs::set_constant(dev_ctx, indices, static_cast<int64_t>(0));
+    return;
+  }
+
+  int64_t len_before = common::product(slice_ddim(in_dims, 0, axis));
+  int64_t len_after =
+      common::product(slice_ddim(in_dims, axis + 1, in_dims.size()));
+  std::vector<int64_t> permute_vec{0, 2, 1};
+  std::vector<int64_t> data_shape{len_before, n, len_after};
+
+  using XPUType = typename XPUTypeTrait<T>::Type;
+
+  XPUArgsort<XPUType>()(dev_ctx.x_context(),
+                        reinterpret_cast<const XPUType*>(input_data),
+                        reinterpret_cast<XPUType*>(output_data),
+                        indices_data,
+                        data_shape,
+                        permute_vec,
+                        descending,
+                        stable);
+}
+}  // namespace phi
+
+PD_REGISTER_KERNEL(argsort,
+                   XPU,
+                   ALL_LAYOUT,
+                   phi::ArgsortKernel,
+                   float,
+                   int,
+                   int64_t,
+                   phi::float16) {
+  kernel->OutputAt(1).SetDataType(phi::DataType::INT64);
+}