chore: import upstream snapshot with attribution

paddle/phi/kernels/split_kernel.h

@@ -0,0 +1,125 @@
+// 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.
+
+#pragma once
+
+#include "paddle/phi/common/int_array.h"
+#include "paddle/phi/common/scalar.h"
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/infermeta/unary.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void SplitKernel(const Context& dev_ctx,
+                 const DenseTensor& x,
+                 const IntArray& sections,
+                 const Scalar& axis,
+                 std::vector<DenseTensor*> out);
+
+template <typename T, typename Context>
+void SplitWithNumKernel(const Context& dev_ctx,
+                        const DenseTensor& x,
+                        int num,
+                        const Scalar& axis,
+                        std::vector<DenseTensor*> out);
+
+template <typename Context>
+void SplitStridedKernel(const Context& dev_ctx,
+                        const DenseTensor& x,
+                        const IntArray& sections,
+                        const Scalar& axis,
+                        std::vector<DenseTensor*> out);
+
+template <typename Context>
+void SplitWithNumStridedKernel(const Context& dev_ctx,
+                               const DenseTensor& x,
+                               int num,
+                               const Scalar& axis,
+                               std::vector<DenseTensor*> out);
+
+template <typename T, typename Context>
+void Split(const Context& dev_ctx,
+           const DenseTensor& x,
+           const IntArray& sections,
+           const Scalar& axis,
+           std::vector<DenseTensor>* result) {
+  size_t out_number = sections.GetData().size();
+
+  std::vector<MetaTensor> out_meta;
+  std::vector<MetaTensor*> out_meta_ptr;
+  out_meta.reserve(out_number);
+  out_meta_ptr.reserve(out_number);
+  result->resize(out_number);
+
+  for (size_t i = 0; i < out_number; ++i) {
+    out_meta.emplace_back(&result->at(i));
+    out_meta_ptr.push_back(&out_meta.back());
+  }
+  SplitInferMeta(x, sections, axis, out_meta_ptr);
+  std::vector<DenseTensor*> outs;
+  outs.reserve(out_meta.size());
+  for (size_t i = 0; i < out_meta.size(); ++i) {
+    outs.push_back(&result->at(i));
+  }
+
+  if (x.initialized()) {
+    SplitKernel<T, Context>(dev_ctx, x, sections, axis, outs);
+  }
+}
+
+template <typename T, typename Context>
+std::vector<DenseTensor> Split(const Context& dev_ctx,
+                               const DenseTensor& x,
+                               const IntArray& sections,
+                               const Scalar& axis) {
+  size_t out_number = sections.GetData().size();
+  std::vector<DenseTensor> result(out_number);
+
+  Split<T, Context>(dev_ctx, x, sections, axis, &result);
+
+  return result;
+}
+
+template <typename T, typename Context>
+std::vector<DenseTensor> SplitWithNum(const Context& dev_ctx,
+                                      const DenseTensor& x,
+                                      int num,
+                                      const Scalar& axis) {
+  size_t out_number = num;
+
+  std::vector<MetaTensor> out_meta;
+  std::vector<MetaTensor*> out_meta_ptr;
+  out_meta.reserve(out_number);
+  out_meta_ptr.reserve(out_number);
+  std::vector<DenseTensor> result(out_number);
+
+  for (size_t i = 0; i < out_number; ++i) {
+    out_meta.emplace_back(&result[i]);
+    out_meta_ptr.push_back(&out_meta.back());
+  }
+  SplitWithNumInferMeta(x, num, axis, out_meta_ptr);
+
+  std::vector<DenseTensor*> outs;
+  outs.reserve(out_meta.size());
+  for (size_t i = 0; i < out_meta.size(); ++i) {
+    outs.push_back(&result[i]);
+  }
+
+  SplitWithNumKernel<T, Context>(dev_ctx, x, num, axis, outs);
+
+  return result;
+}
+
+}  // namespace phi