chore: import upstream snapshot with attribution

paddle/phi/kernels/xpu/split_kernel.cc

@@ -0,0 +1,106 @@
+// 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/split_kernel.h"
+
+#include "paddle/phi/backends/xpu/enforce_xpu.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void SplitKernel(const Context& dev_ctx,
+                 const DenseTensor& x,
+                 const IntArray& sections,
+                 const Scalar& axis_scalar,
+                 std::vector<DenseTensor*> outs) {
+  using XPUType = typename XPUTypeTrait<T>::Type;
+  int axis = axis_scalar.to<int>();
+  auto in_dims = x.dims();
+  auto input_shape = vectorize<int64_t>(in_dims);
+  std::vector<XPUType*> out_ptrs;
+
+  // Vectors to keep track of zero-sized and non-zero-sized outputs
+  std::vector<XPUType*> non_zero_out_ptrs;
+  std::vector<int64_t> non_zero_split_lists;
+
+  for (size_t j = 0; j < outs.size(); ++j) {
+    dev_ctx.template Alloc<T>(outs[j]);
+    out_ptrs.push_back(reinterpret_cast<XPUType*>(outs[j]->data<T>()));
+    int64_t section_size =
+        axis < outs[j]->dims().size() ? outs[j]->dims()[axis] : 1;
+
+    if (section_size > 0) {
+      non_zero_out_ptrs.push_back(
+          reinterpret_cast<XPUType*>(outs[j]->data<T>()));
+      non_zero_split_lists.push_back(section_size);
+    } else {
+      auto zero_dims = in_dims;
+      zero_dims[axis] = 0;
+      outs[j]->Resize(zero_dims);
+    }
+  }
+
+  if (x.numel() == 0) {
+    return;
+  }
+
+  // Perform the split operation only on non-zero sections
+  if (!non_zero_split_lists.empty()) {
+    int r = xpu::split<XPUType>(dev_ctx.x_context(),
+                                reinterpret_cast<const XPUType*>(x.data<T>()),
+                                non_zero_out_ptrs,
+                                input_shape,
+                                non_zero_split_lists,
+                                axis);
+    PADDLE_ENFORCE_XDNN_SUCCESS(r, "split");
+  }
+}
+
+template <typename T, typename Context>
+void SplitWithNumKernel(const Context& dev_ctx,
+                        const DenseTensor& x,
+                        int num,
+                        const Scalar& axis_scalar,
+                        std::vector<DenseTensor*> outs) {
+  int axis_value = axis_scalar.to<int>();
+  int64_t input_axis_dim = x.dims().at(axis_value);
+  std::vector<int64_t> sections_vec;
+  for (int i = 0; i < num; ++i) {
+    sections_vec.push_back(input_axis_dim / static_cast<int64_t>(num));
+  }
+  IntArray sections(sections_vec);
+  SplitKernel<T, Context>(dev_ctx, x, sections, axis_scalar, outs);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(split,
+                   XPU,
+                   ALL_LAYOUT,
+                   phi::SplitKernel,
+                   float,
+                   int64_t,
+                   int,
+                   phi::float16,
+                   phi::bfloat16) {}
+PD_REGISTER_KERNEL(split_with_num,
+                   XPU,
+                   ALL_LAYOUT,
+                   phi::SplitWithNumKernel,
+                   float,
+                   int64_t,
+                   int,
+                   phi::float16,
+                   phi::bfloat16) {}