chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/diagonal_kernel.cc

@@ -0,0 +1,109 @@
+// 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/diagonal_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/diagonal.h"
+namespace phi {
+
+template <typename T, typename Context>
+void DiagonalKernel(const Context& dev_ctx,
+                    const DenseTensor& x,
+                    int offset,
+                    int axis1,
+                    int axis2,
+                    DenseTensor* out) {
+  if (x.numel() == 0) {
+    Full<T, Context>(dev_ctx, out->dims(), 0, out);
+    return;
+  }
+
+  auto* input = &x;
+  const T* input_data = input->data<T>();
+  auto input_dim = vectorize(input->dims());
+  auto input_dim_size = input_dim.size();
+
+  auto* output = out;
+  T* output_data = dev_ctx.template Alloc<T>(output);
+  auto output_dim = vectorize(output->dims());
+  auto output_dim_size = output_dim.size();
+
+  const int64_t offset_ = offset;
+  int64_t axis1_ =
+      static_cast<int64_t>(axis1 < 0 ? input_dim_size + axis1 : axis1);
+  int64_t axis2_ =
+      static_cast<int64_t>(axis2 < 0 ? input_dim_size + axis2 : axis2);
+
+  std::vector<int64_t> input_stride = funcs::ComputeDimStride(input_dim);
+  std::vector<int64_t> output_stride = funcs::ComputeDimStride(output_dim);
+
+  int64_t out_numel = out->numel();
+  for (int64_t idx = 0; idx < out_numel; idx++) {
+    std::vector<int64_t> idx_dim(output_dim_size);
+    int64_t temp = 0;
+    for (size_t i = 0; i < output_dim_size; i++) {
+      idx_dim[i] = (idx - temp) / output_stride[i];
+      temp = temp + idx_dim[i] * output_stride[i];
+    }
+    int64_t tmp = idx_dim[output_dim_size - 1];
+    std::vector<int64_t> list;
+    list.clear();
+    int64_t l = std::min(axis1_, axis2_);
+    int64_t r = std::max(axis1_, axis2_);
+    for (size_t j = 0; j < output_dim_size - 1; j++) {
+      list.push_back(idx_dim[j]);
+    }
+    if (offset_ == 0) {
+      list.insert(list.begin() + l, tmp);
+      list.insert(list.begin() + r, tmp);
+    } else if (offset_ > 0) {
+      if (axis1_ < axis2_) {
+        list.insert(list.begin() + l, tmp);
+        list.insert(list.begin() + r, tmp + offset_);
+      } else {
+        list.insert(list.begin() + l, tmp + offset_);
+        list.insert(list.begin() + r, tmp);
+      }
+    } else if (offset_ < 0) {
+      if (axis1_ < axis2_) {
+        list.insert(list.begin() + l, tmp - offset_);
+        list.insert(list.begin() + r, tmp);
+      } else {
+        list.insert(list.begin() + l, tmp);
+        list.insert(list.begin() + r, tmp - offset_);
+      }
+    }
+
+    int64_t input_offset = 0;
+    for (size_t i = 0; i < input_dim_size; i++) {
+      input_offset = input_offset + list[i] * input_stride[i];
+    }
+    output_data[idx] = input_data[input_offset];
+  }
+}
+}  // namespace phi
+PD_REGISTER_KERNEL(diagonal,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::DiagonalKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t,
+                   phi::complex64,
+                   phi::complex128,
+                   bool) {}