chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/diagonal_grad_kernel.cc

@@ -0,0 +1,100 @@
+// 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_grad_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/diagonal.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void DiagonalGradKernel(const Context& dev_ctx,
+                        const DenseTensor& x UNUSED,
+                        const DenseTensor& out_grad,
+                        int offset,
+                        int axis1,
+                        int axis2,
+                        DenseTensor* in_grad) {
+  if (in_grad->numel() == 0) {
+    dev_ctx.template Alloc<T>(in_grad);
+    return;
+  }
+  const auto* dout = &out_grad;
+  const T* dout_data = dout->data<T>();
+  auto dout_dim = vectorize(dout->dims());
+
+  auto* dx = in_grad;
+  T* dx_data = dev_ctx.template Alloc<T>(dx);
+  auto dx_dim = vectorize(dx->dims());
+  auto dx_dim_size = dx_dim.size();
+
+  const int64_t offset_ = offset;
+  int64_t axis1_ =
+      static_cast<int64_t>(axis1 < 0 ? dx_dim_size + axis1 : axis1);
+  int64_t axis2_ =
+      static_cast<int64_t>(axis2 < 0 ? dx_dim_size + axis2 : axis2);
+
+  std::vector<int64_t> dout_stride = funcs::ComputeDimStride(dout_dim);
+  std::vector<int64_t> dx_stride = funcs::ComputeDimStride(dx_dim);
+
+  int64_t numel = dx->numel();
+
+  for (int64_t idx = 0; idx < numel; idx++) {
+    std::vector<int64_t> idx_dim(dx_dim_size);
+    int64_t temp = 0;
+    for (size_t i = 0; i < dx_dim_size; i++) {
+      idx_dim[i] = (idx - temp) / dx_stride[i];
+      temp = temp + idx_dim[i] * dx_stride[i];
+    }
+
+    int64_t axis1_dim = idx_dim[axis1_];
+    int64_t axis2_dim = idx_dim[axis2_];
+
+    idx_dim.erase(idx_dim.begin() + std::max(axis1_, axis2_));
+    idx_dim.erase(idx_dim.begin() + std::min(axis1_, axis2_));
+
+    bool flag = false;
+    if ((offset_ == 0 && axis1_dim == axis2_dim) ||
+        (offset_ > 0 && (axis1_dim + offset_) == axis2_dim)) {
+      idx_dim.push_back(axis1_dim);
+      flag = true;
+    } else if (offset_ < 0 && (axis1_dim + offset_) == axis2_dim) {
+      idx_dim.push_back(axis2_dim);
+      flag = true;
+    }
+    if (flag) {
+      int64_t idx_output = 0;
+      for (size_t i = 0; i < idx_dim.size(); i++) {
+        idx_output = idx_output + idx_dim[i] * dout_stride[i];
+      }
+      dx_data[idx] = dout_data[idx_output];
+    } else {
+      dx_data[idx] = static_cast<T>(0);
+    }
+  }
+}
+}  // namespace phi
+PD_REGISTER_KERNEL(diagonal_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::DiagonalGradKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t,
+                   bool,
+                   phi::complex64,
+                   phi::complex128) {}