chore: import upstream snapshot with attribution

paddle/phi/kernels/gpu/fill_diagonal_kernel.cu

@@ -0,0 +1,91 @@
+// 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/fill_diagonal_kernel.h"
+
+#include <algorithm>
+#include <vector>
+
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/common_shape.h"
+
+namespace phi {
+
+template <typename T>
+__global__ void fill_constant_kernel(const int64_t featuresize,
+                                     T* in_data,
+                                     int64_t strides,
+                                     int offset,
+                                     T fillvar,
+                                     int dims) {
+  for (int64_t idx = static_cast<int64_t>(blockIdx.x) * featuresize +
+                     static_cast<int64_t>(threadIdx.x);
+       idx * strides + offset < (blockIdx.x + 1) * featuresize;
+       idx += blockDim.x) {
+    // to check if the new position with offset is still in the same line;
+    // this modify should not affect across lines.
+    // out_dims[1] is also work for tensor with dim>2, for which the dims must
+    // be the same number
+    if ((idx * strides) % dims + offset < dims &&
+        (idx * strides) % dims + offset >= 0) {
+      in_data[idx * strides + offset] = fillvar;
+    }
+  }
+}
+
+template <typename T, typename Context>
+void FillDiagonalKernel(const Context& dev_ctx,
+                        const DenseTensor& x,
+                        float value,
+                        int offset,
+                        bool wrap,
+                        DenseTensor* out) {
+  if (out && out->numel() == 0) {
+    dev_ctx.template Alloc<T>(out);
+    return;
+  }
+  const int64_t kMaxBlockDim = 512;
+  Copy(dev_ctx, x, dev_ctx.GetPlace(), false, out);
+
+  T* out_data = dev_ctx.template Alloc<T>(out);
+  auto fill_val = static_cast<T>(value);
+  T temp_var = static_cast<T>(fill_val);
+
+  auto size = out->numel();
+  auto out_dims = out->dims();
+  auto strides = funcs::CalStride(out_dims);
+
+  // The wrap mode supported only the dims equals to 2; In wrap mode, the
+  // value will be filled in cycles
+  if (!wrap) {
+    size = std::min(size, out_dims[1] * out_dims[1]);
+  }
+
+  int64_t kBlockDim = std::min(int64_t(size / strides), kMaxBlockDim);
+  fill_constant_kernel<T><<<1, kBlockDim, 0>>>(
+      size, out_data, strides, offset, temp_var, out_dims[1]);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(fill_diagonal,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::FillDiagonalKernel,
+                   float,
+                   double,
+                   int64_t,
+                   int,
+                   phi::float16,
+                   bool) {}