chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/shape_broadcast_kernel.cc

@@ -0,0 +1,84 @@
+/* Copyright (c) 2024 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/core/dense_tensor.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+#include "paddle/utils/array_ref.h"
+
+namespace phi {
+
+template <typename T>
+std::vector<T> ComputeBroadcastShape(const paddle::array_ref<T>& large_shape,
+                                     const paddle::array_ref<T>& small_shape) {
+  PADDLE_ENFORCE_GE(
+      large_shape.size(),
+      small_shape.size(),
+      common::errors::PreconditionNotMet(
+          "Size of large_shape is expected to be greater or equal size of "
+          "small_shape, but got [%d] >= [%d].",
+          large_shape.size(),
+          small_shape.size()));
+  std::vector<T> output_data;
+  output_data.reserve(large_shape.size());
+  auto rank_gap = large_shape.size() - small_shape.size();
+  for (size_t i = 0; i < rank_gap; ++i) {
+    output_data.push_back(large_shape[i]);
+  }
+  for (size_t i = 0; i < small_shape.size(); ++i) {
+    output_data.push_back(std::max(large_shape[i + rank_gap], small_shape[i]));
+  }
+  return output_data;
+}
+
+template <typename T, typename Context>
+void ShapeBroadcastKernel(const Context& dev_ctx,
+                          const DenseTensor& x_shape,
+                          const DenseTensor& y_shape,
+                          DenseTensor* out) {
+  PADDLE_ENFORCE_EQ(x_shape.dims().size(),
+                    1,
+                    common::errors::InvalidArgument(
+                        "Invalid input tensor. The rank of x_shape "
+                        "should be equal 1, but now received [%d].",
+                        x_shape.dims().size()));
+  PADDLE_ENFORCE_EQ(y_shape.dims().size(),
+                    1,
+                    common::errors::InvalidArgument(
+                        "Invalid input tensor. The rank of y_shape "
+                        "should be equal 1, but now received [%d].",
+                        y_shape.dims().size()));
+  paddle::array_ref<T> x_shape_data(x_shape.data<T>(), x_shape.numel());
+  paddle::array_ref<T> y_shape_data(y_shape.data<T>(), y_shape.numel());
+  const auto& output_data =
+      x_shape_data.size() > y_shape_data.size()
+          ? ComputeBroadcastShape(x_shape_data, y_shape_data)
+          : ComputeBroadcastShape(y_shape_data, x_shape_data);
+  T* out_data = dev_ctx.template HostAlloc<T>(out);
+  int64_t out_numel = out->numel();
+  for (int i = 0; i < out_numel; ++i) {
+    out_data[i] = output_data[i];
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(shape_broadcast,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::ShapeBroadcastKernel,
+                   int32_t,
+                   int64_t) {}