chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/arange_kernel.cc

@@ -0,0 +1,125 @@
+/* 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/arange_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/common/amp_type_traits.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/range_function.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void ArangeFunc(const Context& dev_ctx,
+                const T& start_value,
+                const T& end_value,
+                const T& step_value,
+                DenseTensor* out) {
+  int64_t size = 0;
+  funcs::GetSize(start_value, end_value, step_value, &size);
+  out->Resize({size});
+  T* out_data = dev_ctx.template Alloc<T>(out);
+  T value = start_value;
+  for (int64_t i = 0; i < size; ++i) {
+    out_data[i] = value;
+    value += step_value;
+  }
+}
+
+template <typename T, typename Context>
+void ArangeTensorKernel(const Context& dev_ctx,
+                        const DenseTensor& start,
+                        const DenseTensor& end,
+                        const DenseTensor& step,
+                        DenseTensor* out) {
+  int64_t size = 0;
+  using MPType = typename phi::dtype::MPTypeTrait<T>::Type;
+
+  bool any_float = phi::IsFloatingType(start.dtype()) ||
+                   phi::IsFloatingType(end.dtype()) ||
+                   phi::IsFloatingType(step.dtype());
+
+  Scalar start_scalar(start);
+  Scalar end_scalar(end);
+  Scalar step_scalar(step);
+
+  if (any_float) {
+    double sv = start_scalar.to<double>();
+    double ev = end_scalar.to<double>();
+    double stv = step_scalar.to<double>();
+    funcs::GetSize<double>(sv, ev, stv, &size);
+  } else {
+    int64_t sv = start_scalar.to<int64_t>();
+    int64_t ev = end_scalar.to<int64_t>();
+    int64_t stv = step_scalar.to<int64_t>();
+    funcs::GetSize<int64_t>(sv, ev, stv, &size);
+  }
+  MPType start_value = start_scalar.to<MPType>();
+  MPType step_value = step_scalar.to<MPType>();
+
+  out->Resize({size});
+  T* out_data = dev_ctx.template Alloc<T>(out);
+  MPType value = start_value;
+  for (int64_t i = 0; i < size; ++i) {
+    out_data[i] = static_cast<T>(value);
+    value += step_value;
+  }
+}
+
+template <typename T, typename Context>
+void ArangeKernel(const Context& dev_ctx,
+                  const Scalar& start,
+                  const Scalar& end,
+                  const Scalar& step,
+                  DenseTensor* out) {
+  bool any_float = phi::IsFloatingType(start.dtype()) ||
+                   phi::IsFloatingType(end.dtype()) ||
+                   phi::IsFloatingType(step.dtype());
+  int64_t size = 0;
+  using MPType = typename phi::dtype::MPTypeTrait<T>::Type;
+  if (any_float) {
+    double sv = start.to<double>();
+    double ev = end.to<double>();
+    double stv = step.to<double>();
+    funcs::GetSize<double>(sv, ev, stv, &size);
+  } else {
+    int64_t sv = start.to<int64_t>();
+    int64_t ev = end.to<int64_t>();
+    int64_t stv = step.to<int64_t>();
+    funcs::GetSize<int64_t>(sv, ev, stv, &size);
+  }
+  MPType start_value = start.to<MPType>();
+  MPType step_value = step.to<MPType>();
+  out->Resize({size});
+  T* out_data = dev_ctx.template Alloc<T>(out);
+  MPType value = start_value;
+  for (int64_t i = 0; i < size; ++i) {
+    out_data[i] = static_cast<T>(value);
+    value += step_value;
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(arange_tensor,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::ArangeTensorKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t) {}
+PD_REGISTER_KERNEL(
+    arange, CPU, ALL_LAYOUT, phi::ArangeKernel, float, double, int, int64_t) {}