chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/std_var_kernel.cc

@@ -0,0 +1,84 @@
+// Copyright (c) 2026 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/std_var_kernel.h"
+
+#include "paddle/phi/backends/all_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/activation_kernel.h"
+#include "paddle/phi/kernels/elementwise_multiply_kernel.h"
+#include "paddle/phi/kernels/elementwise_subtract_kernel.h"
+#include "paddle/phi/kernels/full_kernel.h"
+#include "paddle/phi/kernels/reduce_mean_kernel.h"
+#include "paddle/phi/kernels/reduce_sum_kernel.h"
+#include "paddle/phi/kernels/scale_kernel.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void VarKernel(const Context& dev_ctx,
+               const DenseTensor& x,
+               const std::vector<int64_t>& axis,
+               bool keepdim,
+               bool unbiased,
+               double correction,
+               DenseTensor* out) {
+  if (x.numel() == 0) {
+    Full<T, Context>(dev_ctx, out->dims(), static_cast<T>(NAN), out);
+    return;
+  }
+  // 1. Mean
+  // Use keepdim=true for broadcasting in subtraction
+  DenseTensor mean_val = Mean<T, Context>(dev_ctx, x, axis, true);
+
+  // 2. Subtract: x - mean
+  DenseTensor sub_res = Subtract<T, Context>(dev_ctx, x, mean_val);
+
+  // 3. Square: (x - mean)^2
+  DenseTensor sq_res = Multiply<T, Context>(dev_ctx, sub_res, sub_res);
+
+  // 4. Sum: Sum((x - mean)^2)
+  DenseTensor sum = Sum<T, Context>(dev_ctx, sq_res, axis, x.dtype(), keepdim);
+
+  // 5. Divide by (N - correction)
+  double n = static_cast<double>(x.numel()) / static_cast<double>(out->numel());
+  double divisor = 0;
+  if (n - correction >= 0) {
+    divisor = 1.0 / (n - correction);
+  }
+
+  DenseTensor scale_val =
+      FullLike<T, Context>(dev_ctx, *out, static_cast<T>(divisor));
+  MultiplyKernel<T, Context>(dev_ctx, sum, scale_val, out);
+}
+
+template <typename T, typename Context>
+void StdKernel(const Context& dev_ctx,
+               const DenseTensor& x,
+               const std::vector<int64_t>& axis,
+               bool keepdim,
+               bool unbiased,
+               double correction,
+               DenseTensor* out) {
+  if (x.numel() == 0) {
+    Full<T, Context>(dev_ctx, out->dims(), static_cast<T>(NAN), out);
+    return;
+  }
+  VarKernel<T, Context>(dev_ctx, x, axis, keepdim, unbiased, correction, out);
+  SqrtKernel<T, Context>(dev_ctx, *out, out);
+}
+
+}  // namespace phi
+PD_REGISTER_KERNEL(var, CPU, ALL_LAYOUT, phi::VarKernel, float, double) {}
+PD_REGISTER_KERNEL(std, CPU, ALL_LAYOUT, phi::StdKernel, float, double) {}