chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/scale_kernel.cc

@@ -0,0 +1,111 @@
+/* Copyright (c) 2021 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/scale_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/common/scalar.h"
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+
+#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
+namespace phi {
+
+template <typename T, typename Context>
+void ScaleKernel(const Context& dev_ctx,
+                 const DenseTensor& x,
+                 const Scalar& scale,
+                 const Scalar& bias,
+                 bool bias_after_scale,
+                 DenseTensor* out) {
+  // calc
+  dev_ctx.template Alloc<T>(out);
+  auto eigen_out = EigenVector<T>::Flatten(*out);
+  auto eigen_x = EigenVector<T>::Flatten(x);
+  auto& dev = *dev_ctx.eigen_device();
+  // TODO(chenweihang): now the eigen function here need the dtype of scale,
+  // eigen_x, bias should be same, so here need cast for two scalar arg,
+  // maybe we declare that the type of scale and bias is T?
+  if (x.numel() <= 0 || (!x.IsInitialized())) {
+    return;
+  }
+  funcs::EigenScale<std::decay_t<decltype(dev)>, T>::Eval(
+      dev, eigen_out, eigen_x, scale.to<T>(), bias.to<T>(), bias_after_scale);
+}
+
+template <typename T, typename Context>
+void DivScaleKernel(const Context& dev_ctx,
+                    const DenseTensor& x,
+                    const Scalar& scale,
+                    DenseTensor* out) {
+  dev_ctx.template Alloc<T>(out);
+  auto eigen_out = EigenVector<T>::Flatten(*out);
+  auto eigen_x = EigenVector<T>::Flatten(x);
+  auto& dev = *dev_ctx.eigen_device();
+  if (x.numel() <= 0 || (!x.IsInitialized())) {
+    return;
+  }
+  funcs::EigenDiv<std::decay_t<decltype(dev)>, T>::Eval(
+      dev, eigen_out, eigen_x, scale.to<T>());
+}
+
+#ifdef _WIN32
+INSTANCE_SCALAR_KERNEL(int, CPUContext)
+INSTANCE_SCALAR_KERNEL(int64_t, CPUContext)
+INSTANCE_SCALAR_KERNEL(float, CPUContext)
+INSTANCE_SCALAR_KERNEL(double, CPUContext)
+INSTANCE_SCALAR_KERNEL(bfloat16, CPUContext)
+INSTANCE_SCALAR_KERNEL(float16, CPUContext)
+INSTANCE_SCALAR_KERNEL(uint8_t, CPUContext)
+INSTANCE_SCALAR_KERNEL(int8_t, CPUContext)
+INSTANCE_SCALAR_KERNEL(int16_t, CPUContext)
+INSTANCE_SCALAR_KERNEL(complex64, CPUContext)
+INSTANCE_SCALAR_KERNEL(complex128, CPUContext)
+#endif
+}  // namespace phi
+
+PD_REGISTER_KERNEL(scale,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::ScaleKernel,
+                   bool,
+                   float,
+                   double,
+                   phi::bfloat16,
+                   phi::float16,
+                   uint8_t,
+                   int8_t,
+                   int16_t,
+                   int,
+                   int64_t,
+                   phi::complex64,
+                   phi::complex128) {}
+
+PD_REGISTER_KERNEL(div_scale,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::DivScaleKernel,
+                   bool,
+                   float,
+                   double,
+                   phi::bfloat16,
+                   phi::float16,
+                   uint8_t,
+                   int8_t,
+                   int16_t,
+                   int,
+                   int64_t,
+                   phi::complex64,
+                   phi::complex128) {}