chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/clip_grad_kernel_impl.h

@@ -0,0 +1,71 @@
+// 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.
+
+#pragma once
+
+#include "paddle/phi/backends/all_context.h"
+#include "paddle/phi/common/transform.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/clip_kernel.h"
+#if defined(__NVCC__) || defined(__HIPCC__)
+#include "paddle/phi/kernels/funcs/broadcast_function.h"
+#endif
+
+namespace phi {
+
+template <typename T>
+class ClipGradFunctor {
+ public:
+  explicit ClipGradFunctor(const T min, const T max) : min_(min), max_(max) {}
+  HOSTDEVICE T operator()(const T x, const T y) const {
+    return (y >= min_ && y <= max_) ? x : static_cast<T>(0);
+  }
+
+ private:
+  T min_;
+  T max_;
+};
+
+template <typename T, typename Context>
+void ClipGradKernel(const Context& dev_ctx,
+                    const DenseTensor& x,
+                    const DenseTensor& out_grad,
+                    const Scalar& min,
+                    const Scalar& max,
+                    DenseTensor* x_grad) {
+  auto max_ = max.to<T>();
+  auto min_ = min.to<T>();
+
+#if defined(__NVCC__) || defined(__HIPCC__)
+  std::vector<const DenseTensor*> ins = {&out_grad, &x};
+  std::vector<DenseTensor*> outs = {x_grad};
+  auto functor = ClipGradFunctor<T>(min_, max_);
+  dev_ctx.template Alloc<T>(x_grad);
+  funcs::ElementwiseKernel<T>(dev_ctx, ins, &outs, functor);
+#else
+  int64_t numel = out_grad.numel();
+  auto* d_x_data = dev_ctx.template Alloc<T>(x_grad);
+  const T* d_out_data = out_grad.data<T>();
+  const T* x_data = x.data<T>();
+  Transform<Context> trans;
+  trans(dev_ctx,
+        d_out_data,
+        d_out_data + numel,
+        x_data,
+        d_x_data,
+        ClipGradFunctor<T>(min_, max_));
+#endif
+}
+
+}  // namespace phi