chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/add_n_kernel.cc

@@ -0,0 +1,154 @@
+// 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/common/amp_type_traits.h"
+#include "paddle/phi/kernels/impl/add_n_kernel_impl.h"
+
+#include "glog/logging.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void AddNKernel(const Context& dev_ctx,
+                const std::vector<const TensorBase*>& x,
+                DenseTensor* out) {
+  size_t in_num = x.size();
+  dev_ctx.template Alloc<T>(out);
+  if (out && out->numel() == 0) {
+    return;
+  }
+
+  bool in_place = false;
+  if (!x.empty() && x[0]->initialized() && DenseTensor::classof(x[0])) {
+    if ((static_cast<const DenseTensor*>(x[0]))->Holder() == out->Holder()) {
+      in_place = true;
+      if (in_num == 1) {
+        return;
+      }
+    }
+  }
+
+  // using MT to keep precision
+  using MT = typename MPTypeTrait<T>::Type;
+  auto& place = *dev_ctx.eigen_device();
+
+  if constexpr (std::is_same_v<MT, T>) {
+    // compute in out
+    auto result = EigenVector<T>::Flatten(*out);
+
+    if (!in_place) {
+      funcs::SetConstant<Context, T> constant_functor;
+      constant_functor(dev_ctx, out, static_cast<T>(0));
+    }
+
+    funcs::SelectedRowsAddToTensor<Context, T> functor;
+    size_t start = in_place ? 1 : 0;
+    for (size_t i = start; i < in_num; i++) {
+      if (DenseTensor::classof(x[i])) {
+        auto& in_t = *(static_cast<const DenseTensor*>(x[i]));
+        if (!in_t.initialized() || in_t.numel() == 0) {
+          continue;
+        }
+        auto in = EigenVector<T>::Flatten(in_t);
+        result.device(place) = result + in;
+      } else if (SelectedRows::classof(x[i])) {
+        auto& in_t = *(static_cast<const SelectedRows*>(x[i]));
+        functor(dev_ctx, in_t, out);
+      } else {
+        PADDLE_THROW(common::errors::InvalidArgument(
+            "Expected type of Input(X) of %d-th must be Tensor, "
+            "SelectedRows. But got "
+            "unsupported type: %s.",
+            i,
+            x[i]->type_info().name()));
+      }
+    }
+  } else {
+    // compute in temp_out by using MT
+    DenseTensor temp_out;
+    temp_out.Resize(out->dims());
+    dev_ctx.template Alloc<MT>(&temp_out);
+
+    auto result_mp = EigenVector<MT>::Flatten(temp_out);
+
+    // set temp_out
+    funcs::SetConstant<Context, MT> constant_functor;
+    if (in_place && DenseTensor::classof(x[0]) && x[0]->initialized()) {
+      auto& in_0 = *(static_cast<const DenseTensor*>(x[0]));
+      if (in_0.numel()) {
+        auto in_0_e = EigenVector<T>::Flatten(in_0).template cast<MT>();
+        result_mp.device(place) = in_0_e;
+      } else {
+        constant_functor(dev_ctx, &temp_out, static_cast<MT>(0));
+      }
+    } else {
+      constant_functor(dev_ctx, &temp_out, static_cast<MT>(0));
+    }
+
+    funcs::SelectedRowsAddToTensor<Context, MT> functor;
+    size_t start = in_place ? 1 : 0;
+    for (size_t i = start; i < in_num; i++) {
+      if (DenseTensor::classof(x[i])) {
+        auto& in_t = *(static_cast<const DenseTensor*>(x[i]));
+        if (!in_t.initialized() || in_t.numel() == 0) {
+          continue;
+        }
+        auto in = EigenVector<T>::Flatten(in_t).template cast<MT>();
+        result_mp.device(place) = result_mp + in;
+      } else if (SelectedRows::classof(x[i])) {
+        auto& in_t = *(static_cast<const SelectedRows*>(x[i]));
+        functor(dev_ctx, in_t, &temp_out);
+      } else {
+        PADDLE_THROW(common::errors::InvalidArgument(
+            "Expected type of Input(X) of %d-th must be Tensor, "
+            "SelectedRows. But got "
+            "unsupported type: %s.",
+            i,
+            x[i]->type_info().name()));
+      }
+    }
+
+    // cast back to T, and copy to out
+    auto result = EigenVector<T>::Flatten(*out);
+    result.device(place) = result_mp.template cast<T>();
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(add_n,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::AddNKernel,
+                   float,
+                   double,
+                   int,
+                   phi::bfloat16,
+                   phi::float16,
+                   int64_t,
+                   phi::complex64,
+                   phi::complex128) {}
+
+PD_REGISTER_KERNEL(add_n_array,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::AddNArrayKernel,
+                   float,
+                   double,
+                   int,
+                   phi::bfloat16,
+                   phi::float16,
+                   int64_t,
+                   phi::complex64,
+                   phi::complex128) {}