chore: import upstream snapshot with attribution

2026-07-13 12:40:42 +08:00
commit e25996e7db
15472 changed files with 3536181 additions and 0 deletions
@@ -0,0 +1,83 @@
+// 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/common/amp_type_traits.h"
+#include "paddle/phi/kernels/adadelta_kernel.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void AdadeltaKernel(const Context& dev_ctx,
+                    const DenseTensor& param,
+                    const DenseTensor& grad,
+                    const DenseTensor& avg_squared_grad,
+                    const DenseTensor& avg_squared_update,
+                    const DenseTensor& learning_rate,
+                    const optional<DenseTensor>& master_param,
+                    float rho,
+                    float epsilon,
+                    bool multi_precision,
+                    DenseTensor* param_out,
+                    DenseTensor* avg_squared_grad_out,
+                    DenseTensor* avg_squared_update_out,
+                    DenseTensor* master_param_outs) {
+  using MT = typename dtype::template MPTypeTrait<T>::Type;
+  dev_ctx.template Alloc<T>(param_out);
+  dev_ctx.template Alloc<MT>(avg_squared_grad_out);
+  dev_ctx.template Alloc<MT>(avg_squared_update_out);
+
+  MT rho_ = static_cast<MT>(rho);
+  MT epsilon_ = static_cast<MT>(epsilon);
+
+  auto eigen_param = EigenVector<T>::Flatten(param);
+  auto eigen_grad = EigenVector<T>::Flatten(grad);
+  // Squared gradient accumulator
+  auto eigen_avg_squared_grad = EigenVector<MT>::Flatten(avg_squared_grad);
+  // Squared updates accumulator
+  auto eigen_avg_squared_update = EigenVector<MT>::Flatten(avg_squared_update);
+  auto eigen_param_out = EigenVector<T>::Flatten(*param_out);
+  auto eigen_avg_squared_grad_out =
+      EigenVector<MT>::Flatten(*avg_squared_grad_out);
+  auto eigen_avg_squared_update_out =
+      EigenVector<MT>::Flatten(*avg_squared_update_out);
+  auto& place = *dev_ctx.eigen_device();
+  auto eigen_grad_cast = eigen_grad.template cast<MT>();
+  eigen_avg_squared_grad_out.device(place) =
+      rho_ * eigen_avg_squared_grad + (1 - rho_) * eigen_grad_cast.square();
+  auto update =
+      -(((eigen_avg_squared_update + epsilon_).sqrt()) /
+        ((eigen_avg_squared_grad_out + epsilon_).sqrt()) * eigen_grad_cast);
+  Eigen::DSizes<int, 1> m_dsize(avg_squared_update_out->numel());
+  auto lr = EigenVector<MT>::Flatten(learning_rate);
+  if (multi_precision) {
+    auto eigen_master_param_out = EigenVector<MT>::Flatten(*master_param_outs);
+    auto eigen_master_param = EigenVector<MT>::Flatten(*master_param);
+
+    eigen_master_param_out.device(place) =
+        eigen_master_param + lr.broadcast(m_dsize) * update;
+    eigen_param_out.device(place) =
+        (eigen_param.template cast<MT>() + lr.broadcast(m_dsize) * update)
+            .template cast<T>();
+  } else {
+    eigen_param_out.device(place) =
+        eigen_param + (lr.broadcast(m_dsize) * update).template cast<T>();
+  }
+  eigen_avg_squared_update_out.device(place) =
+      rho_ * eigen_avg_squared_update + (1 - rho_) * update.square();
+}
+
+}  // namespace phi