chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/adagrad_kernel.cc

@@ -0,0 +1,118 @@
+// 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/kernels/adagrad_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+#include "paddle/phi/kernels/funcs/selected_rows_functor.h"
+#include "paddle/phi/kernels/impl/adagrad_kernel_impl.h"
+
+namespace phi {
+
+namespace {
+size_t FindPos(const std::vector<int64_t>& rows, int64_t value) {
+  return std::find(rows.begin(), rows.end(), value) - rows.begin();
+}
+}  // namespace
+
+template <typename T>
+struct DenseAdagradFunctor<CPUContext, T> {
+  void operator()(const CPUContext& dev_ctx,
+                  const DenseTensor& param_t,
+                  const DenseTensor& grad_t,
+                  const DenseTensor& moment_t,
+                  const DenseTensor& learning_rate,
+                  const optional<DenseTensor>& master_param,
+                  float epsilon_t,
+                  bool multi_precision,
+                  DenseTensor* param_out_tensor,
+                  DenseTensor* moment_out_tensor,
+                  DenseTensor* master_param_outs) {
+    dev_ctx.template Alloc<T>(param_out_tensor);
+    dev_ctx.template Alloc<T>(moment_out_tensor);
+
+    T epsilon = static_cast<T>(epsilon_t);
+
+    auto param = EigenVector<T>::Flatten(param_t);
+
+    auto grad = EigenVector<T>::Flatten(grad_t);
+
+    auto moment = EigenVector<T>::Flatten(moment_t);
+
+    auto param_out = EigenVector<T>::Flatten(*param_out_tensor);
+    auto moment_out = EigenVector<T>::Flatten(*moment_out_tensor);
+    auto place = *dev_ctx.eigen_device();
+
+    moment_out.device(place) = moment + grad * grad;
+    Eigen::DSizes<int, 1> m_dsize(static_cast<int>(moment_out_tensor->numel()));
+    auto* lr = learning_rate.data<T>();
+    param_out.device(place) =
+        param - lr[0] * grad / (moment_out.sqrt() + epsilon);
+  }
+};
+
+template <typename T>
+struct SparseAdagradFunctor<CPUContext, T> {
+  void operator()(const CPUContext& dev_ctx,
+                  const SelectedRows& grad,
+                  const DenseTensor& learning_rate,
+                  T epsilon,
+                  DenseTensor* moment,
+                  DenseTensor* param) {
+    // 1. g_m.rows = set(g.rows)
+    auto grad_width = grad.value().dims()[1];
+    funcs::scatter::MergeAdd<CPUContext, T> merge_func;
+    auto grad_merge = merge_func(dev_ctx, grad);
+    auto& merge_rows = grad_merge.rows();
+    auto* grad_merge_data = grad_merge.mutable_value()->template data<T>();
+
+    // 2. m += g_m * g_m
+    auto grad_square = SquareSelectedRows<CPUContext, T>(dev_ctx, grad_merge);
+
+    funcs::SelectedRowsAddToTensor<CPUContext, T> functor;
+    functor(dev_ctx, grad_square, moment);
+
+    // 3. update parameter
+    auto* lr = learning_rate.data<T>();
+    auto* param_data = param->data<T>();
+    auto* moment_data = moment->data<T>();
+
+    for (size_t i = 0; i < merge_rows.size(); i++) {
+      for (int64_t j = 0; j < grad_width; j++) {
+        param_data[merge_rows[i] * grad_width + j] -=
+            lr[0] * grad_merge_data[i * grad_width + j] /
+            (std::sqrt(moment_data[merge_rows[i] * grad_width + j]) + epsilon);
+      }
+    }
+  }
+};
+
+template struct SparseAdagradFunctor<CPUContext, float>;
+template struct SparseAdagradFunctor<CPUContext, double>;
+template struct DenseAdagradFunctor<CPUContext, float>;
+template struct DenseAdagradFunctor<CPUContext, double>;
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(
+    adagrad, CPU, ALL_LAYOUT, phi::AdagradDenseKernel, float, double) {}
+
+PD_REGISTER_KERNEL(adagrad_dense_param_sparse_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::AdagradSparseKernel,
+                   float,
+                   double) {}