adam_op.h

/* Copyright (c) 2016 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 <math.h>  // for sqrt in CPU and CUDA
#include <Eigen/Dense>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/algorithm.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
#include "paddle/fluid/platform/for_range.h"

namespace paddle {
namespace operators {

namespace scatter = paddle::operators::math::scatter;

struct GPUAdam;
struct CPUAdam;

template <typename T, typename Flavour>
struct AdamFunctor;

template <typename T>
struct AdamFunctor<T, GPUAdam> {
  T beta1_;
  T beta2_;
  T epsilon_;

  const T* beta1_pow_;
  const T* beta2_pow_;
  const T* moment1_;
  T* moment1_out_;
  const T* moment2_;
  T* moment2_out_;
  const T* lr_;
  const T* grad_;
  const T* param_;
  T* param_out_;

  AdamFunctor(T beta1, T beta2, T epsilon, const T* beta1_pow,
              const T* beta2_pow, const T* mom1, T* mom1_out, const T* mom2,
              T* mom2_out, const T* lr, const T* grad, const T* param,
              T* param_out)
      : beta1_(beta1),
        beta2_(beta2),
        epsilon_(epsilon),
        beta1_pow_(beta1_pow),
        beta2_pow_(beta2_pow),
        moment1_(mom1),
        moment1_out_(mom1_out),
        moment2_(mom2),
        moment2_out_(mom2_out),
        lr_(lr),
        grad_(grad),
        param_(param),
        param_out_(param_out) {}

  inline HOSTDEVICE void operator()(size_t i) const {
    // Merge all memory access together.
    T g = grad_[i];
    T mom1 = moment1_[i];
    T mom2 = moment2_[i];
    T lr = *lr_;
    T beta1_pow = *beta1_pow_;
    T beta2_pow = *beta2_pow_;
    T p = param_[i];

    // Calculation
    lr *= sqrt(1 - beta2_pow) / (1 - beta1_pow);

    mom1 = beta1_ * mom1 + (1 - beta1_) * g;
    mom2 = beta2_ * mom2 + (1 - beta2_) * g * g;
    p -= lr * (mom1 / (sqrt(mom2) + epsilon_));

    // Write back to global memory
    moment1_out_[i] = mom1;
    moment2_out_[i] = mom2;
    param_out_[i] = p;
  }
};

template <typename T>
struct AdamFunctor<T, CPUAdam> {
  T beta1_;
  T beta2_;
  T epsilon_;

  const T* beta1_pow_;
  const T* beta2_pow_;
  const T* moment1_;
  T* moment1_out_;
  const T* moment2_;
  T* moment2_out_;
  const T* lr_;
  const T* grad_;
  const T* param_;
  T* param_out_;

  AdamFunctor(T beta1, T beta2, T epsilon, const T* beta1_pow,
              const T* beta2_pow, const T* mom1, T* mom1_out, const T* mom2,
              T* mom2_out, const T* lr, const T* grad, const T* param,
              T* param_out)
      : beta1_(beta1),
        beta2_(beta2),
        epsilon_(epsilon),
        beta1_pow_(beta1_pow),
        beta2_pow_(beta2_pow),
        moment1_(mom1),
        moment1_out_(mom1_out),
        moment2_(mom2),
        moment2_out_(mom2_out),
        lr_(lr),
        grad_(grad),
        param_(param),
        param_out_(param_out) {}

  void operator()(size_t numel) const {
    Eigen::Map<const Eigen::Array<T, 1, Eigen::Dynamic>> g{
        grad_, static_cast<Eigen::Index>(numel)};
    Eigen::Map<const Eigen::Array<T, 1, Eigen::Dynamic>> mom1{
        moment1_, static_cast<Eigen::Index>(numel)};
    Eigen::Map<const Eigen::Array<T, 1, Eigen::Dynamic>> mom2{
        moment2_, static_cast<Eigen::Index>(numel)};
    Eigen::Map<const Eigen::Array<T, 1, Eigen::Dynamic>> param{
        param_, static_cast<Eigen::Index>(numel)};

    Eigen::Map<Eigen::Array<T, 1, Eigen::Dynamic>> param_out{
        param_out_, static_cast<Eigen::Index>(numel)};
    Eigen::Map<Eigen::Array<T, 1, Eigen::Dynamic>> moment1_out{
        moment1_out_, static_cast<Eigen::Index>(numel)};
    Eigen::Map<Eigen::Array<T, 1, Eigen::Dynamic>> moment2_out{
        moment2_out_, static_cast<Eigen::Index>(numel)};

    T lr = *lr_;
    T beta1_pow = *beta1_pow_;
    T beta2_pow = *beta2_pow_;

    // Calculation
    lr *= sqrt(1 - beta2_pow) / (1 - beta1_pow);

    moment1_out = beta1_ * mom1 + (1 - beta1_) * g;
    moment2_out = beta2_ * mom2 + (1 - beta2_) * g * g;
    param_out = param - lr * (moment1_out / (moment2_out.sqrt() + epsilon_));
  }
};

template <typename T, typename Flavour>
struct SparseAdamFunctor;

template <typename T>
struct SparseAdamFunctor<T, GPUAdam> {
  T beta1_;
  T beta2_;
  T epsilon_;

  const T* beta1_pow_;
  const T* beta2_pow_;
  const T* moment1_;
  T* moment1_out_;
  const T* moment2_;
  T* moment2_out_;
  const T* lr_;
  const T* grad_;
  const T* param_;
  T* param_out_;

  const int64_t* rows_;
  int64_t row_numel_;
  int64_t row_count_;

  SparseAdamFunctor(T beta1, T beta2, T epsilon, const T* beta1_pow,
                    const T* beta2_pow, const T* mom1, T* mom1_out,
                    const T* mom2, T* mom2_out, const T* lr, const T* grad,
                    const T* param, T* param_out, const int64_t* rows,
                    int64_t row_numel, int64_t row_count)
      : beta1_(beta1),
        beta2_(beta2),
        epsilon_(epsilon),
        beta1_pow_(beta1_pow),
        beta2_pow_(beta2_pow),
        moment1_(mom1),
        moment1_out_(mom1_out),
        moment2_(mom2),
        moment2_out_(mom2_out),
        lr_(lr),
        grad_(grad),
        param_(param),
        param_out_(param_out),
        rows_(rows),
        row_numel_(row_numel),
        row_count_(row_count) {}

  inline HOSTDEVICE void operator()(size_t i) const {
    auto row_idx =
        math::BinarySearch<int64_t>(rows_, row_count_, i / row_numel_);
    T g = row_idx >= 0 ? grad_[row_idx * row_numel_ + i % row_numel_] : 0;

    // The following code is the same as dense
    T mom1 = moment1_[i];
    T mom2 = moment2_[i];
    T lr = *lr_;
    T beta1_pow = *beta1_pow_;
    T beta2_pow = *beta2_pow_;
    T p = param_[i];

    // Calculation
    lr *= sqrt(1 - beta2_pow) / (1 - beta1_pow);

    mom1 = beta1_ * mom1 + (1 - beta1_) * g;
    mom2 = beta2_ * mom2 + (1 - beta2_) * g * g;
    p -= lr * (mom1 / (sqrt(mom2) + epsilon_));

    // Write back to global memory
    moment1_out_[i] = mom1;
    moment2_out_[i] = mom2;
    param_out_[i] = p;
  }
};

template <typename T>
struct SparseAdamFunctor<T, CPUAdam> {
  T beta1_;
  T beta2_;
  T epsilon_;

  const T* beta1_pow_;
  const T* beta2_pow_;
  const T* moment1_;
  T* moment1_out_;
  const T* moment2_;
  T* moment2_out_;
  const T* lr_;
  const T* grad_;
  const T* param_;
  T* param_out_;

  const int64_t* rows_;
  int64_t row_numel_;
  int64_t row_count_;

  SparseAdamFunctor(T beta1, T beta2, T epsilon, const T* beta1_pow,
                    const T* beta2_pow, const T* mom1, T* mom1_out,
                    const T* mom2, T* mom2_out, const T* lr, const T* grad,
                    const T* param, T* param_out, const int64_t* rows,
                    int64_t row_numel, int64_t row_count)
      : beta1_(beta1),
        beta2_(beta2),
        epsilon_(epsilon),
        beta1_pow_(beta1_pow),
        beta2_pow_(beta2_pow),
        moment1_(mom1),
        moment1_out_(mom1_out),
        moment2_(mom2),
        moment2_out_(mom2_out),
        lr_(lr),
        grad_(grad),
        param_(param),
        param_out_(param_out),
        rows_(rows),
        row_numel_(row_numel),
        row_count_(row_count) {}

  inline void operator()(size_t numel) const {
    // lr could be reuse
    T lr = *lr_;
    T beta1_pow = *beta1_pow_;
    T beta2_pow = *beta2_pow_;
    lr *= sqrt(1 - beta2_pow) / (1 - beta1_pow);
    size_t row_count = numel / row_numel_;

    for (size_t i = 0U, j = 0U; i != row_count; ++i) {
      if (i == *(rows_ + j)) {
        for (size_t k = 0U; k != row_numel_; ++k) {
          T mom1 = moment1_[i * row_numel_ + k];
          T mom2 = moment2_[i * row_numel_ + k];
          T p = param_[i * row_numel_ + k];

          T g = grad_[j * row_numel_ + k];
          mom1 = beta1_ * mom1 + (1 - beta1_) * g;
          mom2 = beta2_ * mom2 + (1 - beta2_) * g * g;

          p -= lr * (mom1 / (sqrt(mom2) + epsilon_));
          // Write back to global memory
          moment1_out_[i * row_numel_ + k] = mom1;
          moment2_out_[i * row_numel_ + k] = mom2;
          param_out_[i * row_numel_ + k] = p;
        }
        ++j;
      } else {
        for (size_t k = 0U; k != row_numel_; ++k) {
          T mom1 = moment1_[i * row_numel_ + k];
          T mom2 = moment2_[i * row_numel_ + k];
          T p = param_[i * row_numel_ + k];

          mom1 = beta1_ * mom1;
          mom2 = beta2_ * mom2;

          p -= lr * (mom1 / (sqrt(mom2) + epsilon_));
          // Write back to global memory
          moment1_out_[i * row_numel_ + k] = mom1;
          moment2_out_[i * row_numel_ + k] = mom2;
          param_out_[i * row_numel_ + k] = p;
        }
      }
    }
  }
};

template <typename DeviceContext, typename T>
class AdamOpKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    const auto* param_var = ctx.InputVar("Param");
    PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
                   "The Var(%s)'s type should be LoDTensor, "
                   "but the received is %s",
                   ctx.Inputs("Param").front(), param_var->Type().name());

    using paddle::framework::LoDTensor;
    using paddle::operators::detail::Ref;

    T beta1 = static_cast<T>(ctx.Attr<float>("beta1"));
    T beta2 = static_cast<T>(ctx.Attr<float>("beta2"));
    T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
    auto& param = Ref(ctx.Input<LoDTensor>("Param"), "Must set Param");
    // auto& grad = Ref(ctx.Input<LoDTensor>("Grad"), "Must set Grad");
    auto* grad_var = ctx.InputVar("Grad");
    auto& mom1 = Ref(ctx.Input<LoDTensor>("Moment1"), "Must set Moment1");
    auto& mom2 = Ref(ctx.Input<LoDTensor>("Moment2"), "Must set Moment2");
    auto& lr =
        Ref(ctx.Input<LoDTensor>("LearningRate"), "Must set LearningRate");

    auto& beta1_pow =
        Ref(ctx.Input<LoDTensor>("Beta1Pow"), "Must set Beta1Pow");
    auto& beta2_pow =
        Ref(ctx.Input<LoDTensor>("Beta2Pow"), "Must set Beta2Pow");

    auto& param_out =
        Ref(ctx.Output<LoDTensor>("ParamOut"), "Must set ParamOut");
    auto& mom1_out =
        Ref(ctx.Output<LoDTensor>("Moment1Out"), "Must set Moment1Out");
    auto& mom2_out =
        Ref(ctx.Output<LoDTensor>("Moment2Out"), "Must set Moment1Out");

    if (grad_var->IsType<framework::LoDTensor>()) {
      auto& grad = Ref(ctx.Input<LoDTensor>("Grad"), "Must set Grad");

      if (platform::is_cpu_place(ctx.GetPlace())) {
        AdamFunctor<T, CPUAdam> functor(
            beta1, beta2, epsilon, beta1_pow.template data<T>(),
            beta2_pow.template data<T>(), mom1.template data<T>(),
            mom1_out.template mutable_data<T>(ctx.GetPlace()),
            mom2.template data<T>(),
            mom2_out.template mutable_data<T>(ctx.GetPlace()),
            lr.template data<T>(), grad.template data<T>(),
            param.template data<T>(),
            param_out.template mutable_data<T>(ctx.GetPlace()));
        functor(param.numel());
      } else if (platform::is_gpu_place(ctx.GetPlace())) {
        AdamFunctor<T, GPUAdam> functor(
            beta1, beta2, epsilon, beta1_pow.template data<T>(),
            beta2_pow.template data<T>(), mom1.template data<T>(),
            mom1_out.template mutable_data<T>(ctx.GetPlace()),
            mom2.template data<T>(),
            mom2_out.template mutable_data<T>(ctx.GetPlace()),
            lr.template data<T>(), grad.template data<T>(),
            param.template data<T>(),
            param_out.template mutable_data<T>(ctx.GetPlace()));

        platform::ForRange<DeviceContext> for_range(
            static_cast<const DeviceContext&>(ctx.device_context()),
            param.numel());
        for_range(functor);
      }
    } else if (grad_var->IsType<framework::SelectedRows>()) {
      auto& grad =
          Ref(ctx.Input<framework::SelectedRows>("Grad"), "Must set Grad");
      if (grad.rows().size() == 0) {
        VLOG(3) << "grad row size is 0!!";
        return;
      }

      std::vector<int64_t> cpu_rows(grad.rows().begin(), grad.rows().end());
      bool is_strict_sorted = true;
      for (size_t i = 1; i < cpu_rows.size(); ++i) {
        if (cpu_rows[i - 1] >= cpu_rows[i]) {
          is_strict_sorted = false;
          break;
        }
      }

      const framework::SelectedRows* grad_merge_ptr;
      if (is_strict_sorted) {
        grad_merge_ptr = &grad;
      } else {
        // merge duplicated rows if any.
        // The rows of grad_merge have been sorted inside MergeAdd functor
        scatter::MergeAdd<DeviceContext, T> merge_func;
        auto* grad_merge_var = const_cast<framework::Scope&>(ctx.scope())
                                   .Var()
                                   ->GetMutable<framework::SelectedRows>();
        merge_func(ctx.template device_context<DeviceContext>(), grad,
                   grad_merge_var, true);
        grad_merge_ptr = grad_merge_var;
      }

      auto& grad_merge = *grad_merge_ptr;
      auto& grad_tensor = grad_merge.value();
      const T* grad_data = grad_tensor.template data<T>();
      const int64_t* rows = nullptr;
// When compiled without CUDA, the CUDAData() interface should not be
// provided.
#if defined(PADDLE_WITH_CUDA)
      if (platform::is_gpu_place(ctx.GetPlace())) {
        rows = grad_merge.rows().CUDAData(ctx.GetPlace());
      } else {
#endif
        rows = grad_merge.rows().data();
#if defined(PADDLE_WITH_CUDA)
      }
#endif
      auto row_numel = grad_tensor.numel() / grad_merge.rows().size();

      if (platform::is_cpu_place(ctx.GetPlace())) {
        SparseAdamFunctor<T, CPUAdam> functor(
            beta1, beta2, epsilon, beta1_pow.template data<T>(),
            beta2_pow.template data<T>(), mom1.template data<T>(),
            mom1_out.template mutable_data<T>(ctx.GetPlace()),
            mom2.template data<T>(),
            mom2_out.template mutable_data<T>(ctx.GetPlace()),
            lr.template data<T>(), grad_data, param.template data<T>(),
            param_out.template mutable_data<T>(ctx.GetPlace()), rows, row_numel,
            grad_merge.rows().size());

        functor(param.numel());
      } else if (platform::is_gpu_place(ctx.GetPlace())) {
        SparseAdamFunctor<T, GPUAdam> functor(
            beta1, beta2, epsilon, beta1_pow.template data<T>(),
            beta2_pow.template data<T>(), mom1.template data<T>(),
            mom1_out.template mutable_data<T>(ctx.GetPlace()),
            mom2.template data<T>(),
            mom2_out.template mutable_data<T>(ctx.GetPlace()),
            lr.template data<T>(), grad_data, param.template data<T>(),
            param_out.template mutable_data<T>(ctx.GetPlace()), rows, row_numel,
            grad_merge.rows().size());

        // FIXME(minqiyang): remove BinarySearch in GPU later
        platform::ForRange<DeviceContext> for_range(
            static_cast<const DeviceContext&>(ctx.device_context()),
            param.numel());
        for_range(functor);
      }
    } else {
      PADDLE_THROW("Variable type not supported by adam_op");
    }
  }
};

}  // namespace operators
}  // namespace paddle