:github_url: https://github.com/pytorch/pytorch


.. _program_listing_file_torch_csrc_api_include_torch_optim_adamw.h:

Program Listing for File adamw.h
================================

|exhale_lsh| :ref:`Return to documentation for file <file_torch_csrc_api_include_torch_optim_adamw.h>` (``torch/csrc/api/include/torch/optim/adamw.h``)

.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS

.. code-block:: cpp

   #pragma once
   
   #include <torch/nn/module.h>
   #include <torch/optim/optimizer.h>
   #include <torch/optim/serialize.h>
   
   #include <utility>
   #include <vector>
   
   namespace torch::serialize {
   class OutputArchive;
   class InputArchive;
   } // namespace torch::serialize
   
   namespace torch::optim {
   
   struct TORCH_API AdamWOptions : public OptimizerCloneableOptions<AdamWOptions> {
     AdamWOptions(double lr = 1e-3);
     TORCH_ARG(double, lr) = 1e-3;
     typedef std::tuple<double, double> betas_t;
     TORCH_ARG(betas_t, betas) = std::make_tuple(0.9, 0.999);
     TORCH_ARG(double, eps) = 1e-8;
     TORCH_ARG(double, weight_decay) = 1e-2;
     TORCH_ARG(bool, amsgrad) = false;
   
    public:
     void serialize(torch::serialize::InputArchive& archive) override;
     void serialize(torch::serialize::OutputArchive& archive) const override;
     TORCH_API friend bool operator==(
         const AdamWOptions& lhs,
         const AdamWOptions& rhs);
     double get_lr() const override;
     void set_lr(const double lr) override;
   };
   
   struct TORCH_API AdamWParamState
       : public OptimizerCloneableParamState<AdamWParamState> {
     TORCH_ARG(int64_t, step) = 0;
     TORCH_ARG(torch::Tensor, exp_avg);
     TORCH_ARG(torch::Tensor, exp_avg_sq);
     TORCH_ARG(torch::Tensor, max_exp_avg_sq);
   
    public:
     void serialize(torch::serialize::InputArchive& archive) override;
     void serialize(torch::serialize::OutputArchive& archive) const override;
     TORCH_API friend bool operator==(
         const AdamWParamState& lhs,
         const AdamWParamState& rhs);
   };
   
   class TORCH_API AdamW : public Optimizer {
    public:
     explicit AdamW(
         const std::vector<OptimizerParamGroup>& param_groups,
         AdamWOptions defaults = {})
         : Optimizer(param_groups, std::make_unique<AdamWOptions>(defaults)) {
       TORCH_CHECK(defaults.lr() >= 0, "Invalid learning rate: ", defaults.lr());
       TORCH_CHECK(defaults.eps() >= 0, "Invalid epsilon value: ", defaults.eps());
       auto betas = defaults.betas();
       TORCH_CHECK(
           0 <= std::get<0>(betas) && std::get<0>(betas) < 1.0,
           "Invalid beta parameter at index 0: ",
           std::get<0>(betas));
       TORCH_CHECK(
           0 <= std::get<1>(betas) && std::get<1>(betas) < 1.0,
           "Invalid beta parameter at index 1: ",
           std::get<1>(betas));
       TORCH_CHECK(
           defaults.weight_decay() >= 0,
           "Invalid weight_decay value: ",
           defaults.weight_decay());
     }
     explicit AdamW(std::vector<Tensor> params, AdamWOptions defaults = {})
         : AdamW({OptimizerParamGroup(std::move(params))}, std::move(defaults)) {}
   
     torch::Tensor step(LossClosure closure = nullptr) override;
     void save(serialize::OutputArchive& archive) const override;
     void load(serialize::InputArchive& archive) override;
   
    private:
     template <typename Self, typename Archive>
     static void serialize(Self& self, Archive& archive) {
       _TORCH_OPTIM_SERIALIZE_WITH_TEMPLATE_ARG(AdamW);
     }
   };
   } // namespace torch::optim