model_wrappers.py

import copy
from abc import ABC, abstractmethod, abstractclassmethod
from collections import OrderedDict
from typing import Any, Tuple, Callable, Union, Optional, Dict, List

import numpy as np
import torch
import logging
from ding.torch_utils import get_tensor_data
from ding.rl_utils import create_noise_generator


class IModelWrapper(ABC):
    r"""
    Overview:
        the base class of Model Wrappers
    Interfaces:
        register
    """

    def __init__(self, model: Any) -> None:
        self._model = model

    def __getattr__(self, key: str) -> Any:
        r"""
        Overview:
            Get the attrbute in model.
        Arguments:
            - key (:obj:`str`): The key to query.
        Returns:
            - ret (:obj:`Any`): The queried attribute.
        """
        return getattr(self._model, key)

    def info(self, attr_name):
        r"""
        Overview:
            get info of attr_name
        """
        if attr_name in dir(self):
            if isinstance(self._model, IModelWrapper):
                return '{} {}'.format(self.__class__.__name__, self._model.info(attr_name))
            else:
                if attr_name in dir(self._model):
                    return '{} {}'.format(self.__class__.__name__, self._model.__class__.__name__)
                else:
                    return '{}'.format(self.__class__.__name__)
        else:
            if isinstance(self._model, IModelWrapper):
                return '{}'.format(self._model.info(attr_name))
            else:
                return '{}'.format(self._model.__class__.__name__)
        return ''


class BaseModelWrapper(IModelWrapper):
    r"""
    Overview:
        the base class of Model Wrappers
    Interfaces:
        register
    """

    def reset(self, data_id: List[int] = None) -> None:
        r"""
        Overview
            the reset function that the Model Wrappers with states should implement
            used to reset the stored states
        """
        pass


class HiddenStateWrapper(IModelWrapper):

    def __init__(
            self, model: Any, state_num: int, save_prev_state: bool = False, init_fn: Callable = lambda: None
    ) -> None:
        """
        Overview:
            Maintain the hidden state for RNN-base model. Each sample in a batch has its own state. \
            Init the maintain state and state function; Then wrap the ``model.forward`` method with auto \
            saved data ['prev_state'] input, and create the ``model.reset`` method.
        Arguments:
            - model(:obj:`Any`): Wrapped model class, should contain forward method.
            - state_num (:obj:`int`): Number of states to process.
            - save_prev_state (:obj:`bool`): Whether to output the prev state in output['prev_state'].
            - init_fn (:obj:`Callable`): The function which is used to init every hidden state when init and reset. \
                Default return None for hidden states.
        .. note::
            1. This helper must deal with an actual batch with some parts of samples, e.g: 6 samples of state_num 8.
            2. This helper must deal with the single sample state reset.
        """
        super().__init__(model)
        self._state_num = state_num
        self._state = {i: init_fn() for i in range(state_num)}
        self._save_prev_state = save_prev_state
        self._init_fn = init_fn

    def forward(self, data, **kwargs):
        state_id = kwargs.pop('data_id', None)
        valid_id = kwargs.pop('valid_id', None)
        data, state_info = self.before_forward(data, state_id)
        output = self._model.forward(data, **kwargs)
        h = output.pop('next_state', None)
        if h:
            self.after_forward(h, state_info, valid_id)
        if self._save_prev_state:
            prev_state = get_tensor_data(data['prev_state'])
            output['prev_state'] = prev_state
        return output

    def reset(self, *args, **kwargs):
        state = kwargs.pop('state', None)
        state_id = kwargs.get('data_id', None)
        self.reset_state(state, state_id)
        if hasattr(self._model, 'reset'):
            return self._model.reset(*args, **kwargs)

    def reset_state(self, state: Optional[list] = None, state_id: Optional[list] = None) -> None:
        if state_id is None:
            state_id = [i for i in range(self._state_num)]
        if state is None:
            state = [self._init_fn() for i in range(len(state_id))]
        assert len(state) == len(state_id), '{}/{}'.format(len(state), len(state_id))
        for idx, s in zip(state_id, state):
            self._state[idx] = s

    def before_forward(self, data: dict, state_id: Optional[list]) -> Tuple[dict, dict]:
        if state_id is None:
            state_id = [i for i in range(self._state_num)]

        state_info = {idx: self._state[idx] for idx in state_id}
        data['prev_state'] = list(state_info.values())
        return data, state_info

    def after_forward(self, h: Any, state_info: dict, valid_id: Optional[list] = None) -> None:
        assert len(h) == len(state_info), '{}/{}'.format(len(h), len(state_info))
        for i, idx in enumerate(state_info.keys()):
            if valid_id is None:
                self._state[idx] = h[i]
            else:
                if idx in valid_id:
                    self._state[idx] = h[i]


def sample_action(logit=None, prob=None):
    if prob is None:
        prob = torch.softmax(logit, dim=-1)
    shape = prob.shape
    prob += 1e-8
    prob = prob.view(-1, shape[-1])
    # prob can also be treated as weight in multinomial sample
    action = torch.multinomial(prob, 1).squeeze(-1)
    action = action.view(*shape[:-1])
    return action


class ArgmaxSampleWrapper(IModelWrapper):
    r"""
    Overview:
        Used to help the model to sample argmax action
    """

    def forward(self, *args, **kwargs):
        output = self._model.forward(*args, **kwargs)
        assert isinstance(output, dict), "model output must be dict, but find {}".format(type(output))
        logit = output['logit']
        assert isinstance(logit, torch.Tensor) or isinstance(logit, list)
        if isinstance(logit, torch.Tensor):
            logit = [logit]
        if 'action_mask' in output:
            mask = output['action_mask']
            if isinstance(mask, torch.Tensor):
                mask = [mask]
            logit = [l.sub_(1e8 * (1 - m)) for l, m in zip(logit, mask)]
        action = [l.argmax(dim=-1) for l in logit]
        if len(action) == 1:
            action, logit = action[0], logit[0]
        output['action'] = action
        return output


class MultinomialSampleWrapper(IModelWrapper):
    r"""
    Overview:
        Used to helper the model get the corresponding action from the output['logits']
    Interfaces:
        register
    """

    def forward(self, *args, **kwargs):
        output = self._model.forward(*args, **kwargs)
        assert isinstance(output, dict), "model output must be dict, but find {}".format(type(output))
        logit = output['logit']
        assert isinstance(logit, torch.Tensor) or isinstance(logit, list)
        if isinstance(logit, torch.Tensor):
            logit = [logit]
        if 'action_mask' in output:
            mask = output['action_mask']
            if isinstance(mask, torch.Tensor):
                mask = [mask]
            logit = [l.sub_(1e8 * (1 - m)) for l, m in zip(logit, mask)]
        action = [sample_action(logit=l) for l in logit]
        if len(action) == 1:
            action, logit = action[0], logit[0]
        output['action'] = action
        return output


class EpsGreedySampleWrapper(IModelWrapper):
    r"""
    Overview:
        Epsilon greedy sampler used in collector_model to help balance exploratin and exploitation.
    Interfaces:
        register
    """

    def forward(self, *args, **kwargs):
        eps = kwargs.pop('eps')
        output = self._model.forward(*args, **kwargs)
        assert isinstance(output, dict), "model output must be dict, but find {}".format(type(output))
        logit = output['logit']
        assert isinstance(logit, torch.Tensor) or isinstance(logit, list)
        if isinstance(logit, torch.Tensor):
            logit = [logit]
        if 'action_mask' in output:
            mask = output['action_mask']
            if isinstance(mask, torch.Tensor):
                mask = [mask]
            logit = [l.sub_(1e8 * (1 - m)) for l, m in zip(logit, mask)]
        else:
            mask = None
        action = []
        for i, l in enumerate(logit):
            if np.random.random() > eps:
                action.append(l.argmax(dim=-1))
            else:
                if mask:
                    action.append(sample_action(prob=mask[i].float()))
                else:
                    action.append(torch.randint(0, l.shape[-1], size=l.shape[:-1]))
        if len(action) == 1:
            action, logit = action[0], logit[0]
        output['action'] = action
        return output


class ActionNoiseWrapper(IModelWrapper):
    r"""
    Overview:
        Add noise to collector's action output; Do clips on both generated noise and action after adding noise.
    Interfaces:
        register, __init__, add_noise, reset
    Arguments:
        - model (:obj:`Any`): Wrapped model class. Should contain ``forward`` method.
        - noise_type (:obj:`str`): The type of noise that should be generated, support ['gauss', 'ou'].
        - noise_kwargs (:obj:`dict`): Keyword args that should be used in noise init. Depends on ``noise_type``.
        - noise_range (:obj:`Optional[dict]`): Range of noise, used for clipping.
        - action_range (:obj:`Optional[dict]`): Range of action + noise, used for clip, default clip to [-1, 1].
    """

    def __init__(
            self,
            model: Any,
            noise_type: str = 'gauss',
            noise_kwargs: dict = {},
            noise_range: Optional[dict] = None,
            action_range: Optional[dict] = {
                'min': -1,
                'max': 1
            }
    ) -> None:
        super().__init__(model)
        self.noise_generator = create_noise_generator(noise_type, noise_kwargs)
        self.noise_range = noise_range
        self.action_range = action_range

    def forward(self, *args, **kwargs):
        output = self._model.forward(*args, **kwargs)
        assert isinstance(output, dict), "model output must be dict, but find {}".format(type(output))
        if 'action' in output:
            action = output['action']
            assert isinstance(action, torch.Tensor)
            action = self.add_noise(action)
            output['action'] = action
        return output

    def add_noise(self, action: torch.Tensor) -> torch.Tensor:
        r"""
        Overview:
            Generate noise and clip noise if needed. Add noise to action and clip action if needed.
        Arguments:
            - action (:obj:`torch.Tensor`): Model's action output.
        Returns:
            - noised_action (:obj:`torch.Tensor`): Action processed after adding noise and clipping.
        """
        noise = self.noise_generator(action.shape, action.device)
        if self.noise_range is not None:
            noise = noise.clamp(self.noise_range['min'], self.noise_range['max'])
        action += noise
        if self.action_range is not None:
            action = action.clamp(self.action_range['min'], self.action_range['max'])
        return action

    def reset(self) -> None:
        r"""
        Overview:
            Reset noise generator.
        """
        pass


class TargetNetworkWrapper(IModelWrapper):
    r"""
    Overview:
        Maintain and update the target network
    Interfaces:
        update, reset
    """

    def __init__(self, model: Any, update_type: str, update_kwargs: dict):
        super().__init__(model)
        assert update_type in ['momentum', 'assign']
        self._update_type = update_type
        self._update_kwargs = update_kwargs
        self._update_count = 0

    def reset(self, *args, **kwargs):
        self.reset_state()
        if hasattr(self._model, 'reset'):
            return self._model.reset(*args, **kwargs)

    def update(self, state_dict: dict, direct: bool = False) -> None:
        r"""
        Overview:
            Update the target network state dict

        Arguments:
            - state_dict (:obj:`dict`): the state_dict from learner model
            - direct (:obj:`bool`): whether to update the target network directly, \
                if ture then will simply call the load_state_dict method of the model
        """
        if direct:
            self._model.load_state_dict(state_dict, strict=True)
            self._update_count = 0
        elif self._update_type == 'assign':
            if (self._update_count + 1) % self._update_kwargs['freq'] == 0:
                self._model.load_state_dict(state_dict, strict=True)
            self._update_count += 1
        elif self._update_type == 'momentum':
            theta = self._update_kwargs['theta']
            for name, p in self._model.named_parameters():
                # default theta = 0.001
                p.data = (1 - theta) * p.data + theta * state_dict[name]

    def reset_state(self) -> None:
        r"""
        Overview:
            Reset the update_count
        """
        self._update_count = 0


class TeacherNetworkWrapper(IModelWrapper):
    r"""
    Overview:
        Set the teacher Network. Set the model's model.teacher_cfg to the input teacher_cfg

    Interfaces:
        register
    """

    def __init__(self, model, teacher_cfg):
        super().__init__(model)
        self._model._teacher_cfg = teacher_cfg


wrapper_name_map = {
    'base': BaseModelWrapper,
    'hidden_state': HiddenStateWrapper,
    'argmax_sample': ArgmaxSampleWrapper,
    'eps_greedy_sample': EpsGreedySampleWrapper,
    'multinomial_sample': MultinomialSampleWrapper,
    'action_noise': ActionNoiseWrapper,
    # model wrapper
    'target': TargetNetworkWrapper,
    'teacher': TeacherNetworkWrapper,
}


def model_wrap(model, wrapper_name: str = None, **kwargs):
    if wrapper_name in wrapper_name_map:
        if not isinstance(model, IModelWrapper):
            model = wrapper_name_map['base'](model)
        model = wrapper_name_map[wrapper_name](model, **kwargs)
    return model


def register_wrapper(name: str, wrapper_type: type):
    r"""
    Overview:
        Register new wrapper to wrapper_name_map
    Arguments:
        - name (:obj:`str`): the name of the wrapper
        - wrapper_type (subclass of :obj:`IModelWrapper`): the wrapper class added to the plguin_name_map
    """
    assert isinstance(name, str)
    assert issubclass(wrapper_type, IModelWrapper)
    wrapper_name_map[name] = wrapper_type