Tianshou/tianshou/utils/net/common.py

from typing import (
    Any,
    Callable,
    Dict,
    List,
    Optional,
    Sequence,
    Tuple,
    Type,
    Union,
    no_type_check,
)

import numpy as np
import torch
from torch import nn

from tianshou.data.batch import Batch

ModuleType = Type[nn.Module]


def miniblock(
    input_size: int,
    output_size: int = 0,
    norm_layer: Optional[ModuleType] = None,
    activation: Optional[ModuleType] = None,
    linear_layer: Type[nn.Linear] = nn.Linear,
) -> List[nn.Module]:
    """Construct a miniblock with given input/output-size, norm layer and \
    activation."""
    layers: List[nn.Module] = [linear_layer(input_size, output_size)]
    if norm_layer is not None:
        layers += [norm_layer(output_size)]  # type: ignore
    if activation is not None:
        layers += [activation()]
    return layers


class MLP(nn.Module):
    """Simple MLP backbone.

    Create a MLP of size input_dim * hidden_sizes[0] * hidden_sizes[1] * ...
    * hidden_sizes[-1] * output_dim

    :param int input_dim: dimension of the input vector.
    :param int output_dim: dimension of the output vector. If set to 0, there
        is no final linear layer.
    :param hidden_sizes: shape of MLP passed in as a list, not including
        input_dim and output_dim.
    :param norm_layer: use which normalization before activation, e.g.,
        ``nn.LayerNorm`` and ``nn.BatchNorm1d``. Default to no normalization.
        You can also pass a list of normalization modules with the same length
        of hidden_sizes, to use different normalization module in different
        layers. Default to no normalization.
    :param activation: which activation to use after each layer, can be both
        the same activation for all layers if passed in nn.Module, or different
        activation for different Modules if passed in a list. Default to
        nn.ReLU.
    :param device: which device to create this model on. Default to None.
    :param linear_layer: use this module as linear layer. Default to nn.Linear.
    :param bool flatten_input: whether to flatten input data. Default to True.
    """

    def __init__(
        self,
        input_dim: int,
        output_dim: int = 0,
        hidden_sizes: Sequence[int] = (),
        norm_layer: Optional[Union[ModuleType, Sequence[ModuleType]]] = None,
        activation: Optional[Union[ModuleType, Sequence[ModuleType]]] = nn.ReLU,
        device: Optional[Union[str, int, torch.device]] = None,
        linear_layer: Type[nn.Linear] = nn.Linear,
        flatten_input: bool = True,
    ) -> None:
        super().__init__()
        self.device = device
        if norm_layer:
            if isinstance(norm_layer, list):
                assert len(norm_layer) == len(hidden_sizes)
                norm_layer_list = norm_layer
            else:
                norm_layer_list = [norm_layer for _ in range(len(hidden_sizes))]
        else:
            norm_layer_list = [None] * len(hidden_sizes)
        if activation:
            if isinstance(activation, list):
                assert len(activation) == len(hidden_sizes)
                activation_list = activation
            else:
                activation_list = [activation for _ in range(len(hidden_sizes))]
        else:
            activation_list = [None] * len(hidden_sizes)
        hidden_sizes = [input_dim] + list(hidden_sizes)
        model = []
        for in_dim, out_dim, norm, activ in zip(
            hidden_sizes[:-1], hidden_sizes[1:], norm_layer_list, activation_list
        ):
            model += miniblock(in_dim, out_dim, norm, activ, linear_layer)
        if output_dim > 0:
            model += [linear_layer(hidden_sizes[-1], output_dim)]
        self.output_dim = output_dim or hidden_sizes[-1]
        self.model = nn.Sequential(*model)
        self.flatten_input = flatten_input

    @no_type_check
    def forward(self, obs: Union[np.ndarray, torch.Tensor]) -> torch.Tensor:
        if self.device is not None:
            obs = torch.as_tensor(obs, device=self.device, dtype=torch.float32)
        if self.flatten_input:
            obs = obs.flatten(1)
        return self.model(obs)


class Net(nn.Module):
    """Wrapper of MLP to support more specific DRL usage.

    For advanced usage (how to customize the network), please refer to
    :ref:`build_the_network`.

    :param state_shape: int or a sequence of int of the shape of state.
    :param action_shape: int or a sequence of int of the shape of action.
    :param hidden_sizes: shape of MLP passed in as a list.
    :param norm_layer: use which normalization before activation, e.g.,
        ``nn.LayerNorm`` and ``nn.BatchNorm1d``. Default to no normalization.
        You can also pass a list of normalization modules with the same length
        of hidden_sizes, to use different normalization module in different
        layers. Default to no normalization.
    :param activation: which activation to use after each layer, can be both
        the same activation for all layers if passed in nn.Module, or different
        activation for different Modules if passed in a list. Default to
        nn.ReLU.
    :param device: specify the device when the network actually runs. Default
        to "cpu".
    :param bool softmax: whether to apply a softmax layer over the last layer's
        output.
    :param bool concat: whether the input shape is concatenated by state_shape
        and action_shape. If it is True, ``action_shape`` is not the output
        shape, but affects the input shape only.
    :param int num_atoms: in order to expand to the net of distributional RL.
        Default to 1 (not use).
    :param bool dueling_param: whether to use dueling network to calculate Q
        values (for Dueling DQN). If you want to use dueling option, you should
        pass a tuple of two dict (first for Q and second for V) stating
        self-defined arguments as stated in
        class:`~tianshou.utils.net.common.MLP`. Default to None.
    :param linear_layer: use this module as linear layer. Default to nn.Linear.

    .. seealso::

        Please refer to :class:`~tianshou.utils.net.common.MLP` for more
        detailed explanation on the usage of activation, norm_layer, etc.

        You can also refer to :class:`~tianshou.utils.net.continuous.Actor`,
        :class:`~tianshou.utils.net.continuous.Critic`, etc, to see how it's
        suggested be used.
    """

    def __init__(
        self,
        state_shape: Union[int, Sequence[int]],
        action_shape: Union[int, Sequence[int]] = 0,
        hidden_sizes: Sequence[int] = (),
        norm_layer: Optional[ModuleType] = None,
        activation: Optional[ModuleType] = nn.ReLU,
        device: Union[str, int, torch.device] = "cpu",
        softmax: bool = False,
        concat: bool = False,
        num_atoms: int = 1,
        dueling_param: Optional[Tuple[Dict[str, Any], Dict[str, Any]]] = None,
        linear_layer: Type[nn.Linear] = nn.Linear,
    ) -> None:
        super().__init__()
        self.device = device
        self.softmax = softmax
        self.num_atoms = num_atoms
        input_dim = int(np.prod(state_shape))
        action_dim = int(np.prod(action_shape)) * num_atoms
        if concat:
            input_dim += action_dim
        self.use_dueling = dueling_param is not None
        output_dim = action_dim if not self.use_dueling and not concat else 0
        self.model = MLP(
            input_dim, output_dim, hidden_sizes, norm_layer, activation, device,
            linear_layer
        )
        self.output_dim = self.model.output_dim
        if self.use_dueling:  # dueling DQN
            q_kwargs, v_kwargs = dueling_param  # type: ignore
            q_output_dim, v_output_dim = 0, 0
            if not concat:
                q_output_dim, v_output_dim = action_dim, num_atoms
            q_kwargs: Dict[str, Any] = {
                **q_kwargs, "input_dim": self.output_dim,
                "output_dim": q_output_dim,
                "device": self.device
            }
            v_kwargs: Dict[str, Any] = {
                **v_kwargs, "input_dim": self.output_dim,
                "output_dim": v_output_dim,
                "device": self.device
            }
            self.Q, self.V = MLP(**q_kwargs), MLP(**v_kwargs)
            self.output_dim = self.Q.output_dim

    def forward(
        self,
        obs: Union[np.ndarray, torch.Tensor],
        state: Any = None,
        info: Dict[str, Any] = {},
    ) -> Tuple[torch.Tensor, Any]:
        """Mapping: obs -> flatten (inside MLP)-> logits."""
        logits = self.model(obs)
        bsz = logits.shape[0]
        if self.use_dueling:  # Dueling DQN
            q, v = self.Q(logits), self.V(logits)
            if self.num_atoms > 1:
                q = q.view(bsz, -1, self.num_atoms)
                v = v.view(bsz, -1, self.num_atoms)
            logits = q - q.mean(dim=1, keepdim=True) + v
        elif self.num_atoms > 1:
            logits = logits.view(bsz, -1, self.num_atoms)
        if self.softmax:
            logits = torch.softmax(logits, dim=-1)
        return logits, state


class Recurrent(nn.Module):
    """Simple Recurrent network based on LSTM.

    For advanced usage (how to customize the network), please refer to
    :ref:`build_the_network`.
    """

    def __init__(
        self,
        layer_num: int,
        state_shape: Union[int, Sequence[int]],
        action_shape: Union[int, Sequence[int]],
        device: Union[str, int, torch.device] = "cpu",
        hidden_layer_size: int = 128,
    ) -> None:
        super().__init__()
        self.device = device
        self.nn = nn.LSTM(
            input_size=hidden_layer_size,
            hidden_size=hidden_layer_size,
            num_layers=layer_num,
            batch_first=True,
        )
        self.fc1 = nn.Linear(int(np.prod(state_shape)), hidden_layer_size)
        self.fc2 = nn.Linear(hidden_layer_size, int(np.prod(action_shape)))

    def forward(
        self,
        obs: Union[np.ndarray, torch.Tensor],
        state: Optional[Dict[str, torch.Tensor]] = None,
        info: Dict[str, Any] = {},
    ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
        """Mapping: obs -> flatten -> logits.

        In the evaluation mode, `obs` should be with shape ``[bsz, dim]``; in the
        training mode, `obs` should be with shape ``[bsz, len, dim]``. See the code
        and comment for more detail.
        """
        obs = torch.as_tensor(
            obs,
            device=self.device,
            dtype=torch.float32,
        )
        # obs [bsz, len, dim] (training) or [bsz, dim] (evaluation)
        # In short, the tensor's shape in training phase is longer than which
        # in evaluation phase.
        if len(obs.shape) == 2:
            obs = obs.unsqueeze(-2)
        obs = self.fc1(obs)
        self.nn.flatten_parameters()
        if state is None:
            obs, (hidden, cell) = self.nn(obs)
        else:
            # we store the stack data in [bsz, len, ...] format
            # but pytorch rnn needs [len, bsz, ...]
            obs, (hidden, cell) = self.nn(
                obs, (
                    state["hidden"].transpose(0, 1).contiguous(),
                    state["cell"].transpose(0, 1).contiguous()
                )
            )
        obs = self.fc2(obs[:, -1])
        # please ensure the first dim is batch size: [bsz, len, ...]
        return obs, {
            "hidden": hidden.transpose(0, 1).detach(),
            "cell": cell.transpose(0, 1).detach()
        }


class ActorCritic(nn.Module):
    """An actor-critic network for parsing parameters.

    Using ``actor_critic.parameters()`` instead of set.union or list+list to avoid
    issue #449.

    :param nn.Module actor: the actor network.
    :param nn.Module critic: the critic network.
    """

    def __init__(self, actor: nn.Module, critic: nn.Module) -> None:
        super().__init__()
        self.actor = actor
        self.critic = critic


class DataParallelNet(nn.Module):
    """DataParallel wrapper for training agent with multi-GPU.

    This class does only the conversion of input data type, from numpy array to torch's
    Tensor. If the input is a nested dictionary, the user should create a similar class
    to do the same thing.

    :param nn.Module net: the network to be distributed in different GPUs.
    """

    def __init__(self, net: nn.Module) -> None:
        super().__init__()
        self.net = nn.DataParallel(net)

    def forward(self, obs: Union[np.ndarray, torch.Tensor], *args: Any,
                **kwargs: Any) -> Tuple[Any, Any]:
        if not isinstance(obs, torch.Tensor):
            obs = torch.as_tensor(obs, dtype=torch.float32)
        return self.net(obs=obs.cuda(), *args, **kwargs)


class EnsembleLinear(nn.Module):
    """Linear Layer of Ensemble network.

    :param int ensemble_size: Number of subnets in the ensemble.
    :param int inp_feature: dimension of the input vector.
    :param int out_feature: dimension of the output vector.
    :param bool bias: whether to include an additive bias, default to be True.
    """

    def __init__(
        self,
        ensemble_size: int,
        in_feature: int,
        out_feature: int,
        bias: bool = True,
    ) -> None:
        super().__init__()

        # To be consistent with PyTorch default initializer
        k = np.sqrt(1. / in_feature)
        weight_data = torch.rand((ensemble_size, in_feature, out_feature)) * 2 * k - k
        self.weight = nn.Parameter(weight_data, requires_grad=True)

        self.bias: Union[nn.Parameter, None]
        if bias:
            bias_data = torch.rand((ensemble_size, 1, out_feature)) * 2 * k - k
            self.bias = nn.Parameter(bias_data, requires_grad=True)
        else:
            self.bias = None

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = torch.matmul(x, self.weight)
        if self.bias is not None:
            x = x + self.bias
        return x


class BranchingNet(nn.Module):
    """Branching dual Q network.

    Network for the BranchingDQNPolicy, it uses a common network module, a value module
    and action "branches" one for each dimension.It allows for a linear scaling
    of Q-value the output w.r.t. the number of dimensions in the action space.
    For more info please refer to: arXiv:1711.08946.
    :param state_shape: int or a sequence of int of the shape of state.
    :param action_shape: int or a sequence of int of the shape of action.
    :param action_peer_branch: int or a sequence of int of the number of actions in
    each dimension.
    :param common_hidden_sizes: shape of the common MLP network passed in as a list.
    :param value_hidden_sizes: shape of the value MLP network passed in as a list.
    :param action_hidden_sizes: shape of the action MLP network passed in as a list.
    :param norm_layer: use which normalization before activation, e.g.,
    ``nn.LayerNorm`` and ``nn.BatchNorm1d``. Default to no normalization.
    You can also pass a list of normalization modules with the same length
    of hidden_sizes, to use different normalization module in different
    layers. Default to no normalization.
    :param activation: which activation to use after each layer, can be both
    the same activation for all layers if passed in nn.Module, or different
    activation for different Modules if passed in a list. Default to
    nn.ReLU.
    :param device: specify the device when the network actually runs. Default
    to "cpu".
    :param bool softmax: whether to apply a softmax layer over the last layer's
    output.
    """

    def __init__(
        self,
        state_shape: Union[int, Sequence[int]],
        num_branches: int = 0,
        action_per_branch: int = 2,
        common_hidden_sizes: List[int] = [],
        value_hidden_sizes: List[int] = [],
        action_hidden_sizes: List[int] = [],
        norm_layer: Optional[ModuleType] = None,
        activation: Optional[ModuleType] = nn.ReLU,
        device: Union[str, int, torch.device] = "cpu",
    ) -> None:
        super().__init__()
        self.device = device
        self.num_branches = num_branches
        self.action_per_branch = action_per_branch
        # common network
        common_input_dim = int(np.prod(state_shape))
        common_output_dim = 0
        self.common = MLP(
            common_input_dim, common_output_dim, common_hidden_sizes, norm_layer,
            activation, device
        )
        # value network
        value_input_dim = common_hidden_sizes[-1]
        value_output_dim = 1
        self.value = MLP(
            value_input_dim, value_output_dim, value_hidden_sizes, norm_layer,
            activation, device
        )
        # action branching network
        action_input_dim = common_hidden_sizes[-1]
        action_output_dim = action_per_branch
        self.branches = nn.ModuleList(
            [
                MLP(
                    action_input_dim, action_output_dim, action_hidden_sizes,
                    norm_layer, activation, device
                ) for _ in range(self.num_branches)
            ]
        )

    def forward(
        self,
        obs: Union[np.ndarray, torch.Tensor],
        state: Any = None,
        info: Dict[str, Any] = {},
    ) -> Tuple[torch.Tensor, Any]:
        """Mapping: obs -> model -> logits."""
        common_out = self.common(obs)
        value_out = self.value(common_out)
        value_out = torch.unsqueeze(value_out, 1)
        action_out = []
        for b in self.branches:
            action_out.append(b(common_out))
        action_scores = torch.stack(action_out, 1)
        action_scores = action_scores - torch.mean(action_scores, 2, keepdim=True)
        logits = value_out + action_scores
        return logits, state


def get_dict_state_decorator(
    state_shape: Dict[str, Union[int, Sequence[int]]], keys: Sequence[str]
) -> Tuple[Callable, int]:
    """A helper function to make Net or equivalent classes (e.g. Actor, Critic) \
    applicable to dict state.

    The first return item, ``decorator_fn``, will alter the implementation of forward
    function of the given class by preprocessing the observation. The preprocessing is
    basically flatten the observation and concatenate them based on the ``keys`` order.
    The batch dimension is preserved if presented. The result observation shape will
    be equal to ``new_state_shape``, the second return item.

    :param state_shape: A dictionary indicating each state's shape
    :param keys: A list of state's keys. The flatten observation will be according to \
    this list order.
    :returns: a 2-items tuple ``decorator_fn`` and ``new_state_shape``
    """
    original_shape = state_shape
    flat_state_shapes = []
    for k in keys:
        flat_state_shapes.append(int(np.prod(state_shape[k])))
    new_state_shape = sum(flat_state_shapes)

    def preprocess_obs(
        obs: Union[Batch, dict, torch.Tensor, np.ndarray]
    ) -> torch.Tensor:
        if isinstance(obs, dict) or (isinstance(obs, Batch) and keys[0] in obs):
            if original_shape[keys[0]] == obs[keys[0]].shape:
                # No batch dim
                new_obs = torch.Tensor([obs[k] for k in keys]).flatten()
                # new_obs = torch.Tensor([obs[k] for k in keys]).reshape(1, -1)
            else:
                bsz = obs[keys[0]].shape[0]
                new_obs = torch.cat(
                    [torch.Tensor(obs[k].reshape(bsz, -1)) for k in keys], dim=1
                )
        else:
            new_obs = torch.Tensor(obs)
        return new_obs

    @no_type_check
    def decorator_fn(net_class):

        class new_net_class(net_class):

            def forward(
                self,
                obs: Union[np.ndarray, torch.Tensor],
                *args,
                **kwargs,
            ) -> Any:
                return super().forward(preprocess_obs(obs), *args, **kwargs)

        return new_net_class

    return decorator_fn, new_state_shape