Tianshou/tianshou/utils/net/common.py

from abc import ABC, abstractmethod
from collections.abc import Callable, Sequence
from typing import Any, no_type_check

import numpy as np
import torch
from torch import nn

from tianshou.data.batch import Batch
from tianshou.data.types import RecurrentStateBatch

ModuleType = type[nn.Module]
ArgsType = tuple[Any, ...] | dict[Any, Any] | Sequence[tuple[Any, ...]] | Sequence[dict[Any, Any]]


def miniblock(
    input_size: int,
    output_size: int = 0,
    norm_layer: ModuleType | None = None,
    norm_args: tuple[Any, ...] | dict[Any, Any] | None = None,
    activation: ModuleType | None = None,
    act_args: tuple[Any, ...] | dict[Any, Any] | None = 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:
        if isinstance(norm_args, tuple):
            layers += [norm_layer(output_size, *norm_args)]
        elif isinstance(norm_args, dict):
            layers += [norm_layer(output_size, **norm_args)]
        else:
            layers += [norm_layer(output_size)]
    if activation is not None:
        if isinstance(act_args, tuple):
            layers += [activation(*act_args)]
        elif isinstance(act_args, dict):
            layers += [activation(**act_args)]
        else:
            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: ModuleType | Sequence[ModuleType] | None = None,
        norm_args: ArgsType | None = None,
        activation: ModuleType | Sequence[ModuleType] | None = nn.ReLU,
        act_args: ArgsType | None = None,
        device: str | int | torch.device | None = 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
                if isinstance(norm_args, list):
                    assert len(norm_args) == len(hidden_sizes)
                    norm_args_list = norm_args
                else:
                    norm_args_list = [norm_args for _ in range(len(hidden_sizes))]
            else:
                norm_layer_list = [norm_layer for _ in range(len(hidden_sizes))]
                norm_args_list = [norm_args for _ in range(len(hidden_sizes))]
        else:
            norm_layer_list = [None] * len(hidden_sizes)
            norm_args_list = [None] * len(hidden_sizes)
        if activation:
            if isinstance(activation, list):
                assert len(activation) == len(hidden_sizes)
                activation_list = activation
                if isinstance(act_args, list):
                    assert len(act_args) == len(hidden_sizes)
                    act_args_list = act_args
                else:
                    act_args_list = [act_args for _ in range(len(hidden_sizes))]
            else:
                activation_list = [activation for _ in range(len(hidden_sizes))]
                act_args_list = [act_args for _ in range(len(hidden_sizes))]
        else:
            activation_list = [None] * len(hidden_sizes)
            act_args_list = [None] * len(hidden_sizes)
        hidden_sizes = [input_dim, *list(hidden_sizes)]
        model = []
        for in_dim, out_dim, norm, norm_args, activ, act_args in zip(
            hidden_sizes[:-1],
            hidden_sizes[1:],
            norm_layer_list,
            norm_args_list,
            activation_list,
            act_args_list,
            strict=True,
        ):
            model += miniblock(in_dim, out_dim, norm, norm_args, activ, act_args, 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: np.ndarray | torch.Tensor) -> torch.Tensor:
        obs = torch.as_tensor(obs, device=self.device, dtype=torch.float32)
        if self.flatten_input:
            obs = obs.flatten(1)
        return self.model(obs)


class NetBase(nn.Module, ABC):
    """Interface for NNs used in policies."""

    @abstractmethod
    def forward(
        self,
        obs: np.ndarray | torch.Tensor,
        state: Any = None,
        **kwargs: Any,
    ) -> tuple[torch.Tensor, Any]:
        pass


class Net(NetBase):
    """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: int | Sequence[int],
        action_shape: int | Sequence[int] = 0,
        hidden_sizes: Sequence[int] = (),
        norm_layer: ModuleType | Sequence[ModuleType] | None = None,
        norm_args: ArgsType | None = None,
        activation: ModuleType | Sequence[ModuleType] | None = nn.ReLU,
        act_args: ArgsType | None = None,
        device: str | int | torch.device = "cpu",
        softmax: bool = False,
        concat: bool = False,
        num_atoms: int = 1,
        dueling_param: tuple[dict[str, Any], dict[str, Any]] | None = None,
        linear_layer: type[nn.Linear] = nn.Linear,
    ) -> None:
        super().__init__()
        self.device = device
        self.softmax = softmax
        self.num_atoms = num_atoms
        self.Q: MLP | None = None
        self.V: MLP | None = None

        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,
            norm_args,
            activation,
            act_args,
            device,
            linear_layer,
        )
        if self.use_dueling:  # dueling DQN
            assert dueling_param is not None
            kwargs_update = {
                "input_dim": self.model.output_dim,
                "device": self.device,
            }
            # Important: don't change the original dict (e.g., don't use .update())
            q_kwargs = {**dueling_param[0], **kwargs_update}
            v_kwargs = {**dueling_param[1], **kwargs_update}

            q_kwargs["output_dim"] = 0 if concat else action_dim
            v_kwargs["output_dim"] = 0 if concat else num_atoms
            self.Q, self.V = MLP(**q_kwargs), MLP(**v_kwargs)
            self.output_dim = self.Q.output_dim
        else:
            self.output_dim = self.model.output_dim

    def forward(
        self,
        obs: np.ndarray | torch.Tensor,
        state: Any = None,
        **kwargs: Any,
    ) -> tuple[torch.Tensor, Any]:
        """Mapping: obs -> flatten (inside MLP)-> logits.

        :param obs:
        :param state: unused and returned as is
        :param kwargs: unused
        """
        logits = self.model(obs)
        batch_size = logits.shape[0]
        if self.use_dueling:  # Dueling DQN
            assert self.Q is not None
            assert self.V is not None
            q, v = self.Q(logits), self.V(logits)
            if self.num_atoms > 1:
                q = q.view(batch_size, -1, self.num_atoms)
                v = v.view(batch_size, -1, self.num_atoms)
            logits = q - q.mean(dim=1, keepdim=True) + v
        elif self.num_atoms > 1:
            logits = logits.view(batch_size, -1, self.num_atoms)
        if self.softmax:
            logits = torch.softmax(logits, dim=-1)
        return logits, state


class Recurrent(NetBase):
    """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: int | Sequence[int],
        action_shape: int | Sequence[int],
        device: 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: np.ndarray | torch.Tensor,
        state: RecurrentStateBatch | dict[str, torch.Tensor] | None = None,
        **kwargs: 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.

        :param obs:
        :param state: either None or a dict with keys 'hidden' and 'cell'
        :param kwargs: unused
        :return: predicted action, next state as dict with keys 'hidden' and 'cell'
        """
        # Note: the original type of state is Batch but it might also be a dict
        # If it is a Batch, .issubset(state) will not work. However,
        # issubset(state.keys()) always works
        if state is not None and not {"hidden", "cell"}.issubset(state.keys()):
            raise ValueError(
                f"Expected to find keys 'hidden' and 'cell' but instead found {state.keys()}",
            )

        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: 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)  # noqa: B026


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

    :param int ensemble_size: Number of subnets in the ensemble.
    :param int in_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(1.0 / 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_weights: nn.Parameter | None = None
        if bias:
            bias_data = torch.rand((ensemble_size, 1, out_feature)) * 2 * k - k
            self.bias_weights = nn.Parameter(bias_data, requires_grad=True)

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


class BranchingNet(NetBase):
    """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: int | Sequence[int],
        num_branches: int = 0,
        action_per_branch: int = 2,
        common_hidden_sizes: list[int] | None = None,
        value_hidden_sizes: list[int] | None = None,
        action_hidden_sizes: list[int] | None = None,
        norm_layer: ModuleType | None = None,
        norm_args: ArgsType | None = None,
        activation: ModuleType | None = nn.ReLU,
        act_args: ArgsType | None = None,
        device: str | int | torch.device = "cpu",
    ) -> None:
        super().__init__()
        common_hidden_sizes = common_hidden_sizes or []
        value_hidden_sizes = value_hidden_sizes or []
        action_hidden_sizes = action_hidden_sizes or []

        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,
            norm_args,
            activation,
            act_args,
            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,
            norm_args,
            activation,
            act_args,
            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,
                    norm_args,
                    activation,
                    act_args,
                    device,
                )
                for _ in range(self.num_branches)
            ],
        )

    def forward(
        self,
        obs: np.ndarray | torch.Tensor,
        state: Any = None,
        **kwargs: 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, 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: 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: 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