LSTMModule

class torchrl.modules.LSTMModule(*args, **kwargs)

An embedder for an LSTM module.

This class adds the following functionality to torch.nn.LSTM:

• Compatibility with TensorDict: the hidden states are reshaped to match the tensordict batch size.

• Optional multi-step execution: with torch.nn, one has to choose between torch.nn.LSTMCell and torch.nn.LSTM, the former being compatible with single step inputs and the latter being compatible with multi-step. This class enables both usages.

After construction, the module is not set in recurrent mode, ie. it will expect single steps inputs.

If in recurrent mode, it is expected that the last dimension of the tensordict marks the number of steps. There is no constrain on the dimensionality of the tensordict (except that it must be greater than one for temporal inputs).

Note

This class can handle multiple consecutive trajectories along the time dimension but the final hidden values should not be trusted in those cases (ie. they should not be re-used for a consecutive trajectory). The reason is that LSTM returns only the last hidden value, which for the padded inputs we provide can correspont to a 0-filled input.

Parameters:

• input_size – The number of expected features in the input x

• hidden_size – The number of features in the hidden state h

• num_layers – Number of recurrent layers. E.g., setting num_layers=2 would mean stacking two LSTMs together to form a stacked LSTM, with the second LSTM taking in outputs of the first LSTM and computing the final results. Default: 1

• bias – If False, then the layer does not use bias weights b_ih and b_hh. Default: True

• dropout – If non-zero, introduces a Dropout layer on the outputs of each LSTM layer except the last layer, with dropout probability equal to dropout. Default: 0

• python_based – If True, will use a full Python implementation of the LSTM cell. Default: False

Keyword Arguments:

• in_key (str or tuple of str) – the input key of the module. Exclusive use with in_keys. If provided, the recurrent keys are assumed to be [“recurrent_state_h”, “recurrent_state_c”] and the in_key will be appended before these.

• in_keys (list of str) – a triplet of strings corresponding to the input value, first and second hidden key. Exclusive with in_key.

• out_key (str or tuple of str) – the output key of the module. Exclusive use with out_keys. If provided, the recurrent keys are assumed to be [(“next”, “recurrent_state_h”), (“next”, “recurrent_state_c”)] and the out_key will be appended before these.

• out_keys (list of str) –

  a triplet of strings corresponding to the output value, first and second hidden key. .. note:

  For a better integration with TorchRL's environments, the best naming
  for the output hidden key is ``("next", <custom_key>)``, such
  that the hidden values are passed from step to step during a rollout.
  

• device (torch.device or compatible) – the device of the module.

• lstm (torch.nn.LSTM, optional) – an LSTM instance to be wrapped. Exclusive with other nn.LSTM arguments.

• default_recurrent_mode (bool, optional) – if provided, the recurrent mode if it hasn’t been overridden by the set_recurrent_mode context manager / decorator. Defaults to False.

Variables:

recurrent_mode – Returns the recurrent mode of the module.

set_recurrent_mode()

controls whether the module should be executed in recurrent mode.

make_tensordict_primer()

creates the TensorDictPrimer transforms for the environment to be aware of the recurrent states of the RNN.

Note

This module relies on specific recurrent_state keys being present in the input TensorDicts. To generate a TensorDictPrimer transform that will automatically add hidden states to the environment TensorDicts, use the method make_tensordict_primer(). If this class is a submodule in a larger module, the method get_primers_from_module() can be called on the parent module to automatically generate the primer transforms required for all submodules, including this one.

Examples

>>> from torchrl.envs import TransformedEnv, InitTracker
>>> from torchrl.envs import GymEnv
>>> from torchrl.modules import MLP
>>> from torch import nn
>>> from tensordict.nn import TensorDictSequential as Seq, TensorDictModule as Mod
>>> env = TransformedEnv(GymEnv("Pendulum-v1"), InitTracker())
>>> lstm_module = LSTMModule(
...     input_size=env.observation_spec["observation"].shape[-1],
...     hidden_size=64,
...     in_keys=["observation", "rs_h", "rs_c"],
...     out_keys=["intermediate", ("next", "rs_h"), ("next", "rs_c")])
>>> mlp = MLP(num_cells=[64], out_features=1)
>>> policy = Seq(lstm_module, Mod(mlp, in_keys=["intermediate"], out_keys=["action"]))
>>> policy(env.reset())
TensorDict(
    fields={
        action: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, is_shared=False),
        done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
        intermediate: Tensor(shape=torch.Size([64]), device=cpu, dtype=torch.float32, is_shared=False),
        is_init: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
        next: TensorDict(
            fields={
                rs_c: Tensor(shape=torch.Size([1, 64]), device=cpu, dtype=torch.float32, is_shared=False),
                rs_h: Tensor(shape=torch.Size([1, 64]), device=cpu, dtype=torch.float32, is_shared=False)},
            batch_size=torch.Size([]),
            device=cpu,
            is_shared=False),
        observation: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False)},
        terminated: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
        truncated: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False)},
    batch_size=torch.Size([]),
    device=cpu,
    is_shared=False)

forward(tensordict: TensorDictBase = None)

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

make_cudnn_based() → LSTMModule

Transforms the LSTM layer in its CuDNN-based version.

Returns:

self

make_python_based() → LSTMModule

Transforms the LSTM layer in its python-based version.

Returns:

self

make_tensordict_primer()

Makes a tensordict primer for the environment.

A TensorDictPrimer object will ensure that the policy is aware of the supplementary inputs and outputs (recurrent states) during rollout execution. That way, the data can be shared across processes and dealt with properly.

Not including a TensorDictPrimer in the environment may result in poorly defined behaviors, for instance in parallel settings where a step involves copying the new recurrent state from "next" to the root tensordict, which the meth:~torchrl.EnvBase.step_mdp method will not be able to do as the recurrent states are not registered within the environment specs.

See torchrl.modules.utils.get_primers_from_module() for a method to generate all primers for a given module.

Examples

>>> from torchrl.collectors import SyncDataCollector
>>> from torchrl.envs import TransformedEnv, InitTracker
>>> from torchrl.envs import GymEnv
>>> from torchrl.modules import MLP, LSTMModule
>>> from torch import nn
>>> from tensordict.nn import TensorDictSequential as Seq, TensorDictModule as Mod
>>>
>>> env = TransformedEnv(GymEnv("Pendulum-v1"), InitTracker())
>>> lstm_module = LSTMModule(
...     input_size=env.observation_spec["observation"].shape[-1],
...     hidden_size=64,
...     in_keys=["observation", "rs_h", "rs_c"],
...     out_keys=["intermediate", ("next", "rs_h"), ("next", "rs_c")])
>>> mlp = MLP(num_cells=[64], out_features=1)
>>> policy = Seq(lstm_module, Mod(mlp, in_keys=["intermediate"], out_keys=["action"]))
>>> policy(env.reset())
>>> env = env.append_transform(lstm_module.make_tensordict_primer())
>>> data_collector = SyncDataCollector(
...     env,
...     policy,
...     frames_per_batch=10
... )
>>> for data in data_collector:
...     print(data)
...     break

set_recurrent_mode(mode: bool = True)

[DEPRECATED - use torchrl.modules.set_recurrent_mode context manager instead] Returns a new copy of the module that shares the same lstm model but with a different recurrent_mode attribute (if it differs).

A copy is created such that the module can be used with divergent behavior in various parts of the code (inference vs training):

Examples

>>> from torchrl.envs import TransformedEnv, InitTracker, step_mdp
>>> from torchrl.envs import GymEnv
>>> from torchrl.modules import MLP
>>> from tensordict import TensorDict
>>> from torch import nn
>>> from tensordict.nn import TensorDictSequential as Seq, TensorDictModule as Mod
>>> env = TransformedEnv(GymEnv("Pendulum-v1"), InitTracker())
>>> lstm = nn.LSTM(input_size=env.observation_spec["observation"].shape[-1], hidden_size=64, batch_first=True)
>>> lstm_module = LSTMModule(lstm=lstm, in_keys=["observation", "hidden0", "hidden1"], out_keys=["intermediate", ("next", "hidden0"), ("next", "hidden1")])
>>> mlp = MLP(num_cells=[64], out_features=1)
>>> # building two policies with different behaviors:
>>> policy_inference = Seq(lstm_module, Mod(mlp, in_keys=["intermediate"], out_keys=["action"]))
>>> policy_training = Seq(lstm_module.set_recurrent_mode(True), Mod(mlp, in_keys=["intermediate"], out_keys=["action"]))
>>> traj_td = env.rollout(3) # some random temporal data
>>> traj_td = policy_training(traj_td)
>>> # let's check that both return the same results
>>> td_inf = TensorDict(batch_size=traj_td.shape[:-1])
>>> for td in traj_td.unbind(-1):
...     td_inf = td_inf.update(td.select("is_init", "observation", ("next", "observation")))
...     td_inf = policy_inference(td_inf)
...     td_inf = step_mdp(td_inf)
...
>>> torch.testing.assert_close(td_inf["hidden0"], traj_td[..., -1]["next", "hidden0"])