reference/generated/torchrl.modules.GRUModule

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GRUModule

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

An embedder for an GRU module.

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

• 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.GRUCell and torch.nn.GRU, 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).

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 GRUs together to form a stacked GRU, with the second GRU taking in outputs of the first GRU and computing the final results. Default: 1

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

• dropout – If non-zero, introduces a Dropout layer on the outputs of each GRU 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 GRU cell. Default: False

• recurrent_backend – backend used in recurrent mode when trajectories reset in the middle of a batch. "pad" keeps the existing split/pad strategy. "scan" uses a scan loop over the time dimension and avoids materializing padded trajectory chunks via hoptorch. "triton" (prototype, CUDA only) uses Triton kernels where available and otherwise preserves pad-backend recurrent semantics for dropout and bidirectional layers. "auto" uses "pad" in eager mode and "scan" when called under torch.compile(). Default: "pad".

• recurrent_compute_dtype – dtype used for the recurrent matmul inside the "triton" backend (torch.float32 -> TF32 on H100, default; torch.bfloat16 -> bigger SMEM margin, lower precision). Ignored by the other backends. Default: torch.float32.

• recurrent_recompute – when set to "full", trade extra compute for lower backward activation memory. For recurrent_backend="triton" this drops the per-step gate buffers (save_r/z/n/save_gh_n) from the autograd save set and replays the forward kernel during backward. For recurrent_backend="scan" this swaps the torch._higher_order_ops.scan HOP for a python time-loop wrapped with torch.utils.checkpoint.checkpoint(). Only "none" (default) and "full" are accepted; the "pad" backend rejects non-"none" values because cuDNN manages its own backward workspace. Default: "none".

• recurrent_matmul_precision – precision used by tl.dot inside the "triton" backend’s recurrent matmul (and the matching cuBLAS calls in the autograd wrapper). Concrete modes: "ieee" (full IEEE FP32, off tensor cores), "tf32" (matches cuDNN’s default, fastest on Ampere+), "tf32x3" (three-product compensated TF32, ~22 bits of mantissa on tensor cores). GPU-aware presets: "fast" (Ampere+ → "tf32", else "ieee") and "high-prec" (Ampere+ → "tf32x3", else "ieee"). Or "auto" to derive from torch.get_float32_matmul_precision() and the TORCHRL_RNN_PRECISION env var ("highest" → "ieee", "high" → "high-prec", "medium" → "fast"). See torchrl.modules.set_recurrent_matmul_precision(). Ignored by the other backends. Default: "auto".

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”] and the in_key will be appended before this.

• in_keys (list of str) – a pair of strings corresponding to the input value and recurrent entry. 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 [(“recurrent_state”)] and the out_key will be appended before these.

• out_keys (list of str) –

  a pair 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.

• gru (torch.nn.GRU, optional) – a GRU instance to be wrapped. Exclusive with other nn.GRU 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())
>>> gru_module = GRUModule(
...     input_size=env.observation_spec["observation"].shape[-1],
...     hidden_size=64,
...     in_keys=["observation", "rs"],
...     out_keys=["intermediate", ("next", "rs")])
>>> mlp = MLP(num_cells=[64], out_features=1)
>>> policy = Seq(gru_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: 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)
>>> gru_module_training = gru_module.set_recurrent_mode()
>>> policy_training = Seq(gru_module, Mod(mlp, in_keys=["intermediate"], out_keys=["action"]))
>>> traj_td = env.rollout(3) # some random temporal data
>>> traj_td = policy_training(traj_td)
>>> print(traj_td)
TensorDict(
    fields={
        action: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.float32, is_shared=False),
        done: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        intermediate: Tensor(shape=torch.Size([3, 64]), device=cpu, dtype=torch.float32, is_shared=False),
        is_init: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        next: TensorDict(
            fields={
                done: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                is_init: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                observation: Tensor(shape=torch.Size([3, 3]), device=cpu, dtype=torch.float32, is_shared=False),
                reward: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.float32, is_shared=False),
                rs: Tensor(shape=torch.Size([3, 1, 64]), device=cpu, dtype=torch.float32, is_shared=False),
                terminated: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                truncated: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
            batch_size=torch.Size([3]),
            device=cpu,
            is_shared=False),
        observation: Tensor(shape=torch.Size([3, 3]), device=cpu, dtype=torch.float32, is_shared=False),
        terminated: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        truncated: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
    batch_size=torch.Size([3]),
    device=cpu,
    is_shared=False)

property canonical_keys: list[NestedKey]

Return TensorDict keys whose canonical layout matters for this module.

The result is the union of self.in_keys and self.out_keys.

See also

canonicalize(), canonicalize_rnn_subset().

canonicalize(data: TensorDictBase, *, inplace: bool = False) → TensorDictBase

Canonicalize only the RNN-relevant leaves of data.

See LSTMModule.canonicalize() for details.

Parameters:

• data – TensorDict to canonicalize.

• inplace – When True, mutates data in place.

Examples

>>> import torch
>>> from tensordict import TensorDict
>>> from torchrl.modules import GRUModule
>>> module = GRUModule(input_size=3, hidden_size=4, in_key="obs",
...                    out_key="out")
>>> td = TensorDict({"obs": torch.zeros(2, 5, 3)}, batch_size=[2, 5])
>>> module.canonicalize(td)["obs"].is_contiguous()
True

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() → GRUModule

Transforms the GRU layer in its CuDNN-based version.

Returns:

self

make_python_based() → GRUModule

Transforms the GRU 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.

When using batched environments such as ParallelEnv, the transform can be used at the single env instance level (i.e., a batch of transformed envs with tensordict primers set within) or at the batched env instance level (i.e., a transformed batch of regular envs).

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

Examples

>>> from torchrl.collectors import Collector
>>> 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())
>>> gru_module = GRUModule(
...     input_size=env.observation_spec["observation"].shape[-1],
...     hidden_size=64,
...     in_keys=["observation", "rs"],
...     out_keys=["intermediate", ("next", "rs")])
>>> mlp = MLP(num_cells=[64], out_features=1)
>>> policy = Seq(gru_module, Mod(mlp, in_keys=["intermediate"], out_keys=["action"]))
>>> policy(env.reset())
>>> env = env.append_transform(gru_module.make_tensordict_primer())
>>> data_collector = Collector(
...     env,
...     policy,
...     frames_per_batch=10
... )
>>> for data in data_collector:
...     print(data)
...     break