Source code for nemo_rl.environments.interfaces

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import abc
from typing import Dict, List, NamedTuple, Optional, Tuple

from torch import Tensor

from nemo_rl.data.interfaces import LLMMessageLogType
from nemo_rl.distributed.batched_data_dict import BatchedDataDict




class EnvironmentReturn(NamedTuple):
    """Standard batched return type for environment step methods.

    **All elements are batched.**
    observations: New observation from the environment.
                  It's a (batched) 'message' type, which is a dict
                  with keys 'role' and 'content'.
    metadata: Updated metadata from the environment.
    next_stop_strings: The stop strings for the next turn.
                       If your environment is a game or similar,
                       you may want to return a list of stop strings
                       that are valid actions for the next turn or
                       similar. This field lets you control this per turn.
    rewards: the rewards for this turn.
    terminateds: whether the episode ended this turn.
    """

    observations: List[Dict[str, str]]
    metadata: List[Optional[dict]]
    next_stop_strings: List[Optional[List[str]]]
    rewards: Tensor
    terminateds: Tensor





class EnvironmentInterface(abc.ABC):


    @abc.abstractmethod
    def step(
        self,
        message_log_batch: List[LLMMessageLogType],
        metadata: List[Optional[dict]],
        *args,
        **kwargs,
    ) -> EnvironmentReturn:
        """Runs a step in the environment. Allows for asynchrony with remote servers, but it's not required (this function is a ray remote).

        message_log_batch: batch of OpenAI-API-like message logs that represent interactions with the LLM.
                  Each element is a List[Dict[str, Union[str, torch.Tensor]]].
                  For example, if this were a Math Environment, then the message log
                  would be
                  [
                    {"role": "user", "content": "problem"},
                    {"role": "assistant", "content": "response"},
                  ]
                  but if this were a code environment
                  with feedback, it would be:
                  [
                    {"role": "user", "content": "problem"},
                    {"role": "assistant", "content": "response"},
                    {"role": "user", "content": "code result"},
                    {"role": "assistant", "content": "model response"},
                  ]
        metadata:     batch of whatever the environment needs to keep track of. I.e.
                      math solutions, code unit tests, or agent states. Can be None if episode terminated.

        Returns:
        - EnvironmentReturn NamedTuple containing observations, metadata, next_stop_strings, rewards, and terminateds flags.
        """




    @abc.abstractmethod
    def global_post_process_and_metrics(
        self, batch: BatchedDataDict
    ) -> Tuple[BatchedDataDict, dict]:
        """Post processing function after all rollouts are done for the batch and returns metrics."""