Source code for nemo_rl.models.policy.interfaces

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from abc import ABC, abstractmethod
from typing import Any, Optional, TypedDict

import ray
import torch

from nemo_rl.algorithms.interfaces import LossFunction
from nemo_rl.distributed.batched_data_dict import BatchedDataDict
from nemo_rl.models.generation.interfaces import GenerationDatumSpec


[docs] class LogprobOutputSpec(TypedDict): """logprobs: Tensor of log probabilities.""" logprobs: torch.Tensor
[docs] class ReferenceLogprobOutputSpec(TypedDict): """logprobs: Tensor of log probabilities.""" reference_logprobs: torch.Tensor
[docs] class PolicyInterface(ABC): """Abstract base class defining the interface for RL policies."""
[docs] @abstractmethod def get_logprobs( self, data: BatchedDataDict[GenerationDatumSpec] ) -> BatchedDataDict[LogprobOutputSpec]: """Get logprobs of actions from observations. Args: data: BatchedDataDict containing rollouts (tokens) Returns: BatchedDataDict containing: - logprobs: Tensor of logprobs of actions """ pass
[docs] @abstractmethod def get_reference_policy_logprobs( self, data: BatchedDataDict[GenerationDatumSpec] ) -> BatchedDataDict[ReferenceLogprobOutputSpec]: """Get logprobs of actions from observations. Args: data: BatchedDataDict containing rollouts (tokens) Returns: BatchedDataDict containing: - logprobs: Tensor of logprobs of actions """ pass
[docs] @abstractmethod def train(self, data: BatchedDataDict, loss_fn: LossFunction) -> dict[str, Any]: """Train the policy on a global batch of data. Args: data: BatchedDataDict containing rollouts (tokens) """ pass
[docs] @abstractmethod def prepare_for_training(self, *args: Any, **kwargs: Any) -> None: pass
[docs] @abstractmethod def finish_training(self, *args: Any, **kwargs: Any) -> None: pass
[docs] @abstractmethod def save_checkpoint(self, *args: Any, **kwargs: Any) -> None: pass
[docs] @abstractmethod def shutdown(self) -> bool: pass
[docs] class ColocatablePolicyInterface(PolicyInterface):
[docs] @abstractmethod def init_collective( self, ip: str, port: int, world_size: int ) -> list[ray.ObjectRef]: pass
[docs] @abstractmethod def offload_before_refit(self) -> None: pass
[docs] @abstractmethod def offload_after_refit(self) -> None: pass
[docs] @abstractmethod def prepare_refit_info(self) -> Optional[dict[str, Any]]: pass
[docs] @abstractmethod def prepare_weights_for_ipc(self, *args: Any, **kwargs: Any) -> list[list[str]]: pass
[docs] @abstractmethod def get_weights_ipc_handles(self, keys: list[str]) -> dict[str, Any]: pass
[docs] @abstractmethod def broadcast_weights_for_collective(self) -> list[ray.ObjectRef]: pass