Source code for nemo_rl.models.policy

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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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from typing import Optional, TypedDict, Union

from nemo_rl.models.generation.interfaces import GenerationConfig




class DTensorConfig(TypedDict):
    enabled: bool
    cpu_offload: bool
    sequence_parallel: bool
    activation_checkpointing: bool
    tensor_parallel_size: int





class TokenizerConfig(TypedDict):
    name: str
    chat_template: str





class DynamicBatchingConfig(TypedDict):
    # dynamic_batching improves performance by ensuring logprob and training microbatches
    # have a sufficent number of tokens to maximize GPU utilization. Specifically, variable length
    # responses are sorted by sequence length and bucketed into microbatches with a total
    # amount of tokens is approximately close to 'train_mb_tokens' and 'logprob_mb_tokens' for the
    # training and logprob stages respectively.
    enabled: bool
    train_mb_tokens: int
    logprob_mb_tokens: int
    sequence_length_round: int





class PolicyConfig(TypedDict):
    model_name: str
    tokenizer: TokenizerConfig
    train_global_batch_size: int
    train_micro_batch_size: int
    learning_rate: float
    logprob_batch_size: int
    generation: Optional[GenerationConfig]
    precision: str
    dtensor_cfg: DTensorConfig
    dynamic_batching: DynamicBatchingConfig
    make_sequence_length_divisible_by: int
    max_grad_norm: Optional[Union[float, int]]
    fsdp_offload_enabled: bool
    activation_checkpointing_enabled: bool
    refit_buffer_size_gb: int