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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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from typing import Callable, Optional

import lightning.pytorch as pl
import nemo_run as run
import torch
from lightning.pytorch.callbacks.callback import Callback
from megatron.core.distributed import DistributedDataParallelConfig

from nemo import lightning as nl
from nemo.collections.llm import GemmaConfig7B, GemmaModel
from nemo.collections.llm.api import finetune, pretrain
from nemo.collections.llm.gpt.data.mock import MockDataModule
from nemo.collections.llm.peft import PEFT_STR2CLS
from nemo.collections.llm.recipes.finetune_default import default_finetune_recipe
from nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger
from nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing
from nemo.collections.llm.recipes.precision.mixed_precision import bf16_mixed
from nemo.lightning.pytorch.callbacks.megatron_comm_overlap import MegatronCommOverlapCallback
from nemo.utils.exp_manager import TimingCallback

NAME = "gemma_7b"


@run.cli.factory(name=NAME)
def model() -> run.Config[pl.LightningModule]:
    """
    Factory function to create a Gemma 7B model configuration.

    Returns:
        run.Config[pl.LightningModule]: Configuration for the Gemma 7B model.

    Examples:
        CLI usage:
            $ nemo llm pretrain model=gemma_7b ...

        Python API usage:
            >>> model_config = model()
            >>> print(model_config)
    """
    return run.Config(GemmaModel, config=run.Config(GemmaConfig7B))


def trainer(
    tensor_parallelism: int = 2,
    pipeline_parallelism: int = 1,
    pipeline_parallelism_type: Optional[torch.dtype] = None,
    virtual_pipeline_parallelism: Optional[int] = None,
    context_parallelism: int = 2,
    sequence_parallelism: bool = False,
    num_nodes: int = 1,
    num_gpus_per_node: int = 8,
    max_steps: int = 1168251,
    callbacks: Optional[list[run.Config[Callback]]] = None,
) -> run.Config[nl.Trainer]:
    """
    Configure the NeMo Lightning Trainer for Gemma 7B model.

    This function sets up the distributed training strategy and other training parameters.

    Args:
        tensor_parallelism (int): Degree of tensor model parallelism.
        pipeline_parallelism (int): Degree of pipeline model parallelism.
        pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.
        virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.
        context_parallelism (int): Degree of context parallelism.
        sequence_parallelism (bool): Whether to use sequence parallelism.
        num_nodes (int): Number of compute nodes to use.
        num_gpus_per_node (int): Number of GPUs per node.
        max_steps (int): Maximum number of training steps.
        callbacks (Optional[list[run.Config[Callback]]]): List of callback configurations.

    Returns:
        run.Config[nl.Trainer]: Configuration for the NeMo Lightning Trainer.

    Examples:
        CLI usage:
            $ nemo llm pretrain trainer=gemma_7b ...

        Python API usage:
            >>> trainer_config = trainer(num_nodes=2, num_gpus_per_node=8)
            >>> print(trainer_config)

    Note:
        For more information on distributed training strategies, refer to the
        NeMo documentation on multi-GPU and multi-node training.
    """
    strategy = run.Config(
        nl.MegatronStrategy,
        tensor_model_parallel_size=tensor_parallelism,
        pipeline_model_parallel_size=pipeline_parallelism,
        pipeline_dtype=pipeline_parallelism_type,
        virtual_pipeline_model_parallel_size=virtual_pipeline_parallelism,
        context_parallel_size=context_parallelism,
        sequence_parallel=sequence_parallelism,
        gradient_as_bucket_view=True,
        ckpt_async_save=True,
        ckpt_parallel_load=True,
        ddp=run.Config(
            DistributedDataParallelConfig,
            check_for_nan_in_grad=True,
            grad_reduce_in_fp32=True,
            overlap_grad_reduce=True,
            overlap_param_gather=True,
        ),
    )

    trainer = run.Config(
        nl.Trainer,
        accelerator="gpu",
        accumulate_grad_batches=1,
        callbacks=callbacks,
        devices=num_gpus_per_node,
        limit_test_batches=50,
        limit_val_batches=32,
        log_every_n_steps=10,
        max_steps=max_steps,
        num_nodes=num_nodes,
        plugins=bf16_mixed(),
        strategy=strategy,
        use_distributed_sampler=False,
        val_check_interval=2000,
    )

    return trainer


@run.cli.factory(target=pretrain, name=NAME)
def pretrain_recipe(
    dir: Optional[str] = None, name: str = "default", num_nodes: int = 1, num_gpus_per_node: int = 8, fn=pretrain
) -> run.Partial:
    """
    Create a pre-training recipe for Gemma 7B model.

    This function sets up a complete configuration for pre-training, including
    model, trainer, data, logging, optimization, and resumption settings.

    Args:
        dir (Optional[str]): Directory for saving logs and checkpoints.
        name (str): Name of the pre-training run.
        num_nodes (int): Number of compute nodes to use.
        num_gpus_per_node (int): Number of GPUs per node.
        fn (Callable): The pre-training function to use.

    Returns:
        run.Partial: Partial configuration for pre-training.

    Examples:
        CLI usage:
            $ nemo llm pretrain --factory gemma_7b
            $ nemo llm pretrain --factory "gemma_7b(num_nodes=2, name='my_pretrain')"

        Python API usage:
            >>> recipe = pretrain_recipe(name="gemma_7b_pretrain", num_nodes=2)
            >>> print(recipe)
    """

    return run.Partial(
        fn,
        model=model(),
        trainer=trainer(
            num_nodes=num_nodes,
            num_gpus_per_node=num_gpus_per_node,
            callbacks=[run.Config(TimingCallback)],
        ),
        data=run.Config(MockDataModule, seq_length=8192, global_batch_size=512, micro_batch_size=1),
        log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),
        optim=distributed_fused_adam_with_cosine_annealing(max_lr=3e-4),
        resume=default_resume(),
    )


@run.cli.factory(target=pretrain, name=NAME + "_optimized")
def pretrain_recipe_performance(
    dir: Optional[str] = None,
    name: str = "default",
    num_nodes: int = 1,
    num_gpus_per_node: int = 8,
    fn: Callable = pretrain,
) -> run.Partial:
    """
    Create a performance-optimized pre-training recipe for Gemma 7B model.

    This recipe enables performance optimizations that may not be suitable for all use cases.
    It builds upon the standard pre-training recipe and adds additional performance enhancements.

    Args:
        dir (Optional[str]): Directory for saving logs and checkpoints.
        name (str): Name of the pre-training run.
        num_nodes (int): Number of compute nodes to use.
        num_gpus_per_node (int): Number of GPUs per node.
        fn (Callable): The pre-training function to use.

    Returns:
        run.Partial: Partial configuration for performance-optimized pre-training.

    Examples:
            $ nemo llm pretrain --factory gemma_7b_optimized

        Python API usage:
            >>> recipe = pretrain_recipe_performance(name="gemma_7b_perf", num_nodes=4)
            >>> print(recipe)

    Note:
        Use this recipe with caution and only when you need maximum performance.
        It may not be suitable for all hardware configurations or use cases.
    """
    recipe = pretrain_recipe(name=name, dir=dir, num_nodes=num_nodes, num_gpus_per_node=num_gpus_per_node, fn=fn)

    recipe.trainer.callbacks.append(
        run.Config(
            MegatronCommOverlapCallback,
            tp_comm_overlap=False,
        )
    )
    return recipe


@run.cli.factory(target=finetune, name=NAME)
def finetune_recipe(
    dir: Optional[str] = None,
    name: str = "default",
    num_nodes: int = 1,
    num_gpus_per_node: int = 8,
    peft_scheme: Optional[str] = 'lora',
    packed_sequence: bool = False,
) -> run.Partial:
    """
    Create a fine-tuning recipe for Gemma 7B model.

    This function sets up a complete configuration for fine-tuning, including
    model, trainer, data, logging, optimization, and resumption settings.
    The recipe uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, unless peft_scheme is set to None.

    Args:
        dir (Optional[str]): Directory for saving logs and checkpoints.
        name (str): Name of the fine-tuning run.
        num_nodes (int): Number of compute nodes to use.
        num_gpus_per_node (int): Number of GPUs per node.
        peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning.
            Allowed values: 'lora'/'dora'/'none'/None.
        packed_sequence (Optional[bool]): Packing multiple training sequences into one long sequence for training
            efficiency. Default sequence length is 2048.

    Returns:
        run.Partial: Partial configuration for fine-tuning.

    Examples:
        CLI usage:
            $ nemo llm finetune --factory gemma_7b

        Python API usage:
            >>> recipe = finetune_recipe(name="gemma_7b_finetune", num_nodes=2)
            >>> print(recipe)

    Note:
        This recipe uses the SQuAD dataset for fine-tuning.
    """
    recipe = default_finetune_recipe(
        model(), "google/gemma-7b", dir, name, num_nodes, num_gpus_per_node, packed_sequence
    )
    # Gemma requires BOS
    recipe.data.dataset_kwargs = {'add_bos': True}

    if peft_scheme is None or peft_scheme.lower() == 'none':
        recipe.trainer.strategy.tensor_model_parallel_size = 2
        recipe.optim.config.lr = 5e-6
    elif peft_scheme.lower() in ['lora', 'dora']:
        recipe.peft = run.Config(PEFT_STR2CLS[peft_scheme.lower()])
        recipe.optim.config.lr = 1e-4
    else:
        raise ValueError(f"Unrecognized peft scheme: {peft_scheme}")
    return recipe
