baichuan2_7b.py

# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
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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 nemo_run as run
import pytorch_lightning as pl
import torch
from megatron.core.distributed import DistributedDataParallelConfig
from pytorch_lightning.callbacks.callback import Callback

from nemo import lightning as nl
from nemo.collections.llm import Baichuan2Config7B, Baichuan2Model
from nemo.collections.llm.api import finetune, pretrain
from nemo.collections.llm.gpt.data.mock import MockDataModule
from nemo.collections.llm.peft.lora import LoRA
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 = "baichuan2_7b"


@run.cli.factory(name=NAME)
def model() -> run.Config[pl.LightningModule]:
    """
    Factory function to create a Baichuan2 7B model configuration.

    Returns:
        run.Config[pl.LightningModule]: Configuration for the Baichuan2 7B model.

    Examples:
        CLI usage:
            $ nemo llm pretrain model=baichuan2_7b ...

        Python API usage:
            >>> model_config = model()
            >>> print(model_config)
    """
    return run.Config(Baichuan2Model, config=run.Config(Baichuan2Config7B))


def trainer(
    tensor_parallelism: int = 1,
    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 Baichuan2 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=baichuan2_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 Baichuan2 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 baichuan2_7b
            $ nemo llm pretrain --factory "baichuan2_7b(num_nodes=2, name='my_pretrain')"

        Python API usage:
            >>> recipe = pretrain_recipe(name="baichuan2_7b_pretrain", num_nodes=2)
            >>> print(recipe)

    Note:
        For more details on pre-training LLMs with NeMo, see the pre-training
        guide in the `examples/llm/pretrain/` directory.
    """
    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 Baichuan2 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 baichuan2_7b_optimized

        Python API usage:
            >>> recipe = pretrain_recipe_performance(name="baichuan2_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 Baichuan2 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', '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 baichuan2_7b

        Python API usage:
            >>> recipe = finetune_recipe(name="baichuan2_7b_finetune", num_nodes=2)
            >>> print(recipe)

    Note:
        This recipe uses the SQuAD dataset for fine-tuning. For more information
        on fine-tuning LLMs with NeMo, see the fine-tuning guide in the
        `examples/llm/finetune/` directory.
    """
    recipe = default_finetune_recipe(
        model(), "baichuan-inc/Baichuan2-7B-Base", dir, name, num_nodes, num_gpus_per_node, packed_sequence
    )
    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() == 'lora':
        recipe.peft = run.Config(LoRA)
        recipe.optim.config.lr = 1e-4
    else:
        raise ValueError(f"Unrecognized peft scheme: {peft_scheme}")
    return recipe