examples/llm/pretrain/multi_datacenter.py

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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 import llm
from nemo.collections.llm.api import pretrain
from nemo.collections.llm.gpt.data.mock import MockDataModule
from nemo.collections.llm.gpt.model.llama import Llama31Config70B, LlamaModel
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.collections.llm.recipes.tp_overlap_configs.userbuffers import (
    userbuffers_bf16_h100_h16384_tp8_cp2_mbs1_seqlen8192,
)
from nemo.lightning.pytorch.callbacks.megatron_comm_overlap import MegatronCommOverlapCallback
from nemo.utils.exp_manager import TimingCallback

NAME = "llama31_70b_multi_dc"


@run.cli.factory(name=NAME)
def model() -> run.Config[pl.LightningModule]:
    """
    Factory function to create a Llama3.1 70B model configuration.

    Returns:
        run.Config[pl.LightningModule]: Configuration for the Llama3.1 70B model.

    Examples:
        CLI usage:
            $ nemo llm pretrain model=llama31_70b ...

        Python API usage:
            >>> model_config = model()
            >>> print(model_config)
    """
    conf = run.Config(Llama31Config70B)
    conf.seq_length = 8192
    return run.Config(LlamaModel, config=conf)


def trainer(
    tensor_parallelism: int = 4,
    pipeline_parallelism: int = 4,
    pipeline_parallelism_type: Optional[torch.dtype] = torch.bfloat16,
    virtual_pipeline_parallelism: Optional[int] = 5,
    context_parallelism: int = 2,
    sequence_parallelism: bool = True,
    num_nodes: int = 8,
    num_gpus_per_node: int = 8,
    max_steps: int = 1168251,
    num_distributed_optimizer_instances: int = 1,
    nccl_communicator_config_path: Optional[str] = None,
    callbacks: Optional[list[run.Config[Callback]]] = None,
) -> run.Config[nl.Trainer]:
    """
    Configure the NeMo Lightning Trainer for Llama3.1 70B model.

    This function sets up the distributed training strategy optimized for the large 70B model.

    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=llama31_70b ...

        Python API usage:
            >>> trainer_config = trainer(num_nodes=4, num_gpus_per_node=8)
            >>> print(trainer_config)

    Note:
        This configuration uses extensive parallelism to handle the large model size efficiently.
    """
    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,
        num_distributed_optimizer_instances=num_distributed_optimizer_instances,
        nccl_communicator_config_path=nccl_communicator_config_path,
        ddp=run.Config(
            DistributedDataParallelConfig,
            check_for_nan_in_grad=True,
            grad_reduce_in_fp32=True,
            overlap_grad_reduce=True,
            overlap_param_gather=True,
            average_in_collective=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 = 8,
    num_gpus_per_node: int = 8,
    num_distributed_optimizer_instances: int = 1,
    nccl_communicator_config_path: Optional[str] = None,
    performance_mode: bool = False,
    fn: Callable = pretrain,
) -> run.Partial:
    """
    Create a pre-training recipe for Llama3.1 70B 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.
        num_distributed_optimizer_instances (int): Number of distributed optimizer instances to use.
        nccl_communicator_config_path (Optional[str]): Path to the NCCL communicator configuration file.
        performance_mode (bool): If true, enables optimizations for maximum performance.
        fn (Callable): The pre-training function to use.
    Returns:
        run.Partial: Partial configuration for pre-training.

    Examples:
        CLI usage:
            $ nemo llm pretrain --factory llama31_70b
            $ nemo llm pretrain --factory "llama31_70b(num_nodes=4, name='my_70b_pretrain')"

        Python API usage:
            >>> recipe = pretrain_recipe(name="llama31_70b_pretrain", num_nodes=4)
            >>> print(recipe)

    Note:
        This recipe is optimized for the large 70B model and requires significant computational resources.
    """
    recipe = run.Partial(
        fn,
        model=model(),
        trainer=trainer(
            num_nodes=num_nodes,
            num_gpus_per_node=num_gpus_per_node,
            num_distributed_optimizer_instances=num_distributed_optimizer_instances,
            nccl_communicator_config_path=nccl_communicator_config_path,
            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(),
    )

    if performance_mode:
        recipe = pretrain_performance_optimizations(recipe)

    return recipe


def pretrain_performance_optimizations(recipe: run.Partial) -> run.Partial:
    """
    Create a performance-optimized pre-training recipe for Llama3.1 70B model.

    This method 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:
        recipe (run.Partial): Base pre-train recipe to which performance optimizations will be added

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

    Note:
        Use this method with caution and only when you need maximum performance.
        It may not be suitable for all hardware configurations or use cases.
    """

    # 'overlap_param_gather_with_optimizer_step' and 'align_param_gather' params are set automatically
    # by MegatronCommOverlapCallback. They are added here for user's knowledge.
    # overlap_param_gather_with_optimizer_step- Overlap param all-gather of first bucket with optimizer step.
    # align_param_gather- If true, all PP stages launch param all-gathers simultaneously, else
    # each PP stage launches independently as needed.

    recipe.trainer.callbacks.append(
        run.Config(
            MegatronCommOverlapCallback,
            tp_comm_overlap=True,
            tp_comm_overlap_cfg=userbuffers_bf16_h100_h16384_tp8_cp2_mbs1_seqlen8192,
            defer_embedding_wgrad_compute=True,
            wgrad_deferral_limit=50,
            overlap_param_gather_with_optimizer_step=False,  # Currently disabled due to an issue with checkpointing
            align_param_gather=True,
        )
    )

    return recipe


def multi_dc_recipe(nodes: int = 8, gpus_per_node: int = 8):
    pretrain = pretrain_recipe(
        num_nodes=nodes,
        num_gpus_per_node=gpus_per_node,
        num_distributed_optimizer_instances=2,
        nccl_communicator_config_path="/opt/NeMo/examples/llm/pretrain/multi_dc_nccl_communicator_config.yaml",
    )

    return pretrain


if __name__ == "__main__":
    run.cli.main(llm.pretrain, default_factory=multi_dc_recipe)