cosmos1/models/diffusion/nemo/post_training/general.py

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# SPDX-License-Identifier: Apache-2.0
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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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# Unless required by applicable law or agreed to in writing, software
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import os

import nemo_run as run
from huggingface_hub import snapshot_download
from nemo.collections import llm
from nemo.collections.diffusion.models.model import DiT7BConfig, DiT14BConfig
from nemo.collections.diffusion.train import pretrain, videofolder_datamodule
from nemo.lightning.pytorch.strategies.utils import RestoreConfig


@run.cli.factory(target=llm.train)
def cosmos_diffusion_7b_text2world_finetune() -> run.Partial:
    # Model setup
    recipe = pretrain()
    recipe.model.config = run.Config(DiT7BConfig)

    # Trainer setup
    recipe.trainer.max_steps = 1000
    recipe.optim.config.lr = 1e-6

    # Tensor / Sequence parallelism
    recipe.trainer.strategy.tensor_model_parallel_size = 8
    recipe.trainer.strategy.sequence_parallel = True
    recipe.trainer.strategy.ckpt_async_save = False

    # FSDP
    recipe.trainer.strategy.ddp.with_megatron_fsdp_code_path = True
    recipe.trainer.strategy.ddp.data_parallel_sharding_strategy = "MODEL_AND_OPTIMIZER_STATES"
    recipe.trainer.strategy.ddp.overlap_param_gather = True
    recipe.trainer.strategy.ddp.overlap_grad_reduce = True
    recipe.model.config.use_cpu_initialization = True

    # Data setup
    recipe.data = videofolder_datamodule()
    recipe.data.path = ""  # path to folder with processed dataset

    # Checkpoint load
    recipe.resume.restore_config = run.Config(RestoreConfig, load_artifacts=False)
    recipe.resume.restore_config.path = os.path.join(
        snapshot_download("nvidia/Cosmos-1.0-Diffusion-7B-Text2World", allow_patterns=["nemo/*"]), "nemo"
    )  # path to diffusion model checkpoint
    recipe.resume.resume_if_exists = False

    # Directory to save checkpoints / logs
    recipe.log_log_dir = "nemo_experiments/cosmos_diffusion_7b_text2world_finetune"

    return recipe


@run.cli.factory(target=llm.train)
def cosmos_diffusion_14b_text2world_finetune() -> run.Partial:
    # Model setup
    recipe = pretrain()
    recipe.model.config = run.Config(DiT14BConfig)

    # Trainer setup
    recipe.trainer.max_steps = 1000
    recipe.optim.config.lr = 1e-6

    # Tensor / Sequence parallelism
    recipe.trainer.strategy.tensor_model_parallel_size = 8
    recipe.trainer.strategy.sequence_parallel = True
    recipe.trainer.strategy.ckpt_async_save = False

    # FSDP
    recipe.trainer.strategy.ddp.with_megatron_fsdp_code_path = True
    recipe.trainer.strategy.ddp.data_parallel_sharding_strategy = "MODEL_AND_OPTIMIZER_STATES"
    recipe.trainer.strategy.ddp.overlap_param_gather = True
    recipe.trainer.strategy.ddp.overlap_grad_reduce = True
    recipe.model.config.use_cpu_initialization = True

    # Activation Checkpointing
    recipe.model.config.recompute_granularity = "full"
    recipe.model.config.recompute_method = "uniform"
    recipe.model.config.recompute_num_layers = 1

    # Data setup
    recipe.data = videofolder_datamodule()
    recipe.data.path = ""  # path to folder with processed dataset

    # Checkpoint load
    recipe.resume.restore_config = run.Config(RestoreConfig, load_artifacts=False)
    recipe.resume.restore_config.path = os.path.join(
        snapshot_download("nvidia/Cosmos-1.0-Diffusion-14B-Text2World", allow_patterns=["nemo/*"]), "nemo"
    )  # path to diffusion model checkpoint

    recipe.resume.resume_if_exists = False

    # Directory to save checkpoints / logs
    recipe.log_log_dir = "nemo_experiments/cosmos_diffusion_14b_text2world_finetune"

    return recipe


if __name__ == "__main__":
    run.cli.main(llm.train, default_factory=cosmos_diffusion_7b_text2world_finetune)