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# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from os.path import basename, splitext
import fiddle as fdl
import fiddle._src.experimental.dataclasses as fdl_dc
import nemo_run as run
from nemo.collections.llm.recipes.llama3_70b import finetune_recipe, model
from nemo.collections.llm.recipes.tp_overlap_configs.userbuffers import (
userbuffers_fp8_h100_h8192_tp2_mbs1_seqlen4096_lora,
)
from nemo.lightning.run.plugins import MemoryProfilePlugin, NsysPlugin
from ..argument_parser import parse_additional_slurm_params, parse_cli_args
from ..executors import slurm_executor
from ..helpers import (
args_sanity_check,
build_perf_env_plugin,
get_user_configs,
set_exp_logging_configs,
set_primary_perf_configs,
)
from ..utils import (
get_comm_overlap_callback_idx,
hf_tokenizer,
import_ckpt_experiment,
prepare_squad_dataset_experiment,
)
HF_MODEL_URI = "meta-llama/Meta-Llama-3-70B"
# Set this to True if checkpoint is available at 'NEMO_HOME'. If set to False,
# extra Slurm job will be scheduled. In this case, if checkpoint is available
# at 'NEMO_HOME', fine-tuning job will use this checkpoint, else, it will be
# downloaded from HuggingFace
SKIP_IMPORT = False
# Set this to True if dataset is already downloaded. If set to False,
# dataset will be downloaded from HuggingFace
SKIP_DATASET_DOWNLOAD = False
def override_recipe_configs(
args: str,
num_nodes: int,
mbs: int,
gbs: int,
tp_size: int,
pp_size: int,
cp_size: int,
vp_size: int,
ep_size: int,
enable_cuda_graphs: bool,
use_mcore_fsdp: bool,
recompute_layers: int,
activation_offload_layers: int,
):
"""
llama3 70b fine-tuning recipe aimed at achieving best possible performance.
NOTE: Use fp8 precision training with caution. It might not give desirable results.
"""
finetuning_scheme = "none" if args.finetuning == "sft" else args.finetuning
gpu_type = args.gpu.lower()
if gpu_type in ["gb200"] and finetuning_scheme == "lora":
# On GB200 for lora task, we need to enable Cuda Graph for optimal performance.
# However, Cuda Graph increases memory usage, so in order to avoid OOM, we need
# to reduce the sequence length.
recipe = finetune_recipe(peft_scheme=finetuning_scheme, performance_mode=True, seq_length=2048)
else:
recipe = finetune_recipe(peft_scheme=finetuning_scheme, performance_mode=True)
recipe = set_primary_perf_configs(
recipe,
finetuning_scheme,
num_nodes,
args.gpus_per_node,
mbs,
gbs,
args.max_steps,
tp_size,
pp_size,
cp_size,
vp_size,
ep_size,
enable_cuda_graphs=enable_cuda_graphs,
use_mcore_fsdp=use_mcore_fsdp,
use_fsdp_double_buffer=args.use_fsdp_double_buffer,
use_user_buffer_registration=args.use_user_buffer_registration,
recompute_layers=recompute_layers,
activation_offload_layers=activation_offload_layers,
compute_dtype=args.compute_dtype,
fp8_recipe=args.fp8_recipe,
nccl_communicator_config_path=args.nccl_communicator_config_path,
use_sharp=args.use_sharp,
use_te_op_fuser=args.use_te_op_fuser or use_mcore_fsdp,
use_te_act_func=args.use_te_act_func,
act_func_fp8_input_store=args.act_func_fp8_input_store,
)
recipe = set_exp_logging_configs(
recipe,
finetuning_scheme,
"llm",
"llama3",
args.tensorboard,
args.wandb,
args.wandb_prj_name,
args.wandb_job_name,
)
# data module configs
recipe.data.tokenizer = hf_tokenizer(HF_MODEL_URI)
comm_overlap_callback_idx = get_comm_overlap_callback_idx(recipe.trainer.callbacks)
assert comm_overlap_callback_idx is not None, "MegatronCommOverlapCallback missing. Required for performance."
if finetuning_scheme == "lora" and tp_size > 1 and args.compute_dtype.lower() == "fp8":
tp_comm_overlap_cfg = userbuffers_fp8_h100_h8192_tp2_mbs1_seqlen4096_lora if tp_size == 2 else None
if tp_comm_overlap_cfg:
# Enable TP comm overlap with the given config
recipe.trainer.callbacks[comm_overlap_callback_idx].tp_comm_overlap = True
tp_comm_overlap_cfg = fdl.cast(run.Config, fdl_dc.convert_dataclasses_to_configs(tp_comm_overlap_cfg))
recipe.trainer.callbacks[comm_overlap_callback_idx].tp_comm_overlap_cfg = tp_comm_overlap_cfg
# Disable this overlap to allow skipping an all-gather which is redundant for LoRA
recipe.model.config.tp_comm_overlap_disable_qkv = True
# Allow overlapping of dgrad reduce-scatter with dgrad GEMMs
# (instead of wgrad GEMMs which are not done when using LoRA)
recipe.model.config.tp_comm_bulk_dgrad = False
recipe.model.config.tp_comm_overlap_rs_dgrad = True
recipe.optim.config.use_distributed_optimizer = True
recipe.model.config.disable_parameter_transpose_cache = True
return recipe
if __name__ == "__main__":
args = parse_cli_args().parse_args()
args_sanity_check(args)
# Parse additional SLURM parameters if provided
additional_slurm_params = None
if hasattr(args, 'additional_slurm_params') and args.additional_slurm_params:
additional_slurm_params = parse_additional_slurm_params(args.additional_slurm_params)
kwargs = get_user_configs(args.gpu.lower(), args.finetuning, "llama3", "70b", args)
(
num_nodes,
mbs,
gbs,
tp_size,
pp_size,
cp_size,
vp_size,
ep_size,
_,
enable_cuda_graphs,
use_mcore_fsdp,
recompute_layers,
activation_offload_layers,
) = kwargs[:13]
recipe = override_recipe_configs(
args,
num_nodes,
mbs,
gbs,
tp_size,
pp_size,
cp_size,
vp_size,
ep_size,
enable_cuda_graphs,
use_mcore_fsdp,
recompute_layers,
activation_offload_layers,
)
exp_config = f"{num_nodes}nodes_tp{tp_size}_pp{pp_size}_cp{cp_size}_vp{vp_size}_{mbs}mbs_{gbs}gbs"
exp_name = f"{args.finetuning}_{splitext(basename(__file__))[0]}_{args.compute_dtype}_{exp_config}"
executor = slurm_executor(
args.gpu.lower(),
args.account,
args.partition,
args.log_dir,
num_nodes,
args.gpus_per_node,
args.time_limit,
args.container_image,
custom_mounts=args.custom_mounts,
custom_env_vars={},
hf_token=args.hf_token,
nemo_home=args.nemo_home,
wandb_key=args.wandb_key,
network='sharp' if args.use_sharp else None,
additional_slurm_params=additional_slurm_params,
)
plugins = [build_perf_env_plugin(args, pp_size=pp_size)]
if args.enable_nsys:
plugins.append(NsysPlugin(start_step=5, end_step=6))
if args.enable_memory_profile:
assert args.memory_profile_out_path is not None
plugins.append(MemoryProfilePlugin(dir=args.memory_profile_out_path))
with run.Experiment(exp_name) as exp:
if not SKIP_IMPORT:
assert args.hf_token is not None, "HF token is required for importing checkpoint from HuggingFace"
exp.add(*import_ckpt_experiment(executor, model(), source=f"hf://{HF_MODEL_URI}"))
if not SKIP_DATASET_DOWNLOAD:
exp.add(
*prepare_squad_dataset_experiment(executor, HF_MODEL_URI, seq_length=4096, nemo_home=args.nemo_home)
)
exp.add(
recipe,
executor=executor,
name=exp_name,
plugins=plugins,
)
if not args.dryrun:
exp.run(sequential=True, detach=args.detach)
else:
exp.dryrun()
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