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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 nemo_run as run
from nemo.collections.llm.recipes.llama4_e16 import pretrain_recipe
from nemo.collections.nlp.modules.common.tokenizer_utils import get_nmt_tokenizer
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 dump_config_diff_from_base_recipe, hf_tokenizer
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,
etp_size: int,
enable_cuda_graphs: bool,
):
"""
llama4 e16 pre-train recipe aimed at achieving best possible performance and faster
overall runtime.
NOTE: Use fp8 precision training with caution. It might not give desirable results.
"""
recipe = pretrain_recipe(performance_mode=True)
recipe = set_primary_perf_configs(
recipe,
"pre_train",
num_nodes,
args.gpus_per_node,
mbs,
gbs,
args.max_steps,
tp_size,
pp_size,
cp_size,
vp_size,
ep_size,
etp_size,
enable_cuda_graphs=enable_cuda_graphs,
use_mcore_fsdp=args.use_mcore_fsdp,
use_fsdp_double_buffer=args.use_fsdp_double_buffer,
use_user_buffer_registration=args.use_user_buffer_registration,
use_sharp=args.use_sharp,
compute_dtype=args.compute_dtype,
fp8_recipe=args.fp8_recipe,
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, "pre_train", "llm", "llama4", args.tensorboard, args.wandb, args.wandb_prj_name, args.wandb_job_name
)
# data module configs
if args.use_hf_tokenizer:
recipe.data.tokenizer = hf_tokenizer('meta-llama/Llama-4-Scout-17B-16E-Instruct')
else:
recipe.data.tokenizer = run.Config(
get_nmt_tokenizer, library="null", model_name="NullTokenizer", vocab_size=200000
)
recipe.model.tokenizer = recipe.data.tokenizer
recipe.model.config.cross_entropy_fusion_impl = "te"
recipe.model.config.cross_entropy_loss_fusion = True
recipe.model.config.apply_rope_fusion = True
recipe.model.config.moe_permute_fusion = 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(), "pre_train", "llama4", "e16", args)
num_nodes, mbs, gbs, tp_size, pp_size, cp_size, vp_size, ep_size, etp_size, enable_cuda_graphs, _, _, _ = kwargs[
0:13
]
recipe = override_recipe_configs(
args, num_nodes, mbs, gbs, tp_size, pp_size, cp_size, vp_size, ep_size, etp_size, enable_cuda_graphs
)
exp_config = (
f"{num_nodes}nodes_tp{tp_size}_pp{pp_size}_cp{cp_size}_vp{vp_size}_ep{ep_size}_etp{etp_size}_{mbs}mbs_{gbs}gbs"
)
exp_name = f"{splitext(basename(__file__))[0]}_{args.compute_dtype}_{exp_config}"
# Workaround for CUDA graph illegal memory access error
if not enable_cuda_graphs:
custom_env_vars = {"PYTORCH_CUDA_ALLOC_CONF": "expandable_segments:True"}
else:
custom_env_vars = {}
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=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=15, end_step=16, gen_shape=True))
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:
exp.add(
recipe,
executor=executor,
name=exp_name,
plugins=plugins,
)
if not args.dryrun:
exp.run(sequential=True, detach=args.detach)
else:
exp.dryrun()
if args.dump_config_diff_from_base_recipe:
output_dir = exp.jobs[0].executor.job_dir
# dump difference from base recipe
base_recipe = pretrain_recipe(performance_mode=False)
file_name = f"diff_from_base_recipe_{args.compute_dtype}.diff"
dump_config_diff_from_base_recipe(base_recipe, recipe, output_dir, file_name=file_name)
# dump difference from default perf recipe
default_perf_recipe = pretrain_recipe(performance_mode=True)
file_name = f"diff_from_default_perf_recipe_{args.compute_dtype}.diff"
dump_config_diff_from_base_recipe(default_perf_recipe, recipe, output_dir, file_name=file_name)
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