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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.llama31_405b import pretrain_recipe
from nemo.collections.llm.recipes.tp_overlap_configs.userbuffers import (
userbuffers_bf16_b200_h16384_tp4_cp2_mbs1_seqlen8192,
userbuffers_bf16_h100_h16384_tp8_cp2_mbs1_seqlen8192,
userbuffers_fp8_b200_h16384_tp4_cp2_mbs1_seqlen8192,
userbuffers_fp8_h100_h16384_tp8_cp2_mbs1_seqlen8192,
)
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, get_comm_overlap_callback_idx, 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,
enable_cuda_graphs: bool,
use_mcore_fsdp: bool,
recompute_layers: int,
activation_offload_layers: int,
):
"""
llama3 405b pre-train recipe aimed at achieving best possible performance.
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,
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,
use_sharp=args.use_sharp,
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_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, "pre_train", "llm", "llama3", args.tensorboard, args.wandb, args.wandb_prj_name, args.wandb_job_name
)
gpu_type = args.gpu.lower()
# data module configs
if args.use_hf_tokenizer:
recipe.data.tokenizer = hf_tokenizer("meta-llama/Llama-3.1-405B")
else:
recipe.data.tokenizer = run.Config(
get_nmt_tokenizer, library="null", model_name="NullTokenizer", vocab_size=128256
)
recipe.model.tokenizer = recipe.data.tokenizer
userbuffers_bf16_h100_h16384_tp8_cp2_mbs1_seqlen8192.qkv_fprop.aggregate = False
userbuffers_bf16_h100_h16384_tp8_cp2_mbs1_seqlen8192.proj_dgrad.aggregate = False
userbuffers_bf16_h100_h16384_tp8_cp2_mbs1_seqlen8192.fc1_fprop.aggregate = False
userbuffers_bf16_h100_h16384_tp8_cp2_mbs1_seqlen8192.fc2_dgrad.aggregate = False
userbuffers_fp8_h100_h16384_tp8_cp2_mbs1_seqlen8192.qkv_fprop.aggregate = False
userbuffers_fp8_h100_h16384_tp8_cp2_mbs1_seqlen8192.proj_dgrad.aggregate = False
userbuffers_fp8_h100_h16384_tp8_cp2_mbs1_seqlen8192.fc1_fprop.aggregate = False
userbuffers_fp8_h100_h16384_tp8_cp2_mbs1_seqlen8192.fc2_dgrad.aggregate = False
ub_cfg = {
"h100": {
"bf16": userbuffers_bf16_h100_h16384_tp8_cp2_mbs1_seqlen8192,
"fp8": userbuffers_fp8_h100_h16384_tp8_cp2_mbs1_seqlen8192,
},
"b200": {
"bf16": userbuffers_bf16_b200_h16384_tp4_cp2_mbs1_seqlen8192,
"fp8": userbuffers_fp8_b200_h16384_tp4_cp2_mbs1_seqlen8192,
},
"gb200": {
"bf16": userbuffers_bf16_b200_h16384_tp4_cp2_mbs1_seqlen8192,
"fp8": userbuffers_fp8_b200_h16384_tp4_cp2_mbs1_seqlen8192,
},
}
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."
tp_comm_overlap_cfg = ub_cfg[gpu_type][args.compute_dtype]
# needed as tp_overlap_configs.userbuffers are dataclass objects which are unserializable
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
if use_mcore_fsdp and gpu_type == 'gb200':
recipe.trainer.strategy.num_distributed_optimizer_instances = (num_nodes * 4) // 64
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", "llama31", "405b", 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"{splitext(basename(__file__))[0]}_{args.compute_dtype}_{exp_config}"
if use_mcore_fsdp:
# Needed to enable CuDNN LN for FSDP overlap
env_vars = {"NVTE_NORM_FWD_USE_CUDNN": "1", "NVTE_NORM_BWD_USE_CUDNN": "1"}
else:
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=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:
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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