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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
from typing import List, Optional
import nemo_run as run
from nemo.collections.llm.recipes.deepseek_v3 import pretrain_recipe
from nemo.collections.nlp.modules.common.tokenizer_utils import get_nmt_tokenizer
from nemo.lightning.pytorch.callbacks.megatron_enable_experimental_callback import MegatronEnableExperimentalCallback
from nemo.lightning.pytorch.callbacks.moe_token_drop import MegatronTokenDropCallback
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
HF_MODEL_URI = "deepseek-ai/DeepSeek-V3-Base"
USE_TOKEN_DROP = True # Use token drop callback
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,
use_mcore_fsdp: bool,
recompute_layers: int,
activation_offload_layers: int,
recompute_modules: Optional[List[str]] = None,
use_user_buffer_registration: Optional[bool] = None,
use_sharp: Optional[bool] = None,
):
"""
DeepSeek V3 pre-train recipe aimed at achieving best possible performance.
"""
recipe = pretrain_recipe(performance_mode=True)
# reset recompute args in the default recipe
if args.recompute_modules is None:
recipe.model.config.recompute_granularity = None
recipe.model.config.recompute_method = None
recipe.model.config.recompute_num_layers = None
recipe.model.config.recompute_modules = None
if not hasattr(recipe.trainer, "callbacks") or recipe.trainer.callbacks is None:
recipe.trainer.callbacks = []
# Token dispatcher configs. For H100 we use deepEP and for Blackwell,
# because deepEP is not supported yet, we use all-to-all dispatcher with
# token drop. After deepEP is supported, we can use deepEP dispatcher.
if args.gpu.lower() in ['h100']:
recipe.model.config.moe_token_dispatcher_type = "flex"
recipe.model.config.moe_enable_deepep = True
recipe.model.config.moe_shared_expert_overlap = False # not supported for deepEP
# use force load balance for reducing variance in benchmarking
recipe.model.config.moe_router_force_load_balancing = True
else:
recipe.model.config.moe_token_dispatcher_type = "alltoall"
recipe.model.config.moe_enable_deepep = False
recipe.model.config.moe_shared_expert_overlap = True
if USE_TOKEN_DROP:
recipe.trainer.callbacks.append(run.Config(MegatronTokenDropCallback))
# Performance optimization knobs
recipe.model.config.moe_permute_fusion = True
recipe.model.config.apply_rope_fusion = True
recipe.trainer.callbacks.append(run.Config(MegatronEnableExperimentalCallback))
# Pipeline parallelism configs. We infer PP layout from the provided PP and VP size
map_pp_vp_to_layout = {
(1, 1): None,
(4, 1): [['embedding'] + ['decoder'] * 16, ['decoder'] * 16, ['decoder'] * 16, ['decoder'] * 13 + ['loss']],
(8, 1): [['embedding'] + ['decoder'] * 8] + [['decoder'] * 8] * 6 + [['decoder'] * 5 + ['loss']],
(4, 2): [['embedding'] + ['decoder'] * 8] + [['decoder'] * 8] * 6 + [['decoder'] * 5 + ['loss']],
(16, 1): [['embedding'] + ['decoder'] * 4] + [['decoder'] * 4] * 14 + [['decoder', 'loss']],
(8, 2): [['embedding'] + ['decoder'] * 4] + [['decoder'] * 4] * 14 + [['decoder', 'loss']],
(4, 4): [['embedding'] + ['decoder'] * 4] + [['decoder'] * 4] * 14 + [['decoder', 'loss']],
}
pp_size = pp_size or 1
vp_size = vp_size or 1
if (pp_size, vp_size) not in map_pp_vp_to_layout:
raise ValueError(
f"Invalid PP and VP size: {pp_size} and {vp_size} to infer PP layout "
f"for DeepSeek V3. Known PP and VP combinations: {map_pp_vp_to_layout.keys()}"
)
layout = map_pp_vp_to_layout[(pp_size, vp_size)]
if layout is not None:
layout = list([list(x) for x in layout]) # yield all the elements
recipe.trainer.strategy.pipeline_model_parallel_layout = layout
# The following knobs are not needed if we specify layout
recipe.trainer.strategy.account_for_embedding_in_pipeline_split = False
recipe.trainer.strategy.account_for_loss_in_pipeline_split = False
recipe.trainer.strategy.num_layers_in_first_pipeline_stage = None
recipe.trainer.strategy.num_layers_in_last_pipeline_stage = None
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=use_mcore_fsdp,
use_fsdp_double_buffer=args.use_fsdp_double_buffer,
use_user_buffer_registration=use_user_buffer_registration,
use_sharp=use_sharp,
recompute_layers=recompute_layers,
activation_offload_layers=activation_offload_layers,
compute_dtype=args.compute_dtype,
fp8_recipe=args.fp8_recipe,
recompute_modules=recompute_modules,
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",
"deepseekv3",
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(HF_MODEL_URI)
else:
recipe.data.tokenizer = run.Config(
get_nmt_tokenizer, library="null", model_name="NullTokenizer", vocab_size=129280
)
recipe.model.tokenizer = recipe.data.tokenizer
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", "deepseek", "v3", args)
(
num_nodes,
mbs,
gbs,
tp_size,
pp_size,
cp_size,
vp_size,
ep_size,
etp_size,
enable_cuda_graphs,
use_mcore_fsdp,
recompute_layers,
activation_offload_layers,
recompute_modules,
_, # keep_fsdp_fp8_transpose_cache
use_user_buffer_registration,
use_sharp,
) = kwargs[:17]
recipe = override_recipe_configs(
args,
num_nodes,
mbs,
gbs,
tp_size,
pp_size,
cp_size,
vp_size,
ep_size,
etp_size,
enable_cuda_graphs,
use_mcore_fsdp,
recompute_layers,
activation_offload_layers,
recompute_modules,
use_user_buffer_registration,
use_sharp,
)
exp_config = f"{num_nodes}nodes_tp{tp_size}_pp{pp_size}_cp{cp_size}_vp{vp_size}_ep{ep_size}_{mbs}mbs_{gbs}gbs"
exp_name = f"{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 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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