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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.precision.mixed_precision import bf16_with_fp8_mixed
from nemo.collections.nlp.modules.common.tokenizer_utils import get_nmt_tokenizer
from nemo.collections.vlm.recipes.qwen25vl_7b import finetune_recipe
from nemo.lightning.run.plugins import NsysPlugin
from ..argument_parser import 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,
)
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,
):
"""
Qwen25VL-7B finetune recipe aimed at achieving best possible performance.
NOTE: Use fp8 precision training with caution. It might not give desirable results.
"""
recipe = finetune_recipe(num_nodes=num_nodes, num_gpus_per_node=args.gpus_per_node)
recipe = set_primary_perf_configs(
recipe,
"finetune",
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,
compute_dtype=args.compute_dtype,
fp8_recipe=args.fp8_recipe,
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_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,
"finetune",
"vlm",
"qwen25vl",
args.tensorboard,
args.wandb,
args.wandb_prj_name,
args.wandb_job_name,
)
# compute dtype configs
if args.compute_dtype.lower() == "fp8":
recipe.trainer.plugins = bf16_with_fp8_mixed()
recipe.trainer.plugins.grad_reduce_in_fp32 = False
recipe.trainer.strategy.tensor_model_parallel_size = tp_size
recipe.trainer.strategy.pipeline_model_parallel_size = pp_size
recipe.data.tokenizer = run.Config(
get_nmt_tokenizer, library="null", model_name="NullTokenizer", vocab_size=152064
)
return recipe
if __name__ == "__main__":
args = parse_cli_args().parse_args()
args_sanity_check(args)
kwargs = get_user_configs(args.gpu.lower(), "finetune", "qwen25vl", "7b", args)
num_nodes, mbs, gbs, tp_size, pp_size, cp_size, vp_size, ep_size, _, enable_cuda_graphs = kwargs[:10]
recipe = override_recipe_configs(
args, num_nodes, mbs, gbs, tp_size, pp_size, cp_size, vp_size, ep_size, enable_cuda_graphs
)
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"qwen25vl_7b_finetune_{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,
)
plugins = [build_perf_env_plugin(args, pp_size=pp_size)]
if args.enable_nsys:
plugins.append(NsysPlugin(start_step=5, end_step=6))
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()
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