Spaces:
Runtime error
Runtime error
File size: 6,890 Bytes
0558aa4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 |
# 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_e128 import finetune_recipe, model
from nemo.collections.llm.recipes.precision.mixed_precision import bf16_with_fp8_mixed
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 (
args_sanity_check,
get_user_configs,
hf_tokenizer,
import_ckpt_experiment,
prepare_squad_dataset_experiment,
set_exp_logging_configs,
set_primary_perf_configs,
slurm_executor,
)
HF_MODEL_URI = "meta-llama/Llama-4-Maverick-17B-128E-Instruct"
# 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,
etp_size: int,
enable_cuda_graphs: bool,
use_mcore_fsdp: bool,
recompute_layers: int,
activation_offload_layers: int,
):
"""
Llama4 e128 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
recipe = finetune_recipe(peft_scheme=finetuning_scheme, performance_mode=True, packed_sequence=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,
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=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,
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",
"llama4",
args.tensorboard,
args.wandb,
args.wandb_prj_name,
args.wandb_job_name,
)
# data module configs
recipe.data.tokenizer = hf_tokenizer(HF_MODEL_URI)
# 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.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(), "sft", "llama4", "e128", 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,
) = 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,
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}_ep{ep_size}_etp{etp_size}_{mbs}mbs_{gbs}gbs"
)
exp_name = f"{splitext(basename(__file__))[0]}_{args.compute_dtype}_{exp_config}"
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))
executor = slurm_executor(
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,
additional_slurm_params=additional_slurm_params,
)
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()
|