Spaces:
Runtime error
Runtime error
File size: 5,945 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 |
# 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.nemotronh_47b import pretrain_recipe
from nemo.collections.nlp.modules.common.tokenizer_utils import get_nmt_tokenizer
from nemo.lightning.run.plugins import 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,
enable_cuda_graphs: bool,
use_mcore_fsdp: bool,
recompute_layers: int,
activation_offload_layers: int,
):
"""
NemotronH 47b 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,
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_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", "nemotronh", 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("nvidia/Nemotron-H-8B-Base-8K")
else:
recipe.data.tokenizer = run.Config(
get_nmt_tokenizer, library="null", model_name="NullTokenizer", vocab_size=131072
)
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", "nemotronh", "47b", 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}"
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,
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))
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)
|