Spaces:
Runtime error
Runtime error
File size: 2,588 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 |
# 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.
import gc
import os
from pathlib import Path
from typing import Optional
import torch
import torch.distributed
from lightning.pytorch import Trainer
from torch import nn
DEFAULT_NEMO_CACHE_HOME = Path.home() / ".cache" / "nemo"
NEMO_CACHE_HOME = Path(os.getenv("NEMO_HOME", DEFAULT_NEMO_CACHE_HOME))
DEFAULT_NEMO_DATASETS_CACHE = NEMO_CACHE_HOME / "datasets"
NEMO_DATASETS_CACHE = Path(os.getenv("NEMO_DATASETS_CACHE", DEFAULT_NEMO_DATASETS_CACHE))
DEFAULT_NEMO_MODELS_CACHE = NEMO_CACHE_HOME / "models"
NEMO_MODELS_CACHE = Path(os.getenv("NEMO_MODELS_CACHE", DEFAULT_NEMO_MODELS_CACHE))
if os.getenv('TOKENIZERS_PARALLELISM') is None:
os.putenv('TOKENIZERS_PARALLELISM', 'True')
def get_vocab_size(
config,
vocab_size: int,
make_vocab_size_divisible_by: int = 128,
) -> int:
"""returns `vocab size + padding` to make sure sum is dividable by `make_vocab_size_divisible_by`"""
from nemo.utils import logging
after = vocab_size
multiple = make_vocab_size_divisible_by * config.tensor_model_parallel_size
after = ((after + multiple - 1) // multiple) * multiple
logging.info(
f"Padded vocab_size: {after}, original vocab_size: {vocab_size}, dummy tokens:" f" {after - vocab_size}."
)
return after
def teardown(trainer: Trainer, model: Optional[nn.Module] = None) -> None:
"""Destroys distributed environment and cleans up cache / collects garbage"""
# Destroy torch distributed
if torch.distributed.is_initialized():
from megatron.core import parallel_state
parallel_state.destroy_model_parallel()
torch.distributed.destroy_process_group()
trainer._teardown() # noqa: SLF001
if model is not None:
for obj in gc.get_objects():
try:
if torch.is_tensor(obj) and obj.is_cuda:
del obj
except:
pass
gc.collect()
torch.cuda.empty_cache()
__all__ = ["get_vocab_size", "teardown"]
|