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bert_110m.py
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bert_110m.py
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# Copyright (c) 2024, 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 typing import Optional
import lightning.pytorch as pl
import nemo_run as run
import torch
from nemo.collections.llm.api import pretrain
from nemo.collections.llm.bert.data.mock import BERTMockDataModule
from nemo.collections.llm.recipes.bert import bert_model, bert_trainer
from nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger
from nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing
from nemo.utils.exp_manager import TimingCallback
NAME = "bert_110m"
@run.cli.factory(name=NAME)
def model(bert_type: str = "huggingface") -> run.Config[pl.LightningModule]:
"""
Factory function to create a Bert-Base (110 million) model configuration.
Returns:
run.Config[pl.LightningModule]: Configuration for the BERT-Base (110 million) model.
Examples:
CLI usage:
$ nemo llm pretrain model=bert_110m ...
Python API usage:
>>> model_config = model(bert_type="megatron")
>>> print(model_config)
"""
return bert_model(version=NAME, bert_type=bert_type)
@run.cli.factory(target=pretrain, name=NAME)
def pretrain_recipe(
# General
dir: Optional[str] = None,
name: str = "default",
bert_type: str = "megatron",
# Trainer
tensor_parallelism: int = 1,
pipeline_parallelism: int = 1,
pipeline_parallelism_type: Optional[torch.dtype] = torch.bfloat16,
virtual_pipeline_parallelism: Optional[int] = None,
context_parallelism: int = 1,
sequence_parallelism: bool = False,
num_nodes: int = 1,
num_gpus_per_node: int = 8,
max_steps: int = 1168251,
precision: str = "bf16-mixed",
accumulate_grad_batches: int = 1,
gradient_clip_val: float = 1.0,
limit_test_batches: int = 32,
limit_val_batches: int = 32,
log_every_n_steps: int = 10,
val_check_interval: int = 2000,
# Data
global_batch_size=32,
micro_batch_size=2,
seq_length=512,
# Optimizer
warmup_steps=500,
constant_steps=0,
min_lr=1.0e-5,
max_lr=1e-4,
# Training function
fn=pretrain,
) -> run.Partial:
"""
Create a pre-training recipe for BERT-base (110M) model.
This function sets up a complete configuration for pre-training, including
model, trainer, data, logging, optimization, and resumption settings.
Args:
dir (Optional[str]): Directory for saving logs and checkpoints.
name (str): Name of the pre-training run.
bert_type (str): Either "megatron" or "huggingface" type of BERT.
tensor_parallelism (int): Degree of tensor model parallelism.
pipeline_parallelism (int): Degree of pipeline model parallelism.
pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.
virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.
context_parallelism (int): Degree of context parallelism.
sequence_parallelism (bool): Whether to use sequence parallelism.
num_nodes (int): Number of compute nodes to use.
num_gpus_per_node (int): Number of GPUs per node.
max_steps (int): Maximum number of training steps.
precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.
accumulate_grad_batches (int): Number of steps per gradient accumulation.
gradient_clip_val (float): Value for gradient clipping.
limit_test_batches (int): Limit the number of test batches.
limit_val_batches (int): Limit the number of validation batches.
log_every_n_steps (int): Log every n steps.
val_check_interval (int): Run validation every N steps.
global_batch_size (int): Global batch size.
micro_batch_size (int): Micro batch size.
seq_length (int): Sequence length.
warmup_steps (int): Number of warmup steps.
constant_steps (int): Number of constant steps.
min_lr (float): Minimum learning rate.
max_lr (float): Maximum learning rate.
fn (Callable): The pre-training function to use.
Returns:
run.Partial: Partial configuration for pre-training.
Examples:
CLI usage:
$ nemo llm pretrain --factory bert_110m
$ nemo llm pretrain --factory "bert_110m(num_nodes=1, name='my_bert_pretrain')"
Python API usage:
>>> recipe = pretrain_recipe(name="bert_pretrain", num_nodes=1)
>>> print(recipe)
"""
return run.Partial(
fn,
model=model(bert_type=bert_type),
trainer=bert_trainer(
tensor_parallelism=tensor_parallelism,
pipeline_parallelism=pipeline_parallelism,
pipeline_parallelism_type=pipeline_parallelism_type,
virtual_pipeline_parallelism=virtual_pipeline_parallelism,
context_parallelism=context_parallelism,
sequence_parallelism=sequence_parallelism,
num_nodes=num_nodes,
num_gpus_per_node=num_gpus_per_node,
max_steps=max_steps,
precision=precision,
accumulate_grad_batches=accumulate_grad_batches,
limit_test_batches=limit_test_batches,
limit_val_batches=limit_val_batches,
log_every_n_steps=log_every_n_steps,
val_check_interval=val_check_interval,
callbacks=[run.Config(TimingCallback)],
),
data=run.Config(
BERTMockDataModule,
seq_length=seq_length,
global_batch_size=global_batch_size,
micro_batch_size=micro_batch_size,
),
log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),
optim=distributed_fused_adam_with_cosine_annealing(
precision=precision,
warmup_steps=warmup_steps,
constant_steps=constant_steps,
min_lr=min_lr,
max_lr=max_lr,
clip_grad=gradient_clip_val,
),
resume=default_resume(),
)