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## Contributing to LiBai
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README.md
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# Generative Pre-Training2(GPT2)
## 模型介绍
GPT2模型：第二代生成式预训练模型（Generative Pre-Training2）。
## 模型结构
GPT2使用 Transformer 的 Decoder 结构，并对 Transformer Decoder 进行了一些改动，原本的 Decoder 包含了两个 Multi-Head Attention 结构，GPT2 只保留了 Mask Multi-Head Attention。
我们为了用户可以使用OneFlow-Libai快速验证GPT2模型预训练，统计性能或验证精度，提供了一个GPT2网络示例，主要网络参数：
```
model.cfg.num_attention_heads = 16
model.cfg.hidden_size = 384
model.cfg.ffn_hidden_size = 1536
model.cfg.hidden_layers = 6
model.cfg.max_seq_length = 1024
```
完整的GPT2网络配置在configs/common/model/gpt.py中
同时，我们提供了一个更大的GPT2-13B网络为了用户可以快速在DCU集群上使用OneFlow-Libai进行较大规模的混合并行分布式预训练验证（该网络可能并不具有实际训练价值），该网络结构在GPT2基础上进行扩充，主要网络参数如下，参数量共有13.1B：
```
model.cfg.num_attention_heads = 32
model.cfg.hidden_size = 4096
model.cfg.ffn_hidden_size = 4096*4
model.cfg.hidden_layers = 64
model.cfg.max_seq_length = 1024
```
## 数据集
我们在libai目录下集成了部分小数据集供用户快速验证：
./nlp_data
## GPT2预训练
### 环境配置
推荐使用docker方式运行，提供[光源](https://www.sourcefind.cn/#/service-details)拉取的docker镜像：image.sourcefind.cn:5000/dcu/admin/base/oneflow:0.9.1-centos7.6-dtk-22.10.1-py39-latest
进入docker：
cd libai
pip3 install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple
pip3 install pybind11 -i https://mirrors.aliyun.com/pypi/simple
pip3 install -e . -i https://mirrors.aliyun.com/pypi/simple
pip3 install oneflow-0.9.1+dtk2210.git.8ea46d6-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
### 训练
该预训练脚本运行环境为1节点，4张DCU-Z100-16G。
并行配置策略在configs/gpt2_pretrain.py中，使用自动混合精度：
```
train.amp.enabled = True
train.train_micro_batch_size = 4
train.dist.data_parallel_size = 4
train.dist.tensor_parallel_size = 1
train.dist.pipeline_parallel_size = 1
```
预训练命令：
cd libai
bash tools/train.sh tools/train_net.py configs/gpt2_pretrain.py 4
### 性能和收敛性
训练数据：[https://oneflow-static.oss-cn-beijing.aliyuncs.com/ci-files/dataset/libai/gpt_dataset](链接)
使用的GPGPU：4张DCU-Z100-16G。
模型性能及收敛性：
| 卡数 | 分布式工具 | 性能 | 收敛性 |
| :--: | :--------: | :--------------: | :---------------------------: |
| 4 | Libai-main | 129.55 samples/s | total_loss: 4.336/10000 iters |
## GPT2-13B预训练
### 环境配置
要求DCU集群Slurm环境正常。
推荐用户使用预编译好的python3.9包来快速建立python3虚拟环境：
cd libai
export PYTHON3_LIB_PATH=/python_lib_path
virtualenv -p /python_bin_path/python3 --system-site-packages venv_oneflow
source env.sh #进入venv_oneflow虚拟环境
pip3 install --upgrade pip -i https://mirrors.aliyun.com/pypi/simple #更新pip
pip3 install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple
pip3 install pybind11 -i https://mirrors.aliyun.com/pypi/simple
pip3 install -e . -i https://mirrors.aliyun.com/pypi/simple
pip3 install oneflow-0.9.1+dtk2210.git.8ea46d6-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
### 训练
该预训练脚本需要24个节点，每节点4张DCU-Z100-16G。
混合并行配置策略在configs/gpt2-13B_pretrain.py中，使用自动混合精度：
```
train.amp.enabled = True
train.train_micro_batch_size = 2
train.num_accumulation_steps = 4
train.activation_checkpoint.enabled = True
train.zero_optimization.enabled = True
train.zero_optimization.stage = 1
train.dist.data_parallel_size = 6
train.dist.tensor_parallel_size = 4
train.dist.pipeline_parallel_size = 4
```
进入登陆节点，预训练命令：
cd libai
source submit_job.sh
tail -f log/xxx.out.log #查看输出log
tail -f log/xxx.err.log #查看错误log
### 性能和收敛性
训练数据：[https://oneflow-static.oss-cn-beijing.aliyuncs.com/ci-files/dataset/libai/gpt_dataset](链接)
使用的GPGPU：96张DCU-Z100-16G。
模型性能及收敛性：
| 卡数 | 分布式工具 | 性能 | 收敛性 |
| :------: | :------: | :------: |:------: |
| 96 | Libai-main | 2.27 samples/s | total_loss: 5.56/1299 iters |
## 参考
* https://libai.readthedocs.io/en/latest/tutorials/get_started/quick_run.html
* https://github.com/Oneflow-Inc/oneflow
* https://github.com/Oneflow-Inc/libai/blob/main/docs/source/notes/FAQ.md
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## Changelog
### Beta 0.1.0 (22/03/2022)
**New Features:**
- Support Data Parallelism
- Support 1D Tensor Parallelism
- Support Pipeline Parallelism
- Unified distributed Layers for both single-GPU and multi-GPU training
- `LazyConfig` system for more flexible syntax and no predefined structures
- Easy-to-use trainer and engine
- Support both CV and NLP data processing
- Mixed Precision Training
- Activation Checkpointing
- Gradient Accumulation
- Gradient Clipping
- Zero Redundancy Optimizer (ZeRO)
**Supported Models:**
- Support 3D parallel [BERT](https://arxiv.org/abs/1810.04805) model
- Support 3D parallel [GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) model
- Support 3D parallel [T5](https://arxiv.org/abs/1910.10683) model
- Support 3D parallel [Vision Transformer](https://arxiv.org/abs/2010.11929)
- Support Data parallel [Swin Transformer](https://arxiv.org/abs/2103.14030) model
- Support finetune task in [QQP project](/projects/QQP/)
- Support text classification task in [text classification project](/projects/text_classification/)
configs/bert_classification.py 0 → 100644
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from libai.config import LazyCall
from libai.models.bert_model import BertForClassification
from .common.models.bert import cfg as bert_cfg
from .common.models.graph import graph
from .common.train import train
from .common.optim import optim
from .common.data.bert_dataset import tokenization, dataloader
vocab_file = "./data_test/bert_data/bert-base-chinese-vocab.txt"
data_prefix = "./data_test/bert_data/loss_compara_content_sentence"
dataloader.train.dataset[0].data_prefix = data_prefix
dataloader.train.dataset[0].indexed_dataset.data_prefix = data_prefix
bert_cfg.num_labels = 2
bert_cfg.classifier_dropout = 0.1
model = LazyCall(BertForClassification)(cfg=bert_cfg)
tokenization.tokenizer.vocab_file = vocab_file
model.cfg.vocab_size = 21128
model.cfg.intermediate_size = 3072
model.cfg.num_attention_heads = 12
model.cfg.hidden_layers = 12
model.cfg.hidden_size = 768
train.amp.enabled = True
train.activation_checkpoint.enabled = True
train.dist.pipeline_num_layers = model.cfg.hidden_layers
train.output_dir = "output/bert_classification_output"
configs/bert_large_pretrain.py 0 → 100644
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from libai.config import LazyCall
from libai.evaluation import PPLEvaluator
from .common.models.bert import pretrain_model as model
from .common.models.graph import graph
from .common.train import train
from .common.optim import optim
from .common.data.bert_dataset import dataloader, tokenization
vocab_file = "./nlp_data/bert-base-chinese-vocab.txt"
data_prefix = "./nlp_data/data/loss_compara_content_sentence"
tokenization.tokenizer.vocab_file = vocab_file
dataloader.train.dataset[0].data_prefix = data_prefix
dataloader.train.dataset[0].indexed_dataset.data_prefix = data_prefix
# Bert-large model config
model.cfg.num_attention_heads = 40
model.cfg.hidden_size = 5120
model.cfg.intermediate_size = 5120*4
model.cfg.hidden_layers = 20
train.input_placement_device = "cpu"
train.dist.pipeline_num_layers = model.cfg.hidden_layers
train.amp.enabled = True
graph.auto_parallel.enabled = False
train.train_micro_batch_size = 8
train.num_accumulation_steps = 4
train.activation_checkpoint.enabled = True
train.zero_optimization.enabled = True
train.zero_optimization.stage = 1
train.dist.data_parallel_size = 12
train.dist.tensor_parallel_size = 4
train.dist.pipeline_parallel_size = 2
train.dist.custom_pipeline_stage_id = [0]*8 + [1]*12
for ds in dataloader.train.dataset:
ds.max_seq_length = model.cfg.max_position_embeddings
train.evaluation.evaluator = LazyCall(PPLEvaluator)()
train.output_dir = "output/bert_output"
configs/common/data/bert_dataset.py 0 → 100644
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from libai.config import LazyCall
from omegaconf import OmegaConf
from libai.data import build_nlp_test_loader, build_nlp_train_val_test_loader
from libai.data.datasets import BertDataset
from libai.data.data_utils import get_indexed_dataset
from libai.tokenizer import BertTokenizer
tokenization = OmegaConf.create()
tokenization.tokenizer = LazyCall(BertTokenizer)(
vocab_file="bert-base-chinese-vocab.txt",
do_lower_case=True,
do_chinese_wwm=True,
)
tokenization.append_eod = False
tokenization.make_vocab_size_divisible_by = 128
dataloader = OmegaConf.create()
dataloader.train = LazyCall(build_nlp_train_val_test_loader)(
dataset=[
LazyCall(BertDataset)(
name="bert",
data_prefix="/workspace/data/libai_dataset/loss_compara_content_sentence",
indexed_dataset=LazyCall(get_indexed_dataset)(
data_prefix="/workspace/data/libai_dataset/loss_compara_content_sentence",
data_impl="mmap",
skip_warmup=False,
),
max_seq_length=512,
mask_lm_prob=0.15,
short_seq_prob=0.1,
binary_head=True,
seed=1234,
masking_style="bert-cn-wwm",
),
],
train_val_test_num_samples=None, # a hint for deferred assignment
splits=[[949.0, 50.0, 1.0]],
weights=[1.0],
num_workers=4,
)
configs/common/data/cifar100.py 0 → 100644
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from omegaconf import OmegaConf
from flowvision import transforms
from flowvision.data.mixup import Mixup
from flowvision.transforms import InterpolationMode
from flowvision.transforms.functional import str_to_interp_mode
from libai.data.datasets import CIFAR100Dataset
from libai.data.build import build_image_train_loader, build_image_test_loader
from libai.config import LazyCall
# mean and std of cifar100 dataset
CIFAR100_TRAIN_MEAN = (0.5070751592371323, 0.48654887331495095, 0.4409178433670343)
CIFAR100_TRAIN_STD = (0.2673342858792401, 0.2564384629170883, 0.27615047132568404)
train_aug = LazyCall(transforms.Compose)(
transforms=[
LazyCall(transforms.RandomResizedCrop)(
size=(224, 224),
scale=(0.08, 1.0),
ratio=(3.0 / 4.0, 4.0 / 3.0),
interpolation=str_to_interp_mode("bicubic"),
),
LazyCall(transforms.RandomHorizontalFlip)(),
LazyCall(transforms.ToTensor)(),
LazyCall(transforms.Normalize)(mean=CIFAR100_TRAIN_MEAN, std=CIFAR100_TRAIN_STD),
]
)
test_aug = LazyCall(transforms.Compose)(
transforms=[
LazyCall(transforms.Resize)(
size=256,
interpolation=InterpolationMode.BICUBIC,
),
LazyCall(transforms.CenterCrop)(
size=224,
),
LazyCall(transforms.ToTensor)(),
LazyCall(transforms.Normalize)(
mean=CIFAR100_TRAIN_MEAN,
std=CIFAR100_TRAIN_STD,
),
]
)
# Dataloader config
dataloader = OmegaConf.create()
dataloader.train = LazyCall(build_image_train_loader)(
dataset=[
LazyCall(CIFAR100Dataset)(
root="./",
train=True,
download=True,
transform=train_aug,
),
],
num_workers=4,
mixup_func=LazyCall(Mixup)(
mixup_alpha=0.8,
cutmix_alpha=1.0,
prob=1.0,
switch_prob=0.5,
mode="batch",
num_classes=100,
),
)
dataloader.test = [
LazyCall(build_image_test_loader)(
dataset=LazyCall(CIFAR100Dataset)(
root="./",
train=False,
download=True,
transform=test_aug,
),
num_workers=4,
)
]
configs/common/data/gpt_dataset.py 0 → 100644
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from libai.config import LazyCall
from omegaconf import OmegaConf
from libai.data import build_nlp_test_loader, build_nlp_train_val_test_loader
from libai.data.datasets import GPT2Dataset
from libai.data.data_utils import get_indexed_dataset
from libai.tokenizer import GPT2Tokenizer
tokenization = OmegaConf.create()
tokenization.tokenizer = LazyCall(GPT2Tokenizer)(
vocab_file="/workspace/data/gpt_dataset/gpt2-vocab.json",
merges_file="/workspace/data/gpt_dataset/gpt2-merges.txt",
do_lower_case=True,
do_chinese_wwm=True,
)
tokenization.append_eod = False
tokenization.make_vocab_size_divisible_by = 128
dataloader = OmegaConf.create()
dataloader.train = LazyCall(build_nlp_train_val_test_loader)(
dataset=[
LazyCall(GPT2Dataset)(
name="gpt-2",
data_prefix="/workspace/data/libai_dataset/loss_compara_content_sentence",
indexed_dataset=LazyCall(get_indexed_dataset)(
data_prefix="/workspace/data/libai_dataset/loss_compara_content_sentence",
data_impl="mmap",
skip_warmup=False,
),
max_seq_length=1024,
seed=1234,
),
],
train_val_test_num_samples=None, # a hint for deferred assignment
splits=[[949.0, 50.0, 1.0]],
weights=[1.0],
num_workers=4,
)
configs/common/data/imagenet.py 0 → 100644
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from omegaconf import OmegaConf
from flowvision import transforms
from flowvision.transforms import InterpolationMode
from flowvision.transforms.functional import str_to_interp_mode
from flowvision.data.constants import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
)
from flowvision.data.auto_augment import rand_augment_transform
from flowvision.data.random_erasing import RandomErasing
from libai.config import LazyCall
from libai.data.datasets import ImageNetDataset
from libai.data.build import build_image_train_loader, build_image_test_loader
train_aug = LazyCall(transforms.Compose)(
transforms=[
LazyCall(transforms.RandomResizedCrop)(
size=224,
scale=(0.08, 1.0),
ratio=(3.0 / 4.0, 4.0 / 3.0),
interpolation=InterpolationMode.BICUBIC,
),
LazyCall(transforms.RandomHorizontalFlip)(p=0.5),
LazyCall(rand_augment_transform)(
config_str="rand-m9-mstd0.5-inc1",
hparams=dict(
translate_const=int(224 * 0.45),
img_mean=tuple([min(255, round(255 * x)) for x in IMAGENET_DEFAULT_MEAN]),
interpolation=str_to_interp_mode("bicubic"),
),
),
LazyCall(transforms.ToTensor)(),
LazyCall(transforms.Normalize)(
mean=IMAGENET_DEFAULT_MEAN,
std=IMAGENET_DEFAULT_STD,
),
LazyCall(RandomErasing)(
probability=0.25,
mode="pixel",
max_count=1,
num_splits=0,
device="cpu",
),
]
)
test_aug = LazyCall(transforms.Compose)(
transforms=[
LazyCall(transforms.Resize)(
size=256,
interpolation=InterpolationMode.BICUBIC,
),
LazyCall(transforms.CenterCrop)(
size=224,
),
LazyCall(transforms.ToTensor)(),
LazyCall(transforms.Normalize)(
mean=IMAGENET_DEFAULT_MEAN,
std=IMAGENET_DEFAULT_STD,
),
]
)
dataloader = OmegaConf.create()
dataloader.train = LazyCall(build_image_train_loader)(
dataset=[
LazyCall(ImageNetDataset)(
root="./dataset",
train=True,
transform=train_aug,
),
],
num_workers=4,
mixup_func=None,
)
dataloader.test = [
LazyCall(build_image_test_loader)(
dataset=LazyCall(ImageNetDataset)(
root="./dataset",
train=False,
transform=test_aug,
),
num_workers=4,
)
]
configs/common/data/roberta_dataset.py 0 → 100644
View file @ 3b355d3f
from libai.config import LazyCall
from omegaconf import OmegaConf
from libai.data import build_nlp_test_loader, build_nlp_train_val_test_loader
from libai.data.datasets import RobertaDataset
from libai.data.data_utils import get_indexed_dataset
from libai.tokenizer import RobertaTokenizer
tokenization = OmegaConf.create()
tokenization.tokenizer = LazyCall(RobertaTokenizer)(
vocab_file="roberta-vocab.json", merges_file="roberta-merges.txt"
)
tokenization.append_eod = False
tokenization.make_vocab_size_divisible_by = 128
dataloader = OmegaConf.create()
dataloader.train = LazyCall(build_nlp_train_val_test_loader)(
dataset=[
LazyCall(RobertaDataset)(
name="roberta",
data_prefix="/workspace/data/libai_dataset/loss_compara_content_sentence",
indexed_dataset=LazyCall(get_indexed_dataset)(
data_prefix="/workspace/data/libai_dataset/loss_compara_content_sentence",
data_impl="mmap",
skip_warmup=False,
),
max_seq_length=512,
mask_lm_prob=0.15,
short_seq_prob=0.0,
seed=1234,
masking_style="bert",
),
],
train_val_test_num_samples=None, # a hint for deferred assignment
splits=[[949.0, 50.0, 1.0]],
weights=[1.0],
num_workers=4,
)
configs/common/data/t5_dataset.py 0 → 100644
View file @ 3b355d3f
from libai.config import LazyCall
from omegaconf import OmegaConf
from libai.data import build_nlp_test_loader, build_nlp_train_val_test_loader
from libai.data.datasets import T5Dataset
from libai.data.data_utils import get_indexed_dataset
from libai.tokenizer import BertTokenizer
tokenization = OmegaConf.create()
tokenization.setup = True
special_tokens = []
for i in range(100):
special_tokens.append(f"<extra_id_{i}>")
tokenization.tokenizer = LazyCall(BertTokenizer)(
vocab_file="/workspace/data/libai_dataset/bert-base-chinese-vocab.txt",
do_lower_case=True,
do_chinese_wwm=True,
bos_token="[BOS]",
eos_token="[EOS]",
additional_special_tokens=special_tokens,
)
tokenization.append_eod = False
tokenization.make_vocab_size_divisible_by = 128
dataloader = OmegaConf.create()
dataloader.train = LazyCall(build_nlp_train_val_test_loader)(
dataset=[
LazyCall(T5Dataset)(
name="t5",
data_prefix="/workspace/data/libai_dataset/loss_compara_content_sentence",
indexed_dataset=LazyCall(get_indexed_dataset)(
data_prefix="/workspace/data/libai_dataset/" "/loss_compara_content_sentence",
data_impl="mmap",
skip_warmup=False,
),
max_seq_length=512,
max_seq_length_dec=128,
masked_lm_prob=0.15,
short_seq_prob=0.1,
seed=1234,
),
],
train_val_test_num_samples=None, # a hint for deferred assignment
splits=[[949.0, 50.0, 1.0]],
weights=[1.0],
num_workers=4,
)
configs/common/models/bert.py 0 → 100644
View file @ 3b355d3f
from omegaconf import DictConfig
from libai.config import LazyCall
from libai.models import BertModel, BertForPreTraining
cfg = dict(
vocab_size=30522,
hidden_size=768,
hidden_layers=24,
num_attention_heads=12,
intermediate_size=4096,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
num_tokentypes=2,
add_pooling_layer=True,
initializer_range=0.02,
layernorm_eps=1e-5,
bias_gelu_fusion=True,
bias_dropout_fusion=True,
scale_mask_softmax_fusion=True,
apply_query_key_layer_scaling=True,
apply_residual_post_layernorm=False,
add_binary_head=True,
amp_enabled=False,
)
cfg = DictConfig(cfg)
bert_model = LazyCall(BertModel)(cfg=cfg)
pretrain_model = LazyCall(BertForPreTraining)(cfg=cfg)
configs/common/models/gpt.py 0 → 100644
View file @ 3b355d3f
from omegaconf import DictConfig
from libai.config import LazyCall
from libai.models import GPTModel, GPTForPreTraining
cfg = dict(
hidden_layers=6,
vocab_size=30522,
hidden_size=384,
ffn_hidden_size=1536,
num_attention_heads=12,
max_seq_length=1024,
embedding_dropout_prob=0,
attention_dropout_prob=0,
output_dropout_prob=0,
layernorm_epsilon=1e-5,
initializer_range=0.02,
use_scaled_init_for_output_weights=True,
bias_gelu_fusion=True,
bias_dropout_fusion=True,
scale_mask_softmax_fusion=True,
apply_query_key_layer_scaling=True,
apply_residual_post_layernorm=False,
amp_enabled=False,
)
cfg = DictConfig(cfg)
gpt_model = LazyCall(GPTModel)(cfg=cfg)
pretrain_model = LazyCall(GPTForPreTraining)(cfg=cfg)
configs/common/models/graph.py 0 → 100644
View file @ 3b355d3f
from omegaconf import DictConfig
from libai.config import LazyCall
from libai.models.utils import GraphBase
graph = dict(
# options for graph or eager mode
enabled=True,
debug=-1, # debug mode for graph
auto_parallel=dict(
enabled=False,
enable_auto_parallel_ignore_user_sbp_config=False, # ignore all .to_global() in graph
trunk_algo=True, # consider overlapping calculate time and transfer time
sbp_collector=False, # use proxy node when one node transfer to many nodes
),
train_graph=LazyCall(GraphBase)(
is_train=True,
),
eval_graph=LazyCall(GraphBase)(is_train=False),
)
graph = DictConfig(graph)
configs/common/models/resmlp/resmlpB_24.py 0 → 100644
View file @ 3b355d3f
from libai.config import LazyCall
from libai.models import ResMLP
from .resmlp_12 import cfg
cfg.patch_size = 8
cfg.embed_dim = 768
cfg.depth = 24
cfg.init_scale = 1e-6
model = LazyCall(ResMLP)(cfg=cfg)
configs/common/models/resmlp/resmlp_12.py 0 → 100644
View file @ 3b355d3f
from omegaconf import DictConfig
from libai.config import LazyCall
from libai.models import ResMLP
cfg = dict(
img_size=224,
patch_size=16,
in_chans=3,
embed_dim=384,
depth=12,
drop_rate=0.0,
drop_path_rate=0.05,
init_scale=0.1,
num_classes=1000,
loss_func=None,
)
cfg = DictConfig(cfg)
model = LazyCall(ResMLP)(cfg=cfg)
configs/common/models/resmlp/resmlp_24.py 0 → 100644
View file @ 3b355d3f
from libai.config import LazyCall
from libai.models import ResMLP
from .resmlp_12 import cfg
cfg.depth = 24
cfg.init_scale = 1e-5
model = LazyCall(ResMLP)(cfg=cfg)
configs/common/models/resmlp/resmlp_36.py 0 → 100644
View file @ 3b355d3f
from libai.config import LazyCall
from libai.models import ResMLP
from .resmlp_12 import cfg
cfg.depth = 36
cfg.init_scale = 1e-6
model = LazyCall(ResMLP)(cfg=cfg)
configs/common/models/roberta.py 0 → 100644
View file @ 3b355d3f
from omegaconf import DictConfig
from libai.config import LazyCall
from libai.models import RobertaModel, RobertaForPreTraining, RobertaForCausalLM
cfg = dict(
vocab_size=50265,
hidden_size=768,
hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=514,
num_tokentypes=1,
add_pooling_layer=True,
initializer_range=0.02,
layernorm_eps=1e-5,
pad_token_id=1,
bias_gelu_fusion=True,
bias_dropout_fusion=True,
scale_mask_softmax_fusion=True,
apply_query_key_layer_scaling=True,
apply_residual_post_layernorm=False,
amp_enabled=False,
)
cfg = DictConfig(cfg)
roberta_model = LazyCall(RobertaModel)(cfg=cfg)
roberta_causal_lm = LazyCall(RobertaForCausalLM)(cfg=cfg)
pretrain_model = LazyCall(RobertaForPreTraining)(cfg=cfg)
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