Skip to content

GitLab

"examples/vscode:/vscode.git/clone" did not exist on "ce144d6dd05c4c588d9f2970301ace70eee16d5d"
Commit 5988d2cc authored by yuguo960516's avatar yuguo960516
Browse files

bert-large

parent 478602ba
Pipeline #142 canceled with stages
Hide whitespace changes
Inline Side-by-side
Showing with 825 additions and 25 deletions
CONTRIBUTING.md 0 → 100644
View file @ 5988d2cc
## Contributing to LiBai
\ No newline at end of file
LICENSE
View file @ 5988d2cc
BSD 2-Clause License Apache License
Version 2.0, January 2004
Copyright (c) 2023, yuguo960516yuguo http://www.apache.org/licenses/
All rights reserved.
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met: 1. Definitions.
1. Redistributions of source code must retain the above copyright notice, this "License" shall mean the terms and conditions for use, reproduction,
list of conditions and the following disclaimer. and distribution as defined by Sections 1 through 9 of this document.
2. Redistributions in binary form must reproduce the above copyright notice, "Licensor" shall mean the copyright owner or entity authorized by
this list of conditions and the following disclaimer in the documentation the copyright owner that is granting the License.
and/or other materials provided with the distribution.
"Legal Entity" shall mean the union of the acting entity and all
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" other entities that control, are controlled by, or are under common
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE control with that entity. For the purposes of this definition,
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE "control" means (i) the power, direct or indirect, to cause the
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE direction or management of such entity, whether by contract or
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL otherwise, or (ii) ownership of fifty percent (50%) or more of the
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR outstanding shares, or (iii) beneficial ownership of such entity.
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, "You" (or "Your") shall mean an individual or Legal Entity
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE exercising permissions granted by this License.
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
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.
changelog.md 0 → 100644
View file @ 5988d2cc
## 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
View file @ 5988d2cc
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
View file @ 5988d2cc
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 = 16
model.cfg.hidden_size = 768
model.cfg.hidden_layers = 8
train.input_placement_device = "cpu"
train.dist.pipeline_num_layers = model.cfg.hidden_layers
train.train_micro_batch_size = 16
train.amp.enabled = True
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
View file @ 5988d2cc
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
View file @ 5988d2cc
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
View file @ 5988d2cc
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
View file @ 5988d2cc
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 @ 5988d2cc
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 @ 5988d2cc
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 @ 5988d2cc
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 @ 5988d2cc
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 @ 5988d2cc
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 @ 5988d2cc
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 @ 5988d2cc
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 @ 5988d2cc
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 @ 5988d2cc
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 @ 5988d2cc
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)
configs/common/models/swin/swin_base_patch4_window12_384.py 0 → 100644
View file @ 5988d2cc
from libai.config import LazyCall
from libai.models import SwinTransformer
from .swin_tiny_patch4_window7_224 import cfg
cfg.img_size = 384
cfg.embed_dim = 128
cfg.depths = [2, 2, 18, 2]
cfg.num_heads = [4, 8, 16, 32]
cfg.drop_path_rate = 0.1
model = LazyCall(SwinTransformer)(cfg=cfg)
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment