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## Contributing to LiBai
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README.md 0 → 100644
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# BigScience Large Open-science Open-access Multilingual Language Model(BLOOM)
## 模型介绍
Bloom是一个开源的支持最多59种语言和176B参数的大语言模型。它是在Megatron-LM GPT2的基础上修改训练出来的，主要使用了解码器唯一结构，对词嵌入层的归一化，使用GeLU激活函数的线性偏差注意力位置编码等技术。它的训练集包含了45种自然语言和12种编程语言，1.5TB的预处理文本转化为了350B的唯一token。bigscience在hugging face上发布的bloom模型包含多个参数多个版本。
## BLOOM-Inference
当模型规模过于庞大，单个 GPU 设备无法容纳大规模模型参数时，便捷好用的分布式训练和推理需求就相继出现，业内也随之推出相应的工具。
基于 OneFlow 构建的 LiBai 模型库让分布式上手难度降到最低，用户不需要关注模型如何分配在不同的显卡设备，只需要修改几个配置数据就可以设置不同的分布式策略。当然，加速性能更是出众。
用 LiBai 搭建的 BLOOM可以便捷地实现model parallel + pipeline parallel推理, 很好地解决单卡放不下大规模模型的问题。
### 分布式推理具有天然优势
要知道，模型的参数其实就是许多 tensor，也就是以矩阵的形式出现，大模型的参数也就是大矩阵，并行策略就是把大矩阵分为多个小矩阵，并分配到不同的显卡或不同的设备上，基础的 LinearLayer 在LiBai中的实现代码如下：
```python
class Linear1D(nn.Module):
def __init__(self, in_features, out_features, parallel="data", layer_idx=0, ...):
super().__init__()
if parallel == "col":
weight_sbp = dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.split(0)])
elif parallel == "row":
weight_sbp = dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.split(1)])
elif parallel == "data":
weight_sbp = dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.broadcast])
else:
raise KeyError(f"{parallel} is not supported! Only support ('data', 'row' and 'col')")
self.weight = flow.nn.Parameter(
flow.empty(
(out_features, in_features),
dtype=flow.float32,
placement=dist.get_layer_placement(layer_idx), # for pipeline parallelism placement
sbp=weight_sbp,
)
)
init_method(self.weight)
...
def forward(self, x):
...
```
在这里，用户可选择去如何切分 Linear 层的矩阵，如何切分数据矩阵，而OneFlow 中的 SBP 控制竖着切、横着切以及其他拆分矩阵的方案（模型并行、数据并行），以及通过设置 Placement 来控制这个 LinearLayer 是放在第几张显卡上（流水并行）。
所以，根据 LiBai 中各种 layer 的设计原理以及基于 OneFlow 中 tensor 自带的 SBP 和 Placement 属性的天然优势，使得用户搭建的模型能够很简单地就实现数据并行、模型并行以及流水并行操作。
## BLOOMZ-7B1推理
### 环境配置
提供[光源](https://www.sourcefind.cn/#/service-details)拉取的训练以及推理的docker镜像：image.sourcefind.cn:5000/dcu/admin/base/oneflow:0.9.1-centos7.6-dtk-22.10.1-py39-latest
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
需要先准备好模型权重：https://huggingface.co/bigscience/bloomz-7b1/tree/main
### bloomz-7b1的文件结构
```python
$ tree data
path/to/bloomz-7b1
├── tokenizer_config.json
├── tokenizer.json
├── special_tokens_map.json
├── config.json
└── pytorch_model.bin
```
### 推理
采用1节点，4张DCU-Z100-16G，采用tp=4，pp=1的并行配置。
运行以下代码：
cd projects/BLOOM
# 运行前修改 configs/bloom_inference.py 中 `min_length=64`
python3 -m oneflow.distributed.launch --nproc_per_node 4 demo.py
demo.py如下：
# model parallel + pipeline parallel demo
import oneflow as flow
from omegaconf import DictConfig
from transformers import BloomTokenizerFast
from libai.utils import distributed as dist
from projects.BLOOM.configs.bloom_inference import cfg
from projects.BLOOM.modeling.bloom_model import BloomForCausalLM
from projects.BLOOM.utils.model_loader import BlooMLoaderHuggerFace
import time
parallel_config = DictConfig(
dict(
data_parallel_size=1,
tensor_parallel_size=4,
pipeline_parallel_size=1,
pipeline_num_layers=30,
)
)
dist.setup_dist_util(parallel_config)
tokenizer = BloomTokenizerFast.from_pretrained("bloomz-7b1")
res = tokenizer("How to improve sleep quality?")
inputs = {
"input_ids": flow.tensor([res.input_ids]),
"attention_mask": flow.tensor([res.attention_mask]),
}
sbp = dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.broadcast])
placement = dist.get_layer_placement(0)
loader = BlooMLoaderHuggerFace(BloomForCausalLM, cfg, "bloomz-7b1")
model = loader.load()
start_t = time.time()
outputs = model.generate(
inputs=inputs["input_ids"].to_global(sbp=sbp, placement=placement), max_length=128
)
end_t = time.time()
if dist.is_main_process():
print('model.generate: %s秒' % (end_t - start_t))
res = tokenizer.decode(outputs[0])
if dist.is_main_process():
print(res)
输出：
```
>>>model.generate: 50.380842208862305秒
How to improve sleep quality? keep your bedroom dark and quiet. Avoid electronics and bright lights. Keep your bedroom cool. Use a white noise machine. Use a humidifier. Use a diffuser. Use essential oils. Use a sleep aid. Try acupuncture. Try hypnotherapy. Try acupressure.</s>
```
## 性能数据
使用的加速卡：4张DCU-Z100-16G：
| bs | max_input_length | min_gen_length | model.generate耗时/s |
| :------: | :------: | :------: | :------: |
| 1 | 512 | 64 | 50.3 |
## 参考
* https://github.com/Oneflow-Inc/libai
* https://huggingface.co/bigscience/bloomz
changelog.md 0 → 100644
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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 = "./data_test/bert_data/bert-base-chinese-vocab.txt"
data_prefix = "./data_test/bert_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
dataloader.test[0].dataset.data_prefix = data_prefix
dataloader.test[0].dataset.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
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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,
)
dataloader.test = [
LazyCall(build_nlp_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_num_samples=10,
max_seq_length=512,
mask_lm_prob=0.15,
short_seq_prob=0.1,
binary_head=True,
seed=1234,
masking_style="bert-cn-wwm",
),
test_batch_size=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,
)
dataloader.test = [
LazyCall(build_nlp_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,
max_num_samples=10,
seed=1234,
),
test_batch_size=4,
)
]
configs/common/data/imagenet.py 0 → 100644
View file @ 9fdb7dab
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 @ 9fdb7dab
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=514,
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,
)
dataloader.test = [
LazyCall(build_nlp_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_num_samples=10,
max_seq_length=514,
mask_lm_prob=0.15,
short_seq_prob=0.1,
seed=1234,
masking_style="bert",
),
test_batch_size=4,
)
]
configs/common/data/t5_dataset.py 0 → 100644
View file @ 9fdb7dab
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,
)
dataloader.test = [
LazyCall(build_nlp_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_num_samples=10,
max_seq_length=512,
max_seq_length_dec=128,
masked_lm_prob=0.15,
short_seq_prob=0.1,
seed=1234,
),
test_batch_size=4,
)
]
configs/common/models/bert.py 0 → 100644
View file @ 9fdb7dab
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 @ 9fdb7dab
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 @ 9fdb7dab
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 @ 9fdb7dab
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 @ 9fdb7dab
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 @ 9fdb7dab
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 @ 9fdb7dab
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 @ 9fdb7dab
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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