直接使用

请打开基于基于EasyNLP-Diffusion模型的中文文图生成，并点击右上角 “ 在DSW中打开” 。

基于EasyNLP-Diffusion模型的中文文图生成

EasyNLP是阿里云机器学习PAI算法团队基于PyTorch开发的易用且丰富的NLP算法框架( https://github.com/alibaba/EasyNLP )，支持常用的中文预训练模型和大模型落地技术，并且提供了从训练到部署的一站式NLP开发体验。EasyNLP提供了简洁的接口供用户开发NLP模型，包括NLP应用AppZoo和预训练ModelZoo，同时提供技术帮助用户高效地落地超大预训练模型到业务，旨在帮助自然语言开发者方便快捷地构建模型并应用于生产。由于跨模态理解需求的不断增加，EasyNLP也将支持各种跨模态模型，特别是中文领域的跨模态模型，希望能够服务更多的NLP和多模态算法开发者和研究者。

用户生成内容（User Generated Content，UGC）是互联网上多模态内容的重要组成部分，UGC数据级的不断增长促进了各大多模态内容平台的繁荣。在海量多模态数据和深度学习大模型的加持下，AI生成内容（AI Generated Content，AIGC）呈现出爆发性增长趋势。其中，文图生成（Text-to-image Generation）任务是流行的跨模态生成任务，旨在生成与给定文本对应的图像。典型的文图模型例如OpenAI开发的DALL-E和DALL-E2。近期，业界也训练出了更大、更新的文图生成模型，例如Google提出的Parti和Imagen，基于扩散模型的Stable Diffusion等。

PAI-Diffusion模型详解

由于StableDiffusion模型主要使用英文数据进行训练，如果直接使用机器翻译将英文数据翻译成中文进行模型训练，因为中英文在文化和表达上具有很大的差异性，产出的模型通常无法建模中文特有的现象。此外，通用的StableDiffusion模型由于数据源的限制，很难用于生成特定领域、特定场景下的高清图片。PAI-Diffusion系列模型由阿里云机器学习（PAI）团队发布并开源，除了可以用于通用文图生成场景，还具有一系列特定场景的定制化中文Diffusion模型，包括古诗配图、二次元动漫、魔幻现实、世界美食等。

PAI-Diffusion模型Pipeline，分为四部分：

Text Encoder：把中文文本输入转化成 Embedding 向量，我们采用EasyNLP中文CLIP跨模态对齐模型的Text Transformer作为Text Encoder； Latent Diffusion Model：在 Latent 空间中根据文本输入处理随机生成的噪声； Auto Encoder：将 Latent 空间中的张量还原为图片； Super Resolution Model：提升图片分辨率，这里我们使用ESRGAN作为图像超分模型。

我们在使用Wukong数据集中的两千万中文图文数据对 Latent Diffusion Model部分进行了约 20 天的预训练，随后在多个下游数据集中进行了微调。本文将为您介绍如何在PAI-DSW中基于EasyNLP快速使用Diffusion进行模型构建、训练、评估、预测。

运行环境要求

建议用户使用：Python 3.6，Pytorch 1.8镜像，GPU机型V100，内存至少为 32G

EasyNLP安装

建议从GitHub下载EasyNLP源代码进行安装，命令如下：

! echo y | pip uninstall pai-easynlp easynlp
! git clone https://github.com/alibaba/EasyNLP.git
! pip install -r EasyNLP/requirements.txt -i http://mirrors.aliyun.com/pypi/simple
! cd EasyNLP && python setup.py install

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?25h  Created wheel for bs4: filename=bs4-0.0.1-py3-none-any.whl size=1272 sha256=2b3f8fdf4dee516e54d6b3886b5419b4ec216bbcb148fa1f910bebd20450868e
  Stored in directory: /root/.cache/pip/wheels/e9/89/8c/4629169599fe73b70cd41effaf7d0369618a52d0dc5501884b
  Building wheel for jieba (setup.py) ... ?25ldone
?25h  Created wheel for jieba: filename=jieba-0.42.1-py3-none-any.whl size=19314478 sha256=d70a23071a949581b659c6c81dfc76b6e539610ce50d0b5983176b8aa2dfd82a
  Stored in directory: /root/.cache/pip/wheels/da/32/88/27de7b74c07183e879d53a8c97dbf4aaff183483941de4236d
  Building wheel for ftfy (setup.py) ... ?25ldone
?25h  Created wheel for ftfy: filename=ftfy-6.0.3-py3-none-any.whl size=41933 sha256=11794667ae8f31aeaebc29b55e0499b2560fe89feb4aefba55ae20b2612e3598
  Stored in directory: /root/.cache/pip/wheels/10/77/20/74d777f466f7e44c638fd47fd7eaf229d75bf49c645969141a
Successfully built bs4 jieba ftfy
Installing collected packages: scipy, soupsieve, fsspec, dill, xxhash, pyarrow, multiprocess, huggingface-hub, beautifulsoup4, sentencepiece, rouge, jieba, ftfy, datasets, bs4
  Attempting uninstall: scipy
    Found existing installation: scipy 1.5.3
    Uninstalling scipy-1.5.3:
      Successfully uninstalled scipy-1.5.3
  Attempting uninstall: dill
    Found existing installation: dill 0.2.9
    Uninstalling dill-0.2.9:
      Successfully uninstalled dill-0.2.9
  Attempting uninstall: pyarrow
    Found existing installation: pyarrow 0.13.0
    Uninstalling pyarrow-0.13.0:
      Successfully uninstalled pyarrow-0.13.0
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
pyalink-public 1.5.1 requires cloudpickle==1.2.2, but you have cloudpickle 2.1.0 which is incompatible.
pyalink-public 1.5.1 requires pandas<1.0.0,>=0.24.0, but you have pandas 1.1.5 which is incompatible.
easycv 0.2.2 requires Pillow<=6.2.2, but you have pillow 8.3.2 which is incompatible.
apache-flink 1.10.1 requires cloudpickle==1.2.2, but you have cloudpickle 2.1.0 which is incompatible.
apache-flink 1.10.1 requires pyarrow<0.14.0,>=0.11.1, but you have pyarrow 6.0.1 which is incompatible.
apache-flink 1.10.1 requires python-dateutil==2.8.0, but you have python-dateutil 2.8.2 which is incompatible.
apache-beam 2.15.0 requires dill<0.2.10,>=0.2.9, but you have dill 0.3.4 which is incompatible.
apache-beam 2.15.0 requires pyarrow<0.15.0,>=0.11.1; python_version >= "3.0" or platform_system != "Windows", but you have pyarrow 6.0.1 which is incompatible.
apache-beam 2.15.0 requires pyyaml<4.0.0,>=3.12, but you have pyyaml 5.4.1 which is incompatible.
Successfully installed beautifulsoup4-4.11.2 bs4-0.0.1 datasets-2.1.0 dill-0.3.4 fsspec-2022.1.0 ftfy-6.0.3 huggingface-hub-0.4.0 jieba-0.42.1 multiprocess-0.70.12.2 pyarrow-6.0.1 rouge-1.0.1 scipy-1.5.4 sentencepiece-0.1.97 soupsieve-2.3.2.post1 xxhash-3.2.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
running install
running bdist_egg
running egg_info
creating pai_easynlp.egg-info
writing pai_easynlp.egg-info/PKG-INFO
writing dependency_links to pai_easynlp.egg-info/dependency_links.txt
writing entry points to pai_easynlp.egg-info/entry_points.txt
writing requirements to pai_easynlp.egg-info/requires.txt
writing top-level names to pai_easynlp.egg-info/top_level.txt
writing manifest file 'pai_easynlp.egg-info/SOURCES.txt'
reading manifest file 'pai_easynlp.egg-info/SOURCES.txt'
adding license file 'LICENSE'
adding license file 'NOTICE'
writing manifest file 'pai_easynlp.egg-info/SOURCES.txt'
installing library code to build/bdist.linux-x86_64/egg
running install_lib
running build_py
creating build
creating build/lib
creating build/lib/easynlp
copying easynlp/__init__.py -> build/lib/easynlp
copying easynlp/cli.py -> build/lib/easynlp
creating build/lib/easynlp/modelzoo
copying easynlp/modelzoo/generation_logits_process.py -> build/lib/easynlp/modelzoo
copying easynlp/modelzoo/__init__.py -> build/lib/easynlp/modelzoo
copying easynlp/modelzoo/tokenization_utils_fast.py -> build/lib/easynlp/modelzoo
copying easynlp/modelzoo/file_utils.py -> build/lib/easynlp/modelzoo
copying easynlp/modelzoo/activations.py -> build/lib/easynlp/modelzoo
copying easynlp/modelzoo/modeling_outputs.py -> build/lib/easynlp/modelzoo
copying easynlp/modelzoo/generation_utils.py -> build/lib/easynlp/modelzoo
copying easynlp/modelzoo/generation_stopping_criteria.py -> build/lib/easynlp/modelzoo
copying easynlp/modelzoo/modeling_utils.py -> build/lib/easynlp/modelzoo
copying easynlp/modelzoo/convert_slow_tokenizer.py -> build/lib/easynlp/modelzoo
copying easynlp/modelzoo/configuration_utils.py -> build/lib/easynlp/modelzoo
copying easynlp/modelzoo/tokenization_utils.py -> build/lib/easynlp/modelzoo
copying easynlp/modelzoo/tokenization_utils_base.py -> build/lib/easynlp/modelzoo
copying easynlp/modelzoo/generation_beam_search.py -> build/lib/easynlp/modelzoo
creating build/lib/easynlp/appzoo
copying easynlp/appzoo/__init__.py -> build/lib/easynlp/appzoo
copying easynlp/appzoo/dataset.py -> build/lib/easynlp/appzoo
copying easynlp/appzoo/application.py -> build/lib/easynlp/appzoo
copying easynlp/appzoo/api.py -> build/lib/easynlp/appzoo
creating build/lib/easynlp/pipelines
copying easynlp/pipelines/implementation.py -> build/lib/easynlp/pipelines
copying easynlp/pipelines/__init__.py -> build/lib/easynlp/pipelines
creating build/lib/easynlp/fewshot_learning
copying easynlp/fewshot_learning/__init__.py -> build/lib/easynlp/fewshot_learning
copying easynlp/fewshot_learning/fewshot_dataset.py -> build/lib/easynlp/fewshot_learning
copying easynlp/fewshot_learning/fewshot_evaluator.py -> build/lib/easynlp/fewshot_learning
copying easynlp/fewshot_learning/fewshot_predictor.py -> build/lib/easynlp/fewshot_learning
copying easynlp/fewshot_learning/fewshot_application.py -> build/lib/easynlp/fewshot_learning
creating build/lib/easynlp/core
copying easynlp/core/trainer.py -> build/lib/easynlp/core
copying easynlp/core/__init__.py -> build/lib/easynlp/core
copying easynlp/core/predictor.py -> build/lib/easynlp/core
copying easynlp/core/distiller.py -> build/lib/easynlp/core
copying easynlp/core/optimizers.py -> build/lib/easynlp/core
copying easynlp/core/evaluator.py -> build/lib/easynlp/core
creating build/lib/easynlp/utils
copying easynlp/utils/random.py -> build/lib/easynlp/utils
copying easynlp/utils/global_vars.py -> build/lib/easynlp/utils
copying easynlp/utils/adapter.py -> build/lib/easynlp/utils
copying easynlp/utils/io_utils.py -> build/lib/easynlp/utils
copying easynlp/utils/arguments.py -> build/lib/easynlp/utils
copying easynlp/utils/__init__.py -> build/lib/easynlp/utils
copying easynlp/utils/logger.py -> build/lib/easynlp/utils
copying easynlp/utils/losses.py -> build/lib/easynlp/utils
copying easynlp/utils/exporter.py -> build/lib/easynlp/utils
copying easynlp/utils/grads.py -> build/lib/easynlp/utils
copying easynlp/utils/parallel_processes.py -> build/lib/easynlp/utils
copying easynlp/utils/statistics.py -> build/lib/easynlp/utils
copying easynlp/utils/initializer.py -> build/lib/easynlp/utils
creating build/lib/easynlp/distillation
copying easynlp/distillation/__init__.py -> build/lib/easynlp/distillation
copying easynlp/distillation/distill_dataset.py -> build/lib/easynlp/distillation
copying easynlp/distillation/distill_metakd_dataset.py -> build/lib/easynlp/distillation
copying easynlp/distillation/distill_application.py -> build/lib/easynlp/distillation
copying easynlp/distillation/distill_metakd_application.py -> build/lib/easynlp/distillation
creating build/lib/easynlp/modelzoo/mg_utils
copying easynlp/modelzoo/mg_utils/corpora.py -> build/lib/easynlp/modelzoo/mg_utils
copying easynlp/modelzoo/mg_utils/__init__.py -> build/lib/easynlp/modelzoo/mg_utils
copying easynlp/modelzoo/mg_utils/lazy_loader.py -> build/lib/easynlp/modelzoo/mg_utils
copying easynlp/modelzoo/mg_utils/learning_rates.py -> build/lib/easynlp/modelzoo/mg_utils
copying easynlp/modelzoo/mg_utils/configure_data.py -> build/lib/easynlp/modelzoo/mg_utils
copying easynlp/modelzoo/mg_utils/pretrain_glm.py -> build/lib/easynlp/modelzoo/mg_utils
copying easynlp/modelzoo/mg_utils/train_utils.py -> build/lib/easynlp/modelzoo/mg_utils
copying easynlp/modelzoo/mg_utils/tokenization.py -> build/lib/easynlp/modelzoo/mg_utils
copying easynlp/modelzoo/mg_utils/finetune_glm.py -> build/lib/easynlp/modelzoo/mg_utils
copying easynlp/modelzoo/mg_utils/dataset.py -> build/lib/easynlp/modelzoo/mg_utils
copying easynlp/modelzoo/mg_utils/fp16.py -> build/lib/easynlp/modelzoo/mg_utils
copying easynlp/modelzoo/mg_utils/utils.py -> build/lib/easynlp/modelzoo/mg_utils
copying easynlp/modelzoo/mg_utils/samplers.py -> build/lib/easynlp/modelzoo/mg_utils
copying easynlp/modelzoo/mg_utils/blocklm_utils.py -> build/lib/easynlp/modelzoo/mg_utils
creating build/lib/easynlp/modelzoo/models
copying easynlp/modelzoo/models/__init__.py -> build/lib/easynlp/modelzoo/models
creating build/lib/easynlp/modelzoo/utils
copying easynlp/modelzoo/utils/logging.py -> build/lib/easynlp/modelzoo/utils
copying easynlp/modelzoo/utils/__init__.py -> build/lib/easynlp/modelzoo/utils
copying easynlp/modelzoo/utils/dummy_sentencepiece_and_tokenizers_objects.py -> build/lib/easynlp/modelzoo/utils
copying easynlp/modelzoo/utils/model_parallel_utils.py -> build/lib/easynlp/modelzoo/utils
creating build/lib/easynlp/modelzoo/models/bart
copying easynlp/modelzoo/models/bart/__init__.py -> build/lib/easynlp/modelzoo/models/bart
copying easynlp/modelzoo/models/bart/tokenization_bart.py -> build/lib/easynlp/modelzoo/models/bart
copying easynlp/modelzoo/models/bart/tokenization_bart_fast.py -> build/lib/easynlp/modelzoo/models/bart
copying easynlp/modelzoo/models/bart/modeling_bart.py -> build/lib/easynlp/modelzoo/models/bart
copying easynlp/modelzoo/models/bart/configuration_bart.py -> build/lib/easynlp/modelzoo/models/bart
creating build/lib/easynlp/modelzoo/models/wukong
copying easynlp/modelzoo/models/wukong/modeling_wukong.py -> build/lib/easynlp/modelzoo/models/wukong
copying easynlp/modelzoo/models/wukong/__init__.py -> build/lib/easynlp/modelzoo/models/wukong
copying easynlp/modelzoo/models/wukong/configuration_wukong.py -> build/lib/easynlp/modelzoo/models/wukong
creating build/lib/easynlp/modelzoo/models/clip
copying easynlp/modelzoo/models/clip/modeling_openclip.py -> build/lib/easynlp/modelzoo/models/clip
copying easynlp/modelzoo/models/clip/__init__.py -> build/lib/easynlp/modelzoo/models/clip
copying easynlp/modelzoo/models/clip/openclip_tokenizer.py -> build/lib/easynlp/modelzoo/models/clip
copying easynlp/modelzoo/models/clip/modeling_clip.py -> build/lib/easynlp/modelzoo/models/clip
copying easynlp/modelzoo/models/clip/configuration_clip.py -> build/lib/easynlp/modelzoo/models/clip
copying easynlp/modelzoo/models/clip/tokenization_clip.py -> build/lib/easynlp/modelzoo/models/clip
copying easynlp/modelzoo/models/clip/modeling_chineseclip.py -> build/lib/easynlp/modelzoo/models/clip
creating build/lib/easynlp/modelzoo/models/dkplm
copying easynlp/modelzoo/models/dkplm/configuration_dkplm.py -> build/lib/easynlp/modelzoo/models/dkplm
copying easynlp/modelzoo/models/dkplm/__init__.py -> build/lib/easynlp/modelzoo/models/dkplm
copying easynlp/modelzoo/models/dkplm/modeling_dkplm.py -> build/lib/easynlp/modelzoo/models/dkplm
copying easynlp/modelzoo/models/dkplm/tokenization_dkplm_fast.py -> build/lib/easynlp/modelzoo/models/dkplm
copying easynlp/modelzoo/models/dkplm/tokenization_dkplm.py -> build/lib/easynlp/modelzoo/models/dkplm
creating build/lib/easynlp/modelzoo/models/t5
copying easynlp/modelzoo/models/t5/tokenization_t5_fast.py -> build/lib/easynlp/modelzoo/models/t5
copying easynlp/modelzoo/models/t5/__init__.py -> build/lib/easynlp/modelzoo/models/t5
copying easynlp/modelzoo/models/t5/configuration_t5.py -> build/lib/easynlp/modelzoo/models/t5
copying easynlp/modelzoo/models/t5/modeling_t5.py -> build/lib/easynlp/modelzoo/models/t5
copying easynlp/modelzoo/models/t5/tokenization_t5.py -> build/lib/easynlp/modelzoo/models/t5
creating build/lib/easynlp/modelzoo/models/kbert
copying easynlp/modelzoo/models/kbert/__init__.py -> build/lib/easynlp/modelzoo/models/kbert
copying easynlp/modelzoo/models/kbert/configuration_kbert.py -> build/lib/easynlp/modelzoo/models/kbert
copying easynlp/modelzoo/models/kbert/modeling_kbert.py -> build/lib/easynlp/modelzoo/models/kbert
copying easynlp/modelzoo/models/kbert/tokenization_kbert_fast.py -> build/lib/easynlp/modelzoo/models/kbert
copying easynlp/modelzoo/models/kbert/tokenization_kbert.py -> build/lib/easynlp/modelzoo/models/kbert
creating build/lib/easynlp/modelzoo/models/geep
copying easynlp/modelzoo/models/geep/configuration_geep.py -> build/lib/easynlp/modelzoo/models/geep
copying easynlp/modelzoo/models/geep/__init__.py -> build/lib/easynlp/modelzoo/models/geep
copying easynlp/modelzoo/models/geep/modeling_geep.py -> build/lib/easynlp/modelzoo/models/geep
creating build/lib/easynlp/modelzoo/models/pegasus
copying easynlp/modelzoo/models/pegasus/__init__.py -> build/lib/easynlp/modelzoo/models/pegasus
copying easynlp/modelzoo/models/pegasus/modeling_pegasus.py -> build/lib/easynlp/modelzoo/models/pegasus
copying easynlp/modelzoo/models/pegasus/tokenization_pegasus.py -> build/lib/easynlp/modelzoo/models/pegasus
copying easynlp/modelzoo/models/pegasus/configuration_pegasus.py -> build/lib/easynlp/modelzoo/models/pegasus
copying easynlp/modelzoo/models/pegasus/tokenization_pegasus_fast.py -> build/lib/easynlp/modelzoo/models/pegasus
creating build/lib/easynlp/modelzoo/models/mingpt_i2t
copying easynlp/modelzoo/models/mingpt_i2t/__init__.py -> build/lib/easynlp/modelzoo/models/mingpt_i2t
copying easynlp/modelzoo/models/mingpt_i2t/modeling_mingpt_i2t.py -> build/lib/easynlp/modelzoo/models/mingpt_i2t
copying easynlp/modelzoo/models/mingpt_i2t/configuration_mingpt_i2t.py -> build/lib/easynlp/modelzoo/models/mingpt_i2t
creating build/lib/easynlp/modelzoo/models/randeng
copying easynlp/modelzoo/models/randeng/__init__.py -> build/lib/easynlp/modelzoo/models/randeng
copying easynlp/modelzoo/models/randeng/modeling_randeng.py -> build/lib/easynlp/modelzoo/models/randeng
copying easynlp/modelzoo/models/randeng/tokenization_randeng.py -> build/lib/easynlp/modelzoo/models/randeng
copying easynlp/modelzoo/models/randeng/configuration_randeng.py -> build/lib/easynlp/modelzoo/models/randeng
copying easynlp/modelzoo/models/randeng/data_utils.py -> build/lib/easynlp/modelzoo/models/randeng
creating build/lib/easynlp/modelzoo/models/mt5
copying easynlp/modelzoo/models/mt5/modeling_mt5.py -> build/lib/easynlp/modelzoo/models/mt5
copying easynlp/modelzoo/models/mt5/__init__.py -> build/lib/easynlp/modelzoo/models/mt5
copying easynlp/modelzoo/models/mt5/configuration_mt5.py -> build/lib/easynlp/modelzoo/models/mt5
creating build/lib/easynlp/modelzoo/models/glm
copying easynlp/modelzoo/models/glm/__init__.py -> build/lib/easynlp/modelzoo/models/glm
copying easynlp/modelzoo/models/glm/modeling_glm.py -> build/lib/easynlp/modelzoo/models/glm
copying easynlp/modelzoo/models/glm/tokenization_glm.py -> build/lib/easynlp/modelzoo/models/glm
copying easynlp/modelzoo/models/glm/configuration_glm.py -> build/lib/easynlp/modelzoo/models/glm
creating build/lib/easynlp/modelzoo/models/artist
copying easynlp/modelzoo/models/artist/__init__.py -> build/lib/easynlp/modelzoo/models/artist
copying easynlp/modelzoo/models/artist/modeling_artist_knowl.py -> build/lib/easynlp/modelzoo/models/artist
copying easynlp/modelzoo/models/artist/configuration_artist.py -> build/lib/easynlp/modelzoo/models/artist
copying easynlp/modelzoo/models/artist/modeling_artist.py -> build/lib/easynlp/modelzoo/models/artist
creating build/lib/easynlp/modelzoo/models/megatron_bert
copying easynlp/modelzoo/models/megatron_bert/modeling_megatron_bert.py -> build/lib/easynlp/modelzoo/models/megatron_bert
copying easynlp/modelzoo/models/megatron_bert/__init__.py -> build/lib/easynlp/modelzoo/models/megatron_bert
copying easynlp/modelzoo/models/megatron_bert/tokenization_megatron_bert.py -> build/lib/easynlp/modelzoo/models/megatron_bert
copying easynlp/modelzoo/models/megatron_bert/configuration_megatron_bert.py -> build/lib/easynlp/modelzoo/models/megatron_bert
copying easynlp/modelzoo/models/megatron_bert/tokenization_modeling_bert_fast.py -> build/lib/easynlp/modelzoo/models/megatron_bert
creating build/lib/easynlp/modelzoo/models/bloom
copying easynlp/modelzoo/models/bloom/modeling_bloom.py -> build/lib/easynlp/modelzoo/models/bloom
copying easynlp/modelzoo/models/bloom/__init__.py -> build/lib/easynlp/modelzoo/models/bloom
copying easynlp/modelzoo/models/bloom/tokenization_bloom_fast.py -> build/lib/easynlp/modelzoo/models/bloom
copying easynlp/modelzoo/models/bloom/configuration_bloom.py -> build/lib/easynlp/modelzoo/models/bloom
creating build/lib/easynlp/modelzoo/models/bert
copying easynlp/modelzoo/models/bert/__init__.py -> build/lib/easynlp/modelzoo/models/bert
copying easynlp/modelzoo/models/bert/tokenization_bert_fast.py -> build/lib/easynlp/modelzoo/models/bert
copying easynlp/modelzoo/models/bert/configuration_bert.py -> build/lib/easynlp/modelzoo/models/bert
copying easynlp/modelzoo/models/bert/tokenization_bert.py -> build/lib/easynlp/modelzoo/models/bert
copying easynlp/modelzoo/models/bert/modeling_bert.py -> build/lib/easynlp/modelzoo/models/bert
creating build/lib/easynlp/modelzoo/models/kangaroo
copying easynlp/modelzoo/models/kangaroo/configuration_kangaroo.py -> build/lib/easynlp/modelzoo/models/kangaroo
copying easynlp/modelzoo/models/kangaroo/tokenization_kangaroo_fast.py -> build/lib/easynlp/modelzoo/models/kangaroo
copying easynlp/modelzoo/models/kangaroo/__init__.py -> build/lib/easynlp/modelzoo/models/kangaroo
copying easynlp/modelzoo/models/kangaroo/modeling_kangaroo.py -> build/lib/easynlp/modelzoo/models/kangaroo
copying easynlp/modelzoo/models/kangaroo/tokenization_kangaroo.py -> build/lib/easynlp/modelzoo/models/kangaroo
creating build/lib/easynlp/modelzoo/models/roberta
copying easynlp/modelzoo/models/roberta/configuration_roberta.py -> build/lib/easynlp/modelzoo/models/roberta
copying easynlp/modelzoo/models/roberta/__init__.py -> build/lib/easynlp/modelzoo/models/roberta
copying easynlp/modelzoo/models/roberta/tokenization_roberta.py -> build/lib/easynlp/modelzoo/models/roberta
copying easynlp/modelzoo/models/roberta/modeling_roberta.py -> build/lib/easynlp/modelzoo/models/roberta
copying easynlp/modelzoo/models/roberta/tokenization_roberta_fast.py -> build/lib/easynlp/modelzoo/models/roberta
creating build/lib/easynlp/modelzoo/models/auto
copying easynlp/modelzoo/models/auto/tokenization_auto.py -> build/lib/easynlp/modelzoo/models/auto
copying easynlp/modelzoo/models/auto/configuration_auto.py -> build/lib/easynlp/modelzoo/models/auto
copying easynlp/modelzoo/models/auto/__init__.py -> build/lib/easynlp/modelzoo/models/auto
copying easynlp/modelzoo/models/auto/auto_factory.py -> build/lib/easynlp/modelzoo/models/auto
copying easynlp/modelzoo/models/auto/modeling_auto.py -> build/lib/easynlp/modelzoo/models/auto
creating build/lib/easynlp/modelzoo/models/gpt2
copying easynlp/modelzoo/models/gpt2/__init__.py -> build/lib/easynlp/modelzoo/models/gpt2
copying easynlp/modelzoo/models/gpt2/tokenization_gpt2_fast.py -> build/lib/easynlp/modelzoo/models/gpt2
copying easynlp/modelzoo/models/gpt2/modeling_gpt2.py -> build/lib/easynlp/modelzoo/models/gpt2
copying easynlp/modelzoo/models/gpt2/tokenization_gpt2.py -> build/lib/easynlp/modelzoo/models/gpt2
copying easynlp/modelzoo/models/gpt2/configuration_gpt2.py -> build/lib/easynlp/modelzoo/models/gpt2
creating build/lib/easynlp/modelzoo/models/cnn
copying easynlp/modelzoo/models/cnn/__init__.py -> build/lib/easynlp/modelzoo/models/cnn
copying easynlp/modelzoo/models/cnn/tokenization_cnn.py -> build/lib/easynlp/modelzoo/models/cnn
copying easynlp/modelzoo/models/cnn/modeling_cnn.py -> build/lib/easynlp/modelzoo/models/cnn
copying easynlp/modelzoo/models/cnn/configuration_cnn.py -> build/lib/easynlp/modelzoo/models/cnn
creating build/lib/easynlp/modelzoo/models/mg_glm
copying easynlp/modelzoo/models/mg_glm/distributed.py -> build/lib/easynlp/modelzoo/models/mg_glm
copying easynlp/modelzoo/models/mg_glm/__init__.py -> build/lib/easynlp/modelzoo/models/mg_glm
copying easynlp/modelzoo/models/mg_glm/mpu_transformer.py -> build/lib/easynlp/modelzoo/models/mg_glm
copying easynlp/modelzoo/models/mg_glm/sp_tokenizer.py -> build/lib/easynlp/modelzoo/models/mg_glm
copying easynlp/modelzoo/models/mg_glm/prompt.py -> build/lib/easynlp/modelzoo/models/mg_glm
copying easynlp/modelzoo/models/mg_glm/modeling_glm.py -> build/lib/easynlp/modelzoo/models/mg_glm
copying easynlp/modelzoo/models/mg_glm/downstream.py -> build/lib/easynlp/modelzoo/models/mg_glm
creating build/lib/easynlp/modelzoo/models/latent_diffusion
copying easynlp/modelzoo/models/latent_diffusion/model.py -> build/lib/easynlp/modelzoo/models/latent_diffusion
copying easynlp/modelzoo/models/latent_diffusion/ddpm.py -> build/lib/easynlp/modelzoo/models/latent_diffusion
copying easynlp/modelzoo/models/latent_diffusion/bert_tokenizer.py -> build/lib/easynlp/modelzoo/models/latent_diffusion
copying easynlp/modelzoo/models/latent_diffusion/__init__.py -> build/lib/easynlp/modelzoo/models/latent_diffusion
copying easynlp/modelzoo/models/latent_diffusion/RRDBNet_arch.py -> build/lib/easynlp/modelzoo/models/latent_diffusion
copying easynlp/modelzoo/models/latent_diffusion/quantize.py -> build/lib/easynlp/modelzoo/models/latent_diffusion
copying easynlp/modelzoo/models/latent_diffusion/ddim.py -> build/lib/easynlp/modelzoo/models/latent_diffusion
copying easynlp/modelzoo/models/latent_diffusion/distributions.py -> build/lib/easynlp/modelzoo/models/latent_diffusion
copying easynlp/modelzoo/models/latent_diffusion/autoencoder.py -> build/lib/easynlp/modelzoo/models/latent_diffusion
copying easynlp/modelzoo/models/latent_diffusion/wukong.py -> build/lib/easynlp/modelzoo/models/latent_diffusion
copying easynlp/modelzoo/models/latent_diffusion/x_transformer.py -> build/lib/easynlp/modelzoo/models/latent_diffusion
copying easynlp/modelzoo/models/latent_diffusion/modules.py -> build/lib/easynlp/modelzoo/models/latent_diffusion
copying easynlp/modelzoo/models/latent_diffusion/plms.py -> build/lib/easynlp/modelzoo/models/latent_diffusion
copying easynlp/modelzoo/models/latent_diffusion/openaimodel.py -> build/lib/easynlp/modelzoo/models/latent_diffusion
copying easynlp/modelzoo/models/latent_diffusion/ema.py -> build/lib/easynlp/modelzoo/models/latent_diffusion
copying easynlp/modelzoo/models/latent_diffusion/attention.py -> build/lib/easynlp/modelzoo/models/latent_diffusion
copying easynlp/modelzoo/models/latent_diffusion/util.py -> build/lib/easynlp/modelzoo/models/latent_diffusion
creating build/lib/easynlp/appzoo/text2image_generation
copying easynlp/appzoo/text2image_generation/model.py -> build/lib/easynlp/appzoo/text2image_generation
copying easynlp/appzoo/text2image_generation/__init__.py -> build/lib/easynlp/appzoo/text2image_generation
copying easynlp/appzoo/text2image_generation/predictor.py -> build/lib/easynlp/appzoo/text2image_generation
copying easynlp/appzoo/text2image_generation/data.py -> build/lib/easynlp/appzoo/text2image_generation
copying easynlp/appzoo/text2image_generation/vqgan.py -> build/lib/easynlp/appzoo/text2image_generation
copying easynlp/appzoo/text2image_generation/evaluator.py -> build/lib/easynlp/appzoo/text2image_generation
creating build/lib/easynlp/appzoo/geep_classification
copying easynlp/appzoo/geep_classification/model.py -> build/lib/easynlp/appzoo/geep_classification
copying easynlp/appzoo/geep_classification/__init__.py -> build/lib/easynlp/appzoo/geep_classification
copying easynlp/appzoo/geep_classification/predictor.py -> build/lib/easynlp/appzoo/geep_classification
copying easynlp/appzoo/geep_classification/data.py -> build/lib/easynlp/appzoo/geep_classification
copying easynlp/appzoo/geep_classification/evaluator.py -> build/lib/easynlp/appzoo/geep_classification
creating build/lib/easynlp/appzoo/clip
copying easynlp/appzoo/clip/model.py -> build/lib/easynlp/appzoo/clip
copying easynlp/appzoo/clip/__init__.py -> build/lib/easynlp/appzoo/clip
copying easynlp/appzoo/clip/predictor.py -> build/lib/easynlp/appzoo/clip
copying easynlp/appzoo/clip/data.py -> build/lib/easynlp/appzoo/clip
copying easynlp/appzoo/clip/evaluator.py -> build/lib/easynlp/appzoo/clip
creating build/lib/easynlp/appzoo/text2video_retrieval
copying easynlp/appzoo/text2video_retrieval/model.py -> build/lib/easynlp/appzoo/text2video_retrieval
copying easynlp/appzoo/text2video_retrieval/__init__.py -> build/lib/easynlp/appzoo/text2video_retrieval
copying easynlp/appzoo/text2video_retrieval/predictor.py -> build/lib/easynlp/appzoo/text2video_retrieval
copying easynlp/appzoo/text2video_retrieval/data.py -> build/lib/easynlp/appzoo/text2video_retrieval
copying easynlp/appzoo/text2video_retrieval/evaluator.py -> build/lib/easynlp/appzoo/text2video_retrieval
creating build/lib/easynlp/appzoo/sequence_labeling
copying easynlp/appzoo/sequence_labeling/model.py -> build/lib/easynlp/appzoo/sequence_labeling
copying easynlp/appzoo/sequence_labeling/__init__.py -> build/lib/easynlp/appzoo/sequence_labeling
copying easynlp/appzoo/sequence_labeling/labeling_eval_utils.py -> build/lib/easynlp/appzoo/sequence_labeling
copying easynlp/appzoo/sequence_labeling/predictor.py -> build/lib/easynlp/appzoo/sequence_labeling
copying easynlp/appzoo/sequence_labeling/data.py -> build/lib/easynlp/appzoo/sequence_labeling
copying easynlp/appzoo/sequence_labeling/evaluator.py -> build/lib/easynlp/appzoo/sequence_labeling
creating build/lib/easynlp/appzoo/language_modeling
copying easynlp/appzoo/language_modeling/model.py -> build/lib/easynlp/appzoo/language_modeling
copying easynlp/appzoo/language_modeling/__init__.py -> build/lib/easynlp/appzoo/language_modeling
copying easynlp/appzoo/language_modeling/data.py -> build/lib/easynlp/appzoo/language_modeling
copying easynlp/appzoo/language_modeling/evaluator.py -> build/lib/easynlp/appzoo/language_modeling
creating build/lib/easynlp/appzoo/wukong_clip
copying easynlp/appzoo/wukong_clip/model.py -> build/lib/easynlp/appzoo/wukong_clip
copying easynlp/appzoo/wukong_clip/bert_tokenizer.py -> build/lib/easynlp/appzoo/wukong_clip
copying easynlp/appzoo/wukong_clip/__init__.py -> build/lib/easynlp/appzoo/wukong_clip
copying easynlp/appzoo/wukong_clip/predictor.py -> build/lib/easynlp/appzoo/wukong_clip
copying easynlp/appzoo/wukong_clip/data.py -> build/lib/easynlp/appzoo/wukong_clip
copying easynlp/appzoo/wukong_clip/evaluator.py -> build/lib/easynlp/appzoo/wukong_clip
creating build/lib/easynlp/appzoo/feature_vectorization
copying easynlp/appzoo/feature_vectorization/model.py -> build/lib/easynlp/appzoo/feature_vectorization
copying easynlp/appzoo/feature_vectorization/__init__.py -> build/lib/easynlp/appzoo/feature_vectorization
copying easynlp/appzoo/feature_vectorization/predictor.py -> build/lib/easynlp/appzoo/feature_vectorization
creating build/lib/easynlp/appzoo/sequence_classification
copying easynlp/appzoo/sequence_classification/model.py -> build/lib/easynlp/appzoo/sequence_classification
copying easynlp/appzoo/sequence_classification/__init__.py -> build/lib/easynlp/appzoo/sequence_classification
copying easynlp/appzoo/sequence_classification/predictor.py -> build/lib/easynlp/appzoo/sequence_classification
copying easynlp/appzoo/sequence_classification/data.py -> build/lib/easynlp/appzoo/sequence_classification
copying easynlp/appzoo/sequence_classification/evaluator.py -> build/lib/easynlp/appzoo/sequence_classification
creating build/lib/easynlp/appzoo/information_extraction
copying easynlp/appzoo/information_extraction/model.py -> build/lib/easynlp/appzoo/information_extraction
copying easynlp/appzoo/information_extraction/__init__.py -> build/lib/easynlp/appzoo/information_extraction
copying easynlp/appzoo/information_extraction/predictor.py -> build/lib/easynlp/appzoo/information_extraction
copying easynlp/appzoo/information_extraction/data.py -> build/lib/easynlp/appzoo/information_extraction
copying easynlp/appzoo/information_extraction/evaluator.py -> build/lib/easynlp/appzoo/information_extraction
creating build/lib/easynlp/appzoo/text_match
copying easynlp/appzoo/text_match/model.py -> build/lib/easynlp/appzoo/text_match
copying easynlp/appzoo/text_match/__init__.py -> build/lib/easynlp/appzoo/text_match
copying easynlp/appzoo/text_match/predictor.py -> build/lib/easynlp/appzoo/text_match
copying easynlp/appzoo/text_match/data.py -> build/lib/easynlp/appzoo/text_match
copying easynlp/appzoo/text_match/evaluator.py -> build/lib/easynlp/appzoo/text_match
creating build/lib/easynlp/appzoo/video2text_generation
copying easynlp/appzoo/video2text_generation/model.py -> build/lib/easynlp/appzoo/video2text_generation
copying easynlp/appzoo/video2text_generation/clip.py -> build/lib/easynlp/appzoo/video2text_generation
copying easynlp/appzoo/video2text_generation/__init__.py -> build/lib/easynlp/appzoo/video2text_generation
copying easynlp/appzoo/video2text_generation/predictor.py -> build/lib/easynlp/appzoo/video2text_generation
copying easynlp/appzoo/video2text_generation/data.py -> build/lib/easynlp/appzoo/video2text_generation
copying easynlp/appzoo/video2text_generation/evaluator.py -> build/lib/easynlp/appzoo/video2text_generation
copying easynlp/appzoo/video2text_generation/tokenizer.py -> build/lib/easynlp/appzoo/video2text_generation
creating build/lib/easynlp/appzoo/sequence_generation
copying easynlp/appzoo/sequence_generation/model.py -> build/lib/easynlp/appzoo/sequence_generation
copying easynlp/appzoo/sequence_generation/__init__.py -> build/lib/easynlp/appzoo/sequence_generation
copying easynlp/appzoo/sequence_generation/predictor.py -> build/lib/easynlp/appzoo/sequence_generation
copying easynlp/appzoo/sequence_generation/data.py -> build/lib/easynlp/appzoo/sequence_generation
copying easynlp/appzoo/sequence_generation/evaluator.py -> build/lib/easynlp/appzoo/sequence_generation
creating build/lib/easynlp/appzoo/image2text_generation
copying easynlp/appzoo/image2text_generation/model.py -> build/lib/easynlp/appzoo/image2text_generation
copying easynlp/appzoo/image2text_generation/clip.py -> build/lib/easynlp/appzoo/image2text_generation
copying easynlp/appzoo/image2text_generation/__init__.py -> build/lib/easynlp/appzoo/image2text_generation
copying easynlp/appzoo/image2text_generation/predictor.py -> build/lib/easynlp/appzoo/image2text_generation
copying easynlp/appzoo/image2text_generation/data.py -> build/lib/easynlp/appzoo/image2text_generation
copying easynlp/appzoo/image2text_generation/vqgan.py -> build/lib/easynlp/appzoo/image2text_generation
copying easynlp/appzoo/image2text_generation/evaluator.py -> build/lib/easynlp/appzoo/image2text_generation
copying easynlp/appzoo/image2text_generation/tokenizer.py -> build/lib/easynlp/appzoo/image2text_generation
creating build/lib/easynlp/appzoo/machine_reading_comprehension
copying easynlp/appzoo/machine_reading_comprehension/model.py -> build/lib/easynlp/appzoo/machine_reading_comprehension
copying easynlp/appzoo/machine_reading_comprehension/__init__.py -> build/lib/easynlp/appzoo/machine_reading_comprehension
copying easynlp/appzoo/machine_reading_comprehension/predictor.py -> build/lib/easynlp/appzoo/machine_reading_comprehension
copying easynlp/appzoo/machine_reading_comprehension/data.py -> build/lib/easynlp/appzoo/machine_reading_comprehension
copying easynlp/appzoo/machine_reading_comprehension/evaluator.py -> build/lib/easynlp/appzoo/machine_reading_comprehension
creating build/lib/easynlp/appzoo/latent_diffusion
copying easynlp/appzoo/latent_diffusion/model.py -> build/lib/easynlp/appzoo/latent_diffusion
copying easynlp/appzoo/latent_diffusion/__init__.py -> build/lib/easynlp/appzoo/latent_diffusion
copying easynlp/appzoo/latent_diffusion/predictor.py -> build/lib/easynlp/appzoo/latent_diffusion
copying easynlp/appzoo/latent_diffusion/data.py -> build/lib/easynlp/appzoo/latent_diffusion
copying easynlp/appzoo/latent_diffusion/evaluator.py -> build/lib/easynlp/appzoo/latent_diffusion
creating build/lib/easynlp/appzoo/data_augmentation
copying easynlp/appzoo/data_augmentation/model.py -> build/lib/easynlp/appzoo/data_augmentation
copying easynlp/appzoo/data_augmentation/__init__.py -> build/lib/easynlp/appzoo/data_augmentation
copying easynlp/appzoo/data_augmentation/predictor.py -> build/lib/easynlp/appzoo/data_augmentation
creating build/lib/easynlp/appzoo/sequence_generation/mg_seq2seq
copying easynlp/appzoo/sequence_generation/mg_seq2seq/evaluate.py -> build/lib/easynlp/appzoo/sequence_generation/mg_seq2seq
copying easynlp/appzoo/sequence_generation/mg_seq2seq/eval_utils.py -> build/lib/easynlp/appzoo/sequence_generation/mg_seq2seq
copying easynlp/appzoo/sequence_generation/mg_seq2seq/__init__.py -> build/lib/easynlp/appzoo/sequence_generation/mg_seq2seq
copying easynlp/appzoo/sequence_generation/mg_seq2seq/dataset.py -> build/lib/easynlp/appzoo/sequence_generation/mg_seq2seq
copying easynlp/appzoo/sequence_generation/mg_seq2seq/finetune.py -> build/lib/easynlp/appzoo/sequence_generation/mg_seq2seq
copying easynlp/appzoo/sequence_generation/mg_seq2seq/data_utils.py -> build/lib/easynlp/appzoo/sequence_generation/mg_seq2seq
creating build/bdist.linux-x86_64
creating build/bdist.linux-x86_64/egg
creating build/bdist.linux-x86_64/egg/easynlp
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo
copying build/lib/easynlp/modelzoo/generation_logits_process.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo
copying build/lib/easynlp/modelzoo/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo
copying build/lib/easynlp/modelzoo/tokenization_utils_fast.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo
copying build/lib/easynlp/modelzoo/file_utils.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo
copying build/lib/easynlp/modelzoo/activations.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo
copying build/lib/easynlp/modelzoo/modeling_outputs.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/mg_utils
copying build/lib/easynlp/modelzoo/mg_utils/corpora.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/mg_utils
copying build/lib/easynlp/modelzoo/mg_utils/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/mg_utils
copying build/lib/easynlp/modelzoo/mg_utils/lazy_loader.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/mg_utils
copying build/lib/easynlp/modelzoo/mg_utils/learning_rates.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/mg_utils
copying build/lib/easynlp/modelzoo/mg_utils/configure_data.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/mg_utils
copying build/lib/easynlp/modelzoo/mg_utils/pretrain_glm.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/mg_utils
copying build/lib/easynlp/modelzoo/mg_utils/train_utils.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/mg_utils
copying build/lib/easynlp/modelzoo/mg_utils/tokenization.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/mg_utils
copying build/lib/easynlp/modelzoo/mg_utils/finetune_glm.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/mg_utils
copying build/lib/easynlp/modelzoo/mg_utils/dataset.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/mg_utils
copying build/lib/easynlp/modelzoo/mg_utils/fp16.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/mg_utils
copying build/lib/easynlp/modelzoo/mg_utils/utils.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/mg_utils
copying build/lib/easynlp/modelzoo/mg_utils/samplers.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/mg_utils
copying build/lib/easynlp/modelzoo/mg_utils/blocklm_utils.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/mg_utils
copying build/lib/easynlp/modelzoo/generation_utils.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo
copying build/lib/easynlp/modelzoo/generation_stopping_criteria.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/bart
copying build/lib/easynlp/modelzoo/models/bart/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/bart
copying build/lib/easynlp/modelzoo/models/bart/tokenization_bart.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/bart
copying build/lib/easynlp/modelzoo/models/bart/tokenization_bart_fast.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/bart
copying build/lib/easynlp/modelzoo/models/bart/modeling_bart.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/bart
copying build/lib/easynlp/modelzoo/models/bart/configuration_bart.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/bart
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/wukong
copying build/lib/easynlp/modelzoo/models/wukong/modeling_wukong.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/wukong
copying build/lib/easynlp/modelzoo/models/wukong/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/wukong
copying build/lib/easynlp/modelzoo/models/wukong/configuration_wukong.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/wukong
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/clip
copying build/lib/easynlp/modelzoo/models/clip/modeling_openclip.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/clip
copying build/lib/easynlp/modelzoo/models/clip/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/clip
copying build/lib/easynlp/modelzoo/models/clip/openclip_tokenizer.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/clip
copying build/lib/easynlp/modelzoo/models/clip/modeling_clip.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/clip
copying build/lib/easynlp/modelzoo/models/clip/configuration_clip.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/clip
copying build/lib/easynlp/modelzoo/models/clip/tokenization_clip.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/clip
copying build/lib/easynlp/modelzoo/models/clip/modeling_chineseclip.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/clip
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/dkplm
copying build/lib/easynlp/modelzoo/models/dkplm/configuration_dkplm.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/dkplm
copying build/lib/easynlp/modelzoo/models/dkplm/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/dkplm
copying build/lib/easynlp/modelzoo/models/dkplm/modeling_dkplm.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/dkplm
copying build/lib/easynlp/modelzoo/models/dkplm/tokenization_dkplm_fast.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/dkplm
copying build/lib/easynlp/modelzoo/models/dkplm/tokenization_dkplm.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/dkplm
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/t5
copying build/lib/easynlp/modelzoo/models/t5/tokenization_t5_fast.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/t5
copying build/lib/easynlp/modelzoo/models/t5/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/t5
copying build/lib/easynlp/modelzoo/models/t5/configuration_t5.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/t5
copying build/lib/easynlp/modelzoo/models/t5/modeling_t5.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/t5
copying build/lib/easynlp/modelzoo/models/t5/tokenization_t5.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/t5
copying build/lib/easynlp/modelzoo/models/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/kbert
copying build/lib/easynlp/modelzoo/models/kbert/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/kbert
copying build/lib/easynlp/modelzoo/models/kbert/configuration_kbert.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/kbert
copying build/lib/easynlp/modelzoo/models/kbert/modeling_kbert.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/kbert
copying build/lib/easynlp/modelzoo/models/kbert/tokenization_kbert_fast.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/kbert
copying build/lib/easynlp/modelzoo/models/kbert/tokenization_kbert.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/kbert
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/geep
copying build/lib/easynlp/modelzoo/models/geep/configuration_geep.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/geep
copying build/lib/easynlp/modelzoo/models/geep/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/geep
copying build/lib/easynlp/modelzoo/models/geep/modeling_geep.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/geep
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/pegasus
copying build/lib/easynlp/modelzoo/models/pegasus/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/pegasus
copying build/lib/easynlp/modelzoo/models/pegasus/modeling_pegasus.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/pegasus
copying build/lib/easynlp/modelzoo/models/pegasus/tokenization_pegasus.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/pegasus
copying build/lib/easynlp/modelzoo/models/pegasus/configuration_pegasus.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/pegasus
copying build/lib/easynlp/modelzoo/models/pegasus/tokenization_pegasus_fast.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/pegasus
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/mingpt_i2t
copying build/lib/easynlp/modelzoo/models/mingpt_i2t/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/mingpt_i2t
copying build/lib/easynlp/modelzoo/models/mingpt_i2t/modeling_mingpt_i2t.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/mingpt_i2t
copying build/lib/easynlp/modelzoo/models/mingpt_i2t/configuration_mingpt_i2t.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/mingpt_i2t
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/randeng
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copying build/lib/easynlp/modelzoo/models/randeng/modeling_randeng.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/randeng
copying build/lib/easynlp/modelzoo/models/randeng/tokenization_randeng.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/randeng
copying build/lib/easynlp/modelzoo/models/randeng/configuration_randeng.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/randeng
copying build/lib/easynlp/modelzoo/models/randeng/data_utils.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/randeng
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/mt5
copying build/lib/easynlp/modelzoo/models/mt5/modeling_mt5.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/mt5
copying build/lib/easynlp/modelzoo/models/mt5/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/mt5
copying build/lib/easynlp/modelzoo/models/mt5/configuration_mt5.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/mt5
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/glm
copying build/lib/easynlp/modelzoo/models/glm/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/glm
copying build/lib/easynlp/modelzoo/models/glm/modeling_glm.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/glm
copying build/lib/easynlp/modelzoo/models/glm/tokenization_glm.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/glm
copying build/lib/easynlp/modelzoo/models/glm/configuration_glm.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/glm
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/artist
copying build/lib/easynlp/modelzoo/models/artist/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/artist
copying build/lib/easynlp/modelzoo/models/artist/modeling_artist_knowl.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/artist
copying build/lib/easynlp/modelzoo/models/artist/configuration_artist.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/artist
copying build/lib/easynlp/modelzoo/models/artist/modeling_artist.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/artist
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/megatron_bert
copying build/lib/easynlp/modelzoo/models/megatron_bert/modeling_megatron_bert.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/megatron_bert
copying build/lib/easynlp/modelzoo/models/megatron_bert/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/megatron_bert
copying build/lib/easynlp/modelzoo/models/megatron_bert/tokenization_megatron_bert.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/megatron_bert
copying build/lib/easynlp/modelzoo/models/megatron_bert/configuration_megatron_bert.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/megatron_bert
copying build/lib/easynlp/modelzoo/models/megatron_bert/tokenization_modeling_bert_fast.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/megatron_bert
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/bloom
copying build/lib/easynlp/modelzoo/models/bloom/modeling_bloom.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/bloom
copying build/lib/easynlp/modelzoo/models/bloom/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/bloom
copying build/lib/easynlp/modelzoo/models/bloom/tokenization_bloom_fast.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/bloom
copying build/lib/easynlp/modelzoo/models/bloom/configuration_bloom.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/bloom
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/bert
copying build/lib/easynlp/modelzoo/models/bert/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/bert
copying build/lib/easynlp/modelzoo/models/bert/tokenization_bert_fast.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/bert
copying build/lib/easynlp/modelzoo/models/bert/configuration_bert.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/bert
copying build/lib/easynlp/modelzoo/models/bert/tokenization_bert.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/bert
copying build/lib/easynlp/modelzoo/models/bert/modeling_bert.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/bert
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/kangaroo
copying build/lib/easynlp/modelzoo/models/kangaroo/configuration_kangaroo.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/kangaroo
copying build/lib/easynlp/modelzoo/models/kangaroo/tokenization_kangaroo_fast.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/kangaroo
copying build/lib/easynlp/modelzoo/models/kangaroo/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/kangaroo
copying build/lib/easynlp/modelzoo/models/kangaroo/modeling_kangaroo.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/kangaroo
copying build/lib/easynlp/modelzoo/models/kangaroo/tokenization_kangaroo.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/kangaroo
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/roberta
copying build/lib/easynlp/modelzoo/models/roberta/configuration_roberta.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/roberta
copying build/lib/easynlp/modelzoo/models/roberta/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/roberta
copying build/lib/easynlp/modelzoo/models/roberta/tokenization_roberta.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/roberta
copying build/lib/easynlp/modelzoo/models/roberta/modeling_roberta.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/roberta
copying build/lib/easynlp/modelzoo/models/roberta/tokenization_roberta_fast.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/roberta
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/auto
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copying build/lib/easynlp/modelzoo/models/auto/configuration_auto.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/auto
copying build/lib/easynlp/modelzoo/models/auto/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/auto
copying build/lib/easynlp/modelzoo/models/auto/auto_factory.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/auto
copying build/lib/easynlp/modelzoo/models/auto/modeling_auto.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/auto
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/gpt2
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copying build/lib/easynlp/modelzoo/models/gpt2/tokenization_gpt2_fast.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/gpt2
copying build/lib/easynlp/modelzoo/models/gpt2/modeling_gpt2.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/gpt2
copying build/lib/easynlp/modelzoo/models/gpt2/tokenization_gpt2.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/gpt2
copying build/lib/easynlp/modelzoo/models/gpt2/configuration_gpt2.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/gpt2
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/cnn
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copying build/lib/easynlp/modelzoo/models/cnn/tokenization_cnn.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/cnn
copying build/lib/easynlp/modelzoo/models/cnn/modeling_cnn.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/cnn
copying build/lib/easynlp/modelzoo/models/cnn/configuration_cnn.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/cnn
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/mg_glm
copying build/lib/easynlp/modelzoo/models/mg_glm/distributed.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/mg_glm
copying build/lib/easynlp/modelzoo/models/mg_glm/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/mg_glm
copying build/lib/easynlp/modelzoo/models/mg_glm/mpu_transformer.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/mg_glm
copying build/lib/easynlp/modelzoo/models/mg_glm/sp_tokenizer.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/mg_glm
copying build/lib/easynlp/modelzoo/models/mg_glm/prompt.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/mg_glm
copying build/lib/easynlp/modelzoo/models/mg_glm/modeling_glm.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/mg_glm
copying build/lib/easynlp/modelzoo/models/mg_glm/downstream.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/mg_glm
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/latent_diffusion
copying build/lib/easynlp/modelzoo/models/latent_diffusion/model.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/latent_diffusion
copying build/lib/easynlp/modelzoo/models/latent_diffusion/ddpm.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/latent_diffusion
copying build/lib/easynlp/modelzoo/models/latent_diffusion/bert_tokenizer.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/latent_diffusion
copying build/lib/easynlp/modelzoo/models/latent_diffusion/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/latent_diffusion
copying build/lib/easynlp/modelzoo/models/latent_diffusion/RRDBNet_arch.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/latent_diffusion
copying build/lib/easynlp/modelzoo/models/latent_diffusion/quantize.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/latent_diffusion
copying build/lib/easynlp/modelzoo/models/latent_diffusion/ddim.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/latent_diffusion
copying build/lib/easynlp/modelzoo/models/latent_diffusion/distributions.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/latent_diffusion
copying build/lib/easynlp/modelzoo/models/latent_diffusion/autoencoder.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/latent_diffusion
copying build/lib/easynlp/modelzoo/models/latent_diffusion/wukong.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/latent_diffusion
copying build/lib/easynlp/modelzoo/models/latent_diffusion/x_transformer.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/latent_diffusion
copying build/lib/easynlp/modelzoo/models/latent_diffusion/modules.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/latent_diffusion
copying build/lib/easynlp/modelzoo/models/latent_diffusion/plms.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/latent_diffusion
copying build/lib/easynlp/modelzoo/models/latent_diffusion/openaimodel.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/latent_diffusion
copying build/lib/easynlp/modelzoo/models/latent_diffusion/ema.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/latent_diffusion
copying build/lib/easynlp/modelzoo/models/latent_diffusion/attention.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/latent_diffusion
copying build/lib/easynlp/modelzoo/models/latent_diffusion/util.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/models/latent_diffusion
copying build/lib/easynlp/modelzoo/modeling_utils.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo
copying build/lib/easynlp/modelzoo/convert_slow_tokenizer.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo
copying build/lib/easynlp/modelzoo/configuration_utils.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo
copying build/lib/easynlp/modelzoo/tokenization_utils.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo
copying build/lib/easynlp/modelzoo/tokenization_utils_base.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo
creating build/bdist.linux-x86_64/egg/easynlp/modelzoo/utils
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copying build/lib/easynlp/modelzoo/utils/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/utils
copying build/lib/easynlp/modelzoo/utils/dummy_sentencepiece_and_tokenizers_objects.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/utils
copying build/lib/easynlp/modelzoo/utils/model_parallel_utils.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo/utils
copying build/lib/easynlp/modelzoo/generation_beam_search.py -> build/bdist.linux-x86_64/egg/easynlp/modelzoo
copying build/lib/easynlp/__init__.py -> build/bdist.linux-x86_64/egg/easynlp
creating build/bdist.linux-x86_64/egg/easynlp/appzoo
creating build/bdist.linux-x86_64/egg/easynlp/appzoo/text2image_generation
copying build/lib/easynlp/appzoo/text2image_generation/model.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/text2image_generation
copying build/lib/easynlp/appzoo/text2image_generation/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/text2image_generation
copying build/lib/easynlp/appzoo/text2image_generation/predictor.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/text2image_generation
copying build/lib/easynlp/appzoo/text2image_generation/data.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/text2image_generation
copying build/lib/easynlp/appzoo/text2image_generation/vqgan.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/text2image_generation
copying build/lib/easynlp/appzoo/text2image_generation/evaluator.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/text2image_generation
creating build/bdist.linux-x86_64/egg/easynlp/appzoo/geep_classification
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copying build/lib/easynlp/appzoo/geep_classification/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/geep_classification
copying build/lib/easynlp/appzoo/geep_classification/predictor.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/geep_classification
copying build/lib/easynlp/appzoo/geep_classification/data.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/geep_classification
copying build/lib/easynlp/appzoo/geep_classification/evaluator.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/geep_classification
creating build/bdist.linux-x86_64/egg/easynlp/appzoo/clip
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copying build/lib/easynlp/appzoo/clip/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/clip
copying build/lib/easynlp/appzoo/clip/predictor.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/clip
copying build/lib/easynlp/appzoo/clip/data.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/clip
copying build/lib/easynlp/appzoo/clip/evaluator.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/clip
creating build/bdist.linux-x86_64/egg/easynlp/appzoo/text2video_retrieval
copying build/lib/easynlp/appzoo/text2video_retrieval/model.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/text2video_retrieval
copying build/lib/easynlp/appzoo/text2video_retrieval/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/text2video_retrieval
copying build/lib/easynlp/appzoo/text2video_retrieval/predictor.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/text2video_retrieval
copying build/lib/easynlp/appzoo/text2video_retrieval/data.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/text2video_retrieval
copying build/lib/easynlp/appzoo/text2video_retrieval/evaluator.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/text2video_retrieval
creating build/bdist.linux-x86_64/egg/easynlp/appzoo/sequence_labeling
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copying build/lib/easynlp/appzoo/sequence_labeling/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/sequence_labeling
copying build/lib/easynlp/appzoo/sequence_labeling/labeling_eval_utils.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/sequence_labeling
copying build/lib/easynlp/appzoo/sequence_labeling/predictor.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/sequence_labeling
copying build/lib/easynlp/appzoo/sequence_labeling/data.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/sequence_labeling
copying build/lib/easynlp/appzoo/sequence_labeling/evaluator.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/sequence_labeling
creating build/bdist.linux-x86_64/egg/easynlp/appzoo/language_modeling
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copying build/lib/easynlp/appzoo/language_modeling/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/language_modeling
copying build/lib/easynlp/appzoo/language_modeling/data.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/language_modeling
copying build/lib/easynlp/appzoo/language_modeling/evaluator.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/language_modeling
copying build/lib/easynlp/appzoo/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo
creating build/bdist.linux-x86_64/egg/easynlp/appzoo/wukong_clip
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copying build/lib/easynlp/appzoo/wukong_clip/bert_tokenizer.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/wukong_clip
copying build/lib/easynlp/appzoo/wukong_clip/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/wukong_clip
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copying build/lib/easynlp/appzoo/wukong_clip/data.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/wukong_clip
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creating build/bdist.linux-x86_64/egg/easynlp/appzoo/feature_vectorization
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copying build/lib/easynlp/appzoo/feature_vectorization/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/feature_vectorization
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creating build/bdist.linux-x86_64/egg/easynlp/appzoo/sequence_classification
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copying build/lib/easynlp/appzoo/sequence_classification/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/sequence_classification
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copying build/lib/easynlp/appzoo/sequence_classification/data.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/sequence_classification
copying build/lib/easynlp/appzoo/sequence_classification/evaluator.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/sequence_classification
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copying build/lib/easynlp/appzoo/information_extraction/data.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/information_extraction
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creating build/bdist.linux-x86_64/egg/easynlp/appzoo/text_match
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copying build/lib/easynlp/appzoo/text_match/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/text_match
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creating build/bdist.linux-x86_64/egg/easynlp/appzoo/video2text_generation
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copying build/lib/easynlp/appzoo/video2text_generation/clip.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/video2text_generation
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creating build/bdist.linux-x86_64/egg/easynlp/appzoo/sequence_generation
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copying build/lib/easynlp/appzoo/sequence_generation/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/sequence_generation
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copying build/lib/easynlp/appzoo/sequence_generation/mg_seq2seq/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/sequence_generation/mg_seq2seq
copying build/lib/easynlp/appzoo/sequence_generation/mg_seq2seq/dataset.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/sequence_generation/mg_seq2seq
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creating build/bdist.linux-x86_64/egg/easynlp/appzoo/image2text_generation
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copying build/lib/easynlp/appzoo/image2text_generation/clip.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/image2text_generation
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copying build/lib/easynlp/appzoo/image2text_generation/predictor.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/image2text_generation
copying build/lib/easynlp/appzoo/image2text_generation/data.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/image2text_generation
copying build/lib/easynlp/appzoo/image2text_generation/vqgan.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/image2text_generation
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copying build/lib/easynlp/appzoo/image2text_generation/tokenizer.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/image2text_generation
creating build/bdist.linux-x86_64/egg/easynlp/appzoo/machine_reading_comprehension
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copying build/lib/easynlp/appzoo/machine_reading_comprehension/__init__.py -> build/bdist.linux-x86_64/egg/easynlp/appzoo/machine_reading_comprehension
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creating build/bdist.linux-x86_64/egg/easynlp/appzoo/data_augmentation
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byte-compiling build/bdist.linux-x86_64/egg/easynlp/cli.py to cli.cpython-36.pyc
creating build/bdist.linux-x86_64/egg/EGG-INFO
copying pai_easynlp.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO
copying pai_easynlp.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
copying pai_easynlp.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
copying pai_easynlp.egg-info/entry_points.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
copying pai_easynlp.egg-info/requires.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
copying pai_easynlp.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
zip_safe flag not set; analyzing archive contents...
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easynlp.appzoo.__pycache__.__init__.cpython-36: module references __path__
easynlp.appzoo.wukong_clip.__pycache__.bert_tokenizer.cpython-36: module references __file__
easynlp.core.__pycache__.__init__.cpython-36: module references __file__
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easynlp.modelzoo.models.artist.__pycache__.__init__.cpython-36: module references __file__
easynlp.modelzoo.models.artist.__pycache__.__init__.cpython-36: module references __path__
easynlp.modelzoo.models.auto.__pycache__.__init__.cpython-36: module references __file__
easynlp.modelzoo.models.auto.__pycache__.__init__.cpython-36: module references __path__
easynlp.modelzoo.models.bart.__pycache__.__init__.cpython-36: module references __file__
easynlp.modelzoo.models.bart.__pycache__.__init__.cpython-36: module references __path__
easynlp.modelzoo.models.bert.__pycache__.__init__.cpython-36: module references __file__
easynlp.modelzoo.models.bert.__pycache__.__init__.cpython-36: module references __path__
easynlp.modelzoo.models.bloom.__pycache__.__init__.cpython-36: module references __file__
easynlp.modelzoo.models.bloom.__pycache__.__init__.cpython-36: module references __path__
easynlp.modelzoo.models.clip.__pycache__.__init__.cpython-36: module references __file__
easynlp.modelzoo.models.clip.__pycache__.__init__.cpython-36: module references __path__
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easynlp.modelzoo.models.latent_diffusion.__pycache__.util.cpython-36: module MAY be using inspect.stack
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easynlp.modelzoo.models.pegasus.__pycache__.__init__.cpython-36: module references __file__
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creating dist
creating 'dist/pai_easynlp-0.1.2-py3.6.egg' and adding 'build/bdist.linux-x86_64/egg' to it
removing 'build/bdist.linux-x86_64/egg' (and everything under it)
Processing pai_easynlp-0.1.2-py3.6.egg
creating /home/pai/lib/python3.6/site-packages/pai_easynlp-0.1.2-py3.6.egg
Extracting pai_easynlp-0.1.2-py3.6.egg to /home/pai/lib/python3.6/site-packages
Adding pai-easynlp 0.1.2 to easy-install.pth file
Installing easynlp script to /home/pai/bin
Installed /home/pai/lib/python3.6/site-packages/pai_easynlp-0.1.2-py3.6.egg
Processing dependencies for pai-easynlp==0.1.2
Searching for wheel>=0.26
Reading https://pypi.org/simple/wheel/
Downloading https://files.pythonhosted.org/packages/bd/7c/d38a0b30ce22fc26ed7dbc087c6d00851fb3395e9d0dac40bec1f905030c/wheel-0.38.4-py3-none-any.whl#sha256=b60533f3f5d530e971d6737ca6d58681ee434818fab630c83a734bb10c083ce8
Best match: wheel 0.38.4
Processing wheel-0.38.4-py3-none-any.whl
Installing wheel-0.38.4-py3-none-any.whl to /home/pai/lib/python3.6/site-packages
Adding wheel 0.38.4 to easy-install.pth file
Installing wheel script to /home/pai/bin
Installed /home/pai/lib/python3.6/site-packages/wheel-0.38.4-py3.6.egg
Searching for ftfy==6.0.3
Best match: ftfy 6.0.3
Adding ftfy 6.0.3 to easy-install.pth file
Installing ftfy script to /home/pai/bin
Using /home/pai/lib/python3.6/site-packages
Searching for torchvision==0.9.2+cu101
Best match: torchvision 0.9.2+cu101
Adding torchvision 0.9.2+cu101 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for einops==0.4.1
Best match: einops 0.4.1
Adding einops 0.4.1 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for albumentations==1.1.0
Best match: albumentations 1.1.0
Adding albumentations 1.1.0 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for Pillow==8.3.2
Best match: Pillow 8.3.2
Adding Pillow 8.3.2 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for tqdm==4.64.0
Best match: tqdm 4.64.0
Adding tqdm 4.64.0 to easy-install.pth file
Installing tqdm script to /home/pai/bin
Using /home/pai/lib/python3.6/site-packages
Searching for rouge==1.0.1
Best match: rouge 1.0.1
Adding rouge 1.0.1 to easy-install.pth file
Installing rouge script to /home/pai/bin
Using /home/pai/lib/python3.6/site-packages
Searching for torch==1.8.2+pai
Best match: torch 1.8.2+pai
Adding torch 1.8.2+pai to easy-install.pth file
Installing convert-caffe2-to-onnx script to /home/pai/bin
Installing convert-onnx-to-caffe2 script to /home/pai/bin
Using /home/pai/lib/python3.6/site-packages
Searching for datasets==2.1.0
Best match: datasets 2.1.0
Adding datasets 2.1.0 to easy-install.pth file
Installing datasets-cli script to /home/pai/bin
Using /home/pai/lib/python3.6/site-packages
Searching for tokenizers==0.9.4
Best match: tokenizers 0.9.4
Adding tokenizers 0.9.4 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for tensorboard==2.9.0
Best match: tensorboard 2.9.0
Adding tensorboard 2.9.0 to easy-install.pth file
Installing tensorboard script to /home/pai/bin
Using /home/pai/lib/python3.6/site-packages
Searching for sentencepiece==0.1.97
Best match: sentencepiece 0.1.97
Adding sentencepiece 0.1.97 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for scipy==1.5.4
Best match: scipy 1.5.4
Adding scipy 1.5.4 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for scikit-learn==0.24.2
Best match: scikit-learn 0.24.2
Adding scikit-learn 0.24.2 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for sacremoses==0.0.53
Best match: sacremoses 0.0.53
Adding sacremoses 0.0.53 to easy-install.pth file
Installing sacremoses script to /home/pai/bin
Using /home/pai/lib/python3.6/site-packages
Searching for regex==2022.3.2
Best match: regex 2022.3.2
Adding regex 2022.3.2 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for packaging==21.3
Best match: packaging 21.3
Adding packaging 21.3 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for numpy==1.19.5
Best match: numpy 1.19.5
Adding numpy 1.19.5 to easy-install.pth file
Installing f2py script to /home/pai/bin
Installing f2py3 script to /home/pai/bin
Installing f2py3.6 script to /home/pai/bin
Using /home/pai/lib/python3.6/site-packages
Searching for jieba==0.42.1
Best match: jieba 0.42.1
Adding jieba 0.42.1 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for importlib-metadata==4.8.1
Best match: importlib-metadata 4.8.1
Adding importlib-metadata 4.8.1 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for filelock==3.4.1
Best match: filelock 3.4.1
Adding filelock 3.4.1 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for bs4==0.0.1
Best match: bs4 0.0.1
Adding bs4 0.0.1 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for wcwidth==0.2.5
Best match: wcwidth 0.2.5
Adding wcwidth 0.2.5 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for opencv-python-headless==4.4.0.40
Best match: opencv-python-headless 4.4.0.40
Adding opencv-python-headless 4.4.0.40 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for PyYAML==5.4.1
Best match: PyYAML 5.4.1
Adding PyYAML 5.4.1 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for scikit-image==0.17.2
Best match: scikit-image 0.17.2
Adding scikit-image 0.17.2 to easy-install.pth file
Installing skivi script to /home/pai/bin
Using /home/pai/lib/python3.6/site-packages
Searching for qudida==0.0.4
Best match: qudida 0.0.4
Adding qudida 0.0.4 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for importlib-resources==5.4.0
Best match: importlib-resources 5.4.0
Adding importlib-resources 5.4.0 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for six==1.16.0
Best match: six 1.16.0
Adding six 1.16.0 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for dataclasses==0.8
Best match: dataclasses 0.8
Adding dataclasses 0.8 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for typing-extensions==4.1.1
Best match: typing-extensions 4.1.1
Adding typing-extensions 4.1.1 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for pandas==1.1.5
Best match: pandas 1.1.5
Adding pandas 1.1.5 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for pyarrow==6.0.1
Best match: pyarrow 6.0.1
Adding pyarrow 6.0.1 to easy-install.pth file
Installing plasma_store script to /home/pai/bin
Using /home/pai/lib/python3.6/site-packages
Searching for multiprocess==0.70.12.2
Best match: multiprocess 0.70.12.2
Adding multiprocess 0.70.12.2 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for xxhash==3.2.0
Best match: xxhash 3.2.0
Adding xxhash 3.2.0 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for huggingface-hub==0.4.0
Best match: huggingface-hub 0.4.0
Adding huggingface-hub 0.4.0 to easy-install.pth file
Installing huggingface-cli script to /home/pai/bin
Using /home/pai/lib/python3.6/site-packages
Searching for fsspec==2022.1.0
Best match: fsspec 2022.1.0
Adding fsspec 2022.1.0 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for responses==0.17.0
Best match: responses 0.17.0
Adding responses 0.17.0 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
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Best match: aiohttp 3.8.1
Adding aiohttp 3.8.1 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for dill==0.3.4
Best match: dill 0.3.4
Adding dill 0.3.4 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
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Best match: requests 2.27.1
Adding requests 2.27.1 to easy-install.pth file
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Best match: grpcio 1.46.3
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Using /home/pai/lib/python3.6/site-packages
Searching for google-auth-oauthlib==0.4.6
Best match: google-auth-oauthlib 0.4.6
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Installing google-oauthlib-tool script to /home/pai/bin
Using /home/pai/lib/python3.6/site-packages
Searching for google-auth==2.6.6
Best match: google-auth 2.6.6
Adding google-auth 2.6.6 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for Markdown==3.3.7
Best match: Markdown 3.3.7
Adding Markdown 3.3.7 to easy-install.pth file
Installing markdown_py script to /home/pai/bin
Using /home/pai/lib/python3.6/site-packages
Searching for Werkzeug==2.0.3
Best match: Werkzeug 2.0.3
Adding Werkzeug 2.0.3 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for setuptools==58.0.4
Best match: setuptools 58.0.4
Adding setuptools 58.0.4 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for tensorboard-data-server==0.6.1
Best match: tensorboard-data-server 0.6.1
Adding tensorboard-data-server 0.6.1 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for absl-py==1.0.0
Best match: absl-py 1.0.0
Adding absl-py 1.0.0 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for tensorboard-plugin-wit==1.8.1
Best match: tensorboard-plugin-wit 1.8.1
Adding tensorboard-plugin-wit 1.8.1 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for protobuf==3.19.4
Best match: protobuf 3.19.4
Adding protobuf 3.19.4 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
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Best match: joblib 1.0.1
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Using /home/pai/lib/python3.6/site-packages
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Best match: threadpoolctl 3.1.0
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Using /home/pai/lib/python3.6/site-packages
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Best match: click 8.0.4
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Best match: pyparsing 3.0.9
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Using /home/pai/lib/python3.6/site-packages
Searching for zipp==3.6.0
Best match: zipp 3.6.0
Adding zipp 3.6.0 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for beautifulsoup4==4.11.2
Best match: beautifulsoup4 4.11.2
Adding beautifulsoup4 4.11.2 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for PyWavelets==1.1.1
Best match: PyWavelets 1.1.1
Adding PyWavelets 1.1.1 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for matplotlib==3.3.4
Best match: matplotlib 3.3.4
Adding matplotlib 3.3.4 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for imageio==2.9.0
Best match: imageio 2.9.0
Adding imageio 2.9.0 to easy-install.pth file
Installing imageio_download_bin script to /home/pai/bin
Installing imageio_remove_bin script to /home/pai/bin
Using /home/pai/lib/python3.6/site-packages
Searching for networkx==2.7.1
Best match: networkx 2.7.1
Adding networkx 2.7.1 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for tifffile==2020.9.3
Best match: tifffile 2020.9.3
Adding tifffile 2020.9.3 to easy-install.pth file
Installing lsm2bin script to /home/pai/bin
Installing tifffile script to /home/pai/bin
Using /home/pai/lib/python3.6/site-packages
Searching for pytz==2022.1
Best match: pytz 2022.1
Adding pytz 2022.1 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
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Best match: python-dateutil 2.8.2
Adding python-dateutil 2.8.2 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
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Best match: urllib3 1.26.8
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Using /home/pai/lib/python3.6/site-packages
Searching for attrs==21.4.0
Best match: attrs 21.4.0
Adding attrs 21.4.0 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
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Best match: asynctest 0.13.0
Adding asynctest 0.13.0 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for yarl==1.7.2
Best match: yarl 1.7.2
Adding yarl 1.7.2 to easy-install.pth file
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Best match: async-timeout 4.0.2
Adding async-timeout 4.0.2 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for charset-normalizer==2.0.4
Best match: charset-normalizer 2.0.4
Adding charset-normalizer 2.0.4 to easy-install.pth file
Installing normalizer script to /home/pai/bin
Using /home/pai/lib/python3.6/site-packages
Searching for frozenlist==1.2.0
Best match: frozenlist 1.2.0
Adding frozenlist 1.2.0 to easy-install.pth file
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Best match: idna-ssl 1.1.0
Adding idna-ssl 1.1.0 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for multidict==5.2.0
Best match: multidict 5.2.0
Adding multidict 5.2.0 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for aiosignal==1.2.0
Best match: aiosignal 1.2.0
Adding aiosignal 1.2.0 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for certifi==2021.5.30
Best match: certifi 2021.5.30
Adding certifi 2021.5.30 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for idna==3.3
Best match: idna 3.3
Adding idna 3.3 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for requests-oauthlib==1.3.1
Best match: requests-oauthlib 1.3.1
Adding requests-oauthlib 1.3.1 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for pyasn1-modules==0.2.8
Best match: pyasn1-modules 0.2.8
Adding pyasn1-modules 0.2.8 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for cachetools==4.2.4
Best match: cachetools 4.2.4
Adding cachetools 4.2.4 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for rsa==4.8
Best match: rsa 4.8
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Installing pyrsa-decrypt script to /home/pai/bin
Installing pyrsa-encrypt script to /home/pai/bin
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Installing pyrsa-sign script to /home/pai/bin
Installing pyrsa-verify script to /home/pai/bin
Using /home/pai/lib/python3.6/site-packages
Searching for soupsieve==2.3.2.post1
Best match: soupsieve 2.3.2.post1
Adding soupsieve 2.3.2.post1 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for cycler==0.11.0
Best match: cycler 0.11.0
Adding cycler 0.11.0 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for kiwisolver==1.3.1
Best match: kiwisolver 1.3.1
Adding kiwisolver 1.3.1 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for oauthlib==3.2.0
Best match: oauthlib 3.2.0
Adding oauthlib 3.2.0 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Searching for pyasn1==0.4.8
Best match: pyasn1 0.4.8
Adding pyasn1 0.4.8 to easy-install.pth file
Using /home/pai/lib/python3.6/site-packages
Finished processing dependencies for pai-easynlp==0.1.2

安装完成easynlp之后，建议重启notebook，防止环境存在缓存，未更新

您可以使用如下命令验证是否安装成功：

import easynlp
easynlp.__file__

/home/pai/bin/easynlp

如果您系统内已经安装完easynlp的CLI工具，则说明EasyNLP代码库已经安装。

数据准备

首先，您需要下载用于本示例的训练数据与验证数据以及测试数据。命令如下：

! wget https://atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com/release/tutorials/painter_text2image/T2I_train.tsv
! wget https://atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com/release/tutorials/painter_text2image/T2I_val.tsv
! wget https://atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com/release/tutorials/painter_text2image/T2I_test.tsv

--2023-02-06 15:53:29--  https://atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com/release/tutorials/painter_text2image/T2I_train.tsv
Resolving atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com... 47.101.88.27
Connecting to atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com|47.101.88.27|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 63275122 (60M) [text/tab-separated-values]
Saving to: ‘T2I_train.tsv’
T2I_train.tsv       100%[===================>]  60.34M  14.4MB/s    in 4.2s    
2023-02-06 15:53:33 (14.4 MB/s) - ‘T2I_train.tsv’ saved [63275122/63275122]
--2023-02-06 15:53:34--  https://atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com/release/tutorials/painter_text2image/T2I_val.tsv
Resolving atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com... 47.101.88.27
Connecting to atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com|47.101.88.27|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 6805833 (6.5M) [text/tab-separated-values]
Saving to: ‘T2I_val.tsv’
T2I_val.tsv         100%[===================>]   6.49M  29.0MB/s    in 0.2s    
2023-02-06 15:53:34 (29.0 MB/s) - ‘T2I_val.tsv’ saved [6805833/6805833]
--2023-02-06 15:53:35--  https://atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com/release/tutorials/painter_text2image/T2I_test.tsv
Resolving atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com... 47.101.88.27
Connecting to atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com|47.101.88.27|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 225 [text/tab-separated-values]
Saving to: ‘T2I_test.tsv’
T2I_test.tsv        100%[===================>]     225  --.-KB/s    in 0s      
2023-02-06 15:53:35 (44.4 MB/s) - ‘T2I_test.tsv’ saved [225/225]

训练数据验证数据和测试数据都为.tsv文件。每行为一个数据，以制表符\t分隔为两列，第一列为文本，第二列为图片的base64编码。

初始化

在Python 3.6环境下，我们首先从刚刚安装好的EasyNLP中引入模型运行需要的各种库，并做一些初始化。在本教程中，我们使用alibaba-pai/pai-diffusion-general-large-zh作为我们的示例模型。

# 为了避免EasyNLP中的args与Jupyter系统的冲突，需要手动设置，否则无法进行初始化。
# 在命令行或py文件中运行文中代码则可忽略下述代码。
import imp
import sys
import os
import torch
sys.path.append('./')

[2023-02-06 15:53:41,314.314 dsw-201335-596c8bd49b-769ld:319 INFO utils.py:30] NOTICE: PAIDEBUGGER is turned off.

from easynlp.core import Trainer
import torch
from easynlp.appzoo.latent_diffusion.data import LdmDataset
from easynlp.appzoo.latent_diffusion.evaluator import LatentDiffusionModelEvaluator
from easynlp.appzoo.latent_diffusion.model import LatentDiffusion
from easynlp.appzoo.latent_diffusion.predictor import LatentDiffusionPredictor
from easynlp.utils import initialize_easynlp, get_args,get_pretrain_model_path,get_dir_name
from easynlp.utils.global_vars import parse_user_defined_parameters
from easynlp.core import PredictorManager 
import shutil
initialize_easynlp()
args = get_args()
user_defined_parameters = parse_user_defined_parameters('pretrain_model_name_or_path=alibaba-pai/pai-diffusion-general-large-zh reset_model_state_flag=True')
args.pretrained_model_name_or_path = 'alibaba-pai/pai-diffusion-general-large-zh'
args.checkpoint_dir='./tmp/finetune_model'
pretrained_model_name_or_path = 'alibaba-pai/pai-diffusion-general-large-zh'

[2023-02-06 15:53:48,910] [WARNING] [partition_parameters.py:61:<module>] unable to find torch.distributed._all_gather_base. will fall back to torch.distributed.all_gather which will result in suboptimal performance. please consider upgrading your pytorch installation.

/home/pai/lib/python3.6/site-packages/OpenSSL/crypto.py:8: CryptographyDeprecationWarning: Python 3.6 is no longer supported by the Python core team. Therefore, support for it is deprecated in cryptography and will be removed in a future release.
  from cryptography import utils, x509

The following parameters are not recognized: ['-f', '/root/.local/share/jupyter/runtime/kernel-3084ffd4-d55d-4cc8-b4bd-d55940c5500a.json']
> initializing torch distributed ...

[2023-02-06 15:53:52,305.305 dsw-201335-596c8bd49b-769ld:319 INFO distributed_c10d.py:195] Added key: store_based_barrier_key:1 to store for rank: 0

Init dist done. World size: 1, rank 0, l_rank 0
> setting random seeds to 1234 ...

注意：上述代码如果出现“Address already in use”错误，则需要运行以下代码清理端口上正在执行的程序。

netstat -tunlp|grep 6000

kill -9 PID （需要替换成上一行代码执行结果中对应的程序ID）

载入数据

我们使用EasyNLP中自带的LdmDataset，对训练和测试数据进行载入。主要参数如下：

pretrained_model_name_or_path：预训练模型名称路径，这里我们使用封装好的get_pretrain_model_path函数，来处理模型名称"alibaba-pai/pai-diffusion-general-large-zh"以得到其路径，并自动下载模型
max_seq_length：文本最大长度，超过将截断，不足将padding
input_schema：输入tsv数据的格式，逗号分隔的每一项对应数据文件中每行以\t分隔的一项，每项开头为其字段标识，如label、sent1等
first_sequence、second_sequence：用于说明input_schema中哪些字段作为第一/第二列输入数据
is_training：是否为训练过程，train_dataset为True，valid_dataset为False

train_dataset = LdmDataset(
        pretrained_model_name_or_path=get_pretrain_model_path("alibaba-pai/pai-diffusion-general-large-zh"),
        data_file='T2I_train.tsv',
        max_seq_length=288,
        input_schema='idx:str:1,text:str:1,imgbase64:str:1',
        first_sequence='text',
        second_sequence='imgbase64',
        user_defined_parameters=user_defined_parameters,
        is_training=True)
valid_dataset = LdmDataset(
        pretrained_model_name_or_path=get_pretrain_model_path("alibaba-pai/pai-diffusion-general-large-zh"),    
        data_file='T2I_val.tsv',
        max_seq_length=288,
        input_schema='idx:str:1,text:str:1,imgbase64:str:1',
        first_sequence='text',
        second_sequence='imgbase64',
        user_defined_parameters=user_defined_parameters,
        is_training=False)

Trying downloading name_mapping.json
Success
Downloading `alibaba-pai/pai-diffusion-general-large-zh` to /root/.easynlp/modelzoo/alibaba-pai/pai-diffusion-general-large-zh.tgz
****T2I_train.tsv
`/root/.easynlp/modelzoo/alibaba-pai/pai-diffusion-general-large-zh.tgz` already exists
****T2I_val.tsv

模型训练

处理好数据与模型载入后，我们开始训练模型。我们使用EasyNLP中封装好的LatentDiffusion函数进行训练时的模型构建，其参数如下：

pretrained_model_name_or_path：预训练模型名称路径，这里我们使用"alibaba-pai/pai-diffusion-general-large-zh"
args：预先定义参数
user_defined_parameters：用户自定义参数，直接填入刚刚处理好的自定义参数user_defined_parameters

model = LatentDiffusion(pretrained_model_name_or_path=pretrained_model_name_or_path,
                        args=args,
                        user_defined_parameters=user_defined_parameters)

`/root/.easynlp/modelzoo/alibaba-pai/pai-diffusion-general-large-zh.tgz` already exists
making attention of type 'vanilla' with 512 in_channels
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
making attention of type 'vanilla' with 512 in_channels
LatentDiffusionModel: Running in eps-prediction mode
DiffusionWrapper has 859.52 M params.

由于之前我们选用了alibaba-pai/pai-diffusion-general-large-zh，因此这里也会对预训练模型进行自动下载并载入。从日志中可以看出，我们对预训练模型的参数进行了载入。下一步我们使用EasyNLP中的Train类创建训练实例，并进行训练。

evaluator = LatentDiffusionModelEvaluator(valid_dataset=valid_dataset, 
                                          user_defined_parameters=user_defined_parameters)
trainer = Trainer(model=model, 
                  train_dataset=train_dataset, 
                  user_defined_parameters=user_defined_parameters,
                  evaluator=evaluator)
trainer.train()

[2023-02-06 16:00:54,702 INFO] ========== Initializing Tensorboard ==========
[2023-02-06 16:00:54,706 INFO] ========== Training Start ==========
[2023-02-06 16:00:54,708 INFO]   Num of GPUs (all)       = 1
[2023-02-06 16:00:54,709 INFO]   Num dataset examples    = 1000
[2023-02-06 16:00:54,709 INFO]   Num training examples   = 1000
[2023-02-06 16:00:54,710 INFO]   Num validation examples = 100
[2023-02-06 16:00:54,710 INFO]   Train. batch size       = 2
[2023-02-06 16:00:54,711 INFO]   Train. micro batch size = 2
[2023-02-06 16:00:54,712 INFO]   Train. batch no.        = 1500
[2023-02-06 16:00:54,715 INFO]   Evaluation batch size   = 2
[2023-02-06 16:00:54,716 INFO]   Total training steps    = 1500
[2023-02-06 16:00:54,716 INFO]   Sequence length         = 16
[2023-02-06 16:00:54,717 INFO]   Saving steps            = None
[2023-02-06 16:00:54,719 INFO]   Distributed_backend     = nccl
[2023-02-06 16:00:54,727 INFO]   num model params        = 1,045,071,531
[2023-02-06 16:00:54,727 INFO]   num trainable params    = 961,417,668
[2023-02-06 16:00:54,728 INFO] 
[2023-02-06 16:00:54,728 INFO] ========== Model Config ==========
[2023-02-06 16:00:54,729 INFO] {"model": {"base_learning_rate": 5e-08, "params": {"linear_start": 0.00085, "linear_end": 0.012, "num_timesteps_cond": 1, "log_every_t": 200, "timesteps": 1000, "first_stage_key": "image", "cond_stage_key": "caption", "image_size": 32, "channels": 4, "cond_stage_trainable": true, "conditioning_key": "crossattn", "monitor": "val/loss_simple_ema", "scale_factor": 0.18215, "use_ema": false, "scheduler_config": {"params": {"warm_up_steps": [10000], "cycle_lengths": [10000000000000], "f_start": [1e-06], "f_max": [1.0], "f_min": [1.0]}}, "unet_config": {"params": {"image_size": 32, "in_channels": 4, "out_channels": 4, "model_channels": 320, "attention_resolutions": [4, 2, 1], "num_res_blocks": 2, "channel_mult": [1, 2, 4, 4], "num_heads": 8, "use_spatial_transformer": true, "transformer_depth": 1, "context_dim": 768, "use_checkpoint": true, "legacy": false}}, "first_stage_config": {"params": {"embed_dim": 4, "monitor": "val/rec_loss", "ddconfig": {"double_z": true, "z_channels": 4, "resolution": 256, "in_channels": 3, "out_ch": 3, "ch": 128, "ch_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_resolutions": [], "dropout": 0.0}, "lossconfig": {"target": "torch.nn.Identity"}, "ckpt_path": null}}, "cond_stage_config": {"params": {"max_length": 32, "text_encoder": {"context_length": 32, "vocab_size": 21128, "width": 768, "heads": 12, "layers": 12, "output_dim": 768, "return_full_embed": false}, "version": "/root/.easynlp/modelzoo/alibaba-pai/pai-diffusion-general-large-zh"}}}}}

optimizer type: AdamW

/mnt/workspace/EasyNLP/easynlp/core/optimizers.py:441: UserWarning: This overload of add_ is deprecated:
  add_(Number alpha, Tensor other)
Consider using one of the following signatures instead:
  add_(Tensor other, *, Number alpha) (Triggered internally at  /workspace/artifacts/paipytorch1.8/dist/ubuntu18.04-py3.6-cuda10.1/build/src/torch/csrc/utils/python_arg_parser.cpp:1005.)
  exp_avg.mul_(beta1).add_(1.0 - beta1, grad)
/home/pai/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:247: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.
  warnings.warn("To get the last learning rate computed by the scheduler, "
[2023-02-06 16:01:39,753 INFO] Epoch [ 1/ 3], step [100/1500], lr 0.000033, 45.02 s
[2023-02-06 16:01:39,754 INFO]   loss      : 0.0793 
[2023-02-06 16:02:22,736 INFO] Epoch [ 1/ 3], step [200/1500], lr 0.000048, 42.98 s
[2023-02-06 16:02:22,738 INFO]   loss      : 0.0785 
[2023-02-06 16:03:05,903 INFO] Epoch [ 1/ 3], step [300/1500], lr 0.000044, 43.17 s
[2023-02-06 16:03:05,905 INFO]   loss      : 0.0764 
[2023-02-06 16:03:48,912 INFO] Epoch [ 1/ 3], step [400/1500], lr 0.000041, 43.01 s
[2023-02-06 16:03:48,913 INFO]   loss      : 0.0784 
[2023-02-06 16:04:31,935 INFO] Epoch [ 1/ 3], step [500/1500], lr 0.000037, 43.02 s
[2023-02-06 16:04:31,936 INFO]   loss      : 0.0800 
[2023-02-06 16:05:15,965 INFO] Epoch [ 2/ 3], step [600/1500], lr 0.000033, 43.52 s
[2023-02-06 16:05:15,968 INFO]   loss      : 0.0741 
[2023-02-06 16:05:58,990 INFO] Epoch [ 2/ 3], step [700/1500], lr 0.000030, 43.02 s
[2023-02-06 16:05:58,991 INFO]   loss      : 0.0721 
[2023-02-06 16:06:42,209 INFO] Epoch [ 2/ 3], step [800/1500], lr 0.000026, 43.22 s
[2023-02-06 16:06:42,210 INFO]   loss      : 0.0742 
[2023-02-06 16:07:25,266 INFO] Epoch [ 2/ 3], step [900/1500], lr 0.000022, 43.06 s
[2023-02-06 16:07:25,268 INFO]   loss      : 0.0754 
[2023-02-06 16:08:08,291 INFO] Epoch [ 2/ 3], step [1000/1500], lr 0.000019, 43.02 s
[2023-02-06 16:08:08,293 INFO]   loss      : 0.0736 
[2023-02-06 16:08:52,589 INFO] Epoch [ 3/ 3], step [1100/1500], lr 0.000015, 43.81 s
[2023-02-06 16:08:52,591 INFO]   loss      : 0.0811 
[2023-02-06 16:09:35,846 INFO] Epoch [ 3/ 3], step [1200/1500], lr 0.000011, 43.25 s
[2023-02-06 16:09:35,848 INFO]   loss      : 0.0758 
[2023-02-06 16:10:18,884 INFO] Epoch [ 3/ 3], step [1300/1500], lr 0.000007, 43.04 s
[2023-02-06 16:10:18,885 INFO]   loss      : 0.0730 
[2023-02-06 16:11:01,936 INFO] Epoch [ 3/ 3], step [1400/1500], lr 0.000004, 43.05 s
[2023-02-06 16:11:01,937 INFO]   loss      : 0.0702 
[2023-02-06 16:11:44,998 INFO] Epoch [ 3/ 3], step [1500/1500], lr 0.000000, 43.06 s
[2023-02-06 16:11:44,999 INFO]   loss      : 0.0737 
[2023-02-06 16:11:45,482 INFO] Saving best model to ./tmp/finetune_model/pytorch_model.bin...

Training Time: 650.8007695674896, rank 0, gsteps 1500

[2023-02-06 16:12:02,798 INFO] Training Time: 668.1165735721588

模型评估

训练过程结束后，train好的模型被我们保存在一开始指定好的checkpoint_dir中，本地路径为"./tmp/finetune_model/"。我们可以对train好的模型进行效果评估。我们同样先使用EasyNLP中的LatentDiffusion方法构建评估模型。

model = LatentDiffusion(pretrained_model_name_or_path='./tmp/finetune_model/',
                        args=args,
                        user_defined_parameters=user_defined_parameters)

making attention of type 'vanilla' with 512 in_channels
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
making attention of type 'vanilla' with 512 in_channels
LatentDiffusionModel: Running in eps-prediction mode
DiffusionWrapper has 859.52 M params.

之后我们使用EasyNLP中的LatentDiffusionModelEvaluator来初始化evaluator，并指定当前device下的当前模型，进行模型评估。

evaluator = LatentDiffusionModelEvaluator(valid_dataset=valid_dataset, 
                                          user_defined_parameters=user_defined_parameters)
model.to(torch.cuda.current_device())
evaluator.evaluate(model=model)

[2023-02-06 16:15:15,666 INFO] Inference time = 5.33s, [41.6574 ms / sample] 
[2023-02-06 16:15:15,667 INFO] Eval loss: 0.07346365787088871

[('eval_loss', -0.07346365787088871)]

模型预测

我们同样可以使用训练好的模型进行预测。我们首先创建一个predictor，并据此实例化一个PredictorManager实例。我们指定输入为T2I_test.tsv，预测好的结果输出在"./tmp/T2I_outputs.tsv"，并指定输出格式为"idx,text,gen_imgbase64"。

predictor = LatentDiffusionPredictor(model_dir='./tmp/finetune_model/', 
                                     model_cls=LatentDiffusion,
                                     args=args,
                                     user_defined_parameters=user_defined_parameters)
predictor_manager = PredictorManager(
    predictor=predictor,
    input_file='T2I_test.tsv',
    input_schema='idx:str:1,text:str:1',
    output_file='./tmp/T2I_outputs.tsv',
    output_schema='idx,text,gen_imgbase64',
    append_cols=args.append_cols,
    batch_size=2
)
predictor_manager.run()
exit()

making attention of type 'vanilla' with 512 in_channels
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
making attention of type 'vanilla' with 512 in_channels
LatentDiffusionModel: Running in eps-prediction mode
DiffusionWrapper has 859.52 M params.

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一步执行

值得一提的是，上述所有训练/评估/预测代码，都已经被集成在EasyNLP/examples/latent_diffusion/main.py中，此外，我们也预先编写好了多种可供直接执行的脚本。用户可以通过带参数运行main.py中指令，或者直接使用bash文件命令行执行的方式，一步执行上述所有训练/评估/预测操作。

main文件一步执行

用户通过以下代码带参数执行main.py中的指令，可直接对模型进行训练/评估/预测操作。以下需要确保当前路径为EasyNLP/examples/latent_diffusion。

训练代码指令如下。参数中，tables指定了训练集和验证集tsv文件的路径，input_schema表示tsv的数据格式，first_sequence、second_sequence用于说明input_schema中哪些字段用于作为第一/第二列数据。模型存储的路径位于checkpoint_dir，learning_rate、epoch_num、random_seed、save_checkpoint_steps、sequence_length、micro_batch_size等为训练的超参数。在本示例中，预训练模型指定为alibaba-pai/pai-diffusion-general-large-zh。

! python  EasyNLP/examples/latent_diffusion/main.py \
    --mode=train \
    --worker_gpu=1 \
    --tables=T2I_train.tsv,T2I_val.tsv \
    --input_schema=idx:str:1,text:str:1,imgbase64:str:1 \
    --first_sequence=text \
    --second_sequence=imgbase64 \
    --checkpoint_dir=./tmp/finetune_model \
    --learning_rate=4e-5 \
    --epoch_num=3 \
    --random_seed=42 \
    --logging_steps=100 \
    --save_checkpoint_steps=1000 \
    --sequence_length=288 \
    --micro_batch_size=16 \
    --app_name=latent_diffusion \
    --user_defined_parameters='pretrain_model_name_or_path=alibaba-pai/pai-diffusion-general-large-zh size=256 text_len=32 img_len=256 reset_model_state_flag=True' ! python  EasyNLP/examples/latent_diffusion/main.py \
    --mode=train \
    --worker_gpu=1 \
    --tables=T2I_train.tsv,T2I_val.tsv \
    --input_schema=idx:str:1,text:str:1,imgbase64:str:1 \
    --first_sequence=text \
    --second_sequence=imgbase64 \
    --checkpoint_dir=./tmp/finetune_model \
    --learning_rate=4e-5 \
    --epoch_num=3 \
    --random_seed=42 \
    --logging_steps=100 \
    --save_checkpoint_steps=1000 \
    --sequence_length=288 \
    --micro_batch_size=16 \
    --app_name=latent_diffusion \
    --user_defined_parameters='pretrain_model_name_or_path=alibaba-pai/pai-diffusion-general-large-zh size=256 text_len=32 img_len=256 reset_model_state_flag=True'

[2023-02-06 15:16:21,505.505 dsw-201326-79bd7d8585-xksgq:1155 INFO utils.py:30] NOTICE: PAIDEBUGGER is turned off.
**************************************************
running local main...
[2023-02-06 15:16:22,352.352 dsw-201326-79bd7d8585-xksgq:1170 INFO utils.py:30] NOTICE: PAIDEBUGGER is turned off.
[2023-02-06 15:16:22,523] [WARNING] [partition_parameters.py:61:<module>] unable to find torch.distributed._all_gather_base. will fall back to torch.distributed.all_gather which will result in suboptimal performance. please consider upgrading your pytorch installation.
/home/pai/lib/python3.6/site-packages/OpenSSL/crypto.py:8: CryptographyDeprecationWarning: Python 3.6 is no longer supported by the Python core team. Therefore, support for it is deprecated in cryptography and will be removed in a future release.
  from cryptography import utils, x509
log: starts to init...
The following parameters are not recognized: []
> initializing torch distributed ...
[2023-02-06 15:16:24,374.374 dsw-201326-79bd7d8585-xksgq:1170 INFO distributed_c10d.py:195] Added key: store_based_barrier_key:1 to store for rank: 0
dsw-201326-79bd7d8585-xksgq:1170:1170 [0] NCCL INFO Bootstrap : Using eth0:10.247.113.131<0>
dsw-201326-79bd7d8585-xksgq:1170:1170 [0] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation
dsw-201326-79bd7d8585-xksgq:1170:1170 [0] misc/ibvwrap.cc:63 NCCL WARN Failed to open libibverbs.so[.1]
dsw-201326-79bd7d8585-xksgq:1170:1170 [0] NCCL INFO NET/Socket : Using [0]eth0:10.247.113.131<0>
dsw-201326-79bd7d8585-xksgq:1170:1170 [0] NCCL INFO Using network Socket
NCCL version 2.8.3+cuda10.1
dsw-201326-79bd7d8585-xksgq:1170:1203 [0] NCCL INFO NCCL_MAX_NCHANNELS set by environment to 2.
dsw-201326-79bd7d8585-xksgq:1170:1203 [0] NCCL INFO NCCL_MIN_NCHANNELS set by environment to 2.
dsw-201326-79bd7d8585-xksgq:1170:1203 [0] NCCL INFO Channel 00/02 :    0
dsw-201326-79bd7d8585-xksgq:1170:1203 [0] NCCL INFO Channel 01/02 :    0
dsw-201326-79bd7d8585-xksgq:1170:1203 [0] NCCL INFO Trees [0] -1/-1/-1->0->-1 [1] -1/-1/-1->0->-1
dsw-201326-79bd7d8585-xksgq:1170:1203 [0] NCCL INFO Connected all rings
dsw-201326-79bd7d8585-xksgq:1170:1203 [0] NCCL INFO Connected all trees
dsw-201326-79bd7d8585-xksgq:1170:1203 [0] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels per peer
dsw-201326-79bd7d8585-xksgq:1170:1203 [0] NCCL INFO comm 0x7fa184006880 rank 0 nranks 1 cudaDev 0 busId 70 - Init COMPLETE
Init dist done. World size: 1, rank 0, l_rank 0
> setting random seeds to 42 ...
`/root/.easynlp/modelzoo/alibaba-pai/pai-diffusion-general-large-zh.tgz` already exists
log: starts to process user params...
`/root/.easynlp/modelzoo/alibaba-pai/pai-diffusion-general-large-zh.tgz` already exists
log: starts to process dataset...
****T2I_train.tsv
****T2I_val.tsv
making attention of type 'vanilla' with 512 in_channels
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
making attention of type 'vanilla' with 512 in_channels
LatentDiffusionModel: Running in eps-prediction mode
DiffusionWrapper has 859.52 M params.
optimizer type: AdamW
[2023-02-06 15:16:38,248 INFO] ========== Initializing Tensorboard ==========
[2023-02-06 15:16:38,251 INFO] ========== Training Start ==========
[2023-02-06 15:16:38,252 INFO]   Num of GPUs (all)       = 1
[2023-02-06 15:16:38,252 INFO]   Num dataset examples    = 1000
[2023-02-06 15:16:38,252 INFO]   Num training examples   = 1000
[2023-02-06 15:16:38,253 INFO]   Num validation examples = 100
[2023-02-06 15:16:38,253 INFO]   Train. batch size       = 16
[2023-02-06 15:16:38,253 INFO]   Train. micro batch size = 16
[2023-02-06 15:16:38,253 INFO]   Train. batch no.        = 187
[2023-02-06 15:16:38,254 INFO]   Evaluation batch size   = 16
[2023-02-06 15:16:38,254 INFO]   Total training steps    = 189
[2023-02-06 15:16:38,254 INFO]   Sequence length         = 288
[2023-02-06 15:16:38,254 INFO]   Saving steps            = 1000
[2023-02-06 15:16:38,254 INFO]   Distributed_backend     = nccl
[2023-02-06 15:16:38,262 INFO]   num model params        = 1,045,071,531
[2023-02-06 15:16:38,262 INFO]   num trainable params    = 961,417,668
[2023-02-06 15:16:38,262 INFO] 
[2023-02-06 15:16:38,262 INFO] ========== Model Config ==========
[2023-02-06 15:16:38,262 INFO] {"model": {"base_learning_rate": 5e-08, "params": {"linear_start": 0.00085, "linear_end": 0.012, "num_timesteps_cond": 1, "log_every_t": 200, "timesteps": 1000, "first_stage_key": "image", "cond_stage_key": "caption", "image_size": 32, "channels": 4, "cond_stage_trainable": true, "conditioning_key": "crossattn", "monitor": "val/loss_simple_ema", "scale_factor": 0.18215, "use_ema": false, "scheduler_config": {"params": {"warm_up_steps": [10000], "cycle_lengths": [10000000000000], "f_start": [1e-06], "f_max": [1.0], "f_min": [1.0]}}, "unet_config": {"params": {"image_size": 32, "in_channels": 4, "out_channels": 4, "model_channels": 320, "attention_resolutions": [4, 2, 1], "num_res_blocks": 2, "channel_mult": [1, 2, 4, 4], "num_heads": 8, "use_spatial_transformer": true, "transformer_depth": 1, "context_dim": 768, "use_checkpoint": true, "legacy": false}}, "first_stage_config": {"params": {"embed_dim": 4, "monitor": "val/rec_loss", "ddconfig": {"double_z": true, "z_channels": 4, "resolution": 256, "in_channels": 3, "out_ch": 3, "ch": 128, "ch_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_resolutions": [], "dropout": 0.0}, "lossconfig": {"target": "torch.nn.Identity"}, "ckpt_path": null}}, "cond_stage_config": {"params": {"max_length": 32, "text_encoder": {"context_length": 32, "vocab_size": 21128, "width": 768, "heads": 12, "layers": 12, "output_dim": 768, "return_full_embed": false}, "version": "/root/.easynlp/modelzoo/alibaba-pai/pai-diffusion-general-large-zh"}}}}}
/home/pai/lib/python3.6/site-packages/pai_easynlp-0.1.2-py3.6.egg/easynlp/core/optimizers.py:441: UserWarning: This overload of add_ is deprecated:
  add_(Number alpha, Tensor other)
Consider using one of the following signatures instead:
  add_(Tensor other, *, Number alpha) (Triggered internally at  /workspace/artifacts/paipytorch1.8/dist/ubuntu18.04-py3.6-cuda10.1/build/src/torch/csrc/utils/python_arg_parser.cpp:1005.)
  exp_avg.mul_(beta1).add_(1.0 - beta1, grad)
/home/pai/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:247: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.
  warnings.warn("To get the last learning rate computed by the scheduler, "
[2023-02-06 15:18:55,312 INFO] Epoch [ 2/ 3], step [100/189], lr 0.000021, 49.72 s
[2023-02-06 15:18:55,313 INFO]   loss      : 0.0689 
Training Time: 259.2537770271301, rank 0, gsteps 189
[2023-02-06 15:21:00,818 INFO] Inference time = 2.16s, [16.8991 ms / sample] 
[2023-02-06 15:21:00,818 INFO] Eval loss: 0.06386045087128878
[2023-02-06 15:21:00,819 INFO] Saving best model to ./tmp/finetune_model/pytorch_model.bin...
[2023-02-06 15:21:50,050 INFO] Best score: -0.06386045087128878
[2023-02-06 15:21:50,050 INFO] Training Time: 311.82186913490295

评估代码如下，参数含义与训练是一致的。

! python EasyNLP/examples/latent_diffusion/main.py \
      --mode=predict \
      --worker_gpu=1 \
      --tables=T2I_test.tsv \
      --input_schema=idx:str:1,text:str:1 \
      --output_schema=idx,text,gen_imgbase64 \
      --outputs=./tmp/T2I_outputs.tsv \
      --first_sequence=text \
      --checkpoint_dir=./tmp/finetune_model \
      --random_seed=42 \
      --logging_steps=100 \
      --save_checkpoint_steps=500 \
      --sequence_length=32 \
      --micro_batch_size=16 \
      --app_name=latent_diffusion \
      --user_defined_parameters="n_samples=2 write_image=True image_prefix=./output/"

[2023-02-06 15:22:29,715.715 dsw-201326-79bd7d8585-xksgq:1695 INFO utils.py:30] NOTICE: PAIDEBUGGER is turned off.
**************************************************
running local main...
[2023-02-06 15:22:30,556.556 dsw-201326-79bd7d8585-xksgq:1710 INFO utils.py:30] NOTICE: PAIDEBUGGER is turned off.
[2023-02-06 15:22:30,730] [WARNING] [partition_parameters.py:61:<module>] unable to find torch.distributed._all_gather_base. will fall back to torch.distributed.all_gather which will result in suboptimal performance. please consider upgrading your pytorch installation.
/home/pai/lib/python3.6/site-packages/OpenSSL/crypto.py:8: CryptographyDeprecationWarning: Python 3.6 is no longer supported by the Python core team. Therefore, support for it is deprecated in cryptography and will be removed in a future release.
  from cryptography import utils, x509
log: starts to init...
The following parameters are not recognized: []
> initializing torch distributed ...
[2023-02-06 15:22:32,587.587 dsw-201326-79bd7d8585-xksgq:1710 INFO distributed_c10d.py:195] Added key: store_based_barrier_key:1 to store for rank: 0
dsw-201326-79bd7d8585-xksgq:1710:1710 [0] NCCL INFO Bootstrap : Using eth0:10.247.113.131<0>
dsw-201326-79bd7d8585-xksgq:1710:1710 [0] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation
dsw-201326-79bd7d8585-xksgq:1710:1710 [0] misc/ibvwrap.cc:63 NCCL WARN Failed to open libibverbs.so[.1]
dsw-201326-79bd7d8585-xksgq:1710:1710 [0] NCCL INFO NET/Socket : Using [0]eth0:10.247.113.131<0>
dsw-201326-79bd7d8585-xksgq:1710:1710 [0] NCCL INFO Using network Socket
NCCL version 2.8.3+cuda10.1
dsw-201326-79bd7d8585-xksgq:1710:1743 [0] NCCL INFO NCCL_MAX_NCHANNELS set by environment to 2.
dsw-201326-79bd7d8585-xksgq:1710:1743 [0] NCCL INFO NCCL_MIN_NCHANNELS set by environment to 2.
dsw-201326-79bd7d8585-xksgq:1710:1743 [0] NCCL INFO Channel 00/02 :    0
dsw-201326-79bd7d8585-xksgq:1710:1743 [0] NCCL INFO Channel 01/02 :    0
dsw-201326-79bd7d8585-xksgq:1710:1743 [0] NCCL INFO Trees [0] -1/-1/-1->0->-1 [1] -1/-1/-1->0->-1
dsw-201326-79bd7d8585-xksgq:1710:1743 [0] NCCL INFO Connected all rings
dsw-201326-79bd7d8585-xksgq:1710:1743 [0] NCCL INFO Connected all trees
dsw-201326-79bd7d8585-xksgq:1710:1743 [0] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels per peer
dsw-201326-79bd7d8585-xksgq:1710:1743 [0] NCCL INFO comm 0x7f06e0006880 rank 0 nranks 1 cudaDev 0 busId 70 - Init COMPLETE
Init dist done. World size: 1, rank 0, l_rank 0
> setting random seeds to 42 ...
log: starts to process user params...
making attention of type 'vanilla' with 512 in_channels
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
making attention of type 'vanilla' with 512 in_channels
LatentDiffusionModel: Running in eps-prediction mode
DiffusionWrapper has 859.52 M params.
0it [00:00, ?it/s]
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Sampling:   0%|                                           | 0/1 [00:00<?, ?it/s]Data shape for PLMS sampling is (2, 4, 32, 32)
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利用bash文件命令行执行

我们在EasyNLP/examples/latent_diffusion/文件夹下封装好了多种可直接执行的bash脚本，用户同样可以通过直接使用bash文件命令行执行的方式来一步完成模型的训练/预测。以下以run_latent_diffusion_local_user_defined.sh脚本为例。

模型训练：

! cd EasyNLP/examples/latent_diffusion &&  bash run_latent_diffusion_local_user_defined.sh 0 finetune

! cd EasyNLP/examples/latent_diffusion && bash run_latent_diffusion_local_user_defined.sh 0 predict

【DSW Gallery】基于EasyNLP-Diffusion模型的中文文图生成

直接使用

基于EasyNLP-Diffusion模型的中文文图生成

PAI-Diffusion模型详解

运行环境要求

EasyNLP安装

数据准备

初始化

载入数据

模型训练

模型评估

模型预测

一步执行

main文件一步执行

利用bash文件命令行执行

人工智能平台PAI

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