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4 Commits (master)
4 Commits (master)
| Author | SHA1 | Message | Date |
|---|---|---|---|
 yuze.zyz
|
605cd7f44a |
[to #42322933] NLP 1030 Refactor
Features:
1. Refactor the directory structure of nlp models. All model files are placed into either the model folder or the task_model folder
2. Refactor all the comments to google style
3. Add detail comments to important tasks and nlp models, to list the description of the model, and its preprocessor&trainer
4. Model Exporting now supports a direct all to TorchModelExporter(no need to derive from it)
5. Refactor model save_pretrained method to support direct running(independent from trainer)
6. Remove the judgement of Model in the pipeline base class, to support outer register models running in our pipelines
7. Nlp trainer now has a NLPTrainingArguments class , user can pass arguments into the dataclass, and use it as a normal cfg_modify_fn, to simplify the operation of modify cfg.
8. Merge the BACKBONES and the MODELS, so user can get a backbone with the Model.from_pretrained call
9. Model.from_pretrained now support a task argument, so user can use a backbone and load it with a specific task class.
10. Support Preprocessor.from_pretrained method
11. Add standard return classes to important nlp tasks, so some of the pipelines and the models are independent now, the return values of the models will always be tensors, and the pipelines will take care of the conversion to numpy and the following stuffs.
12. Split the file of the nlp preprocessors, to make the dir structure more clear.
Bugs Fixing:
1. Fix a bug that lr_scheduler can be called earlier than the optimizer's step
2. Fix a bug that the direct call of Pipelines (not from pipeline(xxx)) throws error
3. Fix a bug that the trainer will not call the correct TaskDataset class
4. Fix a bug that the internal loading of dataset will throws error in the trainer class
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10490585
|
2 years ago |
|
yuze.zyz
|
21fa71baf0 |
[to #42322933] add/refactor nlp models source code and finetune
1. add sbert,veco,palm,space source code
2. support sbert sequence classification, token classification finetune
3. support veco sequence classification finetune
4. support palm nlg finetune
evaluation result: https://sheet.alibaba-inc.com/#/sheet/f7fdcc7f22bd5105 sheet:Maas
5. add ut for finetunes
6. add veco's taskdataset processor
7. add a common trainer for nlp, and a specific trainer for veco
8. merge some duplicate codes of models, preprocessors, pipelines
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9574105
* add basic class of hook&metrics
* pre-commit passed
* change some comments
* pre commit passed
* 1. remove accuracy's groups 2. remove useless hooks 3. simplify priorities
* pre-commit passed
* fix a comment
* Merge branch 'master' into finetune_hooks_metrics
# Conflicts:
# modelscope/metainfo.py
* pre-commit passed
* add basic class of hook&metrics
* pre-commit passed
* change some comments
* pre commit passed
* 1. remove accuracy's groups 2. remove useless hooks 3. simplify priorities
* pre-commit passed
* fix a comment
* Merge branch 'feat/finetune' of gitlab.alibaba-inc.com:Ali-MaaS/MaaS-lib into feat/finetune
* mv hooks related to modelscope/trainers/hooks
* mv priority back
* add torch mdoel base and test
* update hooks, trainer, import_util
* add torch epoch based trainer and dis utils
* add hooks
* fix warmup
* format code stype and fix warmup and add warmup unittest
* fix impls
* pre-commit check passed
* update hook and add EpochBasedTrainer
* add trainer unittest
* Merge branch 'feat/add_hooks' into feat/add_task
# Conflicts:
# modelscope/models/base_torch.py
# modelscope/trainers/hooks/hook.py
# modelscope/trainers/trainer.py
* update unittest name
* rewrite taskdataset to trainer
* fix trainer and add unittest
* add unittest
* code: run to forward
* run through... but ugly code
* arrange some cls
* fix some errs
* revert some mistakes
* init check in
* Merge branch 'feat/add_hooks' into feat/add_task
# Conflicts:
# modelscope/trainers/trainer.py
* test with bigger epoch and size
* add the default metrics class
* move build metrics code to a method
* merge add_task
* merge origin add_task
* add device initialization
* remove preprocessor arg for bool
* add task models
* move metric collect logic to metrics class
* pre-commit passed
* fix cr comments
* precommit passed
* add task models
* Merge remote-tracking branch 'origin/feat/add_task' into feat/backbone_head
* add comment
* change comment formats.
* fix comments
* fix ut bug
* fix comments
* add wrapper check
* fix comments
* pre commit passed
* fix cr comments
* solve a loop import problem
* fix ut bug
* fix ut errors
* change dummydataset to msdataset
* precommit passed
* merge add task
* backbone-head is build, model is not correctly loaded
* model load states matched
* result matched
* lint
* add veco/palm_v2 code
* merge master
* merge master success running
* add repr model name level
* Merge branch 'feat/veco_palm' into feat/finetune_sbert_veco
* model test for training
* add token-classification metric add formal ut
* fix running bug
* finetune and pipeline are working with backbone-head
* add nli
* add missing code
* finetune and pipeline are working with backbone-head
* Merge branch 'feat/backbone_head' of http://gitlab.alibaba-inc.com/Ali-MaaS/MaaS-lib into feat/backbone_head
* add a test repo for pr
* remove merge conflicted file
* remove merge conflicted file 1
* lint check
* import error
* none type bug fix
* forward input unpacking or dict bug
* move head into models, add build_backbone with registry, no base method
* merge master
* feat: 1. add interleave dataset method 2. support multiple dataset in trainer.build_dataset 3. support 3 sub tasks in sequence_classification task
* unfinished
* update the task model structure in NLP field
* merge master
* update by comments
* keep the default model id as current on production
* unfinished
* unfinished
* veco can run
* Merge remote-tracking branch 'origin/master' into feat/backbone_head
* add taskmodel for module management
* remove forward_input_is_dict
* unfinished
* token classification started
* update base model structure
* move space to backbone
* remove 'type' in build_from_cfg method
* test update
* bug fix
* on tesing, mess code
* Merge branch 'feat/backbone_head' into feat/refactor_nlp_730
# Conflicts:
# modelscope/metrics/builder.py
# modelscope/models/__init__.py
# modelscope/models/nlp/__init__.py
# modelscope/preprocessors/nlp.py
# modelscope/trainers/trainer.py
# requirements/multi-modal.txt
* add missing merge
* add sofa source code
* refactor
* add veco task dataset
* add veco task dataset
* pre-commit passed
* fix bug of log
* add some features
* merge master
* bug fix
* refine nlp models
* fix the training error
* unfinished
* refactor pipeline
* Merge branch 'feat/backbone_head' into feat/refactor_nlp_730
# Conflicts:
# modelscope/metrics/builder.py
# modelscope/models/nlp/__init__.py
# modelscope/models/nlp/backbones/structbert/modeling_sbert.py
# modelscope/models/nlp/palm_v2/palm_for_text_generation.py
# modelscope/preprocessors/base.py
# modelscope/preprocessors/nlp.py
# modelscope/trainers/trainer.py
* Merge commit 'ab04ceafc5453ce7daa9aa09e37a55f703072a10' into feat/refactor_nlp_730
# Conflicts:
# modelscope/metainfo.py
# modelscope/metrics/builder.py
# modelscope/models/__init__.py
# modelscope/models/base/base_torch_model.py
# modelscope/models/nlp/__init__.py
# modelscope/models/nlp/backbones/space/model/intent_unified_transformer.py
# modelscope/models/nlp/backbones/space/model/model_base.py
# modelscope/models/nlp/palm_v2/palm_for_text_generation.py
# modelscope/models/nlp/sbert_for_sequence_classification.py
# modelscope/models/nlp/sequence_classification.py
# modelscope/models/nlp/space/__init__.py
# modelscope/models/nlp/space_for_dialog_intent_prediction.py
# modelscope/models/nlp/space_for_dialog_modeling.py
# modelscope/models/nlp/space_for_dialog_state_tracking.py
# modelscope/models/nlp/task_model.py
# modelscope/pipelines/nlp/sentiment_classification_pipeline.py
# modelscope/preprocessors/base.py
# modelscope/preprocessors/nlp.py
# modelscope/trainers/trainer.py
* revert changes
* unify sentnece classification postprocess
* revert some changes, move some model files
* pipeline first case run through
* ws pipeline passed
* Merge branch 'feat/refactor_nlp_730' into feat/finetune_sbert_veco
* finetune
* revert code
* revert some code
* ws finetune started, only the accuracy is weird
* Merge branch 'feat/veco_taskdataset' into feat/finetune_sbert_veco
# Conflicts:
# modelscope/task_datasets/veco_dataset.py
# tests/taskdataset/test_veco_dataset.py
* veco+nli finetune started
* Merge branch 'master' into feat/finetune_sbert_veco
# Conflicts:
# modelscope/models/nlp/sbert_for_sequence_classification.py
# modelscope/models/nlp/sbert_for_token_classification.py
# modelscope/models/nlp/sbert_for_zero_shot_classification.py
# modelscope/models/nlp/space/space_for_dialog_intent_prediction.py
# modelscope/models/nlp/space/space_for_dialog_modeling.py
# modelscope/trainers/trainer.py
* add trainer for nlp
* trainer: dataset params passed into preprocessor
* test passed by nlptrainer
* fix some bugs
* fix some bugs
* add backbone/head subclass
* fix regression bugs
* fix bug in token-cls finetune
* support cfg modification
* fix bug
* fix bug
* update requirements
* add some comments and fix some t
* add some comments and revert a argument
* split to two test files
* revert code
* fixbug in precessor
(cherry picked from commit
|
3 years ago |
 zhangzhicheng.zzc
|
68fc437044 | [to #42322933] Add backbone-head model structure | 3 years ago |
 wenmeng.zwm
|
231f400133 |
[to #43112534] finetune support and first case
co-contributed with 夕陌&雨泓 * add torch epoch based trainer and dis utils * add hooks including optimizer, lrscheduler, logging, checkpoint, evaluation, time profiling * add torch mdoel base and test * add optimizer and lrscheduler module * add sbert for text classification example * add task_dataset for dataset-level processor Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9338412 |
3 years ago |