From 0326d5fb7adb4b9fea032e77087461f26a37c038 Mon Sep 17 00:00:00 2001 From: shengdinghu Date: Sun, 3 Jul 2022 02:10:18 +0000 Subject: [PATCH 1/8] finish delta center development --- .gitignore | 3 +- README.md | 5 + examples/examples_prompt/README.md | 67 ++- examples/examples_prompt/collect_result.jsonl | 59 --- .../examples_prompt/data_processors/tasks.py | 49 ++- examples/examples_prompt/src/run.py | 2 +- examples/examples_prompt/src/test.py | 10 +- examples/examples_prompt/utils/args.py | 15 + .../{ => legacies}/examples_seq2seq/README.md | 0 .../examples_seq2seq/__init__.py | 0 .../examples_seq2seq/configs/config_gen_bs.py | 0 .../data_processors/__init__.py | 0 .../data_processors/data_collator.py | 0 .../data_processors/postprocessors.py | 0 .../examples_seq2seq/data_processors/tasks.py | 0 .../examples_seq2seq/data_processors/utils.py | 0 .../examples_seq2seq/metrics/__init__.py | 0 .../examples_seq2seq/metrics/metrics.py | 0 .../examples_seq2seq/metrics/qa_utils.py | 0 .../examples_seq2seq/run_seq2seq.py | 0 .../examples_seq2seq/seq2seq_trainer.py | 0 .../examples_seq2seq/trainers/__init__.py | 0 .../examples_seq2seq/trainers/model_args.py | 0 .../trainers/seq2seq_trainer.py | 0 .../examples_seq2seq/trainers/trainer.py | 0 .../examples_seq2seq/trainers/trainer_args.py | 0 .../trainers/trainer_utils.py | 0 .../examples_seq2seq/utils/__init__.py | 0 .../examples_seq2seq/utils/utils.py | 0 .../examples_text-classification/README.md | 0 .../configs/config_gen.py | 0 .../configs/lora_roberta-base/lora_cola.json | 0 .../configs/lora_roberta-base/lora_mnli.json | 0 .../configs/lora_roberta-base/lora_mrpc.json | 0 .../configs/lora_roberta-base/lora_qnli.json | 0 .../configs/lora_roberta-base/lora_qqp.json | 0 .../configs/lora_roberta-base/lora_rte.json | 0 .../configs/lora_roberta-base/lora_sst2.json | 0 .../configs/lora_roberta-base/lora_stsb.json | 0 .../configs/lora_roberta-base/lora_wnli.json | 0 .../configs/prefix_roberta-base/cola.json | 0 .../configs/prefix_roberta-base/mnli.json | 0 .../configs/prefix_roberta-base/mrpc.json | 0 .../configs/prefix_roberta-base/qnli.json | 0 .../configs/prefix_roberta-base/qqp.json | 0 .../configs/prefix_roberta-base/rte.json | 0 .../configs/prefix_roberta-base/sst2.json | 0 .../configs/prefix_roberta-base/stsb.json | 0 .../prefix_roberta-base/superglue-boolq.json | 0 .../prefix_roberta-base/superglue-cb.json | 0 .../prefix_roberta-base/superglue-copa.json | 0 .../superglue-multirc.json | 0 .../prefix_roberta-base/superglue-record.json | 0 .../prefix_roberta-base/superglue-wic.json | 0 .../superglue-wsc.fixed.json | 0 .../metrics/glue.py | 0 .../requirements.txt | 0 .../examples_text-classification/run.sh | 0 .../examples_text-classification/run_glue.py | 0 .../examples_text-classification/util.py | 0 examples/{ => legacies}/setup_seq2seq.py | 0 examples/setup_prompt.py | 48 --- opendelta/auto_delta.py | 34 +- opendelta/delta_configs.py | 171 ++------ opendelta/utils/delta_center.py | 107 +---- opendelta/utils/delta_hub.py | 24 -- opendelta/utils/file_utils.py | 3 + opendelta/utils/saving_loading_utils.py | 406 +++++------------- 68 files changed, 287 insertions(+), 716 deletions(-) delete mode 100644 examples/examples_prompt/collect_result.jsonl rename examples/{ => legacies}/examples_seq2seq/README.md (100%) rename examples/{ => legacies}/examples_seq2seq/__init__.py (100%) rename examples/{ => legacies}/examples_seq2seq/configs/config_gen_bs.py (100%) rename examples/{ => legacies}/examples_seq2seq/data_processors/__init__.py (100%) rename examples/{ => legacies}/examples_seq2seq/data_processors/data_collator.py (100%) rename examples/{ => legacies}/examples_seq2seq/data_processors/postprocessors.py (100%) rename examples/{ => legacies}/examples_seq2seq/data_processors/tasks.py (100%) rename examples/{ => legacies}/examples_seq2seq/data_processors/utils.py (100%) rename examples/{ => legacies}/examples_seq2seq/metrics/__init__.py (100%) rename examples/{ => legacies}/examples_seq2seq/metrics/metrics.py (100%) rename examples/{ => legacies}/examples_seq2seq/metrics/qa_utils.py (100%) rename examples/{ => legacies}/examples_seq2seq/run_seq2seq.py (100%) rename examples/{ => legacies}/examples_seq2seq/seq2seq_trainer.py (100%) rename examples/{ => legacies}/examples_seq2seq/trainers/__init__.py (100%) rename examples/{ => legacies}/examples_seq2seq/trainers/model_args.py (100%) rename examples/{ => legacies}/examples_seq2seq/trainers/seq2seq_trainer.py (100%) rename examples/{ => legacies}/examples_seq2seq/trainers/trainer.py (100%) rename examples/{ => legacies}/examples_seq2seq/trainers/trainer_args.py (100%) rename examples/{ => legacies}/examples_seq2seq/trainers/trainer_utils.py (100%) rename examples/{ => legacies}/examples_seq2seq/utils/__init__.py (100%) rename examples/{ => legacies}/examples_seq2seq/utils/utils.py (100%) rename examples/{ => legacies}/examples_text-classification/README.md (100%) rename examples/{ => legacies}/examples_text-classification/configs/config_gen.py (100%) rename examples/{ => legacies}/examples_text-classification/configs/lora_roberta-base/lora_cola.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/lora_roberta-base/lora_mnli.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/lora_roberta-base/lora_mrpc.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/lora_roberta-base/lora_qnli.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/lora_roberta-base/lora_qqp.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/lora_roberta-base/lora_rte.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/lora_roberta-base/lora_sst2.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/lora_roberta-base/lora_stsb.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/lora_roberta-base/lora_wnli.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/prefix_roberta-base/cola.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/prefix_roberta-base/mnli.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/prefix_roberta-base/mrpc.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/prefix_roberta-base/qnli.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/prefix_roberta-base/qqp.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/prefix_roberta-base/rte.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/prefix_roberta-base/sst2.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/prefix_roberta-base/stsb.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/prefix_roberta-base/superglue-boolq.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/prefix_roberta-base/superglue-cb.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/prefix_roberta-base/superglue-copa.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/prefix_roberta-base/superglue-multirc.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/prefix_roberta-base/superglue-record.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/prefix_roberta-base/superglue-wic.json (100%) rename examples/{ => legacies}/examples_text-classification/configs/prefix_roberta-base/superglue-wsc.fixed.json (100%) rename examples/{ => legacies}/examples_text-classification/metrics/glue.py (100%) rename examples/{ => legacies}/examples_text-classification/requirements.txt (100%) rename examples/{ => legacies}/examples_text-classification/run.sh (100%) rename examples/{ => legacies}/examples_text-classification/run_glue.py (100%) rename examples/{ => legacies}/examples_text-classification/util.py (100%) rename examples/{ => legacies}/setup_seq2seq.py (100%) delete mode 100755 examples/setup_prompt.py delete mode 100644 opendelta/utils/delta_hub.py create mode 100644 opendelta/utils/file_utils.py diff --git a/.gitignore b/.gitignore index db062d8..da9400c 100644 --- a/.gitignore +++ b/.gitignore @@ -38,4 +38,5 @@ log.txt **/output/ **/thunlp/ **/saved_ckpts/ - +**/delta_checkpoints/ +**/outputs/ diff --git a/README.md b/README.md index 06ab183..fbcada9 100644 --- a/README.md +++ b/README.md @@ -72,6 +72,11 @@ python setup.py install python setup.py develop ``` +If you encounter network error using setup.py, please firstly install the dependencies via +```shell +pip install -r requirements.txt && python setup.py develop +``` + ## Must Try ```python diff --git a/examples/examples_prompt/README.md b/examples/examples_prompt/README.md index e9d5249..d6b3329 100644 --- a/examples/examples_prompt/README.md +++ b/examples/examples_prompt/README.md @@ -1,24 +1,59 @@ -# !!!!This example collection is still under develop, please wait for some time to use it. +# Examples of using opendelta together with 🤗 transformers. -## install the repo +In this repo, we construct a very general pipeline to train and test a PLM using +🤗 transformers. + +The pipeline was constructed together with [openpromptu](https://pypi.org/project/openpromptu/), which is a light and +model-agnostic version of [openprompt](https://github.com/thunlp/OpenPrompt). + +## Pool of PLMs +We are going to adapt most of the models in 🤗 transformers +in the repos. The different pipeline, processing, or configurations are specified +in `./backbones/`. You can add your own model in this file to support customized models. + + +### A example script to run the repo in offline mode ```bash -cd ../ -python setup_seq2seq.py develop +conda activate [YOURENV] +PATHBASE=[YOURPATH] + +JOBNAME="adapter_t5-base" +DATASET="superglue-cb" + +cd $PATHBASE/OpenDelta/examples/examples_prompt/ +python configs/gen_t5.py --job $JOBNAME + +export TRANSFORMERS_OFFLINE=1 +export HF_DATASETS_OFFLINE=1 +python src/run.py configs/$JOBNAME/$DATASET.json \ +--model_name_or_path [YOURPATH_TO_T5_BASE] \ +--tokenizer_name [YOURPATH_TO_T5_BASE] \ +--datasets_saved_path [YOURPATH_TO_CB_DATASETS] \ +--finetuned_delta_path ${PATHBASE}/delta_checkpoints/ \ +--num_train_epochs 20 \ +--bottleneck_dim 24 \ +--delay_push True ``` -This will add `examples_seq2seq` to the environment path of the python lib. -## Generating the json configuration file +## A example of quick testing the repo. -```shell -python configs/gen_$BACKBONETYPE.py --job $YOURJOB -#e.g. python configs/gen_beit.py --job lora_beit-base-patch16-224 -``` -The available job configuration (e.g., `--job lora_beit-base-patch16-224`) can be seen from the scripts. You can also -create your only configuration. +```bash +conda activate [YOURENV] +PATHBASE=[YOURPATH] +JOBNAME="adapter_t5-base" +DATASET="superglue-cb" -## Run the code +cd $PATHBASE/OpenDelta/examples/examples_prompt/ -``` -CUDA_VISIBLE_DEVICES=1 python src/run.py configs/lora_beit-base-patch16-224/beans.json -``` +export TRANSFORMERS_OFFLINE=1 +export HF_DATASETS_OFFLINE=1 +export DELTACENTER_OFFLINE=0 +python src/test.py configs/$JOBNAME/$DATASET.json \ +--model_name_or_path [YOURPATH_TO_T5_BASE] \ +--tokenizer_name [YOURPATH_TO_T5_BASE] \ +--datasets_saved_path [YOURPATH_TO_CB_DATASETS] \ +--finetuned_delta_path thunlp/t5-base_adapter_superglue-cb_20220701171436c80 \ +--delta_cache_dir "./delta_checkpoints/" \ +--force_download True +``` \ No newline at end of file diff --git a/examples/examples_prompt/collect_result.jsonl b/examples/examples_prompt/collect_result.jsonl deleted file mode 100644 index 990a2d9..0000000 --- a/examples/examples_prompt/collect_result.jsonl +++ /dev/null @@ -1,59 +0,0 @@ -# the final results will be populated here.{ - "evaluate": { - "epoch": 20.0, - "eval_accuracy": 89.2156862745098, - "eval_average_metrics": 90.76168929110105, - "eval_f1": 92.3076923076923, - "eval_loss": 0.16493959724903107, - "eval_runtime": 1.6391, - "eval_samples_per_second": 124.455 - }, - "repo_name": "DeltaHub/bitfit_t5-base_mrpc", - "test": { - "epoch": 20.0, - "test_accuracy": 88.23529411764706, - "test_average_metrics": 89.97971602434077, - "test_f1": 91.72413793103448, - "test_loss": 0.14968213438987732, - "test_runtime": 1.6344, - "test_samples_per_second": 124.82 - } -} -{ - "evaluate": { - "epoch": 20.0, - "eval_average_metrics": 52.10265668831534, - "eval_loss": 0.3603779077529907, - "eval_matthews_correlation": 52.10265668831534, - "eval_runtime": 1.0808, - "eval_samples_per_second": 482.046 - }, - "repo_name": "DeltaHub/bitfit_t5-base_cola", - "test": { - "epoch": 20.0, - "test_average_metrics": 54.209563471221934, - "test_loss": 0.2853100299835205, - "test_matthews_correlation": 54.209563471221934, - "test_runtime": 1.056, - "test_samples_per_second": 494.304 - } -} -{ - "evaluate": { - "epoch": 20.0, - "eval_average_metrics": 53.80613287067274, - "eval_loss": 0.25723716616630554, - "eval_matthews_correlation": 53.80613287067274, - "eval_runtime": 1.0583, - "eval_samples_per_second": 492.299 - }, - "repo_name": "DeltaHub/bitfit_t5-base_cola", - "test": { - "epoch": 20.0, - "test_average_metrics": 54.32497579543861, - "test_loss": 0.22327613830566406, - "test_matthews_correlation": 54.32497579543861, - "test_runtime": 1.0556, - "test_samples_per_second": 494.507 - } -} diff --git a/examples/examples_prompt/data_processors/tasks.py b/examples/examples_prompt/data_processors/tasks.py index 55048fe..b6ea54b 100644 --- a/examples/examples_prompt/data_processors/tasks.py +++ b/examples/examples_prompt/data_processors/tasks.py @@ -13,12 +13,11 @@ import numpy as np import torch import re import itertools - +import os logger = logging.getLogger(__name__) - from transformers.models.auto.tokenization_auto import tokenizer_class_from_name from typing import List, Dict @@ -63,7 +62,8 @@ class COLA(AbstractTask): } def load_dataset(self, split): - if self.data_args.datasets_load_from_disk: + offline = os.environ.get("HF_DATASETS_OFFLINE", "0") + if offline == '1': return datasets.load_from_disk(f"{self.data_args.datasets_saved_path}/glue.cola")[split] else: return datasets.load_dataset('glue', 'cola', @@ -91,7 +91,8 @@ class SST2(AbstractTask): } def load_dataset(self, split): - if self.data_args.datasets_load_from_disk: + offline = os.environ.get("HF_DATASETS_OFFLINE", "0") + if offline == '1': return datasets.load_from_disk(f"{self.data_args.datasets_saved_path}/glue.sst2")[split] else: return datasets.load_dataset('glue', 'sst2', @@ -118,10 +119,9 @@ class MRPC(AbstractTask): } - - def load_dataset(self, split): - if self.data_args.datasets_load_from_disk: + offline = os.environ.get("HF_DATASETS_OFFLINE", "0") + if offline == '1': return datasets.load_from_disk(f"{self.data_args.datasets_saved_path}/glue.mrpc")[split] else: return datasets.load_dataset('glue', 'mrpc', split=split, script_version="master") @@ -147,7 +147,8 @@ class QQP(AbstractTask): def load_dataset(self, split): - if self.data_args.datasets_load_from_disk: + offline = os.environ.get("HF_DATASETS_OFFLINE", "0") + if offline == '1': return datasets.load_from_disk(f"{self.data_args.datasets_saved_path}/glue.qqp")[split] else: return datasets.load_dataset('glue', 'qqp', @@ -203,7 +204,8 @@ class MNLI(AbstractTask): } def load_dataset(self, split): - if self.data_args.datasets_load_from_disk: + offline = os.environ.get("HF_DATASETS_OFFLINE", "0") + if offline == '1': return datasets.load_from_disk(f"{self.data_args.datasets_saved_path}/glue.mnli")[split] else: return datasets.load_dataset('glue', 'mnli', split=split, script_version="master") @@ -238,7 +240,8 @@ class QNLI(AbstractTask): def load_dataset(self, split): - if self.data_args.datasets_load_from_disk: + offline = os.environ.get("HF_DATASETS_OFFLINE", "0") + if offline == '1': return datasets.load_from_disk(f"{self.data_args.datasets_saved_path}/glue.qnli")[split] else: return datasets.load_dataset('glue', 'qnli', split=split, script_version="master") @@ -274,7 +277,8 @@ class RTE(AbstractTask): } def load_dataset(self, split): - if self.data_args.datasets_load_from_disk: + offline = os.environ.get("HF_DATASETS_OFFLINE", "0") + if offline == '1': return datasets.load_from_disk(f"{self.data_args.datasets_saved_path}/glue.rte")[split] else: return datasets.load_dataset('glue', 'rte', @@ -301,7 +305,8 @@ class WNLI(AbstractTask): def load_dataset(self, split): - if self.data_args.datasets_load_from_disk: + offline = os.environ.get("HF_DATASETS_OFFLINE", "0") + if offline == '1': return datasets.load_from_disk(f"{self.data_args.datasets_saved_path}/glue.wnli")[split] else: return datasets.load_dataset('glue', 'wnli', split=split, script_version="master") @@ -329,7 +334,8 @@ class SuperGLUEBoolQ(AbstractTask): } def load_dataset(self, split): - if self.data_args.datasets_load_from_disk: + offline = os.environ.get("HF_DATASETS_OFFLINE", "0") + if offline == '1': return datasets.load_from_disk(f"{self.data_args.datasets_saved_path}/super_glue.boolq")[split] else: return datasets.load_dataset('super_glue', 'boolq', split=split, script_version="master") @@ -342,7 +348,7 @@ class SuperGLUECB(AbstractTask): split_to_data_split = {"train": "train", "validation": "validation", "test": "validation"} - metric = [metrics.mean_multiclass_f1(num_classes=3), metrics.accuracy] + metric = [metrics.accuracy] metric_names = ["accuracy"] verbalizers = { @@ -356,7 +362,8 @@ class SuperGLUECB(AbstractTask): } def load_dataset(self, split): - if self.data_args.datasets_load_from_disk: + offline = os.environ.get("HF_DATASETS_OFFLINE", "0") + if offline == '1': return datasets.load_from_disk(f"{self.data_args.datasets_saved_path}/super_glue.cb")[split] else: return datasets.load_dataset('super_glue', 'cb', split=split, script_version="master") @@ -382,7 +389,8 @@ class SuperGLUECOPA(AbstractTask): } def load_dataset(self, split): - if self.data_args.datasets_load_from_disk: + offline = os.environ.get("HF_DATASETS_OFFLINE", "0") + if offline == '1': return datasets.load_from_disk(f"{self.data_args.datasets_saved_path}/super_glue.copa")[split] else: return datasets.load_dataset('super_glue', 'copa', split=split, script_version="master") @@ -411,7 +419,8 @@ class SuperGLUEMultiRC(AbstractTask): def load_dataset(self, split): - if self.data_args.datasets_load_from_disk: + offline = os.environ.get("HF_DATASETS_OFFLINE", "0") + if offline == '1': return datasets.load_from_disk(f"{self.data_args.datasets_saved_path}/super_glue.multirc")[split] else: return datasets.load_dataset('super_glue', 'multirc', split=split, script_version="master") @@ -454,7 +463,8 @@ class SuperGLUEWIC(AbstractTask): } def load_dataset(self, split): - if self.data_args.datasets_load_from_disk: + offline = os.environ.get("HF_DATASETS_OFFLINE", "0") + if offline == '1': return datasets.load_from_disk(f"{self.data_args.datasets_saved_path}/super_glue.wic")[split] else: return datasets.load_dataset('super_glue', 'wic', split=split, script_version="master") @@ -544,7 +554,8 @@ class Beans(AbstractTask): def load_dataset(self, split): # from IPython import embed; embed(header="beans") - if self.data_args.datasets_load_from_disk: + offline = os.environ.get("HF_DATASETS_OFFLINE", "0") + if offline == '1': return datasets.load_from_disk(f"{self.data_args.datasets_saved_path}/super_glue.wic")[split] else: return datasets.load_dataset('beans', split=split, script_version="master") diff --git a/examples/examples_prompt/src/run.py b/examples/examples_prompt/src/run.py index 81a3de2..30b534e 100644 --- a/examples/examples_prompt/src/run.py +++ b/examples/examples_prompt/src/run.py @@ -328,7 +328,7 @@ def main(): center_args_pool = {**vars(model_args), **vars(data_args), **vars(training_args), **vars(delta_args)}, list_tags = ['NLI'], dict_tags = {'purpose':'for testing'}, - delay_push=True, + delay_push=delta_args.delay_push, ) diff --git a/examples/examples_prompt/src/test.py b/examples/examples_prompt/src/test.py index 11244da..fb17494 100644 --- a/examples/examples_prompt/src/test.py +++ b/examples/examples_prompt/src/test.py @@ -152,9 +152,13 @@ def main(): Visualization(model).structure_graph() if delta_args.delta_type.lower() != "none": - from opendelta import AutoDeltaConfig,AutoDeltaModel - # delta_config = AutoDeltaConfig.from_dict(vars(delta_args)) - delta_model = AutoDeltaModel.from_finetuned(finetuned_model_path=delta_args.finetuned_model_path, cache_dir="saved_ckpts", backbone_model=model) + from opendelta.delta_models.adapter import AdapterConfig, AdapterModel + delta_config = AdapterConfig.from_finetuned(finetuned_delta_path=delta_args.finetuned_delta_path) + delta_model = AdapterModel.from_finetuned(finetuned_delta_path=delta_args.finetuned_delta_path, + delta_config=delta_config, + backbone_model=model, + force_download=delta_args.force_download, + cache_dir=delta_args.delta_cache_dir) # delta_model.freeze_module(set_state_dict = True) delta_model.log(delta_ratio=True, trainable_ratio=True, visualization=True) diff --git a/examples/examples_prompt/utils/args.py b/examples/examples_prompt/utils/args.py index 7d20750..e403ce4 100644 --- a/examples/examples_prompt/utils/args.py +++ b/examples/examples_prompt/utils/args.py @@ -243,6 +243,21 @@ class DeltaArguments: finetuned_delta_path: Optional[str] = field( default=None, metadata={"help": "the path of the finetuned delta model"} ) + force_download: Optional[bool] = field( + default=False, metadata={"help": "whether to download the checkpoint form delta center no matter whether it exists"} + ) + local_files_only: Optional[bool] = field( + default=False, metadata={"help": "whether not to look for file in delta center"} + ) + delta_cache_dir: Optional[str] = field( + default=None, metadata={"help": "The cache path defined by user. If not set, we will firstly look into the"+ + " working directory and then into the default cache path (ususally ~/.cache/delta_center)."} + ) + delay_push: Optional[bool] = field( + default=True, metadata={ + 'help':'whether push the checkpoint to delta center later.' + } + ) def merge_arguments(self, objb): print(objb) diff --git a/examples/examples_seq2seq/README.md b/examples/legacies/examples_seq2seq/README.md similarity index 100% rename from examples/examples_seq2seq/README.md rename to examples/legacies/examples_seq2seq/README.md diff --git a/examples/examples_seq2seq/__init__.py b/examples/legacies/examples_seq2seq/__init__.py similarity index 100% rename from examples/examples_seq2seq/__init__.py rename to examples/legacies/examples_seq2seq/__init__.py diff --git a/examples/examples_seq2seq/configs/config_gen_bs.py b/examples/legacies/examples_seq2seq/configs/config_gen_bs.py similarity index 100% rename from examples/examples_seq2seq/configs/config_gen_bs.py rename to examples/legacies/examples_seq2seq/configs/config_gen_bs.py diff --git a/examples/examples_seq2seq/data_processors/__init__.py b/examples/legacies/examples_seq2seq/data_processors/__init__.py similarity index 100% rename from examples/examples_seq2seq/data_processors/__init__.py rename to examples/legacies/examples_seq2seq/data_processors/__init__.py diff --git a/examples/examples_seq2seq/data_processors/data_collator.py b/examples/legacies/examples_seq2seq/data_processors/data_collator.py similarity index 100% rename from examples/examples_seq2seq/data_processors/data_collator.py rename to examples/legacies/examples_seq2seq/data_processors/data_collator.py diff --git a/examples/examples_seq2seq/data_processors/postprocessors.py b/examples/legacies/examples_seq2seq/data_processors/postprocessors.py similarity index 100% rename from examples/examples_seq2seq/data_processors/postprocessors.py rename to examples/legacies/examples_seq2seq/data_processors/postprocessors.py diff --git a/examples/examples_seq2seq/data_processors/tasks.py b/examples/legacies/examples_seq2seq/data_processors/tasks.py similarity index 100% rename from examples/examples_seq2seq/data_processors/tasks.py rename to examples/legacies/examples_seq2seq/data_processors/tasks.py diff --git a/examples/examples_seq2seq/data_processors/utils.py b/examples/legacies/examples_seq2seq/data_processors/utils.py similarity index 100% rename from examples/examples_seq2seq/data_processors/utils.py rename to examples/legacies/examples_seq2seq/data_processors/utils.py diff --git a/examples/examples_seq2seq/metrics/__init__.py b/examples/legacies/examples_seq2seq/metrics/__init__.py similarity index 100% rename from examples/examples_seq2seq/metrics/__init__.py rename to examples/legacies/examples_seq2seq/metrics/__init__.py diff --git a/examples/examples_seq2seq/metrics/metrics.py b/examples/legacies/examples_seq2seq/metrics/metrics.py similarity index 100% rename from examples/examples_seq2seq/metrics/metrics.py rename to examples/legacies/examples_seq2seq/metrics/metrics.py diff --git a/examples/examples_seq2seq/metrics/qa_utils.py b/examples/legacies/examples_seq2seq/metrics/qa_utils.py similarity index 100% rename from examples/examples_seq2seq/metrics/qa_utils.py rename to examples/legacies/examples_seq2seq/metrics/qa_utils.py diff --git a/examples/examples_seq2seq/run_seq2seq.py b/examples/legacies/examples_seq2seq/run_seq2seq.py similarity index 100% rename from examples/examples_seq2seq/run_seq2seq.py rename to examples/legacies/examples_seq2seq/run_seq2seq.py diff --git a/examples/examples_seq2seq/seq2seq_trainer.py b/examples/legacies/examples_seq2seq/seq2seq_trainer.py similarity index 100% rename from examples/examples_seq2seq/seq2seq_trainer.py rename to examples/legacies/examples_seq2seq/seq2seq_trainer.py diff --git a/examples/examples_seq2seq/trainers/__init__.py b/examples/legacies/examples_seq2seq/trainers/__init__.py similarity 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a/examples/examples_seq2seq/trainers/trainer_args.py b/examples/legacies/examples_seq2seq/trainers/trainer_args.py similarity index 100% rename from examples/examples_seq2seq/trainers/trainer_args.py rename to examples/legacies/examples_seq2seq/trainers/trainer_args.py diff --git a/examples/examples_seq2seq/trainers/trainer_utils.py b/examples/legacies/examples_seq2seq/trainers/trainer_utils.py similarity index 100% rename from examples/examples_seq2seq/trainers/trainer_utils.py rename to examples/legacies/examples_seq2seq/trainers/trainer_utils.py diff --git a/examples/examples_seq2seq/utils/__init__.py b/examples/legacies/examples_seq2seq/utils/__init__.py similarity index 100% rename from examples/examples_seq2seq/utils/__init__.py rename to examples/legacies/examples_seq2seq/utils/__init__.py diff --git a/examples/examples_seq2seq/utils/utils.py b/examples/legacies/examples_seq2seq/utils/utils.py similarity index 100% rename from examples/examples_seq2seq/utils/utils.py rename to examples/legacies/examples_seq2seq/utils/utils.py diff --git a/examples/examples_text-classification/README.md b/examples/legacies/examples_text-classification/README.md similarity index 100% rename from examples/examples_text-classification/README.md rename to examples/legacies/examples_text-classification/README.md diff --git a/examples/examples_text-classification/configs/config_gen.py b/examples/legacies/examples_text-classification/configs/config_gen.py similarity index 100% rename from examples/examples_text-classification/configs/config_gen.py rename to examples/legacies/examples_text-classification/configs/config_gen.py diff --git a/examples/examples_text-classification/configs/lora_roberta-base/lora_cola.json b/examples/legacies/examples_text-classification/configs/lora_roberta-base/lora_cola.json similarity index 100% rename from examples/examples_text-classification/configs/lora_roberta-base/lora_cola.json rename to 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b/examples/legacies/examples_text-classification/configs/lora_roberta-base/lora_qnli.json similarity index 100% rename from examples/examples_text-classification/configs/lora_roberta-base/lora_qnli.json rename to examples/legacies/examples_text-classification/configs/lora_roberta-base/lora_qnli.json diff --git a/examples/examples_text-classification/configs/lora_roberta-base/lora_qqp.json b/examples/legacies/examples_text-classification/configs/lora_roberta-base/lora_qqp.json similarity index 100% rename from examples/examples_text-classification/configs/lora_roberta-base/lora_qqp.json rename to examples/legacies/examples_text-classification/configs/lora_roberta-base/lora_qqp.json diff --git a/examples/examples_text-classification/configs/lora_roberta-base/lora_rte.json b/examples/legacies/examples_text-classification/configs/lora_roberta-base/lora_rte.json similarity index 100% rename from examples/examples_text-classification/configs/lora_roberta-base/lora_rte.json rename to examples/legacies/examples_text-classification/configs/lora_roberta-base/lora_rte.json diff --git a/examples/examples_text-classification/configs/lora_roberta-base/lora_sst2.json b/examples/legacies/examples_text-classification/configs/lora_roberta-base/lora_sst2.json similarity index 100% rename from examples/examples_text-classification/configs/lora_roberta-base/lora_sst2.json rename to examples/legacies/examples_text-classification/configs/lora_roberta-base/lora_sst2.json diff --git a/examples/examples_text-classification/configs/lora_roberta-base/lora_stsb.json b/examples/legacies/examples_text-classification/configs/lora_roberta-base/lora_stsb.json similarity index 100% rename from examples/examples_text-classification/configs/lora_roberta-base/lora_stsb.json rename to examples/legacies/examples_text-classification/configs/lora_roberta-base/lora_stsb.json diff --git a/examples/examples_text-classification/configs/lora_roberta-base/lora_wnli.json b/examples/legacies/examples_text-classification/configs/lora_roberta-base/lora_wnli.json similarity index 100% rename from examples/examples_text-classification/configs/lora_roberta-base/lora_wnli.json rename to examples/legacies/examples_text-classification/configs/lora_roberta-base/lora_wnli.json diff --git a/examples/examples_text-classification/configs/prefix_roberta-base/cola.json b/examples/legacies/examples_text-classification/configs/prefix_roberta-base/cola.json similarity index 100% rename from examples/examples_text-classification/configs/prefix_roberta-base/cola.json rename to examples/legacies/examples_text-classification/configs/prefix_roberta-base/cola.json diff --git a/examples/examples_text-classification/configs/prefix_roberta-base/mnli.json b/examples/legacies/examples_text-classification/configs/prefix_roberta-base/mnli.json similarity index 100% rename from examples/examples_text-classification/configs/prefix_roberta-base/mnli.json rename to examples/legacies/examples_text-classification/configs/prefix_roberta-base/mnli.json diff --git a/examples/examples_text-classification/configs/prefix_roberta-base/mrpc.json b/examples/legacies/examples_text-classification/configs/prefix_roberta-base/mrpc.json similarity index 100% rename from examples/examples_text-classification/configs/prefix_roberta-base/mrpc.json rename to examples/legacies/examples_text-classification/configs/prefix_roberta-base/mrpc.json diff --git a/examples/examples_text-classification/configs/prefix_roberta-base/qnli.json b/examples/legacies/examples_text-classification/configs/prefix_roberta-base/qnli.json similarity index 100% rename from examples/examples_text-classification/configs/prefix_roberta-base/qnli.json rename to examples/legacies/examples_text-classification/configs/prefix_roberta-base/qnli.json diff --git a/examples/examples_text-classification/configs/prefix_roberta-base/qqp.json b/examples/legacies/examples_text-classification/configs/prefix_roberta-base/qqp.json similarity index 100% rename from examples/examples_text-classification/configs/prefix_roberta-base/qqp.json rename to examples/legacies/examples_text-classification/configs/prefix_roberta-base/qqp.json diff --git a/examples/examples_text-classification/configs/prefix_roberta-base/rte.json b/examples/legacies/examples_text-classification/configs/prefix_roberta-base/rte.json similarity index 100% rename from examples/examples_text-classification/configs/prefix_roberta-base/rte.json rename to examples/legacies/examples_text-classification/configs/prefix_roberta-base/rte.json diff --git a/examples/examples_text-classification/configs/prefix_roberta-base/sst2.json b/examples/legacies/examples_text-classification/configs/prefix_roberta-base/sst2.json similarity index 100% rename from examples/examples_text-classification/configs/prefix_roberta-base/sst2.json rename to examples/legacies/examples_text-classification/configs/prefix_roberta-base/sst2.json diff --git a/examples/examples_text-classification/configs/prefix_roberta-base/stsb.json b/examples/legacies/examples_text-classification/configs/prefix_roberta-base/stsb.json similarity index 100% rename from examples/examples_text-classification/configs/prefix_roberta-base/stsb.json rename to examples/legacies/examples_text-classification/configs/prefix_roberta-base/stsb.json diff --git a/examples/examples_text-classification/configs/prefix_roberta-base/superglue-boolq.json b/examples/legacies/examples_text-classification/configs/prefix_roberta-base/superglue-boolq.json similarity index 100% rename from examples/examples_text-classification/configs/prefix_roberta-base/superglue-boolq.json rename to examples/legacies/examples_text-classification/configs/prefix_roberta-base/superglue-boolq.json diff --git a/examples/examples_text-classification/configs/prefix_roberta-base/superglue-cb.json b/examples/legacies/examples_text-classification/configs/prefix_roberta-base/superglue-cb.json similarity index 100% rename from examples/examples_text-classification/configs/prefix_roberta-base/superglue-cb.json rename to examples/legacies/examples_text-classification/configs/prefix_roberta-base/superglue-cb.json diff --git a/examples/examples_text-classification/configs/prefix_roberta-base/superglue-copa.json b/examples/legacies/examples_text-classification/configs/prefix_roberta-base/superglue-copa.json similarity index 100% rename from examples/examples_text-classification/configs/prefix_roberta-base/superglue-copa.json rename to examples/legacies/examples_text-classification/configs/prefix_roberta-base/superglue-copa.json diff --git a/examples/examples_text-classification/configs/prefix_roberta-base/superglue-multirc.json b/examples/legacies/examples_text-classification/configs/prefix_roberta-base/superglue-multirc.json similarity index 100% rename from examples/examples_text-classification/configs/prefix_roberta-base/superglue-multirc.json rename to examples/legacies/examples_text-classification/configs/prefix_roberta-base/superglue-multirc.json diff --git a/examples/examples_text-classification/configs/prefix_roberta-base/superglue-record.json b/examples/legacies/examples_text-classification/configs/prefix_roberta-base/superglue-record.json similarity index 100% rename from examples/examples_text-classification/configs/prefix_roberta-base/superglue-record.json rename to examples/legacies/examples_text-classification/configs/prefix_roberta-base/superglue-record.json diff --git a/examples/examples_text-classification/configs/prefix_roberta-base/superglue-wic.json b/examples/legacies/examples_text-classification/configs/prefix_roberta-base/superglue-wic.json similarity index 100% rename from examples/examples_text-classification/configs/prefix_roberta-base/superglue-wic.json rename to examples/legacies/examples_text-classification/configs/prefix_roberta-base/superglue-wic.json diff --git a/examples/examples_text-classification/configs/prefix_roberta-base/superglue-wsc.fixed.json b/examples/legacies/examples_text-classification/configs/prefix_roberta-base/superglue-wsc.fixed.json similarity index 100% rename from examples/examples_text-classification/configs/prefix_roberta-base/superglue-wsc.fixed.json rename to examples/legacies/examples_text-classification/configs/prefix_roberta-base/superglue-wsc.fixed.json diff --git a/examples/examples_text-classification/metrics/glue.py b/examples/legacies/examples_text-classification/metrics/glue.py similarity index 100% rename from examples/examples_text-classification/metrics/glue.py rename to examples/legacies/examples_text-classification/metrics/glue.py diff --git a/examples/examples_text-classification/requirements.txt b/examples/legacies/examples_text-classification/requirements.txt similarity index 100% rename from examples/examples_text-classification/requirements.txt rename to examples/legacies/examples_text-classification/requirements.txt diff --git a/examples/examples_text-classification/run.sh b/examples/legacies/examples_text-classification/run.sh similarity index 100% rename from examples/examples_text-classification/run.sh rename to examples/legacies/examples_text-classification/run.sh diff --git a/examples/examples_text-classification/run_glue.py b/examples/legacies/examples_text-classification/run_glue.py similarity index 100% rename from examples/examples_text-classification/run_glue.py rename to examples/legacies/examples_text-classification/run_glue.py diff --git a/examples/examples_text-classification/util.py b/examples/legacies/examples_text-classification/util.py similarity index 100% rename from examples/examples_text-classification/util.py rename to examples/legacies/examples_text-classification/util.py diff --git a/examples/setup_seq2seq.py b/examples/legacies/setup_seq2seq.py similarity index 100% rename from examples/setup_seq2seq.py rename to examples/legacies/setup_seq2seq.py diff --git a/examples/setup_prompt.py b/examples/setup_prompt.py deleted file mode 100755 index 5a9c74d..0000000 --- a/examples/setup_prompt.py +++ /dev/null @@ -1,48 +0,0 @@ -"""Install Compacter.""" -import os -import setuptools -from torch.utils.cpp_extension import BuildExtension, CUDAExtension - -#os.environ['TORCH_CUDA_ARCH_LIST']="3.5;3.7;6.1;7.0;7.5;8.6+PTX" - -def setup_package(): - long_description = "examples_prompt" - setuptools.setup( - name='examples_prompt', - version='0.0.1', - description='textual prompt example', - long_description=long_description, - long_description_content_type='text/markdown', - author='Shengding Hu', - license='MIT License', - packages=setuptools.find_packages( - exclude=['docs', 'tests', 'scripts']), - dependency_links=[ - 'https://download.pytorch.org/whl/torch_stable.html', - ], - classifiers=[ - 'Intended Audience :: Developers', - 'Intended Audience :: Science/Research', - 'License :: OSI Approved :: MIT License', - 'Topic :: Scientific/Engineering :: Artificial Intelligence', - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.7.10', - ], - keywords='text nlp machinelearning', - # ext_modules=[ - # CUDAExtension('seq2seq.projections.fwh_cuda', - # sources=[ - # 'seq2seq/projections/fwh_cuda/fwh_cpp.cpp', - # 'seq2seq/projections/fwh_cuda/fwh_cu.cu', - # ] - # ) - # ] - # , - cmdclass={"build_ext": BuildExtension}, - install_requires=[ - ], - ) - - -if __name__ == '__main__': - setup_package() diff --git a/opendelta/auto_delta.py b/opendelta/auto_delta.py index 87a38c1..ac9fa61 100644 --- a/opendelta/auto_delta.py +++ b/opendelta/auto_delta.py @@ -2,11 +2,9 @@ from copy import deepcopy from typing import Any, Dict, OrderedDict from opendelta.utils.visualization import Visualization import torch.nn as nn -from transformers.file_utils import PushToHubMixin from opendelta.utils.logging import get_logger import importlib from opendelta.delta_configs import BaseDeltaConfig -from opendelta.basemodel import DeltaBase logger = get_logger(__name__) @@ -114,7 +112,7 @@ class AutoDeltaConfig: @classmethod - def from_finetuned(cls, finetuned_model_path, **kwargs): + def from_finetuned(cls, finetuned_delta_path, **kwargs): r""" Instantiate one of the configuration classes of the library from a finetuned delta model configuration. The configuration class to instantiate is selected based on the ``delta_type`` property of the config object that @@ -122,7 +120,7 @@ class AutoDeltaConfig: Parameters: - finetuned_model_path (:obj:`str` or :obj:`os.PathLike`, *optional*): + finetuned_delta_path (:obj:`str` or :obj:`os.PathLike`, *optional*): Can be either: - A string, the *model id* of a finetuned delta model configuration hosted inside a model repo on @@ -173,20 +171,19 @@ class AutoDeltaConfig: """ - kwargs["name_or_path"] = finetuned_model_path - config_dict, _ = BaseDeltaConfig.get_config_dict(finetuned_model_path, **kwargs) + config_dict, kwargs = BaseDeltaConfig.get_config_dict(finetuned_delta_path, **kwargs) if "delta_type" in config_dict: config_class = LAZY_CONFIG_MAPPING[config_dict["delta_type"]] return config_class.from_dict(config_dict, **kwargs) else: # Fallback: use pattern matching on the string. for pattern, config_class in LAZY_CONFIG_MAPPING.items(): - if pattern in str(finetuned_model): + if pattern in str(finetuned_delta_path): return config_class.from_dict(config_dict, **kwargs) raise ValueError( - f"Unrecognized model in {finetuned_model_path}. " + f"Unrecognized model in {finetuned_delta_path}. " f"Should have a `delta_type` key in the loaded config, or contain one of the following strings " f"in its name: {', '.join(LAZY_CONFIG_MAPPING.keys())}" ) @@ -355,14 +352,14 @@ class AutoDeltaModel: ) @classmethod - def from_finetuned(cls, finetuned_model_path, backbone_model, *model_args, **kwargs): + def from_finetuned(cls, finetuned_delta_path, backbone_model, *model_args, **kwargs): r""" Automatically instantiated a delta model and load the finetuned checkpoints based on the - :obj:`finetuned_model_path`, which can either be a string pointing to a local path or a url pointint to + :obj:`finetuned_delta_path`, which can either be a string pointing to a local path or a url pointint to the delta hub. It will check the hash after loading the delta model to see whether the correct backbone and delta checkpoint are used. Args: - finetuned_model_path (:obj:`str` or :obj:`os.PathLike`, *optional*): + finetuned_delta_path (:obj:`str` or :obj:`os.PathLike`, *optional*): Can be either: - A string, the *model id* of a finetuned delta model configuration hosted inside a model repo on @@ -377,6 +374,7 @@ class AutoDeltaModel: backbone_model (:obj:`nn.Module`): The backbone model to be modified. model_args: Other argument for initialize the model. + kwargs: Other kwargs that will be passed into DeltaBase.from_finetuned. Example: @@ -385,15 +383,15 @@ class AutoDeltaModel: delta_model = AutoDeltaModel.from_finetuned("DeltaHub/lora_t5-base-mrpc", backbone_model) """ - config = kwargs.pop("config", None) + delta_config = kwargs.pop("delta_config", None) - if not isinstance(config, BaseDeltaConfig): - config, kwargs = AutoDeltaConfig.from_finetuned( - finetuned_model_path, return_unused_kwargs=True, **kwargs + if not isinstance(delta_config, BaseDeltaConfig): + delta_config, kwargs = AutoDeltaConfig.from_finetuned( + finetuned_delta_path, return_unused_kwargs=True, **kwargs ) - if type(config) in cls._delta_model_mapping.keys(): - model_class = cls._delta_model_mapping[type(config)] - return model_class.from_finetuned(finetuned_model_path, backbone_model, *model_args, **kwargs) + if type(delta_config) in cls._delta_model_mapping.keys(): + model_class = cls._delta_model_mapping[type(delta_config)] + return model_class.from_finetuned(finetuned_delta_path, backbone_model, *model_args, delta_config=delta_config, **kwargs) raise ValueError( f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n" f"Model type should be one of {', '.join(c.__name__ for c in cls._model_mapping.keys())}." diff --git a/opendelta/delta_configs.py b/opendelta/delta_configs.py index fcc8493..73c8e1c 100644 --- a/opendelta/delta_configs.py +++ b/opendelta/delta_configs.py @@ -5,15 +5,6 @@ from opendelta import __version__ as opendelta_version from opendelta.utils import logging from opendelta.utils.signature import get_arg_names, get_arg_names_inside_func import transformers -from transformers.file_utils import ( - PushToHubMixin, - is_offline_mode, - cached_path, - is_remote_url, - get_list_of_files, - hf_bucket_url, -) -from packaging import version import json import copy @@ -26,7 +17,7 @@ logger = logging.get_logger(__name__) FULL_CONFIGURATION_FILE = "config.json" _re_configuration_file = re.compile(r"config\.(.*)\.json") -class BaseDeltaConfig(PushToHubMixin): +class BaseDeltaConfig: r"""Base class for all configuration classes. Handles a few parameters common to all delta models' configurations as well as methods for loading/downloading/saving configurations. @@ -108,7 +99,7 @@ class BaseDeltaConfig(PushToHubMixin): @classmethod - def from_finetuned(cls, finetuned_model_path: Union[str, os.PathLike], **kwargs) -> "BaseDeltaConfig": + def from_finetuned(cls, finetuned_delta_path: Union[str, os.PathLike], **kwargs) -> "BaseDeltaConfig": r""" Instantiate a :obj:`BaseDeltaConfig` (or a derived class) from a finetined delta module configuration. @@ -132,7 +123,7 @@ class BaseDeltaConfig(PushToHubMixin): delta_config = LoraConfig.from_finetuned("DeltaHub/lora_t5-base_mrpc") """ - config_dict, kwargs = cls.get_config_dict(finetuned_model_path, **kwargs) + config_dict, kwargs = cls.get_config_dict(finetuned_delta_path, **kwargs) if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: logger.warn( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " @@ -215,7 +206,7 @@ class BaseDeltaConfig(PushToHubMixin): to_remove.append(key) for key in to_remove: kwargs.pop(key, None) - logger.info(f"Model config {config}") + logger.info(f"Model config\n{config}") if return_unused_kwargs: return config, kwargs @@ -224,101 +215,58 @@ class BaseDeltaConfig(PushToHubMixin): @classmethod def get_config_dict( - cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs + cls, finetuned_delta_path: Union[str, os.PathLike], **kwargs ) -> Tuple[Dict[str, Any], Dict[str, Any]]: """[NODOC] - From a ``pretrained_model_name_or_path``, resolve to a dictionary of parameters, to be used for instantiating a + From a ``finetuned_delta_path``, resolve to a dictionary of parameters, to be used for instantiating a [``PretrainedConfig``] using ``from_dict``. Parameters: - pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`): + finetuned_delta_path (:obj:`str` or :obj:`os.PathLike`): The identifier of the pre-trained checkpoint from which we want the dictionary of parameters. Returns: :obj:`Tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the configuration object. """ - cache_dir = kwargs.pop("cache_dir", None) - force_download = kwargs.pop("force_download", False) - resume_download = kwargs.pop("resume_download", False) - proxies = kwargs.pop("proxies", None) - use_auth_token = kwargs.pop("use_auth_token", None) - local_files_only = kwargs.pop("local_files_only", False) - revision = kwargs.pop("revision", None) + cache_dir = kwargs.get("cache_dir", None) + force_download = kwargs.get("force_download", False) + # resume_download = kwargs.pop("resume_download", False) + # proxies = kwargs.pop("proxies", None) + # use_auth_token = kwargs.pop("use_auth_token", None) + local_files_only = kwargs.get("local_files_only", False) + # revision = kwargs.pop("revision", None) # from_pipeline = kwargs.pop("_from_pipeline", None) - from_auto_class = kwargs.pop("_from_auto", False) + # from_auto_class = kwargs.pop("_from_auto", False) - user_agent = {"file_type": "config", "from_auto_class": from_auto_class} + # user_agent = {"file_type": "config", "from_auto_class": from_auto_class} # if from_pipeline is not None: # user_agent["using_pipeline"] = from_pipeline - if is_offline_mode() and not local_files_only: - logger.info("Offline mode: forcing local_files_only=True") + if os.environ.get("DELTACENTER_OFFLINE", '0') == '1': + logger.info("Delta Center offline mode!") local_files_only = True - pretrained_model_name_or_path = str(pretrained_model_name_or_path) + finetuned_delta_path = str(finetuned_delta_path) - - if os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path): - config_file = pretrained_model_name_or_path + if cache_dir is not None: + cached_finetuned_delta_path = os.path.join(cache_dir, finetuned_delta_path) else: - # configuration_file = get_configuration_file( - # pretrained_model_name_or_path, - # revision=revision, - # use_auth_token=use_auth_token, - # local_files_only=local_files_only, - # ) - print("cache_dir", cache_dir, "|||", "pretrained_model_name_or_path", pretrained_model_name_or_path) + cached_finetuned_delta_path = finetuned_delta_path - if os.path.isdir(pretrained_model_name_or_path): - config_file = os.path.join(pretrained_model_name_or_path, "config.json") - elif os.path.isdir(os.path.join(cache_dir, pretrained_model_name_or_path)): - config_file = os.path.join(cache_dir, pretrained_model_name_or_path, "config.json") - else: - config_file = hf_bucket_url( - pretrained_model_name_or_path, filename=configuration_file, revision=revision, mirror=None - ) + if os.path.isfile(cached_finetuned_delta_path): + local_files_only = True + elif os.path.isdir(cached_finetuned_delta_path): + # cached_finetuned_delta_path = os.path.join(cached_finetuned_delta_path, 'config.json') + local_files_only = True - print("config file!!", config_file) - try: - # Load from URL or cache if already cached - resolved_config_file = cached_path( - config_file, - cache_dir=cache_dir, - force_download=force_download, - proxies=proxies, - resume_download=resume_download, - local_files_only=local_files_only, - use_auth_token=use_auth_token, - user_agent=user_agent, - ) - # Load config dict - config_dict = cls._dict_from_json_file(resolved_config_file) - - except EnvironmentError as err: - logger.error(err) - msg = ( - f"Can't load config for '{pretrained_model_name_or_path}'. Make sure that:\n\n" - f"- '{pretrained_model_name_or_path}' is a correct model identifier listed on 'https://huggingface.co/models'\n" - f" (make sure '{pretrained_model_name_or_path}' is not a path to a local directory with something else, in that case)\n\n" - f"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a {CONFIG_NAME} file\n\n" - ) - - if revision is not None: - msg += f"- or '{revision}' is a valid git identifier (branch name, a tag name, or a commit id) that exists for this model name as listed on its model page on 'https://huggingface.co/models'\n\n" - - raise EnvironmentError(msg) - - except (json.JSONDecodeError, UnicodeDecodeError): - msg = ( - f"Couldn't reach server at '{config_file}' to download configuration file or " - "configuration file is not a valid JSON file. " - f"Please check network or file content here: {resolved_config_file}." - ) - raise EnvironmentError(msg) - - if resolved_config_file == config_file: - logger.info(f"loading configuration file {config_file}") - else: - logger.info(f"loading configuration file {config_file} from cache at {resolved_config_file}") + # if local_files_only: + # config_dict = cls._dict_from_json_file(cached_finetuned_delta_path) + if not local_files_only or force_download: + from .utils.delta_center import download as dcdownload + # try to download from DeltaCenter + cached_finetuned_delta_path = dcdownload(finetuned_delta_path, force_download=force_download, cache_dir=cache_dir) + kwargs['force_download'] = False # Has been downloaded, not more forcing + cached_finetuned_delta_path = os.path.join(cached_finetuned_delta_path, 'config.json') + config_dict = cls._dict_from_json_file(cached_finetuned_delta_path) return config_dict, kwargs @classmethod @@ -432,53 +380,6 @@ class BaseDeltaConfig(PushToHubMixin): -def get_configuration_file( - path_or_repo: Union[str, os.PathLike], - revision: Optional[str] = None, - use_auth_token: Optional[Union[bool, str]] = None, - local_files_only: bool = False, -) -> str: - """ - Get the configuration file to use for this version of transformers. - Args: - path_or_repo (`:obj:str` or `:obj:os.PathLike`): - Can be either the id of a repo on huggingface.co or a path to a *directory*. - revision(`:obj:str`, *optional*, defaults to ``"main"``): - The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a - git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any - identifier allowed by git. - use_auth_token (:obj:`str` or *bool*, *optional*): - The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token generated - when running ``transformers-cli login`` (stored in ``~/.huggingface``). - local_files_only (:obj:`bool`, *optional*, defaults to :obj:`False`): - Whether or not to only rely on local files and not to attempt to download any files. - Returns: - :obj:`str`: The configuration file to use. - """ - # Inspect all files from the repo/folder. - all_files = get_list_of_files( - path_or_repo, revision=revision, use_auth_token=use_auth_token, local_files_only=local_files_only - ) - configuration_files_map = {} - for file_name in all_files: - search = _re_configuration_file.search(file_name) - if search is not None: - v = search.groups()[0] - configuration_files_map[v] = os.path.split(file_name)[-1] - available_versions = sorted(configuration_files_map.keys()) - # Defaults to FULL_CONFIGURATION_FILE and then try to look at some newer versions. - configuration_file = FULL_CONFIGURATION_FILE - # transformers_version_ = version.parse(transformers_version) - for v in available_versions: - # if version.parse(v) <= transformers_version_: - configuration_file = configuration_files_map[v] - # else: - # # No point going further since the versions are sorted. - # break - - return configuration_file - - if __name__ == "__main__": myconfig = BaseDeltaConfig.from_pretrained("../ckpts/lora/") myconfig.save_pretrained("../ckpts/lora.1/") diff --git a/opendelta/utils/delta_center.py b/opendelta/utils/delta_center.py index 3ad5f25..9bf185d 100644 --- a/opendelta/utils/delta_center.py +++ b/opendelta/utils/delta_center.py @@ -1,103 +1,10 @@ - -# from dataclasses import dataclass, field, fields -# from tkinter.messagebox import NO -# from typing import Optional, List, Union -# from xml.sax import default_parser_list -# from opendelta.utils.logging import get_logger - -# logger = get_logger(__name__) +from DeltaCenter import OssClient +from .file_utils import default_cache_path -# alternative_names = { -# "train_tasks": ["train_tasks", "train_task", "task_name"], -# } +def download(finetuned_delta_path, cache_dir=None, force_download=False): + if cache_dir is None: + cache_dir = default_cache_path + path_to_unzip_file = OssClient.download(finetuned_delta_path, dest=cache_dir, force_download=force_download) + return path_to_unzip_file - -# @dataclass -# class DeltaCenterArguments: -# """ -# The arguments that are used to distinguish between different delta models on the DeltaCenter -# """ -# name: str = field(default="", -# metadata={"help": "The name of the delta model checkpoint"} -# ) -# backbone_model: str = field(default="", -# metadata={"help": "The backbone model of the delta model"} -# ) -# model_name_or_path: str = field( -# default = None, -# metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} -# ) -# model_revision: str = field( -# default="main", -# metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, -# ) -# delta_type: str = field( -# default=None, -# metadata={"help": "the type of type model, e.g., adapter, lora, etc."} -# ) -# train_tasks: Optional[Union[List[str], str]]= field( -# default=None, -# metadata={"help": "the task(s) that the delta is trained on"} -# ) -# checkpoint_size: Optional[float] = field( -# default=None, -# metadata={"help": "the size of the checkpoint, in MB"} -# ) -# test_tasks: Optional[Union[List[str], str]] = field( -# default=None, -# metadata={"help": "the task(s) that the delta is tested on"} -# ) -# test_performance: Optional[float] = field( -# default=None, -# metadata={"help": "the performance of the model on the test set"} -# ) -# trainable_ratio: Optional[float] = field( -# default=None, -# metadata={"help": "the ratio of trainable parameters in the model"} -# ) -# delta_ratio: Optional[float] = field( -# default=None, -# metadata={"help": "the ratio of delta parameters in the model"} -# ) - - - - - -# def create_repo_name(prefix="", center_args=None, **kwargs -# ): -# r"""Currently, it's only a simple concatenation of the arguments. -# """ -# if center_args is None: -# center_args = create_delta_center_args(**kwargs) -# reponame = prefix+"_" -# reponame += center_args.model_name_or_path.split()[-1]+"_" if center_args.model_name_or_path is not None else "" -# reponame += center_args.delta_type+"_" if center_args.delta_type is not None else "" - -# # tasks - -# if isinstance(center_args.train_tasks, list): -# train_tasks = "+".join(center_args.train_tasks) -# elif center_args.train_tasks is not None: -# train_tasks = center_args.train_tasks -# else: -# logger.warning("train_tasks are not find in all arguments. Do you miss them?") -# train_tasks = None -# reponame += train_tasks+"_" if train_tasks is not None else "" -# reponame = reponame.strip("_") -# return reponame - -# def create_delta_center_args(**kwargs): -# mdict = {} -# field = fields(DeltaCenterArguments) -# for f in field: -# if f.name in kwargs: -# mdict[f.name] = kwargs[f.name] -# else: -# for altername in alternative_names[f.name]: -# if altername in kwargs: -# mdict[f.name] = kwargs[altername] -# break -# center_args = DeltaCenterArguments(**mdict) -# return center_args \ No newline at end of file diff --git a/opendelta/utils/delta_hub.py b/opendelta/utils/delta_hub.py deleted file mode 100644 index d0da33e..0000000 --- a/opendelta/utils/delta_hub.py +++ /dev/null @@ -1,24 +0,0 @@ - - -def create_hub_repo_name(root = "DeltaHub", - dataset = None, - delta_type = None, - model_name_or_path = None, - ): - r"""Currently, it's only a simple concatenation of the arguments. - """ - repo_name = [] - - repo_name.append(f"{delta_type}") - model_name_or_path = model_name_or_path.split("/")[-1] - repo_name.append(f"{model_name_or_path}") - repo_name.append(f"{dataset}") - - repo_name = "_".join(repo_name) - - repo_name = root+"/"+repo_name - return repo_name - - - - diff --git a/opendelta/utils/file_utils.py b/opendelta/utils/file_utils.py new file mode 100644 index 0000000..2e82768 --- /dev/null +++ b/opendelta/utils/file_utils.py @@ -0,0 +1,3 @@ +import os +default_cache_path = "{}/.cache/delta_center/".format(os.path.expanduser('~')) +WEIGHTS_NAME = 'pytorch_model.bin' \ No newline at end of file diff --git a/opendelta/utils/saving_loading_utils.py b/opendelta/utils/saving_loading_utils.py index dabc735..0173d65 100644 --- a/opendelta/utils/saving_loading_utils.py +++ b/opendelta/utils/saving_loading_utils.py @@ -1,7 +1,4 @@ -from io import RawIOBase -import re -from tarfile import HeaderError from typing import Dict, List, Union, Optional, Callable from opendelta.delta_configs import BaseDeltaConfig from opendelta.utils.model_md5 import gen_model_hash, gen_parameter_hash @@ -9,24 +6,16 @@ import torch import os from opendelta import logging import torch.nn as nn -from transformers.file_utils import ( - WEIGHTS_NAME, - PushToHubMixin, - is_offline_mode, - is_remote_url, - hf_bucket_url, - cached_path, - ) -from transformers.utils.dummy_pt_objects import PreTrainedModel -import hashlib from DeltaCenter import OssClient import yaml from dataclasses import dataclass, field, fields import datetime +from .file_utils import WEIGHTS_NAME logger = logging.get_logger(__name__) + alternative_names = { "train_tasks": ["train_tasks", "train_task", "task_name"], } @@ -86,7 +75,7 @@ class DeltaCenterArguments: -class SaveLoadMixin(PushToHubMixin): +class SaveLoadMixin: def add_configs_when_saving(self,): self.config.backbone_class = self.backbone_model.__class__.__name__ self.config.backbone_checkpoint_name = os.path.split(self.backbone_model.config._name_or_path.strip("/"))[-1] @@ -110,48 +99,49 @@ class SaveLoadMixin(PushToHubMixin): ): r""" Save a model and its configuration file to a directory, so that it can be re-loaded using the - :py:meth:`~DeltaBase.from_finetuned` class method. + :py:meth:`~DeltaBase.save_finetuned` class method. Arguments: - save_directory (:obj:`str` or :obj:`os.PathLike`): - Directory to which to save. Will be created if it doesn't exist. - save_config (:obj:`bool`, *optional*, defaults to :obj:`True`): - Whether or not to save the config of the model. Useful when in distributed training like TPUs and need - to call this function on all processes. In this case, set ``save_config=True`` only on the main process - to avoid race conditions. - state_dict (nested dictionary of :obj:`torch.Tensor`): - The state dictionary of the model to save. Will default to ``self.state_dict()``, but can be used to only - save parts of the model or if special precautions need to be taken when recovering the state dictionary - of a model (like when using model parallelism). - save_function (:obj:`Callable`): - The function to use to save the state dictionary. Useful on distributed training like TPUs when one - need to replace ``torch.save`` by another method. - push_to_dc (:obj:`bool`, *optional*, defaults to :obj:`True`): Whether or not to push the model to the DeltaCenter. - center_args (:obj:`Union[DeltaCenterArguments, dict]`, *optional*, defaults to :obj:`None`): The arguments - that are used to distinguish between different delta models on the DeltaCenter. It has higher priority than the `center_args_pool`. - It will be used to group delta models. - center_args_pool (:obj:`dict`, *optional*, defaults to :obj:`None`): The arguments's pool for DeltaCenter - Together with center_args, they are are used to distinguish between different delta models on the DeltaCenter. - It will be used to group delta models. - list_tags (:obj:`List`, *optional*, defaults to :obj:`None`): The tags in the form of list for the delta model, it is the - optional identifiers that are not expected by `DeltaCenterArgument`. It will not be used to group delta models in the delta center - dict_tags (:obj:`Dict`, *optional*, defaults to :obj:`None`): The tags in the form of dictionary for the delta model, it is the - optional identifiers that are not expected by `DeltaCenterArgument`. It will not be used to group delta models in the delta center. - delay_push (:obj:`bool`, *optional*, defaults to :obj:`False`): Whether or not to delay the push to the DeltaCenter. When set to True, - the delta object will be saved locally to save_directory, you can push it later using - - .. code-block:: shell - - python -m DeltaCenter upload save_directory - + finetuned_delta_path: (optional) path to the directory where the model and its configuration file will be saved. + If not specified, the model will be saved in the directory ``./delta_checkpoints/``, + which is a subdirectory of the current working directory. + save_config: (optional) if ``True``, the configuration file will be saved in the same directory as the + model file. + state_dict: (optional) a dictionary containing the model's state_dict. If not specified, the + state_dict is loaded from the backbone model's trainable parameters. + save_function: (optional) the function used to save the model. Defaults to ``torch.save``. + push_to_dc: (optional) if ``True``, the model will prepare things to pushed to the DeltaCenter. + This includes: + - creating a configuration file for the model + - creating a directory for the model + - saving the model's trainable parameters + - pushing the model to the DeltaCenter + center_args: (optional) the arguments that are used to distinguish between different delta models on the DeltaCenter + center_args_pool: (optional) a dictionary containing the arguments that are used to distinguish between different delta models on the DeltaCenter + list_tags: (optional) a list of tags that will be added to the model's configuration file + dict_tags: (optional) a dictionary of tags that will be added to the model's configuration file + delay_push: (optional) if ``True``, the model will not be pushed to the DeltaCenter. This is useful if you want to + push the model later. """ + + # create the config to save, including model hash, etc. + if save_config: + if not hasattr(self, "config"): + self.create_config_from_model() + self.add_configs_when_saving() + + final_center_args = self.create_delta_center_args(center_args=center_args, + center_args_pool=center_args_pool) + save_directory = finetuned_delta_path if os.path.isfile(save_directory): logger.error(f"Provided path ({save_directory}) should be a directory, not a file") return os.makedirs(save_directory, exist_ok=True) + save_directory = os.path.join(save_directory, final_center_args.name) + os.makedirs(save_directory, exist_ok=True) model_to_save = self.backbone_model# unwrap_model(self) @@ -161,27 +151,24 @@ class SaveLoadMixin(PushToHubMixin): # Save the config if save_config: - if not hasattr(self, "config"): - self.create_config_from_model() - self.add_configs_when_saving() self.config.save_finetuned(save_directory) output_model_file = os.path.join(save_directory, WEIGHTS_NAME) save_function(state_dict, output_model_file) - logger.info(f"Model weights saved in {output_model_file}") - final_center_args = self.create_delta_center_args(center_args=center_args, - center_args_pool=center_args_pool) + logger.info("\n"+"*"*30+f"\nYou delta models has been saved locally to:\n\t{os.path.abspath(save_directory)}" + ) if push_to_dc: + logger.info("Creating yaml file for delta center") self.create_yml(save_directory, final_center_args, list_tags, dict_tags) + if not delay_push: OssClient.upload(base_dir=save_directory) else: - logger.info("\n"+"*"*30+f"\nYou delta models has been saved locally to:\n\t\t{os.path.abspath(save_directory)}\ - \nyou can push it to the delta center later using \n\t\tpython -m DeltaCenter upload {os.path.abspath(save_directory)}\n" + logger.info("Delay push: you can push it to the delta center later using \n\tpython -m DeltaCenter upload {os.path.abspath(save_directory)}\n" +"*"*30) # get absolute path of saved_directory, @@ -201,8 +188,11 @@ class SaveLoadMixin(PushToHubMixin): backbone_model: nn.Module, delta_config = None, cache_dir: Optional[Union[str, os.PathLike]] = None, + state_dict: Optional[dict] = None, *model_args, + force_download: Optional[bool] = False, check_hash: Optional[bool] = True, + local_files_only: Optional[bool] = False, **kwargs): r""" Instantiate a finetuned delta model from a path. @@ -210,250 +200,76 @@ class SaveLoadMixin(PushToHubMixin): To further train the model, you can use the :meth:`freeze_module ` method. Parameters: - - finetuned_model_path (:obj:`str` or :obj:`os.PathLike`, *optional*): - Can be either: - - - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. - Valid model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under a - user or organization name, like ``dbmdz/bert-base-german-cased``. - - A path to a *directory* containing model weights saved using - :meth:`SaveLoadMixin.save_finetuned`, e.g., ``./my_model_directory/``. - - A path or url to a *tensorflow index checkpoint file* (e.g, ``./tf_model/model.ckpt.index``). In - this case, ``from_tf`` should be set to ``True`` and a configuration object should be provided as - ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a - PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards. - - A path or url to a model folder containing a *flax checkpoint file* in *.msgpack* format (e.g, - ``./flax_model/`` containing ``flax_model.msgpack``). In this case, ``from_flax`` should be set to - ``True``. - - ``None`` if you are both providing the configuration and state dictionary (resp. with keyword - arguments ``config`` and ``state_dict``). - backbone_model (:obj:`torch.nn.Module`): The backbone model to be modified. - model_args (sequence of positional arguments, *optional*): - All remaining positional arguments will be passed to the underlying model's ``__init__`` method. - config (Union[:obj:`BaseDeltaConfig`, :obj:`str`, :obj:`os.PathLike`], *optional*): Can be either: - - an instance of a class derived from :class:`~PretrainedConfig`, - - a string or path valid as input to :py:meth:`~PretrainedConfig.from_pretrained`. - - Configuration for the model to use instead of an automatically loaded configuration. Configuration can - be automatically loaded when: - - - The model is a model provided by the library (loaded with the *model id* string of a pretrained - model). - - The model was saved using :py:meth:`~PreTrainedModel.save_pretrained` and is reloaded by supplying the - save directory. - - The model is loaded by supplying a local directory as ``pretrained_model_name_or_path`` and a - configuration JSON file named *config.json* is found in the directory. - state_dict (Dict[:obj:`str`, :obj:`torch.Tensor`], *optional*): - A state dictionary to use instead of a state dictionary loaded from saved weights file. - This option can be used if you want to create a model from a pretrained configuration but load your own - weights. In this case though, you should check if using :py:meth:`~PreTrainedModel.save_pretrained` and - :py:meth:`~PreTrainedModel.from_pretrained` is not a simpler option. - cache_dir (:obj:`Union[str, os.PathLike]`, *optional*): - Path to a directory in which a downloaded pretrained model configuration should be cached if the - standard cache should not be used. - force_download (:obj:`bool`, *optional*, defaults to :obj:`False`): - Whether or not to force the (re-)download of the model weights and configuration files, overriding the - cached versions if they exist. - resume_download (:obj:`bool`, *optional*, defaults to :obj:`False`): - Whether or not to delete incompletely received files. Will attempt to resume the download if such a - file exists. - proxies (:obj:`Dict[str, str]`, *optional*): - A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', - 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. - local_files_only(:obj:`bool`, *optional*, defaults to :obj:`False`): - Whether or not to only look at local files (i.e., do not try to download the model). - use_auth_token (:obj:`str` or *bool*, *optional*): - The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token generated - when running ``transformers-cli login`` (stored in ``~/.huggingface``). - revision(:obj:`str`, *optional*, defaults to ``"main"``): - The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a - git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any - identifier allowed by git. - mirror(:obj:`str`, *optional*): - Mirror source to accelerate downloads in China. If you are from China and have an accessibility - problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. - Please refer to the mirror site for more information. - torch_dtype (:obj:`str` or :obj:`torch.dtype`, *optional*): - Override the default :obj:`torch.dtype` and load the model under this dtype. If ``"auto"`` is passed the dtype - will be automatically derived from the model's weights. - - .. warning:: - - This feature is inherited from HuggingFace. We do not guarantee its usefulness currently. - One should only disable *_fast_init* to ensure backwards compatibility with `transformers.__version__ < - 4.6.0` for seeded model initialization. This argument will be removed at the next major version. See - `pull request 11471 `_ for more information. - kwargs (remaining dictionary of keyword arguments, *optional*): - Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., - ``output_attentions=True``). Behaves differently depending on whether a ``config`` is provided or - automatically loaded: - - - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the - underlying model's ``__init__`` method (we assume all relevant updates to the configuration have - already been done) - - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class - initialization function (:py:meth:`~PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that - corresponds to a configuration attribute will be used to override said attribute with the - supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute - will be passed to the underlying model's ``__init__`` function. - - .. tip:: - Passing ``use_auth_token=True`` is required when you want to use a private model. - - .. code-block:: python - - from transformers import AutoModelForSeq2SeqLM - t5 = AutoModelForSeq2SeqLM.from_pretrained("t5-base") - from opendelta import AutoDeltaModel - delta = AutoDeltaModel.from_finetuned("DeltaHub/lora_t5-base_mrpc", backbone_model=t5) - delta.log() - - - + finetuned_delta_path: (optional) path to the directory where the model and its configuration file will be saved. + If not specified, the model will be loaded from the directory cahce directory. (see ``cache_dir``), + backbone_model: the backbone model that will be used to instantiate the finetuned delta model. + delta_config: (optional) the configuration file of the finetuned delta model. If not specified, the configuration file + is loaded from the directory ``finetuned_delta_path``. + cache_dir: (optional) path to the directory where the model and its configuration file will be saved. + If not specified, we will first look into current working directory, then the cache directory of your system, e.g., ~/.cache/delta_center/, + state_dict: (optional) a dictionary containing the model's state_dict. If not specified, the + state_dict is loaded from the ``finetuned_delta_path``. + force_download: (optional) if ``True``, the model will be downloaded from the internet even if it is already + present in the cache directory. + check_hash: (optional) if ``True``, check whether the hash of the model once it's trained differs from what we load now. + local_files_only: (optional) if ``True``, the model will be loaded from the local cache directory. """ - # config = kwargs.pop("config", None) - state_dict = kwargs.pop("state_dict", None) - # cache_dir = kwargs.pop("cache_dir", None) - # ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False) - force_download = kwargs.pop("force_download", False) - resume_download = kwargs.pop("resume_download", False) - proxies = kwargs.pop("proxies", None) - # output_loading_info = kwargs.pop("output_loading_info", False) - local_files_only = kwargs.pop("local_files_only", False) - use_auth_token = kwargs.pop("use_auth_token", None) - revision = kwargs.pop("revision", None) - mirror = kwargs.pop("mirror", None) - from_pipeline = kwargs.pop("_from_pipeline", None) - from_auto_class = kwargs.pop("_from_auto", False) - # _fast_init = kwargs.pop("_fast_init", True) - torch_dtype = kwargs.pop("torch_dtype", None) - # low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", False) - - user_agent = {"file_type": "model", "framework": "pytorch", "from_auto_class": from_auto_class} - - if is_offline_mode() and not local_files_only: - logger.info("Offline mode: forcing local_files_only=True") + if os.environ.get("DELTACENTER_OFFLINE", '0') == '1': + logger.info("Delta Center offline mode!") local_files_only = True # Load config if we don't provide a configuration + + + finetuned_delta_path = str(finetuned_delta_path) + + if cache_dir is not None: + cached_finetuned_delta_path = os.path.join(cache_dir, finetuned_delta_path) + else: + cached_finetuned_delta_path = finetuned_delta_path + + download_from_dc = False + if os.path.isfile(cached_finetuned_delta_path): + raise RuntimeError( + f"You should pass a directory to load a delta checkpoint instead of a file, " + f"since we need the delta's configuration file." + ) + elif os.path.isdir(cached_finetuned_delta_path): + if os.path.isfile(os.path.join(cached_finetuned_delta_path, WEIGHTS_NAME)): + # Load from a PyTorch checkpoint + weight_file = os.path.join(cached_finetuned_delta_path, WEIGHTS_NAME) + else: + raise EnvironmentError( + f"Error no file named {WEIGHTS_NAME} found in " + f"directory {cached_finetuned_delta_path}." + ) + + else: + # try to download from DeltaCenter + from .delta_center import download as dcdownload + cached_finetuned_delta_path = dcdownload(finetuned_delta_path, cache_dir=cache_dir, force_download=force_download) + download_from_dc = True + weight_file = os.path.join(cached_finetuned_delta_path, WEIGHTS_NAME) + + if state_dict is None: + state_dict = torch.load(weight_file, map_location="cpu") + if not isinstance(delta_config, BaseDeltaConfig): - # config_path = delta_config if delta_config is not None else finetuned_model_path # Todo check delta_config, model_kwargs = cls.config_class.from_finetuned( - finetuned_model_path, - cache_dir=cache_dir, + cached_finetuned_delta_path, + cache_dir=None, return_unused_kwargs=True, - force_download=force_download, - resume_download=resume_download, - proxies=proxies, - local_files_only=local_files_only, - use_auth_token=use_auth_token, - revision=revision, - _from_auto=from_auto_class, - _from_pipeline=from_pipeline, + local_files_only=True if download_from_dc else local_files_only, # has been downloaded **kwargs, ) else: model_kwargs = kwargs - print("delta_config", delta_config) - # Load model - if finetuned_model_path is not None: - finetuned_model_path = str(finetuned_model_path) - if os.path.isdir(finetuned_model_path): - if os.path.isfile(os.path.join(finetuned_model_path, WEIGHTS_NAME)): - # Load from a PyTorch checkpoint - archive_file = os.path.join(finetuned_model_path, WEIGHTS_NAME) - else: - raise EnvironmentError( - f"Error no file named {WEIGHTS_NAME} found in " - f"directory {finetuned_model_path}." - ) - elif os.path.isfile(finetuned_model_path) or is_remote_url(finetuned_model_path): - archive_file = finetuned_model_path - else: - archive_file = hf_bucket_url( - finetuned_model_path, - filename=WEIGHTS_NAME, - revision=revision, - mirror=mirror, - ) - - try: - # Load from URL or cache if already cached #TODO - - resolved_archive_file = cached_path( - archive_file, - cache_dir=cache_dir, - force_download=force_download, - proxies=proxies, - resume_download=resume_download, - local_files_only=local_files_only, - use_auth_token=use_auth_token, - user_agent=user_agent, - ) - except EnvironmentError as err: - logger.error(err) - msg = ( - f"Can't load weights for '{finetuned_model_path}'. Make sure that:\n\n" - ) - - if revision is not None: - msg += f"- or '{revision}' is a valid git identifier (branch name, a tag name, or a commit id) that exists for this model name as listed on its model page on 'https://huggingface.co/models'\n\n" - - raise EnvironmentError(msg) - - if resolved_archive_file == archive_file: - logger.info(f"loading weights file {archive_file}") - else: - logger.info(f"loading weights file {archive_file} from cache at {resolved_archive_file}") - else: - resolved_archive_file = None - - # load pt weights early so that we know which dtype to init the model under - - if state_dict is None: - try: - state_dict = torch.load(resolved_archive_file, map_location="cpu") - except Exception as e: - try: - with open(resolved_archive_file) as f: - if f.read().startswith("version"): - raise OSError( - "You seem to have cloned a repository without having git-lfs installed. Please install " - "git-lfs and run `git lfs install` followed by `git lfs pull` in the folder " - "you cloned." - ) - else: - raise ValueError from e - except (UnicodeDecodeError, ValueError): - raise OSError( - f"Unable to load weights from pytorch checkpoint file for '{finetuned_model_path}' " - f"at '{resolved_archive_file}'. " - "If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True." - ) - - # set dtype to instantiate the model under: - # 1. If torch_dtype is not None, we use that dtype - # 2. If torch_dtype is "auto", we auto-detect dtype from the loaded state_dict, by checking its first - # weights entry - we assume all weights are of the same dtype - # we also may have config.torch_dtype available, but we won't rely on it till v5 - dtype_orig = None - if torch_dtype is not None: - if isinstance(torch_dtype, str): - if torch_dtype == "auto": - torch_dtype = next(iter(state_dict.values())).dtype - else: - raise ValueError( - f"`torch_dtype` can be either a `torch.dtype` or `auto`, but received {torch_dtype}" - ) - dtype_orig = cls._set_default_torch_dtype(torch_dtype) - # Initialize the model from config and attach the delta model to the backbone_model. - delta_model = cls.from_config(config, backbone_model, *model_args, **model_kwargs, ) + delta_model = cls.from_config(delta_config, backbone_model, *model_args, **model_kwargs, ) # load the state_dict into the backbone_model. As the delta model's parameter # is the same object as the deltas in the backbone model with different reference name, @@ -461,14 +277,21 @@ class SaveLoadMixin(PushToHubMixin): delta_model._load_state_dict_into_backbone(backbone_model, state_dict) backbone_hash = gen_model_hash(backbone_model) - if check_hash and hasattr(config, "backbone_hash") and \ - config.backbone_hash is not None and \ - config.backbone_hash != backbone_hash: - logger.warning("The config has an hash of the backbone model, and is" - "different from the hash of the loaded model. This indicates a mismatch" - "between the backbone model that the delta checkpoint is based on and" - "the one you loaded. You propobability need to Train the model instead of" - "directly inference. ") + + if check_hash: + if hasattr(delta_config, "backbone_hash") and \ + delta_config.backbone_hash is not None and \ + delta_config.backbone_hash != backbone_hash: + logger.warning("The config has an hash of the backbone model, and is" + "different from the hash of the loaded model. This indicates a mismatch" + "between the backbone model that the delta checkpoint is based on and" + "the one you loaded. You propobability need to Train the model instead of" + "directly inference. ") + else: + logger.info("Hash-check passed. You can safely use this checkpoint directly.") + else: + logger.warning("Parameters' hash has not been checked!") + # Set model in evaluation mode to deactivate DropOut modules by default backbone_model.eval() @@ -511,7 +334,7 @@ class SaveLoadMixin(PushToHubMixin): # if eventualy name is not set, create a default one if mdict['name'] is None or mdict['name'] == '': - print("Warning: name is not set, use default name") + logger.info("Name is not set, use default name.") mdict['name'] = self.create_default_name(**mdict) @@ -521,7 +344,6 @@ class SaveLoadMixin(PushToHubMixin): def create_default_name(self, **kwargs): r"""Currently, it's only a simple concatenation of the arguments. 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-1,5 +1,5 @@ -__version__ = "0.1.0" +__version__ = "0.2.0" class GlobalSetting: def __init__(self): diff --git a/setup.py b/setup.py index 31e0d58..cfbcac8 100644 --- a/setup.py +++ b/setup.py @@ -4,10 +4,9 @@ import os import os def get_requirements(path): - print("path is :", path) ret = [] - - with open(os.path.join(path, "requirements.txt"), encoding="utf-8") as freq: + # path = "/home/ubuntu/OpenDelta_beta/OpenDelta/" + with open(os.path.join(path, "opendelta.egg-info/requires.txt"), encoding="utf-8") as freq: for line in freq.readlines(): ret.append( line.strip() ) return ret @@ -15,12 +14,11 @@ def get_requirements(path): path = os.path.dirname(os.path.abspath(__file__)) requires = get_requirements(path) -print(requires) with open('README.md', 'r') as f: setuptools.setup( name = 'opendelta', - version = "0.1.0", + version = "0.2.0", description = "An open source framework for delta learning (parameter efficient learning).", long_description=open("README.md", "r", encoding="utf-8").read(), long_description_content_type="text/markdown", @@ -30,10 +28,10 @@ with open('README.md', 'r') as f: url="https://github.com/thunlp/OpenDelta", keywords = ['PLM', 'Parameter-efficient-Learning', 'AI', 'NLP'], python_requires=">=3.6.0", - install_requires=requires, + install_requires=get_requirements(path), package_dir={'opendelta':'opendelta'}, package_data= { - 'opendelta':["utils/interactive/templates/*.html"], + 'opendelta':["utils/interactive/templates/*.html", 'requirments.txt'], }, include_package_data=True, packages=setuptools.find_packages(), From ed2bd8c50f18e003d971101ecdcd2273943c8610 Mon Sep 17 00:00:00 2001 From: shengdinghu Date: Sun, 3 Jul 2022 13:29:30 +0000 Subject: [PATCH 3/8] update requirements.txt --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index a53c347..a2ffa29 100644 --- a/requirements.txt +++ b/requirements.txt @@ -8,3 +8,4 @@ decorator rich web.py gitpython +delta_center_client From b9a0f7cf8930889a964a1b6ab49320824dbe0c73 Mon Sep 17 00:00:00 2001 From: shengdinghu Date: Wed, 6 Jul 2022 14:00:58 +0000 Subject: [PATCH 4/8] small update in save_and_load --- dist/opendelta-0.2.0-py3-none-any.whl | Bin 79495 -> 79495 bytes dist/opendelta-0.2.0.tar.gz | Bin 64421 -> 64441 bytes dist/opendelta-0.2.1-py3-none-any.whl | Bin 0 -> 79504 bytes dist/opendelta-0.2.1.tar.gz | Bin 0 -> 64419 bytes dist/opendelta-0.2.2-py3-none-any.whl | Bin 0 -> 79505 bytes dist/opendelta-0.2.2.tar.gz | Bin 0 -> 64439 bytes opendelta/__init__.py | 2 +- opendelta/delta_configs.py | 1 + opendelta/delta_models/adapter.py | 14 +++++- opendelta/utils/saving_loading_utils.py | 62 +++++++++++++++--------- opendelta/utils/structure_mapping.py | 48 +++++++++++------- setup.py | 32 ++++++++---- 12 files changed, 106 insertions(+), 53 deletions(-) create mode 100644 dist/opendelta-0.2.1-py3-none-any.whl create mode 100644 dist/opendelta-0.2.1.tar.gz create mode 100644 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znnxg|0WNuy!r|F6KTgTT6ed+VnNsqH+`-Vq;vSC_@L3!dzG@l__mv{=g|8~a*{#=y zFL0`xJXT@)qv!bl%fr`O#|0q1bRmg8%m_GzE?X`l9KpY~~=_GzE?X`l9KpY~~=_GzE?X`kQ2^ZzvG_9OuC F1pwd1uciP1 literal 0 HcmV?d00001 diff --git a/examples/examples_prompt/src/run.py b/examples/examples_prompt/src/run.py index 30b534e..51fa3dc 100644 --- a/examples/examples_prompt/src/run.py +++ b/examples/examples_prompt/src/run.py @@ -324,7 +324,7 @@ def main(): delta_model.save_finetuned(finetuned_delta_path=delta_args.finetuned_delta_path, push_to_dc=training_args.push_to_dc, center_args={"test_performance":all_results['test'][data_args.task_name]['test_average_metrics'], - }, + "backbone_model_path_public":model_args.model_name_or_path.split("/")[-1]}, center_args_pool = {**vars(model_args), **vars(data_args), **vars(training_args), **vars(delta_args)}, list_tags = ['NLI'], dict_tags = {'purpose':'for testing'}, diff --git a/opendelta/__init__.py b/opendelta/__init__.py index 4f01cee..6d38799 100644 --- a/opendelta/__init__.py +++ b/opendelta/__init__.py @@ -1,5 +1,5 @@ -__version__ = "0.2.2" +__version__ = "0.2.4" class GlobalSetting: def __init__(self): diff --git a/opendelta/utils/saving_loading_utils.py b/opendelta/utils/saving_loading_utils.py index 8979faa..59f381c 100644 --- a/opendelta/utils/saving_loading_utils.py +++ b/opendelta/utils/saving_loading_utils.py @@ -32,14 +32,10 @@ class DeltaCenterArguments: backbone_model: str = field(default="", metadata={"help": "The backbone model of the delta model"} ) - model_path_public: str = field( + backbone_model_path_public: str = field( default = None, metadata={"help": "Publicly available path (url) to pretrained model or model identifier from huggingface.co/models"} ) - model_revision: str = field( - default="main", - metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, - ) delta_type: str = field( default=None, metadata={"help": "the type of type model, e.g., adapter, lora, etc."} @@ -48,6 +44,10 @@ class DeltaCenterArguments: default=None, metadata={"help": "the task(s) that the delta is trained on"} ) + train_datasets: Optional[Union[List[str], str]]= field( + default=None, + metadata={"help": "the datasets(s) that the delta is trained on"} + ) checkpoint_size: Optional[float] = field( default=None, metadata={"help": "the size of the checkpoint, in MB"} @@ -56,6 +56,10 @@ class DeltaCenterArguments: default=None, metadata={"help": "the task(s) that the delta is tested on"} ) + test_datasets: Optional[Union[List[str], str]] = field( + default=None, + metadata={"help": "the dataset(s) that the delta is tested on"} + ) test_performance: Optional[float] = field( default=None, metadata={"help": "the performance of the model on the test set"} @@ -72,6 +76,14 @@ class DeltaCenterArguments: default=None, metadata={"help": "the ratio of delta parameters in the model"} ) + usage: Optional[str] = field( + default="", + metadata={"help": "the usage code of the model"} + ) + license: Optional[str] = field( + default="apache-2.0", + metadata={"help": "the license of the model"} + ) @@ -96,6 +108,7 @@ class SaveLoadMixin: list_tags: Optional[List] = list(), dict_tags: Optional[Dict] = dict(), delay_push: bool = False, + usage: Optional[str] = "", ): r""" Save a model and its configuration file to a directory, so that it can be re-loaded using the @@ -353,16 +366,27 @@ class SaveLoadMixin: logger.info("Name is not set, use default name.") mdict['name'] = self.create_default_name(**mdict) + if len(mdict['usage']) == 0: + logger.info("Usage is not set, use default usage.") + mdict['usage'] = self.create_default_usage(mdict['name']) + center_args = DeltaCenterArguments(**mdict) return center_args + def create_default_usage(self, name): + usage_str = """from opendelta import AutoDeltaModel\n""" + \ + """delta_model = AutoDeltaModel.from_finetuned('{name_with_userid}', backbone_model=model)\n""" + \ + """delta_model.freeze_module() # if you are going to further train it \n""" + \ + """delta_model.log()""" + return usage_str + def create_default_name(self, **kwargs): r"""Currently, it's only a simple concatenation of the arguments. """ reponame = "" - reponame += kwargs["model_path_public"].split("/")[-1]+"_" if kwargs['model_path_public'] is not None else kwargs['backbone_model'] + reponame += kwargs["backbone_model_path_public"].split("/")[-1]+"_" if kwargs['backbone_model_path_public'] is not None else kwargs['backbone_model'] reponame += kwargs["delta_type"]+"_" if kwargs["delta_type"] is not None else "" # tasks diff --git a/requirements.txt b/requirements.txt index a2ffa29..5345c4e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -8,4 +8,4 @@ decorator rich web.py gitpython -delta_center_client +delta_center_client==0.0.4 diff --git a/setup.py b/setup.py index ef04582..cbaef15 100644 --- a/setup.py +++ b/setup.py @@ -14,7 +14,7 @@ decorator rich web.py gitpython -delta_center_client +delta_center_client==0.0.4 """ def get_requirements(): @@ -30,7 +30,7 @@ def get_requirements(): with open('README.md', 'r') as f: setuptools.setup( name = 'opendelta', - version = "0.2.2", + version = "0.2.4", description = "An open source framework for delta learning (parameter efficient learning).", long_description=open("README.md", "r", encoding="utf-8").read(), long_description_content_type="text/markdown", From 0e8d66f6c73579b906d20d6e35b319a86bc32ee6 Mon Sep 17 00:00:00 2001 From: DingDing <32740627+ShengdingHu@users.noreply.github.com> Date: Sat, 9 Jul 2022 21:19:59 +0800 Subject: [PATCH 6/8] Update t5.py --- examples/examples_prompt/backbones/t5.py | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/examples/examples_prompt/backbones/t5.py b/examples/examples_prompt/backbones/t5.py index 7a6edf0..15e7f21 100644 --- a/examples/examples_prompt/backbones/t5.py +++ b/examples/examples_prompt/backbones/t5.py @@ -26,14 +26,13 @@ def preprocess_function(raw_example, **kwargs): example = InputExample(**raw_example) - try: - example = verbalizer.wrap_one_example(example) - example, other = template.wrap_one_example(example) - input_sentence = tokenizer_wrapper.merge_wrapped_example(example) - model_inputs = tokenizer(input_sentence, max_length=256, - padding="max_length", truncation=True) - except: - from IPython import embed; embed(header="Therer") + + example = verbalizer.wrap_one_example(example) + example, other = template.wrap_one_example(example) + input_sentence = tokenizer_wrapper.merge_wrapped_example(example) + model_inputs = tokenizer(input_sentence, max_length=256, + padding="max_length", truncation=True) + with tokenizer.as_target_tokenizer(): label = tokenizer(other['tgt_text']).input_ids From 73fe77bb72f5c0d41ec388297aa80dd39ed35109 Mon Sep 17 00:00:00 2001 From: DingDing <32740627+ShengdingHu@users.noreply.github.com> Date: Sat, 9 Jul 2022 21:20:30 +0800 Subject: [PATCH 7/8] Update bart.py --- examples/examples_prompt/backbones/bart.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/examples/examples_prompt/backbones/bart.py b/examples/examples_prompt/backbones/bart.py index bab8303..e3ff5d5 100644 --- a/examples/examples_prompt/backbones/bart.py +++ b/examples/examples_prompt/backbones/bart.py @@ -26,14 +26,14 @@ def preprocess_function(raw_example, **kwargs): example = InputExample(**raw_example) - try: - example = verbalizer.wrap_one_example(example) - example, other = template.wrap_one_example(example) - input_sentence = tokenizer_wrapper.merge_wrapped_example(example) - model_inputs = tokenizer(input_sentence, max_length=256, - padding="max_length", truncation=True) - except: - from IPython import embed; embed(header="Therer") + + example = verbalizer.wrap_one_example(example) + example, other = template.wrap_one_example(example) + input_sentence = tokenizer_wrapper.merge_wrapped_example(example) + model_inputs = tokenizer(input_sentence, max_length=256, + padding="max_length", truncation=True) + + with tokenizer.as_target_tokenizer(): label = tokenizer(other['tgt_text']).input_ids From 07896215b42d3ea55b6a53a063748c80eaff6e1c Mon Sep 17 00:00:00 2001 From: DingDing <32740627+ShengdingHu@users.noreply.github.com> Date: Sat, 9 Jul 2022 21:20:54 +0800 Subject: [PATCH 8/8] Update blenderbot.py --- examples/examples_prompt/backbones/blenderbot.py | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/examples/examples_prompt/backbones/blenderbot.py b/examples/examples_prompt/backbones/blenderbot.py index c1e8876..9c402b6 100644 --- a/examples/examples_prompt/backbones/blenderbot.py +++ b/examples/examples_prompt/backbones/blenderbot.py @@ -26,14 +26,13 @@ def preprocess_function(raw_example, **kwargs): example = InputExample(**raw_example) - try: - example = verbalizer.wrap_one_example(example) - example, other = template.wrap_one_example(example) - input_sentence = tokenizer_wrapper.merge_wrapped_example(example) - model_inputs = tokenizer(input_sentence, max_length=data_args.max_source_length, - padding="max_length", truncation=True) - except: - from IPython import embed; embed(header="Therer") + + example = verbalizer.wrap_one_example(example) + example, other = template.wrap_one_example(example) + input_sentence = tokenizer_wrapper.merge_wrapped_example(example) + model_inputs = tokenizer(input_sentence, max_length=data_args.max_source_length, + padding="max_length", truncation=True) + with tokenizer.as_target_tokenizer(): label = tokenizer(other['tgt_text']).input_ids

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a/opendelta/delta_configs.py +++ b/opendelta/delta_configs.py @@ -193,6 +193,7 @@ class BaseDeltaConfig: config_dict.pop(config_key) unused_config_keys.append(config_key) logger.warning(f"The following keys are not used by {cls}.__init__ function: {unused_config_keys}") + config = cls(**config_dict) diff --git a/opendelta/delta_models/adapter.py b/opendelta/delta_models/adapter.py index c5ab43a..e706026 100644 --- a/opendelta/delta_models/adapter.py +++ b/opendelta/delta_models/adapter.py @@ -22,10 +22,18 @@ class InterFaceMixin: self._reverse_axis_order = np.argsort(self._axis_order).tolist() def _transpose(self, tensor): - return tensor.permute(*self._axis_order) + if tensor.dim() == 3: + return tensor.permute(*self._axis_order) + else: + return tensor + + def _reverse_transpose(self, tensor): - return tensor.permute(*self._reverse_axis_order).contiguous() + if tensor.dim() == 3: + return tensor.permute(*self._reverse_axis_order).contiguous() + else: + return tensor def _convert_data_type(self, tensor): self._data_type_record = tensor.dtype @@ -37,6 +45,8 @@ class InterFaceMixin: + + class AdapterLayer(nn.Module, InterFaceMixin): r"""A layer of adapter tuning module. """ diff --git a/opendelta/utils/saving_loading_utils.py b/opendelta/utils/saving_loading_utils.py index 0173d65..8979faa 100644 --- a/opendelta/utils/saving_loading_utils.py +++ b/opendelta/utils/saving_loading_utils.py @@ -91,10 +91,10 @@ class SaveLoadMixin: state_dict: Optional[dict] = None, save_function: Callable = torch.save, push_to_dc: bool = True, - center_args: Optional[Union[DeltaCenterArguments, dict]] = None, - center_args_pool: Optional[dict] = None, - list_tags: Optional[List] = None, - dict_tags: Optional[Dict] = None, + center_args: Optional[Union[DeltaCenterArguments, dict]] = dict(), + center_args_pool: Optional[dict] = dict(), + list_tags: Optional[List] = list(), + dict_tags: Optional[Dict] = dict(), delay_push: bool = False, ): r""" @@ -106,10 +106,11 @@ class SaveLoadMixin: If not specified, the model will be saved in the directory ``./delta_checkpoints/``, which is a subdirectory of the current working directory. save_config: (optional) if ``True``, the configuration file will be saved in the same directory as the - model file. + model file. if ``False``, only the state dict will be saved. state_dict: (optional) a dictionary containing the model's state_dict. If not specified, the state_dict is loaded from the backbone model's trainable parameters. save_function: (optional) the function used to save the model. Defaults to ``torch.save``. + state_dict_only: (optional) if ``True``, only the state_dict will be saved. push_to_dc: (optional) if ``True``, the model will prepare things to pushed to the DeltaCenter. This includes: - creating a configuration file for the model @@ -131,7 +132,8 @@ class SaveLoadMixin: self.create_config_from_model() self.add_configs_when_saving() - final_center_args = self.create_delta_center_args(center_args=center_args, + if push_to_dc: + final_center_args = self.create_delta_center_args(center_args=center_args, center_args_pool=center_args_pool) save_directory = finetuned_delta_path @@ -140,8 +142,10 @@ class SaveLoadMixin: return os.makedirs(save_directory, exist_ok=True) - save_directory = os.path.join(save_directory, final_center_args.name) - os.makedirs(save_directory, exist_ok=True) + + if push_to_dc: + save_directory = os.path.join(save_directory, final_center_args.name) + os.makedirs(save_directory, exist_ok=True) model_to_save = self.backbone_model# unwrap_model(self) @@ -149,13 +153,13 @@ class SaveLoadMixin: if state_dict is None: state_dict = model_to_save.state_dict() - # Save the config - if save_config: - self.config.save_finetuned(save_directory) - output_model_file = os.path.join(save_directory, WEIGHTS_NAME) save_function(state_dict, output_model_file) + # Save the config + if save_config: + self.config.save_finetuned(save_directory) + logger.info("\n"+"*"*30+f"\nYou delta models has been saved locally to:\n\t{os.path.abspath(save_directory)}" ) @@ -164,24 +168,36 @@ class SaveLoadMixin: logger.info("Creating yaml file for delta center") self.create_yml(save_directory, final_center_args, list_tags, dict_tags) - - if not delay_push: - OssClient.upload(base_dir=save_directory) - else: - logger.info("Delay push: you can push it to the delta center later using \n\tpython -m DeltaCenter upload {os.path.abspath(save_directory)}\n" - +"*"*30) - - # get absolute path of saved_directory, + if not delay_push: + OssClient.upload(base_dir=save_directory) + else: + logger.info(f"Delay push: you can push it to the delta center later using \n\tpython -m DeltaCenter upload {os.path.abspath(save_directory)}\n" + +"*"*30) - def create_yml(self, save_dir, config, list_tags=None, dict_tags=None): + + + def create_yml(self, save_dir, config, list_tags=list(), dict_tags=dict()): f = open("{}/config.yml".format(save_dir), 'w') config_dict = vars(config) - config_dict['dict_tags'] = dict_tags if dict_tags is not None else {} - config_dict['list_tags'] = list_tags if list_tags is not None else [] + config_dict['dict_tags'] = dict_tags + config_dict['list_tags'] = list_tags yaml.safe_dump(config_dict, f) f.close() + def load_checkpoint(self, path, load_func=torch.load, backbone_model=None): + r"""Simple method for loading only the checkpoint + """ + if backbone_model is None: + backbone_model = self.backbone_model + self.backbone_model.load_state_dict(load_func(f"{path}/{WEIGHTS_NAME}"), strict=False) + + def save_checkpoint(self, path, save_func=torch.save, backbone_model=None): + r"""Simple method for saving only the checkpoint""" + if backbone_model is None: + backbone_model = self.backbone_model + save_func(backbone_model.state_dict(), f"{path}/{WEIGHTS_NAME}") + @classmethod def from_finetuned(cls, finetuned_delta_path: Optional[Union[str, os.PathLike]], diff --git a/opendelta/utils/structure_mapping.py b/opendelta/utils/structure_mapping.py index bb1f1de..a49b2a5 100644 --- a/opendelta/utils/structure_mapping.py +++ b/opendelta/utils/structure_mapping.py @@ -24,7 +24,7 @@ t5_mapping = { } } }, - "final_layer_norm": {"__name__":"layer_norm"}, + "final_layer_norm": {"__name__":"layer_norm"}, }, "decoder": {"__name__":"decoder", "embed_tokens": {"__name__":"embeddings"}, @@ -199,8 +199,14 @@ distilbert_mapping = { } } + +MAPPINGERROR_MSG = "We haven't provide common structure mapping for this backbone model." + \ + " If it is a common enough PLM, please check whether it is wrapped by other wrapper model, e.g., XXXForSequenceClassification." +\ + "Please manually add the "+\ + "delta models by speicifying 'modified_modules' based on the visualization of model structure. Refer to `https://opendelta.readthedocs.io/en/latest/notes/faq.html` for detail." + def transform(org_key, mapping, strict=True, warning=False, verbose=False): - + chain = org_key.split(".") query = "" node = mapping @@ -215,7 +221,7 @@ def transform(org_key, mapping, strict=True, warning=False, verbose=False): if strict: if warning: print(f"'{org_key}' has no common mapping.") - return + return else: new_chain.append(query) else: @@ -226,19 +232,19 @@ def transform(org_key, mapping, strict=True, warning=False, verbose=False): new_chain.append(query) query = "" else: - query += "." + query += "." if query!="": if strict: if warning: print("A part of the orginial key hasn't been matched!") - return + return else: new_chain.append(query.strip(".")) # tailing query new_key = ".".join(new_chain) if verbose: print(f"{org_key} => {new_key}") return new_key - + @@ -255,7 +261,7 @@ def mapping_for_SequenceClassification(mapping, type): mapping["classifier"] = {"__name__": "classifier"} elif type == "deberta": mapping.pop("lm_predictions.lm_head") - mapping["pooler"] = {"__name__": "classifier"} + mapping["pooler"] = {"__name__": "classifier"} mapping["classifier"] = {"__name__": "classifier"} else: raise NotImplementedError @@ -266,29 +272,30 @@ def mapping_for_ConditionalGeneration(mapping, type): if type == "t5": mapping["lm_head"] = {"__name__":"lm_head.proj"} else: - raise NotImplementedError + raise NotImplementedError(MAPPINGERROR_MSG.format()) return mapping class _LazyLoading(OrderedDict): def __init__(self, mapping): self._mapping_string = mapping self._mapping = {} - + def __getitem__(self, key): if key not in self._mapping_string: - raise KeyError(key) + raise KeyError(MAPPINGERROR_MSG) value = self._mapping_string[key] self._mapping[key] = eval(value) - return self._mapping[key] - + return self._mapping[key] + def keys(self): return list(self._mapping_string.keys()) - + def __contains__(self, item): return item in self._mapping_string + class CommonStructureMap(object): r""" A lazy loading structure map. """ @@ -296,7 +303,6 @@ class CommonStructureMap(object): "RobertaForSequenceClassification": """mapping_for_SequenceClassification(roberta_mapping, "roberta")""", "RobertaForMaskedLM": "roberta_mapping", "BertForMaskedLM": "bert_mapping", - "BertForSequenceClassification": """mapping_for_SequenceClassification(bert_mapping, "bert")""", "T5ForConditionalGeneration": """mapping_for_ConditionalGeneration(t5_mapping, "t5")""", "DebertaV2ForSequenceClassification": """mapping_for_SequenceClassification(debertav2_mapping, "deberta")""" }) @@ -315,8 +321,17 @@ class CommonStructureMap(object): """ backbone_class = type(backbone_model).__name__ if backbone_class not in cls.Mappings: - raise KeyError(backbone_class) - mapping = cls.Mappings[backbone_class] + raise KeyError(MAPPINGERROR_MSG) + + try: + mapping = cls.Mappings[backbone_class] + except KeyError: + logger.error(MAPPINGERROR_MSG) + exit(-1) + + + + if visualize: logger.info("Since you are using the common structure mapping, draw the transformed parameter structure for checking.") vis = Visualization(backbone_model) @@ -346,4 +361,3 @@ if __name__ == "__main__": for name, _ in plm.named_modules(): transform(name, t5_mapping, strict=True, warning=False) - \ No newline at end of file diff --git a/setup.py b/setup.py index cfbcac8..ef04582 100644 --- a/setup.py +++ b/setup.py @@ -3,22 +3,34 @@ import setuptools import os import os -def get_requirements(path): - ret = [] - # path = "/home/ubuntu/OpenDelta_beta/OpenDelta/" - with open(os.path.join(path, "opendelta.egg-info/requires.txt"), encoding="utf-8") as freq: - for line in freq.readlines(): - ret.append( line.strip() ) + +requires = """torch>=1.8.0 +transformers>=4.10.0 +datasets==1.17.0 +sentencepiece>=0.1.96 +tqdm>=4.62.2 +# loralib +decorator +rich +web.py +gitpython +delta_center_client +""" + +def get_requirements(): + ret = [x for x in requires.split("\n") if len(x)>0] + print("requirements:", ret) return ret -path = os.path.dirname(os.path.abspath(__file__)) -requires = get_requirements(path) + +# path = os.path.dirname(os.path.abspath(__file__)) +# requires = get_requirements(path) with open('README.md', 'r') as f: setuptools.setup( name = 'opendelta', - version = "0.2.0", + version = "0.2.2", description = "An open source framework for delta learning (parameter efficient learning).", long_description=open("README.md", "r", encoding="utf-8").read(), long_description_content_type="text/markdown", @@ -28,7 +40,7 @@ with open('README.md', 'r') as f: url="https://github.com/thunlp/OpenDelta", keywords = ['PLM', 'Parameter-efficient-Learning', 'AI', 'NLP'], python_requires=">=3.6.0", - install_requires=get_requirements(path), + install_requires=get_requirements(), package_dir={'opendelta':'opendelta'}, package_data= { 'opendelta':["utils/interactive/templates/*.html", 'requirments.txt'], From 5fb6b7989d13b43f66e7c3049d54d17d9f1a0fab Mon Sep 17 00:00:00 2001 From: shengdinghu Date: Fri, 8 Jul 2022 03:48:02 +0000 Subject: [PATCH 5/8] update delta center arguments --- dist/opendelta-0.2.2-py3-none-any.whl | Bin 79505 -> 79910 bytes dist/opendelta-0.2.2.tar.gz | Bin 64439 -> 64768 bytes dist/opendelta-0.2.3-py3-none-any.whl | Bin 0 -> 79913 bytes dist/opendelta-0.2.3.tar.gz | Bin 0 -> 64736 bytes dist/opendelta-0.2.4-py3-none-any.whl | Bin 0 -> 80065 bytes dist/opendelta-0.2.4.tar.gz | Bin 0 -> 64876 bytes examples/examples_prompt/src/run.py | 2 +- opendelta/__init__.py | 2 +- opendelta/utils/saving_loading_utils.py | 36 ++++++++++++++++++++---- requirements.txt | 2 +- setup.py | 4 +-- 11 files changed, 35 insertions(+), 11 deletions(-) create mode 100644 dist/opendelta-0.2.3-py3-none-any.whl create mode 100644 dist/opendelta-0.2.3.tar.gz create mode 100644 dist/opendelta-0.2.4-py3-none-any.whl create mode 100644 dist/opendelta-0.2.4.tar.gz diff --git a/dist/opendelta-0.2.2-py3-none-any.whl b/dist/opendelta-0.2.2-py3-none-any.whl index 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