[AutoNLP] add compress #4963
[AutoNLP] add compress #4963
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Codecov Report
@@ Coverage Diff @@
## develop #4963 +/- ##
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+ Coverage 47.58% 47.64% +0.05%
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Files 453 453
Lines 65378 65460 +82
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+ Hits 31109 31187 +78
- Misses 34269 34273 +4
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| @@ -104,8 +105,8 @@ def supported_languages(self) -> List[str]: | |||
| return ["Chinese", "English"] | |||
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| @property | |||
| def _default_training_argument(self) -> TrainingArguments: | |||
| return TrainingArguments( | |||
| def _default_training_argument(self) -> CompressionArguments: | |||
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我感觉还是先维持TrainingArguments吧，好像现在也没有太用到CompressionArguments里的内容。后续我们看考虑接入trainer.compress的时候，再看CompressionArguments
| @@ -240,6 +241,7 @@ def _model_candidates(self) -> List[Dict[str, Any]]: | |||
| "trainer_type": "PromptTrainer", | |||
| "template.prompt": "{'mask'}{'soft'}“{'text': '" + self.text_column + "'}”", | |||
| "early_stopping_patience": 5, | |||
| "early_stopping_patience": 5, | |||
| @@ -552,29 +558,35 @@ def _preprocess_dataset( | |||
| processed_dataset = copy.deepcopy(dataset).map(trans_func, lazy=False) | |||
| return processed_dataset | |||
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| def to_taskflow(self, trial_id=None, export_path=None, batch_size=1, precision="fp32"): | |||
| def to_taskflow( | |||
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建议当前先把compress和export做成独立的功能。
export -> to_taskflow
compress -> to_taskflow
这两部能不能分开走？即如果一个trial_id已经被export或者compress过了，都可以直接taskflow直接加载推理？
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但这么设计，相当于compress出来就只有一个模型，不需要选择。现在只有一个策略没有问题，如果未来有多个策略，那么需要一个可以选择的函数。
我的想法是：train->(compress)->（export[training_path]->taskflow）->export[specify_path]，export这个函数是指定路径的，也意味着承载着选择用户最终需要的模型功能。
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Description
新增压缩和预测