Merge pull request #252 from stanford-oval/wip/mehrad/cleanups

Major Cleanup #1

.travis.yml

@@ -25,11 +25,6 @@ jobs:
       stage: test
       script:
         - bash ./tests/test_main_almond.sh
-    -
-      name: "Main tests for almond_multilingual task"
-      stage: test
-      script:
-        - bash ./tests/test_main_almond_multilingual.sh
     -
       name: "Paraphrasing tests"
       stage: test

README.md

@@ -125,7 +125,7 @@ genienlp run-paraphrase --model_name_or_path <model_dir> --temperature 0.3 --rep
 Use the following command for training/ finetuning an NMT model:
 
 ```bash
-genienlp train --train_tasks almond_translate --data <data_directory> --train_languages <src_lang> --eval_languages <tgt_lang> --no_commit --train_iterations <iterations> --preserve_case --save <save_dir> --exist_ok --skip_cache --model TransformerSeq2Seq --pretrained_model <hf_model_name>
+genienlp train --train_tasks almond_translate --data <data_directory> --train_languages <src_lang> --eval_languages <tgt_lang> --no_commit --train_iterations <iterations> --preserve_case --save <save_dir> --exist_ok  --model TransformerSeq2Seq --pretrained_model <hf_model_name>
 ```
 
 We currently support MarianMT, MBART, MT5, and M2M100 models.<br>
@@ -134,7 +134,7 @@ To save a pretrained model in genienlp format without any finetuning, set train_
 To produce translations for an eval/ test set run the following command:
 
 ```bash
-genienlp predict --tasks almond_translate --data <data_directory> --pred_languages <src_lang> --pred_tgt_languages <tgt_lang> --path <path_to_saved_model> --eval_dir <eval_dir> --skip_cache --val_batch_size 4000 --evaluate <valid/test>  --overwrite --silent
+genienlp predict --tasks almond_translate --data <data_directory> --pred_languages <src_lang> --pred_tgt_languages <tgt_lang> --path <path_to_saved_model> --eval_dir <eval_dir>  --val_batch_size 4000 --evaluate <valid/test>  --overwrite --silent
 ```
 
 If your dataset is a document or contains long examples, pass `--translate_example_split` to break the examples down into individual sentences before translation for better results. <br>

genienlp/arguments.py

@@ -67,7 +67,6 @@ def parse_argv(parser):
     parser.add_argument('--data', default='.data/', type=str, help='where to load data from.')
     parser.add_argument('--save', required=True, type=str, help='where to save results.')
     parser.add_argument('--embeddings', default='.embeddings/', type=str, help='where to save embeddings.')
-    parser.add_argument('--cache', default='.cache/', type=str, help='where to save cached files')
 
     parser.add_argument(
         '--train_languages',
@@ -409,10 +408,6 @@ def parse_argv(parser):
         help='Ignore all conditions and use fast version of huggingface tokenizer',
     )
 
-    parser.add_argument('--skip_cache', action='store_true', help='whether to use existing cached splits or generate new ones')
-    parser.add_argument(
-        '--cache_input_data', action='store_true', help='Cache examples from input data for faster subsequent trainings'
-    )
     parser.add_argument('--use_curriculum', action='store_true', help='Use curriculum learning')
     parser.add_argument(
         '--aux_dataset', default='', type=str, help='path to auxiliary dataset (ignored if curriculum is not used)'
@@ -539,11 +534,6 @@ def parse_argv(parser):
     # token classification task args
     parser.add_argument('--num_labels', type=int, help='num_labels for classification tasks')
     parser.add_argument('--crossner_domains', nargs='+', type=str, help='domains to use for CrossNER task')
-    parser.add_argument(
-        '--hf_test_overfit',
-        action='store_true',
-        help='Debugging flag for hf datasets where validation will be performed on train set',
-    )
 
     parser.add_argument(
         '--e2e_dialogue_evaluation',

genienlp/kfserver.py

@@ -39,9 +39,9 @@
 
 
 class KFModelServer(kfserving.KFModel):
-    def __init__(self, name, args, numericalizer, model, device, confidence_estimators, estimator_filenames, ned_model):
+    def __init__(self, name, args, model, device, confidence_estimators, estimator_filenames, ned_model):
         super().__init__(name)
-        self.server = Server(args, numericalizer, model, device, confidence_estimators, estimator_filenames, ned_model)
+        self.server = Server(args, model, device, confidence_estimators, estimator_filenames, ned_model)
 
     def load(self):
         log_model_size(logger, self.server.model, self.server.args.model)
@@ -57,7 +57,7 @@ def predict(self, request):
 def main(args):
     model, device, confidence_estimators, estimator_filenames, ned_model = init(args)
     model_server = KFModelServer(
-        args.inference_name, args, model.numericalizer, model, device, confidence_estimators, estimator_filenames, ned_model
+        args.inference_name, args, model, device, confidence_estimators, estimator_filenames, ned_model
     )
     model_server.load()
     kfserving.KFServer(workers=1).start([model_server])