Add cache_dir to save features in GLUE + Differentiate match/mismatch for MNLI metrics (#4621)

* Glue task cleaup

* Enable writing cache to cache_dir in case dataset lives in readOnly
filesystem.
* Differentiate match vs mismatch for MNLI metrics.

* Style

* Fix pytype

* Fix type

* Use cache_dir in mnli mismatch eval dataset

* Small Tweaks
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

examples/text-classification/run_glue.py

@@ -21,7 +21,7 @@ import logging
 import os
 import sys
 from dataclasses import dataclass, field
-from typing import Dict, Optional
+from typing import Callable, Dict, Optional
 
 import numpy as np
 
@@ -134,16 +134,29 @@ def main():
     )
 
     # Get datasets
-    train_dataset = GlueDataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
-    eval_dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="dev") if training_args.do_eval else None
-    test_dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="test") if training_args.do_predict else None
+    train_dataset = (
+        GlueDataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None
+    )
+    eval_dataset = (
+        GlueDataset(data_args, tokenizer=tokenizer, mode="dev", cache_dir=model_args.cache_dir)
+        if training_args.do_eval
+        else None
+    )
+    test_dataset = (
+        GlueDataset(data_args, tokenizer=tokenizer, mode="test", cache_dir=model_args.cache_dir)
+        if training_args.do_predict
+        else None
+    )
 
-    def compute_metrics(p: EvalPrediction) -> Dict:
-        if output_mode == "classification":
-            preds = np.argmax(p.predictions, axis=1)
-        elif output_mode == "regression":
-            preds = np.squeeze(p.predictions)
-        return glue_compute_metrics(data_args.task_name, preds, p.label_ids)
+    def build_compute_metrics_fn(task_name: str) -> Callable[[EvalPrediction], Dict]:
+        def compute_metrics_fn(p: EvalPrediction):
+            if output_mode == "classification":
+                preds = np.argmax(p.predictions, axis=1)
+            elif output_mode == "regression":
+                preds = np.squeeze(p.predictions)
+            return glue_compute_metrics(task_name, preds, p.label_ids)
+
+        return compute_metrics_fn
 
     # Initialize our Trainer
     trainer = Trainer(
@@ -151,7 +164,7 @@ def main():
         args=training_args,
         train_dataset=train_dataset,
         eval_dataset=eval_dataset,
-        compute_metrics=compute_metrics,
+        compute_metrics=build_compute_metrics_fn(data_args.task_name),
     )
 
     # Training
@@ -174,9 +187,12 @@ def main():
         eval_datasets = [eval_dataset]
         if data_args.task_name == "mnli":
             mnli_mm_data_args = dataclasses.replace(data_args, task_name="mnli-mm")
-            eval_datasets.append(GlueDataset(mnli_mm_data_args, tokenizer=tokenizer, mode="dev"))
+            eval_datasets.append(
+                GlueDataset(mnli_mm_data_args, tokenizer=tokenizer, mode="dev", cache_dir=model_args.cache_dir)
+            )
 
         for eval_dataset in eval_datasets:
+            trainer.compute_metrics = build_compute_metrics_fn(eval_dataset.args.task_name)
             eval_result = trainer.evaluate(eval_dataset=eval_dataset)
 
             output_eval_file = os.path.join(
@@ -196,7 +212,9 @@ def main():
         test_datasets = [test_dataset]
         if data_args.task_name == "mnli":
             mnli_mm_data_args = dataclasses.replace(data_args, task_name="mnli-mm")
-            test_datasets.append(GlueDataset(mnli_mm_data_args, tokenizer=tokenizer, mode="test"))
+            test_datasets.append(
+                GlueDataset(mnli_mm_data_args, tokenizer=tokenizer, mode="test", cache_dir=model_args.cache_dir)
+            )
 
         for test_dataset in test_datasets:
             predictions = trainer.predict(test_dataset=test_dataset).predictions

src/transformers/data/datasets/glue.py

@@ -70,6 +70,7 @@ class GlueDataset(Dataset):
         tokenizer: PreTrainedTokenizer,
         limit_length: Optional[int] = None,
         mode: Union[str, Split] = Split.train,
+        cache_dir: Optional[str] = None,
     ):
         self.args = args
         self.processor = glue_processors[args.task_name]()
@@ -81,7 +82,7 @@ class GlueDataset(Dataset):
                 raise KeyError("mode is not a valid split name")
         # Load data features from cache or dataset file
         cached_features_file = os.path.join(
-            args.data_dir,
+            cache_dir if cache_dir is not None else args.data_dir,
             "cached_{}_{}_{}_{}".format(
                 mode.value, tokenizer.__class__.__name__, str(args.max_seq_length), args.task_name,
             ),

src/transformers/data/metrics/__init__.py

@@ -63,9 +63,9 @@ if _has_sklearn:
         elif task_name == "qqp":
             return acc_and_f1(preds, labels)
         elif task_name == "mnli":
-            return {"acc": simple_accuracy(preds, labels)}
+            return {"mnli/acc": simple_accuracy(preds, labels)}
         elif task_name == "mnli-mm":
-            return {"acc": simple_accuracy(preds, labels)}
+            return {"mnli-mm/acc": simple_accuracy(preds, labels)}
         elif task_name == "qnli":
             return {"acc": simple_accuracy(preds, labels)}
         elif task_name == "rte":

src/transformers/trainer.py

@@ -553,6 +553,7 @@ class Trainer:
         if self.tb_writer:
             for k, v in logs.items():
                 self.tb_writer.add_scalar(k, v, self.global_step)
+            self.tb_writer.flush()
         if is_wandb_available():
             wandb.log(logs, step=self.global_step)
         output = json.dumps({**logs, **{"step": self.global_step}})