Add Flax example tests (#14599)

* add test for glue

* add tests for clm

* fix clm test

* add summrization tests

* more tests

* fix few tests

* add test for t5 mlm

* fix t5 mlm test

* fix tests for multi device

* cleanup

* ci job

* fix metric file name

* make t5 more robust

examples/flax/token-classification/run_flax_ner.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """ Fine-tuning a 🤗 Flax Transformers model on token classification tasks (NER, POS, CHUNKS)"""
+import json
 import logging
 import os
 import random
@@ -675,6 +676,42 @@ def main():
                         repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
         epochs.desc = f"Epoch ... {epoch + 1}/{num_epochs}"
 
+    # Eval after training
+    if training_args.do_eval:
+        eval_metrics = {}
+        eval_loader = eval_data_collator(eval_dataset, eval_batch_size)
+        for batch in tqdm(eval_loader, total=len(eval_dataset) // eval_batch_size, desc="Evaluating ...", position=2):
+            labels = batch.pop("labels")
+            predictions = p_eval_step(state, batch)
+            predictions = np.array([pred for pred in chain(*predictions)])
+            labels = np.array([label for label in chain(*labels)])
+            labels[np.array(chain(*batch["attention_mask"])) == 0] = -100
+            preds, refs = get_labels(predictions, labels)
+            metric.add_batch(predictions=preds, references=refs)
+
+        # evaluate also on leftover examples (not divisible by batch_size)
+        num_leftover_samples = len(eval_dataset) % eval_batch_size
+
+        # make sure leftover batch is evaluated on one device
+        if num_leftover_samples > 0 and jax.process_index() == 0:
+            # take leftover samples
+            batch = eval_dataset[-num_leftover_samples:]
+            batch = {k: np.array(v) for k, v in batch.items()}
+
+            labels = np.array(batch.pop("labels"))
+            predictions = eval_step(unreplicate(state), batch)
+            labels[np.array(batch["attention_mask"]) == 0] = -100
+            preds, refs = get_labels(predictions, labels)
+            metric.add_batch(predictions=preds, references=refs)
+
+        eval_metrics = compute_metrics()
+
+        if jax.process_index() == 0:
+            eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()}
+            path = os.path.join(training_args.output_dir, "eval_results.json")
+            with open(path, "w") as f:
+                json.dump(eval_metrics, f, indent=4, sort_keys=True)
+
 
 if __name__ == "__main__":
     main()