Add Flax example tests

examples/flax/language-modeling/run_clm_flax.py

@@ -21,6 +21,7 @@
 """
 # You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
 
+import json
 import logging
 import math
 import os
@@ -219,6 +220,13 @@ def write_eval_metric(summary_writer, eval_metrics, step):
         summary_writer.scalar(f"eval_{metric_name}", value, step)
 
 
+def save_metrics(split, output_dir, metrics):
+    metrics = {f"{split}_{metric_name}": value for metric_name, value in metrics.items()}
+    path = os.path.join(output_dir, f"{split}_results.json")
+    with open(path, "w") as f:
+        json.dump(metrics, f, indent=4, sort_keys=True)
+
+
 def create_learning_rate_fn(
     train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
 ) -> Callable[[int], jnp.array]:
@@ -672,6 +680,29 @@ def eval_step(params, batch):
                     if training_args.push_to_hub:
                         repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
 
+    # Eval after training
+    if training_args.do_eval:
+        eval_metrics = []
+        eval_loader = data_loader(input_rng, eval_dataset, eval_batch_size)
+        eval_steps = len(eval_dataset) // eval_batch_size
+        for _ in tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False):
+            # Model forward
+            batch = shard(next(eval_loader))
+            metrics = p_eval_step(state.params, batch)
+            eval_metrics.append(metrics)
+
+        # normalize eval metrics
+        eval_metrics = get_metrics(eval_metrics)
+        eval_metrics = jax.tree_map(lambda x: jnp.mean(x).item(), eval_metrics)
+
+        try:
+            eval_metrics["perplexity"] = math.exp(eval_metrics["loss"])
+        except OverflowError:
+            eval_metrics["perplexity"] = float("inf")
+
+        if jax.process_index() == 0:
+            save_metrics("eval", training_args.output_dir, eval_metrics)
+
 
 if __name__ == "__main__":
     main()

examples/flax/summarization/run_summarization_flax.py

@@ -18,6 +18,7 @@
 """
 # You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
 
+import json
 import logging
 import os
 import sys
@@ -278,6 +279,13 @@ def write_metric(summary_writer, train_metrics, eval_metrics, train_time, step):
         summary_writer.scalar(f"eval_{metric_name}", value, step)
 
 
+def save_metrics(split, output_dir, metrics):
+    metrics = {f"{split}_{metric_name}": value for metric_name, value in metrics.items()}
+    path = os.path.join(output_dir, f"{split}_results.json")
+    with open(path, "w") as f:
+        json.dump(metrics, f, indent=4, sort_keys=True)
+
+
 def create_learning_rate_fn(
     train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
 ) -> Callable[[int], jnp.array]:
@@ -816,6 +824,10 @@ def generate_step(params, batch):
         desc = f"Predict Loss: {pred_metrics['loss']} | {rouge_desc})"
         logger.info(desc)
 
+        # save final metrics in json
+        if jax.process_index() == 0:
+            save_metrics("test", training_args.output_dir, rouge_metrics)
+
 
 if __name__ == "__main__":
     main()

examples/flax/test_examples.py

@@ -0,0 +1,154 @@
+# coding=utf-8
+# Copyright 2021 HuggingFace Inc..
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import argparse
+import json
+import logging
+import os
+import sys
+from unittest.mock import patch
+
+from transformers.testing_utils import CaptureLogger, TestCasePlus, get_gpu_count, slow
+
+
+SRC_DIRS = [
+    os.path.join(os.path.dirname(__file__), dirname)
+    for dirname in [
+        "text-classification",
+        "language-modeling",
+        "summarization",
+    ]
+]
+sys.path.extend(SRC_DIRS)
+
+
+if SRC_DIRS is not None:
+    import run_clm_flax
+    import run_flax_glue
+    import run_summarization_flax
+
+
+logging.basicConfig(level=logging.DEBUG)
+
+logger = logging.getLogger()
+
+
+def get_setup_file():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-f")
+    args = parser.parse_args()
+    return args.f
+
+
+def get_results(output_dir, split="eval"):
+    results = {}
+    path = os.path.join(output_dir, f"{split}_results.json")
+    if os.path.exists(path):
+        with open(path, "r") as f:
+            results = json.load(f)
+    else:
+        raise ValueError(f"can't find {path}")
+    return results
+
+
+class ExamplesTests(TestCasePlus):
+    def test_run_glue(self):
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            run_glue.py
+            --model_name_or_path distilbert-base-uncased
+            --output_dir {tmp_dir}
+            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
+            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
+            --per_device_train_batch_size=2
+            --per_device_eval_batch_size=1
+            --learning_rate=1e-4
+            --max_train_steps=10
+            --num_warmup_steps=2
+            --seed=42
+            --max_length=128
+            """.split()
+
+        with patch.object(sys, "argv", testargs):
+            run_flax_glue.main()
+            result = get_results(tmp_dir)
+            self.assertGreaterEqual(result["eval_accuracy"], 0.75)
+
+    def test_run_clm(self):
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            run_clm_flax.py
+            --model_name_or_path distilgpt2
+            --train_file ./tests/fixtures/sample_text.txt
+            --validation_file ./tests/fixtures/sample_text.txt
+            --do_train
+            --do_eval
+            --block_size 128
+            --per_device_train_batch_size 5
+            --per_device_eval_batch_size 5
+            --num_train_epochs 2
+            --logging_steps 2 --eval_steps 2
+            --output_dir {tmp_dir}
+            --overwrite_output_dir
+            """.split()
+
+        # if torch.cuda.device_count() > 1:
+        # Skipping because there are not enough batches to train the model + would need a drop_last to work.
+        # return
+
+        with patch.object(sys, "argv", testargs):
+            run_clm_flax.main()
+            result = get_results(tmp_dir)
+            self.assertLess(result["eval_perplexity"], 100)
+
+    # @slow
+    def test_run_summarization(self):
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            run_summarization.py
+            --model_name_or_path t5-small
+            --train_file tests/fixtures/tests_samples/xsum/sample.json
+            --validation_file tests/fixtures/tests_samples/xsum/sample.json
+            --test_file tests/fixtures/tests_samples/xsum/sample.json
+            --output_dir {tmp_dir}
+            --overwrite_output_dir
+            --max_steps=50
+            --warmup_steps=8
+            --do_train
+            --do_eval
+            --do_predict
+            --learning_rate=2e-4
+            --per_device_train_batch_size=2
+            --per_device_eval_batch_size=1
+            --predict_with_generate
+        """.split()
+
+        with patch.object(sys, "argv", testargs):
+            run_summarization_flax.main()
+            result = get_results(tmp_dir, split="test")
+            self.assertGreaterEqual(result["test_rouge1"], 10)
+            self.assertGreaterEqual(result["test_rouge2"], 2)
+            self.assertGreaterEqual(result["test_rougeL"], 7)
+            self.assertGreaterEqual(result["test_rougeLsum"], 7)

examples/flax/text-classification/run_flax_glue.py

@@ -15,6 +15,7 @@
 # limitations under the License.
 """ Finetuning a 🤗 Flax Transformers model for sequence classification on GLUE."""
 import argparse
+import json
 import logging
 import os
 import random
@@ -259,6 +260,13 @@ def glue_eval_data_collator(dataset: Dataset, batch_size: int):
         yield batch
 
 
+def save_metrics(split, output_dir, metrics):
+    metrics = {f"{split}_{metric_name}": value for metric_name, value in metrics.items()}
+    path = os.path.join(output_dir, f"{split}_results.json")
+    with open(path, "w") as f:
+        json.dump(metrics, f, indent=4, sort_keys=True)
+
+
 def main():
     args = parse_args()
 
@@ -522,6 +530,10 @@ def eval_step(state, batch):
             if args.push_to_hub:
                 repo.push_to_hub(commit_message=f"Saving weights and logs of epoch {epoch}", blocking=False)
 
+    # save the eval metrics in json
+    if jax.process_index() == 0:
+        save_metrics("eval", args.output_dir, eval_metric)
+
 
 if __name__ == "__main__":
     main()