[Flax] Align GLUE training script with mlm training script (#11778)

* speed up flax glue

* remove unnecessary line

* remove folder

* remove run in loop
Co-authored-by: Patrick von Platen <patrick@huggingface.co>

examples/flax/text-classification/README.md

@@ -59,20 +59,19 @@ On the task other than MRPC and WNLI we train for 3 these epochs because this is
 but looking at the training curves of some of them (e.g., SST-2, STS-b), it appears the models
 are undertrained and we could get better results when training longer.
 
-In the Tensorboard results linked below, the random seed of each model is equal to the ID of the run. So in order to reproduce run 1, run the command above with `--seed=1`. The best run used random seed 2, which is the default in the script. The results of all runs are in [this Google Sheet](https://docs.google.com/spreadsheets/d/1wtcjX_fJLjYs6kXkoiej2qGjrl9ByfNhPulPAz71Ky4/edit?usp=sharing).
-
+In the Tensorboard results linked below, the random seed of each model is equal to the ID of the run. So in order to reproduce run 1, run the command above with `--seed=1`. The best run used random seed 2, which is the default in the script. The results of all runs are in [this Google Sheet](https://docs.google.com/spreadsheets/d/1p3XzReMO75m_XdEJvPue-PIq_PN-96J2IJpJW1yS-10/edit?usp=sharing).
 
 | Task  | Metric                       | Acc (best run) | Acc (avg/5runs) | Stdev     | Metrics                                                                  |
 |-------|------------------------------|----------------|-----------------|-----------|--------------------------------------------------------------------------|
-| CoLA  | Matthew's corr               | 59.29          | 56.25           | 2.18      | [tfhub.dev](https://tensorboard.dev/experiment/tNBiYyvsRv69ZlXRI7x0pQ/)  |
-| SST-2 | Accuracy                     | 91.97          | 91.79           | 0.42      | [tfhub.dev](https://tensorboard.dev/experiment/wQto9nBwQHOINUxjKAAblQ/)  |
-| MRPC  | F1/Accuracy                  | 90.39/86.03     | 89.70/85.20     | 0.68/0.91 | [tfhub.dev](https://tensorboard.dev/experiment/Q40mkOtDSYymFRfo4jKsgQ/)  |
-| STS-B | Pearson/Spearman corr.       | 89.19/88.91    | 89.40/89.09     | 0.18/0.14 | [tfhub.dev](https://tensorboard.dev/experiment/a2bfeAy6SveV0X0FjwxMXQ/)  |
-| QQP   | Accuracy/F1                  | 91.02/87.90    | 90.96/87.75     | 0.08/0.14 | [tfhub.dev](https://tensorboard.dev/experiment/kL2vGgoQQeyTVGetehbCpg/)  |
-| MNLI  | Matched acc.                 | 83.82          | 83.65           | 0.28      | [tfhub.dev](https://tensorboard.dev/experiment/nck6178dTpmTOPm7862urA/)  |
-| QNLI  | Accuracy                     | 90.81          | 90.88           | 0.18      | [tfhub.dev](https://tensorboard.dev/experiment/44slZTLKQtqGhWs1Rhedcg/)  |
-| RTE   | Accuracy                     | 69.31          | 66.79           | 1.88      | [tfhub.dev](https://tensorboard.dev/experiment/g0yvpEXKSAytDMvP8TP8Og/)  |
-| WNLI  | Accuracy                     | 56.34          | 36.62           | 12.48      | [tfhub.dev](https://tensorboard.dev/experiment/7DfXdlDnTWWKBEx4pXForA/)  |
+| CoLA  | Matthew's corr               | 60.82          | 59.04           | 1.17      | [tfhub.dev](https://tensorboard.dev/experiment/U2ncNFP3RpWW6YnA9PYJBA/)  |
+| SST-2 | Accuracy                     | 92.43          | 92.13           | 0.38      | [tfhub.dev](https://tensorboard.dev/experiment/vzxoOHZURcm0rO1I33x7uA/)  |
+| MRPC  | F1/Accuracy                  | 89.90/88.98    | 88.98/85.30     | 0.73/2.33 | [tfhub.dev](https://tensorboard.dev/experiment/EWPBIbfYSDGHjiYxrw2a2Q/)  |
+| STS-B | Pearson/Spearman corr.       | 89.04/88.70    | 88.94/88.63     | 0.07/0.07 | [tfhub.dev](https://tensorboard.dev/experiment/3aYHKL10TeiaZYwH1M8ogA/)  |
+| QQP   | Accuracy/F1                  | 90.82/87.54    | 90.75/87.53     | 0.06/0.02 | [tfhub.dev](https://tensorboard.dev/experiment/VfVDLS4AQnqr4NMbng6yUw/)  |
+| MNLI  | Matched acc.                 | 84.10          | 83.84           | 0.16      | [tfhub.dev](https://tensorboard.dev/experiment/Sz9UdhoORaaSjzuOHRB4Jw/)  |
+| QNLI  | Accuracy                     | 91.07          | 90.83           | 0.19      | [tfhub.dev](https://tensorboard.dev/experiment/zk6udb5MQAyAQ4eczrFBaQ/)  |
+| RTE   | Accuracy                     | 66.06          | 64.76           | 1.04      | [tfhub.dev](https://tensorboard.dev/experiment/BwxaUoAEQ5aa3oQilEjADw/)  |
+| WNLI  | Accuracy                     | 46.48          | 37.01           | 6.83      | [tfhub.dev](https://tensorboard.dev/experiment/b2Y8ouwMTRC8iBWzRzVYTA/)  |
 
 Some of these results are significantly different from the ones reported on the test set of GLUE benchmark on the
 website. For QQP and WNLI, please refer to [FAQ #12](https://gluebenchmark.com/faq) on the website.
@@ -85,18 +84,18 @@ overall training time below. For comparison we ran Pytorch's [run_glue.py](https
 
 | Task  | TPU v3-8  | 8 GPU      | [1 GPU](https://tensorboard.dev/experiment/mkPS4Zh8TnGe1HB6Yzwj4Q)  | 1 GPU (Pytorch) |
 |-------|-----------|------------|------------|-----------------|
-| CoLA  |  1m 46s   |  1m 26s    | 3m 9s      | 4m 6s           |
-| SST-2 |  5m 30s   |  6m 28s    | 22m 33s    | 34m 37s         |
-| MRPC  |  1m 32s   |  1m 14s    | 2m 20s     | 2m 56s          |
-| STS-B |  1m 33s   |  1m 12s    | 2m 16s     | 2m 48s          |
-| QQP   | 24m 40s   | 31m 48s    | 1h 59m 41s | 2h 54m          |
-| MNLI  | 26m 30s   | 33m 55s    | 2h 9m 37s  | 3h 7m 6s        |
-| QNLI  |  8m       |  9m 40s    | 34m 40s    | 49m 8s          |
-| RTE   |  1m 21s   |     55s    | 1m 10s     | 1m 16s          |
-| WNLI  |  1m 12s   |     48s    | 39s        | 36s             |
+| CoLA  |  1m 42s   |  1m 26s    | 3m 9s      | 4m 6s           |
+| SST-2 |  5m 12s   |  6m 28s    | 22m 33s    | 34m 37s         |
+| MRPC  |  1m 29s   |  1m 14s    | 2m 20s     | 2m 56s          |
+| STS-B |  1m 30s   |  1m 12s    | 2m 16s     | 2m 48s          |
+| QQP   | 22m 50s   | 31m 48s    | 1h 59m 41s | 2h 54m          |
+| MNLI  | 25m 03s   | 33m 55s    | 2h 9m 37s  | 3h 7m 6s        |
+| QNLI  |  7m30s    |  9m 40s    | 34m 40s    | 49m 8s          |
+| RTE   |  1m 20s   |     55s    | 1m 10s     | 1m 16s          |
+| WNLI  |  1m 11s   |     48s    | 39s        | 36s             |
 |-------|
-| **TOTAL** | 1h 13m | 1h 28m | 5h 16m | 6h 37m      |
-| **COST*** | $9.60     | $29.10     | $13.06 | $16.41          |
+| **TOTAL** | 1h 03m | 1h 28m | 5h 16m | 6h 37m      |
+| **COST*** | $8.56  | $29.10 | $13.06 | $16.41      |
 
 
 *All experiments are ran on Google Cloud Platform. Prices are on-demand prices

examples/flax/text-classification/run_flax_glue.py

@@ -34,7 +34,7 @@ from flax import struct, traverse_util
 from flax.jax_utils import replicate, unreplicate
 from flax.metrics import tensorboard
 from flax.training import train_state
-from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
+from flax.training.common_utils import get_metrics, onehot, shard
 from transformers import AutoConfig, AutoTokenizer, FlaxAutoModelForSequenceClassification, PretrainedConfig
 
 
@@ -407,6 +407,7 @@ def main():
 
     num_epochs = int(args.num_train_epochs)
     rng = jax.random.PRNGKey(args.seed)
+    dropout_rngs = jax.random.split(rng, jax.local_device_count())
 
     train_batch_size = args.per_device_train_batch_size * jax.local_device_count()
     eval_batch_size = args.per_device_eval_batch_size * jax.local_device_count()
@@ -424,6 +425,7 @@ def main():
         state: train_state.TrainState, batch: Dict[str, Array], dropout_rng: PRNGKey
     ) -> Tuple[train_state.TrainState, float]:
         """Trains model with an optimizer (both in `state`) on `batch`, returning a pair `(new_state, loss)`."""
+        dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
         targets = batch.pop("labels")
 
         def loss_fn(params):
@@ -436,7 +438,7 @@ def main():
         grad = jax.lax.pmean(grad, "batch")
         new_state = state.apply_gradients(grads=grad)
         metrics = jax.lax.pmean({"loss": loss, "learning_rate": learning_rate_fn(state.step)}, axis_name="batch")
-        return new_state, metrics
+        return new_state, metrics, new_dropout_rng
 
     p_train_step = jax.pmap(train_step, axis_name="batch", donate_argnums=(0,))
 
@@ -467,9 +469,7 @@ def main():
 
         # train
         for batch in glue_train_data_collator(input_rng, train_dataset, train_batch_size):
-            rng, dropout_rng = jax.random.split(rng)
-            dropout_rngs = shard_prng_key(dropout_rng)
-            state, metrics = p_train_step(state, batch, dropout_rngs)
+            state, metrics, dropout_rngs = p_train_step(state, batch, dropout_rngs)
             train_metrics.append(metrics)
         train_time += time.time() - train_start
         logger.info(f"    Done! Training metrics: {unreplicate(metrics)}")