Unverified Commit 6797cdc0 authored by Marc van Zee's avatar Marc van Zee Committed by GitHub
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Updates README and fixes bug (#11701)

parent f063c56d
......@@ -83,14 +83,24 @@ We also ran each task once on a single V100 GPU, 8 V100 GPUs, and 8 Cloud v3 TPU
overall training time below. For comparison we ran Pytorch's [run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) on a single GPU (last column).
| Task | 8 TPU | 8 GPU | 1 GPU | 1 GPU (Pytorch) |
|-------|---------|---------|------------|-----------------|
| CoLA | 1m 46s | 1m 26s | 3m 6s | 4m 6s |
| SST-2 | 5m 30s | 6m 28s | 22m 6s | 34m 37s |
| MRPC | 1m 32s | 1m 14s | 2m 17s | 2m 56s |
| STS-B | 1m 33s | 1m 12s | 2m 11s | 2m 48s |
| QQP | 24m 40s | 31m 48s | 1h 20m 15s | 2h 54m |
| MNLI | 26m 30s | 33m 55s | 2h 7m 30s | 3u 7m 6s |
| QNLI | 8m | 9m 40s | 34m 20s | 49m 8s |
| RTE | 1m 21s | 55s | 1m 8s | 1m 16s |
| WNLI | 1m 12s | 48s | 38s | 36s |
| Task | TPU v3-8 | 8 GPU | 1 GPU | 1 GPU (Pytorch) |
|-------|-----------|------------|------------|-----------------|
| CoLA | 1m 46s | 1m 26s | 3m 6s | 4m 6s |
| SST-2 | 5m 30s | 6m 28s | 22m 6s | 34m 37s |
| MRPC | 1m 32s | 1m 14s | 2m 17s | 2m 56s |
| STS-B | 1m 33s | 1m 12s | 2m 11s | 2m 48s |
| QQP | 24m 40s | 31m 48s | 1h 20m 15s | 2h 54m |
| MNLI | 26m 30s | 33m 55s | 2h 7m 30s | 3h 7m 6s |
| QNLI | 8m | 9m 40s | 34m 20s | 49m 8s |
| RTE | 1m 21s | 55s | 1m 8s | 1m 16s |
| WNLI | 1m 12s | 48s | 38s | 36s |
|-------|
| **TOTAL** | 1h 13m | 1h 28m | 4h 34m | 6h 37m |
| **COST*** | $9.60 | $29.10 | $11.33 | $16.41 |
*All experiments are ran on Google Cloud Platform. Prices are on-demand prices
(not preemptible), obtained from the following tables:
[TPU pricing table](https://cloud.google.com/tpu/pricing),
[GPU pricing table](https://cloud.google.com/compute/gpus-pricing). GPU
experiments are ran without further optimizations besides JAX transformations.
\ No newline at end of file
......@@ -473,8 +473,8 @@ def main():
dropout_rngs = shard_prng_key(dropout_rng)
state, metrics = 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)}")
train_time += time.time() - train_start
logger.info(f" Done! Training metrics: {unreplicate(metrics)}")
logger.info(" Evaluating...")
rng, input_rng = jax.random.split(rng)
......
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