minor esm2 readme and wandb updates (#1348)

Signed-off-by: Peter St. John <pstjohn@nvidia.com>

Author: pstjohn

bionemo-recipes/models/esm2/README.md

@@ -156,5 +156,6 @@ huggingface-cli upload nvidia/${MODEL_NAME} $PWD/checkpoint_export/${MODEL_NAME}
 Or, upload all models at once with:
 
 ```bash
-for dir in *; do huggingface-cli upload nvidia/$(basename "$dir") "$dir/"; done
+cd checkpoint_export
+for dir in */; do hf upload --repo-type model nvidia/$(basename "$dir") "$dir/"; done
 ```

bionemo-recipes/recipes/esm2_native_te/perf_logger.py

@@ -56,8 +56,8 @@ def __init__(self, dist_config: DistributedConfig, args: DictConfig):
             "train/grad_norm": torchmetrics.MeanMetric(),
             "train/learning_rate": torchmetrics.MeanMetric(),
             "train/step_time": torchmetrics.MeanMetric(),
-            "train/tokens_per_second": torchmetrics.MeanMetric(),
-            "train/unpadded_tokens_per_second": torchmetrics.MeanMetric(),
+            "train/tokens_per_second_per_gpu": torchmetrics.MeanMetric(),
+            "train/unpadded_tokens_per_second_per_gpu": torchmetrics.MeanMetric(),
             "train/total_unpadded_tokens_per_batch": torchmetrics.SumMetric(),
             "train/perplexity": torchmetrics.text.Perplexity(ignore_index=-100),
             "train/gpu_memory_allocated_max_gb": torchmetrics.MaxMetric(),
@@ -102,8 +102,8 @@ def log_step(
         self.metrics["train/learning_rate"].update(lr)
         self.metrics["train/grad_norm"].update(grad_norm)
         self.metrics["train/step_time"].update(step_time)
-        self.metrics["train/tokens_per_second"].update(num_tokens / step_time)
-        self.metrics["train/unpadded_tokens_per_second"].update(num_unpadded_tokens / step_time)
+        self.metrics["train/tokens_per_second_per_gpu"].update(num_tokens / step_time)
+        self.metrics["train/unpadded_tokens_per_second_per_gpu"].update(num_unpadded_tokens / step_time)
         self.metrics["train/total_unpadded_tokens_per_batch"].update(num_unpadded_tokens / self.logging_frequency)
 
         # Handle sequence packing for torchmetrics calculation.