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[training_utils] fix: Fix total train_time in sft_trainer.py and add 1) step time 2) seq len stats for training data. #5579

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[training_utils] fix: Fix total train_time in sft_trainer.py and add 1) step time 2) seq len stats for training data. #5579

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25 changes: 24 additions & 1 deletion verl/trainer/sft_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,8 +22,10 @@
os.environ["TOKENIZERS_PARALLELISM"] = "true"

import logging
import time

import hydra
import numpy as np
import torch
import torch.distributed
from omegaconf import OmegaConf
Expand Down Expand Up @@ -375,8 +377,11 @@ def fit(self):
# start profile in SPMD mode
if global_step == self.start_profile_step:
self.training_client.start_profile()
# train for on batch
# train for one batch
step_start = time.perf_counter()
output = self.training_client.train_batch(data=data)
step_time = time.perf_counter() - step_start
train_time += step_time

if global_step == self.end_profile_step:
self.training_client.stop_profile()
Expand All @@ -390,6 +395,24 @@ def fit(self):
value = metrics.pop(k)
metrics[f"train/{k}"] = value

metrics["train/time(s)"] = step_time

# add sequence length statistics
try:
seq_lens = np.array(batch_seqlens)
metrics.update(
{
"data/train_seq_len_count": len(seq_lens),
"data/train_seq_len_min": int(seq_lens.min()),
"data/train_seq_len_max": int(seq_lens.max()),
"data/train_seq_len_mean": float(seq_lens.mean()),
"data/train_seq_len_std": float(seq_lens.std()),
"data/train_seq_len_median": float(np.median(seq_lens)),
}
)
except Exception as e:
logger.warning("Failed to compute seq_len stats: %s (batch_seqlens=%s)", e, batch_seqlens)

metrics["train/global_tokens"] = torch.sum(
torch.tensor(batch_seqlens, device=self.device_name)
).item()
Expand Down