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feature: log number of seen sequence and frames during training for throughput #230

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11 changes: 9 additions & 2 deletions jasmine/train_dynamics.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,5 @@
import os


os.environ.setdefault("XLA_PYTHON_CLIENT_MEM_FRACTION", "0.98")

from dataclasses import dataclass, field
Expand Down Expand Up @@ -686,7 +685,15 @@ def calculate_validation_metrics(val_dataloader, genie, rng):
# --- Logging ---
if args.log:
if step % args.log_interval == 0 and jax.process_index() == 0:
log_dict = {"loss": loss, "step": step, **metrics}
sequences_seen = step * args.batch_size
frames_seen = step * args.seq_len * args.batch_size
Comment on lines +688 to +689
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Copilot AI Oct 23, 2025

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These calculations count each sequence and frame multiple times in distributed training scenarios. When using data parallelism across multiple devices, each device processes its own batch independently, but step increments on all devices. This means the totals will be multiplied by the number of processes. Consider multiplying by jax.process_count() to get accurate global counts, or divide by process count if tracking per-process metrics.

Suggested change
sequences_seen = step * args.batch_size
frames_seen = step * args.seq_len * args.batch_size
sequences_seen = step * args.batch_size * jax.process_count()
frames_seen = step * args.seq_len * args.batch_size * jax.process_count()

Copilot uses AI. Check for mistakes.
log_dict = {
"loss": loss,
"step": step,
"sequences_seen": sequences_seen,
"frames_seen": frames_seen,
**metrics,
}
if val_results:
log_dict.update(val_results["metrics"])
wandb.log(log_dict)
Expand Down
10 changes: 9 additions & 1 deletion jasmine/train_lam.py
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Original file line number Diff line number Diff line change
Expand Up @@ -506,7 +506,15 @@ def calculate_validation_metrics(val_dataloader, lam):
# --- Logging ---
if args.log:
if step % args.log_interval == 0 and jax.process_index() == 0:
log_dict = {"loss": loss, "step": step, **metrics}
sequences_seen = step * args.batch_size
frames_seen = step * args.seq_len * args.batch_size
Comment on lines +509 to +510
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Copilot AI Oct 23, 2025

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These calculations count each sequence and frame multiple times in distributed training scenarios. When using data parallelism across multiple devices, each device processes its own batch independently, but step increments on all devices. This means the totals will be multiplied by the number of processes. Consider multiplying by jax.process_count() to get accurate global counts, or divide by process count if tracking per-process metrics.

Suggested change
sequences_seen = step * args.batch_size
frames_seen = step * args.seq_len * args.batch_size
sequences_seen = step * args.batch_size * jax.process_count()
frames_seen = step * args.seq_len * args.batch_size * jax.process_count()

Copilot uses AI. Check for mistakes.
log_dict = {
"loss": loss,
"step": step,
"sequences_seen": sequences_seen,
"frames_seen": frames_seen,
**metrics,
}
if val_results:
log_dict.update(val_results["metrics"])
wandb.log(log_dict)
Expand Down
10 changes: 9 additions & 1 deletion jasmine/train_tokenizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -482,7 +482,15 @@ def calculate_validation_metrics(val_dataloader, tokenizer):
# --- Logging ---
if args.log:
if step % args.log_interval == 0 and jax.process_index() == 0:
log_dict = {"loss": loss, "step": step, **metrics}
sequences_seen = step * args.batch_size
frames_seen = step * args.seq_len * args.batch_size
Comment on lines +485 to +486
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Copilot AI Oct 23, 2025

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These calculations count each sequence and frame multiple times in distributed training scenarios. When using data parallelism across multiple devices, each device processes its own batch independently, but step increments on all devices. This means the totals will be multiplied by the number of processes. Consider multiplying by jax.process_count() to get accurate global counts, or divide by process count if tracking per-process metrics.

Suggested change
sequences_seen = step * args.batch_size
frames_seen = step * args.seq_len * args.batch_size
sequences_seen = step * args.batch_size * jax.process_count()
frames_seen = step * args.seq_len * args.batch_size * jax.process_count()

Copilot uses AI. Check for mistakes.
log_dict = {
"loss": loss,
"step": step,
"sequences_seen": sequences_seen,
"frames_seen": frames_seen,
**metrics,
}
if val_results:
log_dict.update(val_results["metrics"])
wandb.log(log_dict)
Expand Down