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allow users to set ndigits for rounding of metrics when logging (#3325)

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* METRIC_PRECISION-> 8

* use ndigits and move env getter to top of log function

---------

Co-authored-by: Ved <ved.work2024@gmail.com>
Co-authored-by: Wing Lian <wing@axolotl.ai>
This commit is contained in:
VED
2025-12-22 19:24:43 +05:30
committed by GitHub
parent 43cef27458
commit faaff6c792
1 changed files with 4 additions and 3 deletions
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7
src/axolotl/core/trainers/base.py
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@@ -604,6 +604,7 @@ class AxolotlTrainer(
"""
# logs either has 'loss' or 'eval_loss'
train_eval = "train" if "loss" in logs else "eval"
metric_ndigits = int(os.getenv("AXOLOTL_METRIC_NDIGITS", "5"))
for key, metric_data in self._stored_metrics[train_eval].items():
values = torch.tensor(metric_data["values"]) # type: ignore[arg-type]
@@ -614,16 +615,16 @@ class AxolotlTrainer(
raise NotImplementedError(
"Metric reduction must be one of [mean, min, max, sum]"
)
logs[key] = round(fn(values).item(), 4)
logs[key] = round(fn(values).item(), metric_ndigits)
if "loss" in logs:
try:
logs["ppl"] = round(math.exp(logs["loss"]), 4)
logs["ppl"] = round(math.exp(logs["loss"]), metric_ndigits)
except OverflowError:
logs["ppl"] = float("inf")
if "eval_loss" in logs:
try:
logs["eval_ppl"] = round(math.exp(logs["eval_loss"]), 4)
logs["eval_ppl"] = round(math.exp(logs["eval_loss"]), metric_ndigits)
except OverflowError:
logs["eval_ppl"] = float("inf")