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Policy metrics in the compiled monolithic loss (#507) #565

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Policy metrics in the compiled monolithic loss (#507) #565

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17 changes: 7 additions & 10 deletions fast_llm/layers/language_model/loss/config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -211,7 +211,7 @@ def loss_class(self) -> "type[LanguageModelZLoss]":
class PolicyMetricsLevel(enum.StrEnum):
none = "none"
basic = "basic"
with_entropy = "with_entropy"
auto = "auto"


@config_class()
Expand All @@ -223,12 +223,13 @@ class LanguageModelPolicyGradientLossConfig(LanguageModelLossConfig):
epsilon_low: float = Field(default=0.2, desc="Lower clip parameter for ratio of log probs")
epsilon_high: float = Field(default=0.2, desc="Upper clip parameter for ratio of log probs")
metrics: PolicyMetricsLevel = Field(
default=PolicyMetricsLevel.none,
default=PolicyMetricsLevel.auto,
desc=(
"Additional diagnostic metrics to log. "
"`basic`: importance-ratio, KL and advantage statistics. "
"`with_entropy`: also log the policy entropy. "
"Not supported with pipeline_parallel > 1."
"Diagnostic metrics to log. "
"`basic`: importance-ratio, KL, advantage statistics and the policy entropy. "
"`auto`: `basic` when `pipeline_parallel == 1`, else `none`. "
"`none`: disable, adding no cost. "
"`basic` is not supported with `pipeline_parallel > 1`."
),
hint=FieldHint.feature,
)
Expand Down Expand Up @@ -286,10 +287,6 @@ def _validate(self) -> None:
)
if loss.use_triton is not None:
raise ValueError(f"Loss `{name}` sets `use_triton`, which has no effect on a fused child loss.")
# GSPO's per-segment metrics need the eager segment aggregation, which the shared-softmax path
# defers to the backward seam; the composite emits only its loss, so metrics would be dropped.
if isinstance(loss, LanguageModelGSPOLossConfig) and loss.metrics != PolicyMetricsLevel.none:
raise ValueError(f"Loss `{name}` requests GSPO metrics, which are unavailable in a monolithic loss.")
# A single softmax serves one effective scale (stacked with the common model scale).
Assert.eq(len({loss.logits_scale_factor for loss in self.losses.values()}), 1)

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