feat: rename logit_bias/logit_scale to logit_mean/logit_sigma for affine score calibration (#39530)

Signed-off-by: Jesus Federico <jefp@amazon.com>
Signed-off-by: wang.yuqi <yuqi.wang@daocloud.io>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: wang.yuqi <yuqi.wang@daocloud.io>

docs/models/pooling_models/classify.md

@@ -273,27 +273,25 @@ Affine Score Calibration, also known as [Platt Scaling](https://en.wikipedia.org
 
 The calibration follows the transformation:
 
-`activation(logit_scale * (logit - logit_bias))`
+`activation((logit - logit_mean) / logit_sigma)`
 
 | Parameter | Default | Description |
 | --------- | ------- | ----------- |
-| `logit_bias` | `None` | Bias subtracted from logits before activation |
-| `logit_scale` | `None` | Scale factor applied to logits after bias subtraction |
-
-Note: `logit_bias` is **subtracted** from the logits (not added), consistent with the `sigmoid_normalize` convention where `sigmoid(x - bias)` centers the sigmoid around the bias value.
+| `logit_mean` | `None` | Mean subtracted from logits (centers scores) |
+| `logit_sigma` | `None` | Standard deviation used to scale logits after mean subtraction |
 
 The computation order is as follows:
 
 ```python
-logits -= logit_bias    # subtract bias (center scores)
-logits *= logit_scale   # scale logits
+logits -= logit_mean   # subtract mean (center scores)
+logits /= logit_sigma  # divide by sigma (scale)
 logits = activation(logits)  # e.g. sigmoid
 ```
 
 Example configuration:
 
 ```bash
---pooler-config '{"use_activation": true, "logit_bias": 4.5, "logit_scale": 1.0}'
+--pooler-config '{"use_activation": true, "logit_mean": 4.5, "logit_sigma": 1.0}'
 ```
 
 ## Removed Features
@@ -301,3 +299,7 @@ Example configuration:
 ### Remove softmax from PoolingParams
 
 We have already removed `softmax` and `activation` from PoolingParams. Instead, use `use_activation`, since we allow `classify` and `token_classify` to use any activation function.
+
+### Remove `logit_bias` and `logit_scale`
+
+`logit_bias` and `logit_scale` are deprecated aliases for `logit_mean` and `logit_sigma` respectively. When using `logit_scale`, it is automatically converted to `logit_sigma = 1/logit_scale`. These deprecated parameters will be removed in v0.21.

vllm/config/pooler.py

@@ -77,17 +77,31 @@ class PoolerConfig:
     Defaults to None (i.e. set to max_model_len).
     """
 
-    ## for classification models
+    ## for classification models — affine score calibration
+    logit_mean: float | None = None
+    """
+    If provided, subtract this value from classification logits before
+    activation. Used for affine score calibration (Platt scaling):
+    activation((logit - logit_mean) / logit_sigma). Defaults to None.
+    """
+
+    logit_sigma: float | None = None
+    """
+    If provided, divide the classification logits by this value after
+    mean subtraction. Used for affine score calibration (Platt scaling):
+    activation((logit - logit_mean) / logit_sigma). Defaults to None.
+    """
+
+    # Deprecated aliases — will be removed in v0.21
     logit_bias: float | None = None
     """
-    If provided, apply classification logit biases. Defaults to None.
+    Deprecated: Use logit_mean instead. Will be removed in v0.21.
     """
 
     logit_scale: float | None = None
     """
-    If provided, scale the classification logits by this factor before
-    activation. Combined with logit_bias, enables affine score calibration:
-    activation(logit_scale * (score - logit_bias)). Defaults to None.
+    Deprecated: Use logit_sigma instead (note: logit_sigma = 1/logit_scale).
+    Will be removed in v0.21.
     """
 
     ## for reward models
@@ -105,6 +119,39 @@ class PoolerConfig:
     """
 
     def __post_init__(self) -> None:
+        # Handle deprecated logit_bias → logit_mean
+        if self.logit_bias is not None:
+            if self.logit_mean is not None:
+                raise ValueError(
+                    "Cannot set both `logit_bias` and `logit_mean`. "
+                    "`logit_bias` is deprecated, use `logit_mean` instead."
+                )
+            logger.warning(
+                "`logit_bias` is deprecated and will be removed in v0.21. "
+                "Use `logit_mean` instead."
+            )
+            self.logit_mean = self.logit_bias
+            self.logit_bias = None
+
+        # Handle deprecated logit_scale → logit_sigma
+        if self.logit_scale is not None:
+            if self.logit_sigma is not None:
+                raise ValueError(
+                    "Cannot set both `logit_scale` and `logit_sigma`. "
+                    "`logit_scale` is deprecated, use `logit_sigma` instead."
+                )
+            logger.warning(
+                "`logit_scale` is deprecated and will be removed in v0.21. "
+                "Use `logit_sigma` instead (logit_sigma = 1/logit_scale)."
+            )
+            if self.logit_scale == 0:
+                raise ValueError("logit_scale cannot be 0 (division by zero)")
+            self.logit_sigma = 1.0 / self.logit_scale
+            self.logit_scale = None
+
+        if self.logit_sigma is not None and self.logit_sigma == 0:
+            raise ValueError("logit_sigma cannot be 0 (division by zero)")
+
         if pooling_type := self.pooling_type:
             if self.seq_pooling_type is not None:
                 raise ValueError(

vllm/model_executor/layers/pooler/seqwise/heads.py

@@ -103,16 +103,16 @@ class ClassifierPoolerHead(SequencePoolerHead):
     def __init__(
         self,
         classifier: ClassifierFn | None = None,
-        logit_bias: float | None = None,
-        logit_scale: float | None = None,
+        logit_mean: float | None = None,
+        logit_sigma: float | None = None,
         head_dtype: torch.dtype | str | None = None,
         activation: ActivationFn | None = None,
     ) -> None:
         super().__init__()
 
         self.classifier = classifier
-        self.logit_bias = logit_bias
-        self.logit_scale = logit_scale
+        self.logit_mean = logit_mean
+        self.logit_sigma = logit_sigma
         self.head_dtype = head_dtype
         self.activation = activation
 
@@ -140,10 +140,11 @@ class ClassifierPoolerHead(SequencePoolerHead):
             logits = pooled_data
 
         # logits shape: [batchsize, num_labels]
-        if self.logit_bias is not None:
-            logits -= self.logit_bias
-        if self.logit_scale is not None:
-            logits *= self.logit_scale
+        # Affine score calibration: activation((logit - mean) / sigma)
+        if self.logit_mean is not None:
+            logits = logits - self.logit_mean
+        if self.logit_sigma is not None:
+            logits = logits / self.logit_sigma
 
         if self.activation is not None:
             flags = [p.use_activation for p in pooling_params]

vllm/model_executor/layers/pooler/seqwise/poolers.py

@@ -118,8 +118,8 @@ def pooler_for_classify(
     head = ClassifierPoolerHead(
         head_dtype=model_config.head_dtype,
         classifier=classifier,
-        logit_bias=model_config.pooler_config.logit_bias,
-        logit_scale=model_config.pooler_config.logit_scale,
+        logit_mean=model_config.pooler_config.logit_mean,
+        logit_sigma=model_config.pooler_config.logit_sigma,
         activation=resolve_classifier_act_fn(
             model_config, static_num_labels=True, act_fn=act_fn
         ),

vllm/model_executor/layers/pooler/tokwise/heads.py

@@ -92,16 +92,16 @@ class TokenClassifierPoolerHead(TokenPoolerHead):
     def __init__(
         self,
         classifier: ClassifierFn | None = None,
-        logit_bias: float | None = None,
-        logit_scale: float | None = None,
+        logit_mean: float | None = None,
+        logit_sigma: float | None = None,
         head_dtype: torch.dtype | str | None = None,
         activation: ActivationFn | None = None,
     ) -> None:
         super().__init__()
 
         self.classifier = classifier
-        self.logit_bias = logit_bias
-        self.logit_scale = logit_scale
+        self.logit_mean = logit_mean
+        self.logit_sigma = logit_sigma
         self.head_dtype = head_dtype
         self.activation = activation
 
@@ -127,10 +127,11 @@ class TokenClassifierPoolerHead(TokenPoolerHead):
             logits = pooled_data
         # logits shape: [n_token, num_labels]
 
-        if self.logit_bias is not None:
-            logits -= self.logit_bias
-        if self.logit_scale is not None:
-            logits *= self.logit_scale
+        # Affine score calibration: activation((logit - mean) / sigma)
+        if self.logit_mean is not None:
+            logits = logits - self.logit_mean
+        if self.logit_sigma is not None:
+            logits = logits / self.logit_sigma
 
         if self.activation is not None and pooling_param.use_activation:
             logits = self.activation(logits)

vllm/model_executor/layers/pooler/tokwise/poolers.py

@@ -127,8 +127,8 @@ def pooler_for_token_classify(
     head = TokenClassifierPoolerHead(
         head_dtype=model_config.head_dtype,
         classifier=classifier,
-        logit_bias=model_config.pooler_config.logit_bias,
-        logit_scale=model_config.pooler_config.logit_scale,
+        logit_mean=model_config.pooler_config.logit_mean,
+        logit_sigma=model_config.pooler_config.logit_sigma,
         activation=resolve_classifier_act_fn(
             model_config, static_num_labels=False, act_fn=act_fn
         ),

vllm/model_executor/models/config.py

@@ -232,8 +232,8 @@ class JinaVLForSequenceClassificationConfig(VerifyAndUpdateConfig):
         config = model_config.hf_config
         config.num_labels = 1
         pooler_config = model_config.pooler_config
-        if pooler_config.logit_bias is None:
-            pooler_config.logit_bias = 2.65
+        if pooler_config.logit_mean is None:
+            pooler_config.logit_mean = 2.65
 
 
 class LlamaBidirectionalConfig(VerifyAndUpdateConfig):