Sync from v0.13

vllm/config/pooler.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Any, Literal
+
+from pydantic.dataclasses import dataclass
+
+from vllm.config.utils import config
+from vllm.logger import init_logger
+from vllm.utils.hashing import safe_hash
+
+logger = init_logger(__name__)
+
+PoolingTypeStr = Literal["LAST", "ALL", "CLS", "STEP", "MEAN"]
+
+
+@config
+@dataclass
+class PoolerConfig:
+    """Controls the behavior of output pooling in pooling models."""
+
+    pooling_type: PoolingTypeStr | None = None
+    """
+    The pooling method of the pooling model. This should be a key in
+    [`vllm.model_executor.layers.pooler.PoolingType`][].
+    """
+
+    ## for embeddings models
+    normalize: bool | None = None
+    """
+    Whether to normalize the embeddings outputs. Defaults to True.
+    """
+    dimensions: int | None = None
+    """
+    Reduce the dimensions of embeddings if model
+    support matryoshka representation. Defaults to None.
+    """
+    enable_chunked_processing: bool | None = None
+    """
+    Whether to enable chunked processing for long inputs that exceed the model's
+    maximum position embeddings. When enabled, long inputs will be split into
+    chunks, processed separately, and then aggregated using weighted averaging.
+    This allows embedding models to handle arbitrarily long text without CUDA
+    errors. Defaults to False.
+    """
+    max_embed_len: int | None = None
+    """
+    Maximum input length allowed for embedding generation. When set, allows
+    inputs longer than max_embed_len to be accepted for embedding models.
+    When an input exceeds max_embed_len, it will be handled according to 
+    the original max_model_len validation logic. 
+    Defaults to None (i.e. set to max_model_len).
+    """
+
+    ## for classification models
+    softmax: float | None = None
+    """
+    softmax will be deprecated, please use use_activation instead.
+    """
+    activation: float | None = None
+    """
+    activation will be deprecated, please use use_activation instead.
+    """
+    use_activation: bool | None = None
+    """
+    Whether to apply activation function to the classification outputs.
+    Defaults to True.
+    """
+    logit_bias: float | None = None
+    """
+    If provided, apply classification logit biases. Defaults to None.
+    """
+
+    ## for reward models
+    step_tag_id: int | None = None
+    """
+    If set, only the score corresponding to the `step_tag_id` in the
+    generated sentence should be returned. Otherwise, the scores for all tokens
+    are returned.
+    """
+    returned_token_ids: list[int] | None = None
+    """
+    A list of indices for the vocabulary dimensions to be extracted,
+    such as the token IDs of `good_token` and `bad_token` in the
+    `math-shepherd-mistral-7b-prm` model.
+    """
+
+    def __post_init__(self):
+        # raise deprecated warning for softmax and activation
+        self.use_activation = get_use_activation(self)
+
+    def compute_hash(self) -> str:
+        """
+        WARNING: Whenever a new field is added to this config,
+        ensure that it is included in the factors list if
+        it affects the computation graph.
+
+        Provide a hash that uniquely identifies all the configs
+        that affect the structure of the computation
+        graph from input ids/embeddings to the final hidden states,
+        excluding anything before input ids/embeddings and after
+        the final hidden states.
+        """
+        # no factors to consider.
+        # this config will not affect the computation graph.
+        factors: list[Any] = []
+        hash_str = safe_hash(str(factors).encode(), usedforsecurity=False).hexdigest()
+        return hash_str
+
+
+def get_use_activation(o: object):
+    if softmax := getattr(o, "softmax", None) is not None:
+        logger.warning_once(
+            "softmax will be deprecated and will be removed in v0.15. "
+            "Please use use_activation instead."
+        )
+        return softmax
+
+    if activation := getattr(o, "activation", None) is not None:
+        logger.warning_once(
+            "activation will be deprecated and will be removed in v0.15. "
+            "Please use use_activation instead."
+        )
+        return activation
+
+    return getattr(o, "use_activation", None)