[Model][1/N] Automatic conversion of CrossEncoding model (#20012)

Signed-off-by: wang.yuqi <noooop@126.com>

vllm/model_executor/models/config.py

@@ -0,0 +1,200 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from copy import deepcopy
+from typing import TYPE_CHECKING
+
+from vllm.logger import init_logger
+
+if TYPE_CHECKING:
+    from vllm.config import VllmConfig
+
+logger = init_logger(__name__)
+
+
+class VerifyAndUpdateConfig:
+
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        raise NotImplementedError
+
+
+class GteNewModelConfig(VerifyAndUpdateConfig):
+
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        config = vllm_config.model_config.hf_config
+
+        assert config.__class__.__name__ == "NewConfig"
+        assert config.hidden_act == "gelu"
+
+        config.hidden_act = "geglu"
+
+        head_dim = config.hidden_size // config.num_attention_heads
+        config.rotary_kwargs = {
+            "head_size": head_dim,
+            "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
+            "max_position": config.max_position_embeddings,
+            "base": config.rope_theta,
+            "rope_scaling": getattr(config, "rope_scaling", None)
+        }
+
+
+class JinaRobertaModelConfig(VerifyAndUpdateConfig):
+
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        config = vllm_config.model_config.hf_config
+
+        if config.position_embedding_type == "rotary":
+            assert config.__class__.__name__ == "XLMRobertaFlashConfig"
+
+            head_dim = config.hidden_size // config.num_attention_heads
+            config.rotary_kwargs = {
+                "head_size": head_dim,
+                "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
+                "max_position": config.max_position_embeddings,
+                "base": getattr(config, "rope_theta", config.rotary_emb_base),
+                "rope_scaling": getattr(config, "rope_scaling", None)
+            }
+
+
+class NomicBertModelConfig(VerifyAndUpdateConfig):
+
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        config = vllm_config.model_config.hf_config
+
+        assert config.__class__.__name__ == "NomicBertConfig"
+        assert config.activation_function in ["swiglu", "gelu"]
+        config.position_embedding_type = getattr(config,
+                                                 "position_embedding_type",
+                                                 "rope")
+
+        if config.activation_function == "swiglu":
+            config.hidden_act = "silu"
+        else:
+            config.hidden_act = config.activation_function
+
+        assert (config.mlp_fc1_bias == config.mlp_fc2_bias ==
+                config.qkv_proj_bias)
+        config.bias = config.qkv_proj_bias
+
+        assert config.rotary_emb_scale_base is None
+        assert not config.rotary_emb_interleaved
+
+        config.layer_norm_eps = config.layer_norm_epsilon
+        config.intermediate_size = config.n_inner
+        config.hidden_size = config.n_embd
+        config.num_hidden_layers = config.n_layer
+
+        head_dim = config.hidden_size // config.num_attention_heads
+        rotary_emb_dim = head_dim * config.rotary_emb_fraction
+        max_trained_positions = getattr(config, "max_trained_positions", 2048)
+        config.rotary_kwargs = {
+            "head_size": head_dim,
+            "rotary_dim": rotary_emb_dim,
+            "max_position": max_trained_positions,
+            "base": getattr(config, "rope_theta", config.rotary_emb_base),
+            "rope_scaling": getattr(config, "rope_scaling", None)
+        }
+
+        # we ignore config.rotary_scaling_factor so that for datasets shorter
+        # than max_trained_positions 2048, the results are consistent
+        # with SentenceTransformer.
+        # The context extension uses vllm style rope_theta and rope_scaling.
+        # See #17785 #18755
+        if (not vllm_config.model_config.hf_overrides
+                and vllm_config.model_config.original_max_model_len is None):
+            # Default
+            # Reset max_model_len to max_trained_positions.
+            # nomic-embed-text-v2-moe the length is set to 512
+            # by sentence_bert_config.json.
+            max_model_len_before = vllm_config.model_config.max_model_len
+            max_model_len = min(vllm_config.model_config.max_model_len,
+                                max_trained_positions)
+
+            vllm_config.recalculate_max_model_len(max_model_len)
+            logger.warning(
+                "Nomic context extension is disabled. "
+                "Changing max_model_len from %s to %s. "
+                "To enable context extension, see: "
+                "https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/context_extension.html",
+                max_model_len_before, vllm_config.model_config.max_model_len)
+        else:
+            # We need to re-verify max_model_len to avoid lengths
+            # greater than position_embedding.
+            model_config = vllm_config.model_config
+            hf_text_config = model_config.hf_text_config
+
+            if isinstance(model_config.hf_overrides, dict):
+                # hf_overrides_kw
+                max_model_len = model_config.hf_overrides.get(
+                    "max_model_len", vllm_config.model_config.max_model_len)
+            else:
+                # hf_overrides_fn
+                # This might be overridden by sentence_bert_config.json.
+                max_model_len = vllm_config.model_config.max_model_len
+
+            # reset hf_text_config for recalculate_max_model_len.
+            if hasattr(hf_text_config, "max_model_len"):
+                delattr(hf_text_config, "max_model_len")
+            hf_text_config.max_position_embeddings = max_trained_positions
+            hf_text_config.rope_scaling = config.rotary_kwargs["rope_scaling"]
+
+            # The priority of sentence_bert_config.json is higher
+            # than max_position_embeddings
+            encoder_config = deepcopy(model_config.encoder_config)
+            encoder_config.pop("max_seq_length", None)
+            model_config.encoder_config = encoder_config
+
+            vllm_config.recalculate_max_model_len(max_model_len)
+
+
+class Qwen3ForSequenceClassificationConfig(VerifyAndUpdateConfig):
+
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        config = vllm_config.model_config.hf_config
+
+        is_original_qwen3_reranker = getattr(config,
+                                             "is_original_qwen3_reranker",
+                                             False)
+
+        if not is_original_qwen3_reranker:
+            return
+
+        tokens = getattr(config, "classifier_from_token", None)
+        assert tokens is not None and len(tokens) == 2, \
+            ("Try loading the original Qwen3 Reranker?, see: "
+             "https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/qwen3_reranker.py")
+        config.num_labels = 1
+
+
+class SnowflakeGteNewModelConfig(VerifyAndUpdateConfig):
+
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        config = vllm_config.model_config.hf_config
+
+        assert config.__class__.__name__ == "GteConfig"
+        assert config.hidden_act == "gelu"
+
+        config.hidden_act = "geglu"
+
+        head_dim = config.hidden_size // config.num_attention_heads
+        config.rotary_kwargs = {
+            "head_size": head_dim,
+            "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
+            "max_position": config.max_position_embeddings,
+            "base": config.rope_theta,
+            "rope_scaling": getattr(config, "rope_scaling", None)
+        }
+
+
+MODELS_CONFIG_MAP: dict[str, type[VerifyAndUpdateConfig]] = {
+    "GteModel": SnowflakeGteNewModelConfig,
+    "GteNewModel": GteNewModelConfig,
+    "NomicBertModel": NomicBertModelConfig,
+    "Qwen3ForSequenceClassification": Qwen3ForSequenceClassificationConfig,
+    "XLMRobertaModel": JinaRobertaModelConfig,
+}