[Deprecation] Remove mean pooling default for Qwen2EmbeddingModel (#18913)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

docs/models/supported_models.md

@@ -401,7 +401,7 @@ Specified using `--task embed`.
 
 !!! note
     `ssmits/Qwen2-7B-Instruct-embed-base` has an improperly defined Sentence Transformers config.
-    You should manually set mean pooling by passing `--override-pooler-config '{"pooling_type": "MEAN"}'`.
+    You need to manually set mean pooling by passing `--override-pooler-config '{"pooling_type": "MEAN"}'`.
 
 !!! note
     For `Alibaba-NLP/gte-Qwen2-*`, you need to enable `--trust-remote-code` for the correct tokenizer to be loaded.

vllm/model_executor/models/qwen2.py

@@ -34,32 +34,27 @@ from vllm.attention import Attention, AttentionType
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
-from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
-from vllm.model_executor.layers.pooler import Pooler, PoolingType
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader, maybe_remap_kv_scale_name)
-from vllm.model_executor.pooling_metadata import PoolingMetadata
 from vllm.model_executor.sampling_metadata import SamplingMetadata
-from vllm.sequence import IntermediateTensors, PoolerOutput
+from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsLoRA, SupportsPP
-from .utils import (AutoWeightsLoader, PPMissingLayer, WeightsMapper,
-                    extract_layer_index, is_pp_missing_parameter,
+from .utils import (AutoWeightsLoader, PPMissingLayer, extract_layer_index,
+                    is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
 
-logger = init_logger(__name__)
-
 
 class Qwen2MLP(nn.Module):
 
@@ -499,69 +494,3 @@ class Qwen2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                            if self.config.tie_word_embeddings else None),
         )
         return loader.load_weights(weights)
-
-
-class Qwen2EmbeddingModel(nn.Module, SupportsLoRA, SupportsPP):
-    packed_modules_mapping = {
-        "qkv_proj": [
-            "q_proj",
-            "k_proj",
-            "v_proj",
-        ],
-        "gate_up_proj": [
-            "gate_proj",
-            "up_proj",
-        ],
-    }
-
-    hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
-
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        super().__init__()
-        config = vllm_config.model_config.hf_config
-        quant_config = vllm_config.quant_config
-        lora_config = vllm_config.lora_config
-        pooler_config = vllm_config.model_config.pooler_config
-
-        self.config = config
-        self.lora_config = lora_config
-
-        self.quant_config = quant_config
-        self.model = Qwen2Model(vllm_config=vllm_config,
-                                prefix=maybe_prefix(prefix, "model"))
-
-        # TODO: Replace this model class with as_embedding_model(
-        # Qwen2ForCausalLM) after changing the default pooling method
-        if pooler_config.pooling_type is None:
-            logger.warning(
-                "This embedding model will default to last-token pooling in "
-                "an upcoming version. To avoid breaking changes, you should "
-                "pass `--override-pooler-config '{\"pooling_type\": \"MEAN\"}'`"
-                " explicitly.")
-
-        self._pooler = Pooler.from_config_with_defaults(
-            pooler_config,
-            pooling_type=PoolingType.MEAN,
-            normalize=True,
-            softmax=False)
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-    ) -> torch.Tensor:
-        return self.model(input_ids, positions, intermediate_tensors)
-
-    def pooler(
-        self,
-        hidden_states: torch.Tensor,
-        pooling_metadata: PoolingMetadata,
-    ) -> Optional[PoolerOutput]:
-        return self._pooler(hidden_states, pooling_metadata)
-
-    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
-        weights = self.hf_to_vllm_mapper.apply(weights)
-        weights = ((name, data) for name, data in weights
-                   if not name.startswith("lm_head."))
-        self.model.load_weights(weights)

vllm/model_executor/models/registry.py

@@ -142,7 +142,7 @@ _EMBEDDING_MODELS = {
     "ModernBertModel": ("modernbert", "ModernBertModel"),
     "NomicBertModel": ("bert_with_rope", "NomicBertModel"),
     "Phi3ForCausalLM": ("phi3", "Phi3ForCausalLM"),
-    "Qwen2Model": ("qwen2", "Qwen2EmbeddingModel"),
+    "Qwen2Model": ("qwen2", "Qwen2ForCausalLM"),
     "Qwen2ForCausalLM": ("qwen2", "Qwen2ForCausalLM"),
     "Qwen2ForRewardModel": ("qwen2_rm", "Qwen2ForRewardModel"),
     "Qwen2ForProcessRewardModel": ("qwen2_rm", "Qwen2ForProcessRewardModel"),