add qwen3

vllm-v0.6.2/vllm/model_executor/models/qwen2_cls.py

@@ -0,0 +1,98 @@
+# Adapted from
+# https://huggingface.co/Qwen/Qwen2.5-Math-RM-72B/blob/main/modeling_qwen2_rm.py
+# Copyright 2024 Kakao Corp. (Kanana-X Team)
+# Copyright 2024 The Qwen team.
+# Copyright 2023 The vLLM team.
+"""Inference-only Qwen2-Classification model compatible with HF weights."""
+from typing import Iterable, List, Optional, Tuple
+
+import torch
+from torch import nn
+
+from vllm.attention import AttentionMetadata
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.linear import RowParallelLinear
+from vllm.model_executor.layers.pooler import Pooler, PoolingType
+from vllm.model_executor.models.qwen2 import Qwen2Model
+from vllm.model_executor.pooling_metadata import PoolingMetadata
+from vllm.sequence import IntermediateTensors, PoolerOutput
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import AutoWeightsLoader, maybe_prefix
+
+
+class Qwen2ForSequenceClassification(nn.Module, SupportsLoRA, SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    supported_lora_modules = [
+        "qkv_proj",
+        "o_proj",
+        "gate_up_proj",
+        "down_proj",
+    ]
+    embedding_modules = {}
+    embedding_padding_modules = []
+
+    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"))
+
+        # hidden_states from Qwen2Model has been reduced,
+        # the input of score layer is not parallelized.
+        self.score = RowParallelLinear(config.hidden_size,
+                                       config.num_labels,
+                                       quant_config=quant_config,
+                                       input_is_parallel=False,
+                                       bias=False,
+                                       prefix=maybe_prefix(prefix, "score"))
+        self._pooler = Pooler.from_config_with_defaults(
+            pooler_config,
+            pooling_type=PoolingType.LAST,
+            normalize=False,
+            softmax=True)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   attn_metadata, intermediate_tensors)
+        logits, _ = self.score(hidden_states)
+        return logits
+
+    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]]):
+        loader = AutoWeightsLoader(self,
+                                   ignore_unexpected_prefixes=["lm_head."])
+        loader.load_weights(weights)