[Model] Support Qwen2ForSequenceClassification (#4609)

Co-authored-by: ximing.wxm <ximing.wxm@antgroup.com>

docs/references/supported_models.md

@@ -54,6 +54,8 @@
   - `python -m sglang.launch_server --model-path internlm/internlm2-7b-reward --is-embedding --trust-remote-code`
 - Qwen2ForRewardModel
   - `python -m sglang.launch_server --model-path Qwen/Qwen2.5-Math-RM-72B --is-embedding --trust-remote-code --tp-size=4`
+- Qwen2ForSequenceClassification
+  - `python -m sglang.launch_server --model-path jason9693/Qwen2.5-1.5B-apeach --is-embedding --trust-remote-code`
 ## How to Support a New Language Model
 
 To support a new model in SGLang, you only need to add a single file under [SGLang Models Directory](https://github.com/sgl-project/sglang/tree/main/python/sglang/srt/models).

python/sglang/srt/configs/model_config.py

@@ -453,6 +453,7 @@ def is_generation_model(model_architectures: List[str], is_embedding: bool = Fal
         or "LlamaForSequenceClassificationWithNormal_Weights" in model_architectures
         or "InternLM2ForRewardModel" in model_architectures
         or "Qwen2ForRewardModel" in model_architectures
+        or "Qwen2ForSequenceClassification" in model_architectures
     ):
         return False
     else:

python/sglang/srt/models/qwen2_classification.py

+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from typing import Iterable, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import Qwen2Config
+
+from sglang.srt.layers.pooler import EmbeddingPoolerOutput, Pooler, PoolingType
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.models.qwen2 import Qwen2ForCausalLM, Qwen2Model
+from sglang.srt.utils import add_prefix
+
+
+class Qwen2ForSequenceClassification(nn.Module):
+    def __init__(
+        self,
+        config: Qwen2Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        self.model = Qwen2Model(
+            config, quant_config=quant_config, prefix=add_prefix("model", prefix)
+        )
+        self.score = nn.Linear(config.hidden_size, config.num_labels)
+        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=False)
+
+        self.eos_token_id = config.eos_token_id
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+        get_embedding: bool = True,
+    ) -> EmbeddingPoolerOutput:
+        assert (
+            get_embedding
+        ), "Qwen2ForSequenceClassification is only used for embedding"
+
+        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
+        logits = self.score(hidden_states)
+        pooled_logits = self.pooler(logits, forward_batch).embeddings
+
+        return EmbeddingPoolerOutput(pooled_logits)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        # Filter out lm_head weights of Qwen2ForCausalLM
+        filtered_weights = [
+            (name, w) for name, w in weights if not name.startswith("lm_head")
+        ]
+        return Qwen2ForCausalLM.load_weights(self, filtered_weights)
+
+
+EntryClass = [
+    Qwen2ForSequenceClassification,
+]

test/srt/models/test_embedding_models.py

@@ -13,18 +13,20 @@
 # ==============================================================================
 
 import multiprocessing as mp
+import random
 import unittest
 
 import torch
 from transformers import AutoConfig, AutoTokenizer
 
 from sglang.test.runners import DEFAULT_PROMPTS, HFRunner, SRTRunner
-from sglang.test.test_utils import get_similarities
+from sglang.test.test_utils import get_similarities, is_in_ci
 
 MODELS = [
     ("Alibaba-NLP/gte-Qwen2-1.5B-instruct", 1, 1e-5),
     ("intfloat/e5-mistral-7b-instruct", 1, 1e-5),
     ("marco/mcdse-2b-v1", 1, 1e-5),
+    ("jason9693/Qwen2.5-1.5B-apeach", 1, 1e-5),
 ]
 TORCH_DTYPES = [torch.float16]
 
@@ -91,7 +93,12 @@ class TestEmbeddingModels(unittest.TestCase):
                 ), "embeddings are not all close"
 
     def test_prefill_logits(self):
-        for model, tp_size, prefill_tolerance in MODELS:
+        models_to_test = MODELS
+
+        if is_in_ci():
+            models_to_test = [random.choice(MODELS)]
+
+        for model, tp_size, prefill_tolerance in models_to_test:
             for torch_dtype in TORCH_DTYPES:
                 self.assert_close_prefill_logits(
                     DEFAULT_PROMPTS, model, tp_size, torch_dtype, prefill_tolerance