[Model] Add classification Task with Qwen2ForSequenceClassification

docs/source/models/supported_models.rst

@@ -361,6 +361,28 @@ Reward Modeling
 .. note::
     As an interim measure, these models are supported via Embeddings API. See `this RFC <https://github.com/vllm-project/vllm/issues/8967>`_ for upcoming changes.
 
+Classification
+---------------
+
+.. list-table::
+  :widths: 25 25 50 5 5
+  :header-rows: 1
+
+  * - Architecture
+    - Models
+    - Example HF Models
+    - :ref:`LoRA <lora>`
+    - :ref:`PP <distributed_serving>`
+  * - :code:`Qwen2ForSequenceClassification`
+    - Qwen2-based
+    - :code:`jason9693/Qwen2.5-1.5B-apeach`, etc.
+    - 
+    - ✅︎
+
+.. note::
+    As an interim measure, these models are supported via Embeddings API. It will be supported via Classification API in the future (no reference APIs exist now).
+
+
 Multimodal Language Models
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
 

tests/conftest.py

@@ -343,6 +343,17 @@ def get_inputs(
 
         return all_inputs
 
+    def classify(self, prompts: List[str]) -> List[str]:
+        # output is final logits
+        all_inputs = self.get_inputs(prompts)
+        outputs = []
+        for inputs in all_inputs:
+            output = self.model(**self.wrap_device(inputs))
+            logits = output.logits.softmax(dim=-1)[0].tolist()
+            outputs.append(logits)
+
+        return outputs
+
     def generate(
         self,
         prompts: List[str],
@@ -688,6 +699,14 @@ def get_inputs(
 
         return inputs
 
+    def classify(self, prompts: List[str]) -> List[str]:
+        req_outputs = self.model.encode(prompts)
+        outputs = []
+        for req_output in req_outputs:
+            embedding = req_output.outputs.embedding
+            outputs.append(embedding)
+        return outputs
+
     def generate(
         self,
         prompts: List[str],

tests/models/embedding/language/test_cls_models.py

@@ -0,0 +1,53 @@
+"""Compare the outputs of HF and vLLM when using greedy sampling.
+
+This test only tests small models. Big models such as 7B should be tested from
+test_big_models.py because it could use a larger instance to run tests.
+
+Run `pytest tests/models/test_cls_models.py`.
+"""
+import pytest
+import torch
+from transformers import AutoModelForSequenceClassification
+
+CLASSIFICATION_MODELS = ["jason9693/Qwen2.5-1.5B-apeach"]
+
+
+@pytest.mark.parametrize("model", CLASSIFICATION_MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+def test_classification_models(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+) -> None:
+    with hf_runner(model,
+                   dtype=dtype,
+                   auto_cls=AutoModelForSequenceClassification) as hf_model:
+        hf_outputs = hf_model.classify(example_prompts)
+
+    with vllm_runner(model, dtype=dtype) as vllm_model:
+        vllm_outputs = vllm_model.classify(example_prompts)
+
+    print(hf_outputs, vllm_outputs)
+
+    # check logits difference
+    for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
+        hf_output = torch.tensor(hf_output)
+        vllm_output = torch.tensor(vllm_output)
+
+        assert torch.allclose(hf_output, vllm_output, 1e-3)
+
+
+@pytest.mark.parametrize("model", CLASSIFICATION_MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+def test_classification_model_print(
+    vllm_runner,
+    model: str,
+    dtype: str,
+) -> None:
+    with vllm_runner(model, dtype=dtype) as vllm_model:
+        # This test is for verifying whether the model's extra_repr
+        # can be printed correctly.
+        print(vllm_model.model.llm_engine.model_executor.driver_worker.
+              model_runner.model)

vllm/model_executor/layers/pooler.py

@@ -28,11 +28,15 @@ class Pooler(nn.Module):
         normalize: Whether to normalize the pooled data.
     """
 
-    def __init__(self, pooling_type: PoolingType, normalize: bool):
+    def __init__(self,
+                 pooling_type: PoolingType,
+                 normalize: bool,
+                 softmax: bool = False):
         super().__init__()
 
         self.pooling_type = pooling_type
         self.normalize = normalize
+        self.softmax = softmax
 
     def forward(
         self,
@@ -64,6 +68,9 @@ def forward(
         if self.normalize:
             pooled_data = nn.functional.normalize(pooled_data, p=2, dim=1)
 
+        if self.softmax:
+            pooled_data = nn.functional.softmax(pooled_data, dim=-1)
+
         pooled_outputs = [
             EmbeddingSequenceGroupOutput(data.tolist()) for data in pooled_data
         ]

vllm/model_executor/models/qwen2_cls.py

@@ -0,0 +1,107 @@
+# coding=utf-8
+# 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 transformers import Qwen2Config
+
+from vllm.attention import AttentionMetadata
+from vllm.config import CacheConfig, LoRAConfig
+from vllm.model_executor.layers.linear import RowParallelLinear
+from vllm.model_executor.layers.pooler import Pooler, PoolingType
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.models.qwen2 import Qwen2Model
+from vllm.model_executor.pooling_metadata import PoolingMetadata
+from vllm.sequence import IntermediateTensors, PoolerOutput
+
+from .utils import AutoWeightsLoader
+
+
+class Qwen2ForSequenceClassification(nn.Module):
+    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,
+        config: Qwen2Config,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        lora_config: Optional[LoRAConfig] = None,
+    ) -> None:
+        # TODO (@robertgshaw2): see if this can be moved out
+        if (cache_config.sliding_window is not None
+                and hasattr(config, "max_window_layers")):
+            raise ValueError("Sliding window for some but all layers is not "
+                             "supported. This model uses sliding window "
+                             "but `max_window_layers` = %s is less than "
+                             "`num_hidden_layers` = %s. Please open an issue "
+                             "to discuss this feature." % (
+                                 config.max_window_layers,
+                                 config.num_hidden_layers,
+                             ))
+
+        super().__init__()
+
+        self.config = config
+        self.lora_config = lora_config
+
+        self.quant_config = quant_config
+        self.model = Qwen2Model(config, cache_config, quant_config)
+
+        self.score = RowParallelLinear(config.hidden_size,
+                                       config.num_labels,
+                                       quant_config=quant_config)
+        self._pooler = Pooler(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)

vllm/model_executor/models/registry.py

@@ -96,6 +96,8 @@
     "Gemma2Model": ("gemma2", "Gemma2EmbeddingModel"),
     "MistralModel": ("llama", "LlamaEmbeddingModel"),
     "Qwen2ForRewardModel": ("qwen2_rm", "Qwen2ForRewardModel"),
+    "Qwen2ForSequenceClassification": (
+        "qwen2_cls", "Qwen2ForSequenceClassification"),
     # [Multimodal]
     "LlavaNextForConditionalGeneration": ("llava_next", "LlavaNextForConditionalGeneration"),  # noqa: E501
     "Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"),