[Model] Support Qwen2 embeddings and use tags to select model tests (#10184)

.buildkite/run-cpu-test-ppc64le.sh

@@ -27,9 +27,9 @@ function cpu_tests() {
       decord einops librosa peft Pillow sentence-transformers soundfile \
       transformers_stream_generator matplotlib datamodel_code_generator
     pip install torchvision --index-url https://download.pytorch.org/whl/cpu
-    pytest -v -s tests/models/embedding/language
+    pytest -v -s tests/models/decoder_only/language -m cpu_model
-    pytest -v -s tests/models/encoder_decoder/language
+    pytest -v -s tests/models/embedding/language -m cpu_model
-    pytest -v -s tests/models/decoder_only/language/test_models.py
+    pytest -v -s tests/models/encoder_decoder/language -m cpu_model
     pytest -v -s tests/models/decoder_only/audio_language -m cpu_model
     pytest -v -s tests/models/decoder_only/vision_language -m cpu_model"
 

.buildkite/run-cpu-test.sh

@@ -38,9 +38,9 @@ function cpu_tests() {
       decord einops librosa peft Pillow sentence-transformers soundfile \
       transformers_stream_generator matplotlib datamodel_code_generator
     pip install torchvision --index-url https://download.pytorch.org/whl/cpu
-    pytest -v -s tests/models/embedding/language
+    pytest -v -s tests/models/decoder_only/language -m cpu_model
-    pytest -v -s tests/models/encoder_decoder/language
+    pytest -v -s tests/models/embedding/language -m cpu_model
-    pytest -v -s tests/models/decoder_only/language/test_models.py
+    pytest -v -s tests/models/encoder_decoder/language -m cpu_model
     pytest -v -s tests/models/decoder_only/audio_language -m cpu_model
     pytest -v -s tests/models/decoder_only/vision_language -m cpu_model"
 

.buildkite/test-pipeline.yaml

@@ -323,62 +323,60 @@ steps:
     - pytest -v -s models/test_registry.py
     - pytest -v -s models/test_initialization.py
 
-- label: Decoder-only Language Models Test (Standard) # 18min
+- label: Language Models Test (Standard) # 42min
   #mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
   - tests/models/decoder_only/language
+  - tests/models/embedding/language
+  - tests/models/encoder_decoder/language
   commands:
-    - pytest -v -s models/decoder_only/language -m core_model
+    - pytest -v -s models/decoder_only/language -m 'core_model or quant_model'
-    - pytest -v -s models/decoder_only/language -m quant_model
+    - pytest -v -s models/embedding/language -m core_model
+    - pytest -v -s models/embedding/vision_language -m core_model
 
-- label: Decoder-only Language Models Test (Extended) # 46min
+- label: Language Models Test (Extended) # 50min
   nightly: true
   source_file_dependencies:
   - vllm/
   - tests/models/decoder_only/language
+  - tests/models/embedding/language
+  - tests/models/encoder_decoder/language
   commands:
     - pytest -v -s models/decoder_only/language -m 'not core_model and not quant_model'
+    - pytest -v -s models/embedding/language -m 'not core_model'
+    - pytest -v -s models/embedding/vision_language -m 'not core_model'
 
-- label: Decoder-only Multi-Modal Models Test (Standard) # 22min
+- label: Multi-Modal Models Test (Standard) # 26min
   #mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
   - tests/models/decoder_only/audio_language
   - tests/models/decoder_only/vision_language
+  - tests/models/embedding/vision_language
+  - tests/models/encoder_decoder/vision_language
   commands:
-    - pytest -v -s models/decoder_only/audio_language -m core_model
+    - pytest -v -s models/decoder_only/audio_language -m 'core_model or quant_model'
-    - pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m core_model
+    - pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'core_model or quant_model'
-    # No tests under this group for now
+    - pytest -v -s models/encoder_decoder/language -m core_model
-    # - pytest -v -s models/decoder_only/audio_language -m quant_model
+    - pytest -v -s models/encoder_decoder/vision_language -m core_model
-    - pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m quant_model
 
-- label: Decoder-only Multi-Modal Models Test (Extended) # 1h10m
+- label: Multi-Modal Models Test (Extended) # 1h15m
   nightly: true
   source_file_dependencies:
   - vllm/
   - tests/models/decoder_only/audio_language
   - tests/models/decoder_only/vision_language
+  - tests/models/embedding/vision_language
+  - tests/models/encoder_decoder/vision_language
   commands:
     - pytest -v -s models/decoder_only/audio_language -m 'not core_model and not quant_model'
     # HACK - run phi3v tests separately to sidestep this transformers bug
     # https://github.com/huggingface/transformers/issues/34307
     - pytest -v -s models/decoder_only/vision_language/test_phi3v.py
     - pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'not core_model and not quant_model'
-
+    - pytest -v -s models/encoder_decoder/language -m 'not core_model'
-- label: Other Models Test # 20min
+    - pytest -v -s models/encoder_decoder/vision_language -m 'not core_model'
-  #mirror_hardwares: [amd]
-  source_file_dependencies:
-  - vllm/
-  - tests/models/embedding/language
-  - tests/models/embedding/vision_language
-  - tests/models/encoder_decoder/language
-  - tests/models/encoder_decoder/vision_language
-  commands:
-    - pytest -v -s models/embedding/language
-    - pytest -v -s models/embedding/vision_language
-    - pytest -v -s models/encoder_decoder/language
-    - pytest -v -s models/encoder_decoder/vision_language
 
 # This test is used only in PR development phase to test individual models and should never run on main
 - label: Custom Models Test

docs/source/models/supported_models.rst

@@ -330,11 +330,16 @@ Text Embedding
     - :code:`BAAI/bge-multilingual-gemma2`, etc.
     - 
     - ✅︎
-  * - :code:`MistralModel`
+  * - :code:`LlamaModel`, :code:`LlamaForCausalLM`, :code:`MistralModel`, etc.
-    - Mistral-based
+    - Llama-based
     - :code:`intfloat/e5-mistral-7b-instruct`, etc.
     - ✅︎
     - ✅︎
+  * - :code:`Qwen2Model`, :code:`Qwen2ForCausalLM`
+    - Qwen2-based
+    - :code:`ssmits/Qwen2-7B-Instruct-embed-base`, :code:`Alibaba-NLP/gte-Qwen2-1.5B-instruct`, etc.
+    - ✅︎
+    - ✅︎
 
 .. important::
   Some model architectures support both generation and embedding tasks.
@@ -355,7 +360,7 @@ Reward Modeling
   * - :code:`Qwen2ForRewardModel`
     - Qwen2-based
     - :code:`Qwen/Qwen2.5-Math-RM-72B`, etc.
-    - 
+    - ✅︎
     - ✅︎
 
 .. note::
@@ -376,7 +381,7 @@ Classification
   * - :code:`Qwen2ForSequenceClassification`
     - Qwen2-based
     - :code:`jason9693/Qwen2.5-1.5B-apeach`, etc.
-    - 
+    - ✅︎
     - ✅︎
 
 .. note::

tests/models/decoder_only/language/test_jamba.py

@@ -33,6 +33,10 @@ def test_models(
 
     with vllm_runner(model, dtype=dtype) as vllm_model:
         vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
+        # 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)
 
     for i in range(len(example_prompts)):
         hf_output_ids, hf_output_str = hf_outputs[i]
@@ -293,17 +297,3 @@ def test_jamba_distributed_produces_identical_generation(
         name_0="vllm_tp_1",
         name_1="vllm_tp_2",
     )
-
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", ["float"])
-def test_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)

tests/models/decoder_only/language/test_mamba.py

@@ -51,6 +51,10 @@ def test_models(
 
     with vllm_runner(model, dtype=dtype) as vllm_model:
         vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
+        # 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)
 
     for i in range(len(example_prompts)):
         hf_output_ids, hf_output_str = hf_outputs[i]
@@ -279,17 +283,3 @@ def test_state_cleanup(
     except ValueError:
         pytest.fail("Mamba inner state wasn't cleaned up between states, "
                     "could be related to finished_requests_ids")
-
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", ["float"])
-def test_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)

tests/models/decoder_only/language/test_models.py

@@ -4,37 +4,52 @@ Run `pytest tests/models/test_models.py`.
 """
 import pytest
 
-from vllm.platforms import current_platform
-
 from ...utils import check_logprobs_close
 
-MODELS = [
+
-    "facebook/opt-125m",  # opt
+@pytest.mark.parametrize(
-    "openai-community/gpt2",  # gpt2
+    "model",
-    # "Milos/slovak-gpt-j-405M",  # gptj
+    [
-    # "bigcode/tiny_starcoder_py",  # gpt_bigcode
+        pytest.param(
-    # "EleutherAI/pythia-70m",  # gpt_neox
             "bigscience/bloom-560m",  # bloom - testing alibi slopes
-    "microsoft/phi-2",  # phi
+            marks=[pytest.mark.core_model, pytest.mark.cpu_model],
-    # "stabilityai/stablelm-3b-4e1t",  # stablelm
+        ),
-    # "bigcode/starcoder2-3b",  # starcoder2
+        pytest.param(
+            "openai-community/gpt2",  # gpt2
+            marks=[pytest.mark.core_model, pytest.mark.cpu_model],
+        ),
+        pytest.param("Milos/slovak-gpt-j-405M"),  # gptj
+        pytest.param("bigcode/tiny_starcoder_py"),  # gpt_bigcode
+        pytest.param("EleutherAI/pythia-70m"),  # gpt_neox
+        pytest.param(
             "google/gemma-1.1-2b-it",  # gemma
-    "Qwen/Qwen2.5-0.5B-Instruct",  # qwen2
+            marks=[pytest.mark.core_model, pytest.mark.cpu_model],
+        ),
+        pytest.param(
             "meta-llama/Llama-3.2-1B-Instruct",  # llama
-]
+            marks=[pytest.mark.core_model, pytest.mark.cpu_model],
-
+        ),
-if not current_platform.is_cpu():
+        pytest.param(
-    MODELS += [
-        # fused_moe which not supported on CPU
             "openbmb/MiniCPM3-4B",
-    ]
+            # fused_moe not supported on CPU
-
+            marks=[pytest.mark.core_model],
-target_dtype = "half"
+        ),
-
+        pytest.param(
-
+            "facebook/opt-125m",  # opt
-@pytest.mark.core_model
+            marks=[pytest.mark.core_model, pytest.mark.cpu_model],
-@pytest.mark.parametrize("model", MODELS)
+        ),
-@pytest.mark.parametrize("dtype", [target_dtype])
+        pytest.param(
+            "microsoft/phi-2",  # phi
+            marks=[pytest.mark.core_model],
+        ),
+        pytest.param(
+            "Qwen/Qwen2.5-0.5B-Instruct",  # qwen2
+            marks=[pytest.mark.core_model],
+        ),
+        pytest.param("stabilityai/stablelm-3b-4e1t"),  # stablelm
+        pytest.param("bigcode/starcoder2-3b"),  # starcoder2
+    ])
+@pytest.mark.parametrize("dtype", ["half"])
 @pytest.mark.parametrize("max_tokens", [32])
 @pytest.mark.parametrize("num_logprobs", [5])
 def test_models(

tests/models/embedding/language/test_cls_models.py

@@ -9,10 +9,14 @@ import pytest
 import torch
 from transformers import AutoModelForSequenceClassification
 
-CLASSIFICATION_MODELS = ["jason9693/Qwen2.5-1.5B-apeach"]
 
-
+@pytest.mark.parametrize(
-@pytest.mark.parametrize("model", CLASSIFICATION_MODELS)
+    "model",
+    [
+        pytest.param("jason9693/Qwen2.5-1.5B-apeach",
+                     marks=[pytest.mark.core_model, pytest.mark.cpu_model]),
+    ],
+)
 @pytest.mark.parametrize("dtype", ["float"])
 def test_classification_models(
     hf_runner,
@@ -23,31 +27,19 @@ def test_classification_models(
 ) -> None:
     with vllm_runner(model, dtype=dtype) as vllm_model:
         vllm_outputs = vllm_model.classify(example_prompts)
+        # 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)
 
     with hf_runner(model,
                    dtype=dtype,
                    auto_cls=AutoModelForSequenceClassification) as hf_model:
         hf_outputs = hf_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)

tests/models/embedding/language/test_embedding.py

@@ -4,25 +4,25 @@ Run `pytest tests/models/embedding/language/test_embedding.py`.
 """
 import pytest
 
-from vllm.utils import current_platform
-
 from ..utils import check_embeddings_close
 
-# Model, Guard
-MODELS = [
-    "intfloat/e5-mistral-7b-instruct",
-    "BAAI/bge-base-en-v1.5",
-    "BAAI/bge-multilingual-gemma2",
-    "intfloat/multilingual-e5-large",
-]
 
-ENCODER_ONLY = [
+@pytest.mark.parametrize(
-    "BAAI/bge-base-en-v1.5",
+    "model",
-    "intfloat/multilingual-e5-large",
+    [
-]
+        # [Encoder-only]
-
+        pytest.param("BAAI/bge-base-en-v1.5",
-
+                     marks=[pytest.mark.core_model, pytest.mark.cpu_model]),
-@pytest.mark.parametrize("model", MODELS)
+        pytest.param("intfloat/multilingual-e5-large"),
+        # [Encoder-decoder]
+        pytest.param("intfloat/e5-mistral-7b-instruct",
+                     marks=[pytest.mark.core_model, pytest.mark.cpu_model]),
+        pytest.param("BAAI/bge-multilingual-gemma2",
+                     marks=[pytest.mark.core_model]),
+        pytest.param("ssmits/Qwen2-7B-Instruct-embed-base"),
+        pytest.param("Alibaba-NLP/gte-Qwen2-1.5B-instruct"),
+    ],
+)
 @pytest.mark.parametrize("dtype", ["half"])
 def test_models(
     hf_runner,
@@ -31,9 +31,6 @@ def test_models(
     model,
     dtype: str,
 ) -> None:
-    if model not in ENCODER_ONLY and current_platform.is_cpu():
-        pytest.skip("Skip large embedding models test on CPU.")
-
     # The example_prompts has ending "\n", for example:
     # "Write a short story about a robot that dreams for the first time.\n"
     # sentence_transformers will strip the input texts, see:
@@ -46,8 +43,13 @@ def test_models(
                    is_sentence_transformer=True) as hf_model:
         hf_outputs = hf_model.encode(example_prompts)
 
-    with vllm_runner(model, dtype=dtype, max_model_len=None) as vllm_model:
+    with vllm_runner(model, task="embedding", dtype=dtype,
+                     max_model_len=None) as vllm_model:
         vllm_outputs = vllm_model.encode(example_prompts)
+        # 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)
 
     check_embeddings_close(
         embeddings_0_lst=hf_outputs,

tests/models/embedding/vision_language/test_llava_next.py

@@ -88,6 +88,7 @@ def _run_test(
 
 @pytest.mark.skipif(transformers.__version__.startswith("4.46"),
                     reason="Model broken with changes in transformers 4.46")
+@pytest.mark.core_model
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["half"])
 def test_models_text(
@@ -112,6 +113,7 @@ def test_models_text(
 
 
 @large_gpu_test(min_gb=48)
+@pytest.mark.core_model
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["half"])
 def test_models_image(

tests/models/embedding/vision_language/test_phi3v.py

@@ -74,6 +74,7 @@ def _run_test(
     )
 
 
+@pytest.mark.core_model
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["half"])
 def test_models_text(
@@ -98,6 +99,7 @@ def test_models_text(
 
 
 @large_gpu_test(min_gb=48)
+@pytest.mark.core_model
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["half"])
 def test_models_image(

tests/models/encoder_decoder/language/test_bart.py

@@ -14,8 +14,6 @@ from ....conftest import (DecoderPromptType, ExplicitEncoderDecoderPrompt,
 from ....utils import multi_gpu_test
 from ...utils import check_logprobs_close
 
-MODELS = ["facebook/bart-base", "facebook/bart-large-cnn"]
-
 
 def vllm_to_hf_output(
     vllm_output: Tuple[List[int], str, Optional[SampleLogprobs]],
@@ -170,7 +168,14 @@ def run_test(
     )
 
 
-@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize(
+    "model",
+    [
+        pytest.param("facebook/bart-base",
+                     marks=[pytest.mark.core_model, pytest.mark.cpu_model]),
+        pytest.param("facebook/bart-large-cnn"),
+    ],
+)
 @pytest.mark.parametrize("dtype", ["float", "bfloat16"])
 @pytest.mark.parametrize("max_tokens", [64])
 @pytest.mark.parametrize("num_logprobs", [5])

tests/models/encoder_decoder/vision_language/test_mllama.py

@@ -233,6 +233,7 @@ def clear_cache():
 
 
 @large_gpu_test(min_gb=48)
+@pytest.mark.core_model
 @pytest.mark.parametrize("model", models)
 @pytest.mark.parametrize(
     "sizes",
@@ -278,6 +279,7 @@ def test_models_single_leading_image(hf_runner, vllm_runner, image_assets,
 
 
 @large_gpu_test(min_gb=48)
+@pytest.mark.core_model
 @pytest.mark.parametrize("model", models)
 @pytest.mark.parametrize("dtype", ["bfloat16"])
 @pytest.mark.parametrize("max_tokens", [128])
@@ -326,6 +328,7 @@ def test_models_multi_leading_images(hf_runner, vllm_runner, image_assets,
 
 
 @large_gpu_test(min_gb=48)
+@pytest.mark.core_model
 @pytest.mark.parametrize("model", models)
 @pytest.mark.parametrize("dtype", ["bfloat16"])
 @pytest.mark.parametrize("max_tokens", [128])

tests/models/registry.py

@@ -129,9 +129,13 @@ _EMBEDDING_EXAMPLE_MODELS = {
     # [Text-only]
     "BertModel": _HfExamplesInfo("BAAI/bge-base-en-v1.5"),
     "Gemma2Model": _HfExamplesInfo("BAAI/bge-multilingual-gemma2"),
+    "LlamaModel": _HfExamplesInfo("llama", is_available_online=False),
     "MistralModel": _HfExamplesInfo("intfloat/e5-mistral-7b-instruct"),
+    "Qwen2Model": _HfExamplesInfo("ssmits/Qwen2-7B-Instruct-embed-base"),
     "Qwen2ForRewardModel": _HfExamplesInfo("Qwen/Qwen2.5-Math-RM-72B"),
     "Qwen2ForSequenceClassification": _HfExamplesInfo("jason9693/Qwen2.5-1.5B-apeach"),  # noqa: E501
+    "RobertaModel": _HfExamplesInfo("sentence-transformers/stsb-roberta-base-v2"),  # noqa: E501
+    "XLMRobertaModel": _HfExamplesInfo("intfloat/multilingual-e5-large"),
     # [Multimodal]
     "LlavaNextForConditionalGeneration": _HfExamplesInfo("royokong/e5-v"),
     "Phi3VForCausalLM": _HfExamplesInfo("TIGER-Lab/VLM2Vec-Full",

tests/models/test_registry.py

@@ -77,8 +77,8 @@ def test_registry_is_pp(model_arch, is_pp, init_cuda):
 
 
 def test_hf_registry_coverage():
-    untested_archs = (HF_EXAMPLE_MODELS.get_supported_archs() -
+    untested_archs = (ModelRegistry.get_supported_archs() -
-                      set(ModelRegistry.get_supported_archs()))
+                      HF_EXAMPLE_MODELS.get_supported_archs())
 
     assert not untested_archs, (
         "Please add the following architectures to "

vllm/model_executor/models/qwen2.py

@@ -37,6 +37,7 @@ 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.sampler import SamplerOutput, get_sampler
@@ -44,8 +45,9 @@ 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
+from vllm.sequence import IntermediateTensors, PoolerOutput
 
 from .interfaces import SupportsLoRA, SupportsPP
 from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
@@ -247,6 +249,18 @@ class Qwen2Model(nn.Module):
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
 
+        # 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` = {} is less than "
+                             "`num_hidden_layers` = {}. Please open an issue "
+                             "to discuss this feature.".format(
+                                 config.max_window_layers,
+                                 config.num_hidden_layers,
+                             ))
+
         self.config = config
         self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
@@ -405,20 +419,9 @@ class Qwen2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
-        cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
-        # TODO (@robertgshaw2): see if this can be moved out
+        pooler_config = vllm_config.model_config.pooler_config
-        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` = {} is less than "
-                             "`num_hidden_layers` = {}. Please open an issue "
-                             "to discuss this feature.".format(
-                                 config.max_window_layers,
-                                 config.num_hidden_layers,
-                             ))
 
         self.config = config
         self.lora_config = lora_config
@@ -438,6 +441,15 @@ class Qwen2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
 
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = get_sampler()
+
+        # The same model class supports both language generation and embedding
+        # because the architecture name is the same
+        self._pooler = Pooler.from_config_with_defaults(
+            pooler_config,
+            pooling_type=PoolingType.LAST,
+            normalize=True,
+            softmax=False)
+
         self.make_empty_intermediate_tensors = (
             self.model.make_empty_intermediate_tensors)
 
@@ -475,6 +487,13 @@ class Qwen2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         next_tokens = self.sampler(logits, sampling_metadata)
         return next_tokens
 
+    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,
@@ -482,3 +501,70 @@ class Qwen2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                            if self.config.tie_word_embeddings else None),
         )
         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",
+        ],
+    }
+
+    # 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"))
+
+        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,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+    ) -> torch.Tensor:
+        return self.model(input_ids, positions, kv_caches, attn_metadata,
+                          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]]):
+        loader = AutoWeightsLoader(self,
+                                   ignore_unexpected_prefixes=["lm_head."])
+        loader.load_weights(weights)

vllm/model_executor/models/qwen2_cls.py

@@ -17,10 +17,11 @@ 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):
+class Qwen2ForSequenceClassification(nn.Module, SupportsLoRA, SupportsPP):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -46,21 +47,9 @@ class Qwen2ForSequenceClassification(nn.Module):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
-        cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
         pooler_config = vllm_config.model_config.pooler_config
-        # 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` = {} is less than "
-                             "`num_hidden_layers` = {}. Please open an issue "
-                             "to discuss this feature.".format(
-                                 config.max_window_layers,
-                                 config.num_hidden_layers,
-                             ))
 
         self.config = config
         self.lora_config = lora_config

vllm/model_executor/models/qwen2_rm.py

@@ -16,7 +16,7 @@ from vllm.model_executor.layers.pooler import Pooler, PoolingType
 from vllm.model_executor.pooling_metadata import PoolingMetadata
 from vllm.sequence import IntermediateTensors, PoolerOutput
 
-from .interfaces import SupportsPP
+from .interfaces import SupportsLoRA, SupportsPP
 from .qwen2 import Qwen2Model
 from .utils import AutoWeightsLoader, maybe_prefix
 
@@ -32,7 +32,7 @@ class ReLU(nn.Module):
         return self.activation(input)
 
 
-class Qwen2ForRewardModel(nn.Module, SupportsPP):
+class Qwen2ForRewardModel(nn.Module, SupportsLoRA, SupportsPP):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -58,21 +58,9 @@ class Qwen2ForRewardModel(nn.Module, SupportsPP):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
-        cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
         pooler_config = vllm_config.model_config.pooler_config
-        # 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` = {} is less than "
-                             "`num_hidden_layers` = {}. Please open an issue "
-                             "to discuss this feature.".format(
-                                 config.max_window_layers,
-                                 config.num_hidden_layers,
-                             ))
 
         self.config = config
         self.lora_config = lora_config

vllm/model_executor/models/registry.py

@@ -11,7 +11,8 @@ import tempfile
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, field
 from functools import lru_cache
-from typing import Callable, Dict, List, Optional, Tuple, Type, TypeVar, Union
+from typing import (AbstractSet, Callable, Dict, List, Optional, Tuple, Type,
+                    TypeVar, Union)
 
 import cloudpickle
 import torch.nn as nn
@@ -110,6 +111,8 @@ _EMBEDDING_MODELS = {
     },
     "MistralModel": ("llama", "LlamaEmbeddingModel"),
     "Phi3ForCausalLM": ("phi3", "Phi3ForCausalLM"),
+    "Qwen2Model": ("qwen2", "Qwen2EmbeddingModel"),
+    "Qwen2ForCausalLM": ("qwen2", "Qwen2ForCausalLM"),
     "Qwen2ForRewardModel": ("qwen2_rm", "Qwen2ForRewardModel"),
     "Qwen2ForSequenceClassification": ("qwen2_cls", "Qwen2ForSequenceClassification"),  # noqa: E501
     # [Multimodal]
@@ -301,8 +304,8 @@ class _ModelRegistry:
     # Keyed by model_arch
     models: Dict[str, _BaseRegisteredModel] = field(default_factory=dict)
 
-    def get_supported_archs(self) -> List[str]:
+    def get_supported_archs(self) -> AbstractSet[str]:
-        return list(self.models.keys())
+        return self.models.keys()
 
     def register_model(
         self,