[CI/Build] Split up models tests (#10069)

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

.buildkite/test-pipeline.yaml

@@ -305,7 +305,7 @@ steps:
 
 #####  models test  #####
 
-- label: Basic Models Test # 3min
+- label: Basic Models Test # 10min
   source_file_dependencies:
   - vllm/
   - tests/models
@@ -314,23 +314,24 @@ steps:
     - pytest -v -s models/test_oot_registration.py # it needs a clean process
     - pytest -v -s models/*.py --ignore=models/test_oot_registration.py
 
-- label: Decoder-only Language Models Test (Standard) # 35min
+- label: Decoder-only Language Models Test (Standard) # 18min
   #mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
   - tests/models/decoder_only/language
   commands:
-    - pytest -v -s models/decoder_only/language/test_models.py
+    - pytest -v -s models/decoder_only/language -m core_model
+    - pytest -v -s models/decoder_only/language -m quant_model
 
-- label: Decoder-only Language Models Test (Extended) # 1h20min
+- label: Decoder-only Language Models Test (Extended) # 46min
   nightly: true
   source_file_dependencies:
   - vllm/
   - tests/models/decoder_only/language
   commands:
-    - pytest -v -s models/decoder_only/language --ignore=models/decoder_only/language/test_models.py
+    - pytest -v -s models/decoder_only/language -m 'not core_model and not quant_model'
 
-- label: Decoder-only Multi-Modal Models Test (Standard) # 26min
+- label: Decoder-only Multi-Modal Models Test (Standard) # 22min
   #mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
@@ -339,21 +340,24 @@ steps:
   commands:
     - pytest -v -s models/decoder_only/audio_language -m core_model
     - pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m core_model
+    # No tests under this group for now
+    # - pytest -v -s models/decoder_only/audio_language -m quant_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)
+- label: Decoder-only Multi-Modal Models Test (Extended) # 1h10m
   nightly: true
   source_file_dependencies:
   - vllm/
   - tests/models/decoder_only/audio_language
   - tests/models/decoder_only/vision_language
   commands:
-    - pytest -v -s models/decoder_only/audio_language -m 'not core_model'
+    - 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'
+    - 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'
 
-- label: Other Models Test # 6min
+- label: Other Models Test # 20min
   #mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/

pyproject.toml

@@ -95,6 +95,7 @@ markers = [
     "skip_global_cleanup",
     "core_model: enable this model test in each PR instead of only nightly",
     "cpu_model: enable this model test in CPU tests",
+    "quant_model: run this model test under Quantized category",
     "distributed_2_gpus: run this test only in distributed tests for 2 GPUs",
     "skip_v1: do not run this test with v1",
 ]

tests/models/decoder_only/language/test_aqlm.py

@@ -38,6 +38,7 @@ ground_truth_generations = [
 ]
 
 
+@pytest.mark.quant_model
 @pytest.mark.skipif(not is_quant_method_supported("aqlm"),
                     reason="AQLM is not supported on this GPU type.")
 @pytest.mark.parametrize("model", ["ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf"])

tests/models/decoder_only/language/test_fp8.py

@@ -15,6 +15,7 @@ from ...utils import check_logprobs_close
 os.environ["TOKENIZERS_PARALLELISM"] = "true"
 
 
+@pytest.mark.quant_model
 @pytest.mark.skipif(not is_quant_method_supported("fp8"),
                     reason="fp8 is not supported on this GPU type.")
 @pytest.mark.parametrize(

tests/models/decoder_only/language/test_gguf.py

@@ -17,26 +17,21 @@ os.environ["TOKENIZERS_PARALLELISM"] = "true"
 
 MAX_MODEL_LEN = 1024
 
-# FIXME: Move this to confest
-MODELS = [
-    ("meta-llama/Llama-3.2-1B-Instruct",
-     hf_hub_download("bartowski/Llama-3.2-1B-Instruct-GGUF",
-                     filename="Llama-3.2-1B-Instruct-Q4_K_M.gguf")),
-    ("meta-llama/Llama-3.2-1B-Instruct",
-     hf_hub_download("bartowski/Llama-3.2-1B-Instruct-GGUF",
-                     filename="Llama-3.2-1B-Instruct-IQ4_XS.gguf")),
-    ("Qwen/Qwen2-1.5B-Instruct",
-     hf_hub_download("Qwen/Qwen2-1.5B-Instruct-GGUF",
-                     filename="qwen2-1_5b-instruct-q4_k_m.gguf")),
-    ("Qwen/Qwen2-1.5B-Instruct",
-     hf_hub_download("legraphista/Qwen2-1.5B-Instruct-IMat-GGUF",
-                     filename="Qwen2-1.5B-Instruct.IQ4_XS.gguf")),
-]
-
 
 @pytest.mark.skipif(not is_quant_method_supported("gguf"),
                     reason="gguf is not supported on this GPU type.")
-@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize(("original_model", "gguf_id", "gguf_path"), [
+    ("meta-llama/Llama-3.2-1B-Instruct",
+     "bartowski/Llama-3.2-1B-Instruct-GGUF",
+     "Llama-3.2-1B-Instruct-Q4_K_M.gguf"),
+    ("meta-llama/Llama-3.2-1B-Instruct",
+     "bartowski/Llama-3.2-1B-Instruct-GGUF",
+     "Llama-3.2-1B-Instruct-IQ4_XS.gguf"),
+    ("Qwen/Qwen2-1.5B-Instruct", "Qwen/Qwen2-1.5B-Instruct-GGUF",
+     "qwen2-1_5b-instruct-q4_k_m.gguf"),
+    ("Qwen/Qwen2-1.5B-Instruct", "legraphista/Qwen2-1.5B-Instruct-IMat-GGUF",
+     "Qwen2-1.5B-Instruct.IQ4_XS.gguf"),
+])
 @pytest.mark.parametrize("dtype", ["half"])
 @pytest.mark.parametrize("max_tokens", [32])
 @pytest.mark.parametrize("num_logprobs", [5])
@@ -45,7 +40,9 @@ def test_models(
     num_gpus_available,
     vllm_runner,
     example_prompts,
-    model,
+    original_model,
+    gguf_id,
+    gguf_path,
     dtype: str,
     max_tokens: int,
     num_logprobs: int,
@@ -54,7 +51,7 @@ def test_models(
     if num_gpus_available < tp_size:
         pytest.skip(f"Not enough GPUs for tensor parallelism {tp_size}")
 
-    original_model, gguf_model = model
+    gguf_model = hf_hub_download(gguf_id, filename=gguf_path)
 
     tokenizer = AutoTokenizer.from_pretrained(original_model)
     messages = [[{

tests/models/decoder_only/language/test_gptq_marlin.py

@@ -33,6 +33,7 @@ MODELS = [
 ]
 
 
+@pytest.mark.quant_model
 @pytest.mark.flaky(reruns=3)
 @pytest.mark.skipif(not is_quant_method_supported("gptq_marlin"),
                     reason="gptq_marlin is not supported on this GPU type.")

tests/models/decoder_only/language/test_gptq_marlin_24.py

@@ -38,6 +38,7 @@ model_pairs = [
 ]
 
 
+@pytest.mark.quant_model
 @pytest.mark.flaky(reruns=2)
 @pytest.mark.skipif(not is_quant_method_supported("gptq_marlin_24"),
                     reason="Marlin24 is not supported on this GPU type.")

tests/models/decoder_only/language/test_granite.py

@@ -7,7 +7,9 @@ import pytest
 from ...utils import check_logprobs_close
 
 MODELS = [
+    # TODO(sang): Sliding window should be tested separately.
     "ibm/PowerLM-3b",
+    "ibm/PowerMoE-3b",
 ]
 
 
@@ -24,7 +26,6 @@ def test_models(
     max_tokens: int,
     num_logprobs: int,
 ) -> None:
-    # TODO(sang): Sliding window should be tested separately.
     with hf_runner(model, dtype=dtype) as hf_model:
         hf_outputs = hf_model.generate_greedy_logprobs_limit(
             example_prompts, max_tokens, num_logprobs)

tests/models/decoder_only/language/test_granitemoe.py

@@ -1,39 +0,0 @@
-"""Compare the outputs of HF and vLLM for Granite models using greedy sampling.
-
-Run `pytest tests/models/test_granite.py`.
-"""
-import pytest
-
-from ...utils import check_logprobs_close
-
-MODELS = [
-    "ibm/PowerMoE-3b",
-]
-
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", ["bfloat16"])
-@pytest.mark.parametrize("max_tokens", [64])
-@pytest.mark.parametrize("num_logprobs", [5])
-def test_models(
-    hf_runner,
-    vllm_runner,
-    example_prompts,
-    model: str,
-    dtype: str,
-    max_tokens: int,
-    num_logprobs: int,
-) -> None:
-    with hf_runner(model, dtype=dtype) as hf_model:
-        hf_outputs = hf_model.generate_greedy_logprobs_limit(
-            example_prompts, max_tokens, num_logprobs)
-
-    with vllm_runner(model, dtype=dtype) as vllm_model:
-        vllm_outputs = vllm_model.generate_greedy_logprobs(
-            example_prompts, max_tokens, num_logprobs)
-    check_logprobs_close(
-        outputs_0_lst=hf_outputs,
-        outputs_1_lst=vllm_outputs,
-        name_0="hf",
-        name_1="vllm",
-    )

tests/models/decoder_only/language/test_modelopt.py

@@ -39,6 +39,7 @@ EXPECTED_STRS_MAP = {
 @pytest.mark.skip(
     reason=
     "Prevent unstable test based on golden strings from breaking the build.")
+@pytest.mark.quant_model
 @pytest.mark.skipif(not is_quant_method_supported("fp8"),
                     reason="fp8 is not supported on this GPU type.")
 @pytest.mark.parametrize("model_name", MODELS)

tests/models/decoder_only/language/test_models.py

@@ -1,8 +1,5 @@
 """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_models.py`.
 """
 import pytest
@@ -35,6 +32,7 @@ if not current_platform.is_cpu():
 target_dtype = "half"
 
 
+@pytest.mark.core_model
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", [target_dtype])
 @pytest.mark.parametrize("max_tokens", [32])

tests/models/decoder_only/vision_language/mm_processor_kwargs/test_llava_next.py

@@ -56,11 +56,13 @@ def test_dummy_data_for_llava_next_feature_size(dummy_data_for_llava_next,
     ctx.model_config.hf_config.image_grid_pinpoints = gridpoints
     seq_len = 5000  # bigger than the max feature size for any image
 
-    seq_data, mm_data = dummy_data_for_llava_next(
+    dummy_data = dummy_data_for_llava_next(
         ctx,
         seq_len=seq_len,
         mm_counts={"image": 1},
     )
+    seq_data = dummy_data.seq_data
+    mm_data = dummy_data.multi_modal_data
 
     # The dummy data dims should match the gridpoint with the biggest feat size
     assert mm_data["image"].height == expected_size[0]

tests/models/decoder_only/vision_language/mm_processor_kwargs/test_phi3v.py

@@ -131,12 +131,13 @@ def test_dummy_data_override(dummy_data_for_phi3v, model: str, num_crops: int,
         mm_processor_kwargs=None,
     )
 
-    sequence_data, _, = dummy_data_for_phi3v(
+    dummy_data = dummy_data_for_phi3v(
         ctx=ctx,
         seq_len=8192,  # Should be bigger than num_imgs * toks_per_img
         mm_counts={"image": num_imgs},
         num_crops=num_crops,
     )
+    sequence_data = dummy_data.seq_data
     # Ensure we have the right number of placeholders per num_crops size
     img_tok_count = sequence_data.get_token_ids().count(_IMAGE_TOKEN_ID)
     assert img_tok_count == toks_per_img * num_imgs

tests/models/decoder_only/vision_language/mm_processor_kwargs/test_qwen2_vl.py

@@ -86,10 +86,17 @@ def test_qwen2_vl_dummy_data(dummy_data_for_qwen2_vl,
 
     # NOTE: video value is required, but isn't actually used
     # when making the dummy data except for error handling currently
-    seq_data, mm_data = dummy_data_for_qwen2_vl(qwen2_vl_context, seq_len, {
+    dummy_data = dummy_data_for_qwen2_vl(
-        "image": 1,
+        ctx=qwen2_vl_context,
-        "video": 0
+        seq_len=seq_len,
-    }, **mm_processor_kwargs)
+        mm_counts={
+            "image": 1,
+            "video": 0
+        },
+        **mm_processor_kwargs,
+    )
+    seq_data = dummy_data.seq_data
+    mm_data = dummy_data.multi_modal_data
 
     # Ensure we have the right number of placeholders for min/max pixel values
     assert seq_data.get_token_ids().count(image_token_id) == token_count

tests/models/decoder_only/vision_language/test_awq.py

@@ -1,4 +1,4 @@
-from typing import List, Optional, Tuple, Type
+from typing import List, Optional, Type
 
 import pytest
 import torch
@@ -19,7 +19,8 @@ HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
 def run_awq_test(
     vllm_runner: Type[VllmRunner],
     image_assets: _ImageAssets,
-    models: Tuple[str, str],
+    source_model: str,
+    quant_model: str,
     *,
     size_factors: List[float],
     dtype: str,
@@ -28,8 +29,6 @@ def run_awq_test(
     tensor_parallel_size: int,
     distributed_executor_backend: Optional[str] = None,
 ):
-    source_model, quant_model = models
-
     images = [asset.pil_image for asset in image_assets]
 
     inputs_per_image = [(
@@ -84,8 +83,11 @@ def run_awq_test(
         )
 
 
+@pytest.mark.quant_model
 @pytest.mark.parametrize(
-    "models", [("OpenGVLab/InternVL2-2B", "OpenGVLab/InternVL2-2B-AWQ")])
+    ("source_model", "quant_model"),
+    [("OpenGVLab/InternVL2-2B", "OpenGVLab/InternVL2-2B-AWQ")],
+)
 @pytest.mark.parametrize(
     "size_factors",
     [
@@ -103,12 +105,13 @@ def run_awq_test(
 @pytest.mark.parametrize("max_tokens", [128])
 @pytest.mark.parametrize("num_logprobs", [5])
 @torch.inference_mode()
-def test_awq_models(vllm_runner, image_assets, models, size_factors,
+def test_awq_models(vllm_runner, image_assets, source_model, quant_model,
-                    dtype: str, max_tokens: int, num_logprobs: int) -> None:
+                    size_factors, dtype, max_tokens, num_logprobs) -> None:
     run_awq_test(
         vllm_runner,
         image_assets,
-        models,
+        source_model,
+        quant_model,
         size_factors=size_factors,
         dtype=dtype,
         max_tokens=max_tokens,

tests/models/decoder_only/vision_language/test_intern_vit.py

@@ -11,21 +11,17 @@ from ....conftest import _ImageAssets
 # we use snapshot_download to prevent conflicts between
 # dynamic_module and trust_remote_code for hf_runner
 DOWNLOAD_PATTERN = ["*.json", "*.py", "*.safetensors", "*.txt", "*.model"]
-models = [
-    snapshot_download("OpenGVLab/InternViT-300M-448px",
-                      allow_patterns=DOWNLOAD_PATTERN),
-    snapshot_download("OpenGVLab/InternViT-6B-448px-V1-5",
-                      allow_patterns=DOWNLOAD_PATTERN),
-]
 
 
 def run_intern_vit_test(
     image_assets: _ImageAssets,
-    model: str,
+    model_id: str,
     *,
     dtype: str,
     distributed_executor_backend: Optional[str] = None,
 ):
+    model = snapshot_download(model_id, allow_patterns=DOWNLOAD_PATTERN)
+
     img_processor = CLIPImageProcessor.from_pretrained(model)
     images = [asset.pil_image for asset in image_assets]
     pixel_values = [
@@ -67,12 +63,15 @@ def run_intern_vit_test(
         assert cos_similar(vllm_output, hf_output).mean() > 0.99
 
 
-@pytest.mark.parametrize("model", models)
+@pytest.mark.parametrize("model_id", [
+    "OpenGVLab/InternViT-300M-448px",
+    "OpenGVLab/InternViT-6B-448px-V1-5",
+])
 @pytest.mark.parametrize("dtype", [torch.half])
 @torch.inference_mode()
-def test_models(dist_init, image_assets, model, dtype: str) -> None:
+def test_models(dist_init, image_assets, model_id, dtype: str) -> None:
     run_intern_vit_test(
         image_assets,
-        model,
+        model_id,
         dtype=dtype,
     )

tests/models/decoder_only/vision_language/test_models.py

@@ -130,8 +130,8 @@ VLM_TEST_SETTINGS = {
         max_num_seqs=2,
         auto_cls=AutoModelForVision2Seq,
         vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,
-        marks=[pytest.mark.core_model, pytest.mark.cpu_model],
         image_size_factors=[(), (0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
+        marks=[pytest.mark.core_model, pytest.mark.cpu_model],
     ),
     #### Extended model tests
     "blip2": VLMTestInfo(
@@ -159,9 +159,9 @@ VLM_TEST_SETTINGS = {
         dtype="bfloat16",
         marks=[
             pytest.mark.skipif(
-                transformers.__version__.startswith("4.46"),
+                transformers.__version__ < "4.46.2",
                 reason="Model broken in HF, see huggingface/transformers#34379"
-            )
+            ),
         ]
     ),
     "fuyu": VLMTestInfo(
@@ -185,8 +185,8 @@ VLM_TEST_SETTINGS = {
         max_num_seqs=2,
         dtype="bfloat16",
         get_stop_token_ids=lambda tok: [151329, 151336, 151338],
-        marks=[large_gpu_mark(min_gb=48)],
         patch_hf_runner=model_utils.glm_patch_hf_runner,
+        marks=[large_gpu_mark(min_gb=48)],
     ),
     "h2ovl": VLMTestInfo(
         models = [
@@ -205,6 +205,22 @@ VLM_TEST_SETTINGS = {
         use_tokenizer_eos=True,
         patch_hf_runner=model_utils.h2ovl_patch_hf_runner,
     ),
+    "idefics3": VLMTestInfo(
+        models=["HuggingFaceM4/Idefics3-8B-Llama3"],
+        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
+        prompt_formatter=lambda img_prompt:f"<|begin_of_text|>User:{img_prompt}<end_of_utterance>\nAssistant:",  # noqa: E501
+        img_idx_to_prompt=lambda idx: "<image>",
+        max_model_len=8192,
+        max_num_seqs=2,
+        auto_cls=AutoModelForVision2Seq,
+        marks=[
+            pytest.mark.skipif(
+                transformers.__version__ < "4.46.0",
+                reason="Model introduced in HF >= 4.46.0"
+            ),
+            large_gpu_mark(min_gb=48),
+        ],
+    ),
     "intern_vl": VLMTestInfo(
         models=[
             "OpenGVLab/InternVL2-1B",
@@ -263,7 +279,6 @@ VLM_TEST_SETTINGS = {
             runner_mm_key="videos",
         )],
     ),
-    # FIXME
     "llava_next_video": VLMTestInfo(
         models=["llava-hf/LLaVA-NeXT-Video-7B-hf"],
         test_type=VLMTestType.VIDEO,
@@ -275,7 +290,7 @@ VLM_TEST_SETTINGS = {
         image_sizes=[((1669, 2560), (2560, 1669), (183, 488), (488, 183))],
         marks=[
             pytest.mark.skipif(
-                transformers.__version__.startswith("4.46"),
+                transformers.__version__ < "4.46.2",
                 reason="Model broken with changes in transformers 4.46"
             )
         ],
@@ -316,6 +331,7 @@ VLM_TEST_SETTINGS = {
         max_model_len=8192,
         max_num_seqs=2,
         auto_cls=AutoModelForVision2Seq,
+        marks=[large_gpu_mark(min_gb=48)],
     ),
     "qwen": VLMTestInfo(
         models=["Qwen/Qwen-VL"],
@@ -327,22 +343,6 @@ VLM_TEST_SETTINGS = {
         vllm_output_post_proc=model_utils.qwen_vllm_to_hf_output,
         prompt_path_encoder=model_utils.qwen_prompt_path_encoder,
     ),
-    "idefics3": VLMTestInfo(
-        models=["HuggingFaceM4/Idefics3-8B-Llama3"],
-        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
-        prompt_formatter=lambda img_prompt:f"<|begin_of_text|>User:{img_prompt}<end_of_utterance>\nAssistant:",  # noqa: E501
-        img_idx_to_prompt=lambda idx: "<image>",
-        max_model_len=8192,
-        max_num_seqs=2,
-        auto_cls=AutoModelForVision2Seq,
-        marks=[
-            pytest.mark.skipif(
-                transformers.__version__ < "4.46.0",
-                reason="Model introduced in HF >= 4.46.0"
-            ),
-            large_gpu_mark(min_gb=48),
-        ],
-    ),
     ### Tensor parallel / multi-gpu broadcast tests
     "broadcast-chameleon": VLMTestInfo(
         models=["facebook/chameleon-7b"],
@@ -362,7 +362,7 @@ VLM_TEST_SETTINGS = {
                 reason="Need at least 2 GPUs to run the test.",
             ),
             pytest.mark.skipif(
-                transformers.__version__.startswith("4.46"),
+                transformers.__version__ < "4.46.2",
                 reason="Model broken in HF, see huggingface/transformers#34379"
             )
         ],

vllm/config.py

@@ -1,7 +1,8 @@
+import copy
 import enum
 import json
 import warnings
-from dataclasses import dataclass, field
+from dataclasses import dataclass, field, replace
 from typing import (TYPE_CHECKING, Any, ClassVar, Dict, Final, List, Literal,
                     Mapping, Optional, Set, Tuple, Type, Union)
 
@@ -2078,6 +2079,12 @@ class VllmConfig:
             return quant_config
         return None
 
+    def with_hf_config(self, hf_config: PretrainedConfig) -> "VllmConfig":
+        model_config = copy.deepcopy(self.model_config)
+        model_config.hf_config = hf_config
+
+        return replace(self, model_config=model_config)
+
     def __post_init__(self):
         """Verify configs are valid & consistent with each other.
         """

vllm/model_executor/models/fuyu.py

@@ -229,7 +229,6 @@ class FuyuForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
     def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None:
         super().__init__()
         config = vllm_config.model_config.hf_config
-        cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         multimodal_config = vllm_config.model_config.multimodal_config
         self.config = config
@@ -246,9 +245,8 @@ class FuyuForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
             quant_config=quant_config,
             gather_output=True,
         )
-        self.language_model = PersimmonForCausalLM(config.text_config,
+        self.language_model = PersimmonForCausalLM(
-                                                   cache_config=cache_config,
+            vllm_config.with_hf_config(config.text_config))
-                                                   quant_config=quant_config)
         self.make_empty_intermediate_tensors = (
             self.language_model.make_empty_intermediate_tensors)
 

vllm/model_executor/models/internlm2_ve.py

@@ -164,10 +164,12 @@ class InternLM2VEForCausalLM(InternLM2ForCausalLM):
         vllm_config: VllmConfig,
         prefix: str = "",
     ) -> None:
+        super().__init__(vllm_config, prefix=prefix)
+
         config = vllm_config.model_config.hf_config
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
-        super().__init__(config, cache_config, quant_config)
+
         self.model = InternLM2VEModel(config,
                                       cache_config,
                                       quant_config,

vllm/model_executor/models/utils.py

@@ -241,11 +241,11 @@ def init_vllm_registered_model(
     based on the arguments passed to the outer vLLM model.
     """
     model_class, _ = ModelRegistry.resolve_model_cls(hf_config.architectures)
-    import copy
-    copied_config = copy.deepcopy(vllm_config)
-    copied_config.model_config.hf_config = hf_config
 
-    return model_class(vllm_config=copied_config, prefix=prefix)
+    return model_class(
+        vllm_config=vllm_config.with_hf_config(hf_config),
+        prefix=prefix,
+    )
 
 
 @overload