[MODEL] Qwen Multimodal Support (Qwen-VL / Qwen-VL-Chat)

docs/source/models/supported_models.rst

@@ -242,6 +242,11 @@ Multimodal Language Models
     - Image
     - :code:`openbmb/MiniCPM-V-2` (see note), :code:`openbmb/MiniCPM-Llama3-V-2_5`, :code:`openbmb/MiniCPM-V-2_6`, etc.
     -
+  * - :code:`QWenLMHeadModel`
+    - Qwen
+    - Image
+    - :code:`Qwen/Qwen-VL`, :code:`Qwen/Qwen-VL-Chat`, etc.
+    -
   * - :code:`UltravoxModel`
     - Ultravox
     - Audio

examples/offline_inference_vision_language.py

@@ -159,6 +159,20 @@ def run_blip2(question):
     return llm, prompt, stop_token_ids
 
 
+# Qwen
+def run_qwen_vl(question):
+
+    llm = LLM(
+        model="Qwen/Qwen-VL",
+        trust_remote_code=True,
+        max_num_seqs=5,
+    )
+
+    prompt = f"{question}Picture 1: <img></img>\n"
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
 model_example_map = {
     "llava": run_llava,
     "llava-next": run_llava_next,
@@ -169,6 +183,7 @@ def run_blip2(question):
     "minicpmv": run_minicpmv,
     "blip-2": run_blip2,
     "internvl_chat": run_internvl,
+    "qwen_vl": run_qwen_vl,
 }
 
 

tests/models/test_qwen.py

@@ -1,48 +1,165 @@
-from typing import Type
+import pathlib
+from typing import List, Optional, Type
 
 import pytest
 
-from ..conftest import HfRunner, VllmRunner
+from vllm.multimodal.utils import rescale_image_size
+
+from ..conftest import IMAGE_ASSETS, HfRunner, VllmRunner, _ImageAssets
 from .utils import check_logprobs_close
 
-models = ["qwen/qwen-vl"]
+pytestmark = pytest.mark.vlm
 
+text_only_models = [
+    "Qwen/Qwen-7B-Chat"  # Has no visual component
+]
 
-@pytest.mark.parametrize("dtype", ["half"])
-@pytest.mark.parametrize("max_tokens", [32])
-@pytest.mark.parametrize("num_logprobs", [5])
-@pytest.mark.parametrize("model", models)
-def test_text_only_qwen_model(
+multimodal_models = ["Qwen/Qwen-VL"]
+
+HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
+    "stop_sign":
+    "Picture 1: <img></img>\nWhat's the content of the image?: ",
+    "cherry_blossom":
+    "Picture 1: <img></img>\nWhat is the season?: ",
+})
+
+
+### Tests for multimodal Qwen models
+def run_test(
+    tmp_path: pathlib.PosixPath,
     hf_runner: Type[HfRunner],
     vllm_runner: Type[VllmRunner],
-    example_prompts,
+    image_assets: _ImageAssets,
     model: str,
     *,
+    size_factors: List[float],
     dtype: str,
     max_tokens: int,
     num_logprobs: int,
+    tensor_parallel_size: int,
+    distributed_executor_backend: Optional[str] = None,
 ):
-    # This test checks language inputs only, since the visual component
-    # for qwen-vl is still unsupported in VLLM. In the near-future, the
-    # implementation and this test will be extended to consider
-    # visual inputs as well.
+    """Inference result should be the same between hf and vllm.
+
+    All the image fixtures for the test is under tests/images.
+    For huggingface runner, we provide the PIL images as input.
+    For vllm runner, we provide MultiModalDataDict objects
+    and corresponding MultiModalConfig as input.
+    Note, the text input is also adjusted to abide by vllm contract.
+    The text output is sanitized to be able to compare with hf.
+    """
+    images = [asset.pil_image for asset in image_assets]
+
+    # Export the images to a tempdir and substitute it into the hf prompt;
+    # the contents between <img>/</img> will be ignored by VLLM, but the
+    # transformers implementation for the visual transformer parses this to
+    # reload it in the forward call; the contents are treated as a URL or a
+    # local path.
+    for idx, asset in enumerate(image_assets):
+        image_tmp_path = tmp_path / f"{asset.name}.jpg"
+        asset.pil_image.save(image_tmp_path)
+        HF_IMAGE_PROMPTS[idx] = HF_IMAGE_PROMPTS[idx].replace(
+            "<img></img>", f"<img>{image_tmp_path}</img>")
+
+    inputs_per_image = [(
+        [prompt for _ in size_factors],
+        [rescale_image_size(image, factor) for factor in size_factors],
+    ) for image, prompt in zip(images, HF_IMAGE_PROMPTS)]
+
+    # NOTE: take care of the order. run vLLM first, and then run HF.
+    # vLLM needs a fresh new process without cuda initialization.
+    # if we run HF first, the cuda initialization will be done and it
+    # will hurt multiprocessing backend with fork method (the default method).
+
+    # max_model_len should be greater than image_feature_size
+    # Qwen encodes images into a fixed content size of 256
+    with vllm_runner(model,
+                     max_model_len=300,
+                     max_num_seqs=1,
+                     dtype=dtype,
+                     tensor_parallel_size=tensor_parallel_size,
+                     distributed_executor_backend=distributed_executor_backend,
+                     enforce_eager=True) as vllm_model:
+        vllm_outputs_per_image = [
+            vllm_model.generate_greedy_logprobs(prompts,
+                                                max_tokens,
+                                                num_logprobs=num_logprobs,
+                                                images=images)
+            for prompts, images in inputs_per_image
+        ]
+
     with hf_runner(model, dtype=dtype) as hf_model:
-        hf_outputs = hf_model.generate_greedy_logprobs_limit(
-            example_prompts,
-            max_tokens,
-            num_logprobs=num_logprobs,
+        hf_outputs_per_image = [
+            hf_model.generate_greedy_logprobs_limit(prompts,
+                                                    max_tokens,
+                                                    num_logprobs=num_logprobs,
+                                                    images=images)
+            for prompts, images in inputs_per_image
+        ]
+
+    for hf_outputs, vllm_outputs in zip(hf_outputs_per_image,
+                                        vllm_outputs_per_image):
+
+        check_logprobs_close(
+            outputs_0_lst=hf_outputs,
+            outputs_1_lst=vllm_outputs,
+            name_0="hf",
+            name_1="vllm",
         )
 
+
+@pytest.mark.parametrize("model", multimodal_models)
+@pytest.mark.parametrize(
+    "size_factors",
+    [
+        # No image
+        [],
+        # Single-scale
+        [1.0],
+        # Single-scale, batched
+        [1.0, 1.0, 1.0],
+        # Multi-scale
+        [0.25, 0.5, 1.0],
+    ],
+)
+@pytest.mark.parametrize("dtype", ["bfloat16"])
+@pytest.mark.parametrize("max_tokens", [8])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_multimodal_models(tmp_path, hf_runner, vllm_runner, image_assets,
+                           model, size_factors, dtype, max_tokens,
+                           num_logprobs) -> None:
+    run_test(
+        tmp_path,
+        hf_runner,
+        vllm_runner,
+        image_assets,
+        model,
+        size_factors=size_factors,
+        dtype=dtype,
+        max_tokens=max_tokens,
+        num_logprobs=num_logprobs,
+        tensor_parallel_size=1,
+    )
+
+
+# Ensure that a text-only Qwen model can still be loaded and
+# used for inference in VLLM without throwing.
+@pytest.mark.parametrize("model", text_only_models)
+@pytest.mark.parametrize("dtype", ["bfloat16"])
+@pytest.mark.parametrize("max_tokens", [32])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_text_only_qwen_model_can_be_loaded_and_run(
+    vllm_runner: Type[VllmRunner],
+    example_prompts,
+    model: str,
+    *,
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+):
     with vllm_runner(model, dtype=dtype) as vllm_model:
-        vllm_outputs = vllm_model.generate_greedy_logprobs(
+        vllm_model.generate_greedy_logprobs(
             example_prompts,
             max_tokens,
             num_logprobs=num_logprobs,
         )
-
-    check_logprobs_close(
-        outputs_0_lst=hf_outputs,
-        outputs_1_lst=vllm_outputs,
-        name_0="hf",
-        name_1="vllm",
-    )

vllm/entrypoints/chat_utils.py

@@ -129,6 +129,8 @@ def add(self, modality: Literal["image", "audio"],
             if model_type in ("blip-2", "chatglm", "fuyu", "paligemma"):
                 # These models do not use image tokens in the prompt
                 return None
+            if model_type == "qwen":
+                return f"Picture {current_count}: <img></img>"
             if model_type.startswith("llava"):
                 return MultiModalItemTracker._cached_token_str(
                     self._tokenizer,