Add multimodal tests for qwen

Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com>

tests/models/test_qwen.py

@@ -1,18 +1,170 @@
-from typing import Type
+import pathlib
+from typing import List, Optional, Type
 
 import pytest
+from transformers import AutoTokenizer
 
-from ..conftest import HfRunner, VllmRunner
+from vllm.model_executor.models.qwen import get_qwen_llm_inputs
+from vllm.multimodal.utils import rescale_image_size
+
+from ..conftest import IMAGE_ASSETS, HfRunner, VllmRunner, _ImageAssets
 from .utils import check_logprobs_close
 
+pytestmark = pytest.mark.vlm
+
 text_only_models = [
     "Qwen/Qwen-7B-Chat"  # Has no visual component
 ]
 
+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
+@pytest.mark.parametrize("hf_input_text,vllm_input_text,num_images", [
+    ("I have no image tags", "I have no image tags", 0),
+    ("Picture 1: <img></img>\n", "Picture 1: <img></img>\n", 1),
+    ("Picture 1: <img></img>\n", "<image>", 1),
+    ("Picture 1: <img></img>\n Picture 2: <img></img>\n", "<image> <image>",
+     2),
+])
+def test_qwen_input_processor_tag_unification(hf_input_text, vllm_input_text,
+                                              num_images):
+    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-VL",
+                                              trust_remote_code=True)
+    hf_tok_ids = tokenizer.encode(hf_input_text)
+    vllm_tok_ids = get_qwen_llm_inputs(
+        vllm_input_text,
+        tokenizer,
+        num_images,
+        multi_modal_data=None,
+    )["prompt_token_ids"]
+    assert len(vllm_tok_ids) == len(hf_tok_ids)
+    assert vllm_tok_ids == hf_tok_ids
+
+
+def run_test(
+    tmp_path: pathlib.PosixPath,
+    hf_runner: Type[HfRunner],
+    vllm_runner: Type[VllmRunner],
+    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,
+):
+    """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).
 
-# Text only tests; the primary purpose of this test is to ensure that we can
-# load Qwen models, e.g., Qwen/Qwen-7B-Chat, that do not have a visual config,
-# without any problems.
+    # max_model_len should be greater than image_feature_size
+    with vllm_runner(model,
+                     max_model_len=2048,
+                     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_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,
+    )
+
+
+### Tests for language only Qwen models
 @pytest.mark.parametrize("dtype", ["half"])
 @pytest.mark.parametrize("max_tokens", [32])
 @pytest.mark.parametrize("num_logprobs", [5])
@@ -27,19 +179,18 @@ def test_text_only_qwen_model(
     max_tokens: int,
     num_logprobs: int,
 ):
-    # 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.
-    with hf_runner(model, dtype=dtype) as hf_model:
-        hf_outputs = hf_model.generate_greedy_logprobs_limit(
+    # the primary purpose of this test is to ensure that we can
+    # load Qwen models, e.g., Qwen/Qwen-7B-Chat, that do not have a visual
+    # config, without any problems.
+    with vllm_runner(model, dtype=dtype) as vllm_model:
+        vllm_outputs = vllm_model.generate_greedy_logprobs(
             example_prompts,
             max_tokens,
             num_logprobs=num_logprobs,
         )
 
-    with vllm_runner(model, dtype=dtype) as vllm_model:
-        vllm_outputs = vllm_model.generate_greedy_logprobs(
+    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,