[Model] Add multi-image support for minicpmv (#7122)

Co-authored-by: hezhihui <hzh7269@modelbest.cn>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>

tests/models/test_minicpmv.py

@@ -14,6 +14,18 @@ from .utils import check_logprobs_close
 
 pytestmark = pytest.mark.vlm
 
+
+class NestedInputs(UserDict):
+
+    def __init__(self, model_inputs: BatchFeature):
+        super().__init__({"model_inputs": model_inputs})
+
+        self.model_inputs = model_inputs
+
+    def to(self, device: torch.types.Device):
+        return NestedInputs(self.model_inputs.to(device))
+
+
 # The image token is placed before "user" on purpose so that the test can pass
 HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
     "stop_sign":
@@ -23,7 +35,7 @@ HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
     "cherry_blossom":
         "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n" \
         "(<image>./</image>)\nWhat is the season?<|eot_id|>" \
-        "<|start_header_id|>assistant<|end_header_id|>\n\n"
+        "<|start_header_id|>assistant<|end_header_id|>\n\n",
 })
 
 models = ["openbmb/MiniCPM-Llama3-V-2_5"]
@@ -94,22 +106,10 @@ def run_test(
         ]
 
     with hf_runner(model, dtype=dtype) as hf_model, torch.no_grad():
-
-        class NestedInputs(UserDict):
-
-            def __init__(self, model_inputs: BatchFeature):
-                super().__init__({"model_inputs": model_inputs})
-
-                self.model_inputs = model_inputs
-
-            def to(self, device: torch.types.Device):
-                return NestedInputs(self.model_inputs.to(device))
-
         hf_processor = hf_model.processor
         hf_model.processor = lambda **kw: NestedInputs(
             hf_processor(**kw)  # type: ignore
         )
-
         hf_outputs_per_image = [
             hf_model.generate_greedy_logprobs_limit(prompts,
                                                     max_tokens,
@@ -161,3 +161,123 @@ def test_models(hf_runner, vllm_runner, image_assets, model, size_factors,
         num_logprobs=num_logprobs,
         tensor_parallel_size=1,
     )
+
+
+HF_MULTIIMAGE_IMAGE_PROMPT = \
+    "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n" \
+    "(<image>./</image>)\n(<image>./</image>)\n" \
+    "Describe these images.<|eot_id|>" \
+    "<|start_header_id|>assistant<|end_header_id|>\n\n"
+
+
+def run_multi_image_test(
+    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 vision language config 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]
+
+    inputs_per_case = [
+        ([HF_MULTIIMAGE_IMAGE_PROMPT for _ in size_factors],
+         [[rescale_image_size(image, factor) for image in images]
+          for factor in size_factors])
+    ]
+
+    # 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
+    with vllm_runner(model,
+                     max_model_len=4096,
+                     max_num_seqs=1,
+                     dtype=dtype,
+                     tensor_parallel_size=tensor_parallel_size,
+                     distributed_executor_backend=distributed_executor_backend,
+                     enforce_eager=True) as vllm_model:
+        tokenizer = vllm_model.model.get_tokenizer()
+        stop_token_ids = [tokenizer.eos_id, tokenizer.eot_id]
+        vllm_outputs_per_case = [
+            vllm_model.generate_greedy_logprobs(prompts,
+                                                max_tokens,
+                                                num_logprobs=num_logprobs,
+                                                images=images,
+                                                stop_token_ids=stop_token_ids)
+            for prompts, images in inputs_per_case
+        ]
+
+    with hf_runner(model, dtype=dtype) as hf_model, torch.no_grad():
+        hf_processor = hf_model.processor
+        hf_model.processor = lambda **kw: NestedInputs(
+            hf_processor(**kw)  # type: ignore
+        )
+        hf_outputs_per_case = [
+            hf_model.generate_greedy_logprobs_limit(prompts,
+                                                    max_tokens,
+                                                    num_logprobs=num_logprobs,
+                                                    images=images,
+                                                    tokenizer=tokenizer)
+            for prompts, images in inputs_per_case
+        ]
+
+    for hf_outputs, vllm_outputs in zip(hf_outputs_per_case,
+                                        vllm_outputs_per_case):
+        check_logprobs_close(
+            outputs_0_lst=[
+                trunc_hf_output(hf_output) for hf_output in hf_outputs
+            ],
+            outputs_1_lst=vllm_outputs,
+            name_0="hf",
+            name_1="vllm",
+        )
+
+
+@pytest.mark.parametrize("model", 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", [target_dtype])
+@pytest.mark.parametrize("max_tokens", [128])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_multi_images_models(hf_runner, vllm_runner, image_assets, model,
+                             size_factors, dtype: str, max_tokens: int,
+                             num_logprobs: int) -> None:
+    run_multi_image_test(
+        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,
+    )