[Core][VLM] Test registration for OOT multimodal models (#8717)

Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>

docs/source/models/adding_model.rst

@@ -85,16 +85,16 @@ When it comes to the linear layers, we provide the following options to parallel
 * :code:`ReplicatedLinear`: Replicates the inputs and weights across multiple GPUs. No memory saving.
 * :code:`RowParallelLinear`: The input tensor is partitioned along the hidden dimension. The weight matrix is partitioned along the rows (input dimension). An *all-reduce* operation is performed after the matrix multiplication to reduce the results. Typically used for the second FFN layer and the output linear transformation of the attention layer.
 * :code:`ColumnParallelLinear`: The input tensor is replicated. The weight matrix is partitioned along the columns (output dimension). The result is partitioned along the column dimension. Typically used for the first FFN layer and the separated QKV transformation of the attention layer in the original Transformer.
-* :code:`MergedColumnParallelLinear`: Column-parallel linear that merges multiple `ColumnParallelLinear` operators. Typically used for the first FFN layer with weighted activation functions (e.g., SiLU). This class handles the sharded weight loading logic of multiple weight matrices.
+* :code:`MergedColumnParallelLinear`: Column-parallel linear that merges multiple :code:`ColumnParallelLinear` operators. Typically used for the first FFN layer with weighted activation functions (e.g., SiLU). This class handles the sharded weight loading logic of multiple weight matrices.
 * :code:`QKVParallelLinear`: Parallel linear layer for the query, key, and value projections of the multi-head and grouped-query attention mechanisms. When number of key/value heads are less than the world size, this class replicates the key/value heads properly. This class handles the weight loading and replication of the weight matrices.
 
-Note that all the linear layers above take `linear_method` as an input. vLLM will set this parameter according to different quantization schemes to support weight quantization.
+Note that all the linear layers above take :code:`linear_method` as an input. vLLM will set this parameter according to different quantization schemes to support weight quantization.
 
 4. Implement the weight loading logic
 -------------------------------------
 
 You now need to implement the :code:`load_weights` method in your :code:`*ForCausalLM` class.
-This method should load the weights from the HuggingFace's checkpoint file and assign them to the corresponding layers in your model. Specifically, for `MergedColumnParallelLinear` and `QKVParallelLinear` layers, if the original model has separated weight matrices, you need to load the different parts separately.
+This method should load the weights from the HuggingFace's checkpoint file and assign them to the corresponding layers in your model. Specifically, for :code:`MergedColumnParallelLinear` and :code:`QKVParallelLinear` layers, if the original model has separated weight matrices, you need to load the different parts separately.
 
 5. Register your model
 ----------------------
@@ -114,6 +114,18 @@ Just add the following lines in your code:
     from your_code import YourModelForCausalLM
     ModelRegistry.register_model("YourModelForCausalLM", YourModelForCausalLM)
 
+If your model imports modules that initialize CUDA, consider instead lazy-importing it to avoid an error like :code:`RuntimeError: Cannot re-initialize CUDA in forked subprocess`:
+
+.. code-block:: python
+
+    from vllm import ModelRegistry
+
+    ModelRegistry.register_model("YourModelForCausalLM", "your_code:YourModelForCausalLM")
+
+.. important::
+    If your model is a multimodal model, make sure the model class implements the :class:`~vllm.model_executor.models.interfaces.SupportsMultiModal` interface.
+    Read more about that :ref:`here <enabling_multimodal_inputs>`.
+
 If you are running api server with :code:`vllm serve <args>`, you can wrap the entrypoint with the following code:
 
 .. code-block:: python

find_cuda_init.py

@@ -0,0 +1,33 @@
+import importlib
+import traceback
+from typing import Callable
+from unittest.mock import patch
+
+
+def find_cuda_init(fn: Callable[[], object]) -> None:
+    """
+    Helper function to debug CUDA re-initialization errors.
+
+    If `fn` initializes CUDA, prints the stack trace of how this happens.
+    """
+    from torch.cuda import _lazy_init
+
+    stack = None
+
+    def wrapper():
+        nonlocal stack
+        stack = traceback.extract_stack()
+        return _lazy_init()
+
+    with patch("torch.cuda._lazy_init", wrapper):
+        fn()
+
+    if stack is not None:
+        print("==== CUDA Initialized ====")
+        print("".join(traceback.format_list(stack)).strip())
+        print("==========================")
+
+
+if __name__ == "__main__":
+    find_cuda_init(
+        lambda: importlib.import_module("vllm.model_executor.models.llava"))

tests/conftest.py

@@ -879,15 +879,16 @@ def num_gpus_available():
 
 
 temp_dir = tempfile.gettempdir()
-_dummy_path = os.path.join(temp_dir, "dummy_opt")
+_dummy_opt_path = os.path.join(temp_dir, "dummy_opt")
+_dummy_llava_path = os.path.join(temp_dir, "dummy_llava")
 
 
 @pytest.fixture
 def dummy_opt_path():
-    json_path = os.path.join(_dummy_path, "config.json")
-    if not os.path.exists(_dummy_path):
+    json_path = os.path.join(_dummy_opt_path, "config.json")
+    if not os.path.exists(_dummy_opt_path):
         snapshot_download(repo_id="facebook/opt-125m",
-                          local_dir=_dummy_path,
+                          local_dir=_dummy_opt_path,
                           ignore_patterns=[
                               "*.bin", "*.bin.index.json", "*.pt", "*.h5",
                               "*.msgpack"
@@ -898,4 +899,23 @@ def dummy_opt_path():
         config["architectures"] = ["MyOPTForCausalLM"]
         with open(json_path, "w") as f:
             json.dump(config, f)
-    return _dummy_path
+    return _dummy_opt_path
+
+
+@pytest.fixture
+def dummy_llava_path():
+    json_path = os.path.join(_dummy_llava_path, "config.json")
+    if not os.path.exists(_dummy_llava_path):
+        snapshot_download(repo_id="llava-hf/llava-1.5-7b-hf",
+                          local_dir=_dummy_llava_path,
+                          ignore_patterns=[
+                              "*.bin", "*.bin.index.json", "*.pt", "*.h5",
+                              "*.msgpack"
+                          ])
+        assert os.path.exists(json_path)
+        with open(json_path, "r") as f:
+            config = json.load(f)
+        config["architectures"] = ["MyLlava"]
+        with open(json_path, "w") as f:
+            json.dump(config, f)
+    return _dummy_llava_path

tests/entrypoints/openai/test_audio.py

@@ -21,7 +21,9 @@ def server():
         "--dtype",
         "bfloat16",
         "--max-model-len",
-        "4096",
+        "2048",
+        "--max-num-seqs",
+        "5",
         "--enforce-eager",
     ]
 

tests/entrypoints/openai/test_vision.py

@@ -23,9 +23,16 @@
 @pytest.fixture(scope="module")
 def server():
     args = [
-        "--dtype", "bfloat16", "--max-model-len", "4096", "--max-num-seqs",
-        "5", "--enforce-eager", "--trust-remote-code", "--limit-mm-per-prompt",
-        f"image={MAXIMUM_IMAGES}"
+        "--dtype",
+        "bfloat16",
+        "--max-model-len",
+        "2048",
+        "--max-num-seqs",
+        "5",
+        "--enforce-eager",
+        "--trust-remote-code",
+        "--limit-mm-per-prompt",
+        f"image={MAXIMUM_IMAGES}",
     ]
 
     with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:

tests/models/test_oot_registration.py

@@ -3,6 +3,7 @@
 import pytest
 
 from vllm import LLM, SamplingParams
+from vllm.assets.image import ImageAsset
 
 from ..utils import fork_new_process_for_each_test
 
@@ -29,3 +30,40 @@ def test_oot_registration(dummy_opt_path):
         # make sure only the first token is generated
         rest = generated_text.replace(first_token, "")
         assert rest == ""
+
+
+image = ImageAsset("cherry_blossom").pil_image.convert("RGB")
+
+
+@fork_new_process_for_each_test
+def test_oot_multimodal_registration(dummy_llava_path):
+    os.environ["VLLM_PLUGINS"] = "register_dummy_model"
+    prompts = [{
+        "prompt": "What's in the image?<image>",
+        "multi_modal_data": {
+            "image": image
+        },
+    }, {
+        "prompt": "Describe the image<image>",
+        "multi_modal_data": {
+            "image": image
+        },
+    }]
+
+    sampling_params = SamplingParams(temperature=0)
+    llm = LLM(model=dummy_llava_path,
+              load_format="dummy",
+              max_num_seqs=1,
+              trust_remote_code=True,
+              gpu_memory_utilization=0.98,
+              max_model_len=4096,
+              enforce_eager=True,
+              limit_mm_per_prompt={"image": 1})
+    first_token = llm.get_tokenizer().decode(0)
+    outputs = llm.generate(prompts, sampling_params)
+
+    for output in outputs:
+        generated_text = output.outputs[0].text
+        # make sure only the first token is generated
+        rest = generated_text.replace(first_token, "")
+        assert rest == ""

tests/plugins/vllm_add_dummy_model/vllm_add_dummy_model/__init__.py

@@ -1,26 +1,14 @@
-from typing import Optional
-
-import torch
-
 from vllm import ModelRegistry
-from vllm.model_executor.models.opt import OPTForCausalLM
-from vllm.model_executor.sampling_metadata import SamplingMetadata
-
-
-class MyOPTForCausalLM(OPTForCausalLM):
-
-    def compute_logits(
-            self, hidden_states: torch.Tensor,
-            sampling_metadata: SamplingMetadata) -> Optional[torch.Tensor]:
-        # this dummy model always predicts the first token
-        logits = super().compute_logits(hidden_states, sampling_metadata)
-        if logits is not None:
-            logits.zero_()
-            logits[:, 0] += 1.0
-        return logits
 
 
 def register():
-    # register our dummy model
+    # Test directly passing the model
+    from .my_opt import MyOPTForCausalLM
+
     if "MyOPTForCausalLM" not in ModelRegistry.get_supported_archs():
         ModelRegistry.register_model("MyOPTForCausalLM", MyOPTForCausalLM)
+
+    # Test passing lazy model
+    if "MyLlava" not in ModelRegistry.get_supported_archs():
+        ModelRegistry.register_model("MyLlava",
+                                     "vllm_add_dummy_model.my_llava:MyLlava")

tests/plugins/vllm_add_dummy_model/vllm_add_dummy_model/my_llava.py

@@ -0,0 +1,28 @@
+from typing import Optional
+
+import torch
+
+from vllm.inputs import INPUT_REGISTRY
+from vllm.model_executor.models.llava import (LlavaForConditionalGeneration,
+                                              dummy_data_for_llava,
+                                              get_max_llava_image_tokens,
+                                              input_processor_for_llava)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+
+
+@MULTIMODAL_REGISTRY.register_image_input_mapper()
+@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_llava_image_tokens)
+@INPUT_REGISTRY.register_dummy_data(dummy_data_for_llava)
+@INPUT_REGISTRY.register_input_processor(input_processor_for_llava)
+class MyLlava(LlavaForConditionalGeneration):
+
+    def compute_logits(
+            self, hidden_states: torch.Tensor,
+            sampling_metadata: SamplingMetadata) -> Optional[torch.Tensor]:
+        # this dummy model always predicts the first token
+        logits = super().compute_logits(hidden_states, sampling_metadata)
+        if logits is not None:
+            logits.zero_()
+            logits[:, 0] += 1.0
+        return logits

tests/plugins/vllm_add_dummy_model/vllm_add_dummy_model/my_opt.py

@@ -0,0 +1,19 @@
+from typing import Optional
+
+import torch
+
+from vllm.model_executor.models.opt import OPTForCausalLM
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+
+
+class MyOPTForCausalLM(OPTForCausalLM):
+
+    def compute_logits(
+            self, hidden_states: torch.Tensor,
+            sampling_metadata: SamplingMetadata) -> Optional[torch.Tensor]:
+        # this dummy model always predicts the first token
+        logits = super().compute_logits(hidden_states, sampling_metadata)
+        if logits is not None:
+            logits.zero_()
+            logits[:, 0] += 1.0
+        return logits

vllm/engine/arg_utils.py

@@ -183,6 +183,8 @@ class EngineArgs:
     def __post_init__(self):
         if self.tokenizer is None:
             self.tokenizer = self.model
+        from vllm.plugins import load_general_plugins
+        load_general_plugins()
 
     @staticmethod
     def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:

vllm/engine/llm_engine.py

@@ -290,9 +290,6 @@ def __init__(
             model_config.mm_processor_kwargs,
         )
         # TODO(woosuk): Print more configs in debug mode.
-        from vllm.plugins import load_general_plugins
-        load_general_plugins()
-
         self.model_config = model_config
         self.cache_config = cache_config
         self.lora_config = lora_config