[Model][LoRA]LoRA support added for MiniCPMV2.5

tests/lora/conftest.py

@@ -194,6 +194,11 @@ def baichuan_zero_lora_files():
     return snapshot_download(repo_id="jeeejeee/baichuan7b-zero-init")
 
 
+@pytest.fixture(scope="session")
+def minicpmv_lora_files():
+    return snapshot_download(repo_id="jeeejeee/minicpmv25-lora-pokemon")
+
+
 @pytest.fixture(scope="session")
 def tinyllama_lora_files():
     return snapshot_download(repo_id="jashing/tinyllama-colorist-lora")

tests/lora/test_minicpmv.py

@@ -0,0 +1,71 @@
+from typing import List
+
+import vllm
+from vllm.assets.image import ImageAsset
+from vllm.lora.request import LoRARequest
+
+MODEL_PATH = "openbmb/MiniCPM-Llama3-V-2_5"
+
+PROMPT_TEMPLATE = (
+    "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n"
+    "(<image>./</image>)\nWhat is in the image?<|eot_id|>"
+    "<|start_header_id|>assistant<|end_header_id|>\n\n")
+
+IMAGE_ASSETS = [
+    ImageAsset("stop_sign"),
+    ImageAsset("cherry_blossom"),
+]
+
+# After fine-tuning with LoRA, all generated content should start begin `A`.
+EXPECTED_OUTPUT = [
+    "A red and white stop sign with a Chinese archway in the background featuring red lanterns and gold accents.",  # noqa: E501
+    "A pink cherry blossom tree with a blue sky in the background.",
+]
+
+
+def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> List[str]:
+    sampling_params = vllm.SamplingParams(
+        temperature=0,
+        max_tokens=256,
+        stop_token_ids=[128001, 128009],  # eos_id, eot_id
+    )
+
+    inputs = [{
+        "prompt": PROMPT_TEMPLATE,
+        "multi_modal_data": {
+            "image": asset.pil_image
+        },
+    } for asset in IMAGE_ASSETS]
+
+    outputs = llm.generate(
+        inputs,
+        sampling_params,
+        lora_request=LoRARequest(str(lora_id), lora_id, lora_path)
+        if lora_id else None,
+    )
+    # Print the outputs.
+    generated_texts: List[str] = []
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text.strip()
+        generated_texts.append(generated_text)
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    return generated_texts
+
+
+def test_minicpmv_lora(minicpmv_lora_files):
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        max_lora_rank=64,
+        trust_remote_code=True,
+    )
+
+    output1 = do_sample(llm, minicpmv_lora_files, lora_id=1)
+    for i in range(len(EXPECTED_OUTPUT)):
+        assert output1[i] == EXPECTED_OUTPUT[i]
+    output2 = do_sample(llm, minicpmv_lora_files, lora_id=2)
+    for i in range(len(EXPECTED_OUTPUT)):
+        assert output2[i] == EXPECTED_OUTPUT[i]

tests/lora/test_minicpmv_tp.py

@@ -0,0 +1,99 @@
+from typing import List
+
+import pytest
+
+import vllm
+from vllm.assets.image import ImageAsset
+from vllm.lora.request import LoRARequest
+
+from ..utils import multi_gpu_test
+
+MODEL_PATH = "openbmb/MiniCPM-Llama3-V-2_5"
+
+PROMPT_TEMPLATE = (
+    "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n"
+    "(<image>./</image>)\nWhat is in the image?<|eot_id|>"
+    "<|start_header_id|>assistant<|end_header_id|>\n\n"
+)
+
+IMAGE_ASSETS = [
+    ImageAsset("stop_sign"),
+    ImageAsset("cherry_blossom"),
+]
+
+# After fine-tuning with LoRA, all generated content should start begin `A`.
+EXPECTED_OUTPUT = [
+    "A red and white stop sign with a Chinese archway in the background featuring red lanterns and gold accents.",  # noqa: E501
+    "A pink cherry blossom tree with a blue sky in the background.",
+]
+
+
+def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> List[str]:
+    sampling_params = vllm.SamplingParams(
+        temperature=0,
+        max_tokens=256,
+        stop_token_ids=[128001, 128009],  # eos_id, eot_id
+    )
+
+    inputs = [
+        {
+            "prompt": PROMPT_TEMPLATE,
+            "multi_modal_data": {"image": asset.pil_image},
+        }
+        for asset in IMAGE_ASSETS
+    ]
+
+    outputs = llm.generate(
+        inputs,
+        sampling_params,
+        lora_request=LoRARequest(str(lora_id), lora_id, lora_path)
+        if lora_id
+        else None,
+    )
+    # Print the outputs.
+    generated_texts: List[str] = []
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text.strip()
+        generated_texts.append(generated_text)
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    return generated_texts
+
+@multi_gpu_test(num_gpus=2)
+@pytest.mark.parametrize("fully_sharded", [True, False])
+def test_minicpmv_tp2(minicpmv_lora_files, fully_sharded):
+    llm = vllm.LLM(
+        MODEL_PATH,
+        enable_lora=True,
+        max_num_seqs=16,
+        max_loras=4,
+        max_lora_rank=64,
+        tensor_parallel_size=2,
+        trust_remote_code=True,
+        fully_sharded_loras=fully_sharded,
+    )
+
+    output_tp = do_sample(llm, minicpmv_lora_files, lora_id=1)
+
+    for i in range(len(EXPECTED_OUTPUT)):
+        assert output_tp[i] == EXPECTED_OUTPUT[i]
+
+
+@multi_gpu_test(num_gpus=4)
+@pytest.mark.parametrize("fully_sharded", [True, False])
+def test_minicpmv_tp4(minicpmv_lora_files, fully_sharded):
+    llm = vllm.LLM(
+        MODEL_PATH,
+        enable_lora=True,
+        max_num_seqs=16,
+        max_loras=4,
+        max_lora_rank=64,
+        tensor_parallel_size=4,
+        trust_remote_code=True,
+        fully_sharded_loras=fully_sharded,
+    )
+
+    output_tp = do_sample(llm, minicpmv_lora_files, lora_id=1)
+
+    for i in range(len(EXPECTED_OUTPUT)):
+        assert output_tp[i] == EXPECTED_OUTPUT[i]

vllm/lora/models.py

@@ -24,7 +24,9 @@
 from vllm.lora.punica import PunicaWrapper
 from vllm.lora.utils import (from_layer, from_layer_logits_processor,
                              parse_fine_tuned_lora_name, replace_submodule)
-from vllm.model_executor.models.interfaces import SupportsLoRA
+from vllm.model_executor.models.interfaces import (SupportsLoRA,
+                                                   supports_multimodal)
+from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.model_executor.models.utils import PPMissingLayer
 from vllm.utils import is_pin_memory_available
 
@@ -332,6 +334,8 @@ def __init__(
                 self.supported_lora_modules.append("rotary_emb")
             self.packed_modules_mapping = copy.deepcopy(
                 self.model.packed_modules_mapping)
+        # Used to indicate whether the model is a multimodal model
+        self.supports_mm: bool = supports_multimodal(self.model)
         self.packed_modules: Dict[str, List[str]] = {}
         self.modules: Dict[str, "BaseLayerWithLoRA"] = {}
         # Dict instead of a Set for compatibility with LRUCache.
@@ -437,12 +441,28 @@ def _create_lora_modules(self):
                 continue
             if not self._match_target_modules(module_name):
                 continue
+            # A temporary approach for multimodal models to support LoRA
+            # TODO: Remove this restriction
+            if self._filter_unsupported_mm_module(module_name):
+                logger.warning(
+                    "Regarding multimodal models, vLLM currently only supports "
+                    "adding LoRA to language model, %s will be ignored.",
+                    module_name,
+                )
+                continue
             parts = module_name.split(".")[-1]
             packed_moduled_lst = self.packed_modules_mapping.get(parts, [])
             new_module = replace_submodule(
                 self.model, module_name,
                 from_layer(module, self.lora_slots, self.lora_config,
                            packed_moduled_lst, self.model.config))
+            # In some models, especially multimodal ones, layers with the same
+            # name may have different types, such as nn.Linear and
+            # ReplicatedLinear. The nn.Linear layers cannot be replaced with
+            # LoRA layers, leading to assertion error. The following check
+            # aims to prevent this error
+            if not isinstance(new_module, BaseLayerWithLoRA):
+                continue
             # LinearScalingRotaryEmbeddingWithLora is used to handle
             # long context lora. Register relevant metadata.
             if isinstance(new_module, LinearScalingRotaryEmbeddingWithLora):
@@ -478,9 +498,10 @@ def create_dummy_lora(
         """Create zero-initialized LoRAModel for warmup."""
         model = LoRAModel(lora_id, rank, {}, scaling_factor)
         for module_name, module in self.model.named_modules():
-            if not self._match_target_modules(module_name) or not isinstance(
-                    module, BaseLayerWithLoRA) or isinstance(
-                        module, LinearScalingRotaryEmbeddingWithLora):
+            if (not self._match_target_modules(module_name)
+                    or not isinstance(module, BaseLayerWithLoRA)
+                    or isinstance(module, LinearScalingRotaryEmbeddingWithLora)
+                    or self._filter_unsupported_mm_module(module_name)):
                 continue
             parts = module_name.split(".")
             if module_name not in self.packed_modules:
@@ -541,6 +562,19 @@ def _match_target_modules(self, module_name: str):
                 module_name) or target_module == module_name
             for target_module in self.supported_lora_modules)
 
+    def _filter_unsupported_mm_module(self, module_name: str) -> bool:
+        """
+        Regarding multimodal models, vLLM currently only supports adding LoRA to
+        language model. LoRA for other modules, such as the vision tower, will 
+        be filtered out.
+        """
+        if self.supports_mm:
+            prefix = module_name.split(".")[0]
+            module_mapping: MultiModelKeys = self.model.get_mm_mapping()
+            return (prefix in module_mapping.connector
+                    or prefix in module_mapping.tower_model)
+        return False
+
     def _register_packed_modules(self, module_full_name: str) -> None:
         parts = module_full_name.split(".")
         module_name = parts[-1]