[Model] Rename MiniCPMVQwen2 to MiniCPMV2.6

docs/source/models/supported_models.rst

@@ -222,7 +222,7 @@ Vision Language Models
     -
   * - :code:`MiniCPMV`
     - MiniCPM-V
-    - :code:`openbmb/MiniCPM-V-2` (see note), :code:`openbmb/MiniCPM-Llama3-V-2_5`, etc.
+    - :code:`openbmb/MiniCPM-V-2` (see note), :code:`openbmb/MiniCPM-Llama3-V-2_5`, :code:`openbmb/MiniCPM-V-2_6`, etc.
     -
 
 .. note::

examples/offline_inference_vision_language.py

@@ -22,26 +22,26 @@ def run_llava(question):
     prompt = f"USER: <image>\n{question}\nASSISTANT:"
 
     llm = LLM(model="llava-hf/llava-1.5-7b-hf")
-
-    return llm, prompt
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
 
 
 # LLaVA-1.6/LLaVA-NeXT
 def run_llava_next(question):
 
     prompt = f"[INST] <image>\n{question} [/INST]"
     llm = LLM(model="llava-hf/llava-v1.6-mistral-7b-hf")
-
-    return llm, prompt
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
 
 
 # Fuyu
 def run_fuyu(question):
 
     prompt = f"{question}\n"
     llm = LLM(model="adept/fuyu-8b")
-
-    return llm, prompt
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
 
 
 # Phi-3-Vision
@@ -59,7 +59,8 @@ def run_phi3v(question):
         trust_remote_code=True,
         max_num_seqs=5,
     )
-    return llm, prompt
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
 
 
 # PaliGemma
@@ -68,16 +69,17 @@ def run_paligemma(question):
     # PaliGemma has special prompt format for VQA
     prompt = "caption en"
     llm = LLM(model="google/paligemma-3b-mix-224")
-
-    return llm, prompt
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
 
 
 # Chameleon
 def run_chameleon(question):
 
     prompt = f"{question}<image>"
     llm = LLM(model="facebook/chameleon-7b")
-    return llm, prompt
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
 
 
 # MiniCPM-V
@@ -89,13 +91,26 @@ def run_minicpmv(question):
     # model_name = "HwwwH/MiniCPM-V-2"
 
     # 2.5
-    model_name = "openbmb/MiniCPM-Llama3-V-2_5"
+    # model_name = "openbmb/MiniCPM-Llama3-V-2_5"
+
+    #2.6
+    model_name = "openbmb/MiniCPM-V-2_6"
     tokenizer = AutoTokenizer.from_pretrained(model_name,
                                               trust_remote_code=True)
     llm = LLM(
         model=model_name,
         trust_remote_code=True,
     )
+    # NOTE The stop_token_ids are different for various versions of MiniCPM-V
+    # 2.0
+    # stop_token_ids = [tokenizer.eos_id]
+
+    # 2.5
+    # stop_token_ids = [tokenizer.eos_id, tokenizer.eot_id]
+
+    # 2.6
+    stop_tokens = ['<|im_end|>', '<|endoftext|>']
+    stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]
 
     messages = [{
         'role': 'user',
@@ -104,7 +119,7 @@ def run_minicpmv(question):
     prompt = tokenizer.apply_chat_template(messages,
                                            tokenize=False,
                                            add_generation_prompt=True)
-    return llm, prompt
+    return llm, prompt, stop_token_ids
 
 
 # InternVL
@@ -118,7 +133,8 @@ def run_internvl(question):
         trust_remote_code=True,
         max_num_seqs=5,
     )
-    return llm, prompt
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
 
 
 # BLIP-2
@@ -128,7 +144,8 @@ def run_blip2(question):
     # See https://huggingface.co/Salesforce/blip2-opt-2.7b/discussions/15#64ff02f3f8cf9e4f5b038262 #noqa
     prompt = f"Question: {question} Answer:"
     llm = LLM(model="Salesforce/blip2-opt-2.7b")
-    return llm, prompt
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
 
 
 model_example_map = {
@@ -149,11 +166,13 @@ def main(args):
     if model not in model_example_map:
         raise ValueError(f"Model type {model} is not supported.")
 
-    llm, prompt = model_example_map[model](question)
+    llm, prompt, stop_token_ids = model_example_map[model](question)
 
     # We set temperature to 0.2 so that outputs can be different
     # even when all prompts are identical when running batch inference.
-    sampling_params = SamplingParams(temperature=0.2, max_tokens=64)
+    sampling_params = SamplingParams(temperature=0.2,
+                                     max_tokens=64,
+                                     stop_token_ids=stop_token_ids)
 
     assert args.num_prompts > 0
     if args.num_prompts == 1:

vllm/model_executor/models/minicpmv.py

@@ -216,7 +216,6 @@ def __init__(
 
         self.query = nn.Parameter(torch.zeros(self.num_queries, embed_dim))
         trunc_normal_(self.query, std=0.02)
-
         if kv_dim is not None and kv_dim != embed_dim:
             self.kv_proj = ReplicatedLinear(kv_dim, embed_dim, bias=False)
         else:
@@ -225,7 +224,6 @@ def __init__(
                 nn.Identity()(*args, **kwargs),
                 None,
             )
-
         self.attn = nn.MultiheadAttention(embed_dim, num_heads)
         self.ln_q = norm_layer(embed_dim)
         self.ln_kv = norm_layer(embed_dim)
@@ -261,7 +259,6 @@ def __init__(
                          norm_layer)
 
         self.adaptive = adaptive
-
         pos_embed_arr = get_2d_sincos_pos_embed(embed_dim,
                                                 grid_size,
                                                 version=(2, 0))
@@ -717,7 +714,7 @@ def is_default_weight_loading(self, name: str) -> bool:
         raise NotImplementedError
 
 
-class MiniCPMV2(MiniCPMVBaseModel):
+class MiniCPMV2_0(MiniCPMVBaseModel):
 
     def __init__(
         self,
@@ -890,10 +887,7 @@ def is_default_weight_loading(self, name: str) -> bool:
         return "resampler" in name
 
 
-# NOTE: Currently, information about this model is unavailable. We are
-# temporarily using `MiniCPMVQwen2` as it's name. The name may need
-# to be modified in the future.
-class MiniCPMVQwen2(MiniCPMVBaseModel):
+class MiniCPMV2_6(MiniCPMVBaseModel):
 
     def __init__(
         self,
@@ -903,6 +897,7 @@ def __init__(
         quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__(config, multimodal_config, cache_config, quant_config)
+        assert self.version == (2, 6)
 
     def init_llm(
         self,
@@ -930,6 +925,7 @@ def init_vision_module(self) -> nn.Module:
 
     def init_resampler(self, embed_dim: int, vision_dim: int) -> nn.Module:
         with set_default_torch_dtype(torch.float16):
+            # The resampler in 2.6 remains consistent with the one in 2.5.
             resampler = Resampler2_5(
                 num_queries=self.config.query_num,
                 embed_dim=embed_dim,
@@ -989,6 +985,13 @@ def is_default_weight_loading(self, name: str) -> bool:
         return "resampler" in name or "vpm" in name
 
 
+_SUPPORT_VERSION = {
+    (2, 0): MiniCPMV2_0,
+    (2, 5): MiniCPMV2_5,
+    (2, 6): MiniCPMV2_6
+}
+
+
 @MULTIMODAL_REGISTRY.register_image_input_mapper()
 @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_minicpmv_image_tokens)
 @INPUT_REGISTRY.register_dummy_data(dummy_data_for_minicpmv)
@@ -1016,11 +1019,9 @@ def __new__(
             version = str(config.version).split(".")
             version = tuple([int(x) for x in version])
         # Dispatch class based on version
-        if version == (2, 0):
-            instance_class = MiniCPMV2
-        elif version == (2, 5):
-            instance_class = MiniCPMV2_5
-        else:
-            instance_class = MiniCPMVQwen2
+        instance_class = _SUPPORT_VERSION.get(version, None)
+        if instance_class is None:
+            raise ValueError(
+                "Currently, MiniCPMV only supports versions 2.0, 2.5, and 2.6")
         return instance_class(config, multimodal_config, cache_config,
                               quant_config)