FEAT: Support MiniCPM-v-2_6

xinference/model/llm/__init__.py

@@ -125,6 +125,7 @@ def _install():
     from .pytorch.internlm2 import Internlm2PytorchChatModel
     from .pytorch.llama_2 import LlamaPytorchChatModel, LlamaPytorchModel
     from .pytorch.minicpmv25 import MiniCPMV25Model
+    from .pytorch.minicpmv26 import MiniCPMV26Model
     from .pytorch.qwen_vl import QwenVLChatModel
     from .pytorch.vicuna import VicunaPytorchChatModel
     from .pytorch.yi_vl import YiVLChatModel
@@ -167,6 +168,7 @@ def _install():
             PytorchModel,
             CogVLM2Model,
             MiniCPMV25Model,
+            MiniCPMV26Model,
             Glm4VModel,
         ]
     )

xinference/model/llm/llm_family.json

@@ -6847,6 +6847,52 @@
       ]
     }
   },
+  {
+    "version":1,
+    "context_length":2048,
+    "model_name":"MiniCPM-V-2.6",
+    "model_lang":[
+      "en",
+      "zh"
+    ],
+    "model_ability":[
+      "chat",
+      "vision"
+    ],
+    "model_description":"MiniCPM-V 2.6 is the latest model in the MiniCPM-V series. The model is built on SigLip-400M and Qwen2-7B with a total of 8B parameters.",
+    "model_specs":[
+      {
+        "model_format":"pytorch",
+        "model_size_in_billions":8,
+        "quantizations":[
+          "none"
+        ],
+        "model_id":"openbmb/MiniCPM-V-2_6",
+        "model_revision":"3f7a8da1b7a8b928b5ee229fae33cf43fd64cf31"
+      },
+      {
+        "model_format":"pytorch",
+        "model_size_in_billions":8,
+        "quantizations":[
+          "int4"
+        ],
+        "model_id":"openbmb/MiniCPM-V-2_6-{quantization}",
+        "model_revision":"051e2df6505f1fc4305f2c9bd42ed90db8bf4874"
+      }
+    ],
+    "prompt_style":{
+      "style_name":"QWEN",
+      "system_prompt":"The role of first msg should be user",
+      "roles":[
+        "user",
+        "assistant"
+      ],
+      "stop": [
+        "<|im_end|>",
+        "<|endoftext|>"
+      ]
+    }
+  },
   {
     "version": 1,
     "context_length": 4096,

xinference/model/llm/llm_family_modelscope.json

@@ -4591,6 +4591,50 @@
       ]
     }
   },
+  {
+    "version":1,
+    "context_length":2048,
+    "model_name":"MiniCPM-V-2.6",
+    "model_lang":[
+      "en",
+      "zh"
+    ],
+    "model_ability":[
+      "chat",
+      "vision"
+    ],
+    "model_description":"MiniCPM-V 2.6 is the latest model in the MiniCPM-V series. The model is built on SigLip-400M and Qwen2-7B with a total of 8B parameters.",
+    "model_specs":[
+      {
+        "model_format":"pytorch",
+        "model_size_in_billions":8,
+        "quantizations":[
+          "none"
+        ],
+        "model_hub": "modelscope",
+        "model_id":"OpenBMB/MiniCPM-V-2_6",
+        "model_revision":"master"
+      },
+      {
+        "model_format":"pytorch",
+        "model_size_in_billions":8,
+        "quantizations":[
+          "int4"
+        ],
+        "model_hub": "modelscope",
+        "model_id":"OpenBMB/MiniCPM-V-2_6-{quantization}",
+        "model_revision":"master"
+      }
+    ],
+    "prompt_style":{
+      "style_name":"OmniLMM",
+      "system_prompt":"The role of first msg should be user",
+      "roles":[
+        "user",
+        "assistant"
+      ]
+    }
+  },
   {
     "version": 1,
     "context_length": 2048,

xinference/model/llm/pytorch/core.py

@@ -72,6 +72,7 @@
     "mini-internvl-chat",
     "cogvlm2",
     "MiniCPM-Llama3-V-2_5",
+    "MiniCPM-V-2.6",
     "glm-4v",
 ]
 

xinference/model/llm/pytorch/minicpmv26.py

@@ -0,0 +1,243 @@
+# Copyright 2022-2023 XProbe Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import base64
+import json
+import logging
+import time
+import uuid
+from concurrent.futures import ThreadPoolExecutor
+from io import BytesIO
+from typing import Dict, Iterator, List, Optional, Union
+
+import requests
+import torch
+from PIL import Image
+
+from ....types import (
+    ChatCompletion,
+    ChatCompletionChunk,
+    ChatCompletionMessage,
+    Completion,
+    CompletionChoice,
+    CompletionChunk,
+    CompletionUsage,
+)
+from ...utils import select_device
+from ..llm_family import LLMFamilyV1, LLMSpecV1
+from .core import PytorchChatModel, PytorchGenerateConfig
+
+logger = logging.getLogger(__name__)
+
+
+class MiniCPMV26Model(PytorchChatModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._device = None
+        self._tokenizer = None
+        self._model = None
+
+    @classmethod
+    def match(
+        cls, model_family: "LLMFamilyV1", model_spec: "LLMSpecV1", quantization: str
+    ) -> bool:
+        family = model_family.model_family or model_family.model_name
+        if "MiniCPM-V-2.6".lower() in family.lower():
+            return True
+        return False
+
+    def _get_model_class(self):
+        from transformers import AutoModel
+
+        return AutoModel
+
+    def load(self, **kwargs):
+        from transformers import AutoModel, AutoTokenizer
+        from transformers.generation import GenerationConfig
+
+        device = self._pytorch_model_config.get("device", "auto")
+        self._device = select_device(device)
+        self._device = "auto" if self._device == "cuda" else self._device
+
+        if "int4" in self.model_path and device == "mps":
+            logger.error(
+                "Error: running int4 model with bitsandbytes on Mac is not supported right now."
+            )
+            exit()
+
+        if self._check_tensorizer_integrity():
+            self._model, self._tokenizer = self._load_tensorizer()
+            return
+
+        if "int4" in self.model_path:
+            model = AutoModel.from_pretrained(self.model_path, trust_remote_code=True)
+        else:
+            model = AutoModel.from_pretrained(
+                self.model_path,
+                trust_remote_code=True,
+                torch_dtype=torch.float16,
+                device_map=self._device,
+            )
+        tokenizer = AutoTokenizer.from_pretrained(
+            self.model_path, trust_remote_code=True
+        )
+        self._model = model.eval()
+        self._tokenizer = tokenizer
+
+        # Specify hyperparameters for generation
+        self._model.generation_config = GenerationConfig.from_pretrained(
+            self.model_path,
+            trust_remote_code=True,
+        )
+        self._save_tensorizer()
+
+    def _message_content_to_chat(self, content):
+        def _load_image(_url):
+            if _url.startswith("data:"):
+                logging.info("Parse url by base64 decoder.")
+                # https://platform.openai.com/docs/guides/vision/uploading-base-64-encoded-images
+                # e.g. f"data:image/jpeg;base64,{base64_image}"
+                _type, data = _url.split(";")
+                _, ext = _type.split("/")
+                data = data[len("base64,") :]
+                data = base64.b64decode(data.encode("utf-8"))
+                return Image.open(BytesIO(data)).convert("RGB")
+            else:
+                try:
+                    response = requests.get(_url)
+                except requests.exceptions.MissingSchema:
+                    return Image.open(_url).convert("RGB")
+                else:
+                    return Image.open(BytesIO(response.content)).convert("RGB")
+
+        if not isinstance(content, str):
+            texts = []
+            image_urls = []
+            for c in content:
+                c_type = c.get("type")
+                if c_type == "text":
+                    texts.append(c["text"])
+                elif c_type == "image_url":
+                    image_urls.append(c["image_url"]["url"])
+            image_futures = []
+            with ThreadPoolExecutor() as executor:
+                for image_url in image_urls:
+                    fut = executor.submit(_load_image, image_url)
+                    image_futures.append(fut)
+            images = [fut.result() for fut in image_futures]
+            text = " ".join(texts)
+            if len(images) == 0:
+                return text, []
+            elif len(images) == 1:
+                return text, images
+            else:
+                raise RuntimeError("Only one image per message is supported")
+        return content, []
+
+    def chat(
+        self,
+        prompt: Union[str, List[Dict]],
+        system_prompt: Optional[str] = None,
+        chat_history: Optional[List[ChatCompletionMessage]] = None,
+        generate_config: Optional[PytorchGenerateConfig] = None,
+    ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]:
+        stream = generate_config.get("stream", False) if generate_config else False
+        content, images_chat = self._message_content_to_chat(prompt)
+
+        msgs = []
+        query_to_response: List[Dict] = []
+        images_history = []
+        for h in chat_history or []:
+            role = h["role"]
+            content_h, images_tmp = self._message_content_to_chat(h["content"])
+            if images_tmp != []:
+                images_history = images_tmp
+            if len(query_to_response) == 0 and role == "user":
+                query_to_response.append({"role": "user", "content": content_h})
+            if len(query_to_response) == 1 and role == "assistant":
+                query_to_response.append({"role": "assistant", "content": content_h})
+            if len(query_to_response) == 2:
+                msgs.extend(query_to_response)
+                query_to_response = []
+        image = None
+        if len(images_chat) > 0:
+            image = images_chat[0]
+        elif len(images_history) > 0:
+            image = images_history[0]
+        msgs.append({"role": "user", "content": content})
+
+        chat = self._model.chat(
+            image=image,
+            msgs=json.dumps(msgs, ensure_ascii=True),
+            tokenizer=self._tokenizer,
+            sampling=True,
+            **generate_config
+        )
+        if stream:
+            it = self.chat_stream(chat)
+            return self._to_chat_completion_chunks(it)
+        else:
+            c = Completion(
+                id=str(uuid.uuid1()),
+                object="text_completion",
+                created=int(time.time()),
+                model=self.model_uid,
+                choices=[
+                    CompletionChoice(
+                        index=0, text=chat, finish_reason="stop", logprobs=None
+                    )
+                ],
+                usage=CompletionUsage(
+                    prompt_tokens=-1, completion_tokens=-1, total_tokens=-1
+                ),
+            )
+            return self._to_chat_completion(c)
+
+    def chat_stream(self, chat) -> Iterator[CompletionChunk]:
+        completion_id = str(uuid.uuid1())
+        for new_text in chat:
+            completion_choice = CompletionChoice(
+                text=new_text, index=0, logprobs=None, finish_reason=None
+            )
+            chunk = CompletionChunk(
+                id=completion_id,
+                object="text_completion",
+                created=int(time.time()),
+                model=self.model_uid,
+                choices=[completion_choice],
+            )
+            completion_usage = CompletionUsage(
+                prompt_tokens=-1,
+                completion_tokens=-1,
+                total_tokens=-1,
+            )
+            chunk["usage"] = completion_usage
+            yield chunk
+
+        completion_choice = CompletionChoice(
+            text="", index=0, logprobs=None, finish_reason="stop"
+        )
+        chunk = CompletionChunk(
+            id=completion_id,
+            object="text_completion",
+            created=int(time.time()),
+            model=self.model_uid,
+            choices=[completion_choice],
+        )
+        completion_usage = CompletionUsage(
+            prompt_tokens=-1,
+            completion_tokens=-1,
+            total_tokens=-1,
+        )
+        chunk["usage"] = completion_usage
+        yield chunk