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FEAT: Support MiniCPM-v-2_6 #2031

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Aug 9, 2024
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2 changes: 2 additions & 0 deletions xinference/model/llm/__init__.py
Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -167,6 +168,7 @@ def _install():
PytorchModel,
CogVLM2Model,
MiniCPMV25Model,
MiniCPMV26Model,
Glm4VModel,
]
)
Expand Down
48 changes: 47 additions & 1 deletion xinference/model/llm/llm_family.json
Original file line number Diff line number Diff line change
Expand Up @@ -6807,7 +6807,7 @@
},
{
"version":1,
"context_length":2048,
"context_length":8192,
"model_name":"MiniCPM-Llama3-V-2_5",
"model_lang":[
"en",
Expand Down Expand Up @@ -6847,6 +6847,52 @@
]
}
},
{
"version":1,
"context_length":32768,
"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":[
"4-bit"
],
"model_id":"openbmb/MiniCPM-V-2_6-int4",
"model_revision":"051e2df6505f1fc4305f2c9bd42ed90db8bf4874"
}
],
"prompt_style":{
"style_name":"QWEN",
"system_prompt":"You are a helpful assistant",
"roles":[
"user",
"assistant"
],
"stop": [
"<|im_end|>",
"<|endoftext|>"
]
}
},
{
"version": 1,
"context_length": 4096,
Expand Down
46 changes: 45 additions & 1 deletion xinference/model/llm/llm_family_modelscope.json
Original file line number Diff line number Diff line change
Expand Up @@ -4549,7 +4549,7 @@
},
{
"version":1,
"context_length":2048,
"context_length":8192,
"model_name":"MiniCPM-Llama3-V-2_5",
"model_lang":[
"en",
Expand Down Expand Up @@ -4591,6 +4591,50 @@
]
}
},
{
"version":1,
"context_length":32768,
"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":[
"4-bit"
],
"model_hub": "modelscope",
"model_id":"OpenBMB/MiniCPM-V-2_6-int4",
"model_revision":"master"
}
],
"prompt_style":{
"style_name":"QWEN",
"system_prompt":"You are a helpful assistant",
"roles":[
"user",
"assistant"
]
}
},
{
"version": 1,
"context_length": 2048,
Expand Down
1 change: 1 addition & 0 deletions xinference/model/llm/pytorch/core.py
Original file line number Diff line number Diff line change
Expand Up @@ -72,6 +72,7 @@
"mini-internvl-chat",
"cogvlm2",
"MiniCPM-Llama3-V-2_5",
"MiniCPM-V-2.6",
"glm-4v",
]

Expand Down
247 changes: 247 additions & 0 deletions xinference/model/llm/pytorch/minicpmv26.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,247 @@
# 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" and self.quantization is None
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
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