You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
530 lines
17 KiB
530 lines
17 KiB
import base64
|
|
import os
|
|
import time
|
|
from io import BytesIO
|
|
from typing import List, Literal, Optional, Tuple, Union
|
|
|
|
import torch
|
|
from PIL import Image
|
|
from pydantic import BaseModel, Field
|
|
from transformers import (
|
|
AutoModelForCausalLM,
|
|
LlamaTokenizer,
|
|
TextIteratorStreamer,
|
|
)
|
|
|
|
from swarms.models.base_multimodal_model import BaseMultiModalModel
|
|
from swarms.utils.logger import logger
|
|
|
|
MODEL_PATH = "THUDM/cogvlm-chat-hf"
|
|
TOKENIZER_PATH = "lmsys/vicuna-7b-v1.5"
|
|
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
|
|
QUANT_ENABLED = False
|
|
|
|
|
|
class ImageUrl(BaseModel):
|
|
url: str
|
|
|
|
|
|
class TextContent(BaseModel):
|
|
type: Literal["text"]
|
|
text: str
|
|
|
|
|
|
class ImageUrlContent(BaseModel):
|
|
type: Literal["image_url"]
|
|
image_url: ImageUrl
|
|
|
|
|
|
ContentItem = Union[TextContent, ImageUrlContent]
|
|
|
|
|
|
class ChatMessageInput(BaseModel):
|
|
role: Literal["user", "assistant", "system"]
|
|
content: Union[str, List[ContentItem]]
|
|
name: Optional[str] = None
|
|
|
|
|
|
class ChatMessageResponse(BaseModel):
|
|
role: Literal["assistant"]
|
|
content: str = None
|
|
name: Optional[str] = None
|
|
|
|
|
|
class DeltaMessage(BaseModel):
|
|
role: Optional[Literal["user", "assistant", "system"]] = None
|
|
content: Optional[str] = None
|
|
|
|
|
|
class ChatCompletionRequest(BaseModel):
|
|
model: str
|
|
messages: List[ChatMessageInput]
|
|
temperature: Optional[float] = 0.8
|
|
top_p: Optional[float] = 0.8
|
|
max_tokens: Optional[int] = None
|
|
stream: Optional[bool] = False
|
|
# Additional parameters
|
|
repetition_penalty: Optional[float] = 1.0
|
|
|
|
|
|
class ChatCompletionResponseChoice(BaseModel):
|
|
index: int
|
|
message: ChatMessageResponse
|
|
|
|
|
|
class ChatCompletionResponseStreamChoice(BaseModel):
|
|
index: int
|
|
delta: DeltaMessage
|
|
|
|
|
|
class UsageInfo(BaseModel):
|
|
prompt_tokens: int = 0
|
|
total_tokens: int = 0
|
|
completion_tokens: Optional[int] = 0
|
|
|
|
|
|
class ChatCompletionResponse(BaseModel):
|
|
model: str
|
|
object: Literal["chat.completion", "chat.completion.chunk"]
|
|
choices: List[
|
|
Union[
|
|
ChatCompletionResponseChoice,
|
|
ChatCompletionResponseStreamChoice,
|
|
]
|
|
]
|
|
created: Optional[int] = Field(
|
|
default_factory=lambda: int(time.time())
|
|
)
|
|
usage: Optional[UsageInfo] = None
|
|
|
|
|
|
# async def create_chat_completion(request: ChatCompletionRequest):
|
|
# global model, tokenizer
|
|
|
|
# gen_params = dict(
|
|
# messages=request.messages,
|
|
# temperature=request.temperature,
|
|
# top_p=request.top_p,
|
|
# max_tokens=request.max_tokens or 1024,
|
|
# echo=False,
|
|
# stream=request.stream,
|
|
# )
|
|
|
|
# # if request.stream:
|
|
# # predict(request.model, gen_params)
|
|
# # response = generate_cogvlm(model, tokenizer, gen_params)
|
|
|
|
# usage = UsageInfo()
|
|
|
|
# message = ChatMessageResponse(
|
|
# role="assistant",
|
|
# content=response["text"],
|
|
# )
|
|
# logger.debug(f"==== message ====\n{message}")
|
|
# choice_data = ChatCompletionResponseChoice(
|
|
# index=0,
|
|
# message=message,
|
|
# )
|
|
# task_usage = UsageInfo.model_validate(response["usage"])
|
|
# for usage_key, usage_value in task_usage.model_dump().items():
|
|
# setattr(
|
|
# usage, usage_key, getattr(usage, usage_key) + usage_value
|
|
# )
|
|
# return ChatCompletionResponse(
|
|
# model=request.model,
|
|
# choices=[choice_data],
|
|
# object="chat.completion",
|
|
# usage=usage,
|
|
# )
|
|
|
|
|
|
class CogVLMMultiModal(BaseMultiModalModel):
|
|
"""
|
|
Initializes the CogVLM model.
|
|
|
|
Args:
|
|
model_name (str): The path or name of the pre-trained model.
|
|
tokenizer (str): The path or name of the tokenizer.
|
|
device (str): The device to run the model on.
|
|
quantize (bool): Whether to enable quantization.
|
|
torch_type (str): The torch data type to use.
|
|
temperature (float): The temperature for sampling.
|
|
top_p (float): The top-p value for sampling.
|
|
max_tokens (int): The maximum number of tokens to generate.
|
|
echo (bool): Whether to echo the input text.
|
|
stream (bool): Whether to stream the output.
|
|
repetition_penalty (float): The repetition penalty for sampling.
|
|
do_sample (bool): Whether to use sampling during generation.
|
|
*args: Additional positional arguments.
|
|
**kwargs: Additional keyword arguments.
|
|
|
|
Methods:
|
|
run: Generates a response using the CogVLM model.
|
|
generate_stream_cogvlm: Generates a stream of responses using the CogVLM model in inference mode.
|
|
process_history_and_images: Processes history messages to extract text, identify the last user query,
|
|
and convert base64 encoded image URLs to PIL images.
|
|
|
|
Example:
|
|
>>> model = CogVLMMultiModal()
|
|
>>> response = model("Describe this image with meticlous details.", "https://example.com/image.jpg")
|
|
>>> print(response)
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
model_name: str = MODEL_PATH,
|
|
tokenizer: str = TOKENIZER_PATH,
|
|
device: str = DEVICE,
|
|
quantize: bool = QUANT_ENABLED,
|
|
torch_type: str = "float16",
|
|
temperature: float = 0.5,
|
|
top_p: float = 0.9,
|
|
max_tokens: int = 3500,
|
|
echo: bool = False,
|
|
stream: bool = False,
|
|
repetition_penalty: float = 1.0,
|
|
do_sample: bool = True,
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
super().__init__()
|
|
self.model_name = model_name
|
|
self.device = device
|
|
self.tokenizer = tokenizer
|
|
self.device = device
|
|
self.quantize = quantize
|
|
self.torch_type = torch_type
|
|
self.temperature = temperature
|
|
self.top_p = top_p
|
|
self.max_tokens = max_tokens
|
|
self.echo = echo
|
|
self.stream = stream
|
|
self.repetition_penalty = repetition_penalty
|
|
self.do_sample = do_sample
|
|
|
|
if os.environ.get("QUANT_ENABLED"):
|
|
pass
|
|
else:
|
|
with torch.cuda.device(device):
|
|
__, total_bytes = torch.cuda.mem_get_info()
|
|
total_gb = total_bytes / (1 << 30)
|
|
if total_gb < 40:
|
|
pass
|
|
|
|
torch.cuda.empty_cache()
|
|
|
|
self.tokenizer = LlamaTokenizer.from_pretrained(
|
|
tokenizer, trust_remote_code=True
|
|
)
|
|
|
|
if (
|
|
torch.cuda.is_available()
|
|
and torch.cuda.get_device_capability()[0] >= 8
|
|
):
|
|
torch_type = torch.bfloat16
|
|
else:
|
|
torch_type = torch.float16
|
|
|
|
print(
|
|
f"========Use torch type as:{torch_type} with"
|
|
f" device:{device}========\n\n"
|
|
)
|
|
|
|
if "cuda" in device:
|
|
if QUANT_ENABLED:
|
|
self.model = AutoModelForCausalLM.from_pretrained(
|
|
model_name,
|
|
load_in_4bit=True,
|
|
trust_remote_code=True,
|
|
torch_dtype=torch_type,
|
|
low_cpu_mem_usage=True,
|
|
*args,
|
|
**kwargs,
|
|
).eval()
|
|
else:
|
|
self.model = (
|
|
AutoModelForCausalLM.from_pretrained(
|
|
model_name,
|
|
load_in_4bit=False,
|
|
trust_remote_code=True,
|
|
torch_dtype=torch_type,
|
|
low_cpu_mem_usage=True,
|
|
*args,
|
|
**kwargs,
|
|
)
|
|
.to(device)
|
|
.eval()
|
|
)
|
|
|
|
else:
|
|
self.model = (
|
|
AutoModelForCausalLM.from_pretrained(
|
|
model_name,
|
|
trust_remote_code=True,
|
|
*args,
|
|
**kwargs,
|
|
)
|
|
.float()
|
|
.to(device)
|
|
.eval()
|
|
)
|
|
|
|
def run(self, task: str, img: str, *args, **kwargs):
|
|
"""
|
|
Generates a response using the CogVLM model. It processes the chat history and image data, if any,
|
|
and then invokes the model to generate a response.
|
|
"""
|
|
messages = [task]
|
|
|
|
params = dict(
|
|
messages=messages,
|
|
temperature=self.temperature,
|
|
repitition_penalty=self.repetition_penalty,
|
|
top_p=self.top_p,
|
|
max_new_tokens=self.max_tokens,
|
|
)
|
|
|
|
for response in self.generate_stream_cogvlm(params):
|
|
pass
|
|
|
|
return response
|
|
|
|
@torch.inference_mode()
|
|
def generate_stream_cogvlm(
|
|
self,
|
|
params: dict,
|
|
):
|
|
"""
|
|
Generates a stream of responses using the CogVLM model in inference mode.
|
|
It's optimized to handle continuous input-output interactions with the model in a streaming manner.
|
|
"""
|
|
messages = params["messages"]
|
|
temperature = float(params.get("temperature", 1.0))
|
|
repetition_penalty = float(
|
|
params.get("repetition_penalty", 1.0)
|
|
)
|
|
top_p = float(params.get("top_p", 1.0))
|
|
max_new_tokens = int(params.get("max_tokens", 256))
|
|
query, history, image_list = self.process_history_and_images(
|
|
messages
|
|
)
|
|
|
|
logger.debug(f"==== request ====\n{query}")
|
|
|
|
input_by_model = self.model.build_conversation_input_ids(
|
|
self.tokenizer,
|
|
query=query,
|
|
history=history,
|
|
images=[image_list[-1]],
|
|
)
|
|
inputs = {
|
|
"input_ids": (
|
|
input_by_model["input_ids"]
|
|
.unsqueeze(0)
|
|
.to(self.device)
|
|
),
|
|
"token_type_ids": (
|
|
input_by_model["token_type_ids"]
|
|
.unsqueeze(0)
|
|
.to(self.device)
|
|
),
|
|
"attention_mask": (
|
|
input_by_model["attention_mask"]
|
|
.unsqueeze(0)
|
|
.to(self.device)
|
|
),
|
|
"images": [
|
|
[
|
|
input_by_model["images"][0]
|
|
.to(self.device)
|
|
.to(self.torch_type)
|
|
]
|
|
],
|
|
}
|
|
if (
|
|
"cross_images" in input_by_model
|
|
and input_by_model["cross_images"]
|
|
):
|
|
inputs["cross_images"] = [
|
|
[
|
|
input_by_model["cross_images"][0]
|
|
.to(self.device)
|
|
.to(self.torch_type)
|
|
]
|
|
]
|
|
|
|
input_echo_len = len(inputs["input_ids"][0])
|
|
streamer = TextIteratorStreamer(
|
|
tokenizer=self.tokenizer,
|
|
timeout=60.0,
|
|
skip_promptb=True,
|
|
skip_special_tokens=True,
|
|
)
|
|
gen_kwargs = {
|
|
"repetition_penalty": repetition_penalty,
|
|
"max_new_tokens": max_new_tokens,
|
|
"do_sample": True if temperature > 1e-5 else False,
|
|
"top_p": top_p if temperature > 1e-5 else 0,
|
|
"streamer": streamer,
|
|
}
|
|
if temperature > 1e-5:
|
|
gen_kwargs["temperature"] = temperature
|
|
|
|
total_len = 0
|
|
generated_text = ""
|
|
with torch.no_grad():
|
|
self.model.generate(**inputs, **gen_kwargs)
|
|
for next_text in streamer:
|
|
generated_text += next_text
|
|
yield {
|
|
"text": generated_text,
|
|
"usage": {
|
|
"prompt_tokens": input_echo_len,
|
|
"completion_tokens": (
|
|
total_len - input_echo_len
|
|
),
|
|
"total_tokens": total_len,
|
|
},
|
|
}
|
|
ret = {
|
|
"text": generated_text,
|
|
"usage": {
|
|
"prompt_tokens": input_echo_len,
|
|
"completion_tokens": total_len - input_echo_len,
|
|
"total_tokens": total_len,
|
|
},
|
|
}
|
|
yield ret
|
|
|
|
def process_history_and_images(
|
|
self,
|
|
messages: List[ChatMessageInput],
|
|
) -> Tuple[
|
|
Optional[str],
|
|
Optional[List[Tuple[str, str]]],
|
|
Optional[List[Image.Image]],
|
|
]:
|
|
"""
|
|
Process history messages to extract text, identify the last user query,
|
|
and convert base64 encoded image URLs to PIL images.
|
|
|
|
Args:
|
|
messages(List[ChatMessageInput]): List of ChatMessageInput objects.
|
|
return: A tuple of three elements:
|
|
- The last user query as a string.
|
|
- Text history formatted as a list of tuples for the model.
|
|
- List of PIL Image objects extracted from the messages.
|
|
"""
|
|
formatted_history = []
|
|
image_list = []
|
|
last_user_query = ""
|
|
|
|
for i, message in enumerate(messages):
|
|
role = message.role
|
|
content = message.content
|
|
|
|
# Extract text content
|
|
if isinstance(content, list): # text
|
|
text_content = " ".join(
|
|
item.text
|
|
for item in content
|
|
if isinstance(item, TextContent)
|
|
)
|
|
else:
|
|
text_content = content
|
|
|
|
# Extract image data
|
|
if isinstance(content, list): # image
|
|
for item in content:
|
|
if isinstance(item, ImageUrlContent):
|
|
image_url = item.image_url.url
|
|
if image_url.startswith(
|
|
"data:image/jpeg;base64,"
|
|
):
|
|
base64_encoded_image = image_url.split(
|
|
"data:image/jpeg;base64,"
|
|
)[1]
|
|
image_data = base64.b64decode(
|
|
base64_encoded_image
|
|
)
|
|
image = Image.open(
|
|
BytesIO(image_data)
|
|
).convert("RGB")
|
|
image_list.append(image)
|
|
|
|
# Format history
|
|
if role == "user":
|
|
if i == len(messages) - 1:
|
|
last_user_query = text_content
|
|
else:
|
|
formatted_history.append((text_content, ""))
|
|
elif role == "assistant":
|
|
if formatted_history:
|
|
if formatted_history[-1][1] != "":
|
|
raise AssertionError(
|
|
"the last query is answered. answer"
|
|
f" again. {formatted_history[-1][0]},"
|
|
f" {formatted_history[-1][1]},"
|
|
f" {text_content}"
|
|
)
|
|
formatted_history[-1] = (
|
|
formatted_history[-1][0],
|
|
text_content,
|
|
)
|
|
else:
|
|
raise AssertionError(
|
|
"assistant reply before user"
|
|
)
|
|
else:
|
|
raise AssertionError(f"unrecognized role: {role}")
|
|
|
|
return last_user_query, formatted_history, image_list
|
|
|
|
async def predict(self, params: dict):
|
|
"""
|
|
Handle streaming predictions. It continuously generates responses for a given input stream.
|
|
This is particularly useful for real-time, continuous interactions with the model.
|
|
"""
|
|
|
|
choice_data = ChatCompletionResponseStreamChoice(
|
|
index=0,
|
|
delta=DeltaMessage(role="assistant"),
|
|
finish_reason=None,
|
|
)
|
|
chunk = ChatCompletionResponse(
|
|
model=self.model_name,
|
|
choices=[choice_data],
|
|
object="chat.completion.chunk",
|
|
)
|
|
yield f"{chunk.model_dump_json(exclude_unset=True)}"
|
|
|
|
previous_text = ""
|
|
for new_response in self.generate_stream_cogvlm(params):
|
|
decoded_unicode = new_response["text"]
|
|
delta_text = decoded_unicode[len(previous_text) :]
|
|
previous_text = decoded_unicode
|
|
delta = DeltaMessage(
|
|
content=delta_text,
|
|
role="assistant",
|
|
)
|
|
choice_data = ChatCompletionResponseStreamChoice(
|
|
index=0,
|
|
delta=delta,
|
|
)
|
|
chunk = ChatCompletionResponse(
|
|
model=self.model_name,
|
|
choices=[choice_data],
|
|
object="chat.completion.chunk",
|
|
)
|
|
yield f"{chunk.model_dump_json(exclude_unset=True)}"
|
|
choice_data = ChatCompletionResponseStreamChoice(
|
|
index=0,
|
|
delta=DeltaMessage(),
|
|
)
|
|
chunk = ChatCompletionResponse(
|
|
model=self.model_name,
|
|
choices=[choice_data],
|
|
object="chat.completion.chunk",
|
|
)
|
|
yield f"{chunk.model_dump_json(exclude_unset=True)}"
|