Kye
11 months ago
parent
afc8df570a
commit
c82f4f9b5a
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import base64
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import os
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import time
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from io import BytesIO
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from typing import List, Literal, Optional, Tuple, Union
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import torch
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from PIL import Image
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from pydantic import BaseModel, Field
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from transformers import (
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AutoModelForCausalLM,
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LlamaTokenizer,
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TextIteratorStreamer,
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)
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from swarms.models.base_multimodal_model import BaseMultiModalModel
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from swarms.utils.logger import logger
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MODEL_PATH = "THUDM/cogvlm-chat-hf"
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TOKENIZER_PATH = "lmsys/vicuna-7b-v1.5"
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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QUANT_ENABLED = False
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class ImageUrl(BaseModel):
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url: str
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class TextContent(BaseModel):
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type: Literal["text"]
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text: str
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class ImageUrlContent(BaseModel):
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type: Literal["image_url"]
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image_url: ImageUrl
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ContentItem = Union[TextContent, ImageUrlContent]
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class ChatMessageInput(BaseModel):
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role: Literal["user", "assistant", "system"]
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content: Union[str, List[ContentItem]]
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name: Optional[str] = None
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class ChatMessageResponse(BaseModel):
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role: Literal["assistant"]
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content: str = None
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name: Optional[str] = None
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class DeltaMessage(BaseModel):
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role: Optional[Literal["user", "assistant", "system"]] = None
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content: Optional[str] = None
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class ChatCompletionRequest(BaseModel):
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model: str
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messages: List[ChatMessageInput]
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temperature: Optional[float] = 0.8
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top_p: Optional[float] = 0.8
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max_tokens: Optional[int] = None
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stream: Optional[bool] = False
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# Additional parameters
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repetition_penalty: Optional[float] = 1.0
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class ChatCompletionResponseChoice(BaseModel):
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index: int
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message: ChatMessageResponse
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class ChatCompletionResponseStreamChoice(BaseModel):
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index: int
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delta: DeltaMessage
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class UsageInfo(BaseModel):
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prompt_tokens: int = 0
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total_tokens: int = 0
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completion_tokens: Optional[int] = 0
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class ChatCompletionResponse(BaseModel):
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model: str
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object: Literal["chat.completion", "chat.completion.chunk"]
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choices: List[
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Union[
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ChatCompletionResponseChoice,
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ChatCompletionResponseStreamChoice,
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]
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]
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created: Optional[int] = Field(
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default_factory=lambda: int(time.time())
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)
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usage: Optional[UsageInfo] = None
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# async def create_chat_completion(request: ChatCompletionRequest):
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# global model, tokenizer
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# gen_params = dict(
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# messages=request.messages,
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# temperature=request.temperature,
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# top_p=request.top_p,
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# max_tokens=request.max_tokens or 1024,
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# echo=False,
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# stream=request.stream,
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# )
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# # if request.stream:
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# # predict(request.model, gen_params)
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# # response = generate_cogvlm(model, tokenizer, gen_params)
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# usage = UsageInfo()
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# message = ChatMessageResponse(
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# role="assistant",
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# content=response["text"],
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# )
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# logger.debug(f"==== message ====\n{message}")
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# choice_data = ChatCompletionResponseChoice(
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# index=0,
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# message=message,
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# )
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# task_usage = UsageInfo.model_validate(response["usage"])
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# for usage_key, usage_value in task_usage.model_dump().items():
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# setattr(
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# usage, usage_key, getattr(usage, usage_key) + usage_value
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# )
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# return ChatCompletionResponse(
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# model=request.model,
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# choices=[choice_data],
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# object="chat.completion",
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# usage=usage,
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# )
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class CogVLMMultiModal(BaseMultiModalModel):
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"""
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Initializes the CogVLM model.
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Args:
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model_name (str): The path or name of the pre-trained model.
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tokenizer (str): The path or name of the tokenizer.
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device (str): The device to run the model on.
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quantize (bool): Whether to enable quantization.
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torch_type (str): The torch data type to use.
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temperature (float): The temperature for sampling.
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top_p (float): The top-p value for sampling.
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max_tokens (int): The maximum number of tokens to generate.
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echo (bool): Whether to echo the input text.
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stream (bool): Whether to stream the output.
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repetition_penalty (float): The repetition penalty for sampling.
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do_sample (bool): Whether to use sampling during generation.
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*args: Additional positional arguments.
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**kwargs: Additional keyword arguments.
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Methods:
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run: Generates a response using the CogVLM model.
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generate_stream_cogvlm: Generates a stream of responses using the CogVLM model in inference mode.
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process_history_and_images: Processes history messages to extract text, identify the last user query, and convert base64 encoded image URLs to PIL images.
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Example:
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>>> model = CogVLMMultiModal()
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>>> response = model("Describe this image with meticlous details.", "https://example.com/image.jpg")
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>>> print(response)
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"""
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def __init__(
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self,
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model_name: str = MODEL_PATH,
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tokenizer: str = TOKENIZER_PATH,
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device: str = DEVICE,
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quantize: bool = QUANT_ENABLED,
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torch_type: str = "float16",
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temperature: float = 0.5,
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top_p: float = 0.9,
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max_tokens: int = 3500,
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echo: bool = False,
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stream: bool = False,
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repetition_penalty: float = 1.0,
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do_sample: bool = True,
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*args,
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**kwargs,
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):
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super().__init__()
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self.model_name = model_name
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self.device = device
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self.tokenizer = tokenizer
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self.device = device
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self.quantize = quantize
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self.torch_type = torch_type
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self.temperature = temperature
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self.top_p = top_p
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self.max_tokens = max_tokens
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self.echo = echo
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self.stream = stream
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self.repetition_penalty = repetition_penalty
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self.do_sample = do_sample
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if os.environ.get("QUANT_ENABLED"):
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pass
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else:
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with torch.cuda.device(device):
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__, total_bytes = torch.cuda.mem_get_info()
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total_gb = total_bytes / (1 << 30)
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if total_gb < 40:
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pass
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else:
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pass
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torch.cuda.empty_cache()
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self.tokenizer = LlamaTokenizer.from_pretrained(
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tokenizer, trust_remote_code=True
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)
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if (
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torch.cuda.is_available()
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and torch.cuda.get_device_capability()[0] >= 8
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):
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torch_type = torch.bfloat16
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else:
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torch_type = torch.float16
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print(
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"========Use torch type as:{} with device:{}========\n\n"
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.format(torch_type, device)
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)
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if "cuda" in device:
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if QUANT_ENABLED:
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self.model = AutoModelForCausalLM.from_pretrained(
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model_name,
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load_in_4bit=True,
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trust_remote_code=True,
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torch_dtype=torch_type,
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low_cpu_mem_usage=True,
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*args,
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**kwargs,
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).eval()
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else:
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self.model = (
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AutoModelForCausalLM.from_pretrained(
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model_name,
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load_in_4bit=False,
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trust_remote_code=True,
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torch_dtype=torch_type,
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low_cpu_mem_usage=True,
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*args,
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**kwargs,
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)
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.to(device)
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.eval()
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)
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else:
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self.model = (
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AutoModelForCausalLM.from_pretrained(
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model_name,
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trust_remote_code=True,
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*args,
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**kwargs,
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)
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.float()
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.to(device)
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.eval()
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)
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def run(self, task: str, img: str, *args, **kwargs):
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"""
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Generates a response using the CogVLM model. It processes the chat history and image data, if any,
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and then invokes the model to generate a response.
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"""
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messages = [task]
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params = dict(
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messages=messages,
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temperature=self.temperature,
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repitition_penalty=self.repetition_penalty,
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top_p=self.top_p,
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max_new_tokens=self.max_tokens,
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)
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for response in self.generate_stream_cogvlm(params):
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pass
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return response
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@torch.inference_mode()
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def generate_stream_cogvlm(
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self,
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params: dict,
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):
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"""
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Generates a stream of responses using the CogVLM model in inference mode.
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It's optimized to handle continuous input-output interactions with the model in a streaming manner.
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"""
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messages = params["messages"]
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temperature = float(params.get("temperature", 1.0))
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repetition_penalty = float(
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params.get("repetition_penalty", 1.0)
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)
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top_p = float(params.get("top_p", 1.0))
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max_new_tokens = int(params.get("max_tokens", 256))
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query, history, image_list = self.process_history_and_images(
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messages
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)
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logger.debug(f"==== request ====\n{query}")
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input_by_model = self.model.build_conversation_input_ids(
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self.tokenizer,
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query=query,
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history=history,
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images=[image_list[-1]],
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)
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inputs = {
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"input_ids": (
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input_by_model["input_ids"]
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.unsqueeze(0)
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.to(self.device)
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),
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"token_type_ids": (
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input_by_model["token_type_ids"]
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.unsqueeze(0)
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.to(self.device)
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),
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"attention_mask": (
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input_by_model["attention_mask"]
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.unsqueeze(0)
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.to(self.device)
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),
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"images": [
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[
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input_by_model["images"][0]
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.to(self.device)
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.to(self.torch_type)
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]
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],
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}
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if (
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"cross_images" in input_by_model
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and input_by_model["cross_images"]
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):
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inputs["cross_images"] = [
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[
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input_by_model["cross_images"][0]
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.to(self.device)
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.to(self.torch_type)
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]
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]
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input_echo_len = len(inputs["input_ids"][0])
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streamer = TextIteratorStreamer(
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tokenizer=self.tokenizer,
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timeout=60.0,
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skip_promptb=True,
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skip_special_tokens=True,
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)
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gen_kwargs = {
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"repetition_penalty": repetition_penalty,
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"max_new_tokens": max_new_tokens,
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"do_sample": True if temperature > 1e-5 else False,
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"top_p": top_p if temperature > 1e-5 else 0,
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"streamer": streamer,
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}
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if temperature > 1e-5:
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gen_kwargs["temperature"] = temperature
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total_len = 0
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generated_text = ""
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with torch.no_grad():
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self.model.generate(**inputs, **gen_kwargs)
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for next_text in streamer:
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generated_text += next_text
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yield {
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"text": generated_text,
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"usage": {
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"prompt_tokens": input_echo_len,
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"completion_tokens": (
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total_len - input_echo_len
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),
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"total_tokens": total_len,
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},
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}
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ret = {
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"text": generated_text,
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"usage": {
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"prompt_tokens": input_echo_len,
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"completion_tokens": total_len - input_echo_len,
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"total_tokens": total_len,
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},
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}
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yield ret
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def process_history_and_images(
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self,
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messages: List[ChatMessageInput],
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) -> Tuple[
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Optional[str],
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Optional[List[Tuple[str, str]]],
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Optional[List[Image.Image]],
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]:
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"""
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Process history messages to extract text, identify the last user query,
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and convert base64 encoded image URLs to PIL images.
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Args:
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messages(List[ChatMessageInput]): List of ChatMessageInput objects.
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return: A tuple of three elements:
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- The last user query as a string.
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- Text history formatted as a list of tuples for the model.
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- List of PIL Image objects extracted from the messages.
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"""
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formatted_history = []
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image_list = []
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last_user_query = ""
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for i, message in enumerate(messages):
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role = message.role
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content = message.content
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# Extract text content
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if isinstance(content, list): # text
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text_content = " ".join(
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item.text
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for item in content
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if isinstance(item, TextContent)
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)
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else:
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text_content = content
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# Extract image data
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if isinstance(content, list): # image
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for item in content:
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if isinstance(item, ImageUrlContent):
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image_url = item.image_url.url
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if image_url.startswith(
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"data:image/jpeg;base64,"
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):
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base64_encoded_image = image_url.split(
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"data:image/jpeg;base64,"
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)[1]
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image_data = base64.b64decode(
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base64_encoded_image
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)
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image = Image.open(
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BytesIO(image_data)
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).convert("RGB")
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image_list.append(image)
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# Format history
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if role == "user":
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if i == len(messages) - 1:
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last_user_query = text_content
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else:
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formatted_history.append((text_content, ""))
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elif role == "assistant":
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if formatted_history:
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if formatted_history[-1][1] != "":
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assert False, (
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"the last query is answered. answer"
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f" again. {formatted_history[-1][0]},"
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f" {formatted_history[-1][1]},"
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f" {text_content}"
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)
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formatted_history[-1] = (
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formatted_history[-1][0],
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text_content,
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)
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else:
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assert False, "assistant reply before user"
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else:
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assert False, f"unrecognized role: {role}"
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return last_user_query, formatted_history, image_list
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async def predict(self, params: dict):
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"""
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Handle streaming predictions. It continuously generates responses for a given input stream.
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This is particularly useful for real-time, continuous interactions with the model.
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"""
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choice_data = ChatCompletionResponseStreamChoice(
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index=0,
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delta=DeltaMessage(role="assistant"),
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finish_reason=None,
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)
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chunk = ChatCompletionResponse(
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model=self.model_name,
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choices=[choice_data],
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object="chat.completion.chunk",
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)
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yield "{}".format(chunk.model_dump_json(exclude_unset=True))
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previous_text = ""
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for new_response in self.generate_stream_cogvlm(params):
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decoded_unicode = new_response["text"]
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delta_text = decoded_unicode[len(previous_text) :]
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previous_text = decoded_unicode
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delta = DeltaMessage(
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content=delta_text,
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role="assistant",
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)
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choice_data = ChatCompletionResponseStreamChoice(
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index=0,
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delta=delta,
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)
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chunk = ChatCompletionResponse(
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model=self.model_name,
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choices=[choice_data],
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object="chat.completion.chunk",
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)
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yield "{}".format(
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chunk.model_dump_json(exclude_unset=True)
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)
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choice_data = ChatCompletionResponseStreamChoice(
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index=0,
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delta=DeltaMessage(),
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)
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chunk = ChatCompletionResponse(
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model=self.model_name,
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choices=[choice_data],
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object="chat.completion.chunk",
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)
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yield "{}".format(chunk.model_dump_json(exclude_unset=True))
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@ -0,0 +1,87 @@
|
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from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
import json
|
||||
from swarms.models.base_llm import AbstractLLM
|
||||
from typing import Any
|
||||
|
||||
|
||||
class FireFunctionCaller(AbstractLLM):
|
||||
"""
|
||||
A class that represents a caller for the FireFunction model.
|
||||
|
||||
Args:
|
||||
model_name (str): The name of the model to be used.
|
||||
device (str): The device to be used.
|
||||
function_spec (Any): The specification of the function.
|
||||
max_tokens (int): The maximum number of tokens.
|
||||
system_prompt (str): The system prompt.
|
||||
*args: Variable length argument list.
|
||||
**kwargs: Arbitrary keyword arguments.
|
||||
|
||||
Methods:
|
||||
run(self, task: str, *args, **kwargs) -> None: Run the function with the given task and arguments.
|
||||
|
||||
Examples:
|
||||
>>> fire_function_caller = FireFunctionCaller()
|
||||
>>> fire_function_caller.run("Add 2 and 3")
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_name: str = "fireworks-ai/firefunction-v1",
|
||||
device: str = "cuda",
|
||||
function_spec: Any = None,
|
||||
max_tokens: int = 3000,
|
||||
system_prompt: str = "You are a helpful assistant with access to functions. Use them if required.",
|
||||
*args,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(model_name, device)
|
||||
self.model_name = model_name
|
||||
self.device = device
|
||||
self.fucntion_spec = function_spec
|
||||
self.max_tokens = max_tokens
|
||||
self.system_prompt = system_prompt
|
||||
|
||||
self.model = AutoModelForCausalLM.from_pretrained(
|
||||
model_name, device_map="auto", *args, **kwargs
|
||||
)
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||
|
||||
self.functions = json.dumps(function_spec, indent=4)
|
||||
|
||||
def run(self, task: str, *args, **kwargs):
|
||||
"""
|
||||
Run the function with the given task and arguments.
|
||||
|
||||
Args:
|
||||
task (str): The task to be performed.
|
||||
*args: Variable length argument list.
|
||||
**kwargs: Arbitrary keyword arguments.
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
messages = [
|
||||
{"role": "functions", "content": self.functions},
|
||||
{
|
||||
"role": "system",
|
||||
"content": self.system_prompt,
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": task,
|
||||
},
|
||||
]
|
||||
|
||||
model_inputs = self.tokenizer.apply_chat_template(
|
||||
messages, return_tensors="pt"
|
||||
).to(self.model.device)
|
||||
|
||||
generated_ids = self.model.generate(
|
||||
model_inputs,
|
||||
max_new_tokens=self.max_tokens,
|
||||
*args,
|
||||
**kwargs,
|
||||
)
|
||||
decoded = self.tokenizer.batch_decode(generated_ids)
|
||||
print(decoded[0])
|
||||
@ -0,0 +1,41 @@
|
||||
import pytest
|
||||
from unittest.mock import MagicMock
|
||||
from swarms.models.fire_function import FireFunctionCaller
|
||||
|
||||
def test_fire_function_caller_run(mocker):
|
||||
# Create mock model and tokenizer
|
||||
model = MagicMock()
|
||||
tokenizer = MagicMock()
|
||||
mocker.patch.object(FireFunctionCaller, 'model', model)
|
||||
mocker.patch.object(FireFunctionCaller, 'tokenizer', tokenizer)
|
||||
|
||||
# Create mock task and arguments
|
||||
task = "Add 2 and 3"
|
||||
args = (2, 3)
|
||||
kwargs = {}
|
||||
|
||||
# Create mock generated_ids and decoded output
|
||||
generated_ids = [1, 2, 3]
|
||||
decoded_output = "5"
|
||||
model.generate.return_value = generated_ids
|
||||
tokenizer.batch_decode.return_value = [decoded_output]
|
||||
|
||||
# Create FireFunctionCaller instance
|
||||
fire_function_caller = FireFunctionCaller()
|
||||
|
||||
# Run the function
|
||||
fire_function_caller.run(task, *args, **kwargs)
|
||||
|
||||
# Assert model.generate was called with the correct inputs
|
||||
model.generate.assert_called_once_with(
|
||||
tokenizer.apply_chat_template.return_value,
|
||||
max_new_tokens=fire_function_caller.max_tokens,
|
||||
*args,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# Assert tokenizer.batch_decode was called with the correct inputs
|
||||
tokenizer.batch_decode.assert_called_once_with(generated_ids)
|
||||
|
||||
# Assert the decoded output is printed
|
||||
assert decoded_output in mocker.patch.object(print, 'call_args_list')
|
||||
@ -0,0 +1,45 @@
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
|
||||
from swarms.models.fire_function import FireFunctionCaller
|
||||
|
||||
|
||||
def test_fire_function_caller_run(mocker):
|
||||
# Create mock model and tokenizer
|
||||
model = MagicMock()
|
||||
tokenizer = MagicMock()
|
||||
mocker.patch.object(FireFunctionCaller, "model", model)
|
||||
mocker.patch.object(FireFunctionCaller, "tokenizer", tokenizer)
|
||||
|
||||
# Create mock task and arguments
|
||||
task = "Add 2 and 3"
|
||||
args = (2, 3)
|
||||
kwargs = {}
|
||||
|
||||
# Create mock generated_ids and decoded output
|
||||
generated_ids = [1, 2, 3]
|
||||
decoded_output = "5"
|
||||
model.generate.return_value = generated_ids
|
||||
tokenizer.batch_decode.return_value = [decoded_output]
|
||||
|
||||
# Create FireFunctionCaller instance
|
||||
fire_function_caller = FireFunctionCaller()
|
||||
|
||||
# Run the function
|
||||
fire_function_caller.run(task, *args, **kwargs)
|
||||
|
||||
# Assert model.generate was called with the correct inputs
|
||||
model.generate.assert_called_once_with(
|
||||
tokenizer.apply_chat_template.return_value,
|
||||
max_new_tokens=fire_function_caller.max_tokens,
|
||||
*args,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# Assert tokenizer.batch_decode was called with the correct inputs
|
||||
tokenizer.batch_decode.assert_called_once_with(generated_ids)
|
||||
|
||||
# Assert the decoded output is printed
|
||||
assert decoded_output in mocker.patch.object(
|
||||
print, "call_args_list"
|
||||
)
|
||||
Loading…
Reference in new issue