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import PIL |
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import torch |
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from .modeling_llava import LlavaForConditionalGeneration |
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from .processing_llava import MLlavaProcessor |
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from ..conversation import conv_mllava_v1_mmtag as default_conv |
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from typing import List, Tuple, Union, Tuple |
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def chat_mllava( |
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text:str, |
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images: List[Union[PIL.Image.Image, str]], |
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model:LlavaForConditionalGeneration, |
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processor:MLlavaProcessor, |
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max_input_length:int=None, |
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history:List[dict]=None, |
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stream:bool=False, |
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**kwargs) -> Tuple[str, List[dict]]: |
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""" |
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Chat with the Mllava model |
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Args: |
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text: str, the text to be sent to the model, where <image> will be the placeholder for the image |
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images: List[PIL.Image.Image], the images to be sent to the model, or None |
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model: LlavaForConditionalGeneration, the model to be used |
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processor: MLlavaProcessor, the processor to be used |
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max_input_length: int, the maximum input length |
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history: List[dict], list of messages in the conversation as history. Each message is a dictionary {"role": "ASSISTANT/USER", "text": "the message"}. If None, the conversation will start from scratch |
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kwargs: dict, the generation kwargs |
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Returns: |
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Tuple[str, List[dict]], the generated text and the history of the conversation |
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""" |
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conv = default_conv.copy() |
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conv.messages = [] |
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if history is not None: |
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for message in history: |
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message["role"] = message["role"].upper() |
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assert message["role"] in conv.roles |
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conv.append_message(message["role"], message["text"]) |
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else: |
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history = [] |
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if text is not None: |
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conv.append_message(conv.roles[0], text) |
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conv.append_message(conv.roles[1], "") |
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history.append({"role": conv.roles[0], "text": text}) |
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history.append({"role": conv.roles[1], "text": ""}) |
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else: |
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assert history, "The history should not be empty if the text is None" |
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assert history[-1]['role'] == conv.roles[1], "The last message in the history should be the assistant, an empty message" |
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assert history[-2]['text'], "The last user message in the history should not be empty" |
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assert history[-1]['text'] == "", "The last assistant message in the history should be empty" |
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prompt = conv.get_prompt() |
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if images: |
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for i in range(len(images)): |
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if isinstance(images[i], str): |
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images[i] = PIL.Image.open(images[i]) |
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inputs = processor(images=images, text=prompt, return_tensors="pt", truncation=True, max_length=max_input_length) |
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inputs = {k: v.to(model.device) if v is not None else v for k, v in inputs.items()} |
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if stream: |
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from transformers import TextIteratorStreamer |
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from threading import Thread |
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streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True) |
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kwargs["streamer"] = streamer |
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inputs.update(kwargs) |
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thread = Thread(target=model.generate, kwargs=inputs) |
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thread.start() |
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for _output in streamer: |
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history[-1]["text"] += _output |
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yield history[-1]["text"], history |
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else: |
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output_ids = model.generate(**inputs, **kwargs) |
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output_ids = output_ids[0] |
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generated_ids = output_ids[inputs["input_ids"].shape[-1]:] |
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generated_text = processor.decode(generated_ids, skip_special_tokens=True) |
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history[-1]["text"] = history[-1]["text"].strip() |
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return generated_text, history |
