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import math
from typing import List, Optional
import json
import torch
import torchvision
from threading import Thread
from copy import deepcopy
from PIL import Image
from transformers import AutoProcessor, Qwen2PreTrainedModel, Qwen2ForCausalLM, TextIteratorStreamer
from .processing_mplugowl3 import mPLUGOwl3Processor
from .image_processing_mplugowl3 import mPLUGOwl3ImageProcessor
from .configuration_mplugowl3 import mPLUGOwl3Config
# from .modeling_navit_siglip import SiglipVisionTransformer
from transformers.models.siglip.modeling_siglip import SiglipVisionTransformer
from .x_sdpa import ScaleDotProductAttention
from .modeling_hyper_qwen2 import HyperQwen2ForCausalLM
from torch import nn
class mPLUGOwl3PreTrainedModel(Qwen2PreTrainedModel):
config_class = mPLUGOwl3Config
class mPLUGOwl3Model(mPLUGOwl3PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.language_model = HyperQwen2ForCausalLM(config)
self.vision_model = self.init_vision_module()
self.vision_dim = self.vision_model.embed_dim
self.embed_dim = self.language_model.config.hidden_size
self.vision2text_model = nn.Linear(self.vision_dim, self.embed_dim)
self.processor = None
self.terminators = ['<|im_end|>', '<|endoftext|>']
def init_vision_module(self):
self.config.vision_config._attn_implementation = self.config.vision_config._attn_implementation
model = SiglipVisionTransformer(self.config.vision_config)
setattr(model, 'embed_dim', model.embeddings.embed_dim)
setattr(model, 'patch_size', model.embeddings.patch_size)
return model
def get_input_embeddings(self):
return self.language_model.get_input_embeddings()
def set_input_embeddings(self, value):
self.language_model.embed_tokens = value
def get_output_embeddings(self):
return self.language_model.lm_head
def set_output_embeddings(self, new_embeddings):
self.language_model.lm_head = new_embeddings
def set_decoder(self, decoder):
self.language_model = decoder
def get_decoder(self):
return self.language_model
def forward_image(self, pixel_values):
if pixel_values is None:
return None
dtype = self.language_model.model.embed_tokens.weight.dtype
with torch.inference_mode():
image_embeds = self.vision_model(pixel_values.to(dtype), output_hidden_states=True).hidden_states[-2]
if self.vision2text_model is not None:
image_embeds = self.vision2text_model(image_embeds)
else:
pass
return image_embeds
def forward(self, pixel_values=None, **kwargs):
image_embeds = self.forward_image(pixel_values)
return self.language_model(
image_embeds=image_embeds,
**kwargs
)
def _decode(self, input_ids, image_embeds, media_offset, tokenizer, attention_mask, decode_text=False, **kwargs):
terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators]
output = self.language_model.generate(
input_ids=input_ids,
image_embeds=image_embeds,
media_offset=media_offset,
pad_token_id=0,
eos_token_id=terminators,
attention_mask=attention_mask,
**kwargs
)
output = output[:,input_ids.shape[1]:]
if decode_text:
return self._decode_text(output, tokenizer)
return output
def _decode_stream(self, input_ids, image_embeds, media_offset, tokenizer, **kwargs):
terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators]
streamer = TextIteratorStreamer(tokenizer=tokenizer)
generation_kwargs = {
'input_ids': input_ids,
'image_embeds': image_embeds,
'media_offset': media_offset,
'pad_token_id': 0,
'eos_token_id': terminators,
'streamer': streamer
}
generation_kwargs.update(kwargs)
thread = Thread(target=self.language_model.generate, kwargs=generation_kwargs)
thread.start()
return streamer
def _decode_text(self, result_ids, tokenizer):
terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators]
result_text = []
for result in result_ids:
result = result[result != 0]
if result[-1] in terminators:
result = result[:-1]
result_text.append(tokenizer.decode(result).strip())
return result_text
def init_processor(self, tokenizer):
ip = mPLUGOwl3ImageProcessor(image_size=384)
self.processor = mPLUGOwl3Processor(image_processor=ip, tokenizer=tokenizer)
processor = self.processor
return processor
def generate(
self,
input_ids=None,
pixel_values=None,
media_offset=None,
attention_mask=None,
tokenizer=None,
return_vision_hidden_states=False,
stream=False,
decode_text=False,
**kwargs
):
assert input_ids is not None
with torch.inference_mode():
image_embeds = self.forward_image(pixel_values)
if stream:
result = self._decode_stream(input_ids=input_ids, image_embeds=image_embeds, media_offset=media_offset, tokenizer=tokenizer, **kwargs)
else:
result = self._decode(input_ids=input_ids, image_embeds=image_embeds, media_offset=media_offset, tokenizer=tokenizer, attention_mask=attention_mask, decode_text=decode_text, **kwargs)
if return_vision_hidden_states:
return result, image_embeds
return result
def chat(
self,
images,
videos,
msgs,
tokenizer,
processor=None,
vision_hidden_states=None,
max_new_tokens=2048,
min_new_tokens=0,
sampling=True,
max_inp_length=8192,
system_prompt='',
stream=False,
max_slice_nums=None,
use_image_id=None,
**kwargs
):
print(msgs)
if processor is None:
if self.processor is None:
self.processor = AutoProcessor.from_pretrained(self.config._name_or_path, trust_remote_code=True)
processor = self.processor
inputs = processor(
prompts_lists,
input_images_lists,
max_slice_nums=max_slice_nums,
use_image_id=use_image_id,
return_tensors="pt",
max_length=max_inp_length
).to(self.device)
if sampling:
generation_config = {
"top_p": 0.8,
"top_k": 100,
"temperature": 0.7,
"do_sample": True,
"repetition_penalty": 1.05
}
else:
generation_config = {
"num_beams": 3,
"repetition_penalty": 1.2,
}
if min_new_tokens > 0:
generation_config['min_new_tokens'] = min_new_tokens
generation_config.update(
(k, kwargs[k]) for k in generation_config.keys() & kwargs.keys()
)
inputs.pop("image_sizes")
with torch.inference_mode():
res = self.generate(
**inputs,
tokenizer=tokenizer,
max_new_tokens=max_new_tokens,
vision_hidden_states=vision_hidden_states,
stream=stream,
decode_text=True,
**generation_config
)
if stream:
def stream_gen():
for text in res:
for term in self.terminators:
text = text.replace(term, '')
yield text
return stream_gen()
else:
if batched:
answer = res
else:
answer = res[0]
return answer
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