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from transformers import Qwen2Config, Qwen2Model, Qwen2ForCausalLM, StoppingCriteria, TextStreamer
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from typing import List, Optional, Tuple, Union
from transformers.cache_utils import Cache
import requests
from PIL import Image
from io import BytesIO
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from .got_vision_b import build_GOT_vit_b
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
import dataclasses
###
DEFAULT_IMAGE_TOKEN = "<image>"
DEFAULT_IMAGE_PATCH_TOKEN = '<imgpad>'
DEFAULT_IM_START_TOKEN = '<img>'
DEFAULT_IM_END_TOKEN = '</img>'
from enum import auto, Enum
class SeparatorStyle(Enum):
"""Different separator style."""
SINGLE = auto()
TWO = auto()
MPT = auto()
@dataclasses.dataclass
class Conversation:
"""A class that keeps all conversation history."""
system: str
roles: List[str]
messages: List[List[str]]
offset: int
sep_style: SeparatorStyle = SeparatorStyle.SINGLE
sep: str = "<|im_end|>"
sep2: str = None
version: str = "Unknown"
skip_next: bool = False
def get_prompt(self):
if self.sep_style == SeparatorStyle.SINGLE:
ret = self.system + self.sep + '\n'
for role, message in self.messages:
if message:
if type(message) is tuple:
message, _, _ = message
ret += role + ": " + message + self.sep
else:
ret += role + ":"
return ret
elif self.sep_style == SeparatorStyle.TWO:
seps = [self.sep, self.sep2]
ret = self.system + seps[0]
for i, (role, message) in enumerate(self.messages):
if message:
if type(message) is tuple:
message, _, _ = message
ret += role + ": " + message + seps[i % 2]
else:
ret += role + ":"
return ret
if self.sep_style == SeparatorStyle.MPT:
if self.system:
ret = self.system + self.sep
else:
ret = ''
for role, message in self.messages:
if message:
if type(message) is tuple:
message, _, _ = message
ret += role + message + self.sep
else:
ret += role
return ret
else:
raise ValueError(f"Invalid style: {self.sep_style}")
def append_message(self, role, message):
self.messages.append([role, message])
def copy(self):
return Conversation(
system=self.system,
roles=self.roles,
messages=[[x, y] for x, y in self.messages],
offset=self.offset,
sep_style=self.sep_style,
sep=self.sep,
sep2=self.sep2)
class KeywordsStoppingCriteria(StoppingCriteria):
def __init__(self, keywords, tokenizer, input_ids):
self.keywords = keywords
self.keyword_ids = [tokenizer(keyword).input_ids for keyword in keywords]
self.keyword_ids = [keyword_id[0] for keyword_id in self.keyword_ids if type(keyword_id) is list and len(keyword_id) == 1]
self.tokenizer = tokenizer
self.start_len = None
self.input_ids = input_ids
def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
if self.start_len is None:
self.start_len = self.input_ids.shape[1]
else:
for keyword_id in self.keyword_ids:
if output_ids[0, -1] == keyword_id:
return True
outputs = self.tokenizer.batch_decode(output_ids[:, self.start_len:], skip_special_tokens=True)[0]
for keyword in self.keywords:
if keyword in outputs:
return True
return False
class GOTImageEvalProcessor:
def __init__(self, image_size=384, mean=None, std=None):
if mean is None:
mean = (0.48145466, 0.4578275, 0.40821073)
if std is None:
std = (0.26862954, 0.26130258, 0.27577711)
self.normalize = transforms.Normalize(mean, std)
self.transform = transforms.Compose(
[
transforms.Resize(
(image_size, image_size), interpolation=InterpolationMode.BICUBIC
),
transforms.ToTensor(),
self.normalize,
]
)
def __call__(self, item):
return self.transform(item)
class GOTConfig(Qwen2Config):
model_type = "GOT"
class GOTQwenModel(Qwen2Model):
config_class = GOTConfig
def __init__(self, config: Qwen2Config):
super(GOTQwenModel, self).__init__(config)
self.vision_tower_high = build_GOT_vit_b()
self.mm_projector_vary = nn.Linear(1024, 1024)
def initialize_vision_modules(
self,
vision_tower,
pretrained_stage1_model=None,
freeze_vision_tower=False,
use_im_start_end=False,
vision_select_layer=-1,
dtype=torch.float16,
device="cpu"
):
image_processor_high = GOTImageEvalProcessor(image_size=1024)
self.vision_tower_high = self.vision_tower_high.to(dtype=dtype, device=device)
self.mm_projector_vary = self.mm_projector_vary.to(dtype=dtype, device=device)
image_token_len = 256
self.config.vision_tower = vision_tower
self.config.image_token_len = image_token_len
self.config.use_im_start_end = True
self.config.vision_select_layer = vision_select_layer
self.config.freeze_vision_tower = freeze_vision_tower
return dict(
image_processor_high=image_processor_high,
image_token_len=image_token_len,
)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
images: Optional[torch.FloatTensor] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
# HACK: replace back original embeddings for LLaVA pretraining
orig_embeds_params = getattr(self, 'orig_embeds_params', None)
if orig_embeds_params is not None:
with torch.no_grad():
self.get_input_embeddings().weight[:-self.num_new_tokens] = orig_embeds_params[:-self.num_new_tokens].data
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
vision_tower_high = getattr(self, 'vision_tower_high', None)
if vision_tower_high is not None and (input_ids.shape[1] != 1 or self.training) and images is not None:
use_im_start_end = getattr(self.config, "use_im_start_end", -1)
vision_select_layer = getattr(self.config, "vision_select_layer", -1)
im_patch_token = getattr(self.config, "im_patch_token", -1)
im_start_token = getattr(self.config, "im_start_token", -1)
im_end_token = getattr(self.config, "im_end_token", -1)
freeze_vision_tower = getattr(self.config, "freeze_vision_tower", False)
im_patch_token = 151859
im_start_token = 151857
im_end_token = 151858
image_features = []
for image in images:
P, C, H, W = image.shape
if P == 1:
with torch.set_grad_enabled(False):
cnn_feature = vision_tower_high(image)
cnn_feature = cnn_feature.flatten(2).permute(0, 2, 1) # 256*1024
image_feature = self.mm_projector_vary(cnn_feature)
image_features.append(image_feature)
else:
image_patches = torch.unbind(image)
image_patches_features = []
for image_patch in image_patches:
image_p = torch.stack([image_patch])
with torch.set_grad_enabled(False):
cnn_feature_p = vision_tower_high(image_p)
cnn_feature_p = cnn_feature_p.flatten(2).permute(0, 2, 1)
image_feature_p = self.mm_projector_vary(cnn_feature_p)
image_patches_features.append(image_feature_p)
image_feature = torch.cat(image_patches_features, dim=1)
image_features.append(image_feature)
dummy_image_features_2 = torch.zeros(256, 1024, device=inputs_embeds.device, dtype=inputs_embeds.dtype)
dummy_image_features = dummy_image_features_2
use_im_start_end = True
new_input_embeds = []
for cur_input_ids, cur_input_embeds, cur_image_features in zip(input_ids, inputs_embeds, image_features):
if (cur_input_ids == im_patch_token).sum() == 0:
cur_input_embeds = cur_input_embeds + (0. * dummy_image_features).sum()
new_input_embeds.append(cur_input_embeds)
continue
if use_im_start_end:
if (cur_input_ids == im_start_token).sum() != (cur_input_ids == im_end_token).sum():
raise ValueError("The number of image start tokens and image end tokens should be the same.")
image_start_tokens = torch.where(cur_input_ids == im_start_token)[0]
for image_start_token_pos, per_cur_image_features in zip(image_start_tokens, cur_image_features):
per_cur_image_features = per_cur_image_features.to(device=cur_input_embeds.device)
num_patches = per_cur_image_features.shape[0]
if cur_input_ids[image_start_token_pos + num_patches + 1] != im_end_token:
raise ValueError("The image end token should follow the image start token.")
cur_input_embeds = torch.cat(
(
cur_input_embeds[:image_start_token_pos+1],
per_cur_image_features,
cur_input_embeds[image_start_token_pos + num_patches + 1:]
),
dim=0
)
new_input_embeds.append(cur_input_embeds)
else:
raise NotImplementedError
inputs_embeds = torch.stack(new_input_embeds, dim=0)
return super(GOTQwenModel, self).forward(
input_ids=None, attention_mask=attention_mask, past_key_values=past_key_values,
inputs_embeds=inputs_embeds, use_cache=use_cache, position_ids = position_ids,
output_attentions=output_attentions, output_hidden_states=output_hidden_states,
return_dict=return_dict
)
class GOTQwenForCausalLM(Qwen2ForCausalLM):
config_class = GOTConfig
# supports_gradient_checkpointing = True
def __init__(self, config):
super(Qwen2ForCausalLM, self).__init__(config)
self.model = GOTQwenModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_model(self):
return self.model
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
images: Optional[torch.FloatTensor] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.model(
input_ids=input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
images=images,
return_dict=return_dict
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
logits = logits.float()
# logits
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
):
# Omit tokens covered by past_key_values
if past_key_values is not None:
if isinstance(past_key_values, Cache):
cache_length = past_key_values.get_seq_length()
past_length = past_key_values.seen_tokens
max_cache_length = past_key_values.get_max_length()
else:
cache_length = past_length = past_key_values[0][0].shape[2]
max_cache_length = None
# Keep only the unprocessed tokens:
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
# input)
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
# input_ids based on the past_length.
elif past_length < input_ids.shape[1]:
input_ids = input_ids[:, past_length:]
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
if (
max_cache_length is not None
and attention_mask is not None
and cache_length + input_ids.shape[1] > max_cache_length
):
attention_mask = attention_mask[:, -max_cache_length:]
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past_key_values:
position_ids = position_ids[:, -input_ids.shape[1] :]
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"position_ids": position_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
"images": kwargs.get("images", None),
}
)
return model_inputs
def initialize_vision_tokenizer(
self,
tokenizer,
freeze_lm_model=False,
pretrained_stage1_model=None,
device="cpu"
):
config = self.get_model().config
self.resize_token_embeddings(len(tokenizer))
config.im_patch_token = 151859
config.use_im_start_end = True
if config.use_im_start_end:
self.resize_token_embeddings(len(tokenizer))
config.im_start_token, config.im_end_token = 151857, 151858
def load_image(self, image_file):
if image_file.startswith('http') or image_file.startswith('https'):
response = requests.get(image_file)
image = Image.open(BytesIO(response.content)).convert('RGB')
else:
image = Image.open(image_file).convert('RGB')
# image = image_file
return image
def disable_torch_init(self):
"""
Disable the redundant torch default initialization to accelerate model creation.
"""
import torch
setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
def chat(self, tokenizer, image_file, ocr_type, ocr_box='', ocr_color='', render=False, save_render_file=None, print_prompt=False, gradio_input=False, stream_flag = False):
self.disable_torch_init()
image_processor_high = GOTImageEvalProcessor(image_size=1024)
use_im_start_end = True
image_token_len = 256
if gradio_input:
image = image_file.copy()
else:
image = self.load_image(image_file)
w, h = image.size
if ocr_type == 'format':
qs = 'OCR with format: '
else:
qs = 'OCR: '
if ocr_box:
bbox = eval(ocr_box)
if len(bbox) == 2:
bbox[0] = int(bbox[0]/w*1000)
bbox[1] = int(bbox[1]/h*1000)
if len(bbox) == 4:
bbox[0] = int(bbox[0]/w*1000)
bbox[1] = int(bbox[1]/h*1000)
bbox[2] = int(bbox[2]/w*1000)
bbox[3] = int(bbox[3]/h*1000)
if ocr_type == 'format':
qs = str(bbox) + ' ' + 'OCR with format: '
else:
qs = str(bbox) + ' ' + 'OCR: '
if ocr_color:
if ocr_type == 'format':
qs = '[' + ocr_color + ']' + ' ' + 'OCR with format: '
else:
qs = '[' + ocr_color + ']' + ' ' + 'OCR: '
if use_im_start_end:
qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_PATCH_TOKEN*image_token_len + DEFAULT_IM_END_TOKEN + '\n' + qs
else:
qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
conv_mpt = Conversation(
system="""<|im_start|>system
You should follow the instructions carefully and explain your answers in detail.""",
# system = None,
roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
version="mpt",
messages=(),
offset=0,
sep_style=SeparatorStyle.MPT,
sep="<|im_end|>",
)
conv = conv_mpt.copy()
conv.append_message(conv.roles[0], qs)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
if print_prompt:
print(prompt)
inputs = tokenizer([prompt])
image_tensor_1 = image_processor_high(image)
input_ids = torch.as_tensor(inputs.input_ids).cpu()
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
if stream_flag:
with torch.autocast("cpu", dtype=torch.bfloat16):
output_ids = self.generate(
input_ids,
images=[image_tensor_1.unsqueeze(0).half().cpu()],
do_sample=False,
num_beams = 1,
no_repeat_ngram_size = 20,
streamer=streamer,
max_new_tokens=4096,
stopping_criteria=[stopping_criteria]
)
else:
with torch.autocast("cpu", dtype=torch.bfloat16):
output_ids = self.generate(
input_ids,
images=[image_tensor_1.unsqueeze(0).half().cpu()],
do_sample=False,
num_beams = 1,
no_repeat_ngram_size = 20,
# streamer=streamer,
max_new_tokens=4096,
stopping_criteria=[stopping_criteria]
)
outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
if outputs.endswith(stop_str):
outputs = outputs[:-len(stop_str)]
outputs = outputs.strip()
response_str = outputs
if render:
print('==============rendering===============')
from .render_tools import svg_to_html, content_mmd_to_html, tik_html, translation_table
if '**kern' in outputs:
import verovio
tk = verovio.toolkit()
tk.loadData(outputs)
tk.setOptions({"pageWidth": 2100, "footer": 'none',
'barLineWidth': 0.5, 'beamMaxSlope': 15,
'staffLineWidth': 0.2, 'spacingStaff': 6})
tk.getPageCount()
svg = tk.renderToSVG()
svg = svg.replace("overflow=\"inherit\"", "overflow=\"visible\"")
svg_to_html(svg, save_render_file)
if ocr_type == 'format' and '**kern' not in outputs:
if '\\begin{tikzpicture}' not in outputs:
html_path_2 = save_render_file
right_num = outputs.count('\\right')
left_num = outputs.count('\left')
if right_num != left_num:
outputs = outputs.replace('\left(', '(').replace('\\right)', ')').replace('\left[', '[').replace('\\right]', ']').replace('\left{', '{').replace('\\right}', '}').replace('\left|', '|').replace('\\right|', '|').replace('\left.', '.').replace('\\right.', '.')
outputs = outputs.replace('"', '``').replace('$', '')
outputs_list = outputs.split('\n')
gt= ''
for out in outputs_list:
gt += '"' + out.replace('\\', '\\\\') + r'\n' + '"' + '+' + '\n'
gt = gt[:-2]
lines = content_mmd_to_html
lines = lines.split("const text =")
new_web = lines[0] + 'const text =' + gt + lines[1]
else:
html_path_2 = save_render_file
outputs = outputs.translate(translation_table)
outputs_list = outputs.split('\n')
gt= ''
for out in outputs_list:
if out:
if '\\begin{tikzpicture}' not in out and '\\end{tikzpicture}' not in out:
while out[-1] == ' ':
out = out[:-1]
if out is None:
break
if out:
if out[-1] != ';':
gt += out[:-1] + ';\n'
else:
gt += out + '\n'
else:
gt += out + '\n'
lines = tik_html
lines = lines.split("const text =")
new_web = lines[0] + gt + lines[1]
with open(html_path_2, 'w') as web_f_new:
web_f_new.write(new_web)
return response_str
def dynamic_preprocess(self, image, min_num=1, max_num=6, image_size=1024, use_thumbnail=True):
def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
best_ratio_diff = float('inf')
best_ratio = (1, 1)
area = width * height
for ratio in target_ratios:
target_aspect_ratio = ratio[0] / ratio[1]
ratio_diff = abs(aspect_ratio - target_aspect_ratio)
if ratio_diff < best_ratio_diff:
best_ratio_diff = ratio_diff
best_ratio = ratio
elif ratio_diff == best_ratio_diff:
if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
best_ratio = ratio
# print(f'width: {width}, height: {height}, best_ratio: {best_ratio}')
return best_ratio
orig_width, orig_height = image.size
aspect_ratio = orig_width / orig_height
# calculate the existing image aspect ratio
target_ratios = set(
(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
i * j <= max_num and i * j >= min_num)
# print(target_ratios)
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
# find the closest aspect ratio to the target
target_aspect_ratio = find_closest_aspect_ratio(
aspect_ratio, target_ratios, orig_width, orig_height, image_size)
# print(target_aspect_ratio)
# calculate the target width and height
target_width = image_size * target_aspect_ratio[0]
target_height = image_size * target_aspect_ratio[1]
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
# resize the image
resized_img = image.resize((target_width, target_height))
processed_images = []
for i in range(blocks):
box = (
(i % (target_width // image_size)) * image_size,
(i // (target_width // image_size)) * image_size,
((i % (target_width // image_size)) + 1) * image_size,
((i // (target_width // image_size)) + 1) * image_size
)
# split the image
split_img = resized_img.crop(box)
processed_images.append(split_img)
assert len(processed_images) == blocks
if use_thumbnail and len(processed_images) != 1:
thumbnail_img = image.resize((image_size, image_size))
processed_images.append(thumbnail_img)
return processed_images
def chat_crop(self, tokenizer, image_file, ocr_type, render=False, save_render_file=None, print_prompt=False, gradio_input=False, stream_flag = False):
# Model
self.disable_torch_init()
multi_page=False
image_processor_high = GOTImageEvalProcessor(image_size=1024)
use_im_start_end = True
image_token_len = 256
image_list = []
# if len(image_file_list)>1:
# multi_page = True
if multi_page:
qs = 'OCR with format across multi pages: '
# only for png files
# import glob
# from natsort import natsorted
# patches = glob.glob(image_file + '/*png')
patches = image_file
# patches = natsorted(patches)
sub_images = []
for sub_image in patches:
sub_images.append(self.load_image(sub_image))
ll = len(patches)
# print(patches)
# print("len ll: ", ll)
else:
if ocr_type == 'format':
qs = 'OCR with format upon the patch reference: '
else:
qs = 'OCR upon the patch reference: '
if gradio_input:
img = image_file.copy()
else:
img = self.load_image(image_file)
sub_images = self.dynamic_preprocess(img)
ll = len(sub_images)
for image in sub_images:
image_tensor_1 = image_processor_high(image)
image_list.append(image_tensor_1)
image_list = torch.stack(image_list)
print('====new images batch size======: \n',image_list.shape)
if use_im_start_end:
qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_PATCH_TOKEN*image_token_len*ll + DEFAULT_IM_END_TOKEN + '\n' + qs
else:
qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
conv_mpt = Conversation(
system="""<|im_start|>system
You should follow the instructions carefully and explain your answers in detail.""",
# system = None,
roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
version="mpt",
messages=(),
offset=0,
sep_style=SeparatorStyle.MPT,
sep="<|im_end|>",
)
conv = conv_mpt.copy()
conv.append_message(conv.roles[0], qs)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
if print_prompt:
print(prompt)
inputs = tokenizer([prompt])
input_ids = torch.as_tensor(inputs.input_ids).cpu()
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
if stream_flag:
with torch.autocast("cpu", dtype=torch.bfloat16):
output_ids = self.generate(
input_ids,
images=[image_list.half().cpu()],
do_sample=False,
num_beams = 1,
# no_repeat_ngram_size = 20,
streamer=streamer,
max_new_tokens=4096,
stopping_criteria=[stopping_criteria]
)
else:
with torch.autocast("cpu", dtype=torch.bfloat16):
output_ids = self.generate(
input_ids,
images=[image_list.half().cpu()],
do_sample=False,
num_beams = 1,
# no_repeat_ngram_size = 20,
# streamer=streamer,
max_new_tokens=4096,
stopping_criteria=[stopping_criteria]
)
outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
if outputs.endswith(stop_str):
outputs = outputs[:-len(stop_str)]
outputs = outputs.strip()
response_str = outputs
if render:
print('==============rendering===============')
from .render_tools import content_mmd_to_html
html_path_2 = save_render_file
right_num = outputs.count('\\right')
left_num = outputs.count('\left')
if right_num != left_num:
outputs = outputs.replace('\left(', '(').replace('\\right)', ')').replace('\left[', '[').replace('\\right]', ']').replace('\left{', '{').replace('\\right}', '}').replace('\left|', '|').replace('\\right|', '|').replace('\left.', '.').replace('\\right.', '.')
outputs = outputs.replace('"', '``').replace('$', '')
outputs_list = outputs.split('\n')
gt= ''
for out in outputs_list:
gt += '"' + out.replace('\\', '\\\\') + r'\n' + '"' + '+' + '\n'
gt = gt[:-2]
lines = content_mmd_to_html
lines = lines.split("const text =")
new_web = lines[0] + 'const text =' + gt + lines[1]
with open(html_path_2, 'w') as web_f_new:
web_f_new.write(new_web)
return response_str |