# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved. # # This code is based on transformers/src/transformers/models/llama/modeling_llama.py # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch InternLMXComposer2 model.""" import os import re import copy import queue import threading from typing import List, Optional, Tuple, Union import torch import torch.utils.checkpoint from PIL import Image import numpy as np import random from torch import nn from torch.nn import CrossEntropyLoss from torchvision import transforms from torchvision.transforms.functional import InterpolationMode from transformers.modeling_outputs import CausalLMOutputWithPast from transformers.utils import (add_start_docstrings_to_model_forward, replace_return_docstrings) from transformers import StoppingCriteria, StoppingCriteriaList from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed try: from transformers.generation.streamers import BaseStreamer except: # noqa # pylint: disable=bare-except BaseStreamer = None import torchvision.transforms as transforms from torchvision.transforms.functional import InterpolationMode from .build_mlp import build_vision_projector, build_vision_tower from .ixc_utils import Image_transform, Video_transform, load_video, frame2img, get_font from .configuration_internlm_xcomposer2 import InternLMXcomposer2Config from .modeling_internlm2 import (InternLM2_INPUTS_DOCSTRING, InternLM2Model, InternLM2PreTrainedModel) _CONFIG_FOR_DOC = 'InternLMXcomposer2Config' image_extensions = {'.jpg', '.jpeg', '.png', '.gif', '.bmp', '.webp'} video_extensions = {'.mp4', '.avi', '.mkv', '.mov', '.wmv'} class StoppingCriteriaSub(StoppingCriteria): def __init__(self, stops=[], encounters=1): super().__init__() self.stops = stops def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor): for stop in self.stops: if torch.all((stop == input_ids[0][-len(stop):])).item(): return True return False def get_stopping_criteria(stop_words_ids): stop_words_ids = [torch.tensor([i]).cuda() for i in stop_words_ids] stopping_criteria = StoppingCriteriaList( [StoppingCriteriaSub(stops=stop_words_ids)]) return stopping_criteria def set_random_seed(seed, set_cudnn=False): """Set the random seed for reproducibility. Parameters: seed (int): The seed to use for generating random numbers. """ torch.manual_seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) # For multi-GPU. np.random.seed(seed) random.seed(seed) if set_cudnn and torch.backends.cudnn.is_available(): torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False class InternLMXComposer2ForCausalLM(InternLM2PreTrainedModel): _auto_class = 'AutoModelForCausalLM' _tied_weights_keys = ['output.weight'] def __init__(self, config): super().__init__(config) self.model = InternLM2Model(config) self.vocab_size = config.vocab_size self.output = nn.Linear( config.hidden_size, config.vocab_size, bias=False) self.tokenizer = None self.hd_num = 25 self.font = get_font() self.max_length = config.max_length print(f'Set max length to {self.max_length}') # Initialize weights and apply final processing self.post_init() self.plora_glb_GN = nn.Parameter(torch.zeros([1, 1, 4096])) self.plora_sub_GN = nn.Parameter(torch.zeros([1, 1, 1, 4096])) self.vit = build_vision_tower() self.vision_proj = build_vision_projector() self.video_mem_proj = build_vision_projector(1536) self.im_size = 490 self.vis_processor = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), ]) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, InternLM2Model): module.gradient_checkpointing = value if value: self.vit.vision_tower.vision_model.encoder.gradient_checkpointing = value def get_input_embeddings(self): return self.model.tok_embeddings def set_input_embeddings(self, value): self.model.tok_embeddings = value def get_output_embeddings(self): return self.output def set_output_embeddings(self, new_embeddings): self.output = new_embeddings def set_decoder(self, decoder): self.model = decoder def get_decoder(self): return self.model def encode_text(self, text, add_special_tokens=False): token = self.tokenizer( text, return_tensors='pt', add_special_tokens=add_special_tokens).input_ids.to(self.device) embs = self.model.tok_embeddings(token) return embs def encode_img(self, image, hd_num=25): if image is None: return None if isinstance(image, str): _, ext = os.path.splitext(image) if ext.lower() in image_extensions: image = Image.open(image) image = Image_transform(image, hd_num = hd_num) elif ext.lower() in video_extensions: image = load_video(image) image = frame2img(image, self.font) image = Video_transform(image, hd_num = hd_num) else: print ('Unknow input format', image) return None image = self.vis_processor(image).unsqueeze(0).to(self.device) else: assert isinstance(image, torch.Tensor) img_embeds, atts_img, img_target = self.img2emb(image) return img_embeds def img2emb(self, image): img_embeds, img_split = self.vit([image], self.plora_glb_GN, self.plora_sub_GN) if len(img_split) > 1: print ('Batch Size >1 is not supported.') assert 0 #print (img_embeds.shape) img_embeds = self.vision_proj(img_embeds) atts_img = torch.ones( img_embeds.size()[:-1], dtype=torch.long).to(img_embeds.device) img_target = torch.ones( img_embeds.size()[:2], dtype=torch.long).to( img_embeds.device) * -100 return img_embeds, atts_img, img_target def prompt_wrap(self, img_embeds, prompt): batch_size = img_embeds.shape[0] p_before, p_after = prompt.split('') p_before_tokens = self.tokenizer( p_before, return_tensors='pt', add_special_tokens=True).to(img_embeds.device) p_before_embeds = self.model.tok_embeddings( p_before_tokens.input_ids).expand(batch_size, -1, -1) wrapped_img_embeds = torch.cat([p_before_embeds, img_embeds], dim=1) wrapped_atts_img = torch.ones( wrapped_img_embeds.size()[:-1], dtype=torch.long).to(img_embeds.device) wrapped_target = torch.ones( batch_size, wrapped_img_embeds.shape[1], dtype=torch.long).to( img_embeds.device) * -100 return wrapped_img_embeds, wrapped_atts_img, wrapped_target def text2emb(self, text, add_special_tokens=False): to_regress_tokens = self.tokenizer( text, return_tensors='pt', padding='longest', truncation=True, max_length=self.max_length, add_special_tokens=add_special_tokens ).to(self.device) targets = self.mask_human_targets(to_regress_tokens.input_ids) targets = targets.to(self.device) return to_regress_tokens, targets def interleav_wrap_chat(self, query, image, history = [], meta_instruction='', max_length=16384, hd_num=24): self.max_length = max_length prompt = '' if meta_instruction: prompt += f"""[UNUSED_TOKEN_146]system\n{meta_instruction}[UNUSED_TOKEN_145]\n""" for record in history: prompt += f"""[UNUSED_TOKEN_146]user\n{record[0]}[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n{record[1]}[UNUSED_TOKEN_145]\n""" prompt += f"""[UNUSED_TOKEN_146]user\n{query}[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n""" image_nums = len(image) if image_nums == 1 and prompt.find('') == -1: #print ('auto append image at the begining') prompt = '' + prompt parts = prompt.split('') wrap_embeds, wrap_im_mask = [], [] temp_len = 0 need_bos = True if len(parts) != image_nums + 1: #raise ValueError('Invalid prompt format.') print ('Waring! The image number != given position!') if image_nums > 1: hd_num = 6 else: hu_num = hd_num for idx, part in enumerate(parts): if need_bos or len(part) > 0: part_tokens = self.tokenizer( part, return_tensors='pt', padding='longest', add_special_tokens=need_bos).to(self.device) if need_bos: need_bos = False part_embeds = self.model.tok_embeddings( part_tokens.input_ids) wrap_embeds.append(part_embeds) wrap_im_mask.append(torch.zeros(part_embeds.shape[:2])) temp_len += part_embeds.shape[1] if idx < image_nums: img = self.encode_img(image[idx], hd_num) wrap_embeds.append(img) wrap_im_mask.append(torch.ones(img.shape[:2])) temp_len += img.shape[1] if temp_len > self.max_length: break wrap_embeds = torch.cat(wrap_embeds, dim=1) wrap_im_mask = torch.cat(wrap_im_mask, dim=1) wrap_embeds = wrap_embeds[:, :self.max_length].to(self.device) wrap_im_mask = wrap_im_mask[:, :self.max_length].to(self.device).bool() inputs = { 'inputs_embeds': wrap_embeds } return inputs, wrap_im_mask, temp_len def interleav_wrap(self, img_list, text_list, image_nums): temp_embeds = [] temp_im_mask = [] temp_tars = [] # encode_image img_embeds, img_split = self.vit(img_list, self.plora_glb_GN, self.plora_sub_GN) img_embeds = self.vision_proj(img_embeds) text_list = text_list[0] for idx, text in enumerate(text_list): image_num = image_nums[idx] im_id = int(np.sum(image_nums[:idx])) images = [] for i in range(image_nums[idx]): st = int(np.sum(img_split[:im_id + i])) sp = img_split[im_id + i] temp_img = img_embeds[:, st:st+sp] images.append(temp_img) atts_img = torch.ones((len(images), images[0].shape[1]), dtype=torch.long).to(self.device) img_target = torch.ones( (len(images), images[0].shape[1]), dtype=torch.long).to( self.device) * -100 if image_num == 1 and text.find('') == -1: text = '' + text parts = text.split('') wrap_tokens, wrap_embeds, wrap_im_mask = [], [], [] temp_len = 0 need_bos = True for idx, part in enumerate(parts): if len(part) > 0: part_tokens = self.tokenizer(part, return_tensors='pt', padding='longest', add_special_tokens=need_bos).to(self.device) if need_bos: need_bos = False wrap_tokens.append(part_tokens.input_ids) part_embeds = self.model.tok_embeddings(part_tokens.input_ids) wrap_embeds.append(part_embeds) wrap_im_mask.append(torch.zeros(part_embeds.shape[:2]).to(self.device)) temp_len += part_embeds.shape[1] if idx < image_num: wrap_embeds.append(images[idx]) wrap_token = torch.ones(images[idx].shape[:2], dtype=torch.long).to(self.device) * -100 wrap_tokens.append(wrap_token) wrap_im_mask.append(torch.ones(images[idx].shape[:2]).to(self.device)) temp_len += images[idx].shape[1] if temp_len > self.max_length: break wrap_tokens = torch.cat(wrap_tokens, dim=1) wrap_embeds = torch.cat(wrap_embeds, dim=1) wrap_im_mask = torch.cat(wrap_im_mask, dim=1) wrap_target = self.mask_human_targets(wrap_tokens).to(self.device) temp_embeds.append(wrap_embeds) temp_im_mask.append(wrap_im_mask) temp_tars.append(wrap_target) temp_max_len = np.max([i.shape[1] for i in temp_embeds]) temp_max_len = min(temp_max_len, self.max_length) final_input, final_atts, final_tars, final_mask = [], [], [], [] pad = torch.ones([1, 1]) * self.tokenizer.pad_token_id pad = pad.long().to(self.device) pad_emb = self.model.tok_embeddings(pad) for idx in range(len(temp_embeds)): temp_len = temp_embeds[idx].shape[1] if temp_len >= temp_max_len: final_input.append(temp_embeds[idx][:, :temp_max_len]) final_atts.append(torch.ones(1, temp_max_len).to(wrap_target.dtype).to(self.device)) final_tars.append(temp_tars[idx][:, :temp_max_len]) final_mask.append(temp_im_mask[idx][:, :temp_max_len]) else: final_input.append(torch.cat([temp_embeds[idx], pad_emb.repeat(1, temp_max_len-temp_len, 1)], dim=1)) final_atts.append(torch.cat([torch.ones(1, temp_len), torch.zeros(1, temp_max_len-temp_len)], dim=1).to(wrap_target.dtype).to(self.device)) final_tars.append(torch.cat([temp_tars[idx], (torch.ones(1, temp_max_len-temp_len)*-100).to(wrap_target.dtype).to(self.device)], dim=1)) final_mask.append(torch.cat([temp_im_mask[idx], (torch.zeros(1, temp_max_len-temp_len)).to(wrap_target.dtype).to(self.device)], dim=1)) inputs_embeds = torch.cat(final_input, dim=0) attention_mask = torch.cat(final_atts, dim=0) targets = torch.cat(final_tars, dim=0) im_mask = torch.cat(final_mask, dim=0) return inputs_embeds, attention_mask, targets, im_mask def mask_human_targets(self, input_ids, pure=False): target_batch = [] for bs in range(input_ids.shape[0]): ids = input_ids[bs] targets = copy.deepcopy(ids) end_count = 0 last_eoa = 0 for i, temp_id in enumerate(ids): if temp_id == 92542: if end_count % 2 == 0: targets[last_eoa:i + 6] = -100 else: last_eoa = i + 1 end_count += 1 # # eos and following pad elif temp_id == 2: # loss on eos, but not on pad targets[i + 1:] = -100 break # trunction, end at last question if temp_id != 2 and end_count % 2 == 0: # mask all after the last answer targets[last_eoa + 1:] = -100 target_batch.append(targets.unsqueeze(0)) target_batch = torch.cat(target_batch, dim=0) return target_batch @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING) @replace_return_docstrings( output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) 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, return_dict: Optional[bool] = None, **kwargs) -> Union[Tuple, CausalLMOutputWithPast]: r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: """ samples = kwargs.get('samples', None) if samples: infer_mode = samples.get('infer_mode', 'base') if samples['data_type'][0] == 'text': has_img = False elif samples['data_type'][0] == 'multi': has_img = True else: raise NotImplementedError # encode text text = samples['text_input'] # encode image if has_img: image = samples['image'][0] bs = len(samples['text_input'][0]) image_nums = [] temp_image = [] for im in image: if type(im) is list: image_nums.append(len(im)) temp_image.extend(im) else: image_nums.append(1) temp_image.append(im) image = temp_image assert type(image) is list and len(image_nums) == bs to_regress_embeds, attention_mask, targets, im_mask = self.interleav_wrap( image, text, image_nums) else: to_regress_tokens, targets = self.text2emb( text, add_special_tokens=True) to_regress_embeds = self.model.tok_embeddings( to_regress_tokens.input_ids) attention_mask = to_regress_tokens.attention_mask im_mask = torch.zeros(to_regress_embeds.shape[:2]).cuda() inputs_embeds = to_regress_embeds[:, :self.max_length] attention_mask = attention_mask[:, :self.max_length] targets = targets[:, :self.max_length] im_mask = im_mask[:, :self.max_length].bool() labels = targets else: im_mask = kwargs.get('im_mask', None) infer_mode = kwargs.get('infer_mode', 'base') if im_mask is None and inputs_embeds is not None: im_mask = torch.zeros(inputs_embeds.shape[:2]).to( inputs_embeds.device) im_mask = im_mask.bool() 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 # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, im_mask=im_mask, infer_mode=infer_mode, ) hidden_states = outputs[0] logits = self.output(hidden_states) logits = logits.float() 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, im_mask=None, infer_mode='base', **kwargs): if past_key_values is not None: past_length = past_key_values[0][0].shape[2] # Some generation methods already pass only the last input ID if input_ids.shape[1] > past_length: remove_prefix_length = past_length else: # Default to old behavior: keep only final ID remove_prefix_length = input_ids.shape[1] - 1 input_ids = input_ids[:, remove_prefix_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} im_mask = im_mask model_inputs.update({ 'position_ids': position_ids, 'past_key_values': past_key_values, 'use_cache': kwargs.get('use_cache'), 'attention_mask': attention_mask, 'im_mask': im_mask, 'infer_mode': infer_mode, }) return model_inputs @staticmethod def _reorder_cache(past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: reordered_past += (tuple( past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), ) return reordered_past def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = [], meta_instruction=''): prompt = '' if meta_instruction: prompt += f"""[UNUSED_TOKEN_146]system\n{meta_instruction}[UNUSED_TOKEN_145]\n""" else: prompt += '' for record in history: prompt += f"""[UNUSED_TOKEN_146]user\n{record[0]}[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n{record[1]}[UNUSED_TOKEN_145]\n""" prompt += f"""[UNUSED_TOKEN_146]user\n{query}[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n""" return tokenizer([prompt], return_tensors='pt') @torch.no_grad() def chat( self, tokenizer, query: str, image: List[Tuple[str, str]] = [], hd_num: int = 24, history: List[Tuple[str, str]] = [], streamer: Optional[BaseStreamer] = None, max_new_tokens: int = 1024, do_sample: bool = True, num_beams: int = 1, temperature: float = 1.0, top_p: float = 0.8, repetition_penalty: float=1.005, infer_mode: str = 'base', use_meta: bool = False, meta_instruction: str = 'You are an AI assistant whose name is InternLM-XComposer (浦语·灵笔).\n' '- InternLM-XComposer (浦语·灵笔) is a multi-modality conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n' '- InternLM-XComposer (浦语·灵笔) can understand and communicate fluently in the language chosen by the user such as English and 中文.\n' '- InternLM-XComposer (浦语·灵笔) is capable of comprehending and articulating responses effectively based on the provided image.', **kwargs, ): if not use_meta: meta_instruction = '' if image is None: inputs = self.build_inputs(tokenizer, query, history, meta_instruction) im_mask = torch.zeros(inputs['input_ids'].shape[:2]).cuda().bool() else: inputs, im_mask, _ = self.interleav_wrap_chat(query, image, history=history, meta_instruction=meta_instruction, hd_num=hd_num) inputs = { k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v) } # also add end-of-assistant token in eos token id to avoid unnecessary generation eos_token_id = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids(['[UNUSED_TOKEN_145]'])[0] ] outputs = self.generate( **inputs, streamer=streamer, max_new_tokens=max_new_tokens, num_beams=num_beams, do_sample=do_sample, temperature=temperature, top_p=top_p, eos_token_id=eos_token_id, repetition_penalty=repetition_penalty, im_mask=im_mask, infer_mode=infer_mode, **kwargs, ) if image is None: outputs = outputs[0].cpu().tolist()[len(inputs['input_ids'][0]):] else: outputs = outputs[0].cpu().tolist() response = tokenizer.decode(outputs, skip_special_tokens=True) response = response.split('[UNUSED_TOKEN_145]')[0] history = history + [(query, response)] return response, history @torch.no_grad() def write_artical( self, inst: str, image: List[Tuple[str, str]] = [], hd_num: int = 25, history: List[Tuple[str, str]] = [], streamer: Optional[BaseStreamer] = None, max_new_tokens: int = 1024, do_sample: bool = True, num_beams: int = 1, temperature: float = 1.0, top_p: float = 0.8, repetition_penalty: float=1.005, max_length: int=8192, seed: int = -1, use_meta: bool = False, **kwargs, ): meta_instruction = """You are an AI assistant whose name is InternLM-XComposer (浦语·灵笔). - InternLM-XComposer (浦语·灵笔) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless. - InternLM-XComposer (浦语·灵笔) can understand and communicate fluently in the language chosen by the user such as English and 中文. """ if seed != -1: set_seed(seed) if len(history): print ('Only chat function support multi round now, history will be ignored in the artical mode') stop_words_ids = [92542] stopping_criteria = get_stopping_criteria(stop_words_ids) if not use_meta: meta_instruction = '' with torch.no_grad(): inputs, im_mask, len_input_tokens = self.interleav_wrap_chat(inst, image, meta_instruction=meta_instruction, max_length=max_length) with torch.autocast(device_type='cuda', dtype=torch.float16): with torch.no_grad(): generate = self.generate(inputs_embeds=inputs['inputs_embeds'], do_sample=do_sample, num_beams=num_beams, temperature=temperature, repetition_penalty=repetition_penalty, stopping_criteria=stopping_criteria, max_new_tokens=max_length - len_input_tokens, top_p=0.8, top_k=40, length_penalty=1.0, im_mask=im_mask, infer_mode='write' ) response = generate[0].tolist() response = self.tokenizer.decode(response, skip_special_tokens=True) # remove eoa response = response.replace('[UNUSED_TOKEN_145]', '') response = response.replace('[UNUSED_TOKEN_146]', '') return response @torch.no_grad() def write_webpage( self, inst: str, image: List[Tuple[str, str]] = [], max_new_tokens: int = 4800, do_sample: bool = True, num_beams: int = 2, temperature: float = 1.0, repetition_penalty: float=3.0, seed: int = -1, use_meta: bool = False, task: str = 'Instruction-aware Webpage Generation', **kwargs, ): if seed != -1: set_random_seed(seed, set_cudnn=True) with torch.no_grad(): inputs, im_mask, len_input_tokens = self.interleav_wrap_chat(inst, image) with torch.autocast(device_type='cuda', dtype=torch.float16): with torch.no_grad(): generate = self.generate(inputs_embeds=inputs['inputs_embeds'], do_sample=do_sample, temperature=temperature, num_beams=num_beams, repetition_penalty=repetition_penalty, max_new_tokens=max_new_tokens, im_mask=im_mask, infer_mode='web' ) response = generate[0].tolist() response = self.tokenizer.decode(response, skip_special_tokens=True) # remove eoa response = response.replace('[UNUSED_TOKEN_145]', '') out = response.replace('[UNUSED_TOKEN_146]', '') image_type = 'random' pattern = r'''https://source\.unsplash\.com/random/(\d+)x(\d+)/\?([^'"]+)''' if image_type == 'placeholder': out = re.sub(pattern, r"https://placehold.co/\1x\2", out) elif image_type == 'random': out = re.sub(pattern, r"https://picsum.photos/\1/\2", out) with open(task.replace(' ', '_') + ".html", "w") as f: f.write(out) return out @torch.no_grad() def resume_2_webpage( self, inst: str, image: List[Tuple[str, str]] = [], max_new_tokens: int = 4800, do_sample: bool = True, num_beams: int = 2, temperature: float = 1.0, repetition_penalty: float=3.0, seed: int = -1, use_meta: bool = False, task: str = 'Resume-to-Personal Page', **kwargs, ): if seed != -1: set_random_seed(seed, set_cudnn=True) try: with open(inst) as fd: resume = fd.read() except: print ('The input should be a resume with markdown format.') inst = ' Generate a personal page using the content in the resume:' + resume with torch.no_grad(): inputs, im_mask, len_input_tokens = self.interleav_wrap_chat(inst, image) with torch.autocast(device_type='cuda', dtype=torch.float16): with torch.no_grad(): generate = self.generate(inputs_embeds=inputs['inputs_embeds'], do_sample=do_sample, temperature=temperature, num_beams=num_beams, repetition_penalty=repetition_penalty, max_new_tokens=max_new_tokens, im_mask=im_mask, infer_mode='web' ) response = generate[0].tolist() response = self.tokenizer.decode(response, skip_special_tokens=True) # remove eoa response = response.replace('[UNUSED_TOKEN_145]', '') html = response.replace('[UNUSED_TOKEN_146]', '') if seed != -1: set_random_seed(seed, set_cudnn=True) js_inst = ' Generate JavaScript events for the html code:' + html with torch.no_grad(): inputs, im_mask, len_input_tokens = self.interleav_wrap_chat(js_inst, image) with torch.autocast(device_type='cuda', dtype=torch.float16): with torch.no_grad(): generate = self.generate(inputs_embeds=inputs['inputs_embeds'], do_sample=do_sample, temperature=temperature, num_beams=num_beams, repetition_penalty=repetition_penalty, max_new_tokens=max_new_tokens, im_mask=im_mask, infer_mode='web' ) response = generate[0].tolist() response = self.tokenizer.decode(response, skip_special_tokens=True) # remove eoa response = response.replace('[UNUSED_TOKEN_145]', '') js = response.replace('[UNUSED_TOKEN_146]', '') if re.search(r'', html): js = re.findall(r')', f'\n{js}\n' + r'\1', html) elif re.search(r'', html): html = re.sub(r'()', f'\n{js}\n' + r'\1', html) out = html image_type = 'random' pattern = r'''https://source\.unsplash\.com/random/(\d+)x(\d+)/\?([^'"]+)''' if image_type == 'placeholder': out = re.sub(pattern, r"https://placehold.co/\1x\2", out) elif image_type == 'random': out = re.sub(pattern, r"https://picsum.photos/\1/\2", out) with open(task.replace(' ', '_') + ".html", "w") as f: f.write(out) return out @torch.no_grad() def screen_2_webpage( self, inst: str, image: List[Tuple[str, str]] = [], max_new_tokens: int = 4800, do_sample: bool = True, num_beams: int = 2, temperature: float = 1.0, repetition_penalty: float=3.0, seed: int = -1, use_meta: bool = False, task: str = 'Screenshot-to-Webpage', **kwargs, ): if seed != -1: set_random_seed(seed, set_cudnn=True) if len(image) == 0: print ('No image is provided, skip') return '' inst = ' Generate the HTML code of this web image with Tailwind CSS.' with torch.no_grad(): inputs, im_mask, len_input_tokens = self.interleav_wrap_chat(inst, image) with torch.autocast(device_type='cuda'): with torch.no_grad(): generate = self.generate(inputs_embeds=inputs['inputs_embeds'], do_sample=do_sample, temperature=temperature, num_beams=num_beams, repetition_penalty=repetition_penalty, max_new_tokens=max_new_tokens, im_mask=im_mask, infer_mode='web' ) response = generate[0].tolist() response = self.tokenizer.decode(response, skip_special_tokens=True) # remove eoa response = response.replace('[UNUSED_TOKEN_145]', '') out = response.replace('[UNUSED_TOKEN_146]', '') image_type = 'random' pattern = r'''https://source\.unsplash\.com/random/(\d+)x(\d+)/\?([^'"]+)''' if image_type == 'placeholder': out = re.sub(pattern, r"https://placehold.co/\1x\2", out) elif image_type == 'random': out = re.sub(pattern, r"https://picsum.photos/\1/\2", out) with open(task.replace(' ', '_') + ".html", "w") as f: f.write(out) return out