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# coding=utf-8
# Copyright 2025 MMaDA team.
#
# 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.
reserved_token_mapping = {
'<|soi|>': 126084,
'<|eoi|>': 126085,
'<|sov|>': 126086,
'<|eov|>': 126087,
'<|t2i|>': 126088,
'<|mmu|>': 126089,
'<|t2v|>': 126090,
'<|v2v|>': 126091,
'<|lvg|>': 126092,
'[iPAD]': 126093,
'<|r2i|>': 126094,
}
import torch
class UniversalPrompting():
def __init__(self, text_tokenizer,
special_tokens=("<|soi|>", "<|eoi|>", "<|sov|>", "<|eov|>", "<|t2i|>", "<|mmu|>", "<|t2v|>", "<|v2v|>", "<|lvg|>"),
max_text_len=8000, max_seq_len=377, ignore_id=-100, cond_dropout_prob=0.1, use_reserved_token=False):
"""
:param text_tokenizer: original text tokenizer
"""
if not use_reserved_token:
self.text_tokenizer = text_tokenizer
self.text_tokenizer.add_special_tokens({'pad_token': '[PAD]'})
self.text_tokenizer.add_tokens(list(special_tokens))
self.sptids_dict = {token: torch.tensor(self.text_tokenizer.convert_tokens_to_ids([token])) for token in
special_tokens}
self.sptids_dict['<|sot|>'] = torch.tensor([self.text_tokenizer.bos_token_id])
self.sptids_dict['<|eot|>'] = torch.tensor([self.text_tokenizer.eos_token_id])
self.sptids_dict['<|pad|>'] = torch.tensor([self.text_tokenizer.pad_token_id])
else:
self.text_tokenizer = text_tokenizer
self.sptids_dict = {}
for token, token_id in reserved_token_mapping.items():
self.sptids_dict[token] = torch.tensor([token_id])
self.sptids_dict['<|sot|>'] = torch.tensor([self.text_tokenizer.bos_token_id])
self.sptids_dict['<|eot|>'] = torch.tensor([self.text_tokenizer.eos_token_id])
end_header_tokens = self.text_tokenizer.convert_tokens_to_ids(['<|end_header_id|>'])
if end_header_tokens and len(end_header_tokens) > 0 and end_header_tokens[0]:
self.sptids_dict['<|end_header_id|>'] = torch.tensor(end_header_tokens)
self.sptids_dict['<|eot_id|>'] = torch.tensor(self.text_tokenizer.convert_tokens_to_ids(['<|eot_id|>']))
self.sptids_dict['<|start_header_id|>'] = torch.tensor(self.text_tokenizer.convert_tokens_to_ids(['<|start_header_id|>']))
else:
special_tokens_dict = {
'additional_special_tokens': [
'<|start_header_id|>',
'<|end_header_id|>',
'<|eot_id|>'
]
}
num_added = self.text_tokenizer.add_special_tokens(special_tokens_dict)
new_token_id = self.text_tokenizer.convert_tokens_to_ids(['<|end_header_id|>'])
self.sptids_dict['<|end_header_id|>'] = torch.tensor(new_token_id)
self.sptids_dict['<|eot_id|>'] = torch.tensor(self.text_tokenizer.convert_tokens_to_ids(['<|eot_id|>']))
self.sptids_dict['<|start_header_id|>'] = torch.tensor(self.text_tokenizer.convert_tokens_to_ids(['<|start_header_id|>']))
# plus 1 because at this time we add a task token before
print(f"self.sptids_dict: {self.sptids_dict}")
self.max_text_len = max_text_len + 1
self.pad_id = reserved_token_mapping['[iPAD]']
self.ignore_id = ignore_id
self.cond_dropout_prob = cond_dropout_prob
def t2i_prompt(self, text_ids, image_ids, labels):
device = image_ids.device
sequence_ids = []
attention_masks = []
label_ids = []
probs = torch.rand(len(text_ids))
for i in range(len(text_ids)):
if len(text_ids[i]) == 0:
text_ids[i] = [self.text_tokenizer.bos_token_id]
elif text_ids[i][0] != self.text_tokenizer.bos_token_id:
text_ids[i] = [self.text_tokenizer.bos_token_id] + text_ids[i]
temp_ids = [int(self.sptids_dict['<|t2i|>'])] + text_ids[i] + [self.text_tokenizer.eos_token_id]
# randomly dropout text condition
if probs[i] < self.cond_dropout_prob:
temp_ids = [int(self.sptids_dict['<|t2i|>']), self.text_tokenizer.bos_token_id, self.text_tokenizer.eos_token_id]
if self.max_text_len >= len(temp_ids):
old_len = len(temp_ids)
temp_ids = [self.pad_id] * (self.max_text_len - len(temp_ids)) + temp_ids
temp_masks = [0] * (self.max_text_len - old_len) + [1] * (old_len + image_ids.shape[-1] + 2)
else:
# should add the eos token
temp_ids = temp_ids[:self.max_text_len - 1] + [self.text_tokenizer.eos_token_id]
temp_masks = [1] * (len(temp_ids) + image_ids.shape[-1] + 2) # +2 for two special tokens
# prompting -- [task token] [sot] [text tokens] [eot] [soi] [image tokens] [eoi]
temp_label_ids = torch.cat([
# should we predict text tokens when doing image reconstruction?
torch.tensor(temp_ids).to(device),
self.sptids_dict['<|soi|>'].to(device),
labels[i],
self.sptids_dict['<|eoi|>'].to(device)
], dim=0)
temp_label_ids = torch.where(temp_label_ids == self.pad_id, self.ignore_id, temp_label_ids)
temp_ids = torch.cat([
torch.tensor(temp_ids).to(device),
self.sptids_dict['<|soi|>'].to(device),
image_ids[i],
self.sptids_dict['<|eoi|>'].to(device)
], dim=0)
# sequence_ids: [pad]...[pad] <|t2i|> <bos> text_1 ... text_n <eos> <|soi|> image_1 ... image_m <|eoi|>
temp_masks = torch.tensor(temp_masks).to(device)
sequence_ids.append(temp_ids.unsqueeze(0))
attention_masks.append(temp_masks.unsqueeze(0))
label_ids.append(temp_label_ids.unsqueeze(0))
return torch.cat(sequence_ids, dim=0), torch.cat(attention_masks, dim=0), torch.cat(label_ids, dim=0)
def t2i_gen_prompt(self, text_ids, image_ids):
device = image_ids.device
sequence_ids = []
attention_masks = []
for i in range(len(text_ids)):
if len(text_ids[i]) == 0:
text_ids[i] = [self.text_tokenizer.bos_token_id]
elif text_ids[i][0] != self.text_tokenizer.bos_token_id:
text_ids[i] = [self.text_tokenizer.bos_token_id] + text_ids[i]
# note that, llama3 tokenizer automatically add the bot token at first but without eot
temp_ids = [int(self.sptids_dict['<|t2i|>'])] + text_ids[i] + [self.text_tokenizer.eos_token_id]
if self.max_text_len >= len(temp_ids):
old_len = len(temp_ids)
temp_ids = [self.pad_id] * (self.max_text_len - len(temp_ids)) + temp_ids
temp_masks = [0] * (self.max_text_len - old_len) + [1] * (old_len + image_ids.shape[-1] + 2)
else:
# should add the eos token
temp_ids = temp_ids[:self.max_text_len - 1] + [self.text_tokenizer.eos_token_id]
temp_masks = [1] * (len(temp_ids) + image_ids.shape[-1] + 2) # +2 for two special tokens
# prompting -- [task token] [sot] [text tokens] [eot] [soi] [image tokens] [eoi]
temp_ids = torch.cat([
torch.tensor(temp_ids).to(device),
self.sptids_dict['<|soi|>'].to(device),
image_ids[i],
self.sptids_dict['<|eoi|>'].to(device)
], dim=0)
temp_masks = torch.tensor(temp_masks).to(device)
sequence_ids.append(temp_ids.unsqueeze(0))
attention_masks.append(temp_masks.unsqueeze(0))
return torch.cat(sequence_ids, dim=0), torch.cat(attention_masks, dim=0)
# language modeling
def lm_prompt(self, text_ids, max_seq_len):
sequence_ids = []
attention_masks = []
label_ids = []
for i in range(len(text_ids)):
if len(text_ids[i]) == 0:
text_ids[i] = [self.text_tokenizer.bos_token_id]
elif text_ids[i][0] != self.text_tokenizer.bos_token_id:
text_ids[i] = [self.text_tokenizer.bos_token_id] + text_ids[i]
temp_ids = text_ids[i] + [self.text_tokenizer.eos_token_id]
if max_seq_len >= len(temp_ids):
temp_labels_ids = temp_ids + [self.text_tokenizer.eos_token_id] * (max_seq_len - len(temp_ids))
temp_ids = temp_ids + [self.text_tokenizer.eos_token_id] * (max_seq_len - len(temp_ids))
temp_masks = [1] * len(temp_ids) + [0] * (max_seq_len - len(temp_ids))
else:
# In language modeling, we only process text tokens. We do not add the eos token if the text length
# exceeds the max sequence length
temp_labels_ids = temp_ids[:max_seq_len]
temp_ids = temp_ids[:max_seq_len]
temp_masks = [1] * len(temp_ids) # +2 for two special tokens
# prompting -- [task token] [sot] [text tokens] [eot] [soi] [image tokens] [eoi]
temp_ids = torch.tensor(temp_ids)
temp_masks = torch.tensor(temp_masks)
temp_labels_ids = torch.tensor(temp_labels_ids)
sequence_ids.append(temp_ids.unsqueeze(0))
attention_masks.append(temp_masks.unsqueeze(0))
label_ids.append(temp_labels_ids.unsqueeze(0))
# input_ids, masks, labels
return torch.cat(sequence_ids, dim=0), torch.cat(attention_masks, dim=0), torch.cat(label_ids, dim=0)
# language modeling
def lm_chat_prompt(self, text_ids, max_seq_len):
sequence_ids = []
prompt_masks = []
label_ids = []
for i in range(len(text_ids)):
if len(text_ids[i]) == 0:
text_ids[i] = [self.text_tokenizer.bos_token_id]
elif text_ids[i][0] != self.text_tokenizer.bos_token_id:
text_ids[i] = [self.text_tokenizer.bos_token_id] + text_ids[i]
temp_ids = text_ids[i] + [self.text_tokenizer.eos_token_id]
if max_seq_len >= len(temp_ids):
temp_labels_ids = temp_ids + [self.text_tokenizer.eos_token_id] * (max_seq_len - len(temp_ids))
temp_ids = temp_ids + [self.text_tokenizer.eos_token_id] * (max_seq_len - len(temp_ids))
else:
# In language modeling, we only process text tokens. We do not add the eos token if the text length
# exceeds the max sequence length
temp_labels_ids = temp_ids[:max_seq_len]
temp_ids = temp_ids[:max_seq_len]
end_header_id = int(self.sptids_dict['<|end_header_id|>'])
end_header_pos = -1
for pos in range(len(temp_ids) - 1, -1, -1): # 尝试从文本序列中寻找<|end_header_id|>
if temp_ids[pos] == end_header_id:
end_header_pos = pos
break
if end_header_pos != -1:
prompt_length = end_header_pos + 1
else:
prompt_length = 0
temp_masks = [1] * prompt_length + [0] * (len(temp_ids) - prompt_length)
# prompting -- [task token] [sot] [text tokens] [eot] [soi] [image tokens] [eoi]
temp_ids = torch.tensor(temp_ids)
temp_masks = torch.tensor(temp_masks)
temp_labels_ids = torch.tensor(temp_labels_ids)
sequence_ids.append(temp_ids.unsqueeze(0))
prompt_masks.append(temp_masks.unsqueeze(0))
label_ids.append(temp_labels_ids.unsqueeze(0))
# input_ids, masks, labels
return torch.cat(sequence_ids, dim=0), torch.cat(prompt_masks, dim=0), torch.cat(label_ids, dim=0)
def mmu_prompt(self, image_ids, text_ids):
device = image_ids.device
sequence_ids = []
prompt_masks = []
label_ids = []
max_text_len = self.max_text_len - 1
for i in range(len(text_ids)):
# note that, llama3 tokenizer automatically add the bot token at first but without eot
# for empty list []
if len(text_ids[i]) == 0:
text_ids[i] = [self.text_tokenizer.bos_token_id]
elif text_ids[i][0] != self.text_tokenizer.bos_token_id:
text_ids[i] = [self.text_tokenizer.bos_token_id] + text_ids[i]
temp_ids = text_ids[i] + [self.text_tokenizer.eos_token_id]
if max_text_len >= len(temp_ids):
# minus 1 because task token was prepended to the former image tokens
temp_ids = temp_ids + [self.text_tokenizer.eos_token_id] * (max_text_len - len(temp_ids))
temp_masks = [1] * (len(temp_ids) + image_ids.shape[-1] + 3) + [0] * (max_text_len - len(temp_ids))
else:
# should add the eos token
temp_ids = temp_ids[:max_text_len - 1] + [self.text_tokenizer.eos_token_id]
temp_masks = [1] * (len(temp_ids) + image_ids.shape[-1] + 3) # +2 for two special tokens
# prompting -- [task token] [sot] [text tokens] [eot] [soi] [image tokens] [eoi]
temp_label_ids = torch.cat([
torch.tensor([self.ignore_id]).to(device),
torch.tensor([self.ignore_id]).to(device),
torch.ones_like(image_ids[i]) * self.ignore_id,
torch.tensor([self.ignore_id]).to(device),
torch.tensor(temp_ids).to(device),
], dim=0)
temp_label_ids = torch.where(temp_label_ids == self.pad_id, self.ignore_id, temp_label_ids)
return_temp_ids = torch.cat([
self.sptids_dict['<|mmu|>'].to(device), # task token
self.sptids_dict['<|soi|>'].to(device),
image_ids[i],
self.sptids_dict['<|eoi|>'].to(device),
torch.tensor(temp_ids).to(device),
], dim=0)
end_header_id = int(self.sptids_dict['<|end_header_id|>'])
end_header_pos = -1
for pos in range(len(temp_ids) - 1, -1, -1):
if temp_ids[pos] == end_header_id:
end_header_pos = pos
break
if end_header_pos != -1:
prompt_length = len(return_temp_ids) - len(temp_ids) + end_header_pos + 1
else:
prompt_length = len(return_temp_ids) - len(temp_ids)
predict_length = len(return_temp_ids) - prompt_length
prompt_mask = [1] * prompt_length + [0] * predict_length
prompt_mask = torch.tensor(prompt_mask).to(device)
sequence_ids.append(return_temp_ids.unsqueeze(0))
prompt_masks.append(prompt_mask.unsqueeze(0))
label_ids.append(temp_label_ids.unsqueeze(0))
return torch.cat(sequence_ids, dim=0), torch.cat(prompt_masks, dim=0), torch.cat(label_ids, dim=0)
def mmu_gen_prompt(self, image_ids, text_ids):
device = image_ids.device
sequence_ids = []
prompt_masks = []
max_text_len = self.max_text_len - 1
for i in range(len(text_ids)):
if len(text_ids[i]) == 0:
text_ids[i] = [self.text_tokenizer.bos_token_id]
elif text_ids[i][0] != self.text_tokenizer.bos_token_id:
text_ids[i] = [self.text_tokenizer.bos_token_id] + text_ids[i]
temp_ids = text_ids[i] + [self.text_tokenizer.eos_token_id]
if max_text_len >= len(temp_ids):
# minus 1 because task token was prepended to the former image tokens
temp_ids = temp_ids + [self.text_tokenizer.eos_token_id] * (max_text_len - len(temp_ids))
else:
# should add the eos token
temp_ids = temp_ids[:max_text_len - 1] + [self.text_tokenizer.eos_token_id]
# print(f"mmu temp_ids: {temp_ids}")
return_temp_ids = torch.cat([
self.sptids_dict['<|mmu|>'].to(device), # task token
self.sptids_dict['<|soi|>'].to(device),
image_ids[i],
self.sptids_dict['<|eoi|>'].to(device),
torch.tensor(temp_ids).to(device),
], dim=0)
end_header_id = int(self.sptids_dict['<|end_header_id|>'])
end_header_pos = -1
for pos in range(len(temp_ids) - 1, -1, -1):
if temp_ids[pos] == end_header_id:
end_header_pos = pos
break
if end_header_pos != -1:
prompt_length = len(return_temp_ids) - len(temp_ids) + end_header_pos + 1
else:
prompt_length = len(return_temp_ids) - len(temp_ids)
predict_length = len(temp_ids) - prompt_length
print(f"prompt_length: {prompt_length}, predict_length: {predict_length}, all length: {len(return_temp_ids)}, {return_temp_ids[-predict_length:]}")
prompt_mask = [1] * prompt_length + [0] * predict_length
prompt_mask = torch.tensor(prompt_mask).to(device)
sequence_ids.append(return_temp_ids.unsqueeze(0))
prompt_masks.append(prompt_mask.unsqueeze(0))
return torch.cat(sequence_ids, dim=0), torch.cat(prompt_masks, dim=0)
def r2i_prompt(self, image_ids, text_ids):
device = image_ids.device
sequence_ids = []
prompt_masks = []
label_ids = []
r2i_id = int(self.sptids_dict['<|r2i|>'])
soi_id = int(self.sptids_dict['<|soi|>'])
eoi_id = int(self.sptids_dict['<|eoi|>'])
max_text_len = self.max_text_len - 1 # 512，include BOS text EOS
for i in range(len(text_ids)):
# note that, llama3 tokenizer automatically add the bot token at first but without eot
# for empty list []
if len(text_ids[i]) == 0:
text_ids[i] = [self.text_tokenizer.bos_token_id]
elif text_ids[i][0]!= self.text_tokenizer.bos_token_id:
text_ids[i] = [self.text_tokenizer.bos_token_id] + text_ids[i]
text_ids_with_bos_eos = text_ids[i] + [self.text_tokenizer.eos_token_id]
if max_text_len >= len(text_ids_with_bos_eos):
# minus 1 because task token was prepended to the former image tokens
text_ids_full_len = text_ids_with_bos_eos + [self.text_tokenizer.eos_token_id] * (max_text_len - len(text_ids_with_bos_eos))
else:
# should add the eos token
text_ids_full_len = text_ids_with_bos_eos[:max_text_len - 1] + [self.text_tokenizer.eos_token_id]
sequence_ids.append(torch.cat([
torch.tensor([r2i_id]).to(device), # task token
torch.tensor(text_ids_full_len).to(device),
torch.tensor([soi_id]).to(device),
image_ids[i],
torch.tensor([eoi_id]).to(device),
], dim=0).unsqueeze(0))
end_header_id = int(self.sptids_dict['<|end_header_id|>'])
end_header_pos = -1
for pos in range(len(text_ids_full_len) - 1, -1, -1):
if text_ids_full_len[pos] == end_header_id:
end_header_pos = pos
break
prompt_mask = torch.zeros(sequence_ids[i].size(1)).to(device)
prompt_mask[0] = 1 # task_id
if end_header_pos != -1:
prompt_mask[1:end_header_pos+2] = 1
else:
prompt_mask[1:len(text_ids_full_len)+1] = 1
prompt_mask[len(text_ids_full_len)+1] = 1
prompt_mask[len(text_ids_full_len)+2+len(image_ids[i])] = 1
prompt_masks.append(prompt_mask.unsqueeze(0))
return torch.cat(sequence_ids, dim=0), torch.cat(prompt_masks, dim=0), torch.cat(sequence_ids, dim=0)
def mask_prompt(self):
pass
def __call__(self, input, task, padding=True, config=None):
"""
input (tuple) : data pairs contain text(str), image(tensor), or videos(tensor).
task (str) : a flag indicates the current task.
"""
if task == "t2i":
text_ids = self.text_tokenizer(input[0])['input_ids'] # (B, max_len)
image_ids = input[1] # (B, #tokens)
sequence_ids_with_masks = self.t2i_prompt(text_ids, image_ids, input[2])
elif task == "t2v":
text_ids = self.text_tokenizer(input[0])['input_ids'] # (B, max_len)
image_ids = input[1] # (B, #tokens)
sequence_ids_with_masks = self.t2v_prompt(text_ids, image_ids, input[2])
elif task == "t2i_plus_lm":
text_ids = self.text_tokenizer(input[0])['input_ids'] # (B, max_len)
image_ids = input[1] # (B, #tokens)
sequence_ids_with_masks = self.t2i_prompt(text_ids[:config.training.batch_size], image_ids,
input[2])
sequence_ids_with_masks_lm = self.lm_prompt(text_ids[config.training.batch_size:], input[3])
return sequence_ids_with_masks, sequence_ids_with_masks_lm
elif task == "t2i_gen":
text_ids = self.text_tokenizer(input[0])['input_ids'] # (B, max_len)
image_ids = input[1] # (B, #tokens)
sequence_ids_with_masks = self.t2i_gen_prompt(text_ids, image_ids)
elif task == "t2v_gen":
text_ids = self.text_tokenizer(input[0])['input_ids'] # (B, max_len)
image_ids = input[1] # (B, #tokens)
sequence_ids_with_masks = self.t2v_gen_prompt(text_ids, image_ids)
elif task == "lm":
text_ids = self.text_tokenizer(input[0], truncation=True)['input_ids'] # (B, max_len)
sequence_ids_with_masks = self.lm_prompt(text_ids, input[1])
elif task == "lm_chat":
text_ids = self.text_tokenizer(input[0], truncation=True)['input_ids'] # (B, max_len)
sequence_ids_with_masks = self.lm_chat_prompt(text_ids, input[1])
elif task == "mmu":
image_ids = input[0]
text_ids = self.text_tokenizer(input[1])['input_ids']
sequence_ids_with_masks = self.mmu_prompt(image_ids, text_ids)
elif task == "r2i":
image_ids = input[0]
text_ids = self.text_tokenizer(input[1])['input_ids']
sequence_ids_with_masks = self.r2i_prompt(image_ids, text_ids)
else:
raise NotImplementedError
return sequence_ids_with_masks
if __name__ == '__main__':
pass |