"""This file contains implementation for MaskGIT model.
Copyright (2024) Bytedance Ltd. and/or its affiliates
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.
Reference:
https://github.com/huggingface/open-muse
https://github.com/baaivision/MUSE-Pytorch
https://github.com/sail-sg/MDT/blob/main/masked_diffusion/models.py
"""
import torch
from torch import nn
import numpy as np
import math
import torch.utils.checkpoint
from transformers import BertConfig, BertModel
from einops import rearrange
import json
from huggingface_hub import PyTorchModelHubMixin
from omegaconf import OmegaConf
from pathlib import Path
from modeling.modules.base_model import BaseModel
from modeling.modules.blocks import UViTBlock
class ImageBert(BaseModel, PyTorchModelHubMixin, tags=["arxiv:2406.07550", "image-generation"], repo_url="https://github.com/bytedance/1d-tokenizer", license="apache-2.0"):
def __init__(self, config):
if isinstance(config, dict):
config = OmegaConf.create(config)
super().__init__()
self.config = config
self.target_codebook_size = config.model.vq_model.codebook_size
self.condition_num_classes = config.model.generator.condition_num_classes
self.image_seq_len = config.model.generator.image_seq_len
self.mask_token_id = self.target_codebook_size
self.hidden_size = config.model.generator.hidden_size
self.num_hidden_layers = config.model.generator.num_hidden_layers
self.num_attention_heads = config.model.generator.num_attention_heads
self.intermediate_size = config.model.generator.intermediate_size
self.model = BertModel(BertConfig(
vocab_size=self.target_codebook_size + self.condition_num_classes + 2,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act='gelu',
hidden_dropout_prob=config.model.generator.dropout,
attention_probs_dropout_prob=config.model.generator.attn_drop,
max_position_embeddings=config.model.generator.image_seq_len + 1,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=None,
position_embedding_type="absolute",
use_cache=True
), add_pooling_layer=False)
self.model.lm_head = nn.Linear(self.hidden_size, self.target_codebook_size, bias=True)
self.model.post_init()
def _save_pretrained(self, save_directory: Path) -> None:
"""Save weights and config to a local directory."""
# Assume 'self.config' is your DictConfig object
# Convert to a regular dictionary
dict_config = OmegaConf.to_container(self.config)
# Save as JSON
file_path = Path(save_directory) / "config.json"
with open(file_path, 'w') as json_file:
json.dump(dict_config, json_file, indent=4)
super()._save_pretrained(save_directory)
def forward(self, input_ids=None, condition=None, cond_drop_prob=0.1):
# Token space:
# [0, codebook_size - 1] : those are the learned quantized image tokens
# codebook_size : the mask token used to mask image tokens
# [codebook_size + 1, codebook_size + nclass] : the imagenet class tokens
# codebook_size + 1 + nclass : the class drop label
drop_label_mask = torch.rand_like(condition, dtype=torch.float) < cond_drop_prob
# Shift the classes
condition = condition + self.target_codebook_size + 1 # [0, 999] -> [codebook_size + 1, codebook_size + 999]
condition[drop_label_mask] = self.condition_num_classes + self.target_codebook_size + 1
# prepend condition token
if input_ids is not None:
input_ids = torch.cat([condition.view(condition.shape[0], -1),
input_ids.view(input_ids.shape[0], -1),], dim=1)
else:
# at least there should be masked token
raise NotImplementedError
model_output = self.model(input_ids=input_ids)
model_output = model_output[0]
return self.model.lm_head(model_output[:, 1:]) # remove cond
# ref: https://github.com/baaivision/MUSE-Pytorch/blob/master/libs/muse.py#L40
@torch.no_grad()
def generate(self,
condition,
guidance_scale=3.0,
guidance_decay="constant",
guidance_scale_pow=3.0,
randomize_temperature=4.5,
softmax_temperature_annealing=False,
num_sample_steps=8):
if guidance_decay not in ["constant", "linear", "power-cosine"]:
# contstant: constant guidance scale
# linear: linear increasing the guidance scale as in MUSE
# power-cosine: the guidance schedule from MDT
raise ValueError(f"Unsupported guidance decay {guidance_decay}")
device = condition.device
ids = torch.full((condition.shape[0], self.image_seq_len),
self.mask_token_id, device=device)
cfg_scale = guidance_scale if guidance_decay == "constant" else 0.
for step in range(num_sample_steps):
ratio = 1. * (step + 1) / num_sample_steps
annealed_temp = randomize_temperature * (1.0 - ratio)
is_mask = (ids == self.mask_token_id)
if guidance_decay == "power-cosine":
# ref: https://github.com/sail-sg/MDT/blob/main/masked_diffusion/models.py#L501
guidance_scale_pow = torch.ones((1), device=device) * guidance_scale_pow
scale_step = (1 - torch.cos(((step / num_sample_steps) ** guidance_scale_pow) * torch.pi)) * 1/2
cfg_scale = (guidance_scale - 1) * scale_step + 1
if cfg_scale != 0:
cond_logits = self.forward(
ids, condition, cond_drop_prob=0.0
)
uncond_logits = self.forward(
ids, condition, cond_drop_prob=1.0
)
if guidance_decay == "power-cosine":
logits = uncond_logits + (cond_logits - uncond_logits) * cfg_scale
else:
logits = cond_logits + (cond_logits - uncond_logits) * cfg_scale
else:
logits = self.forward(
ids, condition, cond_drop_prob=0.0
)
if softmax_temperature_annealing:
softmax_temperature = 0.5 + 0.8 * (1 - ratio)
logits = logits / softmax_temperature
# Add gumbel noise
def log(t, eps=1e-20):
return torch.log(t.clamp(min=eps))
def gumbel_noise(t):
noise = torch.zeros_like(t).uniform_(0, 1)
return -log(-log(noise))
def add_gumbel_noise(t, temperature):
return t + temperature * gumbel_noise(t)
sampled_ids = add_gumbel_noise(logits, annealed_temp).argmax(dim=-1)
sampled_logits = torch.squeeze(
torch.gather(logits, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1)
sampled_ids = torch.where(is_mask, sampled_ids, ids)
sampled_logits = torch.where(is_mask, sampled_logits, +np.inf).float()
# masking
mask_ratio = np.arccos(ratio) / (math.pi * 0.5)
mask_len = torch.Tensor([np.floor(self.image_seq_len * mask_ratio)]).to(device)
mask_len = torch.maximum(torch.Tensor([1]).to(device),
torch.minimum(torch.sum(is_mask, dim=-1, keepdims=True) - 1,
mask_len))[0].squeeze()
confidence = add_gumbel_noise(sampled_logits, annealed_temp)
sorted_confidence, _ = torch.sort(confidence, axis=-1)
cut_off = sorted_confidence[:, mask_len.long() - 1:mask_len.long()]
masking = (confidence <= cut_off)
if step == num_sample_steps - 1:
ids = sampled_ids
else:
ids = torch.where(masking, self.mask_token_id, sampled_ids)
if guidance_decay == "linear":
cfg_scale = ratio * guidance_scale
return ids
def masking_input_tokens(self, input_tokens):
batch_size, seq_len = input_tokens.shape
device = input_tokens.device
timesteps = torch.zeros((batch_size,), device=device).float().uniform_(0, 1.0)
mask_ratio = torch.acos(timesteps) / (math.pi * 0.5) # arccos schedule
mask_ratio = torch.clamp(mask_ratio, min=1e-6, max=1.)
num_token_masked = (seq_len * mask_ratio).round().clamp(min=1)
batch_randperm = torch.rand(batch_size, seq_len, device=device).argsort(dim=-1)
masks = batch_randperm < rearrange(num_token_masked, 'b -> b 1')
masked_tokens = torch.where(masks, self.mask_token_id, input_tokens)
return masked_tokens, masks
class UViTBert(ImageBert):
def __init__(self, config):
super().__init__(config=config)
del self.model
self.embeddings = nn.Embedding(
self.target_codebook_size + self.condition_num_classes + 2,
self.hidden_size)
self.pos_embed = nn.init.trunc_normal_(
nn.Parameter(torch.zeros(1, self.config.model.generator.image_seq_len + 1, self.hidden_size)), 0., 0.02)
self.in_blocks = nn.ModuleList([
UViTBlock(
dim=self.hidden_size, num_heads=self.num_attention_heads, mlp_ratio=(self.intermediate_size / self.hidden_size),
qkv_bias=False, qk_scale=None, norm_layer=nn.LayerNorm, use_checkpoint=False)
for _ in range(self.num_hidden_layers // 2)])
self.mid_block = UViTBlock(
dim=self.hidden_size, num_heads=self.num_attention_heads, mlp_ratio=(self.intermediate_size / self.hidden_size),
qkv_bias=False, qk_scale=None, norm_layer=nn.LayerNorm, use_checkpoint=False)
self.out_blocks = nn.ModuleList([
UViTBlock(
dim=self.hidden_size, num_heads=self.num_attention_heads, mlp_ratio=(self.intermediate_size / self.hidden_size),
qkv_bias=False, qk_scale=None, norm_layer=nn.LayerNorm, skip=True, use_checkpoint=False)
for _ in range(self.num_hidden_layers // 2)])
self.norm = nn.LayerNorm(self.hidden_size)
self.lm_head = nn.Linear(self.hidden_size,
self.target_codebook_size, bias=True)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
nn.init.trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Embedding):
m.weight.data = nn.init.trunc_normal_(m.weight.data, mean=0.0, std=0.02)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward(self, input_ids=None, condition=None, cond_drop_prob=0.1):
# Token space:
# [0, codebook_size - 1] : those are the learned quantized image tokens
# codebook_size : the mask token used to mask image tokens
# [codebook_size + 1, codebook_size + nclass] : the imagenet class tokens
# codebook_size + 1 + nclass : the class drop label
drop_label_mask = torch.rand_like(condition, dtype=torch.float) < cond_drop_prob
# Shift the classes
condition = condition + self.target_codebook_size + 1 # [0, 999] -> [codebook_size + 1, codebook_size + 999]
condition[drop_label_mask] = self.condition_num_classes + self.target_codebook_size + 1
# prepend condition token
if input_ids is not None:
input_ids = torch.cat([condition.view(condition.shape[0], -1),
input_ids.view(input_ids.shape[0], -1),], dim=1)
else:
# at least there should be masked token
raise NotImplementedError
# UViT forward
embeddings = self.embeddings(input_ids)
x = embeddings + self.pos_embed[:, :embeddings.shape[1]]
skips = []
for blk in self.in_blocks:
x = blk(x)
skips.append(x)
x = self.mid_block(x)
for blk in self.out_blocks:
x = blk(x, skips.pop())
x = self.norm(x)
return self.lm_head(x[:, 1:]) # remove cond