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"""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
"""
import torch
from torch import nn
import numpy as np
import math
import torch.utils.checkpoint
from transformers import BertConfig, BertModel
class ImageBert(nn.Module):
def __init__(self, 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.model = BertModel(BertConfig(
vocab_size=self.target_codebook_size + self.condition_num_classes + 2,
hidden_size=768,
num_hidden_layers=24,
num_attention_heads=16,
intermediate_size=3072,
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(768, self.target_codebook_size, bias=True)
self.model.post_init()
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,
randomize_temperature=4.5,
num_sample_steps=8):
device = condition.device
ids = torch.full((condition.shape[0], self.image_seq_len),
self.mask_token_id, device=device)
cfg_scale = guidance_scale
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 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
)
logits = cond_logits + (cond_logits - uncond_logits) * cfg_scale
else:
logits = self.forward(
ids, condition, cond_drop_prob=0.0
)
# 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)
return ids |