# coding=utf-8
# Copyright 2024 The HuggingFace Inc. 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.
from typing import Dict, Union
import torch
import torch.nn.functional as F
from torch import nn
from torch.utils.checkpoint import checkpoint
from ...configuration_utils import ConfigMixin, register_to_config
from ...loaders import PeftAdapterMixin
from ..attention import BasicTransformerBlock, SkipFFTransformerBlock
from ..attention_processor import (
ADDED_KV_ATTENTION_PROCESSORS,
CROSS_ATTENTION_PROCESSORS,
AttentionProcessor,
AttnAddedKVProcessor,
AttnProcessor,
)
from ..embeddings import TimestepEmbedding, get_timestep_embedding
from ..modeling_utils import ModelMixin
from ..normalization import GlobalResponseNorm, RMSNorm
from ..resnet import Downsample2D, Upsample2D
class UVit2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
# global config
hidden_size: int = 1024,
use_bias: bool = False,
hidden_dropout: float = 0.0,
# conditioning dimensions
cond_embed_dim: int = 768,
micro_cond_encode_dim: int = 256,
micro_cond_embed_dim: int = 1280,
encoder_hidden_size: int = 768,
# num tokens
vocab_size: int = 8256, # codebook_size + 1 (for the mask token) rounded
codebook_size: int = 8192,
# `UVit2DConvEmbed`
in_channels: int = 768,
block_out_channels: int = 768,
num_res_blocks: int = 3,
downsample: bool = False,
upsample: bool = False,
block_num_heads: int = 12,
# `TransformerLayer`
num_hidden_layers: int = 22,
num_attention_heads: int = 16,
# `Attention`
attention_dropout: float = 0.0,
# `FeedForward`
intermediate_size: int = 2816,
# `Norm`
layer_norm_eps: float = 1e-6,
ln_elementwise_affine: bool = True,
sample_size: int = 64,
):
super().__init__()
self.encoder_proj = nn.Linear(encoder_hidden_size, hidden_size, bias=use_bias)
self.encoder_proj_layer_norm = RMSNorm(hidden_size, layer_norm_eps, ln_elementwise_affine)
self.embed = UVit2DConvEmbed(
in_channels, block_out_channels, vocab_size, ln_elementwise_affine, layer_norm_eps, use_bias
)
self.cond_embed = TimestepEmbedding(
micro_cond_embed_dim + cond_embed_dim, hidden_size, sample_proj_bias=use_bias
)
self.down_block = UVitBlock(
block_out_channels,
num_res_blocks,
hidden_size,
hidden_dropout,
ln_elementwise_affine,
layer_norm_eps,
use_bias,
block_num_heads,
attention_dropout,
downsample,
False,
)
self.project_to_hidden_norm = RMSNorm(block_out_channels, layer_norm_eps, ln_elementwise_affine)
self.project_to_hidden = nn.Linear(block_out_channels, hidden_size, bias=use_bias)
self.transformer_layers = nn.ModuleList(
[
BasicTransformerBlock(
dim=hidden_size,
num_attention_heads=num_attention_heads,
attention_head_dim=hidden_size // num_attention_heads,
dropout=hidden_dropout,
cross_attention_dim=hidden_size,
attention_bias=use_bias,
norm_type="ada_norm_continuous",
ada_norm_continous_conditioning_embedding_dim=hidden_size,
norm_elementwise_affine=ln_elementwise_affine,
norm_eps=layer_norm_eps,
ada_norm_bias=use_bias,
ff_inner_dim=intermediate_size,
ff_bias=use_bias,
attention_out_bias=use_bias,
)
for _ in range(num_hidden_layers)
]
)
self.project_from_hidden_norm = RMSNorm(hidden_size, layer_norm_eps, ln_elementwise_affine)
self.project_from_hidden = nn.Linear(hidden_size, block_out_channels, bias=use_bias)
self.up_block = UVitBlock(
block_out_channels,
num_res_blocks,
hidden_size,
hidden_dropout,
ln_elementwise_affine,
layer_norm_eps,
use_bias,
block_num_heads,
attention_dropout,
downsample=False,
upsample=upsample,
)
self.mlm_layer = ConvMlmLayer(
block_out_channels, in_channels, use_bias, ln_elementwise_affine, layer_norm_eps, codebook_size
)
self.gradient_checkpointing = False
def _set_gradient_checkpointing(self, module, value: bool = False) -> None:
pass
def forward(self, input_ids, encoder_hidden_states, pooled_text_emb, micro_conds, cross_attention_kwargs=None):
encoder_hidden_states = self.encoder_proj(encoder_hidden_states)
encoder_hidden_states = self.encoder_proj_layer_norm(encoder_hidden_states)
micro_cond_embeds = get_timestep_embedding(
micro_conds.flatten(), self.config.micro_cond_encode_dim, flip_sin_to_cos=True, downscale_freq_shift=0
)
micro_cond_embeds = micro_cond_embeds.reshape((input_ids.shape[0], -1))
pooled_text_emb = torch.cat([pooled_text_emb, micro_cond_embeds], dim=1)
pooled_text_emb = pooled_text_emb.to(dtype=self.dtype)
pooled_text_emb = self.cond_embed(pooled_text_emb).to(encoder_hidden_states.dtype)
hidden_states = self.embed(input_ids)
hidden_states = self.down_block(
hidden_states,
pooled_text_emb=pooled_text_emb,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
)
batch_size, channels, height, width = hidden_states.shape
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels)
hidden_states = self.project_to_hidden_norm(hidden_states)
hidden_states = self.project_to_hidden(hidden_states)
for layer in self.transformer_layers:
if torch.is_grad_enabled() and self.gradient_checkpointing:
def layer_(*args):
return checkpoint(layer, *args)
else:
layer_ = layer
hidden_states = layer_(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs={"pooled_text_emb": pooled_text_emb},
)
hidden_states = self.project_from_hidden_norm(hidden_states)
hidden_states = self.project_from_hidden(hidden_states)
hidden_states = hidden_states.reshape(batch_size, height, width, channels).permute(0, 3, 1, 2)
hidden_states = self.up_block(
hidden_states,
pooled_text_emb=pooled_text_emb,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
)
logits = self.mlm_layer(hidden_states)
return logits
@property
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
indexed by its weight name.
"""
# set recursively
processors = {}
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
if hasattr(module, "get_processor"):
processors[f"{name}.processor"] = module.get_processor()
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(name, module, processors)
return processors
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
r"""
Sets the attention processor to use to compute attention.
Parameters:
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
The instantiated processor class or a dictionary of processor classes that will be set as the processor
for **all** `Attention` layers.
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
processor. This is strongly recommended when setting trainable attention processors.
"""
count = len(self.attn_processors.keys())
if isinstance(processor, dict) and len(processor) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
)
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
if hasattr(module, "set_processor"):
if not isinstance(processor, dict):
module.set_processor(processor)
else:
module.set_processor(processor.pop(f"{name}.processor"))
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
for name, module in self.named_children():
fn_recursive_attn_processor(name, module, processor)
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
processor = AttnAddedKVProcessor()
elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
processor = AttnProcessor()
else:
raise ValueError(
f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
)
self.set_attn_processor(processor)
class UVit2DConvEmbed(nn.Module):
def __init__(self, in_channels, block_out_channels, vocab_size, elementwise_affine, eps, bias):
super().__init__()
self.embeddings = nn.Embedding(vocab_size, in_channels)
self.layer_norm = RMSNorm(in_channels, eps, elementwise_affine)
self.conv = nn.Conv2d(in_channels, block_out_channels, kernel_size=1, bias=bias)
def forward(self, input_ids):
embeddings = self.embeddings(input_ids)
embeddings = self.layer_norm(embeddings)
embeddings = embeddings.permute(0, 3, 1, 2)
embeddings = self.conv(embeddings)
return embeddings
class UVitBlock(nn.Module):
def __init__(
self,
channels,
num_res_blocks: int,
hidden_size,
hidden_dropout,
ln_elementwise_affine,
layer_norm_eps,
use_bias,
block_num_heads,
attention_dropout,
downsample: bool,
upsample: bool,
):
super().__init__()
if downsample:
self.downsample = Downsample2D(
channels,
use_conv=True,
padding=0,
name="Conv2d_0",
kernel_size=2,
norm_type="rms_norm",
eps=layer_norm_eps,
elementwise_affine=ln_elementwise_affine,
bias=use_bias,
)
else:
self.downsample = None
self.res_blocks = nn.ModuleList(
[
ConvNextBlock(
channels,
layer_norm_eps,
ln_elementwise_affine,
use_bias,
hidden_dropout,
hidden_size,
)
for i in range(num_res_blocks)
]
)
self.attention_blocks = nn.ModuleList(
[
SkipFFTransformerBlock(
channels,
block_num_heads,
channels // block_num_heads,
hidden_size,
use_bias,
attention_dropout,
channels,
attention_bias=use_bias,
attention_out_bias=use_bias,
)
for _ in range(num_res_blocks)
]
)
if upsample:
self.upsample = Upsample2D(
channels,
use_conv_transpose=True,
kernel_size=2,
padding=0,
name="conv",
norm_type="rms_norm",
eps=layer_norm_eps,
elementwise_affine=ln_elementwise_affine,
bias=use_bias,
interpolate=False,
)
else:
self.upsample = None
def forward(self, x, pooled_text_emb, encoder_hidden_states, cross_attention_kwargs):
if self.downsample is not None:
x = self.downsample(x)
for res_block, attention_block in zip(self.res_blocks, self.attention_blocks):
x = res_block(x, pooled_text_emb)
batch_size, channels, height, width = x.shape
x = x.view(batch_size, channels, height * width).permute(0, 2, 1)
x = attention_block(
x, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs
)
x = x.permute(0, 2, 1).view(batch_size, channels, height, width)
if self.upsample is not None:
x = self.upsample(x)
return x
class ConvNextBlock(nn.Module):
def __init__(
self, channels, layer_norm_eps, ln_elementwise_affine, use_bias, hidden_dropout, hidden_size, res_ffn_factor=4
):
super().__init__()
self.depthwise = nn.Conv2d(
channels,
channels,
kernel_size=3,
padding=1,
groups=channels,
bias=use_bias,
)
self.norm = RMSNorm(channels, layer_norm_eps, ln_elementwise_affine)
self.channelwise_linear_1 = nn.Linear(channels, int(channels * res_ffn_factor), bias=use_bias)
self.channelwise_act = nn.GELU()
self.channelwise_norm = GlobalResponseNorm(int(channels * res_ffn_factor))
self.channelwise_linear_2 = nn.Linear(int(channels * res_ffn_factor), channels, bias=use_bias)
self.channelwise_dropout = nn.Dropout(hidden_dropout)
self.cond_embeds_mapper = nn.Linear(hidden_size, channels * 2, use_bias)
def forward(self, x, cond_embeds):
x_res = x
x = self.depthwise(x)
x = x.permute(0, 2, 3, 1)
x = self.norm(x)
x = self.channelwise_linear_1(x)
x = self.channelwise_act(x)
x = self.channelwise_norm(x)
x = self.channelwise_linear_2(x)
x = self.channelwise_dropout(x)
x = x.permute(0, 3, 1, 2)
x = x + x_res
scale, shift = self.cond_embeds_mapper(F.silu(cond_embeds)).chunk(2, dim=1)
x = x * (1 + scale[:, :, None, None]) + shift[:, :, None, None]
return x
class ConvMlmLayer(nn.Module):
def __init__(
self,
block_out_channels: int,
in_channels: int,
use_bias: bool,
ln_elementwise_affine: bool,
layer_norm_eps: float,
codebook_size: int,
):
super().__init__()
self.conv1 = nn.Conv2d(block_out_channels, in_channels, kernel_size=1, bias=use_bias)
self.layer_norm = RMSNorm(in_channels, layer_norm_eps, ln_elementwise_affine)
self.conv2 = nn.Conv2d(in_channels, codebook_size, kernel_size=1, bias=use_bias)
def forward(self, hidden_states):
hidden_states = self.conv1(hidden_states)
hidden_states = self.layer_norm(hidden_states.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
logits = self.conv2(hidden_states)
return logits