# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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 dataclasses import dataclass
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.embeddings import ImagePositionalEmbeddings
from diffusers.utils import BaseOutput, deprecate, maybe_allow_in_graph
from diffusers.models.attention import FeedForward, AdaLayerNorm, AdaLayerNormZero, Attention
from diffusers.models.embeddings import PatchEmbed
from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear
from diffusers.models.modeling_utils import ModelMixin
from diffusers.utils.import_utils import is_xformers_available
from einops import rearrange
import pdb
import random
if is_xformers_available():
import xformers
import xformers.ops
else:
xformers = None
@dataclass
class TransformerMV2DModelOutput(BaseOutput):
"""
The output of [`Transformer2DModel`].
Args:
sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
distributions for the unnoised latent pixels.
"""
sample: torch.FloatTensor
class TransformerMV2DModel(ModelMixin, ConfigMixin):
"""
A 2D Transformer model for image-like data.
Parameters:
num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
in_channels (`int`, *optional*):
The number of channels in the input and output (specify if the input is **continuous**).
num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
This is fixed during training since it is used to learn a number of position embeddings.
num_vector_embeds (`int`, *optional*):
The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**).
Includes the class for the masked latent pixel.
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
num_embeds_ada_norm ( `int`, *optional*):
The number of diffusion steps used during training. Pass if at least one of the norm_layers is
`AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are
added to the hidden states.
During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`.
attention_bias (`bool`, *optional*):
Configure if the `TransformerBlocks` attention should contain a bias parameter.
"""
@register_to_config
def __init__(
self,
num_attention_heads: int = 16,
attention_head_dim: int = 88,
in_channels: Optional[int] = None,
out_channels: Optional[int] = None,
num_layers: int = 1,
dropout: float = 0.0,
norm_num_groups: int = 32,
cross_attention_dim: Optional[int] = None,
attention_bias: bool = False,
sample_size: Optional[int] = None,
num_vector_embeds: Optional[int] = None,
patch_size: Optional[int] = None,
activation_fn: str = "geglu",
num_embeds_ada_norm: Optional[int] = None,
use_linear_projection: bool = False,
only_cross_attention: bool = False,
upcast_attention: bool = False,
norm_type: str = "layer_norm",
norm_elementwise_affine: bool = True,
num_views: int = 1,
joint_attention: bool=False,
joint_attention_twice: bool=False,
multiview_attention: bool=True,
cross_domain_attention: bool=False
):
super().__init__()
self.use_linear_projection = use_linear_projection
self.num_attention_heads = num_attention_heads
self.attention_head_dim = attention_head_dim
inner_dim = num_attention_heads * attention_head_dim
# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
# Define whether input is continuous or discrete depending on configuration
self.is_input_continuous = (in_channels is not None) and (patch_size is None)
self.is_input_vectorized = num_vector_embeds is not None
self.is_input_patches = in_channels is not None and patch_size is not None
if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
deprecation_message = (
f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
" incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."
" Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"
" results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"
" would be very nice if you could open a Pull request for the `transformer/config.json` file"
)
deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False)
norm_type = "ada_norm"
if self.is_input_continuous and self.is_input_vectorized:
raise ValueError(
f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
" sure that either `in_channels` or `num_vector_embeds` is None."
)
elif self.is_input_vectorized and self.is_input_patches:
raise ValueError(
f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"
" sure that either `num_vector_embeds` or `num_patches` is None."
)
elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
raise ValueError(
f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"
f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."
)
# 2. Define input layers
if self.is_input_continuous:
self.in_channels = in_channels
self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
if use_linear_projection:
self.proj_in = LoRACompatibleLinear(in_channels, inner_dim)
else:
self.proj_in = LoRACompatibleConv(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
elif self.is_input_vectorized:
assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"
self.height = sample_size
self.width = sample_size
self.num_vector_embeds = num_vector_embeds
self.num_latent_pixels = self.height * self.width
self.latent_image_embedding = ImagePositionalEmbeddings(
num_embed=num_vector_embeds, embed_dim=inner_dim, height=self.height, width=self.width
)
elif self.is_input_patches:
assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"
self.height = sample_size
self.width = sample_size
self.patch_size = patch_size
self.pos_embed = PatchEmbed(
height=sample_size,
width=sample_size,
patch_size=patch_size,
in_channels=in_channels,
embed_dim=inner_dim,
)
# 3. Define transformers blocks
self.transformer_blocks = nn.ModuleList(
[
BasicMVTransformerBlock(
inner_dim,
num_attention_heads,
attention_head_dim,
dropout=dropout,
cross_attention_dim=cross_attention_dim,
activation_fn=activation_fn,
num_embeds_ada_norm=num_embeds_ada_norm,
attention_bias=attention_bias,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
norm_type=norm_type,
norm_elementwise_affine=norm_elementwise_affine,
num_views=num_views,
joint_attention=joint_attention,
joint_attention_twice=joint_attention_twice,
multiview_attention=multiview_attention,
cross_domain_attention=cross_domain_attention
)
for d in range(num_layers)
]
)
# 4. Define output layers
self.out_channels = in_channels if out_channels is None else out_channels
if self.is_input_continuous:
# TODO: should use out_channels for continuous projections
if use_linear_projection:
self.proj_out = LoRACompatibleLinear(inner_dim, in_channels)
else:
self.proj_out = LoRACompatibleConv(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
elif self.is_input_vectorized:
self.norm_out = nn.LayerNorm(inner_dim)
self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1)
elif self.is_input_patches:
self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim)
self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels)
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
timestep: Optional[torch.LongTensor] = None,
class_labels: Optional[torch.LongTensor] = None,
cross_attention_kwargs: Dict[str, Any] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
return_dict: bool = True,
):
"""
The [`Transformer2DModel`] forward method.
Args:
hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
Input `hidden_states`.
encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
self-attention.
timestep ( `torch.LongTensor`, *optional*):
Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
`AdaLayerZeroNorm`.
encoder_attention_mask ( `torch.Tensor`, *optional*):
Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:
* Mask `(batch, sequence_length)` True = keep, False = discard.
* Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.
If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
above. This bias will be added to the cross-attention scores.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
tuple.
Returns:
If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
`tuple` where the first element is the sample tensor.
"""
# ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
# we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
# we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
# expects mask of shape:
# [batch, key_tokens]
# adds singleton query_tokens dimension:
# [batch, 1, key_tokens]
# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
# [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)
# [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
if attention_mask is not None and attention_mask.ndim == 2:
# assume that mask is expressed as:
# (1 = keep, 0 = discard)
# convert mask into a bias that can be added to attention scores:
# (keep = +0, discard = -10000.0)
attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
attention_mask = attention_mask.unsqueeze(1)
# convert encoder_attention_mask to a bias the same way we do for attention_mask
if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
# 1. Input
if self.is_input_continuous:
batch, _, height, width = hidden_states.shape
residual = hidden_states
hidden_states = self.norm(hidden_states)
if not self.use_linear_projection:
hidden_states = self.proj_in(hidden_states)
inner_dim = hidden_states.shape[1]
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
else:
inner_dim = hidden_states.shape[1]
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
hidden_states = self.proj_in(hidden_states)
elif self.is_input_vectorized:
hidden_states = self.latent_image_embedding(hidden_states)
elif self.is_input_patches:
hidden_states = self.pos_embed(hidden_states)
# 2. Blocks
for block in self.transformer_blocks:
hidden_states = block(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
timestep=timestep,
cross_attention_kwargs=cross_attention_kwargs,
class_labels=class_labels,
)
# 3. Output
if self.is_input_continuous:
if not self.use_linear_projection:
hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
hidden_states = self.proj_out(hidden_states)
else:
hidden_states = self.proj_out(hidden_states)
hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
output = hidden_states + residual
elif self.is_input_vectorized:
hidden_states = self.norm_out(hidden_states)
logits = self.out(hidden_states)
# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
logits = logits.permute(0, 2, 1)
# log(p(x_0))
output = F.log_softmax(logits.double(), dim=1).float()
elif self.is_input_patches:
# TODO: cleanup!
conditioning = self.transformer_blocks[0].norm1.emb(
timestep, class_labels, hidden_dtype=hidden_states.dtype
)
shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]
hidden_states = self.proj_out_2(hidden_states)
# unpatchify
height = width = int(hidden_states.shape[1] ** 0.5)
hidden_states = hidden_states.reshape(
shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)
)
hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
output = hidden_states.reshape(
shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)
)
if not return_dict:
return (output,)
return TransformerMV2DModelOutput(sample=output)
@maybe_allow_in_graph
class BasicMVTransformerBlock(nn.Module):
r"""
A basic Transformer block.
Parameters:
dim (`int`): The number of channels in the input and output.
num_attention_heads (`int`): The number of heads to use for multi-head attention.
attention_head_dim (`int`): The number of channels in each head.
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
only_cross_attention (`bool`, *optional*):
Whether to use only cross-attention layers. In this case two cross attention layers are used.
double_self_attention (`bool`, *optional*):
Whether to use two self-attention layers. In this case no cross attention layers are used.
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
num_embeds_ada_norm (:
obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
attention_bias (:
obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
"""
def __init__(
self,
dim: int,
num_attention_heads: int,
attention_head_dim: int,
dropout=0.0,
cross_attention_dim: Optional[int] = None,
activation_fn: str = "geglu",
num_embeds_ada_norm: Optional[int] = None,
attention_bias: bool = False,
only_cross_attention: bool = False,
double_self_attention: bool = False,
upcast_attention: bool = False,
norm_elementwise_affine: bool = True,
norm_type: str = "layer_norm",
final_dropout: bool = False,
num_views: int = 1,
joint_attention: bool = False,
joint_attention_twice: bool = False,
multiview_attention: bool = True,
cross_domain_attention: bool = False
):
super().__init__()
self.only_cross_attention = only_cross_attention
self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
)
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
elif self.use_ada_layer_norm_zero:
self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
else:
self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
self.multiview_attention = multiview_attention
self.cross_domain_attention = cross_domain_attention
self.attn1 = CustomAttention(
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
cross_attention_dim=cross_attention_dim if only_cross_attention else None,
upcast_attention=upcast_attention,
processor=MVAttnProcessor()
)
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
self.norm2 = (
AdaLayerNorm(dim, num_embeds_ada_norm)
if self.use_ada_layer_norm
else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
)
self.attn2 = Attention(
query_dim=dim,
cross_attention_dim=cross_attention_dim if not double_self_attention else None,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
upcast_attention=upcast_attention,
) # is self-attn if encoder_hidden_states is none
else:
self.norm2 = None
self.attn2 = None
# 3. Feed-forward
self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)
# let chunk size default to None
self._chunk_size = None
self._chunk_dim = 0
self.num_views = num_views
self.joint_attention = joint_attention
if self.joint_attention:
# Joint task -Attn
self.attn_joint = CustomJointAttention(
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
cross_attention_dim=cross_attention_dim if only_cross_attention else None,
upcast_attention=upcast_attention,
processor=JointAttnProcessor()
)
nn.init.zeros_(self.attn_joint.to_out[0].weight.data)
self.norm_joint = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
self.joint_attention_twice = joint_attention_twice
if self.joint_attention_twice:
print("joint twice")
# Joint task -Attn
self.attn_joint_twice = CustomJointAttention(
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
cross_attention_dim=cross_attention_dim if only_cross_attention else None,
upcast_attention=upcast_attention,
processor=JointAttnProcessor()
)
nn.init.zeros_(self.attn_joint_twice.to_out[0].weight.data)
self.norm_joint_twice = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int):
# Sets chunk feed-forward
self._chunk_size = chunk_size
self._chunk_dim = dim
def forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
timestep: Optional[torch.LongTensor] = None,
cross_attention_kwargs: Dict[str, Any] = None,
class_labels: Optional[torch.LongTensor] = None,
):
assert attention_mask is None # not supported yet
# Notice that normalization is always applied before the real computation in the following blocks.
# 1. Self-Attention
if self.use_ada_layer_norm:
norm_hidden_states = self.norm1(hidden_states, timestep)
elif self.use_ada_layer_norm_zero:
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
)
else:
norm_hidden_states = self.norm1(hidden_states)
cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
attn_output = self.attn1(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
attention_mask=attention_mask,
num_views=self.num_views,
multiview_attention=self.multiview_attention,
cross_domain_attention=self.cross_domain_attention,
**cross_attention_kwargs,
)
if self.use_ada_layer_norm_zero:
attn_output = gate_msa.unsqueeze(1) * attn_output
hidden_states = attn_output + hidden_states
# joint attention twice
if self.joint_attention_twice:
norm_hidden_states = (
self.norm_joint_twice(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_joint_twice(hidden_states)
)
hidden_states = self.attn_joint_twice(norm_hidden_states) + hidden_states
# 2. Cross-Attention
if self.attn2 is not None:
norm_hidden_states = (
self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
)
attn_output = self.attn2(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
**cross_attention_kwargs,
)
hidden_states = attn_output + hidden_states
# 3. Feed-forward
norm_hidden_states = self.norm3(hidden_states)
if self.use_ada_layer_norm_zero:
norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
)
num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
ff_output = torch.cat(
[self.ff(hid_slice) for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)],
dim=self._chunk_dim,
)
else:
ff_output = self.ff(norm_hidden_states)
if self.use_ada_layer_norm_zero:
ff_output = gate_mlp.unsqueeze(1) * ff_output
hidden_states = ff_output + hidden_states
if self.joint_attention:
norm_hidden_states = (
self.norm_joint(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_joint(hidden_states)
)
hidden_states = self.attn_joint(norm_hidden_states) + hidden_states
return hidden_states
class CustomAttention(Attention):
def set_use_memory_efficient_attention_xformers(
self, use_memory_efficient_attention_xformers: bool, *args, **kwargs
):
processor = XFormersMVAttnProcessor()
self.set_processor(processor)
# print("using xformers attention processor")
class CustomJointAttention(Attention):
def set_use_memory_efficient_attention_xformers(
self, use_memory_efficient_attention_xformers: bool, *args, **kwargs
):
processor = XFormersJointAttnProcessor()
self.set_processor(processor)
# print("using xformers attention processor")
class MVAttnProcessor:
r"""
Default processor for performing attention-related computations.
"""
def __call__(
self,
attn: Attention,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
temb=None,
num_views=1,
multiview_attention=True
):
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
# print('query', query.shape, 'key', key.shape, 'value', value.shape)
#([bx4, 1024, 320]) key torch.Size([bx4, 1024, 320]) value torch.Size([bx4, 1024, 320])
# pdb.set_trace()
# multi-view self-attention
if multiview_attention:
if num_views <= 6:
# after use xformer; possible to train with 6 views
key = rearrange(key, "(b t) d c -> b (t d) c", t=num_views).repeat_interleave(num_views, dim=0)
value = rearrange(value, "(b t) d c -> b (t d) c", t=num_views).repeat_interleave(num_views, dim=0)
else:# apply sparse attention
pass
# print("use sparse attention")
# # seems that the sparse random sampling cause problems
# # don't use random sampling, just fix the indexes
# onekey = rearrange(key, "(b t) d c -> b t d c", t=num_views)
# onevalue = rearrange(value, "(b t) d c -> b t d c", t=num_views)
# allkeys = []
# allvalues = []
# all_indexes = {
# 0 : [0, 2, 3, 4],
# 1: [0, 1, 3, 5],
# 2: [0, 2, 3, 4],
# 3: [0, 2, 3, 4],
# 4: [0, 2, 3, 4],
# 5: [0, 1, 3, 5]
# }
# for jj in range(num_views):
# # valid_index = [x for x in range(0, num_views) if x!= jj]
# # indexes = random.sample(valid_index, 3) + [jj] + [0]
# indexes = all_indexes[jj]
# indexes = torch.tensor(indexes).long().to(key.device)
# allkeys.append(onekey[:, indexes])
# allvalues.append(onevalue[:, indexes])
# keys = torch.stack(allkeys, dim=1) # checked, should be dim=1
# values = torch.stack(allvalues, dim=1)
# key = rearrange(keys, 'b t f d c -> (b t) (f d) c')
# value = rearrange(values, 'b t f d c -> (b t) (f d) c')
query = attn.head_to_batch_dim(query).contiguous()
key = attn.head_to_batch_dim(key).contiguous()
value = attn.head_to_batch_dim(value).contiguous()
attention_probs = attn.get_attention_scores(query, key, attention_mask)
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
class XFormersMVAttnProcessor:
r"""
Default processor for performing attention-related computations.
"""
def __call__(
self,
attn: Attention,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
temb=None,
num_views=1.,
multiview_attention=True,
cross_domain_attention=False,
):
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# from yuancheng; here attention_mask is None
if attention_mask is not None:
# expand our mask's singleton query_tokens dimension:
# [batch*heads, 1, key_tokens] ->
# [batch*heads, query_tokens, key_tokens]
# so that it can be added as a bias onto the attention scores that xformers computes:
# [batch*heads, query_tokens, key_tokens]
# we do this explicitly because xformers doesn't broadcast the singleton dimension for us.
_, query_tokens, _ = hidden_states.shape
attention_mask = attention_mask.expand(-1, query_tokens, -1)
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key_raw = attn.to_k(encoder_hidden_states)
value_raw = attn.to_v(encoder_hidden_states)
# print('query', query.shape, 'key', key.shape, 'value', value.shape)
#([bx4, 1024, 320]) key torch.Size([bx4, 1024, 320]) value torch.Size([bx4, 1024, 320])
# pdb.set_trace()
# multi-view self-attention
if multiview_attention:
key = rearrange(key_raw, "(b t) d c -> b (t d) c", t=num_views).repeat_interleave(num_views, dim=0)
value = rearrange(value_raw, "(b t) d c -> b (t d) c", t=num_views).repeat_interleave(num_views, dim=0)
if cross_domain_attention:
# memory efficient, cross domain attention
key_0, key_1 = torch.chunk(key_raw, dim=0, chunks=2) # keys shape (b t) d c
value_0, value_1 = torch.chunk(value_raw, dim=0, chunks=2)
key_cross = torch.concat([key_1, key_0], dim=0)
value_cross = torch.concat([value_1, value_0], dim=0) # shape (b t) d c
key = torch.cat([key, key_cross], dim=1)
value = torch.cat([value, value_cross], dim=1) # shape (b t) (t+1 d) c
else:
# print("don't use multiview attention.")
key = key_raw
value = value_raw
query = attn.head_to_batch_dim(query)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
class XFormersJointAttnProcessor:
r"""
Default processor for performing attention-related computations.
"""
def __call__(
self,
attn: Attention,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
temb=None,
num_tasks=2
):
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# from yuancheng; here attention_mask is None
if attention_mask is not None:
# expand our mask's singleton query_tokens dimension:
# [batch*heads, 1, key_tokens] ->
# [batch*heads, query_tokens, key_tokens]
# so that it can be added as a bias onto the attention scores that xformers computes:
# [batch*heads, query_tokens, key_tokens]
# we do this explicitly because xformers doesn't broadcast the singleton dimension for us.
_, query_tokens, _ = hidden_states.shape
attention_mask = attention_mask.expand(-1, query_tokens, -1)
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
assert num_tasks == 2 # only support two tasks now
key_0, key_1 = torch.chunk(key, dim=0, chunks=2) # keys shape (b t) d c
value_0, value_1 = torch.chunk(value, dim=0, chunks=2)
key = torch.cat([key_0, key_1], dim=1) # (b t) 2d c
value = torch.cat([value_0, value_1], dim=1) # (b t) 2d c
key = torch.cat([key]*2, dim=0) # ( 2 b t) 2d c
value = torch.cat([value]*2, dim=0) # (2 b t) 2d c
query = attn.head_to_batch_dim(query).contiguous()
key = attn.head_to_batch_dim(key).contiguous()
value = attn.head_to_batch_dim(value).contiguous()
hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
class JointAttnProcessor:
r"""
Default processor for performing attention-related computations.
"""
def __call__(
self,
attn: Attention,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
temb=None,
num_tasks=2
):
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
assert num_tasks == 2 # only support two tasks now
key_0, key_1 = torch.chunk(key, dim=0, chunks=2) # keys shape (b t) d c
value_0, value_1 = torch.chunk(value, dim=0, chunks=2)
key = torch.cat([key_0, key_1], dim=1) # (b t) 2d c
value = torch.cat([value_0, value_1], dim=1) # (b t) 2d c
key = torch.cat([key]*2, dim=0) # ( 2 b t) 2d c
value = torch.cat([value]*2, dim=0) # (2 b t) 2d c
query = attn.head_to_batch_dim(query).contiguous()
key = attn.head_to_batch_dim(key).contiguous()
value = attn.head_to_batch_dim(value).contiguous()
attention_probs = attn.get_attention_scores(query, key, attention_mask)
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states