MuseV/musev/models/transformer_2d.py

# 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 __future__ import annotations
from dataclasses import dataclass
from typing import Any, Dict, List, Literal, Optional
import logging

from einops import rearrange

import torch
import torch.nn.functional as F
from torch import nn

from diffusers.models.transformer_2d import (
 Transformer2DModelOutput,
 Transformer2DModel as DiffusersTransformer2DModel,
)

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.embeddings import ImagePositionalEmbeddings
from diffusers.utils import BaseOutput, deprecate
from diffusers.models.attention import (
 BasicTransformerBlock as DiffusersBasicTransformerBlock,
)
from diffusers.models.embeddings import PatchEmbed
from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear
from diffusers.models.modeling_utils import ModelMixin
from diffusers.utils.constants import USE_PEFT_BACKEND

from .attention import BasicTransformerBlock

logger = logging.getLogger(__name__)

# 本部分 与 diffusers/models/transformer_2d.py 几乎一样
# 更新部分
# 1. 替换自定义 BasicTransformerBlock 类
# 2. 在forward 里增加了 self_attn_block_embs 用于 提取 self_attn 中的emb

# this module is same as diffusers/models/transformer_2d.py. The update part is
# 1 redefine BasicTransformerBlock
# 2. add self_attn_block_embs in forward to extract emb from self_attn


class Transformer2DModel(DiffusersTransformer2DModel):
 """
 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: int | None = None,
 out_channels: int | None = None,
 num_layers: int = 1,
 dropout: float = 0,
 norm_num_groups: int = 32,
 cross_attention_dim: int | None = None,
 attention_bias: bool = False,
 sample_size: int | None = None,
 num_vector_embeds: int | None = None,
 patch_size: int | None = None,
 activation_fn: str = "geglu",
 num_embeds_ada_norm: int | None = None,
 use_linear_projection: bool = False,
 only_cross_attention: bool = False,
 double_self_attention: bool = False,
 upcast_attention: bool = False,
 norm_type: str = "layer_norm",
 norm_elementwise_affine: bool = True,
 attention_type: str = "default",
 cross_attn_temporal_cond: bool = False,
 ip_adapter_cross_attn: bool = False,
 need_t2i_facein: bool = False,
 need_t2i_ip_adapter_face: bool = False,
 image_scale: float = 1.0,
 ):
 super().__init__(
 num_attention_heads,
 attention_head_dim,
 in_channels,
 out_channels,
 num_layers,
 dropout,
 norm_num_groups,
 cross_attention_dim,
 attention_bias,
 sample_size,
 num_vector_embeds,
 patch_size,
 activation_fn,
 num_embeds_ada_norm,
 use_linear_projection,
 only_cross_attention,
 double_self_attention,
 upcast_attention,
 norm_type,
 norm_elementwise_affine,
 attention_type,
 )
 inner_dim = num_attention_heads * attention_head_dim
 self.transformer_blocks = nn.ModuleList(
 [
 BasicTransformerBlock(
 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,
 double_self_attention=double_self_attention,
 upcast_attention=upcast_attention,
 norm_type=norm_type,
 norm_elementwise_affine=norm_elementwise_affine,
 attention_type=attention_type,
 cross_attn_temporal_cond=cross_attn_temporal_cond,
 ip_adapter_cross_attn=ip_adapter_cross_attn,
 need_t2i_facein=need_t2i_facein,
 need_t2i_ip_adapter_face=need_t2i_ip_adapter_face,
 image_scale=image_scale,
 )
 for d in range(num_layers)
 ]
 )
 self.num_layers = num_layers
 self.cross_attn_temporal_cond = cross_attn_temporal_cond
 self.ip_adapter_cross_attn = ip_adapter_cross_attn

 self.need_t2i_facein = need_t2i_facein
 self.need_t2i_ip_adapter_face = need_t2i_ip_adapter_face
 self.image_scale = image_scale
 self.print_idx = 0

 def forward(
 self,
 hidden_states: torch.Tensor,
 encoder_hidden_states: Optional[torch.Tensor] = None,
 timestep: Optional[torch.LongTensor] = None,
 added_cond_kwargs: Dict[str, torch.Tensor] = 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,
 self_attn_block_embs: Optional[List[torch.Tensor]] = None,
 self_attn_block_embs_mode: Literal["read", "write"] = "write",
 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`.
 cross_attention_kwargs ( `Dict[str, Any]`, *optional*):
 A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
 `self.processor` in
 [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
 attention_mask ( `torch.Tensor`, *optional*):
 An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
 is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
 negative values to the attention scores corresponding to "discard" tokens.
 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)

 # Retrieve lora scale.
 lora_scale = (
 cross_attention_kwargs.get("scale", 1.0)
 if cross_attention_kwargs is not None
 else 1.0
 )

 # 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, scale=lora_scale)
 if not USE_PEFT_BACKEND
 else 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, scale=lora_scale)
 if not USE_PEFT_BACKEND
 else self.proj_in(hidden_states)
 )

 elif self.is_input_vectorized:
 hidden_states = self.latent_image_embedding(hidden_states)
 elif self.is_input_patches:
 height, width = (
 hidden_states.shape[-2] // self.patch_size,
 hidden_states.shape[-1] // self.patch_size,
 )
 hidden_states = self.pos_embed(hidden_states)

 if self.adaln_single is not None:
 if self.use_additional_conditions and added_cond_kwargs is None:
 raise ValueError(
 "`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`."
 )
 batch_size = hidden_states.shape[0]
 timestep, embedded_timestep = self.adaln_single(
 timestep,
 added_cond_kwargs,
 batch_size=batch_size,
 hidden_dtype=hidden_states.dtype,
 )

 # 2. Blocks
 if self.caption_projection is not None:
 batch_size = hidden_states.shape[0]
 encoder_hidden_states = self.caption_projection(encoder_hidden_states)
 encoder_hidden_states = encoder_hidden_states.view(
 batch_size, -1, hidden_states.shape[-1]
 )

 for block in self.transformer_blocks:
 if self.training and self.gradient_checkpointing:
 hidden_states = torch.utils.checkpoint.checkpoint(
 block,
 hidden_states,
 attention_mask,
 encoder_hidden_states,
 encoder_attention_mask,
 timestep,
 cross_attention_kwargs,
 class_labels,
 self_attn_block_embs,
 self_attn_block_embs_mode,
 use_reentrant=False,
 )
 else:
 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,
 self_attn_block_embs=self_attn_block_embs,
 self_attn_block_embs_mode=self_attn_block_embs_mode,
 )
 # 将 转换 self_attn_emb的尺寸
 if (
 self_attn_block_embs is not None
 and self_attn_block_embs_mode.lower() == "write"
 ):
 self_attn_idx = block.spatial_self_attn_idx
 if self.print_idx == 0:
 logger.debug(
 f"self_attn_block_embs, num={len(self_attn_block_embs)}, before, shape={self_attn_block_embs[self_attn_idx].shape}, height={height}, width={width}"
 )
 self_attn_block_embs[self_attn_idx] = rearrange(
 self_attn_block_embs[self_attn_idx],
 "bt (h w) c->bt c h w",
 h=height,
 w=width,
 )
 if self.print_idx == 0:
 logger.debug(
 f"self_attn_block_embs, num={len(self_attn_block_embs)}, after ,shape={self_attn_block_embs[self_attn_idx].shape}, height={height}, width={width}"
 )

 if self.proj_out is None:
 return hidden_states

 # 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, scale=lora_scale)
 if not USE_PEFT_BACKEND
 else self.proj_out(hidden_states)
 )
 else:
 hidden_states = (
 self.proj_out(hidden_states, scale=lora_scale)
 if not USE_PEFT_BACKEND
 else 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()

 if self.is_input_patches:
 if self.config.norm_type != "ada_norm_single":
 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)
 elif self.config.norm_type == "ada_norm_single":
 shift, scale = (
 self.scale_shift_table[None] + embedded_timestep[:, None]
 ).chunk(2, dim=1)
 hidden_states = self.norm_out(hidden_states)
 # Modulation
 hidden_states = hidden_states * (1 + scale) + shift
 hidden_states = self.proj_out(hidden_states)
 hidden_states = hidden_states.squeeze(1)

 # unpatchify
 if self.adaln_single is None:
 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,
 )
 )
 self.print_idx += 1
 if not return_dict:
 return (output,)

 return Transformer2DModelOutput(sample=output)