391 lines
15 KiB
Python
391 lines
15 KiB
Python
# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Any, Dict, Optional
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import torch
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import torch.nn.functional as F
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from torch import nn
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from ..utils import maybe_allow_in_graph
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from .activations import get_activation
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from .attention_processor import Attention
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from .embeddings import CombinedTimestepLabelEmbeddings
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from .lora import LoRACompatibleLinear
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@maybe_allow_in_graph
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class BasicTransformerBlock(nn.Module):
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r"""
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A basic Transformer block.
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Parameters:
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dim (`int`): The number of channels in the input and output.
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num_attention_heads (`int`): The number of heads to use for multi-head attention.
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attention_head_dim (`int`): The number of channels in each head.
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dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
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cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
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only_cross_attention (`bool`, *optional*):
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Whether to use only cross-attention layers. In this case two cross attention layers are used.
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double_self_attention (`bool`, *optional*):
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Whether to use two self-attention layers. In this case no cross attention layers are used.
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activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
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num_embeds_ada_norm (:
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obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
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attention_bias (:
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obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
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"""
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def __init__(
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self,
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dim: int,
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num_attention_heads: int,
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attention_head_dim: int,
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dropout=0.0,
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cross_attention_dim: Optional[int] = None,
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activation_fn: str = "geglu",
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num_embeds_ada_norm: Optional[int] = None,
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attention_bias: bool = False,
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only_cross_attention: bool = False,
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double_self_attention: bool = False,
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upcast_attention: bool = False,
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norm_elementwise_affine: bool = True,
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norm_type: str = "layer_norm",
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final_dropout: bool = False,
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):
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super().__init__()
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self.only_cross_attention = only_cross_attention
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self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
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self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
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if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
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raise ValueError(
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f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
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f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
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)
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# Define 3 blocks. Each block has its own normalization layer.
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# 1. Self-Attn
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if self.use_ada_layer_norm:
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self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
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elif self.use_ada_layer_norm_zero:
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self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
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else:
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self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
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self.attn1 = Attention(
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query_dim=dim,
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heads=num_attention_heads,
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dim_head=attention_head_dim,
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dropout=dropout,
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bias=attention_bias,
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cross_attention_dim=cross_attention_dim if only_cross_attention else None,
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upcast_attention=upcast_attention,
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)
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# 2. Cross-Attn
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if cross_attention_dim is not None or double_self_attention:
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# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
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# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
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# the second cross attention block.
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self.norm2 = (
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AdaLayerNorm(dim, num_embeds_ada_norm)
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if self.use_ada_layer_norm
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else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
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)
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self.attn2 = Attention(
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query_dim=dim,
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cross_attention_dim=cross_attention_dim if not double_self_attention else None,
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heads=num_attention_heads,
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dim_head=attention_head_dim,
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dropout=dropout,
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bias=attention_bias,
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upcast_attention=upcast_attention,
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) # is self-attn if encoder_hidden_states is none
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else:
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self.norm2 = None
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self.attn2 = None
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# 3. Feed-forward
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self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
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self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)
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# let chunk size default to None
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self._chunk_size = None
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self._chunk_dim = 0
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def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int):
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# Sets chunk feed-forward
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self._chunk_size = chunk_size
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self._chunk_dim = dim
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def forward(
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self,
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hidden_states: torch.FloatTensor,
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attention_mask: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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timestep: Optional[torch.LongTensor] = None,
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cross_attention_kwargs: Dict[str, Any] = None,
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class_labels: Optional[torch.LongTensor] = None,
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):
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# Notice that normalization is always applied before the real computation in the following blocks.
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# 1. Self-Attention
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if self.use_ada_layer_norm:
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norm_hidden_states = self.norm1(hidden_states, timestep)
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elif self.use_ada_layer_norm_zero:
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norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
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hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
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)
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else:
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norm_hidden_states = self.norm1(hidden_states)
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cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
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attn_output = self.attn1(
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norm_hidden_states,
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encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
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attention_mask=attention_mask,
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**cross_attention_kwargs,
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)
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if self.use_ada_layer_norm_zero:
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attn_output = gate_msa.unsqueeze(1) * attn_output
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hidden_states = attn_output + hidden_states
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# 2. Cross-Attention
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if self.attn2 is not None:
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norm_hidden_states = (
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self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
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)
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attn_output = self.attn2(
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norm_hidden_states,
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encoder_hidden_states=encoder_hidden_states,
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attention_mask=encoder_attention_mask,
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**cross_attention_kwargs,
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)
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hidden_states = attn_output + hidden_states
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# 3. Feed-forward
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norm_hidden_states = self.norm3(hidden_states)
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if self.use_ada_layer_norm_zero:
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norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
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if self._chunk_size is not None:
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# "feed_forward_chunk_size" can be used to save memory
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if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
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raise ValueError(
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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`."
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)
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num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
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ff_output = torch.cat(
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[self.ff(hid_slice) for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)],
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dim=self._chunk_dim,
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)
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else:
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ff_output = self.ff(norm_hidden_states)
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if self.use_ada_layer_norm_zero:
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ff_output = gate_mlp.unsqueeze(1) * ff_output
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hidden_states = ff_output + hidden_states
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return hidden_states
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class FeedForward(nn.Module):
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r"""
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A feed-forward layer.
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Parameters:
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dim (`int`): The number of channels in the input.
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dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
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mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
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dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
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activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
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final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
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"""
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def __init__(
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self,
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dim: int,
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dim_out: Optional[int] = None,
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mult: int = 4,
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dropout: float = 0.0,
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activation_fn: str = "geglu",
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final_dropout: bool = False,
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):
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super().__init__()
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inner_dim = int(dim * mult)
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dim_out = dim_out if dim_out is not None else dim
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if activation_fn == "gelu":
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act_fn = GELU(dim, inner_dim)
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if activation_fn == "gelu-approximate":
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act_fn = GELU(dim, inner_dim, approximate="tanh")
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elif activation_fn == "geglu":
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act_fn = GEGLU(dim, inner_dim)
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elif activation_fn == "geglu-approximate":
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act_fn = ApproximateGELU(dim, inner_dim)
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self.net = nn.ModuleList([])
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# project in
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self.net.append(act_fn)
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# project dropout
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self.net.append(nn.Dropout(dropout))
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# project out
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self.net.append(LoRACompatibleLinear(inner_dim, dim_out))
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# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
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if final_dropout:
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self.net.append(nn.Dropout(dropout))
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def forward(self, hidden_states):
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for module in self.net:
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hidden_states = module(hidden_states)
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return hidden_states
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class GELU(nn.Module):
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r"""
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GELU activation function with tanh approximation support with `approximate="tanh"`.
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"""
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def __init__(self, dim_in: int, dim_out: int, approximate: str = "none"):
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super().__init__()
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self.proj = nn.Linear(dim_in, dim_out)
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self.approximate = approximate
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def gelu(self, gate):
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if gate.device.type != "mps":
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return F.gelu(gate, approximate=self.approximate)
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# mps: gelu is not implemented for float16
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return F.gelu(gate.to(dtype=torch.float32), approximate=self.approximate).to(dtype=gate.dtype)
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def forward(self, hidden_states):
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hidden_states = self.proj(hidden_states)
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hidden_states = self.gelu(hidden_states)
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return hidden_states
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class GEGLU(nn.Module):
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r"""
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A variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202.
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Parameters:
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dim_in (`int`): The number of channels in the input.
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dim_out (`int`): The number of channels in the output.
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"""
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def __init__(self, dim_in: int, dim_out: int):
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super().__init__()
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self.proj = LoRACompatibleLinear(dim_in, dim_out * 2)
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def gelu(self, gate):
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if gate.device.type != "mps":
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return F.gelu(gate)
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# mps: gelu is not implemented for float16
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return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype)
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def forward(self, hidden_states):
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hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1)
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return hidden_states * self.gelu(gate)
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class ApproximateGELU(nn.Module):
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"""
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The approximate form of Gaussian Error Linear Unit (GELU)
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For more details, see section 2: https://arxiv.org/abs/1606.08415
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"""
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def __init__(self, dim_in: int, dim_out: int):
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super().__init__()
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self.proj = nn.Linear(dim_in, dim_out)
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def forward(self, x):
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x = self.proj(x)
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return x * torch.sigmoid(1.702 * x)
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class AdaLayerNorm(nn.Module):
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"""
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Norm layer modified to incorporate timestep embeddings.
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"""
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def __init__(self, embedding_dim, num_embeddings):
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super().__init__()
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self.emb = nn.Embedding(num_embeddings, embedding_dim)
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self.silu = nn.SiLU()
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self.linear = nn.Linear(embedding_dim, embedding_dim * 2)
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self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False)
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def forward(self, x, timestep):
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emb = self.linear(self.silu(self.emb(timestep)))
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scale, shift = torch.chunk(emb, 2)
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x = self.norm(x) * (1 + scale) + shift
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return x
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class AdaLayerNormZero(nn.Module):
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"""
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Norm layer adaptive layer norm zero (adaLN-Zero).
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"""
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def __init__(self, embedding_dim, num_embeddings):
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super().__init__()
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self.emb = CombinedTimestepLabelEmbeddings(num_embeddings, embedding_dim)
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self.silu = nn.SiLU()
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self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True)
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self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6)
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def forward(self, x, timestep, class_labels, hidden_dtype=None):
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emb = self.linear(self.silu(self.emb(timestep, class_labels, hidden_dtype=hidden_dtype)))
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shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.chunk(6, dim=1)
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x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
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return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
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class AdaGroupNorm(nn.Module):
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"""
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GroupNorm layer modified to incorporate timestep embeddings.
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"""
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def __init__(
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self, embedding_dim: int, out_dim: int, num_groups: int, act_fn: Optional[str] = None, eps: float = 1e-5
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):
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super().__init__()
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self.num_groups = num_groups
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self.eps = eps
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if act_fn is None:
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self.act = None
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else:
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self.act = get_activation(act_fn)
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self.linear = nn.Linear(embedding_dim, out_dim * 2)
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def forward(self, x, emb):
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if self.act:
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emb = self.act(emb)
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emb = self.linear(emb)
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emb = emb[:, :, None, None]
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scale, shift = emb.chunk(2, dim=1)
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x = F.group_norm(x, self.num_groups, eps=self.eps)
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x = x * (1 + scale) + shift
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return x
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