import inspect from importlib import import_module from typing import Any, Dict, Optional, Tuple import torch import torch.nn.functional as F from diffusers.models.activations import GEGLU, GELU, ApproximateGELU from diffusers.models.attention import _chunked_feed_forward from diffusers.models.attention_processor import ( LoRAAttnAddedKVProcessor, LoRAAttnProcessor, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, SpatialNorm, ) from diffusers.models.lora import LoRACompatibleLinear from diffusers.models.normalization import RMSNorm from diffusers.utils import deprecate, logging from diffusers.utils.torch_utils import maybe_allow_in_graph from einops import rearrange from torch import nn try: from torch_xla.experimental.custom_kernel import flash_attention except ImportError: # workaround for automatic tests. Currently this function is manually patched # to the torch_xla lib on setup of container pass # code adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py logger = logging.get_logger(__name__) @maybe_allow_in_graph class BasicTransformerBlock(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. 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. 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. upcast_attention (`bool`, *optional*): Whether to upcast the attention computation to float32. This is useful for mixed precision training. norm_elementwise_affine (`bool`, *optional*, defaults to `True`): Whether to use learnable elementwise affine parameters for normalization. qk_norm (`str`, *optional*, defaults to None): Set to 'layer_norm' or `rms_norm` to perform query and key normalization. adaptive_norm (`str`, *optional*, defaults to `"single_scale_shift"`): The type of adaptive norm to use. Can be `"single_scale_shift"`, `"single_scale"` or "none". standardization_norm (`str`, *optional*, defaults to `"layer_norm"`): The type of pre-normalization to use. Can be `"layer_norm"` or `"rms_norm"`. final_dropout (`bool` *optional*, defaults to False): Whether to apply a final dropout after the last feed-forward layer. attention_type (`str`, *optional*, defaults to `"default"`): The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`. positional_embeddings (`str`, *optional*, defaults to `None`): The type of positional embeddings to apply to. num_positional_embeddings (`int`, *optional*, defaults to `None`): The maximum number of positional embeddings to apply. """ 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, # pylint: disable=unused-argument attention_bias: bool = False, only_cross_attention: bool = False, double_self_attention: bool = False, upcast_attention: bool = False, norm_elementwise_affine: bool = True, adaptive_norm: str = "single_scale_shift", # 'single_scale_shift', 'single_scale' or 'none' standardization_norm: str = "layer_norm", # 'layer_norm' or 'rms_norm' norm_eps: float = 1e-5, qk_norm: Optional[str] = None, final_dropout: bool = False, attention_type: str = "default", # pylint: disable=unused-argument ff_inner_dim: Optional[int] = None, ff_bias: bool = True, attention_out_bias: bool = True, use_tpu_flash_attention: bool = False, use_rope: bool = False, ): super().__init__() self.only_cross_attention = only_cross_attention self.use_tpu_flash_attention = use_tpu_flash_attention self.adaptive_norm = adaptive_norm assert standardization_norm in ["layer_norm", "rms_norm"] assert adaptive_norm in ["single_scale_shift", "single_scale", "none"] make_norm_layer = ( nn.LayerNorm if standardization_norm == "layer_norm" else RMSNorm ) # Define 3 blocks. Each block has its own normalization layer. # 1. Self-Attn self.norm1 = make_norm_layer( dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps ) self.attn1 = Attention( 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, out_bias=attention_out_bias, use_tpu_flash_attention=use_tpu_flash_attention, qk_norm=qk_norm, use_rope=use_rope, ) # 2. Cross-Attn if cross_attention_dim is not None or double_self_attention: 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, out_bias=attention_out_bias, use_tpu_flash_attention=use_tpu_flash_attention, qk_norm=qk_norm, use_rope=use_rope, ) # is self-attn if encoder_hidden_states is none if adaptive_norm == "none": self.attn2_norm = make_norm_layer( dim, norm_eps, norm_elementwise_affine ) else: self.attn2 = None self.attn2_norm = None self.norm2 = make_norm_layer(dim, norm_eps, norm_elementwise_affine) # 3. Feed-forward self.ff = FeedForward( dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout, inner_dim=ff_inner_dim, bias=ff_bias, ) # 5. Scale-shift for PixArt-Alpha. if adaptive_norm != "none": num_ada_params = 4 if adaptive_norm == "single_scale" else 6 self.scale_shift_table = nn.Parameter( torch.randn(num_ada_params, dim) / dim**0.5 ) # let chunk size default to None self._chunk_size = None self._chunk_dim = 0 def set_use_tpu_flash_attention(self, device): r""" Function sets the flag in this object and propagates down the children. The flag will enforce the usage of TPU attention kernel. """ if device == "xla": self.use_tpu_flash_attention = True self.attn1.set_use_tpu_flash_attention(device) self.attn2.set_use_tpu_flash_attention(device) def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0): # Sets chunk feed-forward self._chunk_size = chunk_size self._chunk_dim = dim def forward( self, hidden_states: torch.FloatTensor, freqs_cis: Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] = None, 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, added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, ) -> torch.FloatTensor: if cross_attention_kwargs is not None: if cross_attention_kwargs.get("scale", None) is not None: logger.warning( "Passing `scale` to `cross_attention_kwargs` is depcrecated. `scale` will be ignored." ) # Notice that normalization is always applied before the real computation in the following blocks. # 0. Self-Attention batch_size = hidden_states.shape[0] norm_hidden_states = self.norm1(hidden_states) # Apply ada_norm_single if self.adaptive_norm in ["single_scale_shift", "single_scale"]: assert timestep.ndim == 3 # [batch, 1 or num_tokens, embedding_dim] num_ada_params = self.scale_shift_table.shape[0] ada_values = self.scale_shift_table[None, None] + timestep.reshape( batch_size, timestep.shape[1], num_ada_params, -1 ) if self.adaptive_norm == "single_scale_shift": shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( ada_values.unbind(dim=2) ) norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa else: scale_msa, gate_msa, scale_mlp, gate_mlp = ada_values.unbind(dim=2) norm_hidden_states = norm_hidden_states * (1 + scale_msa) elif self.adaptive_norm == "none": scale_msa, gate_msa, scale_mlp, gate_mlp = None, None, None, None else: raise ValueError(f"Unknown adaptive norm type: {self.adaptive_norm}") norm_hidden_states = norm_hidden_states.squeeze( 1 ) # TODO: Check if this is needed # 1. Prepare GLIGEN inputs cross_attention_kwargs = ( cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {} ) attn_output = self.attn1( norm_hidden_states, freqs_cis=freqs_cis, encoder_hidden_states=( encoder_hidden_states if self.only_cross_attention else None ), attention_mask=attention_mask, **cross_attention_kwargs, ) if gate_msa is not None: attn_output = gate_msa * attn_output hidden_states = attn_output + hidden_states if hidden_states.ndim == 4: hidden_states = hidden_states.squeeze(1) # 3. Cross-Attention if self.attn2 is not None: if self.adaptive_norm == "none": attn_input = self.attn2_norm(hidden_states) else: attn_input = hidden_states attn_output = self.attn2( attn_input, freqs_cis=freqs_cis, encoder_hidden_states=encoder_hidden_states, attention_mask=encoder_attention_mask, **cross_attention_kwargs, ) hidden_states = attn_output + hidden_states # 4. Feed-forward norm_hidden_states = self.norm2(hidden_states) if self.adaptive_norm == "single_scale_shift": norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp elif self.adaptive_norm == "single_scale": norm_hidden_states = norm_hidden_states * (1 + scale_mlp) elif self.adaptive_norm == "none": pass else: raise ValueError(f"Unknown adaptive norm type: {self.adaptive_norm}") if self._chunk_size is not None: # "feed_forward_chunk_size" can be used to save memory ff_output = _chunked_feed_forward( self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size ) else: ff_output = self.ff(norm_hidden_states) if gate_mlp is not None: ff_output = gate_mlp * ff_output hidden_states = ff_output + hidden_states if hidden_states.ndim == 4: hidden_states = hidden_states.squeeze(1) return hidden_states @maybe_allow_in_graph class Attention(nn.Module): r""" A cross attention layer. Parameters: query_dim (`int`): The number of channels in the query. cross_attention_dim (`int`, *optional*): The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`. heads (`int`, *optional*, defaults to 8): The number of heads to use for multi-head attention. dim_head (`int`, *optional*, defaults to 64): The number of channels in each head. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. bias (`bool`, *optional*, defaults to False): Set to `True` for the query, key, and value linear layers to contain a bias parameter. upcast_attention (`bool`, *optional*, defaults to False): Set to `True` to upcast the attention computation to `float32`. upcast_softmax (`bool`, *optional*, defaults to False): Set to `True` to upcast the softmax computation to `float32`. cross_attention_norm (`str`, *optional*, defaults to `None`): The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`. cross_attention_norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the group norm in the cross attention. added_kv_proj_dim (`int`, *optional*, defaults to `None`): The number of channels to use for the added key and value projections. If `None`, no projection is used. norm_num_groups (`int`, *optional*, defaults to `None`): The number of groups to use for the group norm in the attention. spatial_norm_dim (`int`, *optional*, defaults to `None`): The number of channels to use for the spatial normalization. out_bias (`bool`, *optional*, defaults to `True`): Set to `True` to use a bias in the output linear layer. scale_qk (`bool`, *optional*, defaults to `True`): Set to `True` to scale the query and key by `1 / sqrt(dim_head)`. qk_norm (`str`, *optional*, defaults to None): Set to 'layer_norm' or `rms_norm` to perform query and key normalization. only_cross_attention (`bool`, *optional*, defaults to `False`): Set to `True` to only use cross attention and not added_kv_proj_dim. Can only be set to `True` if `added_kv_proj_dim` is not `None`. eps (`float`, *optional*, defaults to 1e-5): An additional value added to the denominator in group normalization that is used for numerical stability. rescale_output_factor (`float`, *optional*, defaults to 1.0): A factor to rescale the output by dividing it with this value. residual_connection (`bool`, *optional*, defaults to `False`): Set to `True` to add the residual connection to the output. _from_deprecated_attn_block (`bool`, *optional*, defaults to `False`): Set to `True` if the attention block is loaded from a deprecated state dict. processor (`AttnProcessor`, *optional*, defaults to `None`): The attention processor to use. If `None`, defaults to `AttnProcessor2_0` if `torch 2.x` is used and `AttnProcessor` otherwise. """ def __init__( self, query_dim: int, cross_attention_dim: Optional[int] = None, heads: int = 8, dim_head: int = 64, dropout: float = 0.0, bias: bool = False, upcast_attention: bool = False, upcast_softmax: bool = False, cross_attention_norm: Optional[str] = None, cross_attention_norm_num_groups: int = 32, added_kv_proj_dim: Optional[int] = None, norm_num_groups: Optional[int] = None, spatial_norm_dim: Optional[int] = None, out_bias: bool = True, scale_qk: bool = True, qk_norm: Optional[str] = None, only_cross_attention: bool = False, eps: float = 1e-5, rescale_output_factor: float = 1.0, residual_connection: bool = False, _from_deprecated_attn_block: bool = False, processor: Optional["AttnProcessor"] = None, out_dim: int = None, use_tpu_flash_attention: bool = False, use_rope: bool = False, ): super().__init__() self.inner_dim = out_dim if out_dim is not None else dim_head * heads self.query_dim = query_dim self.use_bias = bias self.is_cross_attention = cross_attention_dim is not None self.cross_attention_dim = ( cross_attention_dim if cross_attention_dim is not None else query_dim ) self.upcast_attention = upcast_attention self.upcast_softmax = upcast_softmax self.rescale_output_factor = rescale_output_factor self.residual_connection = residual_connection self.dropout = dropout self.fused_projections = False self.out_dim = out_dim if out_dim is not None else query_dim self.use_tpu_flash_attention = use_tpu_flash_attention self.use_rope = use_rope # we make use of this private variable to know whether this class is loaded # with an deprecated state dict so that we can convert it on the fly self._from_deprecated_attn_block = _from_deprecated_attn_block self.scale_qk = scale_qk self.scale = dim_head**-0.5 if self.scale_qk else 1.0 if qk_norm is None: self.q_norm = nn.Identity() self.k_norm = nn.Identity() elif qk_norm == "rms_norm": self.q_norm = RMSNorm(dim_head * heads, eps=1e-5) self.k_norm = RMSNorm(dim_head * heads, eps=1e-5) elif qk_norm == "layer_norm": self.q_norm = nn.LayerNorm(dim_head * heads, eps=1e-5) self.k_norm = nn.LayerNorm(dim_head * heads, eps=1e-5) else: raise ValueError(f"Unsupported qk_norm method: {qk_norm}") self.heads = out_dim // dim_head if out_dim is not None else heads # for slice_size > 0 the attention score computation # is split across the batch axis to save memory # You can set slice_size with `set_attention_slice` self.sliceable_head_dim = heads self.added_kv_proj_dim = added_kv_proj_dim self.only_cross_attention = only_cross_attention if self.added_kv_proj_dim is None and self.only_cross_attention: raise ValueError( "`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`." ) if norm_num_groups is not None: self.group_norm = nn.GroupNorm( num_channels=query_dim, num_groups=norm_num_groups, eps=eps, affine=True ) else: self.group_norm = None if spatial_norm_dim is not None: self.spatial_norm = SpatialNorm( f_channels=query_dim, zq_channels=spatial_norm_dim ) else: self.spatial_norm = None if cross_attention_norm is None: self.norm_cross = None elif cross_attention_norm == "layer_norm": self.norm_cross = nn.LayerNorm(self.cross_attention_dim) elif cross_attention_norm == "group_norm": if self.added_kv_proj_dim is not None: # The given `encoder_hidden_states` are initially of shape # (batch_size, seq_len, added_kv_proj_dim) before being projected # to (batch_size, seq_len, cross_attention_dim). The norm is applied # before the projection, so we need to use `added_kv_proj_dim` as # the number of channels for the group norm. norm_cross_num_channels = added_kv_proj_dim else: norm_cross_num_channels = self.cross_attention_dim self.norm_cross = nn.GroupNorm( num_channels=norm_cross_num_channels, num_groups=cross_attention_norm_num_groups, eps=1e-5, affine=True, ) else: raise ValueError( f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'" ) linear_cls = nn.Linear self.linear_cls = linear_cls self.to_q = linear_cls(query_dim, self.inner_dim, bias=bias) if not self.only_cross_attention: # only relevant for the `AddedKVProcessor` classes self.to_k = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias) self.to_v = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias) else: self.to_k = None self.to_v = None if self.added_kv_proj_dim is not None: self.add_k_proj = linear_cls(added_kv_proj_dim, self.inner_dim) self.add_v_proj = linear_cls(added_kv_proj_dim, self.inner_dim) self.to_out = nn.ModuleList([]) self.to_out.append(linear_cls(self.inner_dim, self.out_dim, bias=out_bias)) self.to_out.append(nn.Dropout(dropout)) # set attention processor # We use the AttnProcessor2_0 by default when torch 2.x is used which uses # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1 if processor is None: processor = AttnProcessor2_0() self.set_processor(processor) def set_use_tpu_flash_attention(self, device_type): r""" Function sets the flag in this object. The flag will enforce the usage of TPU attention kernel. """ if device_type == "xla": self.use_tpu_flash_attention = True def set_processor(self, processor: "AttnProcessor") -> None: r""" Set the attention processor to use. Args: processor (`AttnProcessor`): The attention processor to use. """ # if current processor is in `self._modules` and if passed `processor` is not, we need to # pop `processor` from `self._modules` if ( hasattr(self, "processor") and isinstance(self.processor, torch.nn.Module) and not isinstance(processor, torch.nn.Module) ): logger.info( f"You are removing possibly trained weights of {self.processor} with {processor}" ) self._modules.pop("processor") self.processor = processor def get_processor( self, return_deprecated_lora: bool = False ) -> "AttentionProcessor": # noqa: F821 r""" Get the attention processor in use. Args: return_deprecated_lora (`bool`, *optional*, defaults to `False`): Set to `True` to return the deprecated LoRA attention processor. Returns: "AttentionProcessor": The attention processor in use. """ if not return_deprecated_lora: return self.processor # TODO(Sayak, Patrick). The rest of the function is needed to ensure backwards compatible # serialization format for LoRA Attention Processors. It should be deleted once the integration # with PEFT is completed. is_lora_activated = { name: module.lora_layer is not None for name, module in self.named_modules() if hasattr(module, "lora_layer") } # 1. if no layer has a LoRA activated we can return the processor as usual if not any(is_lora_activated.values()): return self.processor # If doesn't apply LoRA do `add_k_proj` or `add_v_proj` is_lora_activated.pop("add_k_proj", None) is_lora_activated.pop("add_v_proj", None) # 2. else it is not posssible that only some layers have LoRA activated if not all(is_lora_activated.values()): raise ValueError( f"Make sure that either all layers or no layers have LoRA activated, but have {is_lora_activated}" ) # 3. And we need to merge the current LoRA layers into the corresponding LoRA attention processor non_lora_processor_cls_name = self.processor.__class__.__name__ lora_processor_cls = getattr( import_module(__name__), "LoRA" + non_lora_processor_cls_name ) hidden_size = self.inner_dim # now create a LoRA attention processor from the LoRA layers if lora_processor_cls in [ LoRAAttnProcessor, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, ]: kwargs = { "cross_attention_dim": self.cross_attention_dim, "rank": self.to_q.lora_layer.rank, "network_alpha": self.to_q.lora_layer.network_alpha, "q_rank": self.to_q.lora_layer.rank, "q_hidden_size": self.to_q.lora_layer.out_features, "k_rank": self.to_k.lora_layer.rank, "k_hidden_size": self.to_k.lora_layer.out_features, "v_rank": self.to_v.lora_layer.rank, "v_hidden_size": self.to_v.lora_layer.out_features, "out_rank": self.to_out[0].lora_layer.rank, "out_hidden_size": self.to_out[0].lora_layer.out_features, } if hasattr(self.processor, "attention_op"): kwargs["attention_op"] = self.processor.attention_op lora_processor = lora_processor_cls(hidden_size, **kwargs) lora_processor.to_q_lora.load_state_dict(self.to_q.lora_layer.state_dict()) lora_processor.to_k_lora.load_state_dict(self.to_k.lora_layer.state_dict()) lora_processor.to_v_lora.load_state_dict(self.to_v.lora_layer.state_dict()) lora_processor.to_out_lora.load_state_dict( self.to_out[0].lora_layer.state_dict() ) elif lora_processor_cls == LoRAAttnAddedKVProcessor: lora_processor = lora_processor_cls( hidden_size, cross_attention_dim=self.add_k_proj.weight.shape[0], rank=self.to_q.lora_layer.rank, network_alpha=self.to_q.lora_layer.network_alpha, ) lora_processor.to_q_lora.load_state_dict(self.to_q.lora_layer.state_dict()) lora_processor.to_k_lora.load_state_dict(self.to_k.lora_layer.state_dict()) lora_processor.to_v_lora.load_state_dict(self.to_v.lora_layer.state_dict()) lora_processor.to_out_lora.load_state_dict( self.to_out[0].lora_layer.state_dict() ) # only save if used if self.add_k_proj.lora_layer is not None: lora_processor.add_k_proj_lora.load_state_dict( self.add_k_proj.lora_layer.state_dict() ) lora_processor.add_v_proj_lora.load_state_dict( self.add_v_proj.lora_layer.state_dict() ) else: lora_processor.add_k_proj_lora = None lora_processor.add_v_proj_lora = None else: raise ValueError(f"{lora_processor_cls} does not exist.") return lora_processor def forward( self, hidden_states: torch.FloatTensor, freqs_cis: Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, **cross_attention_kwargs, ) -> torch.Tensor: r""" The forward method of the `Attention` class. Args: hidden_states (`torch.Tensor`): The hidden states of the query. encoder_hidden_states (`torch.Tensor`, *optional*): The hidden states of the encoder. attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied. **cross_attention_kwargs: Additional keyword arguments to pass along to the cross attention. Returns: `torch.Tensor`: The output of the attention layer. """ # The `Attention` class can call different attention processors / attention functions # here we simply pass along all tensors to the selected processor class # For standard processors that are defined here, `**cross_attention_kwargs` is empty attn_parameters = set( inspect.signature(self.processor.__call__).parameters.keys() ) unused_kwargs = [ k for k, _ in cross_attention_kwargs.items() if k not in attn_parameters ] if len(unused_kwargs) > 0: logger.warning( f"cross_attention_kwargs {unused_kwargs} are not expected by" f" {self.processor.__class__.__name__} and will be ignored." ) cross_attention_kwargs = { k: w for k, w in cross_attention_kwargs.items() if k in attn_parameters } return self.processor( self, hidden_states, freqs_cis=freqs_cis, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, **cross_attention_kwargs, ) def batch_to_head_dim(self, tensor: torch.Tensor) -> torch.Tensor: r""" Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads` is the number of heads initialized while constructing the `Attention` class. Args: tensor (`torch.Tensor`): The tensor to reshape. Returns: `torch.Tensor`: The reshaped tensor. """ head_size = self.heads batch_size, seq_len, dim = tensor.shape tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim) tensor = tensor.permute(0, 2, 1, 3).reshape( batch_size // head_size, seq_len, dim * head_size ) return tensor def head_to_batch_dim(self, tensor: torch.Tensor, out_dim: int = 3) -> torch.Tensor: r""" Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is the number of heads initialized while constructing the `Attention` class. Args: tensor (`torch.Tensor`): The tensor to reshape. out_dim (`int`, *optional*, defaults to `3`): The output dimension of the tensor. If `3`, the tensor is reshaped to `[batch_size * heads, seq_len, dim // heads]`. Returns: `torch.Tensor`: The reshaped tensor. """ head_size = self.heads if tensor.ndim == 3: batch_size, seq_len, dim = tensor.shape extra_dim = 1 else: batch_size, extra_dim, seq_len, dim = tensor.shape tensor = tensor.reshape( batch_size, seq_len * extra_dim, head_size, dim // head_size ) tensor = tensor.permute(0, 2, 1, 3) if out_dim == 3: tensor = tensor.reshape( batch_size * head_size, seq_len * extra_dim, dim // head_size ) return tensor def get_attention_scores( self, query: torch.Tensor, key: torch.Tensor, attention_mask: torch.Tensor = None, ) -> torch.Tensor: r""" Compute the attention scores. Args: query (`torch.Tensor`): The query tensor. key (`torch.Tensor`): The key tensor. attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied. Returns: `torch.Tensor`: The attention probabilities/scores. """ dtype = query.dtype if self.upcast_attention: query = query.float() key = key.float() if attention_mask is None: baddbmm_input = torch.empty( query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device, ) beta = 0 else: baddbmm_input = attention_mask beta = 1 attention_scores = torch.baddbmm( baddbmm_input, query, key.transpose(-1, -2), beta=beta, alpha=self.scale, ) del baddbmm_input if self.upcast_softmax: attention_scores = attention_scores.float() attention_probs = attention_scores.softmax(dim=-1) del attention_scores attention_probs = attention_probs.to(dtype) return attention_probs def prepare_attention_mask( self, attention_mask: torch.Tensor, target_length: int, batch_size: int, out_dim: int = 3, ) -> torch.Tensor: r""" Prepare the attention mask for the attention computation. Args: attention_mask (`torch.Tensor`): The attention mask to prepare. target_length (`int`): The target length of the attention mask. This is the length of the attention mask after padding. batch_size (`int`): The batch size, which is used to repeat the attention mask. out_dim (`int`, *optional*, defaults to `3`): The output dimension of the attention mask. Can be either `3` or `4`. Returns: `torch.Tensor`: The prepared attention mask. """ head_size = self.heads if attention_mask is None: return attention_mask current_length: int = attention_mask.shape[-1] if current_length != target_length: if attention_mask.device.type == "mps": # HACK: MPS: Does not support padding by greater than dimension of input tensor. # Instead, we can manually construct the padding tensor. padding_shape = ( attention_mask.shape[0], attention_mask.shape[1], target_length, ) padding = torch.zeros( padding_shape, dtype=attention_mask.dtype, device=attention_mask.device, ) attention_mask = torch.cat([attention_mask, padding], dim=2) else: # TODO: for pipelines such as stable-diffusion, padding cross-attn mask: # we want to instead pad by (0, remaining_length), where remaining_length is: # remaining_length: int = target_length - current_length # TODO: re-enable tests/models/test_models_unet_2d_condition.py#test_model_xattn_padding attention_mask = F.pad(attention_mask, (0, target_length), value=0.0) if out_dim == 3: if attention_mask.shape[0] < batch_size * head_size: attention_mask = attention_mask.repeat_interleave(head_size, dim=0) elif out_dim == 4: attention_mask = attention_mask.unsqueeze(1) attention_mask = attention_mask.repeat_interleave(head_size, dim=1) return attention_mask def norm_encoder_hidden_states( self, encoder_hidden_states: torch.Tensor ) -> torch.Tensor: r""" Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the `Attention` class. Args: encoder_hidden_states (`torch.Tensor`): Hidden states of the encoder. Returns: `torch.Tensor`: The normalized encoder hidden states. """ assert ( self.norm_cross is not None ), "self.norm_cross must be defined to call self.norm_encoder_hidden_states" if isinstance(self.norm_cross, nn.LayerNorm): encoder_hidden_states = self.norm_cross(encoder_hidden_states) elif isinstance(self.norm_cross, nn.GroupNorm): # Group norm norms along the channels dimension and expects # input to be in the shape of (N, C, *). In this case, we want # to norm along the hidden dimension, so we need to move # (batch_size, sequence_length, hidden_size) -> # (batch_size, hidden_size, sequence_length) encoder_hidden_states = encoder_hidden_states.transpose(1, 2) encoder_hidden_states = self.norm_cross(encoder_hidden_states) encoder_hidden_states = encoder_hidden_states.transpose(1, 2) else: assert False return encoder_hidden_states @staticmethod def apply_rotary_emb( input_tensor: torch.Tensor, freqs_cis: Tuple[torch.FloatTensor, torch.FloatTensor], ) -> Tuple[torch.Tensor, torch.Tensor]: cos_freqs = freqs_cis[0] sin_freqs = freqs_cis[1] t_dup = rearrange(input_tensor, "... (d r) -> ... d r", r=2) t1, t2 = t_dup.unbind(dim=-1) t_dup = torch.stack((-t2, t1), dim=-1) input_tensor_rot = rearrange(t_dup, "... d r -> ... (d r)") out = input_tensor * cos_freqs + input_tensor_rot * sin_freqs return out class AttnProcessor2_0: r""" Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). """ def __init__(self): pass def __call__( self, attn: Attention, hidden_states: torch.FloatTensor, freqs_cis: Tuple[torch.FloatTensor, torch.FloatTensor], encoder_hidden_states: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, temb: Optional[torch.FloatTensor] = None, *args, **kwargs, ) -> torch.FloatTensor: if len(args) > 0 or kwargs.get("scale", None) is not None: deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." deprecate("scale", "1.0.0", deprecation_message) 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 ) if (attention_mask is not None) and (not attn.use_tpu_flash_attention): attention_mask = attn.prepare_attention_mask( attention_mask, sequence_length, batch_size ) # scaled_dot_product_attention expects attention_mask shape to be # (batch, heads, source_length, target_length) attention_mask = attention_mask.view( batch_size, attn.heads, -1, attention_mask.shape[-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) query = attn.q_norm(query) if encoder_hidden_states is not None: if attn.norm_cross: encoder_hidden_states = attn.norm_encoder_hidden_states( encoder_hidden_states ) key = attn.to_k(encoder_hidden_states) key = attn.k_norm(key) else: # if no context provided do self-attention encoder_hidden_states = hidden_states key = attn.to_k(hidden_states) key = attn.k_norm(key) if attn.use_rope: key = attn.apply_rotary_emb(key, freqs_cis) query = attn.apply_rotary_emb(query, freqs_cis) value = attn.to_v(encoder_hidden_states) inner_dim = key.shape[-1] head_dim = inner_dim // attn.heads query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) # the output of sdp = (batch, num_heads, seq_len, head_dim) if attn.use_tpu_flash_attention: # use tpu attention offload 'flash attention' q_segment_indexes = None if ( attention_mask is not None ): # if mask is required need to tune both segmenIds fields # attention_mask = torch.squeeze(attention_mask).to(torch.float32) attention_mask = attention_mask.to(torch.float32) q_segment_indexes = torch.ones( batch_size, query.shape[2], device=query.device, dtype=torch.float32 ) assert ( attention_mask.shape[1] == key.shape[2] ), f"ERROR: KEY SHAPE must be same as attention mask [{key.shape[2]}, {attention_mask.shape[1]}]" assert ( query.shape[2] % 128 == 0 ), f"ERROR: QUERY SHAPE must be divisible by 128 (TPU limitation) [{query.shape[2]}]" assert ( key.shape[2] % 128 == 0 ), f"ERROR: KEY SHAPE must be divisible by 128 (TPU limitation) [{key.shape[2]}]" # run the TPU kernel implemented in jax with pallas hidden_states = flash_attention( q=query, k=key, v=value, q_segment_ids=q_segment_indexes, kv_segment_ids=attention_mask, sm_scale=attn.scale, ) else: hidden_states = F.scaled_dot_product_attention( query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False, ) hidden_states = hidden_states.transpose(1, 2).reshape( batch_size, -1, attn.heads * head_dim ) hidden_states = hidden_states.to(query.dtype) # 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 AttnProcessor: r""" Default processor for performing attention-related computations. """ def __call__( self, attn: Attention, hidden_states: torch.FloatTensor, encoder_hidden_states: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, temb: Optional[torch.FloatTensor] = None, *args, **kwargs, ) -> torch.Tensor: if len(args) > 0 or kwargs.get("scale", None) is not None: deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." deprecate("scale", "1.0.0", deprecation_message) 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) query = attn.head_to_batch_dim(query) key = attn.head_to_batch_dim(key) value = attn.head_to_batch_dim(value) query = attn.q_norm(query) key = attn.k_norm(key) 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 FeedForward(nn.Module): r""" A feed-forward layer. Parameters: dim (`int`): The number of channels in the input. dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`. mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. final_dropout (`bool` *optional*, defaults to False): Apply a final dropout. bias (`bool`, defaults to True): Whether to use a bias in the linear layer. """ def __init__( self, dim: int, dim_out: Optional[int] = None, mult: int = 4, dropout: float = 0.0, activation_fn: str = "geglu", final_dropout: bool = False, inner_dim=None, bias: bool = True, ): super().__init__() if inner_dim is None: inner_dim = int(dim * mult) dim_out = dim_out if dim_out is not None else dim linear_cls = nn.Linear if activation_fn == "gelu": act_fn = GELU(dim, inner_dim, bias=bias) elif activation_fn == "gelu-approximate": act_fn = GELU(dim, inner_dim, approximate="tanh", bias=bias) elif activation_fn == "geglu": act_fn = GEGLU(dim, inner_dim, bias=bias) elif activation_fn == "geglu-approximate": act_fn = ApproximateGELU(dim, inner_dim, bias=bias) else: raise ValueError(f"Unsupported activation function: {activation_fn}") self.net = nn.ModuleList([]) # project in self.net.append(act_fn) # project dropout self.net.append(nn.Dropout(dropout)) # project out self.net.append(linear_cls(inner_dim, dim_out, bias=bias)) # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout if final_dropout: self.net.append(nn.Dropout(dropout)) def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tensor: compatible_cls = (GEGLU, LoRACompatibleLinear) for module in self.net: if isinstance(module, compatible_cls): hidden_states = module(hidden_states, scale) else: hidden_states = module(hidden_states) return hidden_states