from math import floor, log, pi from typing import Any, List, Optional, Sequence, Tuple, Union from .utils import * import torch import torch.nn as nn from einops import rearrange, reduce, repeat from einops.layers.torch import Rearrange from einops_exts import rearrange_many from torch import Tensor, einsum """ Utils """ class AdaLayerNorm(nn.Module): def __init__(self, style_dim, channels, eps=1e-5): super().__init__() self.channels = channels self.eps = eps self.fc = nn.Linear(style_dim, channels*2) def forward(self, x, s): x = x.transpose(-1, -2) x = x.transpose(1, -1) h = self.fc(s) h = h.view(h.size(0), h.size(1), 1) gamma, beta = torch.chunk(h, chunks=2, dim=1) gamma, beta = gamma.transpose(1, -1), beta.transpose(1, -1) x = F.layer_norm(x, (self.channels,), eps=self.eps) x = (1 + gamma) * x + beta return x.transpose(1, -1).transpose(-1, -2) class StyleTransformer1d(nn.Module): def __init__( self, num_layers: int, channels: int, num_heads: int, head_features: int, multiplier: int, use_context_time: bool = True, use_rel_pos: bool = False, context_features_multiplier: int = 1, rel_pos_num_buckets: Optional[int] = None, rel_pos_max_distance: Optional[int] = None, context_features: Optional[int] = None, context_embedding_features: Optional[int] = None, embedding_max_length: int = 512, ): super().__init__() self.blocks = nn.ModuleList( [ StyleTransformerBlock( features=channels + context_embedding_features, head_features=head_features, num_heads=num_heads, multiplier=multiplier, style_dim=context_features, use_rel_pos=use_rel_pos, rel_pos_num_buckets=rel_pos_num_buckets, rel_pos_max_distance=rel_pos_max_distance, ) for i in range(num_layers) ] ) self.to_out = nn.Sequential( Rearrange("b t c -> b c t"), nn.Conv1d( in_channels=channels + context_embedding_features, out_channels=channels, kernel_size=1, ), ) use_context_features = exists(context_features) self.use_context_features = use_context_features self.use_context_time = use_context_time if use_context_time or use_context_features: context_mapping_features = channels + context_embedding_features self.to_mapping = nn.Sequential( nn.Linear(context_mapping_features, context_mapping_features), nn.GELU(), nn.Linear(context_mapping_features, context_mapping_features), nn.GELU(), ) if use_context_time: assert exists(context_mapping_features) self.to_time = nn.Sequential( TimePositionalEmbedding( dim=channels, out_features=context_mapping_features ), nn.GELU(), ) if use_context_features: assert exists(context_features) and exists(context_mapping_features) self.to_features = nn.Sequential( nn.Linear( in_features=context_features, out_features=context_mapping_features ), nn.GELU(), ) self.fixed_embedding = FixedEmbedding( max_length=embedding_max_length, features=context_embedding_features ) def get_mapping( self, time: Optional[Tensor] = None, features: Optional[Tensor] = None ) -> Optional[Tensor]: """Combines context time features and features into mapping""" items, mapping = [], None # Compute time features if self.use_context_time: assert_message = "use_context_time=True but no time features provided" assert exists(time), assert_message items += [self.to_time(time)] # Compute features if self.use_context_features: assert_message = "context_features exists but no features provided" assert exists(features), assert_message items += [self.to_features(features)] # Compute joint mapping if self.use_context_time or self.use_context_features: mapping = reduce(torch.stack(items), "n b m -> b m", "sum") mapping = self.to_mapping(mapping) return mapping def run(self, x, time, embedding, features): mapping = self.get_mapping(time, features) x = torch.cat([x.expand(-1, embedding.size(1), -1), embedding], axis=-1) mapping = mapping.unsqueeze(1).expand(-1, embedding.size(1), -1) for block in self.blocks: x = x + mapping x = block(x, features) x = x.mean(axis=1).unsqueeze(1) x = self.to_out(x) x = x.transpose(-1, -2) return x def forward(self, x: Tensor, time: Tensor, embedding_mask_proba: float = 0.0, embedding: Optional[Tensor] = None, features: Optional[Tensor] = None, embedding_scale: float = 1.0) -> Tensor: b, device = embedding.shape[0], embedding.device fixed_embedding = self.fixed_embedding(embedding) if embedding_mask_proba > 0.0: # Randomly mask embedding batch_mask = rand_bool( shape=(b, 1, 1), proba=embedding_mask_proba, device=device ) embedding = torch.where(batch_mask, fixed_embedding, embedding) if embedding_scale != 1.0: # Compute both normal and fixed embedding outputs out = self.run(x, time, embedding=embedding, features=features) out_masked = self.run(x, time, embedding=fixed_embedding, features=features) # Scale conditional output using classifier-free guidance return out_masked + (out - out_masked) * embedding_scale else: return self.run(x, time, embedding=embedding, features=features) return x class StyleTransformerBlock(nn.Module): def __init__( self, features: int, num_heads: int, head_features: int, style_dim: int, multiplier: int, use_rel_pos: bool, rel_pos_num_buckets: Optional[int] = None, rel_pos_max_distance: Optional[int] = None, context_features: Optional[int] = None, ): super().__init__() self.use_cross_attention = exists(context_features) and context_features > 0 self.attention = StyleAttention( features=features, style_dim=style_dim, num_heads=num_heads, head_features=head_features, use_rel_pos=use_rel_pos, rel_pos_num_buckets=rel_pos_num_buckets, rel_pos_max_distance=rel_pos_max_distance, ) if self.use_cross_attention: self.cross_attention = StyleAttention( features=features, style_dim=style_dim, num_heads=num_heads, head_features=head_features, context_features=context_features, use_rel_pos=use_rel_pos, rel_pos_num_buckets=rel_pos_num_buckets, rel_pos_max_distance=rel_pos_max_distance, ) self.feed_forward = FeedForward(features=features, multiplier=multiplier) def forward(self, x: Tensor, s: Tensor, *, context: Optional[Tensor] = None) -> Tensor: x = self.attention(x, s) + x if self.use_cross_attention: x = self.cross_attention(x, s, context=context) + x x = self.feed_forward(x) + x return x class StyleAttention(nn.Module): def __init__( self, features: int, *, style_dim: int, head_features: int, num_heads: int, context_features: Optional[int] = None, use_rel_pos: bool, rel_pos_num_buckets: Optional[int] = None, rel_pos_max_distance: Optional[int] = None, ): super().__init__() self.context_features = context_features mid_features = head_features * num_heads context_features = default(context_features, features) self.norm = AdaLayerNorm(style_dim, features) self.norm_context = AdaLayerNorm(style_dim, context_features) self.to_q = nn.Linear( in_features=features, out_features=mid_features, bias=False ) self.to_kv = nn.Linear( in_features=context_features, out_features=mid_features * 2, bias=False ) self.attention = AttentionBase( features, num_heads=num_heads, head_features=head_features, use_rel_pos=use_rel_pos, rel_pos_num_buckets=rel_pos_num_buckets, rel_pos_max_distance=rel_pos_max_distance, ) def forward(self, x: Tensor, s: Tensor, *, context: Optional[Tensor] = None) -> Tensor: assert_message = "You must provide a context when using context_features" assert not self.context_features or exists(context), assert_message # Use context if provided context = default(context, x) # Normalize then compute q from input and k,v from context x, context = self.norm(x, s), self.norm_context(context, s) q, k, v = (self.to_q(x), *torch.chunk(self.to_kv(context), chunks=2, dim=-1)) # Compute and return attention return self.attention(q, k, v) class Transformer1d(nn.Module): def __init__( self, num_layers: int, channels: int, num_heads: int, head_features: int, multiplier: int, use_context_time: bool = True, use_rel_pos: bool = False, context_features_multiplier: int = 1, rel_pos_num_buckets: Optional[int] = None, rel_pos_max_distance: Optional[int] = None, context_features: Optional[int] = None, context_embedding_features: Optional[int] = None, embedding_max_length: int = 512, ): super().__init__() self.blocks = nn.ModuleList( [ TransformerBlock( features=channels + context_embedding_features, head_features=head_features, num_heads=num_heads, multiplier=multiplier, use_rel_pos=use_rel_pos, rel_pos_num_buckets=rel_pos_num_buckets, rel_pos_max_distance=rel_pos_max_distance, ) for i in range(num_layers) ] ) self.to_out = nn.Sequential( Rearrange("b t c -> b c t"), nn.Conv1d( in_channels=channels + context_embedding_features, out_channels=channels, kernel_size=1, ), ) use_context_features = exists(context_features) self.use_context_features = use_context_features self.use_context_time = use_context_time if use_context_time or use_context_features: context_mapping_features = channels + context_embedding_features self.to_mapping = nn.Sequential( nn.Linear(context_mapping_features, context_mapping_features), nn.GELU(), nn.Linear(context_mapping_features, context_mapping_features), nn.GELU(), ) if use_context_time: assert exists(context_mapping_features) self.to_time = nn.Sequential( TimePositionalEmbedding( dim=channels, out_features=context_mapping_features ), nn.GELU(), ) if use_context_features: assert exists(context_features) and exists(context_mapping_features) self.to_features = nn.Sequential( nn.Linear( in_features=context_features, out_features=context_mapping_features ), nn.GELU(), ) self.fixed_embedding = FixedEmbedding( max_length=embedding_max_length, features=context_embedding_features ) def get_mapping( self, time: Optional[Tensor] = None, features: Optional[Tensor] = None ) -> Optional[Tensor]: """Combines context time features and features into mapping""" items, mapping = [], None # Compute time features if self.use_context_time: assert_message = "use_context_time=True but no time features provided" assert exists(time), assert_message items += [self.to_time(time)] # Compute features if self.use_context_features: assert_message = "context_features exists but no features provided" assert exists(features), assert_message items += [self.to_features(features)] # Compute joint mapping if self.use_context_time or self.use_context_features: mapping = reduce(torch.stack(items), "n b m -> b m", "sum") mapping = self.to_mapping(mapping) return mapping def run(self, x, time, embedding, features): mapping = self.get_mapping(time, features) x = torch.cat([x.expand(-1, embedding.size(1), -1), embedding], axis=-1) mapping = mapping.unsqueeze(1).expand(-1, embedding.size(1), -1) for block in self.blocks: x = x + mapping x = block(x) x = x.mean(axis=1).unsqueeze(1) x = self.to_out(x) x = x.transpose(-1, -2) return x def forward(self, x: Tensor, time: Tensor, embedding_mask_proba: float = 0.0, embedding: Optional[Tensor] = None, features: Optional[Tensor] = None, embedding_scale: float = 1.0) -> Tensor: b, device = embedding.shape[0], embedding.device fixed_embedding = self.fixed_embedding(embedding) if embedding_mask_proba > 0.0: # Randomly mask embedding batch_mask = rand_bool( shape=(b, 1, 1), proba=embedding_mask_proba, device=device ) embedding = torch.where(batch_mask, fixed_embedding, embedding) if embedding_scale != 1.0: # Compute both normal and fixed embedding outputs out = self.run(x, time, embedding=embedding, features=features) out_masked = self.run(x, time, embedding=fixed_embedding, features=features) # Scale conditional output using classifier-free guidance return out_masked + (out - out_masked) * embedding_scale else: return self.run(x, time, embedding=embedding, features=features) return x """ Attention Components """ class RelativePositionBias(nn.Module): def __init__(self, num_buckets: int, max_distance: int, num_heads: int): super().__init__() self.num_buckets = num_buckets self.max_distance = max_distance self.num_heads = num_heads self.relative_attention_bias = nn.Embedding(num_buckets, num_heads) @staticmethod def _relative_position_bucket( relative_position: Tensor, num_buckets: int, max_distance: int ): num_buckets //= 2 ret = (relative_position >= 0).to(torch.long) * num_buckets n = torch.abs(relative_position) max_exact = num_buckets // 2 is_small = n < max_exact val_if_large = ( max_exact + ( torch.log(n.float() / max_exact) / log(max_distance / max_exact) * (num_buckets - max_exact) ).long() ) val_if_large = torch.min( val_if_large, torch.full_like(val_if_large, num_buckets - 1) ) ret += torch.where(is_small, n, val_if_large) return ret def forward(self, num_queries: int, num_keys: int) -> Tensor: i, j, device = num_queries, num_keys, self.relative_attention_bias.weight.device q_pos = torch.arange(j - i, j, dtype=torch.long, device=device) k_pos = torch.arange(j, dtype=torch.long, device=device) rel_pos = rearrange(k_pos, "j -> 1 j") - rearrange(q_pos, "i -> i 1") relative_position_bucket = self._relative_position_bucket( rel_pos, num_buckets=self.num_buckets, max_distance=self.max_distance ) bias = self.relative_attention_bias(relative_position_bucket) bias = rearrange(bias, "m n h -> 1 h m n") return bias def FeedForward(features: int, multiplier: int) -> nn.Module: mid_features = features * multiplier return nn.Sequential( nn.Linear(in_features=features, out_features=mid_features), nn.GELU(), nn.Linear(in_features=mid_features, out_features=features), ) class AttentionBase(nn.Module): def __init__( self, features: int, *, head_features: int, num_heads: int, use_rel_pos: bool, out_features: Optional[int] = None, rel_pos_num_buckets: Optional[int] = None, rel_pos_max_distance: Optional[int] = None, ): super().__init__() self.scale = head_features ** -0.5 self.num_heads = num_heads self.use_rel_pos = use_rel_pos mid_features = head_features * num_heads if use_rel_pos: assert exists(rel_pos_num_buckets) and exists(rel_pos_max_distance) self.rel_pos = RelativePositionBias( num_buckets=rel_pos_num_buckets, max_distance=rel_pos_max_distance, num_heads=num_heads, ) if out_features is None: out_features = features self.to_out = nn.Linear(in_features=mid_features, out_features=out_features) def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor: # Split heads q, k, v = rearrange_many((q, k, v), "b n (h d) -> b h n d", h=self.num_heads) # Compute similarity matrix sim = einsum("... n d, ... m d -> ... n m", q, k) sim = (sim + self.rel_pos(*sim.shape[-2:])) if self.use_rel_pos else sim sim = sim * self.scale # Get attention matrix with softmax attn = sim.softmax(dim=-1) # Compute values out = einsum("... n m, ... m d -> ... n d", attn, v) out = rearrange(out, "b h n d -> b n (h d)") return self.to_out(out) class Attention(nn.Module): def __init__( self, features: int, *, head_features: int, num_heads: int, out_features: Optional[int] = None, context_features: Optional[int] = None, use_rel_pos: bool, rel_pos_num_buckets: Optional[int] = None, rel_pos_max_distance: Optional[int] = None, ): super().__init__() self.context_features = context_features mid_features = head_features * num_heads context_features = default(context_features, features) self.norm = nn.LayerNorm(features) self.norm_context = nn.LayerNorm(context_features) self.to_q = nn.Linear( in_features=features, out_features=mid_features, bias=False ) self.to_kv = nn.Linear( in_features=context_features, out_features=mid_features * 2, bias=False ) self.attention = AttentionBase( features, out_features=out_features, num_heads=num_heads, head_features=head_features, use_rel_pos=use_rel_pos, rel_pos_num_buckets=rel_pos_num_buckets, rel_pos_max_distance=rel_pos_max_distance, ) def forward(self, x: Tensor, *, context: Optional[Tensor] = None) -> Tensor: assert_message = "You must provide a context when using context_features" assert not self.context_features or exists(context), assert_message # Use context if provided context = default(context, x) # Normalize then compute q from input and k,v from context x, context = self.norm(x), self.norm_context(context) q, k, v = (self.to_q(x), *torch.chunk(self.to_kv(context), chunks=2, dim=-1)) # Compute and return attention return self.attention(q, k, v) """ Transformer Blocks """ class TransformerBlock(nn.Module): def __init__( self, features: int, num_heads: int, head_features: int, multiplier: int, use_rel_pos: bool, rel_pos_num_buckets: Optional[int] = None, rel_pos_max_distance: Optional[int] = None, context_features: Optional[int] = None, ): super().__init__() self.use_cross_attention = exists(context_features) and context_features > 0 self.attention = Attention( features=features, num_heads=num_heads, head_features=head_features, use_rel_pos=use_rel_pos, rel_pos_num_buckets=rel_pos_num_buckets, rel_pos_max_distance=rel_pos_max_distance, ) if self.use_cross_attention: self.cross_attention = Attention( features=features, num_heads=num_heads, head_features=head_features, context_features=context_features, use_rel_pos=use_rel_pos, rel_pos_num_buckets=rel_pos_num_buckets, rel_pos_max_distance=rel_pos_max_distance, ) self.feed_forward = FeedForward(features=features, multiplier=multiplier) def forward(self, x: Tensor, *, context: Optional[Tensor] = None) -> Tensor: x = self.attention(x) + x if self.use_cross_attention: x = self.cross_attention(x, context=context) + x x = self.feed_forward(x) + x return x """ Time Embeddings """ class SinusoidalEmbedding(nn.Module): def __init__(self, dim: int): super().__init__() self.dim = dim def forward(self, x: Tensor) -> Tensor: device, half_dim = x.device, self.dim // 2 emb = torch.tensor(log(10000) / (half_dim - 1), device=device) emb = torch.exp(torch.arange(half_dim, device=device) * -emb) emb = rearrange(x, "i -> i 1") * rearrange(emb, "j -> 1 j") return torch.cat((emb.sin(), emb.cos()), dim=-1) class LearnedPositionalEmbedding(nn.Module): """Used for continuous time""" def __init__(self, dim: int): super().__init__() assert (dim % 2) == 0 half_dim = dim // 2 self.weights = nn.Parameter(torch.randn(half_dim)) def forward(self, x: Tensor) -> Tensor: x = rearrange(x, "b -> b 1") freqs = x * rearrange(self.weights, "d -> 1 d") * 2 * pi fouriered = torch.cat((freqs.sin(), freqs.cos()), dim=-1) fouriered = torch.cat((x, fouriered), dim=-1) return fouriered def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module: return nn.Sequential( LearnedPositionalEmbedding(dim), nn.Linear(in_features=dim + 1, out_features=out_features), ) class FixedEmbedding(nn.Module): def __init__(self, max_length: int, features: int): super().__init__() self.max_length = max_length self.embedding = nn.Embedding(max_length, features) def forward(self, x: Tensor) -> Tensor: batch_size, length, device = *x.shape[0:2], x.device assert_message = "Input sequence length must be <= max_length" assert length <= self.max_length, assert_message position = torch.arange(length, device=device) fixed_embedding = self.embedding(position) fixed_embedding = repeat(fixed_embedding, "n d -> b n d", b=batch_size) return fixed_embedding