from functools import partial import torch from torch import nn, einsum import torch.nn.functional as F from einops import rearrange, repeat try: import xformers import xformers.ops XFORMERS_IS_AVAILBLE = True except: XFORMERS_IS_AVAILBLE = False from lvdm.common import ( checkpoint, exists, default, ) from lvdm.basics import ( zero_module, ) def generate_weight_sequence(n): if n % 2 == 0: max_weight = n // 2 weight_sequence = list(range(1, max_weight + 1, 1)) + list(range(max_weight, 0, -1)) else: max_weight = (n + 1) // 2 weight_sequence = list(range(1, max_weight, 1)) + [max_weight] + list(range(max_weight - 1, 0, -1)) return weight_sequence class RelativePosition(nn.Module): """ https://github.com/evelinehong/Transformer_Relative_Position_PyTorch/blob/master/relative_position.py """ def __init__(self, num_units, max_relative_position): super().__init__() self.num_units = num_units self.max_relative_position = max_relative_position self.embeddings_table = nn.Parameter(torch.Tensor(max_relative_position * 2 + 1, num_units)) nn.init.xavier_uniform_(self.embeddings_table) def forward(self, length_q, length_k): device = self.embeddings_table.device range_vec_q = torch.arange(length_q, device=device) range_vec_k = torch.arange(length_k, device=device) distance_mat = range_vec_k[None, :] - range_vec_q[:, None] distance_mat_clipped = torch.clamp(distance_mat, -self.max_relative_position, self.max_relative_position) final_mat = distance_mat_clipped + self.max_relative_position final_mat = final_mat.long() embeddings = self.embeddings_table[final_mat] return embeddings class CrossAttention(nn.Module): def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., relative_position=False, temporal_length=None, img_cross_attention=False, injection=False): super().__init__() inner_dim = dim_head * heads context_dim = default(context_dim, query_dim) self.scale = dim_head**-0.5 self.heads = heads self.dim_head = dim_head self.to_q = nn.Linear(query_dim, inner_dim, bias=False) self.to_k = nn.Linear(context_dim, inner_dim, bias=False) self.to_v = nn.Linear(context_dim, inner_dim, bias=False) self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)) self.image_cross_attention_scale = 1.0 self.text_context_len = 77 self.img_cross_attention = img_cross_attention if self.img_cross_attention: self.to_k_ip = nn.Linear(context_dim, inner_dim, bias=False) self.to_v_ip = nn.Linear(context_dim, inner_dim, bias=False) self.relative_position = relative_position if self.relative_position: assert(temporal_length is not None) self.relative_position_k = RelativePosition(num_units=dim_head, max_relative_position=temporal_length) self.relative_position_v = RelativePosition(num_units=dim_head, max_relative_position=temporal_length) else: ## only used for spatial attention, while NOT for temporal attention if XFORMERS_IS_AVAILBLE and temporal_length is None: self.forward = self.efficient_forward self.injection = injection def forward(self, x, context=None, mask=None, context_next=None, use_injection=False): sa_flag = False if context is None: sa_flag = True h = self.heads all_q = self.to_q(x) context = default(context, x) ## considering image token additionally if context is not None and self.img_cross_attention: context, context_img = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:] all_k = self.to_k(context) all_v = self.to_v(context) all_k_ip = self.to_k_ip(context_img) all_v_ip = self.to_v_ip(context_img) else: all_k = self.to_k(context) all_v = self.to_v(context) count = torch.zeros_like(all_k) value = torch.zeros_like(all_k) if (sa_flag) and (context_next is not None): all_q, all_k, all_v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (all_q, all_k, all_v)) if context is not None and self.img_cross_attention: all_k_ip, all_v_ip = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (all_k_ip, all_v_ip)) for t_start, t_end in context_next: weight_sequence = generate_weight_sequence(t_end - t_start) weight_tensor = torch.ones_like(count[:, t_start:t_end]) weight_tensor = weight_tensor * torch.Tensor(weight_sequence).to(x.device).unsqueeze(0).unsqueeze(-1) q = all_q[:, t_start:t_end] k = all_k[:, t_start:t_end] v = all_v[:, t_start:t_end] sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale if self.relative_position: len_q, len_k, len_v = q.shape[1], k.shape[1], v.shape[1] k2 = self.relative_position_k(len_q, len_k) sim2 = einsum('b t d, t s d -> b t s', q, k2) * self.scale # TODO check sim += sim2 del k if exists(mask): ## feasible for causal attention mask only max_neg_value = -torch.finfo(sim.dtype).max mask = repeat(mask, 'b i j -> (b h) i j', h=h) sim.masked_fill_(~(mask>0.5), max_neg_value) # attention, what we cannot get enough of sim = sim.softmax(dim=-1) out = torch.einsum('b i j, b j d -> b i d', sim, v) if self.relative_position: v2 = self.relative_position_v(len_q, len_v) out2 = einsum('b t s, t s d -> b t d', sim, v2) # TODO check out += out2 out = rearrange(out, '(b h) n d -> b n (h d)', h=h) ## considering image token additionally if context is not None and self.img_cross_attention: k_ip = all_k_ip[:, t_start:t_end] v_ip = all_v_ip[:, t_start:t_end] sim_ip = torch.einsum('b i d, b j d -> b i j', q, k_ip) * self.scale del k_ip sim_ip = sim_ip.softmax(dim=-1) out_ip = torch.einsum('b i j, b j d -> b i d', sim_ip, v_ip) out_ip = rearrange(out_ip, '(b h) n d -> b n (h d)', h=h) out = out + self.image_cross_attention_scale * out_ip del q value[:,t_start:t_end] += out * weight_tensor count[:,t_start:t_end] += weight_tensor final_out = torch.where(count>0, value/count, value) else: q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (all_q, all_k, all_v)) sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale if self.relative_position: len_q, len_k, len_v = q.shape[1], k.shape[1], v.shape[1] k2 = self.relative_position_k(len_q, len_k) sim2 = einsum('b t d, t s d -> b t s', q, k2) * self.scale # TODO check sim += sim2 del k if exists(mask): ## feasible for causal attention mask only max_neg_value = -torch.finfo(sim.dtype).max mask = repeat(mask, 'b i j -> (b h) i j', h=h) sim.masked_fill_(~(mask>0.5), max_neg_value) # attention, what we cannot get enough of sim = sim.softmax(dim=-1) out = torch.einsum('b i j, b j d -> b i d', sim, v) if self.relative_position: v2 = self.relative_position_v(len_q, len_v) out2 = einsum('b t s, t s d -> b t d', sim, v2) # TODO check out += out2 final_out = rearrange(out, '(b h) n d -> b n (h d)', h=h) ## considering image token additionally if context is not None and self.img_cross_attention: k_ip, v_ip = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (all_k_ip, all_v_ip)) sim_ip = torch.einsum('b i d, b j d -> b i j', q, k_ip) * self.scale del k_ip sim_ip = sim_ip.softmax(dim=-1) out_ip = torch.einsum('b i j, b j d -> b i d', sim_ip, v_ip) out_ip = rearrange(out_ip, '(b h) n d -> b n (h d)', h=h) final_out = final_out + self.image_cross_attention_scale * out_ip del q return self.to_out(final_out) def efficient_forward(self, x, context=None, mask=None, context_next=None, use_injection=False): sa_flag = False if context is None: sa_flag = True q = self.to_q(x) context = default(context, x) if not sa_flag: sq_size = x.shape[0] if self.injection and use_injection: context_new = context[-sq_size:] else: context_new = context[:sq_size] else: context_new = context.clone() ## considering image token additionally if context is not None and self.img_cross_attention: context, context_img = context_new[:,:self.text_context_len,:], context_new[:,self.text_context_len:,:] k = self.to_k(context) v = self.to_v(context) k_ip = self.to_k_ip(context_img) v_ip = self.to_v_ip(context_img) else: k = self.to_k(context_new) v = self.to_v(context_new) b, _, _ = q.shape q, k, v = map( lambda t: t.unsqueeze(3) .reshape(b, t.shape[1], self.heads, self.dim_head) .permute(0, 2, 1, 3) .reshape(b * self.heads, t.shape[1], self.dim_head) .contiguous(), (q, k, v), ) # actually compute the attention, what we cannot get enough of out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=None) ## considering image token additionally if context is not None and self.img_cross_attention: k_ip, v_ip = map( lambda t: t.unsqueeze(3) .reshape(b, t.shape[1], self.heads, self.dim_head) .permute(0, 2, 1, 3) .reshape(b * self.heads, t.shape[1], self.dim_head) .contiguous(), (k_ip, v_ip), ) out_ip = xformers.ops.memory_efficient_attention(q, k_ip, v_ip, attn_bias=None, op=None) out_ip = ( out_ip.unsqueeze(0) .reshape(b, self.heads, out.shape[1], self.dim_head) .permute(0, 2, 1, 3) .reshape(b, out.shape[1], self.heads * self.dim_head) ) if exists(mask): raise NotImplementedError out = ( out.unsqueeze(0) .reshape(b, self.heads, out.shape[1], self.dim_head) .permute(0, 2, 1, 3) .reshape(b, out.shape[1], self.heads * self.dim_head) ) if context is not None and self.img_cross_attention: out = out + self.image_cross_attention_scale * out_ip return self.to_out(out) class BasicTransformerBlock(nn.Module): def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, disable_self_attn=False, attention_cls=None, img_cross_attention=False, injection=False): super().__init__() attn_cls = CrossAttention if attention_cls is None else attention_cls self.disable_self_attn = disable_self_attn self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, context_dim=context_dim if self.disable_self_attn else None, injection=injection) self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, dropout=dropout, img_cross_attention=img_cross_attention, injection=injection) self.norm1 = nn.LayerNorm(dim) self.norm2 = nn.LayerNorm(dim) self.norm3 = nn.LayerNorm(dim) self.checkpoint = checkpoint def forward(self, x, context=None, mask=None, context_next=None, use_injection=False, **kwargs): ## implementation tricks: because checkpointing doesn't support non-tensor (e.g. None or scalar) arguments input_tuple = (x,) ## should not be (x), otherwise *input_tuple will decouple x into multiple arguments if context is not None: input_tuple = (x, context) if mask is not None: forward_mask = partial(self._forward, mask=mask) return checkpoint(forward_mask, (x,), self.parameters(), self.checkpoint) if context is not None and mask is not None: input_tuple = (x, context, mask) input_tuple = (x, context, mask, context_next, use_injection) return checkpoint(self._forward, input_tuple, self.parameters(), self.checkpoint) def _forward(self, x, context=None, mask=None, context_next=None, use_injection=False): x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None, mask=mask, context_next=context_next, use_injection=False) + x x = self.attn2(self.norm2(x), context=context, mask=mask, context_next=context_next, use_injection=use_injection) + x x = self.ff(self.norm3(x)) + x return x class SpatialTransformer(nn.Module): """ Transformer block for image-like data in spatial axis. First, project the input (aka embedding) and reshape to b, t, d. Then apply standard transformer action. Finally, reshape to image NEW: use_linear for more efficiency instead of the 1x1 convs """ def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None, use_checkpoint=True, disable_self_attn=False, use_linear=False, img_cross_attention=False, injection=False): super().__init__() self.in_channels = in_channels inner_dim = n_heads * d_head self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) if not use_linear: self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) else: self.proj_in = nn.Linear(in_channels, inner_dim) self.transformer_blocks = nn.ModuleList([ BasicTransformerBlock( inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim, img_cross_attention=img_cross_attention, disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, injection=injection) for d in range(depth) ]) if not use_linear: self.proj_out = zero_module(nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)) else: self.proj_out = zero_module(nn.Linear(inner_dim, in_channels)) self.use_linear = use_linear def forward(self, x, context=None, **kwargs): b, c, h, w = x.shape x_in = x x = self.norm(x) if not self.use_linear: x = self.proj_in(x) x = rearrange(x, 'b c h w -> b (h w) c').contiguous() if self.use_linear: x = self.proj_in(x) for i, block in enumerate(self.transformer_blocks): x = block(x, context=context, **kwargs) if self.use_linear: x = self.proj_out(x) x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous() if not self.use_linear: x = self.proj_out(x) return x + x_in class TemporalTransformer(nn.Module): """ Transformer block for image-like data in temporal axis. First, reshape to b, t, d. Then apply standard transformer action. Finally, reshape to image """ def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None, use_checkpoint=True, use_linear=False, only_self_att=True, causal_attention=False, relative_position=False, temporal_length=None, injection=False): super().__init__() self.only_self_att = only_self_att self.relative_position = relative_position self.causal_attention = causal_attention self.in_channels = in_channels inner_dim = n_heads * d_head self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) if not use_linear: self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) else: self.proj_in = nn.Linear(in_channels, inner_dim) if relative_position: assert(temporal_length is not None) attention_cls = partial(CrossAttention, relative_position=True, temporal_length=temporal_length) else: attention_cls = partial(CrossAttention, temporal_length=temporal_length) if self.causal_attention: assert(temporal_length is not None) self.mask = torch.tril(torch.ones([1, temporal_length, temporal_length])) if self.only_self_att: context_dim = None self.transformer_blocks = nn.ModuleList([ BasicTransformerBlock( inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim, attention_cls=attention_cls, checkpoint=use_checkpoint, injection=injection) for d in range(depth) ]) if not use_linear: self.proj_out = zero_module(nn.Conv1d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)) else: self.proj_out = zero_module(nn.Linear(inner_dim, in_channels)) self.use_linear = use_linear def forward(self, x, context=None, **kwargs): b, c, t, h, w = x.shape x_in = x x = self.norm(x) x = rearrange(x, 'b c t h w -> (b h w) c t').contiguous() if not self.use_linear: x = self.proj_in(x) x = rearrange(x, 'bhw c t -> bhw t c').contiguous() if self.use_linear: x = self.proj_in(x) if self.causal_attention: mask = self.mask.to(x.device) mask = repeat(mask, 'l i j -> (l bhw) i j', bhw=b*h*w) else: mask = None if self.only_self_att: ## note: if no context is given, cross-attention defaults to self-attention for i, block in enumerate(self.transformer_blocks): x = block(x, mask=mask, **kwargs) x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous() else: x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous() context = rearrange(context, '(b t) l con -> b t l con', t=t).contiguous() for i, block in enumerate(self.transformer_blocks): # calculate each batch one by one (since number in shape could not greater then 65,535 for some package) for j in range(b): context_j = repeat( context[j], 't l con -> (t r) l con', r=(h * w) // t, t=t).contiguous() ## note: causal mask will not applied in cross-attention case x[j] = block(x[j], context=context_j, **kwargs) if self.use_linear: x = self.proj_out(x) x = rearrange(x, 'b (h w) t c -> b c t h w', h=h, w=w).contiguous() if not self.use_linear: x = rearrange(x, 'b hw t c -> (b hw) c t').contiguous() x = self.proj_out(x) x = rearrange(x, '(b h w) c t -> b c t h w', b=b, h=h, w=w).contiguous() return x + x_in class GEGLU(nn.Module): def __init__(self, dim_in, dim_out): super().__init__() self.proj = nn.Linear(dim_in, dim_out * 2) def forward(self, x): x, gate = self.proj(x).chunk(2, dim=-1) return x * F.gelu(gate) class FeedForward(nn.Module): def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): super().__init__() inner_dim = int(dim * mult) dim_out = default(dim_out, dim) project_in = nn.Sequential( nn.Linear(dim, inner_dim), nn.GELU() ) if not glu else GEGLU(dim, inner_dim) self.net = nn.Sequential( project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out) ) def forward(self, x): return self.net(x) class LinearAttention(nn.Module): def __init__(self, dim, heads=4, dim_head=32): super().__init__() self.heads = heads hidden_dim = dim_head * heads self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False) self.to_out = nn.Conv2d(hidden_dim, dim, 1) def forward(self, x): b, c, h, w = x.shape qkv = self.to_qkv(x) q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3) k = k.softmax(dim=-1) context = torch.einsum('bhdn,bhen->bhde', k, v) out = torch.einsum('bhde,bhdn->bhen', context, q) out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w) return self.to_out(out) class SpatialSelfAttention(nn.Module): def __init__(self, in_channels): super().__init__() self.in_channels = in_channels self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) def forward(self, x, **kwargs): h_ = x h_ = self.norm(h_) q = self.q(h_) k = self.k(h_) v = self.v(h_) # compute attention b,c,h,w = q.shape q = rearrange(q, 'b c h w -> b (h w) c') k = rearrange(k, 'b c h w -> b c (h w)') w_ = torch.einsum('bij,bjk->bik', q, k) w_ = w_ * (int(c)**(-0.5)) w_ = torch.nn.functional.softmax(w_, dim=2) # attend to values v = rearrange(v, 'b c h w -> b c (h w)') w_ = rearrange(w_, 'b i j -> b j i') h_ = torch.einsum('bij,bjk->bik', v, w_) h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h) h_ = self.proj_out(h_) return x+h_