import math import torch import xformers import open_clip import xformers.ops import torch.nn as nn from torch import einsum from einops import rearrange from functools import partial import torch.nn.functional as F import torch.nn.init as init from rotary_embedding_torch import RotaryEmbedding from fairscale.nn.checkpoint import checkpoint_wrapper def sinusoidal_embedding(timesteps, dim): # check input half = dim // 2 timesteps = timesteps.float() # compute sinusoidal embedding sinusoid = torch.outer( timesteps, torch.pow(10000, -torch.arange(half).to(timesteps).div(half))) x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1) if dim % 2 != 0: x = torch.cat([x, torch.zeros_like(x[:, :1])], dim=1) return x def exists(x): return x is not None def default(val, d): if exists(val): return val return d() if callable(d) else d def prob_mask_like(shape, prob, device): if prob == 1: return torch.ones(shape, device = device, dtype = torch.bool) elif prob == 0: return torch.zeros(shape, device = device, dtype = torch.bool) else: mask = torch.zeros(shape, device = device).float().uniform_(0, 1) < prob ### aviod mask all, which will cause find_unused_parameters error if mask.all(): mask[0]=False return mask class MemoryEfficientCrossAttention(nn.Module): def __init__(self, query_dim, max_bs=4096, context_dim=None, heads=8, dim_head=64, dropout=0.0): super().__init__() inner_dim = dim_head * heads context_dim = default(context_dim, query_dim) self.max_bs = max_bs 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.attention_op: Optional[Any] = None def forward(self, x, context=None, mask=None): q = self.to_q(x) context = default(context, x) # print("=======context.shape====", context.shape) k = self.to_k(context) v = self.to_v(context) 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 if q.shape[0] > self.max_bs: q_list = torch.chunk(q, q.shape[0] // self.max_bs, dim=0) k_list = torch.chunk(k, k.shape[0] // self.max_bs, dim=0) v_list = torch.chunk(v, v.shape[0] // self.max_bs, dim=0) out_list = [] for q_1, k_1, v_1 in zip(q_list, k_list, v_list): out = xformers.ops.memory_efficient_attention( q_1, k_1, v_1, attn_bias=None, op=self.attention_op) out_list.append(out) out = torch.cat(out_list, dim=0) else: out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op) 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) ) return self.to_out(out) class RelativePositionBias(nn.Module): def __init__( self, heads = 8, num_buckets = 32, max_distance = 128 ): super().__init__() self.num_buckets = num_buckets self.max_distance = max_distance self.relative_attention_bias = nn.Embedding(num_buckets, heads) @staticmethod def _relative_position_bucket(relative_position, num_buckets = 32, max_distance = 128): ret = 0 n = -relative_position num_buckets //= 2 ret += (n < 0).long() * num_buckets n = torch.abs(n) max_exact = num_buckets // 2 is_small = n < max_exact val_if_large = max_exact + ( torch.log(n.float() / max_exact) / math.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, n, device): q_pos = torch.arange(n, dtype = torch.long, device = device) k_pos = torch.arange(n, dtype = torch.long, device = device) rel_pos = rearrange(k_pos, 'j -> 1 j') - rearrange(q_pos, 'i -> i 1') rp_bucket = self._relative_position_bucket(rel_pos, num_buckets = self.num_buckets, max_distance = self.max_distance) values = self.relative_attention_bias(rp_bucket) return rearrange(values, 'i j h -> h i j') class SpatialTransformer(nn.Module): """ Transformer block for image-like data. 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, disable_self_attn=False, use_linear=False, use_checkpoint=True, pose_attention = False,para = False, our_weight = 1): super().__init__() if exists(context_dim) and not isinstance(context_dim, list): context_dim = [context_dim] self.in_channels = in_channels inner_dim = n_heads * d_head self.our_weight = our_weight 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[d], disable_self_attn=disable_self_attn, checkpoint=use_checkpoint) 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(in_channels, inner_dim)) self.use_linear = use_linear self.pose_attention = pose_attention self.para = para if pose_attention: self.pose_attention = SpatialTransformer_pose( in_channels, n_heads, d_head, depth = depth, context_dim= context_dim, disable_self_attn = disable_self_attn, use_linear = use_linear, para =para,our_weight = self.our_weight ) def forward(self, x, context=None, pose_embedding = None): # note: if no context is given, cross-attention defaults to self-attention if not isinstance(context, list): context = [context] 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): # if context[i]!=None: # print("no _p elf.transformer_blocks_ context[i].shape===>>>", x.shape, context[i].shape) # torch.Size([33, 81, 1024]) # else: # print("no _p elf.transformer_blocks_ context[i].shape===>>> is none") x = block(x, context=context[i]) 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) x = x + x_in if self.pose_attention == False: return x elif self.para == False: x = self.pose_attention(x, pose_embedding ) # print('here') return x else: x = x + self.pose_attention(x_in, pose_embedding ) # print('here') return x class SpatialTransformer_pose(nn.Module): """ Transformer block for image-like data. 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, disable_self_attn=False, use_linear=False, use_checkpoint=True, para = False, our_weight = 1): super().__init__() if exists(context_dim) and not isinstance(context_dim, list): context_dim = [context_dim] self.in_channels = in_channels inner_dim = n_heads * d_head self.our_weight = our_weight self.norm_p = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) if not use_linear: self.proj_in_p = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) else: self.proj_in_p = nn.Linear(in_channels, inner_dim) self.transformer_blocks_p = nn.ModuleList( [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], disable_self_attn=disable_self_attn, checkpoint=use_checkpoint) for d in range(depth)] ) if not use_linear: self.proj_out_p = zero_module(nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)) else: self.proj_out_p = zero_module(nn.Linear(in_channels, inner_dim)) self.use_linear = use_linear self.para = para def forward(self, x, context=None): # if context!=None: # print("context.shape===>>>", context.shape) # print("x.shape===>>>", x.shape) # note: if no context is given, cross-attention defaults to self-attention if not isinstance(context, list): context = [context] b, c, h, w = x.shape x_in = x x = self.norm_p(x) if not self.use_linear: x = self.proj_in_p(x) x = rearrange(x, 'b c h w -> b (h w) c').contiguous() if self.use_linear: # print("before = self.proj_in_ x.shape===>>>", x.shape) x = self.proj_in_p(x) # print("after = self.proj_in_ x.shape===>>>", x.shape) for i, block in enumerate(self.transformer_blocks_p): # if context[i]!=None: # print("_p elf.transformer_blocks_ context[i].shape===>>>", context[i].shape) # else: # print("_p elf.transformer_blocks_ context[i].shape===>>> is none") # print("before = block(x, c x.shape===>>>", x.shape) x = block(x, context=context[i]) # print("after = block(x, c x.shape===>>>", x.shape) if self.use_linear: # print("before =x = self.proj_out x.shape===>>>", x.shape) x = self.proj_out_p(x) # print("after =x = self.proj_out x.shape===>>>", x.shape) 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_p(x) if self.para: return x else: # print('---------x*self.self.our_weight + x_in: ---------', self.our_weight) return x*self.our_weight + x_in import os _ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32") class CrossAttention(nn.Module): def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.): super().__init__() inner_dim = dim_head * heads context_dim = default(context_dim, query_dim) self.scale = dim_head ** -0.5 self.heads = heads 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) ) def forward(self, x, context=None, mask=None): h = self.heads q = self.to_q(x) context = default(context, x) k = self.to_k(context) v = self.to_v(context) q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) # force cast to fp32 to avoid overflowing if _ATTN_PRECISION =="fp32": with torch.autocast(enabled=False, device_type = 'cuda'): q, k = q.float(), k.float() sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale else: sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale del q, k if exists(mask): mask = rearrange(mask, 'b ... -> b (...)') max_neg_value = -torch.finfo(sim.dtype).max mask = repeat(mask, 'b j -> (b h) () j', h=h) sim.masked_fill_(~mask, 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) out = rearrange(out, '(b h) n d -> b n (h d)', h=h) return self.to_out(out) class Adapter(nn.Module): def __init__(self, in_dim, hidden_dim, condition_dim=None): super().__init__() self.down_linear = nn.Linear(in_dim, hidden_dim) self.up_linear = nn.Linear(hidden_dim, in_dim) self.condition_dim = condition_dim if condition_dim is not None: self.condition_linear = nn.Linear(condition_dim, in_dim) init.zeros_(self.up_linear.weight) init.zeros_(self.up_linear.bias) def forward(self, x, condition=None, condition_lam=1): x_in = x if self.condition_dim is not None and condition is not None: x = x + condition_lam * self.condition_linear(condition) x = self.down_linear(x) x = F.gelu(x) x = self.up_linear(x) x += x_in return x class MemoryEfficientCrossAttention_attemask(nn.Module): def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0): super().__init__() inner_dim = dim_head * heads context_dim = default(context_dim, query_dim) 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.attention_op: Optional[Any] = None def forward(self, x, context=None, mask=None): q = self.to_q(x) context = default(context, x) k = self.to_k(context) v = self.to_v(context) 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=xformers.ops.LowerTriangularMask(), op=self.attention_op) 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) ) return self.to_out(out) class BasicTransformerBlock_attemask(nn.Module): # ATTENTION_MODES = { # "softmax": CrossAttention, # vanilla attention # "softmax-xformers": MemoryEfficientCrossAttention # } def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, disable_self_attn=False): super().__init__() # attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax" # assert attn_mode in self.ATTENTION_MODES # attn_cls = CrossAttention attn_cls = MemoryEfficientCrossAttention_attemask 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) # is a self-attention if not self.disable_self_attn 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) # is self-attn if context is none self.norm1 = nn.LayerNorm(dim) self.norm2 = nn.LayerNorm(dim) self.norm3 = nn.LayerNorm(dim) self.checkpoint = checkpoint def forward_(self, x, context=None): return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint) def forward(self, x, context=None): x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x x = self.attn2(self.norm2(x), context=context) + x x = self.ff(self.norm3(x)) + x return x class BasicTransformerBlockWithAdapter(nn.Module): # ATTENTION_MODES = { # "softmax": CrossAttention, # vanilla attention # "softmax-xformers": MemoryEfficientCrossAttention # } def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, disable_self_attn=False, adapter_list=[], adapter_position_list=['parallel', 'parallel', 'parallel'], adapter_hidden_dim=None, adapter_condition_dim=None ): super().__init__() # attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax" # assert attn_mode in self.ATTENTION_MODES # attn_cls = CrossAttention attn_cls = MemoryEfficientCrossAttention 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) # is a self-attention if not self.disable_self_attn 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) # is self-attn if context is none self.norm1 = nn.LayerNorm(dim) self.norm2 = nn.LayerNorm(dim) self.norm3 = nn.LayerNorm(dim) self.checkpoint = checkpoint # adapter self.adapter_list = adapter_list self.adapter_position_list = adapter_position_list hidden_dim = dim//2 if not adapter_hidden_dim else adapter_hidden_dim if "self_attention" in adapter_list: self.attn_adapter = Adapter(dim, hidden_dim, adapter_condition_dim) if "cross_attention" in adapter_list: self.cross_attn_adapter = Adapter(dim, hidden_dim, adapter_condition_dim) if "feedforward" in adapter_list: self.ff_adapter = Adapter(dim, hidden_dim, adapter_condition_dim) def forward_(self, x, context=None, adapter_condition=None, adapter_condition_lam=1): return checkpoint(self._forward, (x, context, adapter_condition, adapter_condition_lam), self.parameters(), self.checkpoint) def forward(self, x, context=None, adapter_condition=None, adapter_condition_lam=1): if "self_attention" in self.adapter_list: if self.adapter_position_list[0] == 'parallel': # parallel x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + self.attn_adapter(x, adapter_condition, adapter_condition_lam) elif self.adapter_position_list[0] == 'serial': # serial x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x x = self.attn_adapter(x, adapter_condition, adapter_condition_lam) else: x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x if "cross_attention" in self.adapter_list: if self.adapter_position_list[1] == 'parallel': # parallel x = self.attn2(self.norm2(x), context=context) + self.cross_attn_adapter(x, adapter_condition, adapter_condition_lam) elif self.adapter_position_list[1] == 'serial': x = self.attn2(self.norm2(x), context=context) + x x = self.cross_attn_adapter(x, adapter_condition, adapter_condition_lam) else: x = self.attn2(self.norm2(x), context=context) + x if "feedforward" in self.adapter_list: if self.adapter_position_list[2] == 'parallel': x = self.ff(self.norm3(x)) + self.ff_adapter(x, adapter_condition, adapter_condition_lam) elif self.adapter_position_list[2] == 'serial': x = self.ff(self.norm3(x)) + x x = self.ff_adapter(x, adapter_condition, adapter_condition_lam) else: x = self.ff(self.norm3(x)) + x return x class BasicTransformerBlock(nn.Module): # ATTENTION_MODES = { # "softmax": CrossAttention, # vanilla attention # "softmax-xformers": MemoryEfficientCrossAttention # } def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, disable_self_attn=False): super().__init__() # attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax" # assert attn_mode in self.ATTENTION_MODES # attn_cls = CrossAttention attn_cls = MemoryEfficientCrossAttention 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) # is a self-attention if not self.disable_self_attn 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) # is self-attn if context is none self.norm1 = nn.LayerNorm(dim) self.norm2 = nn.LayerNorm(dim) self.norm3 = nn.LayerNorm(dim) self.checkpoint = checkpoint def forward_(self, x, context=None): return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint) def forward(self, x, context=None): x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x # if context==None: # print("context==None: x.shape: ", x.shape) # else: # print("context!=None: x.shape: ", x.shape, "context.shape:", context.shape) # context!=None: x.shape: torch.Size([33, 6144, 320]) context.shape: torch.Size([33, 81, 1024]) # x = self.attn2(self.norm2(x), context=context) + x x_tmp = self.norm2(x) # print(self.norm2) # if context == None: # print("x_tmp.shape, ", x_tmp.shape) # else: # print("x_tmp.shape, context is not none", x_tmp.shape) # print(self.attn2) # 目前的：torch.Size([33, 6144, 320]) ； torch.Size([1536, 33, 640]) torch.Size([6144, 33, 512]) orch.Size([6144, 33, 320]) x = self.attn2(x_tmp, context=context) + x # _tmp.shape, context is none torch.Size([33, 6144, 320]) # print("after: ", x.shape) x = self.ff(self.norm3(x)) + x return x # feedforward 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) def zero_module(module): """ Zero out the parameters of a module and return it. """ for p in module.parameters(): p.detach().zero_() return module 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 Upsample(nn.Module): """ An upsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then upsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims if use_conv: self.conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=padding) def forward(self, x): assert x.shape[1] == self.channels if self.dims == 3: x = F.interpolate( x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" ) else: x = F.interpolate(x, scale_factor=2, mode="nearest") if self.use_conv: x = self.conv(x) return x class UpsampleSR600(nn.Module): """ An upsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then upsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims if use_conv: self.conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=padding) def forward(self, x): assert x.shape[1] == self.channels if self.dims == 3: x = F.interpolate( x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" ) else: x = F.interpolate(x, scale_factor=2, mode="nearest") # TODO: to match input_blocks, remove elements of two sides x = x[..., 1:-1, :] if self.use_conv: x = self.conv(x) return x class ResBlock(nn.Module): """ A residual block that can optionally change the number of channels. :param channels: the number of input channels. :param emb_channels: the number of timestep embedding channels. :param dropout: the rate of dropout. :param out_channels: if specified, the number of out channels. :param use_conv: if True and out_channels is specified, use a spatial convolution instead of a smaller 1x1 convolution to change the channels in the skip connection. :param dims: determines if the signal is 1D, 2D, or 3D. :param use_checkpoint: if True, use gradient checkpointing on this module. :param up: if True, use this block for upsampling. :param down: if True, use this block for downsampling. """ def __init__( self, channels, emb_channels, dropout, out_channels=None, use_conv=False, use_scale_shift_norm=False, dims=2, up=False, down=False, use_temporal_conv=True, use_image_dataset=False, ): super().__init__() self.channels = channels self.emb_channels = emb_channels self.dropout = dropout self.out_channels = out_channels or channels self.use_conv = use_conv self.use_scale_shift_norm = use_scale_shift_norm self.use_temporal_conv = use_temporal_conv self.in_layers = nn.Sequential( nn.GroupNorm(32, channels), nn.SiLU(), nn.Conv2d(channels, self.out_channels, 3, padding=1), ) self.updown = up or down if up: self.h_upd = Upsample(channels, False, dims) self.x_upd = Upsample(channels, False, dims) elif down: self.h_upd = Downsample(channels, False, dims) self.x_upd = Downsample(channels, False, dims) else: self.h_upd = self.x_upd = nn.Identity() self.emb_layers = nn.Sequential( nn.SiLU(), nn.Linear( emb_channels, 2 * self.out_channels if use_scale_shift_norm else self.out_channels, ), ) self.out_layers = nn.Sequential( nn.GroupNorm(32, self.out_channels), nn.SiLU(), nn.Dropout(p=dropout), zero_module( nn.Conv2d(self.out_channels, self.out_channels, 3, padding=1) ), ) if self.out_channels == channels: self.skip_connection = nn.Identity() elif use_conv: self.skip_connection = conv_nd( dims, channels, self.out_channels, 3, padding=1 ) else: self.skip_connection = nn.Conv2d(channels, self.out_channels, 1) if self.use_temporal_conv: self.temopral_conv = TemporalConvBlock_v2(self.out_channels, self.out_channels, dropout=0.1, use_image_dataset=use_image_dataset) # self.temopral_conv_2 = TemporalConvBlock(self.out_channels, self.out_channels, dropout=0.1, use_image_dataset=use_image_dataset) def forward(self, x, emb, batch_size): """ Apply the block to a Tensor, conditioned on a timestep embedding. :param x: an [N x C x ...] Tensor of features. :param emb: an [N x emb_channels] Tensor of timestep embeddings. :return: an [N x C x ...] Tensor of outputs. """ return self._forward(x, emb, batch_size) def _forward(self, x, emb, batch_size): if self.updown: in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] h = in_rest(x) h = self.h_upd(h) x = self.x_upd(x) h = in_conv(h) else: h = self.in_layers(x) emb_out = self.emb_layers(emb).type(h.dtype) while len(emb_out.shape) < len(h.shape): emb_out = emb_out[..., None] if self.use_scale_shift_norm: out_norm, out_rest = self.out_layers[0], self.out_layers[1:] scale, shift = th.chunk(emb_out, 2, dim=1) h = out_norm(h) * (1 + scale) + shift h = out_rest(h) else: h = h + emb_out h = self.out_layers(h) h = self.skip_connection(x) + h if self.use_temporal_conv: h = rearrange(h, '(b f) c h w -> b c f h w', b=batch_size) h = self.temopral_conv(h) # h = self.temopral_conv_2(h) h = rearrange(h, 'b c f h w -> (b f) c h w') return h class Downsample(nn.Module): """ A downsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then downsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims stride = 2 if dims != 3 else (1, 2, 2) if use_conv: self.op = nn.Conv2d(self.channels, self.out_channels, 3, stride=stride, padding=padding) else: assert self.channels == self.out_channels self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) def forward(self, x): assert x.shape[1] == self.channels return self.op(x) class Resample(nn.Module): def __init__(self, in_dim, out_dim, mode): assert mode in ['none', 'upsample', 'downsample'] super(Resample, self).__init__() self.in_dim = in_dim self.out_dim = out_dim self.mode = mode def forward(self, x, reference=None): if self.mode == 'upsample': assert reference is not None x = F.interpolate(x, size=reference.shape[-2:], mode='nearest') elif self.mode == 'downsample': x = F.adaptive_avg_pool2d(x, output_size=tuple(u // 2 for u in x.shape[-2:])) return x class ResidualBlock(nn.Module): def __init__(self, in_dim, embed_dim, out_dim, use_scale_shift_norm=True, mode='none', dropout=0.0): super(ResidualBlock, self).__init__() self.in_dim = in_dim self.embed_dim = embed_dim self.out_dim = out_dim self.use_scale_shift_norm = use_scale_shift_norm self.mode = mode # layers self.layer1 = nn.Sequential( nn.GroupNorm(32, in_dim), nn.SiLU(), nn.Conv2d(in_dim, out_dim, 3, padding=1)) self.resample = Resample(in_dim, in_dim, mode) self.embedding = nn.Sequential( nn.SiLU(), nn.Linear(embed_dim, out_dim * 2 if use_scale_shift_norm else out_dim)) self.layer2 = nn.Sequential( nn.GroupNorm(32, out_dim), nn.SiLU(), nn.Dropout(dropout), nn.Conv2d(out_dim, out_dim, 3, padding=1)) self.shortcut = nn.Identity() if in_dim == out_dim else nn.Conv2d(in_dim, out_dim, 1) # zero out the last layer params nn.init.zeros_(self.layer2[-1].weight) def forward(self, x, e, reference=None): identity = self.resample(x, reference) x = self.layer1[-1](self.resample(self.layer1[:-1](x), reference)) e = self.embedding(e).unsqueeze(-1).unsqueeze(-1).type(x.dtype) if self.use_scale_shift_norm: scale, shift = e.chunk(2, dim=1) x = self.layer2[0](x) * (1 + scale) + shift x = self.layer2[1:](x) else: x = x + e x = self.layer2(x) x = x + self.shortcut(identity) return x class AttentionBlock(nn.Module): def __init__(self, dim, context_dim=None, num_heads=None, head_dim=None): # consider head_dim first, then num_heads num_heads = dim // head_dim if head_dim else num_heads head_dim = dim // num_heads assert num_heads * head_dim == dim super(AttentionBlock, self).__init__() self.dim = dim self.context_dim = context_dim self.num_heads = num_heads self.head_dim = head_dim self.scale = math.pow(head_dim, -0.25) # layers self.norm = nn.GroupNorm(32, dim) self.to_qkv = nn.Conv2d(dim, dim * 3, 1) if context_dim is not None: self.context_kv = nn.Linear(context_dim, dim * 2) self.proj = nn.Conv2d(dim, dim, 1) # zero out the last layer params nn.init.zeros_(self.proj.weight) def forward(self, x, context=None): r"""x: [B, C, H, W]. context: [B, L, C] or None. """ identity = x b, c, h, w, n, d = *x.size(), self.num_heads, self.head_dim # compute query, key, value x = self.norm(x) q, k, v = self.to_qkv(x).view(b, n * 3, d, h * w).chunk(3, dim=1) if context is not None: ck, cv = self.context_kv(context).reshape(b, -1, n * 2, d).permute(0, 2, 3, 1).chunk(2, dim=1) k = torch.cat([ck, k], dim=-1) v = torch.cat([cv, v], dim=-1) # compute attention attn = torch.matmul(q.transpose(-1, -2) * self.scale, k * self.scale) attn = F.softmax(attn, dim=-1) # gather context x = torch.matmul(v, attn.transpose(-1, -2)) x = x.reshape(b, c, h, w) # output x = self.proj(x) return x + identity class TemporalAttentionBlock(nn.Module): def __init__( self, dim, heads = 4, dim_head = 32, rotary_emb = None, use_image_dataset = False, use_sim_mask = False ): super().__init__() # consider num_heads first, as pos_bias needs fixed num_heads # heads = dim // dim_head if dim_head else heads dim_head = dim // heads assert heads * dim_head == dim self.use_image_dataset = use_image_dataset self.use_sim_mask = use_sim_mask self.scale = dim_head ** -0.5 self.heads = heads hidden_dim = dim_head * heads self.norm = nn.GroupNorm(32, dim) self.rotary_emb = rotary_emb self.to_qkv = nn.Linear(dim, hidden_dim * 3)#, bias = False) self.to_out = nn.Linear(hidden_dim, dim)#, bias = False) # nn.init.zeros_(self.to_out.weight) # nn.init.zeros_(self.to_out.bias) def forward( self, x, pos_bias = None, focus_present_mask = None, video_mask = None ): identity = x n, height, device = x.shape[2], x.shape[-2], x.device x = self.norm(x) x = rearrange(x, 'b c f h w -> b (h w) f c') qkv = self.to_qkv(x).chunk(3, dim = -1) if exists(focus_present_mask) and focus_present_mask.all(): # if all batch samples are focusing on present # it would be equivalent to passing that token's values （v=qkv[-1]） through to the output values = qkv[-1] out = self.to_out(values) out = rearrange(out, 'b (h w) f c -> b c f h w', h = height) return out + identity # split out heads # q, k, v = rearrange_many(qkv, '... n (h d) -> ... h n d', h = self.heads) # shape [b (hw) h n c/h], n=f q= rearrange(qkv[0], '... n (h d) -> ... h n d', h = self.heads) k= rearrange(qkv[1], '... n (h d) -> ... h n d', h = self.heads) v= rearrange(qkv[2], '... n (h d) -> ... h n d', h = self.heads) # scale q = q * self.scale # rotate positions into queries and keys for time attention if exists(self.rotary_emb): q = self.rotary_emb.rotate_queries_or_keys(q) k = self.rotary_emb.rotate_queries_or_keys(k) # similarity # shape [b (hw) h n n], n=f sim = torch.einsum('... h i d, ... h j d -> ... h i j', q, k) # relative positional bias if exists(pos_bias): # print(sim.shape,pos_bias.shape) sim = sim + pos_bias if (focus_present_mask is None and video_mask is not None): #video_mask: [B, n] mask = video_mask[:, None, :] * video_mask[:, :, None] # [b,n,n] mask = mask.unsqueeze(1).unsqueeze(1) #[b,1,1,n,n] sim = sim.masked_fill(~mask, -torch.finfo(sim.dtype).max) elif exists(focus_present_mask) and not (~focus_present_mask).all(): attend_all_mask = torch.ones((n, n), device = device, dtype = torch.bool) attend_self_mask = torch.eye(n, device = device, dtype = torch.bool) mask = torch.where( rearrange(focus_present_mask, 'b -> b 1 1 1 1'), rearrange(attend_self_mask, 'i j -> 1 1 1 i j'), rearrange(attend_all_mask, 'i j -> 1 1 1 i j'), ) sim = sim.masked_fill(~mask, -torch.finfo(sim.dtype).max) if self.use_sim_mask: sim_mask = torch.tril(torch.ones((n, n), device = device, dtype = torch.bool), diagonal=0) sim = sim.masked_fill(~sim_mask, -torch.finfo(sim.dtype).max) # numerical stability sim = sim - sim.amax(dim = -1, keepdim = True).detach() attn = sim.softmax(dim = -1) # aggregate values out = torch.einsum('... h i j, ... h j d -> ... h i d', attn, v) out = rearrange(out, '... h n d -> ... n (h d)') out = self.to_out(out) out = rearrange(out, 'b (h w) f c -> b c f h w', h = height) if self.use_image_dataset: out = identity + 0*out else: out = identity + out return out class TemporalTransformer(nn.Module): """ Transformer block for image-like data. First, project the input (aka embedding) and 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, disable_self_attn=False, use_linear=False, use_checkpoint=True, only_self_att=True, multiply_zero=False): super().__init__() self.multiply_zero = multiply_zero self.only_self_att = only_self_att self.use_adaptor = False if self.only_self_att: context_dim = None if not isinstance(context_dim, list): context_dim = [context_dim] 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.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) else: self.proj_in = nn.Linear(in_channels, inner_dim) if self.use_adaptor: self.adaptor_in = nn.Linear(frames, frames) self.transformer_blocks = nn.ModuleList( [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], checkpoint=use_checkpoint) 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(in_channels, inner_dim)) if self.use_adaptor: self.adaptor_out = nn.Linear(frames, frames) self.use_linear = use_linear def forward(self, x, context=None): # note: if no context is given, cross-attention defaults to self-attention if self.only_self_att: context = None if not isinstance(context, list): context = [context] b, c, f, h, w = x.shape x_in = x x = self.norm(x) if not self.use_linear: x = rearrange(x, 'b c f h w -> (b h w) c f').contiguous() x = self.proj_in(x) # [16384, 16, 320] if self.use_linear: x = rearrange(x, '(b f) c h w -> b (h w) f c', f=self.frames).contiguous() x = self.proj_in(x) if self.only_self_att: x = rearrange(x, 'bhw c f -> bhw f c').contiguous() for i, block in enumerate(self.transformer_blocks): x = block(x) x = rearrange(x, '(b hw) f c -> b hw f c', b=b).contiguous() else: x = rearrange(x, '(b hw) c f -> b hw f c', b=b).contiguous() for i, block in enumerate(self.transformer_blocks): # context[i] = repeat(context[i], '(b f) l con -> b (f r) l con', r=(h*w)//self.frames, f=self.frames).contiguous() context[i] = rearrange(context[i], '(b f) l con -> b f l con', f=self.frames).contiguous() # 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_i_j = repeat(context[i][j], 'f l con -> (f r) l con', r=(h*w)//self.frames, f=self.frames).contiguous() x[j] = block(x[j], context=context_i_j) if self.use_linear: x = self.proj_out(x) x = rearrange(x, 'b (h w) f c -> b f c h w', h=h, w=w).contiguous() if not self.use_linear: # x = rearrange(x, 'bhw f c -> bhw c f').contiguous() x = rearrange(x, 'b hw f c -> (b hw) c f').contiguous() x = self.proj_out(x) x = rearrange(x, '(b h w) c f -> b c f h w', b=b, h=h, w=w).contiguous() if self.multiply_zero: x = 0.0 * x + x_in else: x = x + x_in return x class Attention(nn.Module): def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.): super().__init__() inner_dim = dim_head * heads project_out = not (heads == 1 and dim_head == dim) self.heads = heads self.scale = dim_head ** -0.5 self.attend = nn.Softmax(dim = -1) self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False) self.to_out = nn.Sequential( nn.Linear(inner_dim, dim), nn.Dropout(dropout) ) if project_out else nn.Identity() def forward(self, x): b, n, _, h = *x.shape, self.heads qkv = self.to_qkv(x).chunk(3, dim = -1) q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv) dots = torch.einsum('b h i d, b h j d -> b h i j', q, k) * self.scale attn = self.attend(dots) out = torch.einsum('b h i j, b h j d -> b h i d', attn, v) out = rearrange(out, 'b h n d -> b n (h d)') return self.to_out(out) class PreNormattention(nn.Module): def __init__(self, dim, fn): super().__init__() self.norm = nn.LayerNorm(dim) self.fn = fn def forward(self, x, **kwargs): return self.fn(self.norm(x), **kwargs) + x class TemporalAttentionMultiBlock(nn.Module): def __init__( self, dim, heads=4, dim_head=32, rotary_emb=None, use_image_dataset=False, use_sim_mask=False, temporal_attn_times=1, ): super().__init__() self.att_layers = nn.ModuleList( [TemporalAttentionBlock(dim, heads, dim_head, rotary_emb, use_image_dataset, use_sim_mask) for _ in range(temporal_attn_times)] ) def forward( self, x, pos_bias = None, focus_present_mask = None, video_mask = None ): for layer in self.att_layers: x = layer(x, pos_bias, focus_present_mask, video_mask) return x class InitTemporalConvBlock(nn.Module): def __init__(self, in_dim, out_dim=None, dropout=0.0,use_image_dataset=False): super(InitTemporalConvBlock, self).__init__() if out_dim is None: out_dim = in_dim#int(1.5*in_dim) self.in_dim = in_dim self.out_dim = out_dim self.use_image_dataset = use_image_dataset # conv layers self.conv = nn.Sequential( nn.GroupNorm(32, out_dim), nn.SiLU(), nn.Dropout(dropout), nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding = (1, 0, 0))) # zero out the last layer params,so the conv block is identity # nn.init.zeros_(self.conv1[-1].weight) # nn.init.zeros_(self.conv1[-1].bias) nn.init.zeros_(self.conv[-1].weight) nn.init.zeros_(self.conv[-1].bias) def forward(self, x): identity = x x = self.conv(x) if self.use_image_dataset: x = identity + 0*x else: x = identity + x return x class TemporalConvBlock(nn.Module): def __init__(self, in_dim, out_dim=None, dropout=0.0, use_image_dataset= False): super(TemporalConvBlock, self).__init__() if out_dim is None: out_dim = in_dim#int(1.5*in_dim) self.in_dim = in_dim self.out_dim = out_dim self.use_image_dataset = use_image_dataset # conv layers self.conv1 = nn.Sequential( nn.GroupNorm(32, in_dim), nn.SiLU(), nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding = (1, 0, 0))) self.conv2 = nn.Sequential( nn.GroupNorm(32, out_dim), nn.SiLU(), nn.Dropout(dropout), nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding = (1, 0, 0))) # zero out the last layer params,so the conv block is identity # nn.init.zeros_(self.conv1[-1].weight) # nn.init.zeros_(self.conv1[-1].bias) nn.init.zeros_(self.conv2[-1].weight) nn.init.zeros_(self.conv2[-1].bias) def forward(self, x): identity = x x = self.conv1(x) x = self.conv2(x) if self.use_image_dataset: x = identity + 0*x else: x = identity + x return x class TemporalConvBlock_v2(nn.Module): def __init__(self, in_dim, out_dim=None, dropout=0.0, use_image_dataset=False): super(TemporalConvBlock_v2, self).__init__() if out_dim is None: out_dim = in_dim # int(1.5*in_dim) self.in_dim = in_dim self.out_dim = out_dim self.use_image_dataset = use_image_dataset # conv layers self.conv1 = nn.Sequential( nn.GroupNorm(32, in_dim), nn.SiLU(), nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding = (1, 0, 0))) self.conv2 = nn.Sequential( nn.GroupNorm(32, out_dim), nn.SiLU(), nn.Dropout(dropout), nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding = (1, 0, 0))) self.conv3 = nn.Sequential( nn.GroupNorm(32, out_dim), nn.SiLU(), nn.Dropout(dropout), nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding = (1, 0, 0))) self.conv4 = nn.Sequential( nn.GroupNorm(32, out_dim), nn.SiLU(), nn.Dropout(dropout), nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding = (1, 0, 0))) # zero out the last layer params,so the conv block is identity nn.init.zeros_(self.conv4[-1].weight) nn.init.zeros_(self.conv4[-1].bias) def forward(self, x): identity = x x = self.conv1(x) x = self.conv2(x) x = self.conv3(x) x = self.conv4(x) if self.use_image_dataset: x = identity + 0.0 * x else: x = identity + x return x class DropPath(nn.Module): r"""DropPath but without rescaling and supports optional all-zero and/or all-keep. """ def __init__(self, p): super(DropPath, self).__init__() self.p = p def forward(self, *args, zero=None, keep=None): if not self.training: return args[0] if len(args) == 1 else args # params x = args[0] b = x.size(0) n = (torch.rand(b) < self.p).sum() # non-zero and non-keep mask mask = x.new_ones(b, dtype=torch.bool) if keep is not None: mask[keep] = False if zero is not None: mask[zero] = False # drop-path index index = torch.where(mask)[0] index = index[torch.randperm(len(index))[:n]] if zero is not None: index = torch.cat([index, torch.where(zero)[0]], dim=0) # drop-path multiplier multiplier = x.new_ones(b) multiplier[index] = 0.0 output = tuple(u * self.broadcast(multiplier, u) for u in args) return output[0] if len(args) == 1 else output def broadcast(self, src, dst): assert src.size(0) == dst.size(0) shape = (dst.size(0), ) + (1, ) * (dst.ndim - 1) return src.view(shape)