import math from inspect import isfunction from typing import Any, Optional import torch import torch.nn.functional as F # from einops._torch_specific import allow_ops_in_compiled_graph # allow_ops_in_compiled_graph() from einops import rearrange, repeat from packaging import version from torch import nn if version.parse(torch.__version__) >= version.parse("2.0.0"): SDP_IS_AVAILABLE = True from torch.backends.cuda import SDPBackend, sdp_kernel BACKEND_MAP = { SDPBackend.MATH: { "enable_math": True, "enable_flash": False, "enable_mem_efficient": False, }, SDPBackend.FLASH_ATTENTION: { "enable_math": False, "enable_flash": True, "enable_mem_efficient": False, }, SDPBackend.EFFICIENT_ATTENTION: { "enable_math": False, "enable_flash": False, "enable_mem_efficient": True, }, None: {"enable_math": True, "enable_flash": True, "enable_mem_efficient": True}, } else: from contextlib import nullcontext SDP_IS_AVAILABLE = False sdp_kernel = nullcontext BACKEND_MAP = {} print( f"No SDP backend available, likely because you are running in pytorch versions < 2.0. In fact, " f"you are using PyTorch {torch.__version__}. You might want to consider upgrading." ) try: import xformers import xformers.ops XFORMERS_IS_AVAILABLE = True except: XFORMERS_IS_AVAILABLE = False print("no module 'xformers'. Processing without...") from .diffusionmodules.util import checkpoint def exists(val): return val is not None def uniq(arr): return {el: True for el in arr}.keys() def default(val, d): if exists(val): return val return d() if isfunction(d) else d def max_neg_value(t): return -torch.finfo(t.dtype).max def init_(tensor): dim = tensor.shape[-1] std = 1 / math.sqrt(dim) tensor.uniform_(-std, std) return tensor # 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) class FeedForward(nn.Module): def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0): 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) def zero_module(module): """ Zero out the parameters of a module and return it. """ for p in module.parameters(): p.detach().zero_() return module def Normalize(in_channels): return torch.nn.GroupNorm( num_groups=32, num_channels=in_channels, eps=1e-6, affine=True ) 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 = Normalize(in_channels) 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): 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_ class CrossAttention(nn.Module): def __init__( self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, backend=None, ): 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) ) self.backend = backend def forward( self, x, context=None, mask=None, additional_tokens=None, n_times_crossframe_attn_in_self=0, ): h = self.heads if additional_tokens is not None: # get the number of masked tokens at the beginning of the output sequence n_tokens_to_mask = additional_tokens.shape[1] # add additional token x = torch.cat([additional_tokens, x], dim=1) q = self.to_q(x) context = default(context, x) k = self.to_k(context) v = self.to_v(context) if n_times_crossframe_attn_in_self: # reprogramming cross-frame attention as in https://arxiv.org/abs/2303.13439 assert x.shape[0] % n_times_crossframe_attn_in_self == 0 n_cp = x.shape[0] // n_times_crossframe_attn_in_self k = repeat( k[::n_times_crossframe_attn_in_self], "b ... -> (b n) ...", n=n_cp ) v = repeat( v[::n_times_crossframe_attn_in_self], "b ... -> (b n) ...", n=n_cp ) q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v)) ## old """ sim = 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 = einsum('b i j, b j d -> b i d', sim, v) """ ## new with sdp_kernel(**BACKEND_MAP[self.backend]): # print("dispatching into backend", self.backend, "q/k/v shape: ", q.shape, k.shape, v.shape) out = F.scaled_dot_product_attention( q, k, v, attn_mask=mask ) # scale is dim_head ** -0.5 per default del q, k, v out = rearrange(out, "b h n d -> b n (h d)", h=h) if additional_tokens is not None: # remove additional token out = out[:, n_tokens_to_mask:] return self.to_out(out) class MemoryEfficientCrossAttention(nn.Module): # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 def __init__( self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, **kwargs ): super().__init__() print( f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using " f"{heads} heads with a dimension of {dim_head}." ) 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, additional_tokens=None, n_times_crossframe_attn_in_self=0, ): if additional_tokens is not None: # get the number of masked tokens at the beginning of the output sequence n_tokens_to_mask = additional_tokens.shape[1] # add additional token x = torch.cat([additional_tokens, x], dim=1) q = self.to_q(x) context = default(context, x) k = self.to_k(context) v = self.to_v(context) if n_times_crossframe_attn_in_self: # reprogramming cross-frame attention as in https://arxiv.org/abs/2303.13439 assert x.shape[0] % n_times_crossframe_attn_in_self == 0 # n_cp = x.shape[0]//n_times_crossframe_attn_in_self k = repeat( k[::n_times_crossframe_attn_in_self], "b ... -> (b n) ...", n=n_times_crossframe_attn_in_self, ) v = repeat( v[::n_times_crossframe_attn_in_self], "b ... -> (b n) ...", n=n_times_crossframe_attn_in_self, ) 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=self.attention_op ) # TODO: Use this directly in the attention operation, as a bias 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 additional_tokens is not None: # remove additional token out = out[:, n_tokens_to_mask:] return self.to_out(out) class BasicTransformerBlock(nn.Module): ATTENTION_MODES = { "softmax": CrossAttention, # vanilla attention "softmax-xformers": MemoryEfficientCrossAttention, # ampere } def __init__( self, dim, n_heads, d_head, dropout=0.0, context_dim=None, gated_ff=True, checkpoint=True, disable_self_attn=False, attn_mode="softmax", sdp_backend=None, ): super().__init__() assert attn_mode in self.ATTENTION_MODES if attn_mode != "softmax" and not XFORMERS_IS_AVAILABLE: print( f"Attention mode '{attn_mode}' is not available. Falling back to native attention. " f"This is not a problem in Pytorch >= 2.0. FYI, you are running with PyTorch version {torch.__version__}" ) attn_mode = "softmax" elif attn_mode == "softmax" and not SDP_IS_AVAILABLE: print( "We do not support vanilla attention anymore, as it is too expensive. Sorry." ) if not XFORMERS_IS_AVAILABLE: assert ( False ), "Please install xformers via e.g. 'pip install xformers==0.0.16'" else: print("Falling back to xformers efficient attention.") attn_mode = "softmax-xformers" attn_cls = self.ATTENTION_MODES[attn_mode] if version.parse(torch.__version__) >= version.parse("2.0.0"): assert sdp_backend is None or isinstance(sdp_backend, SDPBackend) else: assert sdp_backend is None 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, backend=sdp_backend, ) # 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, backend=sdp_backend, ) # 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 if self.checkpoint: print(f"{self.__class__.__name__} is using checkpointing") def forward( self, x, context=None, additional_tokens=None, n_times_crossframe_attn_in_self=0 ): kwargs = {"x": x} if context is not None: kwargs.update({"context": context}) if additional_tokens is not None: kwargs.update({"additional_tokens": additional_tokens}) if n_times_crossframe_attn_in_self: kwargs.update( {"n_times_crossframe_attn_in_self": n_times_crossframe_attn_in_self} ) # return mixed_checkpoint(self._forward, kwargs, self.parameters(), self.checkpoint) return checkpoint( self._forward, (x, context), self.parameters(), self.checkpoint ) def _forward( self, x, context=None, additional_tokens=None, n_times_crossframe_attn_in_self=0 ): x = ( self.attn1( self.norm1(x), context=context if self.disable_self_attn else None, additional_tokens=additional_tokens, n_times_crossframe_attn_in_self=n_times_crossframe_attn_in_self if not self.disable_self_attn else 0, ) + x ) x = ( self.attn2( self.norm2(x), context=context, additional_tokens=additional_tokens ) + x ) x = self.ff(self.norm3(x)) + x return x class BasicTransformerSingleLayerBlock(nn.Module): ATTENTION_MODES = { "softmax": CrossAttention, # vanilla attention "softmax-xformers": MemoryEfficientCrossAttention # on the A100s not quite as fast as the above version # (todo might depend on head_dim, check, falls back to semi-optimized kernels for dim!=[16,32,64,128]) } def __init__( self, dim, n_heads, d_head, dropout=0.0, context_dim=None, gated_ff=True, checkpoint=True, attn_mode="softmax", ): super().__init__() assert attn_mode in self.ATTENTION_MODES attn_cls = self.ATTENTION_MODES[attn_mode] self.attn1 = attn_cls( query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, context_dim=context_dim, ) self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) self.norm1 = nn.LayerNorm(dim) self.norm2 = 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) + x x = self.ff(self.norm2(x)) + x return x 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=0.0, context_dim=None, disable_self_attn=False, use_linear=False, attn_type="softmax", use_checkpoint=True, # sdp_backend=SDPBackend.FLASH_ATTENTION sdp_backend=None, ): super().__init__() print( f"constructing {self.__class__.__name__} of depth {depth} w/ {in_channels} channels and {n_heads} heads" ) from omegaconf import ListConfig if exists(context_dim) and not isinstance(context_dim, (list, ListConfig)): context_dim = [context_dim] if exists(context_dim) and isinstance(context_dim, list): if depth != len(context_dim): print( f"WARNING: {self.__class__.__name__}: Found context dims {context_dim} of depth {len(context_dim)}, " f"which does not match the specified 'depth' of {depth}. Setting context_dim to {depth * [context_dim[0]]} now." ) # depth does not match context dims. assert all( map(lambda x: x == context_dim[0], context_dim) ), "need homogenous context_dim to match depth automatically" context_dim = depth * [context_dim[0]] elif context_dim is None: context_dim = [None] * depth self.in_channels = in_channels inner_dim = n_heads * d_head self.norm = Normalize(in_channels) 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, attn_mode=attn_type, checkpoint=use_checkpoint, sdp_backend=sdp_backend, ) 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.proj_out = zero_module(nn.Linear(inner_dim, in_channels)) self.use_linear = use_linear def forward(self, x, context=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 i > 0 and len(context) == 1: i = 0 # use same context for each block 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) return x + x_in