636 lines
20 KiB
Python
636 lines
20 KiB
Python
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import math
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from inspect import isfunction
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from typing import Any, Optional
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import torch
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import torch.nn.functional as F
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# from einops._torch_specific import allow_ops_in_compiled_graph
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# allow_ops_in_compiled_graph()
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from einops import rearrange, repeat
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from packaging import version
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from torch import nn
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if version.parse(torch.__version__) >= version.parse("2.0.0"):
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SDP_IS_AVAILABLE = True
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from torch.backends.cuda import SDPBackend, sdp_kernel
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BACKEND_MAP = {
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SDPBackend.MATH: {
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"enable_math": True,
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"enable_flash": False,
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"enable_mem_efficient": False,
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},
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SDPBackend.FLASH_ATTENTION: {
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"enable_math": False,
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"enable_flash": True,
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"enable_mem_efficient": False,
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},
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SDPBackend.EFFICIENT_ATTENTION: {
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"enable_math": False,
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"enable_flash": False,
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"enable_mem_efficient": True,
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},
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None: {"enable_math": True, "enable_flash": True, "enable_mem_efficient": True},
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}
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else:
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from contextlib import nullcontext
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SDP_IS_AVAILABLE = False
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sdp_kernel = nullcontext
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BACKEND_MAP = {}
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print(
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f"No SDP backend available, likely because you are running in pytorch versions < 2.0. In fact, "
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f"you are using PyTorch {torch.__version__}. You might want to consider upgrading."
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)
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try:
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import xformers
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import xformers.ops
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XFORMERS_IS_AVAILABLE = True
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except:
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XFORMERS_IS_AVAILABLE = False
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print("no module 'xformers'. Processing without...")
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from .diffusionmodules.util import checkpoint
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def exists(val):
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return val is not None
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def uniq(arr):
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return {el: True for el in arr}.keys()
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def default(val, d):
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if exists(val):
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return val
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return d() if isfunction(d) else d
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def max_neg_value(t):
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return -torch.finfo(t.dtype).max
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def init_(tensor):
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dim = tensor.shape[-1]
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std = 1 / math.sqrt(dim)
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tensor.uniform_(-std, std)
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return tensor
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# feedforward
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class GEGLU(nn.Module):
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def __init__(self, dim_in, dim_out):
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super().__init__()
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self.proj = nn.Linear(dim_in, dim_out * 2)
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def forward(self, x):
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x, gate = self.proj(x).chunk(2, dim=-1)
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return x * F.gelu(gate)
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class FeedForward(nn.Module):
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def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
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super().__init__()
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inner_dim = int(dim * mult)
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dim_out = default(dim_out, dim)
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project_in = (
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nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
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if not glu
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else GEGLU(dim, inner_dim)
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)
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self.net = nn.Sequential(
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project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)
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)
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def forward(self, x):
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return self.net(x)
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def zero_module(module):
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"""
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Zero out the parameters of a module and return it.
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"""
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for p in module.parameters():
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p.detach().zero_()
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return module
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def Normalize(in_channels):
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return torch.nn.GroupNorm(
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num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
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)
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class LinearAttention(nn.Module):
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def __init__(self, dim, heads=4, dim_head=32):
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super().__init__()
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self.heads = heads
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hidden_dim = dim_head * heads
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self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
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self.to_out = nn.Conv2d(hidden_dim, dim, 1)
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def forward(self, x):
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b, c, h, w = x.shape
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qkv = self.to_qkv(x)
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q, k, v = rearrange(
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qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3
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)
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k = k.softmax(dim=-1)
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context = torch.einsum("bhdn,bhen->bhde", k, v)
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out = torch.einsum("bhde,bhdn->bhen", context, q)
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out = rearrange(
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out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w
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)
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return self.to_out(out)
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class SpatialSelfAttention(nn.Module):
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def __init__(self, in_channels):
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super().__init__()
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self.in_channels = in_channels
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self.norm = Normalize(in_channels)
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self.q = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.k = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.v = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.proj_out = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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def forward(self, x):
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h_ = x
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h_ = self.norm(h_)
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q = self.q(h_)
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k = self.k(h_)
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v = self.v(h_)
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# compute attention
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b, c, h, w = q.shape
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q = rearrange(q, "b c h w -> b (h w) c")
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k = rearrange(k, "b c h w -> b c (h w)")
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w_ = torch.einsum("bij,bjk->bik", q, k)
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w_ = w_ * (int(c) ** (-0.5))
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w_ = torch.nn.functional.softmax(w_, dim=2)
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# attend to values
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v = rearrange(v, "b c h w -> b c (h w)")
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w_ = rearrange(w_, "b i j -> b j i")
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h_ = torch.einsum("bij,bjk->bik", v, w_)
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h_ = rearrange(h_, "b c (h w) -> b c h w", h=h)
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h_ = self.proj_out(h_)
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return x + h_
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class CrossAttention(nn.Module):
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def __init__(
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self,
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query_dim,
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context_dim=None,
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heads=8,
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dim_head=64,
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dropout=0.0,
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backend=None,
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):
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super().__init__()
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inner_dim = dim_head * heads
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context_dim = default(context_dim, query_dim)
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self.scale = dim_head**-0.5
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self.heads = heads
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self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
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self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
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self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
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self.to_out = nn.Sequential(
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nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)
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)
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self.backend = backend
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def forward(
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self,
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x,
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context=None,
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mask=None,
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additional_tokens=None,
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n_times_crossframe_attn_in_self=0,
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):
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h = self.heads
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if additional_tokens is not None:
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# get the number of masked tokens at the beginning of the output sequence
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n_tokens_to_mask = additional_tokens.shape[1]
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# add additional token
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x = torch.cat([additional_tokens, x], dim=1)
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q = self.to_q(x)
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context = default(context, x)
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k = self.to_k(context)
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v = self.to_v(context)
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if n_times_crossframe_attn_in_self:
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# reprogramming cross-frame attention as in https://arxiv.org/abs/2303.13439
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assert x.shape[0] % n_times_crossframe_attn_in_self == 0
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n_cp = x.shape[0] // n_times_crossframe_attn_in_self
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k = repeat(
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k[::n_times_crossframe_attn_in_self], "b ... -> (b n) ...", n=n_cp
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)
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v = repeat(
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v[::n_times_crossframe_attn_in_self], "b ... -> (b n) ...", n=n_cp
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)
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q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v))
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## old
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"""
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sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
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del q, k
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if exists(mask):
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mask = rearrange(mask, 'b ... -> b (...)')
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max_neg_value = -torch.finfo(sim.dtype).max
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mask = repeat(mask, 'b j -> (b h) () j', h=h)
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sim.masked_fill_(~mask, max_neg_value)
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# attention, what we cannot get enough of
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sim = sim.softmax(dim=-1)
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out = einsum('b i j, b j d -> b i d', sim, v)
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"""
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## new
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with sdp_kernel(**BACKEND_MAP[self.backend]):
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# print("dispatching into backend", self.backend, "q/k/v shape: ", q.shape, k.shape, v.shape)
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out = F.scaled_dot_product_attention(
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q, k, v, attn_mask=mask
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) # scale is dim_head ** -0.5 per default
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del q, k, v
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out = rearrange(out, "b h n d -> b n (h d)", h=h)
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if additional_tokens is not None:
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# remove additional token
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out = out[:, n_tokens_to_mask:]
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return self.to_out(out)
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class MemoryEfficientCrossAttention(nn.Module):
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# https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
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def __init__(
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self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, **kwargs
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):
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super().__init__()
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print(
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f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
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f"{heads} heads with a dimension of {dim_head}."
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)
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inner_dim = dim_head * heads
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context_dim = default(context_dim, query_dim)
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self.heads = heads
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self.dim_head = dim_head
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self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
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self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
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self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
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self.to_out = nn.Sequential(
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nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)
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)
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self.attention_op: Optional[Any] = None
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def forward(
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self,
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x,
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context=None,
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mask=None,
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additional_tokens=None,
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n_times_crossframe_attn_in_self=0,
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):
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if additional_tokens is not None:
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# get the number of masked tokens at the beginning of the output sequence
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n_tokens_to_mask = additional_tokens.shape[1]
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# add additional token
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x = torch.cat([additional_tokens, x], dim=1)
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q = self.to_q(x)
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context = default(context, x)
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k = self.to_k(context)
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v = self.to_v(context)
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if n_times_crossframe_attn_in_self:
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# reprogramming cross-frame attention as in https://arxiv.org/abs/2303.13439
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assert x.shape[0] % n_times_crossframe_attn_in_self == 0
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# n_cp = x.shape[0]//n_times_crossframe_attn_in_self
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k = repeat(
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k[::n_times_crossframe_attn_in_self],
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"b ... -> (b n) ...",
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n=n_times_crossframe_attn_in_self,
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)
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v = repeat(
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v[::n_times_crossframe_attn_in_self],
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"b ... -> (b n) ...",
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n=n_times_crossframe_attn_in_self,
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)
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b, _, _ = q.shape
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q, k, v = map(
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lambda t: t.unsqueeze(3)
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.reshape(b, t.shape[1], self.heads, self.dim_head)
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.permute(0, 2, 1, 3)
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.reshape(b * self.heads, t.shape[1], self.dim_head)
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.contiguous(),
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(q, k, v),
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)
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# actually compute the attention, what we cannot get enough of
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out = xformers.ops.memory_efficient_attention(
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q, k, v, attn_bias=None, op=self.attention_op
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)
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# TODO: Use this directly in the attention operation, as a bias
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if exists(mask):
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raise NotImplementedError
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out = (
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out.unsqueeze(0)
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||
|
|
.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
|