mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
350 lines
12 KiB
Python
350 lines
12 KiB
Python
from inspect import isfunction
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import math
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import torch
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import torch.nn.functional as F
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from torch import nn, einsum
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from einops import rearrange, repeat
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from ldm.modules.diffusionmodules.util import checkpoint
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import psutil
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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.):
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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 = nn.Sequential(
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nn.Linear(dim, inner_dim),
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nn.GELU()
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) if not glu else GEGLU(dim, inner_dim)
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self.net = nn.Sequential(
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project_in,
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nn.Dropout(dropout),
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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(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
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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(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
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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(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
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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(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.k = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.v = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.proj_out = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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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__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
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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),
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nn.Dropout(dropout)
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)
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if not torch.cuda.is_available():
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mem_av = psutil.virtual_memory().available / (1024**3)
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if mem_av > 32:
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self.einsum_op = self.einsum_op_v1
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elif mem_av > 12:
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self.einsum_op = self.einsum_op_v2
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else:
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self.einsum_op = self.einsum_op_v3
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del mem_av
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else:
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self.einsum_op = self.einsum_op_v4
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# mps 64-128 GB
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def einsum_op_v1(self, q, k, v, r1):
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if q.shape[1] <= 4096: # for 512x512: the max q.shape[1] is 4096
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s1 = einsum('b i d, b j d -> b i j', q, k) * self.scale # aggressive/faster: operation in one go
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s2 = s1.softmax(dim=-1, dtype=q.dtype)
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del s1
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r1 = einsum('b i j, b j d -> b i d', s2, v)
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del s2
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else:
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# q.shape[0] * q.shape[1] * slice_size >= 2**31 throws err
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# needs around half of that slice_size to not generate noise
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slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
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for i in range(0, q.shape[1], slice_size):
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end = i + slice_size
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s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale
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s2 = s1.softmax(dim=-1, dtype=r1.dtype)
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del s1
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r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
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del s2
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return r1
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# mps 16-32 GB (can be optimized)
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def einsum_op_v2(self, q, k, v, r1):
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slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
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for i in range(0, q.shape[1], slice_size): # conservative/less mem: operation in steps
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end = i + slice_size
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s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale
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s2 = s1.softmax(dim=-1, dtype=r1.dtype)
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del s1
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r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
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del s2
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return r1
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# mps 8 GB
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def einsum_op_v3(self, q, k, v, r1):
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slice_size = 1
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for i in range(0, q.shape[0], slice_size): # iterate over q.shape[0]
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end = min(q.shape[0], i + slice_size)
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s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end]) # adapted einsum for mem
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s1 *= self.scale
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s2 = s1.softmax(dim=-1, dtype=r1.dtype)
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del s1
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r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end]) # adapted einsum for mem
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del s2
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return r1
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# cuda
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def einsum_op_v4(self, q, k, v, r1):
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stats = torch.cuda.memory_stats(q.device)
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mem_active = stats['active_bytes.all.current']
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mem_reserved = stats['reserved_bytes.all.current']
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mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
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mem_free_torch = mem_reserved - mem_active
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mem_free_total = mem_free_cuda + mem_free_torch
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gb = 1024 ** 3
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tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * 4
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mem_required = tensor_size * 2.5
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steps = 1
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if mem_required > mem_free_total:
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steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
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if steps > 64:
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max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
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raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
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f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')
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slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
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for i in range(0, q.shape[1], slice_size):
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end = min(q.shape[1], i + slice_size)
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s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale
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s2 = s1.softmax(dim=-1, dtype=r1.dtype)
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del s1
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r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
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del s2
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return r1
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def forward(self, x, context=None, mask=None):
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h = self.heads
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q_in = self.to_q(x)
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context = default(context, x)
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k_in = self.to_k(context)
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v_in = self.to_v(context)
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device_type = 'mps' if x.device.type == 'mps' else 'cuda'
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del context, x
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
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del q_in, k_in, v_in
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r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
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r1 = self.einsum_op(q, k, v, r1)
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del q, k, v
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r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
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del r1
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return self.to_out(r2)
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class BasicTransformerBlock(nn.Module):
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def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
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super().__init__()
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self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is a self-attention
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self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
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self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
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heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none
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self.norm1 = nn.LayerNorm(dim)
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self.norm2 = nn.LayerNorm(dim)
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self.norm3 = nn.LayerNorm(dim)
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self.checkpoint = checkpoint
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def forward(self, x, context=None):
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return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
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def _forward(self, x, context=None):
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x = x.contiguous() if x.device.type == 'mps' else x
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x = self.attn1(self.norm1(x)) + x
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x = self.attn2(self.norm2(x), context=context) + x
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x = self.ff(self.norm3(x)) + x
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return x
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class SpatialTransformer(nn.Module):
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"""
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Transformer block for image-like data.
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First, project the input (aka embedding)
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and reshape to b, t, d.
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Then apply standard transformer action.
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Finally, reshape to image
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"""
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def __init__(self, in_channels, n_heads, d_head,
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depth=1, dropout=0., context_dim=None):
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super().__init__()
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self.in_channels = in_channels
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inner_dim = n_heads * d_head
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self.norm = Normalize(in_channels)
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self.proj_in = nn.Conv2d(in_channels,
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inner_dim,
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kernel_size=1,
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stride=1,
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padding=0)
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self.transformer_blocks = nn.ModuleList(
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[BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
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for d in range(depth)]
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)
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self.proj_out = zero_module(nn.Conv2d(inner_dim,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0))
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def forward(self, x, context=None):
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# note: if no context is given, cross-attention defaults to self-attention
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b, c, h, w = x.shape
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x_in = x
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x = self.norm(x)
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x = self.proj_in(x)
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x = rearrange(x, 'b c h w -> b (h w) c')
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for block in self.transformer_blocks:
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x = block(x, context=context)
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x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
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x = self.proj_out(x)
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return x + x_in
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