mirror of
https://github.com/brycedrennan/imaginAIry
synced 2024-11-19 03:25:41 +00:00
494 lines
16 KiB
Python
494 lines
16 KiB
Python
import math
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from functools import lru_cache
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import psutil
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import torch
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import torch.nn.functional as F
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from einops import rearrange
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from torch import einsum, nn
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from imaginairy.modules.diffusion.util import checkpoint as checkpoint_eval
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from imaginairy.utils import get_device
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XFORMERS_IS_AVAILABLE = False
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try:
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if get_device() == "cuda":
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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 ImportError:
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pass
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ALLOW_SPLITMEM = True
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ATTENTION_PRECISION_OVERRIDE = "default"
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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 = dim_out if dim_out is not None else 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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def get_mem_free_total(device):
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device_type = "mps" if device.type == "mps" else "cuda"
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if device_type == "cuda":
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stats = torch.cuda.memory_stats(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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mem_free_total *= 0.9
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else:
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# if we don't add a buffer, larger images come out as noise
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mem_free_total = psutil.virtual_memory().available * 0.6
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return mem_free_total
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@lru_cache(maxsize=1)
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def get_mps_gb_ram():
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return psutil.virtual_memory().total / (1024**3)
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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=0.0):
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super().__init__()
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inner_dim = dim_head * heads
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context_dim = context_dim if context_dim is not None else 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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def forward(self, x, context=None, mask=None):
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# from imaginairy.api import _global_mask_hack
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#
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# if mask is None and _global_mask_hack is not None:
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# mask = _global_mask_hack.to(torch.bool)
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if get_device() == "cuda" or "mps" in get_device(): # noqa
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if not XFORMERS_IS_AVAILABLE and ALLOW_SPLITMEM:
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return self.forward_splitmem(x, context=context, mask=mask)
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h = self.heads
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# print(x.shape)
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q = self.to_q(x)
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context = context if context is not None else x
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k = self.to_k(context) * self.scale
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v = self.to_v(context)
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q, k, v = (rearrange(t, "b n (h d) -> (b h) n d", h=h) for t in (q, k, v))
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# force cast to fp32 to avoid overflowing
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if ATTENTION_PRECISION_OVERRIDE == "fp32":
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with torch.autocast(enabled=False, device_type=get_device()):
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q, k = q.float(), k.float()
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sim = einsum("b i d, b j d -> b i j", q, k)
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else:
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sim = einsum("b i d, b j d -> b i j", q, k)
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# print(sim.shape)
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# print("*" * 100)
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del q, k
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# if mask is not None:
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# if sim.shape[2] == 320 and False:
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# mask = [mask] * 2
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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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attn = sim.softmax(dim=-1)
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out = einsum("b i j, b j d -> b i d", attn, v)
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out = rearrange(out, "(b h) n d -> b n (h d)", h=h)
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return self.to_out(out)
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def forward_splitmem(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 = context if context is not None else x
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k_in = self.to_k(context) * self.scale
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v_in = self.to_v(context)
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del context, x
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q, k, v = (
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rearrange(t, "b n (h d) -> (b h) n d", h=h) for t in (q_in, k_in, v_in)
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)
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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)
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mem_free_total = get_mem_free_total(q.device)
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gb = 1024**3
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tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
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modifier = 3 if q.element_size() == 2 else 2.5
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mem_required = tensor_size * modifier
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steps = 1
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if "mps" in get_device():
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# https://github.com/brycedrennan/imaginAIry/issues/175
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# https://github.com/invoke-ai/InvokeAI/issues/1244
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mps_gb = get_mps_gb_ram()
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factor = 32 / mps_gb
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slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1] * 16 * factor))
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else:
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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 = (
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math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
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)
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msg = f"Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free"
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raise RuntimeError(msg)
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slice_size = (
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q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
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)
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# steps = len(range(0, q.shape[1], slice_size))
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# print(f"Splitting attention into {steps} steps of {slice_size} slices")
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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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# force cast to fp32 to avoid overflowing
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if ATTENTION_PRECISION_OVERRIDE == "fp32":
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with torch.autocast(enabled=False, device_type=get_device()):
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q, k = q.float(), k.float()
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s1 = einsum("b i d, b j d -> b i j", q[:, i:end], k)
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else:
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s1 = einsum("b i d, b j d -> b i j", q[:, i:end], k)
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s2 = s1.softmax(dim=-1, dtype=q.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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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 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__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
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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."
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# )
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inner_dim = dim_head * heads
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context_dim = context_dim if context_dim is not None else 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 = None
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def forward(self, x, context=None, mask=None):
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q = self.to_q(x)
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context = context if context is not None else x
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k = self.to_k(context)
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v = self.to_v(context)
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b, _, _ = q.shape
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q, k, v = (
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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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for t in (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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if mask is not None:
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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)
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.permute(0, 2, 1, 3)
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.reshape(b, out.shape[1], self.heads * self.dim_head)
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)
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return self.to_out(out)
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class BasicTransformerBlock(nn.Module):
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ATTENTION_MODES = {
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"softmax": CrossAttention, # vanilla attention
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"softmax-xformers": MemoryEfficientCrossAttention,
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}
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def __init__(
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self,
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dim,
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n_heads,
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d_head,
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dropout=0.0,
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context_dim=None,
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gated_ff=True,
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checkpoint=True,
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disable_self_attn=False,
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):
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super().__init__()
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attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILABLE else "softmax"
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assert attn_mode in self.ATTENTION_MODES
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attn_cls = self.ATTENTION_MODES[attn_mode]
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self.disable_self_attn = disable_self_attn
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self.attn1 = attn_cls(
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query_dim=dim,
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heads=n_heads,
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dim_head=d_head,
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dropout=dropout,
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context_dim=context_dim if self.disable_self_attn else None,
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) # is a self-attention if not self.disable_self_attn
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self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
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self.attn2 = attn_cls(
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query_dim=dim,
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context_dim=context_dim,
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heads=n_heads,
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dim_head=d_head,
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dropout=dropout,
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) # 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_eval(
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self._forward, (x, context), self.parameters(), self.checkpoint
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)
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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 = (
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self.attn1(
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self.norm1(x), context=context if self.disable_self_attn else None
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)
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+ x
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)
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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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NEW: use_linear for more efficiency instead of the 1x1 convs.
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"""
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def __init__(
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self,
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in_channels,
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n_heads,
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d_head,
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depth=1,
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dropout=0.0,
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context_dim=None,
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disable_self_attn=False,
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use_linear=False,
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use_checkpoint=True,
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):
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super().__init__()
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if context_dim is not None and not isinstance(context_dim, list):
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context_dim = [context_dim]
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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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if not use_linear:
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self.proj_in = nn.Conv2d(
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in_channels, inner_dim, kernel_size=1, stride=1, padding=0
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)
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else:
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self.proj_in = nn.Linear(in_channels, inner_dim)
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self.transformer_blocks = nn.ModuleList(
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[
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BasicTransformerBlock(
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inner_dim,
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n_heads,
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d_head,
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dropout=dropout,
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context_dim=context_dim[d],
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disable_self_attn=disable_self_attn,
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checkpoint=use_checkpoint,
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)
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for d in range(depth)
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]
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)
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if not use_linear:
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self.proj_out = zero_module(
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nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
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)
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else:
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self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
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self.use_linear = use_linear
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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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if not isinstance(context, list):
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context = [context]
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b, c, h, w = x.shape
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x_in = x
|
|
x = self.norm(x)
|
|
if self.use_linear:
|
|
x = rearrange(x, "b c h w -> b (h w) c").contiguous()
|
|
x = self.proj_in(x)
|
|
for i, block in enumerate(self.transformer_blocks):
|
|
x = block(x, context=context[i])
|
|
x = self.proj_out(x)
|
|
x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w).contiguous()
|
|
else:
|
|
x = self.proj_in(x)
|
|
x = rearrange(x, "b c h w -> b (h w) c")
|
|
for i, block in enumerate(self.transformer_blocks):
|
|
x = block(x, context=context[i])
|
|
x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
|
|
x = self.proj_out(x)
|
|
|
|
return x + x_in
|