mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
383 lines
14 KiB
Python
383 lines
14 KiB
Python
from inspect import isfunction
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import math
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from typing import Callable, Optional
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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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def get_mem_free_total(device):
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#only on cuda
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if not torch.cuda.is_available():
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return None
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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(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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return mem_free_total
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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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self.mem_total_gb = psutil.virtual_memory().total // (1 << 30)
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self.cached_mem_free_total = None
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self.attention_slice_wrangler = None
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self.slicing_strategy_getter = None
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def set_attention_slice_wrangler(self, wrangler: Optional[Callable[[nn.Module, torch.Tensor, int, int, int], torch.Tensor]]):
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'''
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Set custom attention calculator to be called when attention is calculated
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:param wrangler: Callback, with args (module, suggested_attention_slice, dim, offset, slice_size),
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which returns either the suggested_attention_slice or an adjusted equivalent.
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`module` is the current CrossAttention module for which the callback is being invoked.
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`suggested_attention_slice` is the default-calculated attention slice
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`dim` is -1 if the attenion map has not been sliced, or 0 or 1 for dimension-0 or dimension-1 slicing.
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If `dim` is >= 0, `offset` and `slice_size` specify the slice start and length.
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Pass None to use the default attention calculation.
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:return:
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'''
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self.attention_slice_wrangler = wrangler
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def set_slicing_strategy_getter(self, getter: Optional[Callable[[nn.Module], tuple[int,int]]]):
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self.slicing_strategy_getter = getter
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def cache_free_memory_count(self, device):
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self.cached_mem_free_total = get_mem_free_total(device)
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print("free cuda memory: ", self.cached_mem_free_total)
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def clear_cached_free_memory_count(self):
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self.cached_mem_free_total = None
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def einsum_lowest_level(self, q, k, v, dim, offset, slice_size):
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# calculate attention scores
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attention_scores = einsum('b i d, b j d -> b i j', q, k)
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# calculate attention slice by taking the best scores for each latent pixel
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default_attention_slice = attention_scores.softmax(dim=-1, dtype=attention_scores.dtype)
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attention_slice_wrangler = self.attention_slice_wrangler
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if attention_slice_wrangler is not None:
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attention_slice = attention_slice_wrangler(self, default_attention_slice, dim, offset, slice_size)
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else:
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attention_slice = default_attention_slice
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return einsum('b i j, b j d -> b i d', attention_slice, v)
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def einsum_op_slice_dim0(self, q, k, v, slice_size):
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r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
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for i in range(0, q.shape[0], slice_size):
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end = i + slice_size
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r[i:end] = self.einsum_lowest_level(q[i:end], k[i:end], v[i:end], dim=0, offset=i, slice_size=slice_size)
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return r
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def einsum_op_slice_dim1(self, q, k, v, slice_size):
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r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
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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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r[:, i:end] = self.einsum_lowest_level(q[:, i:end], k, v, dim=1, offset=i, slice_size=slice_size)
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return r
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def einsum_op_mps_v1(self, q, k, v):
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if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
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return self.einsum_lowest_level(q, k, v, None, None, None)
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else:
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slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
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return self.einsum_op_slice_dim1(q, k, v, slice_size)
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def einsum_op_mps_v2(self, q, k, v):
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if self.mem_total_gb > 8 and q.shape[1] <= 4096:
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return self.einsum_lowest_level(q, k, v, None, None, None)
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else:
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return self.einsum_op_slice_dim0(q, k, v, 1)
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def einsum_op_tensor_mem(self, q, k, v, max_tensor_mb):
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size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
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if size_mb <= max_tensor_mb:
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return self.einsum_lowest_level(q, k, v, None, None, None)
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div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
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if div <= q.shape[0]:
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return self.einsum_op_slice_dim0(q, k, v, q.shape[0] // div)
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return self.einsum_op_slice_dim1(q, k, v, max(q.shape[1] // div, 1))
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def einsum_op_cuda(self, q, k, v):
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# check if we already have a slicing strategy (this should only happen during cross-attention controlled generation)
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slicing_strategy_getter = self.slicing_strategy_getter
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if slicing_strategy_getter is not None:
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(dim, slice_size) = slicing_strategy_getter(self)
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if dim is not None:
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# print("using saved slicing strategy with dim", dim, "slice size", slice_size)
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if dim == 0:
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return self.einsum_op_slice_dim0(q, k, v, slice_size)
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elif dim == 1:
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return self.einsum_op_slice_dim1(q, k, v, slice_size)
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# fallback for when there is no saved strategy, or saved strategy does not slice
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mem_free_total = self.cached_mem_free_total or get_mem_free_total(q.device)
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# Divide factor of safety as there's copying and fragmentation
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return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
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def get_attention_mem_efficient(self, q, k, v):
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if q.device.type == 'cuda':
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#print("in get_attention_mem_efficient with q shape", q.shape, ", k shape", k.shape, ", free memory is", get_mem_free_total(q.device))
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return self.einsum_op_cuda(q, k, v)
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if q.device.type == 'mps':
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if self.mem_total_gb >= 32:
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return self.einsum_op_mps_v1(q, k, v)
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return self.einsum_op_mps_v2(q, k, v)
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# Smaller slices are faster due to L2/L3/SLC caches.
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# Tested on i7 with 8MB L3 cache.
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return self.einsum_op_tensor_mem(q, k, v, 32)
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def forward(self, x, context=None, mask=None):
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h = self.heads
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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) * self.scale
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v = self.to_v(context)
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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, k, v))
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r = self.get_attention_mem_efficient(q, k, v)
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hidden_states = rearrange(r, '(b h) n d -> b n (h d)', h=h)
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return self.to_out(hidden_states)
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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.clone()))
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x += self.attn2(self.norm2(x.clone()), context=context)
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x += self.ff(self.norm3(x.clone()))
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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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