540 lines
19 KiB
Python
540 lines
19 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 typing import Optional, Any
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from ldm.modules.diffusionmodules.util import checkpoint
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from .sub_quadratic_attention import efficient_dot_product_attention
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try:
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import xformers
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import xformers.ops
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XFORMERS_IS_AVAILBLE = True
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except:
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XFORMERS_IS_AVAILBLE = False
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# CrossAttn precision handling
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import os
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_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
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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 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 CrossAttentionBirchSan(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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def forward(self, x, context=None, mask=None):
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h = self.heads
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query = self.to_q(x)
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context = default(context, x)
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key = self.to_k(context)
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value = self.to_v(context)
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del context, x
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query = query.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1)
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key_t = key.transpose(1,2).unflatten(1, (self.heads, -1)).flatten(end_dim=1)
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del key
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value = value.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1)
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dtype = query.dtype
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upcast_attention = _ATTN_PRECISION =="fp32" and query.dtype != torch.float32
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if upcast_attention:
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bytes_per_token = torch.finfo(torch.float32).bits//8
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else:
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bytes_per_token = torch.finfo(query.dtype).bits//8
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batch_x_heads, q_tokens, _ = query.shape
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_, _, k_tokens = key_t.shape
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qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
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stats = torch.cuda.memory_stats(query.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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chunk_threshold_bytes = mem_free_torch * 0.5 #Using only this seems to work better on AMD
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kv_chunk_size_min = None
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query_chunk_size_x = 1024 * 4
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kv_chunk_size_min_x = None
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kv_chunk_size_x = (int((chunk_threshold_bytes // (batch_x_heads * bytes_per_token * query_chunk_size_x)) * 2.0) // 1024) * 1024
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if kv_chunk_size_x < 1024:
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kv_chunk_size_x = None
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if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
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# the big matmul fits into our memory limit; do everything in 1 chunk,
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# i.e. send it down the unchunked fast-path
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query_chunk_size = q_tokens
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kv_chunk_size = k_tokens
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else:
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query_chunk_size = query_chunk_size_x
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kv_chunk_size = kv_chunk_size_x
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kv_chunk_size_min = kv_chunk_size_min_x
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hidden_states = efficient_dot_product_attention(
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query,
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key_t,
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value,
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query_chunk_size=query_chunk_size,
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kv_chunk_size=kv_chunk_size,
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kv_chunk_size_min=kv_chunk_size_min,
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use_checkpoint=self.training,
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upcast_attention=upcast_attention,
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)
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hidden_states = hidden_states.to(dtype)
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hidden_states = hidden_states.unflatten(0, (-1, self.heads)).transpose(1,2).flatten(start_dim=2)
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out_proj, dropout = self.to_out
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hidden_states = out_proj(hidden_states)
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hidden_states = dropout(hidden_states)
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return hidden_states
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class CrossAttentionDoggettx(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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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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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)
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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] * 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 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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# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
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# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
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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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# print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size)
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first_op_done = False
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cleared_cache = False
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while True:
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try:
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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 = i + slice_size
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if _ATTN_PRECISION =="fp32":
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with torch.autocast(enabled=False, device_type = 'cuda'):
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s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * self.scale
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else:
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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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first_op_done = True
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s2 = s1.softmax(dim=-1)
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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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break
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except torch.cuda.OutOfMemoryError as e:
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if first_op_done == False:
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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if cleared_cache == False:
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cleared_cache = True
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print("out of memory error, emptying cache and trying again")
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continue
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steps *= 2
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if steps > 64:
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raise e
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print("out of memory error, increasing steps and trying again", steps)
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else:
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raise e
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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 OriginalCrossAttention(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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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)
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v = self.to_v(context)
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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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# force cast to fp32 to avoid overflowing
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if _ATTN_PRECISION =="fp32":
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with torch.autocast(enabled=False, device_type = 'cuda'):
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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) * self.scale
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else:
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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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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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import sys
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if "--use-split-cross-attention" in sys.argv:
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print("Using split optimization for cross attention")
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class CrossAttention(CrossAttentionDoggettx):
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pass
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else:
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print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
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class CrossAttention(CrossAttentionBirchSan):
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pass
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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(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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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(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
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self.attention_op: Optional[Any] = 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 = 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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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(q, k, v, attn_bias=None, op=self.attention_op)
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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)
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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__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
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disable_self_attn=False):
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super().__init__()
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attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE 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(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
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context_dim=context_dim if self.disable_self_attn else None) # 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(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)
|
|
self.norm2 = nn.LayerNorm(dim)
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|
self.norm3 = nn.LayerNorm(dim)
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|
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 if self.disable_self_attn else None) + x
|
|
x = self.attn2(self.norm2(x), context=context) + x
|
|
x = self.ff(self.norm3(x)) + x
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|
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., context_dim=None,
|
|
disable_self_attn=False, use_linear=False,
|
|
use_checkpoint=True):
|
|
super().__init__()
|
|
if exists(context_dim) and not isinstance(context_dim, list):
|
|
context_dim = [context_dim]
|
|
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, checkpoint=use_checkpoint)
|
|
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.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):
|
|
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
|
|
|