disable neonpixel optimizations on M1 hardware (#414)

* disable neonpixel optimizations on M1 hardware

* fix typo that was causing random noise images on m1

ldm/modules/attention.py

@@ -1,9 +1,10 @@
-from inspect import isfunction
 import math
+from inspect import isfunction
+
 import torch
 import torch.nn.functional as F
-from torch import nn, einsum
 from einops import rearrange, repeat
+from torch import nn, einsum
 
 from ldm.modules.diffusionmodules.util import checkpoint
 
@@ -13,7 +14,7 @@ def exists(val):
 
 
 def uniq(arr):
- return{el: True for el in arr}.keys()
+ return {el: True for el in arr}.keys()
 
 
 def default(val, d):
@@ -82,14 +83,14 @@ def __init__(self, dim, heads=4, dim_head=32):
  super().__init__()
  self.heads = heads
  hidden_dim = dim_head * heads
- self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
+ self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
  self.to_out = nn.Conv2d(hidden_dim, dim, 1)
 
  def forward(self, x):
  b, c, h, w = x.shape
  qkv = self.to_qkv(x)
- q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
- k = k.softmax(dim=-1) 
+ q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads=self.heads, qkv=3)
+ k = k.softmax(dim=-1)
  context = torch.einsum('bhdn,bhen->bhde', k, v)
  out = torch.einsum('bhde,bhdn->bhen', context, q)
  out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
@@ -131,12 +132,12 @@ def forward(self, x):
  v = self.v(h_)
 
  # compute attention
- b,c,h,w = q.shape
+ b, c, h, w = q.shape
  q = rearrange(q, 'b c h w -> b (h w) c')
  k = rearrange(k, 'b c h w -> b c (h w)')
  w_ = torch.einsum('bij,bjk->bik', q, k)
 
- w_ = w_ * (int(c)**(-0.5))
+ w_ = w_ * (int(c) ** (-0.5))
  w_ = torch.nn.functional.softmax(w_, dim=2)
 
  # attend to values
@@ -146,7 +147,7 @@ def forward(self, x):
  h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
  h_ = self.proj_out(h_)
 
- return x+h_
+ return x + h_
 
 
 class CrossAttention(nn.Module):
@@ -174,6 +175,7 @@ def forward(self, x, context=None, mask=None):
  context = default(context, x)
  k = self.to_k(context)
  v = self.to_v(context)
+ device_type = x.device.type
  del context, x
 
  q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
@@ -188,9 +190,11 @@ def forward(self, x, context=None, mask=None):
  sim.masked_fill_(~mask, max_neg_value)
  del mask
 
- # attention, what we cannot get enough of, by halves
- sim[4:] = sim[4:].softmax(dim=-1)
- sim[:4] = sim[:4].softmax(dim=-1)
+ if device_type == 'mps': #special case for M1 - disable neonsecret optimization
+ sim = sim.softmax(dim=-1)
+ else:
+ sim[4:] = sim[4:].softmax(dim=-1)
+ sim[:4] = sim[:4].softmax(dim=-1)
 
  sim = einsum('b i j, b j d -> b i d', sim, v)
  sim = rearrange(sim, '(b h) n d -> b n (h d)', h=h)
@@ -200,7 +204,8 @@ def forward(self, x, context=None, mask=None):
 class BasicTransformerBlock(nn.Module):
  def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
  super().__init__()
- self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is a self-attention
+ self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head,
+ dropout=dropout) # is a self-attention
  self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
  self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
  heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none
@@ -228,6 +233,7 @@ class SpatialTransformer(nn.Module):
  Then apply standard transformer action.
  Finally, reshape to image
  """
+
  def __init__(self, in_channels, n_heads, d_head,
  depth=1, dropout=0., context_dim=None):
  super().__init__()
@@ -243,7 +249,7 @@ def __init__(self, in_channels, n_heads, d_head,
 
  self.transformer_blocks = nn.ModuleList(
  [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
-  for d in range(depth)]
+ for d in range(depth)]
  )
 
  self.proj_out = zero_module(nn.Conv2d(inner_dim,