Dead code removal (#48)

* Remove old commented-out attention code

* Mark two functions as likely unused

* Use exists() and default() from sgm.util

sgm/modules/attention.py

@@ -1,6 +1,5 @@
 import logging
 import math
-from inspect import isfunction
 from typing import Any, Optional
 
 import torch
@@ -8,6 +7,8 @@ import torch.nn.functional as F
 from einops import rearrange, repeat
 from packaging import version
 from torch import nn
+from ..util import exists, default
+
 
 
 logger = logging.getLogger(__name__)
@@ -58,25 +59,11 @@ except:
 from .diffusionmodules.util import checkpoint
 
 
-def exists(val):
-    return val is not None
-
-
-def uniq(arr):
+def uniq(arr):  # TODO: this seems unused
     return {el: True for el in arr}.keys()
 
 
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def max_neg_value(t):
-    return -torch.finfo(t.dtype).max
-
-
-def init_(tensor):
+def init_(tensor):  # TODO: this seems unused
     dim = tensor.shape[-1]
     std = 1 / math.sqrt(dim)
     tensor.uniform_(-std, std)
@@ -256,23 +243,6 @@ class CrossAttention(nn.Module):
 
         q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v))
 
-        ## old
-        """
-        sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-        del q, k
-
-        if exists(mask):
-            mask = rearrange(mask, 'b ... -> b (...)')
-            max_neg_value = -torch.finfo(sim.dtype).max
-            mask = repeat(mask, 'b j -> (b h) () j', h=h)
-            sim.masked_fill_(~mask, max_neg_value)
-
-        # attention, what we cannot get enough of
-        sim = sim.softmax(dim=-1)
-
-        out = einsum('b i j, b j d -> b i d', sim, v)
-        """
-        ## new
         with sdp_kernel(**BACKEND_MAP[self.backend]):
             # print("dispatching into backend", self.backend, "q/k/v shape: ", q.shape, k.shape, v.shape)
             out = F.scaled_dot_product_attention(