add xformers attention

modules/sd_hijack_optimizations.py

 import math
 import torch
 from torch import einsum
-
+import xformers.ops
+import functorch
+xformers._is_functorch_available=True
 from ldm.util import default
 from einops import rearrange
 
@@ -92,6 +94,41 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 
     return self.to_out(r2)
 
+def _maybe_init(self, x):
+    """
+    Initialize the attention operator, if required We expect the head dimension to be exposed here, meaning that x
+    : B, Head, Length
+    """
+    if self.attention_op is not None:
+        return
+    _, M, K = x.shape
+    try:
+        self.attention_op = xformers.ops.AttentionOpDispatch(
+            dtype=x.dtype,
+            device=x.device,
+            k=K,
+            attn_bias_type=type(None),
+            has_dropout=False,
+            kv_len=M,
+            q_len=M,
+        ).op
+    except NotImplementedError as err:
+        raise NotImplementedError(f"Please install xformers with the flash attention / cutlass components.\n{err}")
+
+def xformers_attention_forward(self, x, context=None, mask=None):
+    h = self.heads
+    q_in = self.to_q(x)
+    context = default(context, x)
+    k_in = self.to_k(context)
+    v_in = self.to_v(context)
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+    del q_in, k_in, v_in
+    self._maybe_init(q)
+    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
+
+    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+    return self.to_out(out)
+
 def cross_attention_attnblock_forward(self, x):
         h_ = x
         h_ = self.norm(h_)