better support for xformers flash attention on older versions of torch

modules/errors.py

@@ -24,6 +24,18 @@ See https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable
         """)
 
 
+already_displayed = {}
+
+
+def display_once(e: Exception, task):
+    if task in already_displayed:
+        return
+
+    display(e, task)
+
+    already_displayed[task] = 1
+
+
 def run(code, task):
     try:
         code()

modules/sd_hijack_optimizations.py

@@ -9,7 +9,7 @@ from torch import einsum
 from ldm.util import default
 from einops import rearrange
 
-from modules import shared
+from modules import shared, errors
 from modules.hypernetworks import hypernetwork
 
 from .sub_quadratic_attention import efficient_dot_product_attention
@@ -279,6 +279,21 @@ def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_
     )
 
 
+def get_xformers_flash_attention_op(q, k, v):
+    if not shared.cmd_opts.xformers_flash_attention:
+        return None
+
+    try:
+        flash_attention_op = xformers.ops.MemoryEfficientAttentionFlashAttentionOp
+        fw, bw = flash_attention_op
+        if fw.supports(xformers.ops.fmha.Inputs(query=q, key=k, value=v, attn_bias=None)):
+            return flash_attention_op
+    except Exception as e:
+        errors.display_once(e, "enabling flash attention")
+
+    return None
+
+
 def xformers_attention_forward(self, x, context=None, mask=None):
     h = self.heads
     q_in = self.to_q(x)
@@ -291,18 +306,7 @@ def xformers_attention_forward(self, x, context=None, mask=None):
     q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
     del q_in, k_in, v_in
 
-    if shared.cmd_opts.xformers_flash_attention:
-        op = xformers.ops.MemoryEfficientAttentionFlashAttentionOp
-        fw, bw = op
-        if not fw.supports(xformers.ops.fmha.Inputs(query=q, key=k, value=v, attn_bias=None)):
-            # print('xformers_attention_forward', q.shape, k.shape, v.shape)
-            # Flash Attention is not availabe for the input arguments. 
-            # Fallback to default xFormers' backend.
-            op = None
-    else:
-        op = None
-        
-    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=op)
+    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v))
 
     out = rearrange(out, 'b n h d -> b n (h d)', h=h)
     return self.to_out(out)
@@ -377,17 +381,7 @@ def xformers_attnblock_forward(self, x):
         q = q.contiguous()
         k = k.contiguous()
         v = v.contiguous()
-        if shared.cmd_opts.xformers_flash_attention:
-            op = xformers.ops.MemoryEfficientAttentionFlashAttentionOp
-            fw, bw = op
-            if not fw.supports(xformers.ops.fmha.Inputs(query=q, key=k, value=v)):
-                # print('xformers_attnblock_forward', q.shape, k.shape, v.shape)
-                # Flash Attention is not availabe for the input arguments. 
-                # Fallback to default xFormers' backend.
-                op = None
-        else:
-            op = None
-        out = xformers.ops.memory_efficient_attention(q, k, v, op=op)
+        out = xformers.ops.memory_efficient_attention(q, k, v, op=get_xformers_flash_attention_op(q, k, v))
         out = rearrange(out, 'b (h w) c -> b c h w', h=h)
         out = self.proj_out(out)
         return x + out