better support for xformers flash attention on older versions of torch

This commit is contained in:
AUTOMATIC 2023-01-23 16:40:20 +03:00
parent 3fa482076a
commit 59146621e2
2 changed files with 30 additions and 24 deletions

View file

@ -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()

View file

@ -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