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Only test for xformers when enabling them #1773 (#1776) 

* only check for xformers when xformers are enabled

* only test for xformers when enabling them
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Ilmari Heikkinen 2022-12-20 08:38:28 +08:00 committed by GitHub
parent 847daf25c7
commit 8e4733b3c3
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1 changed files with 47 additions and 45 deletions
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src/diffusers/models/attention.py
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@ -288,28 +288,29 @@ class AttentionBlock(nn.Module):
self._use_memory_efficient_attention_xformers = False
def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool):
if not is_xformers_available():
raise ModuleNotFoundError(
"Refer to https://github.com/facebookresearch/xformers for more information on how to install"
" xformers",
name="xformers",
)
elif not torch.cuda.is_available():
raise ValueError(
"torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only"
" available for GPU "
)
else:
try:
# Make sure we can run the memory efficient attention
_ = xformers.ops.memory_efficient_attention(
torch.randn((1, 2, 40), device="cuda"),
torch.randn((1, 2, 40), device="cuda"),
torch.randn((1, 2, 40), device="cuda"),
if use_memory_efficient_attention_xformers:
if not is_xformers_available():
raise ModuleNotFoundError(
"Refer to https://github.com/facebookresearch/xformers for more information on how to install"
" xformers",
name="xformers",
)
except Exception as e:
raise e
self._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
elif not torch.cuda.is_available():
raise ValueError(
"torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only"
" available for GPU "
)
else:
try:
# Make sure we can run the memory efficient attention
_ = xformers.ops.memory_efficient_attention(
torch.randn((1, 2, 40), device="cuda"),
torch.randn((1, 2, 40), device="cuda"),
torch.randn((1, 2, 40), device="cuda"),
)
except Exception as e:
raise e
self._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
def reshape_heads_to_batch_dim(self, tensor):
batch_size, seq_len, dim = tensor.shape
@ -450,31 +451,32 @@ class BasicTransformerBlock(nn.Module):
self.norm3 = nn.LayerNorm(dim)
def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool):
if not is_xformers_available():
print("Here is how to install it")
raise ModuleNotFoundError(
"Refer to https://github.com/facebookresearch/xformers for more information on how to install"
" xformers",
name="xformers",
)
elif not torch.cuda.is_available():
raise ValueError(
"torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only"
" available for GPU "
)
else:
try:
# Make sure we can run the memory efficient attention
_ = xformers.ops.memory_efficient_attention(
torch.randn((1, 2, 40), device="cuda"),
torch.randn((1, 2, 40), device="cuda"),
torch.randn((1, 2, 40), device="cuda"),
if use_memory_efficient_attention_xformers:
if not is_xformers_available():
print("Here is how to install it")
raise ModuleNotFoundError(
"Refer to https://github.com/facebookresearch/xformers for more information on how to install"
" xformers",
name="xformers",
)
except Exception as e:
raise e
self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
if self.attn2 is not None:
self.attn2._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
elif not torch.cuda.is_available():
raise ValueError(
"torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only"
" available for GPU "
)
else:
try:
# Make sure we can run the memory efficient attention
_ = xformers.ops.memory_efficient_attention(
torch.randn((1, 2, 40), device="cuda"),
torch.randn((1, 2, 40), device="cuda"),
torch.randn((1, 2, 40), device="cuda"),
)
except Exception as e:
raise e
self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
if self.attn2 is not None:
self.attn2._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None):
# 1. Self-Attention