[MPS] CrossAttention: (partial) fix for NaN attention_scores on PyTorch nightly

[Birch-san]

Some history

when attention_mask is None, we set baddbmm's beta = 0.

If beta is 0, then input will be ignored, and nan and inf in it will not be propagated.

in other words, baddbmm_input is an unused tensor that we supply just because the API requires it. beta = 0, so the tensor isn't even used.
this is why when I originally introduced it: I knew there was no need to even initialize the memory (hence empty instead of zero).

The bug

on MPS backend: this has been fine in some PyTorch versions, but on the recent nightly I tried (2.1.0.dev20230310): allocating such a large bias tensor (40, 4096, 4096) causes baddbmm() to return NaN attention_scores (and consequently black images). even though it's unused!

so, I've changed it to do a smaller allocation, and expand the tensor to the expected size.
at this allocation size (one element): I'm unconcerned about whether initializing the memory costs anything, so I've changed it to use zeroes instead of uninitialized memory.

a (1, 1, 1) tensor would work too (baddbmm supports broadcasting), but I think the explicit shape is helpful documentation, and doesn't cost much with expand(). it might also help model conversion (such as coremltools) with expressing the baddbmm in terms of unfused operations (GEMM * scale + bias).

=====

the reason I call this a partial fix for black images (in this PyTorch nightly, on MPS) is because I found one more fix was necessary:

CrossAttention#get_attention_scores:

 attention_scores = torch.baddbmm(
attention_bias,
query,
key.transpose(-1, -2),
beta=beta,
alpha=self.scale,
)
+ # accessing the tensor seems to help prevent black images on MPS on PyTorch nightly+ assert not attention_scores.isnan().any().item()

AttnProcessor2_0#__call__:

 # the output of sdp = (batch, num_heads, seq_len, head_dim)
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
+ # accessing the tensor seems to help prevent black images on MPS on PyTorch nightly+ assert not hidden_states.isnan().any().item()

but that half of the fix is a bit harder to justify upstreaming (since it's not free, and doesn't make sense). better to report that half as a bug to pytorch, but I haven't succeeded in making a minimal repro.

====

there's also a third problem causing black images on MPS/pytorch@2.1.0.dev20230310, for larger tensor sizes:
pytorch/pytorch#96602

[HuggingFaceDocBuilderDev]

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint.

[patrickvonplaten]

Thanks a lot for the PR @Birch-san!

This looks good to me - @pcuenca could you also take a quick look?

[pcuenca]

Hi @Birch-san! Your code looks good and your explanations are great, but I'm unable to reproduce the problem. I've tried with the nightly you mentioned and also with the one from today, running inference on various models. Do you happen to have a reproducible code snippet I can try?

Regarding the nasty workaround to access the tensors, I'm not opposed to use it inside a check that makes sure it only runs for mps, and maybe only for certain versions of PyTorch. As you know, we've done similar things in the past. That way users can enjoy diffusers while we report upstream and the problem gets solved.

Thanks a lot for your help!

[Birch-san]

@pcuenca

Regarding the nasty workaround to access the tensors, I'm not opposed to use it inside a check that makes sure it only runs for mps, and maybe only for certain versions of PyTorch

aye, that'd be fine, but I don't know which versions are affected. I tried to produce a minimal repro, but the minimal version ran without trouble. something odd's going on.

I'm on Python 3.11, PyTorch 2.1.0.dev20230310, macOS 13.3 beta. they're all new, so any one of those could be responsible for Very Weird Stuff.

[pcuenca]

I'm also on macOS 13.3 beta and tested that same daily (and today's), but I tested on Python 3.9. I'll try Python 3.11, but it looks like it's not going to be easy to reproduce given your comment :)

[williamberman]

Nice! @Birch-san could you move the changes into attention_processor.py given this recent refactor?

e828232

[ZXStudio]

I also encountered the same problem, but I am window10

[nipunjindal]

Can you please illustrate what's needed to push this PR forward?

[Birch-san]

@nipunjindal it just needs the changes to move to here:

diffusers/src/diffusers/models/attention_processor.py

Line 300 in 6ba0efb

baddbmm_input=torch.empty(

are you experiencing this problem too?

[nipunjindal]

Yes, I do and I did apply the patch to attention_processor. I can help in case you need help to merge main to this.
Please let me know.

[patrickvonplaten]

Yes we renamed the class to attention_processor.py so we would just have to transfer the changes to this file! Sorry about this filename change :-/

[Birch-san]

@nipunjindal I've now rebased; thanks

[patrickvonplaten]

Does this problem also appear on torch 2.0.0 or torch 2.0.1? Torch nightly is often not in the best state

[patrickvonplaten]

cc @pcuenca

[github-actions]

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[wuchangping]

diffusers 0.16.1, diffusers 0.20.0, stable-diffusion-2-1 ,dreambooth, fp16 , sd_scripts v0.6.5 reproduce the issue.

 def get_attention_scores(self, query, key, attention_mask=None):
dtype = query.dtype
if self.upcast_attention:
query = query.float()
key = key.float()
print("0 get_attention_scores:", attention_mask)
if attention_mask is None:
# baddbmm_input = torch.zeros(
# query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device
# )
batch_x_heads, query_tokens, _ = query.shape
_, key_tokens, _ = key.shape
# expanding dims isn't strictly necessary (baddbmm supports broadcasting bias),
# but documents the expected shape without allocating any additional memory
attention_bias = torch.zeros(1, 1, 1, dtype=query.dtype, device=query.device).expand(
batch_x_heads, query_tokens, key_tokens
)
beta = 0
print("0-1 attention_bias:", attention_bias)
else:
attention_bias = attention_mask
# baddbmm_input = attention_mask
beta = 1
# baddbmm_input,
print("0-2 attention_bias:", attention_bias)
print("0-3 query:", query)
print("0-4 key:", key)
print("0-5 beta:", beta)
print("0-6 self.scale:", self.scale)
attention_scores = torch.baddbmm(
attention_bias,
query,
key.transpose(-1, -2),
beta=beta,
alpha=self.scale,
)
print("1 attention_scores:", attention_scores )
if self.upcast_softmax:
attention_scores = attention_scores.float()
print("2 attention_scores:", attention_scores )
attention_probs = attention_scores.softmax(dim=-1)
print("3 attention_probs:", attention_probs)
print("3 attention_probs dtype:", dtype)
attention_probs = attention_probs.to(dtype)
print("4 attention_probs:", attention_probs)
return attention_probs

logs

0 get_attention_scores: None
0-1 attention_bias: tensor([[[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]],
[[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]],
[[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
..........................
0-3 query: tensor([[[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
...,
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan]],
[[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
...,
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan]],
[[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
...,
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan]],
[[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
...,
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan]],
[[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
...,
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan]]], device='xla:0',

-5 beta: 0
0-6 self.scale: 0.125
1 attention_scores: tensor([[[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ...,
[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]],
[[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ...,
[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]], [[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
...,
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan]],
[[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
...,
[nan, nan, nan, ..., nan, nan, nan],