Meet error when using xformers and doing loss backward

[eeyrw]

🐛 Bug

Associated issue: https://github.com/huggingface/diffusers/issues/1314 Get error when I enable xformers of UNet and try to do backward:

Traceback (most recent call last):
  File "f:/diffusers-test/vae_expr.py", line 66, in <module>
    loss.backward()
  File "C:\Users\uuu\.virtualenvs\stable-diffusion\lib\site-packages\torch\_tensor.py", line 396, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
  File "C:\Users\uuu\.virtualenvs\stable-diffusion\lib\site-packages\torch\autograd\__init__.py", line 175, in backward
    allow_unreachable=True, accumulate_grad=True)  # Calls into the C++ engine to run the backward pass
  File "C:\Users\uuu\.virtualenvs\stable-diffusion\lib\site-packages\torch\autograd\function.py", line 253, in apply   
    return user_fn(self, *args)
  File "f:\xformers\xformers\ops\memory_efficient_attention.py", line 414, in backward
    causal=ctx.causal,
  File "C:\Users\uuu\.virtualenvs\stable-diffusion\lib\site-packages\torch\_ops.py", line 143, in __call__
    return self._op(*args, **kwargs or {})
RuntimeError: p.gQ_strideM() == grad_q.stride(1) INTERNAL ASSERT FAILED at "F:\\xformers\\xformers\\components\\attention\\csrc\\cuda\\mem_eff_attention\\attention_backward_generic.cu":181, please report a bug to PyTorch.

Command

To Reproduce

Steps to reproduce the behavior:

import argparse
import logging
import math
import os
import random
from pathlib import Path
from typing import Iterable, Optional

import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from diffusers import AutoencoderKL, DDPMScheduler, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel
from diffusers.optimization import get_scheduler
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer

pretrained_model_name_or_path = r'F:\diffusers-weight'
# Load models and create wrapper for stable diffusion
tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(pretrained_model_name_or_path, subfolder="text_encoder")
vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae")
unet = UNet2DConditionModel.from_pretrained(pretrained_model_name_or_path, subfolder="unet")
noise_scheduler = DDPMScheduler.from_config(pretrained_model_name_or_path, subfolder="scheduler")

# Freeze vae and text_encoder
vae.requires_grad_(False)
text_encoder.requires_grad_(False)


weight_dtype = torch.bfloat16
# Move text_encode and vae to gpu.
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
text_encoder.to('cuda', dtype=weight_dtype)
vae.to('cuda', dtype=weight_dtype)
unet.to('cuda', dtype=weight_dtype)
unet.set_use_memory_efficient_attention_xformers(True)

                # Convert images to latent space
images = torch.randn(1,3,512,512).to('cuda', dtype=weight_dtype)
latents = vae.encode(images).latent_dist.sample()
latents = latents * 0.18215
# Convert images to latent space
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
inputs = tokenizer('Terwt dsfs gsdgs sg"', max_length=tokenizer.model_max_length, padding="do_not_pad", truncation=True)
input_ids = [inputs["input_ids"]]
padded_tokens = tokenizer.pad({"input_ids": input_ids}, padding=True, return_tensors="pt")
input_ids = padded_tokens.input_ids.to('cuda', dtype=torch.int)
encoder_hidden_states = text_encoder(input_ids)[0]

# Predict the noise residual and compute loss
noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
loss = F.mse_loss(noise_pred.float(), noise.float(), reduction="mean")
loss.backward()

Expected behavior

Nothing should happen.

Environment

System Info diffusers version: 0.7.2 Platform: Windows-10-10.0.19041-SP0 Python version: 3.7.7 PyTorch version (GPU?): 1.12.0+cu113 (True) Huggingface_hub version: 0.10.1 Transformers version: 4.24.0 Using GPU in script?: Yes Using distributed or parallel set-up in script?: No xformers version: efdca026381a13319be082f079c60275cc871301 https://github.com/facebookresearch/xformers/commit/efdca026381a13319be082f079c60275cc871301

[danthe3rd]

Thanks for reporting! Do you mind sharing what is the resolution you are using? Also can you report the output of python -m xformers.info ?

[eeyrw]

@danthe3rd (stable-diffusion) PS F:\diffusers-test> python -m xformers.info A matching Triton is not available, some optimizations will not be enabled. Error caught was: No module named 'triton' xFormers 0.0.15.dev+efdca02.d20221116 memory_efficient_attention.flshatt: available - requires GPU with compute capability 7.5+ memory_efficient_attention.cutlass: available memory_efficient_attention.small_k: available swiglu.fused.p.cpp: available is_triton_available: False is_functorch_available: False pytorch.version: 1.12.0+cu113 pytorch.cuda: available gpu.compute_capability: 8.6 gpu.name: NVIDIA GeForce RTX 3060

[eeyrw]

For convenience of reproducing, I make a more concise test case:

import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from diffusers import UNet2DConditionModel

weight_dtype = torch.float32

cfg = {
    "_class_name": "UNet2DConditionModel",
    "_diffusers_version": "0.8.0.dev0",
    "act_fn": "silu",
    "attention_head_dim": 8,
    "block_out_channels": [
        320,
        640,
        1280,
        1280
    ],
    "center_input_sample": False,
    "cross_attention_dim": 768,
    "down_block_types": [
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "DownBlock2D"
    ],
    "downsample_padding": 1,
    "flip_sin_to_cos": True,
    "freq_shift": 0,
    "in_channels": 4,
    "layers_per_block": 2,
    "mid_block_scale_factor": 1,
    "norm_eps": 1e-05,
    "norm_num_groups": 32,
    "out_channels": 4,
    "sample_size": 32,
    "up_block_types": [
        "UpBlock2D",
        "CrossAttnUpBlock2D",
        "CrossAttnUpBlock2D",
        "CrossAttnUpBlock2D"
    ]
}
unet = UNet2DConditionModel(**cfg)

unet.to('cuda', dtype=weight_dtype)
unet.set_use_memory_efficient_attention_xformers(True)
noise = torch.randn(1, 4, 64, 64).to('cuda', dtype=weight_dtype)
noisy_latents = torch.randn(1, 4, 64, 64).to('cuda', dtype=weight_dtype)
timesteps = torch.tensor(543, device='cuda', dtype=torch.int64)
encoder_hidden_states = torch.randn(1, 10, 768).to('cuda', dtype=weight_dtype)
noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
loss = F.mse_loss(noise_pred.float(), noise.float(), reduction="mean")
loss.backward()

[danthe3rd]

Thanks a lot - this is really useful! @artkorenev has been working on this and should have a fix coming soon

[leeruibin]

Is there any solution now, I also have the same problem when I train stablediffusion model. I can get the result with forward function. However, when I calculate loss can execute backward, it raises

raise NotImplementedError(f"No operator found for this attention: {inp}") NotImplementedError: No operator found for this attention: Inputs

this Error is raised in file "xformers/ops/fmha/dispatch.py", line 68, in _dispatch_bw", the "inp" has the shape {query:(64,256,1,128),key:(64,77,1,128),value:(64,77,1,128)}, I guess maybe the 1 dim cause this dispatch error?

[danthe3rd]

Closing this as it's resolved now.

@leeruibin this is a different / unrelated issue. Can you open a new one with the entire stacktrace/log of the error? num_heads=1 should be supported without issue. Also include the output of python -m xformers.info

[eeyrw]

Closing this as it's resolved now.

@leeruibin this is a different / unrelated issue. Can you open a new one with the entire stacktrace/log of the error? num_heads=1 should be supported without issue. Also include the output of python -m xformers.info

Just wonder the fix in which release or dev version?

[leeruibin]

Thank for your reply, I have open a new issue in this link. https://github.com/facebookresearch/xformers/issues/628#issue-1525498690

[danthe3rd]

Just wonder the fix in which release or dev version?

Woops I forgot to circle back here. It has been fixed in https://github.com/facebookresearch/xformers/commit/3ea73074f41617857fe001f1e7d46dee2e03517a