Merge branch 'main' of github.com:pesser/stable-diffusion into main
This commit is contained in:
Robin Rombach
commit
a681f02fdd
|
|
@ -0,0 +1,135 @@
|
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model:
|
||||
base_learning_rate: 1.0e-04
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target: ldm.models.diffusion.ddpm.LatentDiffusion
|
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params:
|
||||
linear_start: 0.00085
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||||
linear_end: 0.0120
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num_timesteps_cond: 1
|
||||
log_every_t: 200
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||||
timesteps: 1000
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||||
first_stage_key: "jpg"
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cond_stage_key: "txt"
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||||
image_size: 64
|
||||
channels: 4
|
||||
cond_stage_trainable: false # Note: different from the one we trained before
|
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conditioning_key: crossattn
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monitor: val/loss_simple_ema
|
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scale_factor: 0.18215
|
||||
|
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scheduler_config: # 10000 warmup steps
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||||
target: ldm.lr_scheduler.LambdaLinearScheduler
|
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params:
|
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warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch
|
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cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
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f_start: [ 1.e-6 ]
|
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f_max: [ 1. ]
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f_min: [ 1. ]
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||||
|
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unet_config:
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target: ldm.modules.diffusionmodules.openaimodel.UNetModel
|
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params:
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image_size: 32 # unused
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in_channels: 4
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out_channels: 4
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model_channels: 320
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attention_resolutions: [ 4, 2, 1 ]
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num_res_blocks: 2
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channel_mult: [ 1, 2, 4, 4 ]
|
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num_heads: 8
|
||||
use_spatial_transformer: True
|
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transformer_depth: 1
|
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context_dim: 768
|
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use_checkpoint: True
|
||||
legacy: False
|
||||
|
||||
first_stage_config:
|
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target: ldm.models.autoencoder.AutoencoderKL
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||||
params:
|
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embed_dim: 4
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||||
monitor: val/rec_loss
|
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ddconfig:
|
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double_z: true
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z_channels: 4
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||||
resolution: 256
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in_channels: 3
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||||
out_ch: 3
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ch: 128
|
||||
ch_mult:
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- 1
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- 2
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- 4
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- 4
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num_res_blocks: 2
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attn_resolutions: []
|
||||
dropout: 0.0
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lossconfig:
|
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target: torch.nn.Identity
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||||
|
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cond_stage_config:
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target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
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|
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|
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data:
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target: ldm.data.laion.WebDataModuleFromConfig
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params:
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tar_base: "pipe:aws s3 cp s3://s-datasets/laion-high-resolution/"
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batch_size: 4
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num_workers: 4
|
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multinode: True
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train:
|
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shards: '{00000..17279}.tar -'
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shuffle: 10000
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image_key: jpg
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image_transforms:
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- target: torchvision.transforms.Resize
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params:
|
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size: 512
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interpolation: 3
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- target: torchvision.transforms.RandomCrop
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params:
|
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size: 512
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|
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# NOTE use enough shards to avoid empty validation loops in workers
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validation:
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shards: '{17280..17535}.tar -'
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shuffle: 0
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image_key: jpg
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image_transforms:
|
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- target: torchvision.transforms.Resize
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params:
|
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size: 512
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interpolation: 3
|
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- target: torchvision.transforms.CenterCrop
|
||||
params:
|
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size: 512
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|
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|
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lightning:
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find_unused_parameters: False
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|
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modelcheckpoint:
|
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params:
|
||||
every_n_train_steps: 5000
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|
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callbacks:
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image_logger:
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target: main.ImageLogger
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||||
params:
|
||||
batch_frequency: 5000
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max_images: 4
|
||||
increase_log_steps: False
|
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log_first_step: False
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log_images_kwargs:
|
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use_ema_scope: False
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||||
inpaint: False
|
||||
plot_progressive_rows: False
|
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plot_diffusion_rows: False
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N: 4
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unconditional_guidance_scale: 3.0
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unconditional_guidance_label: [""]
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|
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trainer:
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benchmark: True
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val_check_interval: 5000000 # really sorry
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num_sanity_val_steps: 0
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accumulate_grad_batches: 2
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|
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@ -105,6 +105,7 @@ def dict_collation_fn(samples, combine_tensors=True, combine_scalars=True):
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class WebDataModuleFromConfig(pl.LightningDataModule):
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def __init__(self, tar_base, batch_size, train=None, validation=None,
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test=None, num_workers=4, multinode=True, min_size=None,
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max_pwatermark=1.0,
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**kwargs):
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super().__init__(self)
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print(f'Setting tar base to {tar_base}')
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@ -116,6 +117,7 @@ class WebDataModuleFromConfig(pl.LightningDataModule):
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self.test = test
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self.multinode = multinode
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self.min_size = min_size # filter out very small images
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self.max_pwatermark = max_pwatermark # filter out watermarked images
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|
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def make_loader(self, dataset_config, train=True):
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if 'image_transforms' in dataset_config:
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|
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@ -184,7 +186,7 @@ class WebDataModuleFromConfig(pl.LightningDataModule):
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if self.min_size is None:
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return True
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try:
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return x['json']['original_width'] >= self.min_size and x['json']['original_height'] >= self.min_size
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return x['json']['original_width'] >= self.min_size and x['json']['original_height'] >= self.min_size and x['json']['pwatermark'] <= self.max_pwatermark
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except Exception:
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return False
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|
||||
|
|
@ -300,8 +302,7 @@ def example01():
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print("next epoch.")
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|
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if __name__ == "__main__":
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#example01()
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def example02():
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from omegaconf import OmegaConf
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from torch.utils.data.distributed import DistributedSampler
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from torch.utils.data import IterableDataset
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|
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@ -318,3 +319,82 @@ if __name__ == "__main__":
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print(batch.keys())
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print(batch["jpg"].shape)
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break
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|
||||
|
||||
def example03():
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# improved aesthetics
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||||
tars = "pipe:aws s3 cp s3://s-laion/improved-aesthetics-laion-2B-en-subsets/aesthetics_tars/{000000..060207}.tar -"
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dataset = wds.WebDataset(tars)
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||||
|
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def filter_keys(x):
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try:
|
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return ("jpg" in x) and ("txt" in x)
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except Exception:
|
||||
return False
|
||||
|
||||
def filter_size(x):
|
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try:
|
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return x['json']['original_width'] >= 512 and x['json']['original_height'] >= 512
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except Exception:
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||||
return False
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||||
|
||||
def filter_watermark(x):
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try:
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||||
return x['json']['pwatermark'] < 0.5
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||||
except Exception:
|
||||
return False
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||||
|
||||
dataset = (dataset
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||||
.select(filter_keys)
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.decode('pil', handler=wds.warn_and_continue))
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n_total = 0
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n_large = 0
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||||
n_large_nowm = 0
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for i, example in enumerate(dataset):
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||||
n_total += 1
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||||
if filter_size(example):
|
||||
n_large += 1
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||||
if filter_watermark(example):
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||||
n_large_nowm += 1
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||||
|
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if i%500 == 0:
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print(i)
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print(f"Large: {n_large}/{n_total} | {n_large/n_total*100:.2f}%")
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if n_large > 0:
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print(f"No Watermark: {n_large_nowm}/{n_large} | {n_large_nowm/n_large*100:.2f}%")
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||||
|
||||
|
||||
|
||||
def example04():
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# improved aesthetics
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for i_shard in range(60208)[::-1]:
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print(i_shard)
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tars = "pipe:aws s3 cp s3://s-laion/improved-aesthetics-laion-2B-en-subsets/aesthetics_tars/{:06}.tar -".format(i_shard)
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dataset = wds.WebDataset(tars)
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||||
|
||||
def filter_keys(x):
|
||||
try:
|
||||
return ("jpg" in x) and ("txt" in x)
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||||
except Exception:
|
||||
return False
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||||
|
||||
def filter_size(x):
|
||||
try:
|
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return x['json']['original_width'] >= 512 and x['json']['original_height'] >= 512
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except Exception:
|
||||
return False
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|
||||
dataset = (dataset
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||||
.select(filter_keys)
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||||
.decode('pil', handler=wds.warn_and_continue))
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||||
try:
|
||||
example = next(iter(dataset))
|
||||
except Exception:
|
||||
print(f"Error @ {i_shard}")
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||||
|
||||
|
||||
if __name__ == "__main__":
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#example01()
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||||
#example02()
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||||
example03()
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||||
#example04()
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||||
|
|
|
|||
|
|
@ -0,0 +1,32 @@
|
|||
import os
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import subprocess
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import time
|
||||
import fire
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||||
|
||||
|
||||
class Checker(object):
|
||||
def __init__(self, filename, interval=60):
|
||||
self._cached_stamp = 0
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||||
self.filename = filename
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self.interval = interval
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|
||||
def check(self, cmd):
|
||||
while True:
|
||||
stamp = os.stat(self.filename).st_mtime
|
||||
if stamp != self._cached_stamp:
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self._cached_stamp = stamp
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print(f"{self.__class__.__name__}: Detected a new file at {self.filename}, running evaluation commands on it.")
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subprocess.run(cmd, shell=True)
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else:
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||||
time.sleep(self.interval)
|
||||
|
||||
|
||||
def run(filename, cmd):
|
||||
checker = Checker(filename, interval=60)
|
||||
checker.check(cmd)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(run)
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
import os
|
||||
import torch
|
||||
import fire
|
||||
|
||||
|
||||
def printit(p):
|
||||
print(f"printin' in path: {p}")
|
||||
size_initial = os.path.getsize(p)
|
||||
nsd = dict()
|
||||
sd = torch.load(p, map_location="cpu")
|
||||
if "global_step" in sd:
|
||||
print(f"This is global step {sd['global_step']}.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(printit)
|
||||
|
|
@ -14,6 +14,8 @@ def prune_it(p):
|
|||
nsd[k] = sd[k]
|
||||
else:
|
||||
print(f"removing optimizer states for path {p}")
|
||||
if "global_step" in sd:
|
||||
print(f"This is global step {sd['global_step']}.")
|
||||
fn = f"{os.path.splitext(p)[0]}-pruned.ckpt"
|
||||
print(f"saving pruned checkpoint at: {fn}")
|
||||
torch.save(nsd, fn)
|
||||
|
|
@ -24,4 +26,4 @@ def prune_it(p):
|
|||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(prune_it)
|
||||
print("done.")
|
||||
print("done.")
|
||||
|
|
|
|||
|
|
@ -0,0 +1,46 @@
|
|||
#!/bin/bash
|
||||
|
||||
# mpi version for node rank
|
||||
H=`hostname`
|
||||
THEID=`echo -e $HOSTNAMES | python3 -c "import sys;[sys.stdout.write(str(i)) for i,line in enumerate(next(sys.stdin).split(' ')) if line.strip() == '$H'.strip()]"`
|
||||
export NODE_RANK=${THEID}
|
||||
echo THEID=$THEID
|
||||
|
||||
echo "##########################################"
|
||||
echo MASTER_ADDR=${MASTER_ADDR}
|
||||
echo MASTER_PORT=${MASTER_PORT}
|
||||
echo NODE_RANK=${NODE_RANK}
|
||||
echo WORLD_SIZE=${WORLD_SIZE}
|
||||
echo "##########################################"
|
||||
# debug environment worked great so we stick with it
|
||||
# no magic there, just a miniconda python=3.9, pytorch=1.12, cudatoolkit=11.3
|
||||
# env with pip dependencies from stable diffusion's requirements.txt
|
||||
eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
|
||||
#conda activate stable
|
||||
conda activate torch111
|
||||
cd /fsx/stable-diffusion/stable-diffusion
|
||||
|
||||
CONFIG="/fsx/stable-diffusion/stable-diffusion/configs/stable-diffusion/v1_improvedaesthetics.yaml"
|
||||
|
||||
# resume and set new seed to reshuffle data
|
||||
#EXTRA="--seed 718 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/checkpoints2/v1pp/v1pp-flatline.ckpt"
|
||||
EXTRA="--seed 718 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-22T07-45-07_v1_improvedaesthetics/checkpoints/last.ckpt"
|
||||
|
||||
# only images >= 512 and pwatermark <= 0.4999
|
||||
EXTRA="${EXTRA} data.params.min_size=512 data.params.max_pwatermark=0.4999"
|
||||
|
||||
# postfix
|
||||
EXTRA="${EXTRA} -f v1_iahr_torch111"
|
||||
|
||||
# time to decay
|
||||
#EXTRA="${EXTRA} model.params.scheduler_config.params.cycle_lengths=[50000] model.params.scheduler_config.params.f_min=[1e-6]"
|
||||
|
||||
# custom logdir
|
||||
#EXTRA="${EXTRA} --logdir rlogs"
|
||||
|
||||
# debugging
|
||||
#EXTRA="${EXTRA} -d True lightning.callbacks.image_logger.params.batch_frequency=50"
|
||||
|
||||
/bin/bash /fsx/stable-diffusion/stable-diffusion/scripts/test_gpu.sh
|
||||
|
||||
python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False $EXTRA
|
||||
|
|
@ -0,0 +1,43 @@
|
|||
#!/bin/bash
|
||||
#SBATCH --partition=compute-od-gpu
|
||||
#SBATCH --job-name=stable-diffusion-v1-iahr-torch111
|
||||
#SBATCH --nodes 32
|
||||
#SBATCH --ntasks-per-node 1
|
||||
#SBATCH --cpus-per-gpu=4
|
||||
#SBATCH --gres=gpu:8
|
||||
#SBATCH --exclusive
|
||||
#SBATCH --output=%x_%j.out
|
||||
#SBATCH --comment "Key=Monitoring,Value=ON"
|
||||
|
||||
module load intelmpi
|
||||
source /opt/intel/mpi/latest/env/vars.sh
|
||||
export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-install/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH
|
||||
#export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-inst
|
||||
#all/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH
|
||||
export NCCL_PROTO=simple
|
||||
export PATH=/opt/amazon/efa/bin:$PATH
|
||||
export LD_PRELOAD="/opt/nccl/build/lib/libnccl.so"
|
||||
export FI_EFA_FORK_SAFE=1
|
||||
export FI_LOG_LEVEL=1
|
||||
export FI_EFA_USE_DEVICE_RDMA=1 # use for p4dn
|
||||
export NCCL_DEBUG=info
|
||||
export PYTHONFAULTHANDLER=1
|
||||
export CUDA_LAUNCH_BLOCKING=0
|
||||
export OMPI_MCA_mtl_base_verbose=1
|
||||
export FI_EFA_ENABLE_SHM_TRANSFER=0
|
||||
export FI_PROVIDER=efa
|
||||
export FI_EFA_TX_MIN_CREDITS=64
|
||||
export NCCL_TREE_THRESHOLD=0
|
||||
|
||||
# sent to sub script
|
||||
export HOSTNAMES=`scontrol show hostnames "$SLURM_JOB_NODELIST"`
|
||||
export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
|
||||
export MASTER_PORT=12802
|
||||
export COUNT_NODE=`scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l`
|
||||
export WORLD_SIZE=$COUNT_NODE
|
||||
|
||||
echo go $COUNT_NODE
|
||||
echo $HOSTNAMES
|
||||
echo $WORLD_SIZE
|
||||
|
||||
mpirun -n $COUNT_NODE -perhost 1 /fsx/stable-diffusion/stable-diffusion/scripts/slurm/v1_iahr_torch111/launcher.sh
|
||||
|
|
@ -22,7 +22,10 @@ cd /fsx/stable-diffusion/stable-diffusion
|
|||
CONFIG="/fsx/stable-diffusion/stable-diffusion/configs/stable-diffusion/v1_improvedaesthetics.yaml"
|
||||
|
||||
# resume and set new seed to reshuffle data
|
||||
EXTRA="--seed 714 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-11T20-16-11_txt2img-1p4B-multinode-clip-encoder-high-res-512_improvedaesthetic/checkpoints/last.ckpt"
|
||||
#EXTRA="--seed 714 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-11T20-16-11_txt2img-1p4B-multinode-clip-encoder-high-res-512_improvedaesthetic/checkpoints/last.ckpt"
|
||||
EXTRA="--seed 715 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-14T23-26-13_v1_improvedaesthetics/checkpoints/last.ckpt"
|
||||
# time to decay
|
||||
EXTRA="${EXTRA} model.params.scheduler_config.params.cycle_lengths=[50000] model.params.scheduler_config.params.f_min=[1e-6]"
|
||||
|
||||
# custom logdir
|
||||
#EXTRA="${EXTRA} --logdir rlogs"
|
||||
|
|
|
|||
|
|
@ -0,0 +1,38 @@
|
|||
#!/bin/bash
|
||||
|
||||
# mpi version for node rank
|
||||
H=`hostname`
|
||||
THEID=`echo -e $HOSTNAMES | python3 -c "import sys;[sys.stdout.write(str(i)) for i,line in enumerate(next(sys.stdin).split(' ')) if line.strip() == '$H'.strip()]"`
|
||||
export NODE_RANK=${THEID}
|
||||
echo THEID=$THEID
|
||||
|
||||
echo "##########################################"
|
||||
echo MASTER_ADDR=${MASTER_ADDR}
|
||||
echo MASTER_PORT=${MASTER_PORT}
|
||||
echo NODE_RANK=${NODE_RANK}
|
||||
echo WORLD_SIZE=${WORLD_SIZE}
|
||||
echo "##########################################"
|
||||
# debug environment worked great so we stick with it
|
||||
# no magic there, just a miniconda python=3.9, pytorch=1.12, cudatoolkit=11.3
|
||||
# env with pip dependencies from stable diffusion's requirements.txt
|
||||
eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
|
||||
conda activate torch111
|
||||
cd /fsx/stable-diffusion/stable-diffusion
|
||||
|
||||
CONFIG="/fsx/stable-diffusion/stable-diffusion/configs/stable-diffusion/v1_improvedaesthetics.yaml"
|
||||
|
||||
# resume and set new seed to reshuffle data
|
||||
#EXTRA="--seed 714 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-11T20-16-11_txt2img-1p4B-multinode-clip-encoder-high-res-512_improvedaesthetic/checkpoints/last.ckpt"
|
||||
#EXTRA="--seed 715 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-14T23-26-13_v1_improvedaesthetics/checkpoints/last.ckpt"
|
||||
#EXTRA="--seed 716 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-18T23-11-11_v1_improvedaesthetics/checkpoints/last.ckpt"
|
||||
EXTRA="--seed 717 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-19T19-03-04_v1_improvedaesthetics/checkpoints/last.ckpt"
|
||||
# time to decay
|
||||
EXTRA="${EXTRA} model.params.scheduler_config.params.cycle_lengths=[50000] model.params.scheduler_config.params.f_min=[1e-6]"
|
||||
|
||||
# custom logdir
|
||||
#EXTRA="${EXTRA} --logdir rlogs"
|
||||
|
||||
# debugging
|
||||
#EXTRA="${EXTRA} -d True lightning.callbacks.image_logger.params.batch_frequency=50"
|
||||
|
||||
python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False $EXTRA
|
||||
|
|
@ -0,0 +1,42 @@
|
|||
#!/bin/bash
|
||||
#SBATCH --partition=compute-od-gpu
|
||||
#SBATCH --job-name=stable-diffusion-v1-improvedaesthetics-torch111
|
||||
#SBATCH --nodes 20
|
||||
#SBATCH --ntasks-per-node 1
|
||||
#SBATCH --cpus-per-gpu=4
|
||||
#SBATCH --gres=gpu:8
|
||||
#SBATCH --exclusive
|
||||
#SBATCH --output=%x_%j.out
|
||||
#SBATCH --comment "Key=Monitoring,Value=ON"
|
||||
|
||||
module load intelmpi
|
||||
source /opt/intel/mpi/latest/env/vars.sh
|
||||
export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-inst
|
||||
all/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH
|
||||
export NCCL_PROTO=simple
|
||||
export PATH=/opt/amazon/efa/bin:$PATH
|
||||
export LD_PRELOAD="/opt/nccl/build/lib/libnccl.so"
|
||||
export FI_EFA_FORK_SAFE=1
|
||||
export FI_LOG_LEVEL=1
|
||||
export FI_EFA_USE_DEVICE_RDMA=1 # use for p4dn
|
||||
export NCCL_DEBUG=info
|
||||
export PYTHONFAULTHANDLER=1
|
||||
export CUDA_LAUNCH_BLOCKING=0
|
||||
export OMPI_MCA_mtl_base_verbose=1
|
||||
export FI_EFA_ENABLE_SHM_TRANSFER=0
|
||||
export FI_PROVIDER=efa
|
||||
export FI_EFA_TX_MIN_CREDITS=64
|
||||
export NCCL_TREE_THRESHOLD=0
|
||||
|
||||
# sent to sub script
|
||||
export HOSTNAMES=`scontrol show hostnames "$SLURM_JOB_NODELIST"`
|
||||
export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
|
||||
export MASTER_PORT=12802
|
||||
export COUNT_NODE=`scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l`
|
||||
export WORLD_SIZE=$COUNT_NODE
|
||||
|
||||
echo go $COUNT_NODE
|
||||
echo $HOSTNAMES
|
||||
echo $WORLD_SIZE
|
||||
|
||||
mpirun -n $COUNT_NODE -perhost 1 /fsx/stable-diffusion/stable-diffusion/scripts/slurm/v1_improvedaesthetics_torch111/launcher.sh
|
||||
|
|
@ -0,0 +1,36 @@
|
|||
#!/bin/bash
|
||||
|
||||
# mpi version for node rank
|
||||
H=`hostname`
|
||||
THEID=`echo -e $HOSTNAMES | python3 -c "import sys;[sys.stdout.write(str(i)) for i,line in enumerate(next(sys.stdin).split(' ')) if line.strip() == '$H'.strip()]"`
|
||||
export NODE_RANK=${THEID}
|
||||
echo THEID=$THEID
|
||||
|
||||
echo "##########################################"
|
||||
echo MASTER_ADDR=${MASTER_ADDR}
|
||||
echo MASTER_PORT=${MASTER_PORT}
|
||||
echo NODE_RANK=${NODE_RANK}
|
||||
echo WORLD_SIZE=${WORLD_SIZE}
|
||||
echo "##########################################"
|
||||
# debug environment worked great so we stick with it
|
||||
# no magic there, just a miniconda python=3.9, pytorch=1.12, cudatoolkit=11.3
|
||||
# env with pip dependencies from stable diffusion's requirements.txt
|
||||
eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
|
||||
conda activate torch111
|
||||
cd /fsx/stable-diffusion/stable-diffusion
|
||||
|
||||
CONFIG="/fsx/stable-diffusion/stable-diffusion/configs/stable-diffusion/v1_laionhr.yaml"
|
||||
|
||||
# resume and set new seed to reshuffle data
|
||||
EXTRA="--seed 718 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/checkpoints2/v1pp/v1pp-flatline.ckpt"
|
||||
|
||||
# time to decay
|
||||
#EXTRA="${EXTRA} model.params.scheduler_config.params.cycle_lengths=[50000] model.params.scheduler_config.params.f_min=[1e-6]"
|
||||
|
||||
# custom logdir
|
||||
#EXTRA="${EXTRA} --logdir rlogs"
|
||||
|
||||
# debugging
|
||||
#EXTRA="${EXTRA} -d True lightning.callbacks.image_logger.params.batch_frequency=50"
|
||||
|
||||
python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False $EXTRA
|
||||
|
|
@ -0,0 +1,42 @@
|
|||
#!/bin/bash
|
||||
#SBATCH --partition=compute-od-gpu
|
||||
#SBATCH --job-name=stable-diffusion-v1-laionhr-torch111
|
||||
#SBATCH --nodes 20
|
||||
#SBATCH --ntasks-per-node 1
|
||||
#SBATCH --cpus-per-gpu=4
|
||||
#SBATCH --gres=gpu:8
|
||||
#SBATCH --exclusive
|
||||
#SBATCH --output=%x_%j.out
|
||||
#SBATCH --comment "Key=Monitoring,Value=ON"
|
||||
|
||||
module load intelmpi
|
||||
source /opt/intel/mpi/latest/env/vars.sh
|
||||
export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-inst
|
||||
all/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH
|
||||
export NCCL_PROTO=simple
|
||||
export PATH=/opt/amazon/efa/bin:$PATH
|
||||
export LD_PRELOAD="/opt/nccl/build/lib/libnccl.so"
|
||||
export FI_EFA_FORK_SAFE=1
|
||||
export FI_LOG_LEVEL=1
|
||||
export FI_EFA_USE_DEVICE_RDMA=1 # use for p4dn
|
||||
export NCCL_DEBUG=info
|
||||
export PYTHONFAULTHANDLER=1
|
||||
export CUDA_LAUNCH_BLOCKING=0
|
||||
export OMPI_MCA_mtl_base_verbose=1
|
||||
export FI_EFA_ENABLE_SHM_TRANSFER=0
|
||||
export FI_PROVIDER=efa
|
||||
export FI_EFA_TX_MIN_CREDITS=64
|
||||
export NCCL_TREE_THRESHOLD=0
|
||||
|
||||
# sent to sub script
|
||||
export HOSTNAMES=`scontrol show hostnames "$SLURM_JOB_NODELIST"`
|
||||
export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
|
||||
export MASTER_PORT=12802
|
||||
export COUNT_NODE=`scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l`
|
||||
export WORLD_SIZE=$COUNT_NODE
|
||||
|
||||
echo go $COUNT_NODE
|
||||
echo $HOSTNAMES
|
||||
echo $WORLD_SIZE
|
||||
|
||||
mpirun -n $COUNT_NODE -perhost 1 /fsx/stable-diffusion/stable-diffusion/scripts/slurm/v1_laionhr_torch111/launcher.sh
|
||||
|
|
@ -16,13 +16,16 @@ echo "##########################################"
|
|||
# no magic there, just a miniconda python=3.9, pytorch=1.12, cudatoolkit=11.3
|
||||
# env with pip dependencies from stable diffusion's requirements.txt
|
||||
eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
|
||||
conda activate stable
|
||||
#conda activate stable
|
||||
# torch 1.11 to avoid bug in ckpt restoring
|
||||
conda activate torch111
|
||||
cd /fsx/stable-diffusion/stable-diffusion
|
||||
|
||||
CONFIG=configs/stable-diffusion/v3_pretraining.yaml
|
||||
|
||||
# resume and set new seed to reshuffle data
|
||||
EXTRA="--seed 714 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/rlogs/2022-07-11T22-57-10_txt2img-v2-clip-encoder-improved_aesthetics-256/checkpoints/last.ckpt"
|
||||
#EXTRA="--seed 714 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/rlogs/2022-07-11T22-57-10_txt2img-v2-clip-encoder-improved_aesthetics-256/checkpoints/last.ckpt"
|
||||
EXTRA="--seed 715 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-14T21-03-49_txt2img-v2-clip-encoder-improved_aesthetics-256/checkpoints/last.ckpt"
|
||||
|
||||
# custom logdir
|
||||
#EXTRA="${EXTRA} --logdir rlogs"
|
||||
|
|
@ -30,4 +33,7 @@ EXTRA="--seed 714 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/
|
|||
# debugging
|
||||
#EXTRA="${EXTRA} -d True lightning.callbacks.image_logger.params.batch_frequency=50"
|
||||
|
||||
# detect bad gpus early on
|
||||
/bin/bash /fsx/stable-diffusion/stable-diffusion/scripts/test_gpu.sh
|
||||
|
||||
python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False $EXTRA
|
||||
|
|
|
|||
|
|
@ -11,8 +11,7 @@
|
|||
|
||||
module load intelmpi
|
||||
source /opt/intel/mpi/latest/env/vars.sh
|
||||
export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-inst
|
||||
all/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH
|
||||
export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-install/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH
|
||||
export NCCL_PROTO=simple
|
||||
export PATH=/opt/amazon/efa/bin:$PATH
|
||||
export LD_PRELOAD="/opt/nccl/build/lib/libnccl.so"
|
||||
|
|
|
|||
|
|
@ -0,0 +1,30 @@
|
|||
import socket
|
||||
try:
|
||||
import torch
|
||||
n_gpus = torch.cuda.device_count()
|
||||
print(f"checking {n_gpus} gpus.")
|
||||
for i_gpu in range(n_gpus):
|
||||
print(i_gpu)
|
||||
device = torch.device(f"cuda:{i_gpu}")
|
||||
torch.backends.cuda.matmul.allow_tf32 = False
|
||||
torch.backends.cudnn.benchmark = True
|
||||
torch.backends.cudnn.deterministic = False
|
||||
torch.backends.cudnn.allow_tf32 = True
|
||||
data = torch.randn([4, 640, 32, 32], dtype=torch.float, device=device, requires_grad=True)
|
||||
net = torch.nn.Conv2d(640, 640, kernel_size=[3, 3], padding=[1, 1], stride=[1, 1], dilation=[1, 1], groups=1)
|
||||
net = net.to(device=device).float()
|
||||
out = net(data)
|
||||
out.backward(torch.randn_like(out))
|
||||
torch.cuda.synchronize()
|
||||
except RuntimeError as err:
|
||||
import requests
|
||||
import datetime
|
||||
import os
|
||||
device = os.environ.get("CUDA_VISIBLE_DEVICES", "?")
|
||||
hostname = socket.gethostname()
|
||||
ts = datetime.datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S')
|
||||
resp = requests.get('http://169.254.169.254/latest/meta-data/instance-id')
|
||||
print(f'ERROR at {ts} on {hostname}/{resp.text} (CUDA_VISIBLE_DEVICES={device}): {type(err).__name__}: {err}', flush=True)
|
||||
raise err
|
||||
else:
|
||||
print(f"checked {socket.gethostname()}")
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
#!/bin/bash
|
||||
eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
|
||||
conda activate stable
|
||||
cd /fsx/stable-diffusion/stable-diffusion
|
||||
python scripts/test_gpu.py
|
||||
Loading…
Reference in New Issue