patch-forcing/configs/config.yaml

defaults:
  - _self_
  - model: dit-b
  - data: dummy256    # dummy data
  - autoencoder: tiny_ae
  - lr_scheduler: null
  - trainer: flow
  - experiment: null    # must be last in defaults list as it can override all others

  # disable hydra logging
  - override hydra/hydra_logging: disabled
  - override hydra/job_logging: disabled
  
# ----------------------------------------
name: debug/your_exp

# ----------------------------------------
# logging
use_wandb: False
use_wandb_offline: False
wandb_project: patch-forcing

tags: []

# checkpoint loading
load_weights: null
load_strict: True
# resume_step: 0    # can be used for load_weights to specify step (not required for resume_checkpoint)
resume_checkpoint: null

# checkpoint saving (lightning callback)
checkpoint_params:              # filename refers to number of gradient updates
    every_n_train_steps: 10000  # gradient update steps
    save_top_k: -1              # needs to be -1, otherwise it overwrites
    verbose: True
    save_last: True
    auto_insert_metric_name: False

# ----------------------------------------
train_params:
  max_steps: -1
  max_epochs: -1
  num_sanity_val_steps: 0
  accumulate_grad_batches: 1
  log_every_n_steps: 1        # gradient update steps
  limit_val_batches: 8        # per GPU
  val_check_interval: 5000    # steps, regardless of gradient accumulation
  precision: bf16-mixed
  clip_grad_norm: 1.0

callbacks:
  - target: lightning.pytorch.callbacks.LearningRateMonitor
    params:
      logging_interval: 'step'

# ----------------------------------------
# profiling
profile: false
profiling:
  warmup: 40
  active: 1
  filename: profile.json
  cpu: true
  cuda: true
  record_shapes: false
  profile_memory: false
  with_flops: false

# ----------------------------------------
# distributed
num_nodes: 1
devices: -1
auto_requeue: False
tqdm_refresh_rate: 1        # set higher on slurm (otherwise prints tqdm every step)
deepspeed_stage: 0
p2p_disable: False
slurm_id: null
cuda_prefetch: False
cuda_prefetch_factor: 2
ddp_kwargs:
  find_unused_parameters: False   # default: False
  gradient_as_bucket_view: True   # default: False
  bucket_cap_mb: 100              # default: 25
  broadcast_buffers: True         # default: True

# ----------------------------------------
user: ${oc.env:USER}

# don't log and save files
hydra:
  output_subdir: null
  run:
    dir: .