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config.yaml

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+defaults:
+  - _self_
+  - /callbacks: [checkpoint_every_n_steps, checkpoint_monitor, learning_rate_monitor]
+  - /data: Korean_dataset
+  - /model: tiny-ar
+  - /strategy: ddp
+  - /noise: loglinear
+  - /lr_scheduler: constant_warmup
+
+mode: sample_eval  # train / ppl_eval / sample_eval
+diffusion: absorbing_state
+backbone: ar  # dit / dimamba / ar
+parameterization: ar  # subs / d3pm / sedd
+time_conditioning: False
+T: 0  # 0 (continuous time) / 1000 
+subs_masking: False
+
+seed: 1
+
+loader:
+  global_batch_size: 32
+  eval_global_batch_size: ${.global_batch_size}
+  # Note: batch_size and eval_batch_size are **per machine**
+  batch_size: ${div_up:${.global_batch_size}, ${eval:${trainer.devices} * ${trainer.num_nodes}}}
+  eval_batch_size: 1  
+  #${div_up:${.eval_global_batch_size}, ${eval:${trainer.devices} * ${trainer.num_nodes}}}
+  num_workers: ${eval:"len(__import__('os').sched_getaffinity(0))"}
+  pin_memory: True
+
+sampling:
+  predictor: ddpm_cache  # analytic, ddpm, ddpm_cache
+  steps: 128
+  noise_removal: True
+  # TODO(yair): @subham, why aren't these params under `eval`?
+  num_sample_batches: 1  # Total samples: `num_gpus` * `loader.eval_batch_size` * num_sample_batches
+  num_sample_log: 1
+  semi_ar: False
+  stride_length: 1
+  num_strides: 1
+
+training:
+  ema: 0.9999
+  antithetic_sampling: True
+  importance_sampling: False
+  sampling_eps: 1e-3
+  change_of_variables: False
+
+eval:
+  checkpoint_path: /home/elicer/lhb01/mdlm/outputs/parkseongjun/psjkodata/2025.04.05/051927/checkpoints/best.ckpt  # Used to evaluate a checkpoint after training.
+  disable_ema: False
+  compute_generative_perplexity: True
+  perplexity_batch_size: 8
+  compute_perplexity_on_sanity: False
+  gen_ppl_eval_model_name_or_path: gpt2-large  # gpt2-large, meta-llama/Llama-2-7b-hf
+  generate_samples: True
+
+optim:
+  weight_decay: 0.01
+  lr: 5e-5
+  beta1: 0.9
+  beta2: 0.999
+  eps: 1e-8
+
+trainer:
+  _target_: lightning.Trainer
+  accelerator: cuda
+  num_nodes: 1
+  devices: ${device_count:}
+  accumulate_grad_batches: ${div_up:${loader.global_batch_size}, ${eval:${trainer.devices} * ${loader.batch_size} * ${trainer.num_nodes}}}
+  gradient_clip_val: 1.0
+  precision: 'bf16'
+  num_sanity_val_steps: 0
+  max_steps: 50000
+  log_every_n_steps: 10
+  limit_train_batches: 1.0  # train on full dataset, can be used to toggle quick run
+  limit_val_batches: 1.0  # validate on full dataset, can be used to toggle quick run
+  val_check_interval: 0.5
+
+wandb:
+  project: test-ar
+  mode: online
+  notes: Mulan for text
+  resume: must
+  group: null
+  job_type: null
+  name: ar
+  id: f12b7c5e-07c9-48ae-96fa-4798823b8492
+  tags:
+    - ${noise.type}
+    - ${data.train}
+    - ${data.valid}
+
+hydra:
+  run:
+    dir: ./outputs/${data.train}/${now:%Y.%m.%d}/${now:%H%M%S}
+  job:
+    chdir: true
+
+checkpointing:
+  # Use custom `save_dir` if, e.g., saving to S3 bucket, otherwise leave this parameter as is
+  save_dir: ${cwd:}
+  # Note: `checkpoints` path should correspond to `checkpoint_every_n_steps.dirpath`
+  resume_from_ckpt: true
+  resume_ckpt_path: /home/elicer/lhb01/mdlm/outputs/parkseongjun/psjkodata/2025.04.05/045928/checkpoints/last.ckpt