config file with esm embedding guidance

configs/config_emb_guidance.yaml

@@ -0,0 +1,133 @@
+defaults:
+  - _self_
+  - /callbacks: [checkpoint_every_n_steps, checkpoint_monitor, learning_rate_monitor]
+  - /data: peptide
+  - /model: small
+  - /strategy: ddp
+  - /noise: loglinear
+  - /lr_scheduler: cosine_decay_warmup  # constant_warmup
+  - /classifier_model: null
+  - /guidance: cbg
+
+mode: ppl_eval  # train / train_classifier / ppl_eval
+diffusion: uniform  # absorbing_state / uniform
+backbone: dit  # dit / dimamba / ar
+classifier_backbone: null
+parameterization: d3pm  # subs / d3pm / ar
+time_conditioning: True  # UDLM is conditioned on time
+subs_masking: False
+zero_recon_loss: True  # Use for UDLM
+T: 0  # 0 (continuous time) / 1000
+# device: "cuda"
+
+
+is_vision: False
+seed: 42
+
+loader:
+  global_batch_size: 512
+  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: ${div_up:${.eval_global_batch_size}, ${eval:${trainer.devices} * ${trainer.num_nodes}}}
+  num_workers: 0 # ${eval:"len(__import__('os').sched_getaffinity(0))"}
+  pin_memory: True
+  persistent_workers: False # True
+
+sampling:
+  use_cache: True
+  steps: 32
+  # Note: batch_size is **per machine**
+  batch_size: 1 # ${loader.eval_batch_size}
+  num_sample_batches: 5  # Total samples: `num_gpus` * `batch_size` * `num_sample_batches`
+  use_float64: False
+  # IL2 uniprot seq
+  original_binder: "MYRMQLLSCIALSLALVTNSAPTSSSTKKTQLQLEHLLLDLQMILNGINNYKNPKLTRMLTFKFYMPKKATELKHLQCLEEELKPLEEVLNLAQSKNFHLRPRDLISNINVIVLELKGSETTFMCEYADETATIVEFLNRWITFCQSIISTLT"
+
+eval:
+  # checkpoint_path: '/home/tc415/discrete-diffusion-guidance/outputs/peptide/2024.12.31/122818/checkpoints/best.ckpt'  # Used to evaluate a checkpoint after training.
+  # Tong uploaded checkpoint to A100, accessing through it
+  checkpoint_path: '/workspace/moPPIt-v2/PeptideUDLM.ckpt'
+
+  # # IL2RG gamma chain
+  # target_sequence: 'MLKPSLPFTSLLFLQLPLLGVGLNTTILTPNGNEDTTADFFLTTMPTDSLSVSTLPLPEVQCFVFNVEYMNCTWNSSSEPQPTNLTLHYWYKNSDNDKVQKCSHYLFSEEITSGCQLQKKEIHLYQTFVVQLQDPREPRRQATQMLKLQNLVIPWAPENLTLHKLSESQLELNWNNRFLNHCLEHLVQYRTDWDHSWTEQSVDYRHKFSLPSVDGQKRYTFRVRSRFNPLCGSAQHWSEWSHPIHWGSNTSKENPFLFALEAVVISVGSMGLIISLLCVYFWLERTMPRIPTLKNLEDLVTEYHGNFSAWSGVSKGLAESLQPDYSERLCLVSEIPPKGGALGEGPGASPCNQHSPYWAPPCYTLKPET'
+  # # motifs for gamma chain
+  # target_motifs: "125, 126, 147, 149, 181, 182, 204, 229, 230, 231, 233"
+
+  # IL2RA alpha chain
+  target_sequence: 'MDSYLLMWGLLTFIMVPGCQAELCDDDPPEIPHATFKAMAYKEGTMLNCECKRGFRRIKSGSLYMLCTGNSSHSSWDNQCQCTSSATRNTTKQVTPQPEEQKERKTTEMQSPMQPVDQASLPGHCREPPPWENEATERIYHFVVGQMVYYQCVQGYRALHRGPAESVCKMTHGKTRWTQPQLICTGEMETSQFPGEEKPQASPEGRPESETSCLVTTTDFQIQTEMAATMETSIFTTEYQVAVAGCVFLLISVLLLSGLTWQRRQRKSRRTI'
+  # motifs for alpha chain
+  target_motifs: "22, 23, 25, 27, 46, 48, 50, 51, 56, 57, 59, 60, 62, 63, 64, 66, 78, 139, 141"
+
+  # # IL2RB beta chain
+  # target_sequence: 'MAAPALSWRLPLLILLLPLATSWASAAVNGTSQFTCFYNSRANISCVWSQDGALQDTSCQVHAWPDRRRWNQTCELLPVSQASWACNLILGAPDSQKLTTVDIVTLRVLCREGVRWRVMAIQDFKPFENLRLMAPISLQVVHVETHRCNISWEISQASHYFERHLEFEARTLSPGHTWEEAPLLTLKQKQEWICLETLTPDTQYEFQVRVKPLQGEFTTWSPWSQPLAFRTKPAALGKDTIPWLGHLLVGLSGAFGFIILVYLLINCRNTGPWLKKVLKCNTPDPSKFFSQLSSEHGGDVQKWLSSPFPSSSFSPGGLAPEISPLEVLERDKVTQLLLQQDKVPEPASLSSNHSLTSCFTNQGYFFFHLPDALEIEACQVYFTYDPYSEEDPDEGVAGAPTGSSPQPLQPLSGEDDAYCTFPSRDDLLLFSPSLLGGPSPPSTAPGGSGAGEERMPPSLQERVPRDWDPQPLGPPTPGVPDLVDFQPPPELVLREAGEEVPDAGPREGVSFPWSRPPGQGEFRALNARLPLNTDAYLSLQELQGQDPTHLV'
+  # # motifs for beta chain
+  # target_motifs: "67, 68, 95, 96, 97, 99, 100, 101, 102, 127, 159, 160, 162, 164, 214"
+
+  disable_ema: False
+  generate_samples: True
+  generated_samples_path: ''
+  max_samples: 50000
+
+
+training:
+  ema: 0.9999
+  antithetic_sampling: True
+  importance_sampling: False
+  sampling_eps: 1e-3
+  change_of_variables: False
+  compute_loss_on_pad_tokens: True
+  use_simple_ce_loss: False  # Ignore ELBO; just use CE
+  guidance: null # Can turn off with `training.guidance: null`
+    # cond_dropout: 0.0
+
+optim:
+  weight_decay: 1e-4
+  lr: 1e-5
+  beta1: 0.9
+  beta2: 0.999
+  eps: 1e-8
+
+trainer:
+  _target_: lightning.Trainer
+  accelerator: cuda
+  num_nodes: 1
+  devices: 2 # ${device_count:}
+  accumulate_grad_batches: 1 # ${div_up:${loader.global_batch_size}, ${eval:${trainer.devices} * ${loader.batch_size} * ${trainer.num_nodes}}}
+  gradient_clip_val: 1.0
+  precision: 'bf16-mixed'
+  num_sanity_val_steps: 2
+  # max_epochs: 10
+  max_steps: 1652000
+  log_every_n_steps: 100
+  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: 16520 # 2545
+
+wandb:
+  project: moPPIt-v2
+  job_type: model-training
+  name: protein_medium_100epochs_lr1e-5_gradclip1_wd1e-4_dropout0.1 #epochs10_lr3e-4_bsz8_64-true_all-params_gradclip1_beta-one0.9_beta-two0.999
+  id: ${.name}
+
+hydra:
+  run:
+    dir: ./outputs/${wandb.name} # ./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: False
+  resume_ckpt_path: ${.save_dir}/checkpoints/last.ckpt
+
+
+  # target_sequence: 'MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD'
+  # target_motifs: '305-313' # P53_1
+  # target_motifs: '371-382' # P53_2
+  # target_motifs: '351-393' # P53_3
+  # target_motifs: '210-230' # P53_4
+  # target_sequence: 'MLQTKDLIWTLFFLGTAVSLQVDIVPSQGEISVGESKFFLCQVAGDAKDKDISWFSPNGEKLTPNQQRISVVWNDDSSSTLTIYNANIDDAGIYKCVVTGEDGSESEATVNVKIFQKLMFKNAPTPQEFREGEDAVIVCDVVSSLPPTIIWKHKGRDVILKKDVRFIVLSNNYLQIRGIKKTDEGTYRCEGRILARGEINFKDIQVIVNVPPTIQARQNIVNATANLGQSVTLVCDAEGFPEPTMSWTKDGEQIEQEEDDEKYIFSDDSSQLTIKKVDKNDEAEYICIAENKAGEQDATIHLKVFAKPKITYVENQTAMELEEQVTLTCEASGDPIPSITWRTSTRNISSEEKTLDGHMVVRSHARVSSLTLKSIQYTDAGEYICTASNTIGQDSQSMYLEVQYAPKLQGPVAVYTWEGNQVNITCEVFAYPSATISWFRDGQLLPSSNYSNIKIYNTPSASYLEVTPDSENDFGNYNCTAVNRIGQESLEFILVQADTPSSPSIDQVEPYSSTAQVQFDEPEATGGVPILKYKAEWRAVGEEVWHSKWYDAKEASMEGIVTIVGLKPETTYAVRLAALNGKGLGEISAASEFKTQPVHSPPP'
+  # target_motifs: '28-39' # NCAM1_ECD