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#
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#
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# Multi-node (4 nodes) fine-tune of Gemma4 31B with TP=4, PP=4 and DP=2.
# Topology: 4 nodes x 8 GPUs = 32 GPUs total (TP4 x PP4 x DP2).
# Gemma4 31B has 46 decoder layers; PP=4 splits them across 4 stages.
#
# Per-node launch (set MASTER_ADDR to the rendezvous host and NODE_RANK
# per node, e.g. via SLURM/MPI):
#
#   torchrun --nnodes=4 --nproc-per-node=8 \
#            --master-addr=$MASTER_ADDR --master-port=29500 \
#            --node-rank=$NODE_RANK \
#            examples/vlm_finetune/finetune.py \
#            -c examples/vlm_finetune/gemma4/gemma4_31b_tp4_pp4.yaml

recipe: FinetuneRecipeForVLM

step_scheduler:
  global_batch_size: 8
  local_batch_size: 1
  ckpt_every_steps: 500
  val_every_steps: 500
  num_epochs: 2

dist_env:
  backend: nccl
  timeout_minutes: 60

rng:
  _target_: nemo_automodel.components.training.rng.StatefulRNG
  seed: 42
  ranked: true

model:
  _target_: nemo_automodel.NeMoAutoModelForImageTextToText.from_pretrained
  pretrained_model_name_or_path: google/gemma-4-31B-it
  torch_dtype: torch.bfloat16
  use_liger_kernel: true
  use_sdpa_patching: false
  attn_implementation: sdpa
  text_config:
    use_cache: false

checkpoint:
  enabled: true
  checkpoint_dir: vlm_checkpoints/gemma4_31b_it_tp4_pp4/
  model_save_format: torch_save
  save_consolidated: false

distributed:
  strategy: fsdp2
  dp_size: none
  tp_size: 4
  cp_size: 1
  pp_size: 4
  ep_size: 1

  sequence_parallel: false
  activation_checkpointing: true

  pipeline:
    pp_schedule: 1f1b
    pp_microbatch_size: 1
    scale_grads_in_schedule: false

loss_fn:
  _target_: nemo_automodel.components.loss.masked_ce.MaskedCrossEntropy

dataset:
  _target_: nemo_automodel.components.datasets.vlm.datasets.make_medpix_dataset
  path_or_dataset: mmoukouba/MedPix-VQA
  split: train[:1000]

dataloader:
  _target_: torchdata.stateful_dataloader.StatefulDataLoader
  num_workers: 0
  pin_memory: true
  drop_last: true
  collate_fn:
    _target_: nemo_automodel.components.datasets.vlm.collate_fns.gemma4_prefix_collate_fn

validation_dataset:
  _target_: nemo_automodel.components.datasets.vlm.datasets.make_medpix_dataset
  path_or_dataset: mmoukouba/MedPix-VQA
  split: validation[:500]

validation_dataloader:
  _target_: torchdata.stateful_dataloader.StatefulDataLoader
  collate_fn:
    _target_: nemo_automodel.components.datasets.vlm.collate_fns.gemma4_prefix_collate_fn

optimizer:
  _target_: torch.optim.AdamW
  lr: 1e-5
  weight_decay: 0.01
  betas: [0.9, 0.95]

lr_scheduler:
  lr_decay_style: cosine

freeze_config:
  freeze_embeddings: true
  freeze_vision_tower: true
  freeze_audio_tower: true
  freeze_language_model: false
