experiment:
  name: "progressive_reasoning_8gpu_fsdp"
  base_model: "google/gemma-2-2b-it"  # Can scale to much larger models with FSDP
  output_dir: "./outputs"
  use_wandb: true
  wandb_project: "matsuo-llm-comp-2025"

model:
  load_in_4bit: false
  device_map: null  # Let FSDP handle device placement
  gradient_checkpointing: true
  use_flash_attention_2: true
  use_eager_attention: false

# FSDP Configuration
fsdp:
  fsdp_transformer_layer_cls_to_wrap: "Gemma2DecoderLayer"  # Wrap at layer level
  fsdp_sharding_strategy: "FULL_SHARD"  # Shard parameters, gradients, and optimizer states
  fsdp_cpu_offload: false  # Keep on GPU for speed
  fsdp_mixed_precision: true  # Use BF16 mixed precision
  fsdp_auto_wrap_policy: "TRANSFORMER_BASED_WRAP"
  fsdp_min_num_params: 1000000  # Wrap layers with >1M parameters
  fsdp_sync_module_states: true
  fsdp_forward_prefetch: true
  fsdp_use_orig_params: true  # Important for LoRA compatibility

progressive_stages:
  - name: "basic_cot"
    description: "Basic Chain-of-Thought reasoning"
    dataset_path: "./data/basic_cot/"
    adapter_config:
      r: 64  # Can use larger ranks with FSDP
      lora_alpha: 128
      lora_dropout: 0.1
      target_modules: ["q_proj", "k_proj", "v_proj", "o_proj"]
      init_lora_weights: true
    training:
      num_epochs: 2
      per_device_batch_size: 32  # Very large batch size with FSDP
      gradient_accumulation_steps: 1
      learning_rate: 5e-4
      warmup_steps: 100
      max_length: 2048
      bf16: true
      max_grad_norm: 1.0
      weight_decay: 0.001
      save_steps: 50
      logging_steps: 10
      dataloader_num_workers: 8
      dataloader_pin_memory: true

  - name: "math_reasoning"
    description: "Mathematical reasoning with OpenR1-Math-220k dataset"
    dataset_path: "open-r1/OpenR1-Math-220k"
    inherit_from: "basic_cot"
    adapter_config:
      r: 128
      lora_alpha: 256
      lora_dropout: 0.1
      target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
      init_lora_weights: true
    training:
      num_epochs: 1
      per_device_batch_size: 16
      gradient_accumulation_steps: 2
      learning_rate: 3e-4
      warmup_steps: 200
      max_length: 4096
      bf16: true
      max_grad_norm: 1.0
      weight_decay: 0.001
      save_steps: 100
      logging_steps: 20
      dataloader_num_workers: 8
    dataset_config:
      streaming: true
      max_samples: 200000  # Process even more data
      split: "train"

  - name: "complex_reasoning"
    description: "Complex multi-step reasoning with Mixture-of-Thoughts"
    dataset_path: "open-r1/Mixture-of-Thoughts"
    inherit_from: "math_reasoning"
    adapter_config:
      r: 256  # Very large rank possible with FSDP
      lora_alpha: 512
      lora_dropout: 0.1
      target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
      init_lora_weights: true
    training:
      num_epochs: 1
      per_device_batch_size: 8
      gradient_accumulation_steps: 4
      learning_rate: 2e-4
      warmup_steps: 300
      max_length: 8192
      bf16: true
      max_grad_norm: 1.0
      weight_decay: 0.001
      save_steps: 200
      logging_steps: 50
      dataloader_num_workers: 8
    dataset_config:
      streaming: true
      max_samples: 100000
      split: "train"

evaluation:
  benchmarks:
    - "HLE"
    - "Do-Not-Answer"
  save_results: true
  results_dir: "./outputs/evaluation_results"