113 lines
No EOL
3.4 KiB
YAML
113 lines
No EOL
3.4 KiB
YAML
experiment:
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name: "progressive_reasoning_8gpu_fsdp"
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base_model: "google/gemma-2-2b-it" # Can scale to much larger models with FSDP
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output_dir: "./outputs"
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use_wandb: true
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wandb_project: "matsuo-llm-comp-2025"
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model:
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load_in_4bit: false
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device_map: null # Let FSDP handle device placement
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gradient_checkpointing: true
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use_flash_attention_2: true
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use_eager_attention: false
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# FSDP Configuration
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fsdp:
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fsdp_transformer_layer_cls_to_wrap: "Gemma2DecoderLayer" # Wrap at layer level
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fsdp_sharding_strategy: "FULL_SHARD" # Shard parameters, gradients, and optimizer states
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fsdp_cpu_offload: false # Keep on GPU for speed
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fsdp_mixed_precision: true # Use BF16 mixed precision
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fsdp_auto_wrap_policy: "TRANSFORMER_BASED_WRAP"
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fsdp_min_num_params: 1000000 # Wrap layers with >1M parameters
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fsdp_sync_module_states: true
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fsdp_forward_prefetch: true
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fsdp_use_orig_params: true # Important for LoRA compatibility
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progressive_stages:
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- name: "basic_cot"
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description: "Basic Chain-of-Thought reasoning"
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dataset_path: "./data/basic_cot/"
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adapter_config:
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r: 64 # Can use larger ranks with FSDP
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lora_alpha: 128
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lora_dropout: 0.1
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target_modules: ["q_proj", "k_proj", "v_proj", "o_proj"]
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init_lora_weights: true
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training:
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num_epochs: 2
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per_device_batch_size: 32 # Very large batch size with FSDP
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gradient_accumulation_steps: 1
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learning_rate: 5e-4
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warmup_steps: 100
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max_length: 2048
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bf16: true
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max_grad_norm: 1.0
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weight_decay: 0.001
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save_steps: 50
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logging_steps: 10
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dataloader_num_workers: 8
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dataloader_pin_memory: true
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- name: "math_reasoning"
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description: "Mathematical reasoning with OpenR1-Math-220k dataset"
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dataset_path: "open-r1/OpenR1-Math-220k"
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inherit_from: "basic_cot"
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adapter_config:
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r: 128
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lora_alpha: 256
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lora_dropout: 0.1
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target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
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init_lora_weights: true
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training:
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num_epochs: 1
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per_device_batch_size: 16
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gradient_accumulation_steps: 2
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learning_rate: 3e-4
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warmup_steps: 200
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max_length: 4096
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bf16: true
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max_grad_norm: 1.0
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weight_decay: 0.001
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save_steps: 100
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logging_steps: 20
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dataloader_num_workers: 8
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dataset_config:
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streaming: true
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max_samples: 200000 # Process even more data
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split: "train"
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- name: "complex_reasoning"
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description: "Complex multi-step reasoning with Mixture-of-Thoughts"
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dataset_path: "open-r1/Mixture-of-Thoughts"
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inherit_from: "math_reasoning"
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adapter_config:
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r: 256 # Very large rank possible with FSDP
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lora_alpha: 512
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lora_dropout: 0.1
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target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
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init_lora_weights: true
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training:
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num_epochs: 1
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per_device_batch_size: 8
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gradient_accumulation_steps: 4
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learning_rate: 2e-4
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warmup_steps: 300
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max_length: 8192
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bf16: true
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max_grad_norm: 1.0
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weight_decay: 0.001
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save_steps: 200
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logging_steps: 50
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dataloader_num_workers: 8
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dataset_config:
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streaming: true
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max_samples: 100000
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split: "train"
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evaluation:
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benchmarks:
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- "HLE"
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- "Do-Not-Answer"
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save_results: true
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results_dir: "./outputs/evaluation_results" |