# Config for multi-device knowledge distillation in knowledge_distillation_distributed.py
# using a teacher and student model
#
# This config assumes that you've ran the following commands before launching KD:
# First download the student and teacher models
#   tune download Qwen/Qwen2-0.5B-Instruct --output-dir /tmp/Qwen2-0.5B-Instruct
#   tune download Qwen/Qwen2-1.5B-Instruct --output-dir /tmp/Qwen2-1.5B-Instruct
#
# You get better results using KD if the teacher model has already been fine-tuned on the target dataset:
#   tune run --nnodes 1 --nproc_per_node 2 lora_finetune_distributed --config qwen2/1.5B_lora
#
# To launch on 2 devices, run the following command from root:
#   tune run --nnodes 1 --nproc_per_node 2 knowledge_distillation_distributed --config qwen2/1.5_to_0.5B_KD_lora_distributed
#
# This config works best for distilling on 2+ devices.


output_dir: /tmp/torchtune/qwen2_1_5_to_0_5B/KD_lora_distributed # /tmp may be deleted by your system. Change it to your preference.

# Model Arguments
model:
  _component_: torchtune.models.qwen2.lora_qwen2_0_5b
  lora_attn_modules: ['q_proj', 'v_proj', 'output_proj']
  apply_lora_to_mlp: True
  lora_rank: 32  # higher increases accuracy and memory
  lora_alpha: 64  # usually alpha=2*rank

teacher_model:
  _component_: torchtune.models.qwen2.qwen2_1_5b

tokenizer:
  _component_: torchtune.models.qwen2.qwen2_tokenizer
  path: /tmp/Qwen2-0.5B-Instruct/vocab.json
  merges_file: /tmp/Qwen2-0.5B-Instruct/merges.txt
  max_seq_len: null

checkpointer:
  _component_: torchtune.training.FullModelHFCheckpointer
  checkpoint_dir: /tmp/Qwen2-0.5B-Instruct
  checkpoint_files: [
    model.safetensors
  ]
  recipe_checkpoint: null
  output_dir: ${output_dir}
  model_type: QWEN2

teacher_checkpointer:
  _component_: torchtune.training.FullModelHFCheckpointer
  checkpoint_dir: /tmp/Qwen2-1.5B-Instruct
  checkpoint_files: [
    model.safetensors
  ]
  recipe_checkpoint: null
  output_dir: ${output_dir}
  model_type: QWEN2

resume_from_checkpoint: False

# Dataset and Sampler
dataset:
  _component_: torchtune.datasets.alpaca_cleaned_dataset
  packed: False  # True increases speed
seed: null
shuffle: True
batch_size: 8

# Optimizer and Scheduler
optimizer:
  _component_: torch.optim.AdamW
  weight_decay: 0.01
  lr: 3e-4
lr_scheduler:
  _component_: torchtune.training.lr_schedulers.get_cosine_schedule_with_warmup
  num_warmup_steps: 100

loss:
  _component_: torchtune.modules.loss.CEWithChunkedOutputLoss

kd_loss:
  _component_: torchtune.modules.loss.ForwardKLWithChunkedOutputLoss
kd_ratio: 0.5

# Training
epochs: 1
max_steps_per_epoch: null
compile: False  # torch.compile the model + loss, True increases speed + decreases memory
gradient_accumulation_steps: 8  # Use to increase effective batch size

# Logging
metric_logger:
  _component_: torchtune.training.metric_logging.DiskLogger
  log_dir: ${output_dir}/logs
log_every_n_steps: 1
log_peak_memory_stats: False

# Environment
device: cuda
dtype: bf16
enable_activation_checkpointing: False  # True reduces memory
enable_activation_offloading: False  # True reduces memory

# Show case the usage of pytorch profiler
# Set enabled to False as it's only needed for debugging training
profiler:
  _component_: torchtune.training.setup_torch_profiler

  enabled: False

  #Output directory of trace artifacts
  output_dir: ${output_dir}/profiling_outputs

  #`torch.profiler.ProfilerActivity` types to trace
  cpu: True
  cuda: True

  #trace options passed to `torch.profiler.profile`
  profile_memory: False
  with_stack: False
  record_shapes: True
  with_flops: False

  # `torch.profiler.schedule` options:
  # wait_steps -> wait, warmup_steps -> warmup, active_steps -> active, num_cycles -> repeat
  wait_steps: 5
  warmup_steps: 5
  active_steps: 2
  num_cycles: 1
