2B_qlora_single_device.yaml

# Config for multi-device QLoRA finetuning in lora_finetune_single_device.py
# using a gemma2 2B model
#
# This config assumes that you've run the following command before launching
# this run:
#   tune download google/gemma-2-2b --ignore-patterns "gemma-2-2b.gguf"  --hf-token <HF_TOKEN>
#
# To launch on a single device, run the following command from root:
#   tune run lora_finetune_single_device --config gemma2/2B_qlora_single_device
#
# You can add specific overrides through the command line. For example
# to override the checkpointer directory while launching training
# you can run:
#   tune run lora_finetune_single_device --config gemma2/2B_qlora_single_device checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
#
# This config works only for training on single device.

# Tokenizer
tokenizer:
  _component_: torchtune.models.gemma.gemma_tokenizer
  path: /tmp/gemma-2-2b/tokenizer.model

# Dataset
dataset:
  packed: False # Set to true for great speed ups
  _component_: torchtune.datasets.alpaca_dataset
seed: null
shuffle: True

# Model Arguments
model:
  _component_: torchtune.models.gemma2.qlora_gemma2_2b
  lora_attn_modules: ['q_proj', 'k_proj', 'v_proj']
  apply_lora_to_mlp: True
  lora_rank: 64
  lora_alpha: 128
  lora_dropout: 0.0

checkpointer:
  _component_: torchtune.training.FullModelHFCheckpointer
  checkpoint_dir: /tmp/gemma-2-2b/
  checkpoint_files: [
    model-00001-of-00003.safetensors,
    model-00002-of-00003.safetensors,
    model-00003-of-00003.safetensors,
  ]
  recipe_checkpoint: null
  output_dir: /tmp/gemma-2-2b
  model_type: GEMMA2
resume_from_checkpoint: False
save_adapter_weights_only: False

optimizer:
  _component_: torch.optim.AdamW
  fused: True
  lr: 2e-5

lr_scheduler:
  _component_: torchtune.training.lr_schedulers.get_cosine_schedule_with_warmup
  num_warmup_steps: 10

loss:
  _component_: torchtune.modules.loss.CEWithChunkedOutputLoss

# Fine-tuning arguments
batch_size: 4
epochs: 1
max_steps_per_epoch: null
gradient_accumulation_steps: 4
compile: False  # pytorch compile, set to true for perf/memory improvement

# Training env
device: cuda

# Memory management
enable_activation_checkpointing: True
enable_activation_offloading: False

# Reduced precision
dtype: bf16

# Logging
metric_logger:
  _component_: torchtune.training.metric_logging.DiskLogger
  log_dir: ${output_dir}
output_dir: /tmp/alpaca-gemma2-lora
log_every_n_steps: 1
log_peak_memory_stats: True

# 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