# Config for single device full finetuning in full_finetune_single_device.py
# using a Llama3.1 8B Instruct model
#
# This config assumes that you've run the following command before launching
# this run:
#   tune download meta-llama/Meta-Llama-3.1-8B-Instruct --output-dir /tmp/Meta-Llama-3.1-8B-Instruct --ignore-patterns "original/consolidated.00.pth"
#
# The default config uses an optimizer from bitsandbytes. If you do not have it installed,
# you can install it with
#   pip install bitsandbytes
#
# To launch on a single device, run the following command from root:
#   tune run full_finetune_single_device --config llama3_1/8B_full_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 full_finetune_single_device --config llama3_1/8B_full_single_device checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
#
# This config works only for training on single device.

# Tokenizer
tokenizer:
  _component_: torchtune.models.llama3.llama3_tokenizer
  path: ${oc.env:ARTIFACT_LOCATION}/Meta-Llama-3.1-8B-Instruct/original/tokenizer.model
  max_seq_len: 3200

# Dataset
dataset:
  _component_: torchtune.datasets.alpaca_dataset
  packed: True  # True increases speed
seed: null
shuffle: True

# Model Arguments
model:
  _component_: torchtune.models.llama3_1.llama3_1_8b

checkpointer:
  _component_: torchtune.training.FullModelHFCheckpointer
  checkpoint_dir: ${oc.env:ARTIFACT_LOCATION}/Meta-Llama-3.1-8B-Instruct
  checkpoint_files: [
    model-00001-of-00004.safetensors,
    model-00002-of-00004.safetensors,
    model-00003-of-00004.safetensors,
    model-00004-of-00004.safetensors
  ]
  recipe_checkpoint: null
  output_dir: ${oc.env:ARTIFACT_LOCATION}/${oc.env:EXPERIMENT_NAME}_${oc.env:RUN_NAME}/checkpoints
  model_type: LLAMA3
resume_from_checkpoint: False

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

lr_scheduler:
  _component_: torchtune.modules.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: 1  # Use to increase virtual batch size
optimizer_in_bwd: True  # True saves memory. Requires gradient_accumulation_steps=1
compile: True  # pytorch compile, set to true for better perf/memory

# Training environment
device: cuda

# Memory management
enable_activation_checkpointing: True  # True reduces memory
enable_activation_offloading: True  # True reduces memory

# Reduced precision
dtype: bf16

# Logging
metric_logger:
  # _component_: torchtune.training.metric_logging.DiskLogger
  _component_: utils.MLflowLogger
  log_dir: ${output_dir}
  # Only used for MLflow
  experiment_name: ${oc.env:EXPERIMENT_NAME}   # Set Experiment Name
  run_name: ${oc.env:RUN_NAME}                 # Set Run Name
  autolog: True                             # Set to False to disable autologging
output_dir: ${oc.env:ARTIFACT_LOCATION}/${oc.env:EXPERIMENT_NAME}_${oc.env:RUN_NAME}
log_every_n_steps: 1
log_peak_memory_stats: True

# Profiler (disabled)
profiler:
  _component_: torchtune.training.setup_torch_profiler
  enabled: True

  #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: True
  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: 3
  active_steps: 2
  num_cycles: 1