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405B_qlora.yaml
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405B_qlora.yaml
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# Config for multi-device QLoRA in lora_finetune_fsdp2.py
# using a Llama3.1 405B model
#
# This config requires PyTorch nightlies to run.
# See https://pytorch.org/torchtune/main/install.html#install-instructions
# for setup instructions.
#
# This config assumes that you've run the following command before launching
# this run:
# tune download meta-llama/Meta-Llama-3.1-405B-Instruct --ignore-patterns "original/consolidated*" --hf-token <HF_TOKEN>
#
# This config needs 8 GPUs to run
# # tune run --nproc_per_node 8 lora_finetune_distributed --config llama3_1/405B_qlora
#
output_dir: /tmp/torchtune/llama3_1_405B/qlora # /tmp may be deleted by your system. Change it to your preference.
# Model Arguments
model:
_component_: torchtune.models.llama3_1.qlora_llama3_1_405b
lora_attn_modules: ['q_proj', 'v_proj', 'output_proj']
apply_lora_to_mlp: True
apply_lora_to_output: False
lora_rank: 16 # higher increases accuracy and memory
lora_alpha: 32 # usually alpha=2*rank
tokenizer:
_component_: torchtune.models.llama3.llama3_tokenizer
path: /tmp/Meta-Llama-3.1-405B-Instruct/original/mp8/tokenizer.model
checkpointer:
_component_: torchtune.training.FullModelHFCheckpointer
checkpoint_dir: /tmp/Meta-Llama-3.1-405B-Instruct/
checkpoint_files:
filename_format: model-{}-of-{}.safetensors
max_filename: 00191
recipe_checkpoint: null
output_dir: ${output_dir}
model_type: LLAMA3
resume_from_checkpoint: False
save_adapter_weights_only: True # Set to false to save the whole model + adapter merged
# Dataset and Sampler
dataset:
_component_: torchtune.datasets.alpaca_dataset
packed: False # True increases speed
train_on_input: True
seed: null
shuffle: True
batch_size: 1
# Optimizer and Scheduler
optimizer:
_component_: torch.optim.AdamW
weight_decay: 0.01
lr: 3e-4
fused: True
lr_scheduler:
_component_: torchtune.training.lr_schedulers.get_cosine_schedule_with_warmup
num_warmup_steps: 100
loss:
_component_: torchtune.modules.loss.CEWithChunkedOutputLoss
fsdp:
cpu_offload: False
# Training
epochs: 1
max_steps_per_epoch: null
gradient_accumulation_steps: 8 # Use to increase effective batch size
compile: False # torch.compile the model + loss, True increases speed + decreases memory
# Logging
metric_logger:
_component_: torchtune.training.metric_logging.DiskLogger
log_dir: ${output_dir}/logs
log_every_n_steps: 1
log_peak_memory_stats: True
# Environment
device: cuda
dtype: bf16
enable_activation_checkpointing: True # True reduces memory
enable_activation_offloading: False # True reduces memory
# Profiler (disabled)
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: 3
active_steps: 2
num_cycles: 1