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7B_qat_full.yaml
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7B_qat_full.yaml
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# Config for multi-device QAT finetuning in qat_distributed.py
# using a Llama2 7B model
#
# This config assumes that you've run the following command before launching
# this run:
# tune download meta-llama/Llama-2-7b-hf --output-dir /tmp/Llama-2-7b-hf --ignore-patterns "*.safetensors" --hf-token <HF_TOKEN>
#
# To launch on 4 devices, run the following command from root:
# tune run --nnodes 1 --nproc_per_node 4 qat_distributed --config llama2/7B_qat_full
#
# You can add specific overrides through the command line. For example
# to override the checkpointer directory while launching training
# you can run:
# tune run --nnodes 1 --nproc_per_node 4 qat_distributed --config llama2/7B_qat_full checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
output_dir: /tmp/torchtune/llama2_7B/qat_full # /tmp may be deleted by your system. Change it to your preference.
# Tokenizer
tokenizer:
_component_: torchtune.models.llama2.llama2_tokenizer
path: /tmp/Llama-2-7b-hf/tokenizer.model
max_seq_len: null
# Dataset
dataset:
_component_: torchtune.datasets.alpaca_dataset
packed: False # True increases speed
seed: null
shuffle: True
# Model Arguments
model:
_component_: torchtune.models.llama2.llama2_7b
checkpointer:
_component_: torchtune.training.FullModelHFCheckpointer
checkpoint_dir: /tmp/Llama-2-7b-hf
checkpoint_files: [
pytorch_model-00001-of-00002.bin,
pytorch_model-00002-of-00002.bin
]
recipe_checkpoint: null
output_dir: ${output_dir}
model_type: LLAMA2
resume_from_checkpoint: False
# Fine-tuning arguments
batch_size: 2
epochs: 1
optimizer:
_component_: torch.optim.AdamW
fused: True
lr: 2e-5
loss:
_component_: torchtune.modules.loss.CEWithChunkedOutputLoss
max_steps_per_epoch: null
gradient_accumulation_steps: 1 # Use to increase effective batch size
compile: False # torch.compile the model + loss, True increases speed + decreases memory
optimizer_in_bwd: False # True saves memory. Requires gradient_accumulation_steps=1
# QAT arguments
quantizer:
_component_: torchtune.training.quantization.Int8DynActInt4WeightQATQuantizer
groupsize: 256
# Training env
device: cuda
# Memory management
enable_activation_checkpointing: True # True reduces memory
enable_activation_offloading: False # True reduces memory
# Reduced precision
dtype: bf16
# Logging
metric_logger:
_component_: torchtune.training.metric_logging.DiskLogger
log_dir: ${output_dir}/logs
log_every_n_steps: 1
log_peak_memory_stats: True
# 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