LLM Training
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Introduction
HuggingFace
Hub
Libraries
Transformers
module load Transformers
Datasets
module load datasets
TRL
(software module coming soon, can be installed in venv meanwhile)
python3 -m venv ~/trl_venv
source ~/trl_venv/bin/activate
pip install --require-virtualenv trl
Compute Resources
| Identifier | Accelerator Resources | Methods | Training Duration | Notes |
|---|---|---|---|---|
| alpaca.1xMIG.A100 | LoRA (4-bit), FlashAttention2 | Planned | ||
| alpaca.1xL4 | 1 * Nvidia L4 (24GB VRAM each) | LoRA (4-bit), FlashAttention2 | Pending | |
| alpaca.3xL4 | 3 * Nvidia L4 (24GB VRAM each) | LoRA (4-bit), FlashAttention2 | Pending | |
| alpaca.4xL4 | 4 * Nvidia L4 (24GB VRAM each) | LoRA (4-bit), FlashAttention2 | Pending | |
| alpaca.1xMI210 | 1 * AMD MI210 (64GB VRAM each) | LoRA (4-bit), FlashAttention2 | Pending | |
| alpaca.3xMI210 | 3 * AMD MI210 (64GB VRAM each) | LoRA (4-bit), FlashAttention2 | Pending | |
| alpaca.1xA100 | 1 * Nvidia A100 (80GB VRAM each) | LoRA (4-bit) | Pending | |
| alpaca.1xA100 | 1 * Nvidia A100 (80GB VRAM each) | LoRA (4-bit), FlashAttention2 | PDBS: 1 | |
| alpaca.3xA100 | 1 * Nvidia A100 (80GB VRAM each) | LoRA (4-bit), FlashAttention2 | PDBS: 1 | |
| alpaca.4xA100 | 1 * Nvidia A100 (80GB VRAM each) | LoRA (4-bit), FlashAttention2 | PDBS: 1 | |
| alpaca.1xH100 | 1 * Nvidia H100 (80GB VRAM each) | LoRA (4-bit), FlashAttention2 | Planned | |
| alpaca.3xH100 | 1 * Nvidia H100 (80GB VRAM each) | LoRA (4-bit), FlashAttention2 | Planned | |
| alpaca.4xH100 | 1 * Nvidia H100 (80GB VRAM each) | LoRA (4-bit), FlashAttention2 | Planned |
Training Script (w/HuggingFace)
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments
from datasets import load_from_disk
from trl import SFTTrainer, ORPOTrainer, AutoModelForCausalLMWithValueHead, ModelConfig, get_peft_config, get_quantization_config, get_kbit_device_map, DataCollatorForCompletionOnlyLM
from peft import PeftModel, TaskType, LoraConfig, get_peft_model
base_model = "/scratch/ks98810/llm/models/hf/Meta-Llama-3-8B"
output_dir = "/lscratch/ks98810/guac0/"
report_to = "wandb"
attn_implementation = "flash_attention_2"
def prompt_formatting_func(self, article):
output_texts = []
for i in range(len(article['hash'])):
text = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n{% for message in messages %}\n{% if message['role'] == 'system' %}### Instruction:\n{% elif message['role'] == 'user' %}### Input:\n{% elif message['role'] == 'assistant' %}### Response:\n{% endif %}{{message['content']}}\n{% endfor %}\n### Response:\n".render(messages = article['messages'][i])
output_texts.append(text)
return output_texts
if __name__ == "__main__":
# Basic model config
model_config = ModelConfig(
model_name_or_path = base_model,
attn_implementation = attn_implementation,
)
quant_config = get_quantization_config(model_config)
model_kwargs = dict(
torch_dtype = "auto",
load_in_4bit = True,
trust_remote_code = False, # Don't
attn_implementation = attn_implementation,
use_cache = False, # false if grad chkpnting
quantization_config = get_quantization_config(model_config),
device_map = get_kbit_device_map(),
)
# Load model & tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, **model_kwargs)
tokenizer.pad_token = tokenizer.eos_token
lora_config = LoraConfig(
r = 64,
lora_alpha = 16,
lora_dropout = 0.05,
bias = "none",
task_type = "CAUSAL_LM",
)
model = get_peft_model(model, lora_config)
train_dataset = load_from_disk("/scratch/ks98810/llm/datasets/guac-merge0")
training_args = TrainingArguments(
logging_strategy = "steps",
logging_steps = 500,
logging_first_step = True,
report_to = report_to,
num_train_epochs = 3,
output_dir = kwargs.get("output_dir", defaults["output_path"]),
per_device_train_batch_size = 1,
learning_rate = 2e-4,
)
response_template = "### Response:\n"
collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer = tokenizer)
trainer = SFTTrainer(
model,
args = training_args,
train_dataset = train_dataset,
dataset_text_field = "text",
max_seq_length = 4096,
peft_config = lora_config,
formatting_func = prompt_formatting_func,
data_collator = collator,
)
trainer.train()
trainer.save_model(kwargs.get("output_dir", output_dir))
Job Submission Script
#!/usr/bin/env bash
#SBATCH --job-name=train_guac0_4xA100
#SBATCH --cpus-per-task=32
#SBATCH --partition=gpu_p
#SBATCH --gres=gpu:A100:4
#SBATCH --ntasks=1
#SBATCH --mem=256gb
#SBATCH --time=06:00:00
#SBATCH --output=logs/%x.%j.out
#SBATCH --error=logs/%x.%j.err
#SBATCH --mail-type=ALL
#SBATCH --mail-user=ks98810@uga.edu
export JOB_CUSTODIAN="ks98810"
export JOB_GROUP="gclab"
export PROJECT_DIR="/work/$JOB_GROUP/$JOB_CUSTODIAN/"
export SCRATCH_DIR="/scratch/$JOB_CUSTODIAN/"
export LSCRATCH_DIR="/lscratch/$JOB_CUSTODIAN/"
export PROJECT_NAME="guac0"
export PROJECT_VARIANT="flash-attn0"
export PROJECT_TITLE="$PROJECT_NAME.$PROJECT_VARIANT.$SLURM_JOBID"
export RESULT_DEPOT="$SCRATCH_DIR/llm/models/hf/$PROJECT_TITLE"
export TRAINING_OUTPUT="$LSCRATCH_DIR/$PROJECT_TITLE"
export OMP_NUM_THREADS=16
export PER_DEVICE_BATCH_SIZE=1
export GPUS_PER_NODE=4
export TRAINING_EPOCHS=3
export TRAINING_VENV="/scratch/ks98810/llm/projects/workbench/venv/"
export TRAINING_SCRIPT="/scratch/ks98810/llm/projects/guac/scripts/training/train_guac0.py"
export TRAINING_ARGS="-b $PER_DEVICE_BATCH_SIZE -o $TRAINING_OUTPUT -e $TRAINING_EPOCHS"
export WANDB_PROJECT="$PROJECT_NAME"
export WANDB_LOG_MODEL="checkpoint"
export WANDB_JOB_TYPE="training"
export WANDB_NAME="$PROJECT_TITLE"
export CUDA_VERSION="12.1.1"
export RDZV_BACKEND="c10d"
export RDZV_ID=2299
export RDZV_PORT=29500
cd $SLURM_SUBMIT_DIR
module load CUDA/$CUDA_VERSION diffusers ccache wandb flash-attn
head_node_ip=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
export LAUNCHER="python -m torch.distributed.run \
--nproc_per_node $GPUS_PER_NODE \
--nnodes $SLURM_NNODES \
--rdzv_id $RDZV_ID \
--rdzv_backend $RDZV_BACKEND \
--rdzv_endpoint $head_node_ip:$RDZV_PORT \
"
source $TRAINING_VENV/bin/activate
export CMD="$LAUNCHER $TRAINING_SCRIPT $TRAINING_ARGS"
srun --jobid $SLURM_JOB_ID bash -c "$CMD"
deactivate
rsync -r $TRAINING_OUTPUT $RESULT_DEPOT