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import os
import subprocess
import psutil
import math
from typing import Any, Dict, List, Optional, Tuple, Union
from numba import cuda
import nvidia_smi
from .utils.lru_cache import LRUCache
from .utils.model_lru_cache import ModelLRUCache
from .lib.finetune import train
class Global:
version = None
data_dir: str = ""
load_8bit: bool = False
default_base_model_name: str = ""
base_model_name: str = ""
tokenizer_name = None
base_model_choices: List[str] = []
trust_remote_code = False
# Functions
train_fn: Any = train
# Training Control
should_stop_training = False
# Generation Control
should_stop_generating = False
generation_force_stopped_at = None
# Model related
loaded_models = ModelLRUCache(1)
loaded_tokenizers = LRUCache(1)
new_base_model_that_is_ready_to_be_used = None
name_of_new_base_model_that_is_ready_to_be_used = None
# GPU Info
gpu_cc = None # GPU compute capability
gpu_sms = None # GPU total number of SMs
gpu_total_cores = None # GPU total cores
gpu_total_memory = None
# WandB
enable_wandb = False
wandb_api_key = None
default_wandb_project = "llama-lora-tuner"
# UI related
ui_title: str = "LLaMA-LoRA Tuner"
ui_emoji: str = "🦙🎛️"
ui_subtitle: str = "Toolkit for evaluating and fine-tuning LLaMA models with low-rank adaptation (LoRA)."
ui_show_sys_info: bool = True
ui_dev_mode: bool = False
ui_dev_mode_title_prefix: str = "[UI DEV MODE] "
def get_package_dir():
current_file_path = os.path.abspath(__file__)
parent_directory_path = os.path.dirname(current_file_path)
return os.path.abspath(parent_directory_path)
def get_git_commit_hash():
try:
original_cwd = os.getcwd()
project_dir = get_package_dir()
try:
os.chdir(project_dir)
commit_hash = subprocess.check_output(
['git', 'rev-parse', 'HEAD']).strip().decode('utf-8')
return commit_hash
except Exception as e:
print(f"Cannot get git commit hash: {e}")
finally:
os.chdir(original_cwd)
except Exception as e:
print(f"Cannot get git commit hash: {e}")
commit_hash = get_git_commit_hash()
if commit_hash:
Global.version = commit_hash[:8]
def load_gpu_info():
print("")
try:
cc_cores_per_SM_dict = {
(2, 0): 32,
(2, 1): 48,
(3, 0): 192,
(3, 5): 192,
(3, 7): 192,
(5, 0): 128,
(5, 2): 128,
(6, 0): 64,
(6, 1): 128,
(7, 0): 64,
(7, 5): 64,
(8, 0): 64,
(8, 6): 128,
(8, 9): 128,
(9, 0): 128
}
# the above dictionary should result in a value of "None" if a cc match
# is not found. The dictionary needs to be extended as new devices become
# available, and currently does not account for all Jetson devices
device = cuda.get_current_device()
device_sms = getattr(device, 'MULTIPROCESSOR_COUNT')
device_cc = device.compute_capability
cores_per_sm = cc_cores_per_SM_dict.get(device_cc)
total_cores = cores_per_sm*device_sms
print("GPU compute capability: ", device_cc)
print("GPU total number of SMs: ", device_sms)
print("GPU total cores: ", total_cores)
Global.gpu_cc = device_cc
Global.gpu_sms = device_sms
Global.gpu_total_cores = total_cores
nvidia_smi.nvmlInit()
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0)
info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
total_memory = info.total
total_memory_mb = total_memory / (1024 ** 2)
total_memory_gb = total_memory / (1024 ** 3)
# Print the memory size
print(
f"GPU total memory: {total_memory} bytes ({total_memory_mb:.2f} MB) ({total_memory_gb:.2f} GB)")
Global.gpu_total_memory = total_memory
available_cpu_ram = psutil.virtual_memory().available
available_cpu_ram_mb = available_cpu_ram / (1024 ** 2)
available_cpu_ram_gb = available_cpu_ram / (1024 ** 3)
print(
f"CPU available memory: {available_cpu_ram} bytes ({available_cpu_ram_mb:.2f} MB) ({available_cpu_ram_gb:.2f} GB)")
preserve_loaded_models_count = math.floor((available_cpu_ram * 0.8) / total_memory) - 1
if preserve_loaded_models_count > 1:
print(f"Will keep {preserve_loaded_models_count} offloaded models in CPU RAM.")
Global.loaded_models = ModelLRUCache(preserve_loaded_models_count)
Global.loaded_tokenizers = LRUCache(preserve_loaded_models_count)
except Exception as e:
print(f"Notice: cannot get GPU info: {e}")
print("")
load_gpu_info()
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