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Deploy Intelligent Distributed LLaMA Framework
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import torch
import random
import numpy as np
import builtins
import sys
def print(*args, is_print_rank=True, **kwargs):
""" solves multi-process interleaved print problem """
if not is_print_rank: return
# Windows-compatible version without fcntl
builtins.print(*args, **kwargs)
def set_all_seed(seed):
for module in [random, np.random]: module.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed)
def to_readable_format(num, precision=3):
num_str = str(num)
length = len(num_str)
def format_with_precision(main, decimal, suffix):
if precision == 0:
return f"{main}{suffix}"
return f"{main}.{decimal[:precision]}{suffix}"
if length > 12: # Trillions
return format_with_precision(num_str[:-12], num_str[-12:], 'T')
elif length > 9: # Billions
return format_with_precision(num_str[:-9], num_str[-9:], 'B')
elif length > 6: # Millions
return format_with_precision(num_str[:-6], num_str[-6:], 'M')
elif length > 3: # Thousands
return format_with_precision(num_str[:-3], num_str[-3:], 'K')
else:
return num_str