File size: 1,776 Bytes
262fa3f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | """Shared utilities for lr_whisper training and evaluation."""
import os
import random
import sys
from typing import Optional
class Logger(object):
"""Tee stdout/stderr to a log file."""
def __init__(self, filename: str = "train_latent.log") -> None:
self.terminal = sys.stdout
self.log = open(filename, "a", encoding="utf-8")
def write(self, message: str) -> None:
self.terminal.write(message)
self.log.write(message)
self.log.flush()
def flush(self) -> None:
self.terminal.flush()
self.log.flush()
def set_seed(seed: int = 42) -> None:
"""Set seeds for deterministic behaviour across multiple libraries."""
import numpy as np
import torch
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def env_flag(name: str, default: bool = False) -> bool:
"""Read a boolean environment variable."""
val = os.getenv(name)
if val is None:
return default
return val.strip().lower() in {"1", "true", "yes", "y", "on"}
def normalize_train_mode(mode: str) -> str:
"""Normalise training mode aliases to a canonical string."""
m = (mode or "").strip().lower()
aliases = {
"prompt": "prompt_tuning",
"prompt-tuning": "prompt_tuning",
"prompt_tune": "prompt_tuning",
"lora": "lora_r16",
"lora16": "lora_r16",
"lora-16": "lora_r16",
"lora_r16": "lora_r16",
}
return aliases.get(m, m)
def mode_label(train_mode: str) -> str:
"""Return a human-readable label for a training mode."""
if train_mode == "prompt_tuning":
return "prompt-tuning"
if train_mode == "lora_r16":
return "lora-r16"
return train_mode
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