Upload kaggle/notebook_director_lora.py with huggingface_hub

kaggle/notebook_director_lora.py

@@ -31,12 +31,20 @@ FILES = {"director_lora/sft.jsonl": SFT, "kaggle/director_lora_train.py": TRAIN}
 PIP = ("pip install -q --upgrade "
        "'transformers==5.22.2' 'huggingface_hub>=1.31,<2' "
        "'peft==0.21.2' 'accelerate>=1.1'")
+# The Kaggle image ships torchao 0.10.0. peft 0.21.2 raises ImportError when
+# it finds torchao < 0.16 instead of ignoring it, even for plain fp16 LoRA.
+# We never quantize here, so remove it; the training script also patches the
+# peft probe as a fallback.
+UNINSTALL = "pip uninstall -y -q torchao"
 CHECK = (sys.executable + " -c \""
-         "import torch, transformers, peft, huggingface_hub; "
+         "import importlib.util, torch, transformers, peft, huggingface_hub; "
          "print('torch', torch.__version__, 'cuda', torch.version.cuda, "
          "'gpus', torch.cuda.device_count()); "
          "print('transformers', transformers.__version__, "
-         "'peft', peft.__version__, 'hub', huggingface_hub.__version__)\"")
+         "'peft', peft.__version__, 'hub', huggingface_hub.__version__); "
+         "print('torchao spec', importlib.util.find_spec('torchao')); "
+         "assert importlib.util.find_spec('torchao') is None, "
+         "'torchao still installed'\"")
 
 
 def log(msg: str) -> None:
@@ -74,8 +82,10 @@ def main() -> None:
 
     if run(PIP) != 0:
         raise SystemExit("dependency install failed")
+    if run(UNINSTALL) != 0:
+        log("WARN: torchao uninstall returned non-zero; peft fallback will handle it")
     if run(CHECK) != 0:
-        raise SystemExit("environment check failed (torch/hub mismatch?)")
+        raise SystemExit("environment check failed (torch/hub/torchao mismatch?)")
 
     env = dict(os.environ, WORK_ROOT=str(ROOT), DATA=str(SFT), LORA_OUT=str(OUT),
                CUDA_VISIBLE_DEVICES="0", HF_HUB_DISABLE_XET="1",
@@ -86,7 +96,8 @@ def main() -> None:
                MAX_LEN=os.environ.get("MAX_LEN", "2048"),
                ACCUM=os.environ.get("ACCUM", "4"),
                DTYPE=os.environ.get("DTYPE", "auto"),
-               GRAD_CKPT=os.environ.get("GRAD_CKPT", "0"))
+               GRAD_CKPT=os.environ.get("GRAD_CKPT", "0"),
+               MAX_TRAIN_SECONDS=os.environ.get("MAX_TRAIN_SECONDS", "4200"))
     log(f"training on {SFT}")
     rc = subprocess.run([sys.executable, str(TRAIN)], env=env).returncode
     log(f"train rc={rc}; outputs in {OUT}")