fix(studio): prevent ModuleNotFoundError in dataset.map() on Windows (#4473)

* fix(studio): prevent ModuleNotFoundError in dataset.map() on Windows

On Windows, dataset.map() uses "spawn", which requires workers to
import compiled modules from disk. Previously, clear_unsloth_compiled_cache()
deleted the entire directory, causing workers to crash when looking for
UnslothSFTTrainer.py.

Changes:
1. Added `preserve_patterns` to cache cleanup to keep `Unsloth*Trainer.py`
   on Windows while clearing model-specific files.
2. Added the cache directory to PYTHONPATH for spawn workers.
Linux/macOS behavior is unchanged.

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* Fix spawn-platform coverage, CWD path mismatch, and race condition for PR #4473

- Extend platform guard from win32-only to include macOS (also uses spawn
  since Python 3.8, same ModuleNotFoundError would occur)
- Replace fragile CWD-based PYTHONPATH registration with centralized
  register_compiled_cache_on_path() that uses the same __file__-relative
  _CACHE_DIRS already used by cache_cleanup -- fixes path mismatch when
  studio is launched from a directory other than the repo root
- Move PYTHONPATH registration to the top of _train_worker(), before any
  dataset.map() call (previously it ran late in config assembly, after
  dataset formatting which also calls dataset.map())
- Update inference.py model-unload to preserve trainer files on spawn
  platforms, preventing a race where unloading a model via inference tab
  would delete UnslothSFTTrainer.py while training workers are importing it

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* Fix cache-dir precedence reversal in register_compiled_cache_on_path()

Iterating _CACHE_DIRS in forward order while calling insert(0) each time
reverses the declared priority: later entries shadow earlier ones. When
multiple compiled-cache directories exist, spawned workers could import a
stale trainer from the wrong cache.

Fix: iterate in reverse so that the highest-priority entry (first in
_CACHE_DIRS) is inserted last and ends up at position 0 in sys.path and
PYTHONPATH.

* fix: harden worker-count helpers against cpu_count=None and desired<=0

- safe_num_proc: guard os.cpu_count() with `or 1`, clamp multi-GPU
  path with max(1, min(4, desired)), clamp return with max(1, desired)
- safe_thread_num_proc: same os.cpu_count() guard and return clamp
- Add regression tests (31 L1 unit + 10 sandbox edge-case tests)

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* remove regression tests from PR

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>

studio/backend/utils/hardware/hardware.py

@@ -521,14 +521,14 @@ def safe_num_proc(desired: Optional[int] = None) -> int:
 
     visible = get_visible_gpu_count()
     if visible > 1:
-        capped = min(4, desired)
+        capped = max(1, min(4, desired))
         logger.info(
             f"Multi-GPU detected ({visible} visible GPUs) -- "
             f"capping num_proc {desired} -> {capped} to avoid fork deadlocks"
         )
         return capped
 
-    return desired
+    return max(1, desired)
 
 
 def safe_thread_num_proc(desired: Optional[int] = None) -> int:
@@ -551,7 +551,7 @@ def safe_thread_num_proc(desired: Optional[int] = None) -> int:
     if desired is None or not isinstance(desired, int):
         desired = max(1, (os.cpu_count() or 1) // 3)
 
-    return desired
+    return max(1, desired)
 
 
 def dataset_map_num_proc(desired: Optional[int] = None) -> Optional[int]: