DeepSpeed

ded23491 - Validate fp16 dynamic loss scaling parameters are positive (#8050)

Validate fp16 dynamic loss scaling parameters are positive (#8050) ## What `fp16.loss_scale_window` and `fp16.min_loss_scale` drive dynamic loss scaling but are not validated, so invalid values initialize silently and fail later during training: - **`loss_scale_window`** is used as `stable_interval % self.scale_window` in `DynamicLossScaler.update_scale` (`deepspeed/runtime/fp16/loss_scaler.py`), so a value of `0` raises `ZeroDivisionError` mid-training. - **`min_loss_scale`** is the loss-scale floor (`max(cur_scale / scale_factor, min_scale)`); a value `<= 0` collapses dynamic loss scaling. This is the same class of silent-misconfiguration bug as `fp16.loss_scale` accepting `inf`, fixed in #7889. ## Change Add a single Pydantic `mode="before"` field validator on `DeepSpeedFP16Config` covering both fields. It rejects `bool`, non-numeric, non-finite (`inf`/`-inf`/`nan`), and non-positive values, raising a clear `ValidationError` (e.g. `fp16.loss_scale_window must be > 0`). Following the #7889 review, `mode="before"` runs prior to type coercion (so `True` is rejected), and `float()` is wrapped in `try/except` so `[]`/`{}` surface a clear `ValidationError` rather than a raw `TypeError`. ## Tests Adds `tests/unit/runtime/test_precision_config_dynamic_scale.py`, parametrized over both fields: - invalid: `0, -1, inf, nan, True, [], {}` -> `ValidationError` - valid: `1, 1000, "2"` -> accepted ```bash pytest -q tests/unit/runtime/test_precision_config_dynamic_scale.py ``` The validator logic was verified against the full matrix locally; the import-level test runs under CI. --------- Signed-off-by: Aryan <aryansputta@gmail.com> Co-authored-by: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com>