scheduler:
- Restore numpy default (float64) on the non-reference timestep linspace
path. The earlier `dtype=np.float32` silently changed rounded timestep
values for every model already using this scheduler. Float32 is now
only applied on the new `use_reference_discrete_timesteps=True` branch.
- Document `use_reference_discrete_timesteps` in the class docstring with
default + when-to-enable + backward-compat notes.
qwen3 encoder:
- `torch.arange(...)` for position_ids now passes `dtype=torch.long`
explicitly so embedding/RoPE indices are unambiguous.
- Drop the redundant `isinstance(x.device.type, str)` check; `device.type`
is always a str. Keep the mps -> cpu fallback for torch.autocast.
encoder parity test:
- `AutoModel.from_pretrained` uses `torch_dtype=` to match repo convention
(the rest of the repo and existing transformers callers all use this
keyword). transformers 4.57.3 accepts both, but `torch_dtype` keeps the
test green across the older transformers versions that some downstream
forks pin.
Raghav
92168e5020