Match the canonical FastVideo dtype pattern (per WanVideo DiT). Remove
all 7 hardcoded `.to(torch.bfloat16)` casts that were verbatim copies of
upstream daVinci-MagiHuman/inference/model/dit/dit_module.py. Replace
with `orig_dtype = self.linear_qkv.weight.dtype` (or equivalent
loader-owned dtype) + `.to(orig_dtype)` pattern, mirroring
fastvideo/models/dits/wanvideo.py:325-392.
Refactored sites: attention pre_norm output, q/k/v post-RoPE, attention
output, MLP pre_norm, MLP activation output, top-level block-input cast.
Model dtype is now loader-owned via pipeline_config.dit_precision, not
hardcoded. Production bf16 behavior is bit-identical (loader sets
default_dtype=bf16, all params/inputs naturally bf16, orig_dtype=bf16,
outputs preserved as bf16). fp32 parity now works end-to-end on the FV
side; remaining drift in fp32 parity tests is upstream's still-hardcoded
bf16 casts (tracked as OQ-9 in tests/local_tests/magi-human.md).
SolitaryThinker
8f0493637e