7098f1b363
Codex review: the M=1 modulation/embedder exclusion was wired only into the dense runtime quantiser; the offline builder scripts/build_prequant_checkpoint.py called make_filter_fn(min_features) with no exclusion. So an int8 prequant checkpoint quantised the AdaLN modulation and conditioning-embedder linears, and loading it via transformer_prequant_path (the load path only loads already-quantised tensors, it can't re-skip them) reintroduced the torch._int_mm M=1 crash this phase fixes for the runtime path. Extracted int8_exclude_name_tokens(scheme) as the single source of truth (int8 -> the M=1 exclusion, every other scheme -> none) and use it in both the runtime quantiser and the builder, so a prequant artifact's quantised-layer set always matches the runtime. fp8/fp4/mx artifacts are byte-identical (empty exclusion). Test: int8_exclude_name_tokens returns the exclusion for int8 and () for fp8/nvfp4/mxfp8. |
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| .. | ||
| data_recipe | ||
| export | ||
| inference | ||
| rag | ||
| training | ||
| __init__.py | ||
| _torchao_stub.py | ||
| import_guards.py | ||
| tool_healing.py | ||