5ef000f912
The prequant-checkpoint builder applied the dense quant filter without the int8-only M=1 modulation / conditioning-embedder exclusion the runtime path uses, so a built int8 checkpoint baked those projections as int8 and crashed (torch._int_mm needs M>16) at the first denoise step on Flux / Qwen. Factor the scheme->exclusion decision into a shared exclude_tokens_for_scheme() used by both the runtime quantise path and the offline builder so they can never drift, and apply it in build_prequant_checkpoint.py. int8 prequant now produces a working checkpoint on every supported model, giving int8 (the consumer-preferred scheme) the same ~2x load-VRAM and download reduction fp8 already had. |
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| .. | ||
| data_recipe | ||
| export | ||
| inference | ||
| rag | ||
| training | ||
| __init__.py | ||
| _torchao_stub.py | ||
| import_guards.py | ||
| tool_healing.py | ||