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S25-B: MXFP4 routed-expert RTN requantization (N8.5) - #270
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michaellchung wants to merge 1 commit into
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New yeto/rl/quant/mxfp4.py implements the compressed-tensors mxfp4-pack-quantized rules: E8M0 shared scale per 32 inputs (BF16 amax rounded to a power of two at mantissa 1.75, minus 2), FP4 E2M1 ties to even with saturation at 6, sign bit for w < 0, even column in the low nibble. requant.py now selects a QuantScheme from the template's quantization_config.format: w4a8-int4-channel (int-quantized, unchanged behaviour) or mxfp4-g32-rtn (weight_packed + weight_scale, refuses AWQ smoothing). compare_checkpoints reports FP4 and E8M0 code match shares. The release CLI takes its scheme tag from the template. Checked on CPU against yetalabs/apex-flash-1-mxfp4-experts@70e86a0: layer-10 routed experts (296 modules) equal in every FP4 and E8M0 code. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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Testing: Full remote test suite on the stack tip: 0 failures. Not validated on PPU/NPU hardware (machines busy).
Part of the S25 stack (NPU+PPU heterogeneous GLM5.3-Flash full-param RL). Draft; do not merge before the S24 stack (#262–#268).
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