Commit History
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sm_120 sa3-m: add the fp8 medium-DiT tier (176 fp8 GEMM + 96 bf16 fused MHA, 1.43-1.95x vs fp16mixed)
Verified signatureAdds the fp8 medium-DiT tier for sm_120 (Blackwell). Built from the calibrated `onnx/sa3-m/dit_fp8.onnx` (commit 8383b7d0) with the same recipe as sm_90 — no code changes needed, the `sa3-m-fp8` build target is arch-agnostic. Selectable with `--dit medium --precision fp8`; the default stays fp16mixed. ## Identity matches sm_90 exactly Verified with `scripts/verify_fp8_rope.py` on DETAILED-verbosity twins of both engines: | | sm_120 | sm_90 (published) | |---|---|---| | fp8 E4M3 GEMMs | **176 / 181** | **176 / 181** | | fused MHA (`_gemm_mha_v2`) | **96, all BF16** | **96, all BF16** | | runtime cos/sin layers | 2 | 2 | | engine size | 1421.19 MB | 1423.75 MB | ## Speed — the gain is larger on sm_120 than on sm_90 ms per forward, vs the published fp16mixed on each arch, median of 9, CUDA events on the work's own stream: | L | sm_90 fp8 | vs fp16mixed | sm_120 fp8 | vs fp16mixed | |---|---|---|---|---| | 256 | 4.99 | 1.48x | 8.32 | **1.95x** | | 1292 | 9.34 | 1.33x | 28.42 | **1.5…af5680d -
sa3-m fp8: calibrated dit_fp8.onnx (real-conditioning activation + per-channel weight scales, @ryanontheinside #47; refs unchanged dit_fp8lin.onnx.data)
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sa3-m fp8: ship calibrated-bakedmin engine (176 fp8 GEMM + 96 bf16 fused MHA + baked fp32 RoPE; calibration by @ryanontheinside #47)
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sm_120 SAME-L: AOT (graph-capturable) engines + tensorRT/README.md (#6)
- sm_120 SAME-L: AOT (graph-capturable) engines + tensorRT/README.md (1d2139821d87b6160099860d29a82d535269a33b)a05f2d4
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Fix the bf16 medium DiT: bake RoPE's tables into the graph (clipping 3.112% -> 0.014%) (#5)
- Fix the bf16 medium DiT: bake RoPE's tables into the graph (clipping 3.112% -> 0.014%) (59bd967d4635c6e32b0b2a677d56a21984654b9d)6152b1d -
Rebuild medium DiT fp16-mixed engines (sm_90 + new sm_120): attention now fuses, 4.3x faster (#4)
- Rebuild medium DiT fp16-mixed engines (sm_90 + new sm_120): attention now fuses, 4.3x faster (f3263ec28281132dda00915ad3a83920f3b98730) - Complete the sm_120 engine set (mirror sm_90's 15 files) (dcca4a7a8676aa3846ca92ecf7607d572706d685)06debc3 -
Fix medium DiT FP16-mixed ONNX: bound the RoPE island so attention fuses (4.3x faster, cos 1.0000) (#3)
- Fix medium DiT FP16-mixed ONNX: bound the RoPE island so attention fuses (4.3x faster, cos 1.0000) (b4300a99fc3b12b6c59e2d10218399f672087340)fb17708
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Add MLX base-model DiT weights for LoRA training (#2)
- Add MLX base-model DiT weights for LoRA training (be6b0350d3677f5981e36fb9c4a1dd2c75307dd9) Co-authored-by: CJ Carr <cortexelus@users.noreply.huggingface.co>08c64b9 -
Add bf16 medium DiT TRT engine (sm_90) — FMHA-fused, medium default (#1)
- Add bf16 medium DiT TRT engine (sm_90) — FMHA-fused, medium default (c8cb8ba88ddea67804bcacbbb9265afcbff03b6f) Co-authored-by: CJ Carr <cortexelus@users.noreply.huggingface.co>9a38efb
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tflite: replace naive same-l/enc_w8a8-dyn.tflite with GPTQ (torch-free, real-audio calibrated)
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tflite: replace naive same-l/enc_w8a32.tflite with GPTQ (torch-free, real-audio calibrated)
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tflite: replace naive same-s/enc_w8a8-dyn.tflite with GPTQ (torch-free, real-audio calibrated)
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tflite: replace naive same-s/enc_w8a32.tflite with GPTQ (torch-free, real-audio calibrated)
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tflite: add same-l/enc_w8a8-dyn.tflite (quantized encoder, varlen)
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tflite: add same-l/enc_w8a32.tflite (quantized encoder, varlen)
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tflite: add same-l/enc_w16a32.tflite (quantized encoder, varlen)
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tflite: add same-s/enc_w8a8-dyn.tflite (quantized encoder, varlen)
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tflite: add same-s/enc_w8a32.tflite (quantized encoder, varlen)
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tflite: add same-s/enc_w16a32.tflite (quantized encoder, varlen)
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tflite: add sa3-m/dit_w16a32.tflite (quantized, varlen+varbatch)
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tflite: add sa3-m/dit_w8a32.tflite (quantized, varlen+varbatch)
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tflite: add sa3-m/dit_w8a8-dyn.tflite (quantized, varlen+varbatch)
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tflite: add same-l/dec_w16a32.tflite (quantized, varlen+varbatch)
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tflite: add sa3-sm-sfx/dit_w16a32.tflite (quantized, varlen+varbatch)
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tflite: add sa3-sm-music/dit_w16a32.tflite (quantized, varlen+varbatch)
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tflite: add same-l/dec_w8a32.tflite (quantized, varlen+varbatch)
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tflite: add same-l/dec_w8a8-dyn.tflite (quantized, varlen+varbatch)
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tflite: add sa3-sm-sfx/dit_w8a32.tflite (quantized, varlen+varbatch)
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tflite: add sa3-sm-music/dit_w8a32.tflite (quantized, varlen+varbatch)
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tflite: add sa3-sm-sfx/dit_w8a8-dyn.tflite (quantized, varlen+varbatch)
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tflite: add sa3-sm-music/dit_w8a8-dyn.tflite (quantized, varlen+varbatch)
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tflite: add same-s/dec_w16a32.tflite (quantized, varlen+varbatch)
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tflite: add same-s/dec_w8a32.tflite (quantized, varlen+varbatch)
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tflite: add same-s/dec_w8a8-dyn.tflite (quantized, varlen+varbatch)
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varbatch DiT: dynamic batch axis (bit-exact at batch=1; enables batched CFG at batch=2)
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varbatch DiT: dynamic batch axis (bit-exact at batch=1; enables batched CFG at batch=2)
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varbatch DiT: dynamic batch axis (bit-exact at batch=1; enables batched CFG at batch=2)
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Fix missing 2π factor in seconds_freqs across all dit.onnx (FP32 source ONNX)
Cortexelus +1: Claude Opus 4.7 (1M context)The base FP32 dit.onnx files were exported by an older version of the producer (fast_load/build_dit.py) before the 2π fix was applied. The post-surgery dit_fp16mixed.onnx files DID get the fix re-injected (their seconds_freqs has max ≈ 62832 = 2π × 10000), but the source dit.onnx files were never re-uploaded and still have max=10000. Effect: any consumer building from dit.onnx (e.g., `--precision fp32` path that reads sa3-m/dit.onnx, or anyone re-running build_dit_fp16mixed locally) would inherit the silence-after-~5s bug — the seconds_total conditioner produces frequencies 2π× too low, so the Fourier features collapse and the model can't condition on duration beyond ~5s. Surgical fix: read the (128,) seconds_freqs initializer in each dit.onnx, multiply by 2π, write back. For sa3-m the buffer is in the .data sidecar at offset 3072+512 bytes — read it from there, multiply, and inline the result into the .onnx so the 5.8 GB .data file doesn't need to be re-uploaded. For sa3-sm-music/sf…ecc70ac