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+# BiRefNet
+
+This family builds BiRefNet (`ZhengPeng7/BiRefNet_lite`) for `segmentation` as
+one TensorRT engine: a Swin-T backbone run at full and half resolution, then a
+convolutional decoder with deformable-convolution ASPP blocks and gating
+convolutions. Input 1024 x 1024, batch 1, fp16 or fp32 (the manifest uses
+fp16). The output is one mask logit per pixel. The family has no quantized
+build.
+
+## FP8 quantization study (not shipped)
+
+FP8 self-quantization of the Swin backbone linears was built and measured on
+TensorRT 11.4 (and 11.1) on B200 and B300. It is not shipped: the engine is
+1.01x (B200) to 1.05x (B300) faster than fp16 and the plan is 31% to 35%
+smaller, but the mean mask IoU against the fp16 engine drops to 0.985 and the
+worst held-out image drops to 0.75 IoU (the fp16 engine is at 0.999 / 0.99
+against PyTorch). This section records the recipe, the method and the numbers,
+so that the study does not have to be repeated. The FP8 code was not merged;
+this family has no FP8 build. All numbers come from a local study.
+
+### Summary and decision
+
+| Item | fp16 (manifest) | FP8 (fp16 engine) | FP8 vs fp16 |
+|---|---|---|---|
+| Latency, batch 1, B200 / B300 (TensorRT 11.4) | 6.338 / 6.102 ms | 6.262 / 5.838 ms | 1.01x / 1.05x |
+| Plan size, B200 / B300 (TensorRT 11.4) | 154.3 / 148.4 MiB | 100.9 / 102.9 MiB | -35% / -31% |
+| Mask IoU vs PyTorch fp32, mean / min, B200 | 0.9990 / 0.9920 | 0.9844 / 0.7450 | -1.5 points mean |
+| Mask IoU vs PyTorch fp32, mean / min, B300 | 0.9989 / 0.9899 | 0.9845 / 0.7444 | -1.4 points mean |
+| Non-finite outputs | 0 | 0 | none |
+
+Decision: do not ship FP8 for BiRefNet.
+
+- Speed: the Swin linears are only about 11% of the engine time. FP8 makes
+  them about 13% faster (1.26 to 1.10 ms in the B200 layer profile) and nothing
+  else changes. The decoder (convolutions, deformable-convolution sampling,
+  fused pointwise kernels) holds most of the time and stays fp16.
+- Quality: the FP8 masks differ from fp16 far more than fp16 differs from
+  PyTorch, and single images lose up to a quarter of the mask IoU. The
+  manifest bar (`min_mask_iou` 0.9) would not hold on every image.
+- The plan size gain alone does not justify the quality loss.
+
+### Recipe that was measured
+
+No FP8 or NVFP4 checkpoint of BiRefNet exists on the Hub, and no official
+quantization config exists for it. The recipe follows the public ViT and Swin
+recipes and the ModelOpt vision example: quantize the transformer linears,
+keep convolutions, normalization and attention math in fp16.
+
+| Choice | Decision |
+|---|---|
+| Quantized (51 projections) | `attn.qkv`, `attn.proj`, `mlp.fc1`, `mlp.fc2` of the 12 Swin blocks and the 3 patch-merging `reduction` layers |
+| fp16 | patch-embedding convolution, layer norms, window-attention matmuls and softmax, GELU, all decoder convolutions, the deformable-convolution sampling, the gating convolutions |
+| Two backbone passes | the full- and half-resolution passes share weights, so one scale per projection covers both (204 FP8 layers in the plan) |
+| Weight scale | per tensor, amax / 448, at build time |
+| Activation scale | static, per tensor; ModelOpt 0.46.1 `FP8_DEFAULT_CFG` (max calibration), restricted to the Swin projections, on the checkpoint's own PyTorch code, 128 COCO 2017 validation images at 1024 x 1024 with the checkpoint's preprocessing |
+| Outlier rule | a scale above 20x the median of its kind stays fp16; measured: no outlier |
+| Dequantize type | fp16 (the GEMM writes fp16 and the bias add fuses into it); no non-finite value, fp32 Dequantize was not needed |
+| Builder | the fused dual-GEMM path was turned off for FP8 builds (it failed to compile with scalar-scale FP8 weights) |
+
+### Method
+
+- GPUs: B200 and B300, one GPU per job; TensorRT 11.4.0.106 (target) and
+  11.1.0.106. torch 2.14.0 (CUDA 13), ModelOpt 0.46.1.
+- Checkpoint: `ZhengPeng7/BiRefNet_lite` at the manifest revision.
+- Data: the first 300 validation images of `detection-datasets/coco`; 128
+  evenly spread images for calibration, 40 of the others held out for
+  evaluation.
+- Quality: sigmoid mask at 0.5; mask IoU and mean absolute probability error
+  against the fp32 PyTorch model (the checkpoint's own code) and against the
+  fp16 engine. No ground-truth masks were used.
+- Speed: all variants built and timed in one process on the same GPU; 5
+  interleaved rounds of 100 calls (plain enqueue), median.
+
+### Results
+
+Speed and size, batch 1 (x is fp16 time over FP8 time):
+
+| GPU | TensorRT | fp32 ms | fp16 ms | FP8 ms | x | plan MiB fp16 to FP8 |
+|---|---|---|---|---|---|---|
+| B200 | 11.4 | 9.904 | 6.338 | 6.262 | 1.01 | 154.3 to 100.9 |
+| B300 | 11.4 | 9.402 | 6.102 | 5.838 | 1.05 | 148.4 to 102.9 |
+| B200 | 11.1 | - | 6.035 | 5.989 | 1.01 | 170.3 to 102.0 |
+| B300 | 11.1 | - | 5.732 | 5.623 | 1.02 | 141.2 to 102.3 |
+
+The 11.1 rows come from a separate job per GPU (fp32 not built). In those
+jobs the fp16 engine is 5% (B200) to 6% (B300) faster on 11.1 than in the 11.4
+jobs; this was not checked on the same node.
+
+Quality, 40 held-out images, TensorRT 11.4:
+
+| GPU | Engine | mask IoU vs PyTorch fp32 (mean / min) | mask IoU vs fp16 engine (mean / min) | probability error vs fp16 engine (mean / max image) |
+|---|---|---|---|---|
+| B200 | fp16 | 0.9990 / 0.9920 | - | - |
+| B200 | fp32 | 0.9998 / 0.9980 | 0.9990 / 0.9935 | 0.00018 / 0.0012 |
+| B200 | FP8 | 0.9844 / 0.7450 | 0.9845 / 0.7491 | 0.0027 / 0.0207 |
+| B300 | fp16 | 0.9989 / 0.9899 | - | - |
+| B300 | fp32 | 0.9998 / 0.9980 | 0.9989 / 0.9916 | 0.00017 / 0.0007 |
+| B300 | FP8 | 0.9845 / 0.7444 | 0.9847 / 0.7492 | 0.0027 / 0.0214 |
+
+TensorRT 11.1 gives the same picture: FP8 mask IoU vs PyTorch fp32 0.9843 /
+0.7442 (B200) and 0.9846 / 0.7460 (B300), fp16 0.9990 / 0.9909 and 0.9990 /
+0.9948; no non-finite value.
+
+Kernel time per group, batch 1, TensorRT 11.4 (layer profiler; the sum is
+larger than the engine time because profiling serializes layers), ms:
+
+| Kernel group | B200 fp16 | B200 FP8 | B300 fp16 | B300 FP8 |
+|---|---|---|---|---|
+| Swin linears | 1.255 | 1.097 | 1.353 | 1.051 |
+| window attention | 0.493 | 0.479 | 0.478 | 0.467 |
+| convolutions | 0.713 | 0.722 | 0.668 | 0.648 |
+| deformable-convolution sampling and gating | 1.074 | 1.076 | 1.020 | 1.020 |
+| other fused pointwise and decoder kernels | 7.538 | 7.581 | 7.489 | 7.622 |
+| sum | 11.07 | 10.95 | 11.01 | 10.81 |
+
+### NVFP4 (not tried)
+
+The only matmuls are the Swin linears, about 11% of the engine time, so even
+a perfect 2x over FP8 on them would give a few percent end to end, with more
+quality loss than FP8. TensorRT does not support NVFP4 on convolutions, where
+most of the time is.
+
+### What was not tried
+
+| Item | Note |
+|---|---|
+| FP8 decoder convolutions or deformable-convolution taps (about 80% of the time) | not measured |
+| Excluding the most sensitive Swin projections, percentile or entropy calibration | not measured |
+| Batch greater than 1 | the family builds batch 1 only |
+| `ZhengPeng7/BiRefNet` (Swin-L) | not supported by the family |
+| Matting or DIS benchmark metrics with ground-truth masks, H100, native runtime with an FP8 bundle | not measured |
+
+### When to revisit
+
+- An FP8 recipe for the decoder convolutions that TensorRT runs faster than
+  fp16 without breaking the fused pointwise kernels: that is where the time is.
+- A calibration or a selective recipe that keeps the worst-image mask IoU near
+  the fp16 engine (above 0.99); the Swin-only recipe above gains too little to
+  justify its loss.