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+# timm DPN
+
+This family builds timm Dual Path Network classifiers (`timm/dpn68b.ra_in1k`,
+`timm/dpn92.mx_in1k` in the manifests; dpn68, 98, 107 and 131 build from the
+same code path) as one TensorRT engine: 224 x 224 input, batch 1, fp16 or fp32
+(the manifests use fp16), builder optimization level 1. The output contains
+every class logit in checkpoint order. The family has no quantized build.
+
+## FP8 quantization study (not shipped)
+
+FP8 self-quantization of the convolutions was built and measured on TensorRT
+11.4 (and 11.1) for `timm/dpn68b.ra_in1k` and `timm/dpn92.mx_in1k`. It is not
+shipped: with every convolution in FP8, dpn68b is 7x slower than fp16, because
+TensorRT 11.4 runs each FP8 grouped 3x3 convolution as one convolution per
+group. With the 1x1 convolutions only in FP8, dpn68b runs at 0.95x to 0.99x of
+fp16, and ImageNet top-1 drops by 2.2 to 3.0 points on both models. 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.
+
+### Summary and decision
+
+| Item | fp16 (manifest) | FP8, all convolutions | FP8, 1x1 only | FP8 1x1 only vs fp16 |
+|---|---|---|---|---|
+| dpn68b latency, batch 1, B200 / B300 (TensorRT 11.4) | 0.424 / 0.416 ms | 3.073 / 2.817 ms | 0.436 / 0.422 ms | 0.97x / 0.99x |
+| dpn68b latency, batch 32, B200 / B300 (TensorRT 11.4) | 1.109 / 1.036 ms | 7.180 / 6.635 ms | 1.140 / 1.087 ms | 0.97x / 0.95x |
+| dpn68b plan size, batch 1, B200 | 25.4 MiB | 17.0 MiB | 16.1 MiB | -37% |
+| dpn68b ImageNet top-1, B200 (PyTorch fp32 78.60%) | 78.60% | 75.73% | 76.20% | -2.4 points |
+| dpn92 latency, batch 1, B200 / B300 (TensorRT 11.4) | 5.512 / 4.919 ms | - | 4.396 / 3.876 ms | 1.25x / 1.27x |
+| dpn92 ImageNet top-1, B200 (PyTorch fp32 80.93%) | 80.87% | - | 77.93% | -2.9 points |
+| Non-finite outputs | 0 | 0 | 0 | none |
+
+Decision: do not ship FP8 for timm DPN.
+
+- dpn68b (the premerge model): no speed gain in any layout. With the grouped
+  3x3 convolutions in FP8, TensorRT 11.4 lowers each of them (32 groups) into
+  one FP8 convolution per group: the plan grows from 131 to 1059 layers (774
+  FP8 convolution kernels) and batch 1 is 7x slower. With the 1x1 convolutions
+  only, FP8 is 0.95x to 0.99x of fp16 (0.67x to 0.87x on TensorRT 11.1).
+- dpn92: the 1x1-only FP8 engine is 1.25x faster at batch 1 only because the
+  fp16 baseline is slow (see "fp16 grouped convolutions in dpn92" below), and it
+  loses 2.6 to 2.9 top-1 points. At batch 32 on B300 it is at 1.00x.
+- Quality: 2.2 to 3.0 top-1 points lost against PyTorch fp32 with per-channel
+  weights (up to 3.7 with per-tensor weights), KL divergence 0.09 to 0.13.
+  The input of each 1x1 convolution is batch norm + ReLU of the concatenated
+  dual-path state, whose channels span a wide range; one static per-tensor
+  activation scale per convolution loses the small channels.
+- The only gain is plan size.
+
+### Recipe that was measured
+
+No FP8 or NVFP4 checkpoint of any timm DPN exists on the Hub, and no official
+quantization config exists for one. Each quantized convolution is built as
+`input -> Quantize -> Dequantize(fp16) -> Convolution(+bias)`, with the kernel
+`FP8 constant -> Dequantize(fp16, axis 0)`, on the family's own graph.
+
+| Choice | Decision |
+|---|---|
+| Variant A, all convolutions (92 in dpn68b) | every convolution except the stem: 1x1 `c1x1_a`, projections `c1x1_w_*`, grouped 3x3 `c3x3_b` (32 groups), block outputs `c1x1_c` / `c1x1_c1` / `c1x1_c2` |
+| Variant B, 1x1 only (70 in dpn68b, 64 in dpn92) | the 1x1 convolutions; the grouped 3x3 convolutions stay fp16 |
+| fp16 | the 7x7 stem (3 input channels), batch norm + ReLU, pooling, the classifier |
+| Weight scale | per output channel, amax / 448 (per tensor measured as a variant) |
+| Activation scale | static, per tensor; ModelOpt `FP8_DEFAULT_CFG` (max calibration) on the timm PyTorch model, 500 ImageNet validation images |
+| Dequantize type | fp16; no non-finite value, fp32 Dequantize was not needed |
+
+### Method
+
+- GPUs: B200 and B300, one GPU per job, all variants of a job built and timed
+  together; TensorRT 11.4.0.106 (target) and 11.1 (sanity check, B200).
+  Family builder settings (optimization level 1).
+- Data: 2000 ImageNet validation images: 500 for calibration, the other 1500
+  held out. Family preprocessing.
+- Quality: top-1 against the labels, top-1 agreement and KL divergence against
+  the timm PyTorch fp32 model on the 1500 held-out images, batch 1.
+- Non-finite scan: the 1500 held-out images at batch 1 and at batch 32, all
+  variants, B200 and B300.
+- Speed: CUDA graph replay, median of 5 interleaved rounds of 200 calls, spread
+  at most 0.6%. Batch 32 is a scratch build (the family builds batch 1 only).
+
+### Results
+
+Speed and size, dpn68b (ms per engine call; fp16 and variant B from one job,
+fp32 and variant A from another job, whose fp16 is in brackets):
+
+| GPU | TensorRT | Batch | fp32 ms | fp16 ms | FP8 A ms | FP8 B ms | B vs fp16 | plan MiB fp16 / A / B |
+|---|---|---|---|---|---|---|---|---|
+| B200 | 11.4 | 1 | 0.629 | 0.424 (0.437) | 3.073 | 0.436 | 0.97x | 25.4 / 17.0 / 16.1 |
+| B200 | 11.4 | 32 | 2.703 | 1.109 (1.103) | 7.180 | 1.140 | 0.97x | 29.2 / 22.4 / 19.4 |
+| B300 | 11.4 | 1 | 0.608 | 0.416 (0.418) | 2.817 | 0.422 | 0.99x | 24.9 / 16.7 / 15.7 |
+| B300 | 11.4 | 32 | 2.706 | 1.036 (1.035) | 6.635 | 1.087 | 0.95x | 29.3 / 22.6 / 19.5 |
+| B200 | 11.1 | 1 | - | 0.451 | - | 0.519 | 0.87x | 25.4 / - / 16.3 |
+| B200 | 11.1 | 32 | - | 1.464 | - | 2.195 | 0.67x | 29.6 / - / 19.9 |
+
+Speed and size, dpn92 (variant B only):
+
+| GPU | TensorRT | Batch | fp16 ms | FP8 B ms | B vs fp16 | plan MiB fp16 / B | layers fp16 / B |
+|---|---|---|---|---|---|---|---|
+| B200 | 11.4 | 1 | 5.512 | 4.396 | 1.25x | 78.7 / 46.6 | 2515 / 1784 |
+| B200 | 11.4 | 32 | 7.926 | 7.020 | 1.13x | 82.1 / 50.0 | 2515 / 1784 |
+| B300 | 11.4 | 1 | 4.919 | 3.876 | 1.27x | 77.8 / 46.1 | 2259 / 1656 |
+| B300 | 11.4 | 32 | 5.277 | 5.265 | 1.00x | 77.6 / 46.8 | 979 / 1016 |
+| B200 | 11.1 | 1 | 5.561 | 4.431 | 1.25x | 78.8 / 46.8 | 2515 / 1811 |
+| B200 | 11.1 | 32 | 8.916 | 8.714 | 1.02x | 82.6 / 50.6 | 2515 / 1811 |
+
+TensorRT 11.1 gives the same top-1 as 11.4 within 0.1 point (dpn68b fp16
+78.67%, FP8 B 76.20%; dpn92 fp16 80.87%, FP8 B 78.20%, B200).
+
+Layers in the plan, dpn68b, batch 1, TensorRT 11.4 (B200 / B300):
+
+| Engine | Layers | Convolution kernels | FP8 convolution kernels |
+|---|---|---|---|
+| fp16 | 131 / 130 | 93 | 0 |
+| FP8 A (all convolutions) | 1059 / 1058 | 775 | 774 |
+| FP8 B (1x1 only) | 153 / 152 | 93 | 70 |
+
+Quality (1500 held-out images, batch 1; B200 / B300):
+
+| Model | Engine | Top-1 | Agreement with PyTorch top-1 | KL mean | KL p99 |
+|---|---|---|---|---|---|
+| dpn68b | PyTorch fp32 | 78.60% | - | - | - |
+| dpn68b | fp16 | 78.60% / 78.53% | 99.6% | 0.00006 | 0.0004 |
+| dpn68b | FP8 A, per-channel weights | 75.73% / 76.33% | 86.2% / 86.8% | 0.095 | 0.81 |
+| dpn68b | FP8 A, per-tensor weights | 74.87% / 75.53% | 84.4% / 85.1% | 0.107 | 0.81 |
+| dpn68b | FP8 B, per-channel weights | 76.20% / 76.07% | 86.6% / 86.6% | 0.091 | 0.74 |
+| dpn92 | PyTorch fp32 | 80.93% | - | - | - |
+| dpn92 | fp16 | 80.87% / 80.87% | 99.5% / 99.7% | 0.00006 | 0.0009 |
+| dpn92 | FP8 B, per-channel weights | 77.93% / 78.27% | 89.5% / 90.3% | 0.133 | 1.5 |
+
+### fp16 grouped convolutions in dpn92 (TensorRT 11.4 behavior)
+
+Independent of FP8: at builder optimization level 1, TensorRT 11.4 splits each
+fp16 grouped 3x3 convolution of dpn92 into per-group convolutions with slice
+and concatenation kernels (2515 layers on B200, 2259 on B300; 740 group
+convolutions, 640 slice and 608 concatenation kernels). Batch 1 is launch bound:
+5.51 ms (B200) and 4.92 ms (B300), almost as long as batch 32 (7.93 / 5.28 ms).
+dpn68b, also with 32 groups but fewer channels per group, builds 131 layers and
+runs batch 1 in 0.42 ms. TensorRT 11.1 builds the same 2515-layer fp16 plan on
+B200. The FP8 B speedup on dpn92 comes from this slow baseline, not from FP8
+math.
+
+Builder optimization level 3 (proposed in #2123, not merged with this note)
+changes the fp16 baseline. Measured on TensorRT 11.4, batch 1, fp16 (separate
+job, 500 held-out images):
+
+| Model | GPU | Level 1 ms (layers) | Level 3 ms (layers) | Level 3 vs level 1 |
+|---|---|---|---|---|
+| dpn68b | B200 | 0.436 (131) | 0.377 (128) | 1.16x |
+| dpn68b | B300 | 0.418 (130) | 0.358 (128) | 1.17x |
+| dpn92 | B200 | 5.685 (2515) | 3.932 (1232) | 1.45x |
+| dpn92 | B300 | 4.881 (2259) | 3.721 (1233) | 1.31x |
+
+At level 3 the fp16 engines are faster than the level-1 FP8 B engines measured
+above on dpn68b, and close to them on dpn92 (3.93 vs 4.40 ms on B200, 3.72 vs
+3.88 ms on B300; FP8 was not rebuilt at level 3).
+
+### NVFP4 (not tried)
+
+TensorRT does not support NVFP4 on convolutions (block-scaled FP4 needs a 2-D
+GEMM), FP8 already loses 2.2 to 3.0 top-1 points, and the engines are small
+(plan under 80 MiB).
+
+### What was not tried
+
+| Item | Note |
+|---|---|
+| Per-channel activation scales, percentile or entropy calibration, quantization-aware training | not measured; could recover accuracy, not speed |
+| FP8 at builder optimization level 3 | not measured |
+| dpn68, dpn98, dpn107, dpn131, H100, native runtime with an FP8 bundle | not measured |
+
+### When to revisit
+
+- A TensorRT release with an FP8 kernel for grouped convolutions that keeps
+  one layer per convolution (variant A).
+- A calibration that keeps the small channels of the concatenated dual-path
+  state (for example per-channel activation scales), so that the loss drops
+  well below 1 top-1 point.
+- A TensorRT release that builds the fp16 grouped convolutions of dpn92 as one
+  layer each; then compare FP8 with that baseline.