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All variants take and return fp32 tensors — swap the
.pte
file, keep your app code.
precision
file
size (MB)
parity vs fp32 eager (worst corr)
Mac median (ms)*
fp32
modnet_portrait_matting_xnnpack_fp32.pte
26.1
1.000000
64.9
fp16
modnet_portrait_matting_xnnpack_fp16.pte
24.4
1.000000
107.8
int8
modnet_portrait_matting_xnnpack_int8.pte
6.8
0.999949
46.5
*Mac arm64, single process, median of 10 — a reference point for relative cost
only, not a device number (torch eager fp32 on the same machine: 120.1 ms).
Checked in the task's own units
Correlation is a first filter. These are the numbers that decide:
int8
— measured in the units that matter for this model: mask IoU at 0.5, median 0.9922 over five real images (worst 0.9613) against the fp32 build.
Verification (executorch 1.4.0, torch 2.13.0)
Parity is measured against the fp32 eager model on real image input;
corr
is
the correlation over all elements of each output tensor.
output
shape
max_abs_diff
corr
0
[1, 1, 512, 512]
1.476e-04
1.000000
XNNPACK delegate coverage (fp32): 94.7% (302/319 ops); ops left on the portable kernels:
aten._native_batch_norm_legit.no_stats
x16,
aten.expand_copy.default
x1
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