mlboydaisuke/CLIP-ViT-B32-ExecuTorch

clip_vit_b32_image — ExecuTorch

• Source: openai/clip-vit-base-patch32
• License: MIT
• Input: [[1, 3, 224, 224]] — RGB, CLIP norm (mean .481/.458/.408, std .269/.261/.276), 224x224
• Output: image embedding [1,512] (unnormalized; L2-normalize before cosine)

Variants

All variants take and return fp32 tensors — swap the .pte file, keep your app code.

build	file	size (MB)	parity vs fp32 eager (worst corr)	Mac median (ms)*
fp32	clip_vit_b32_image_xnnpack_fp32.pte	351.6	1.000000	19.0
fp16	clip_vit_b32_image_xnnpack_fp16.pte	180.7	0.999996	26.1
int8 (dynamic)	clip_vit_b32_image_xnnpack_int8.pte	95.9	0.995739	18.4
Core ML (fp16, iOS)	clip_vit_b32_image_coreml_all.pte	176.2	0.999998	3.5

The Core ML build is the same graph lowered to Apple's Neural Engine instead of XNNPACK, which is CPU-only. Measured on an iPhone 17 Pro across seven models, it runs 3.5x to 13.9x faster (median 12x) at roughly half the file size — for example Depth-Anything-V2-Small at 500.8 ms against 42.7 ms, and MODNet at 81.7 ms against 5.9 ms. It computes in fp16 and is iOS-only; the XNNPACK files stay the portable option and are what runs on Android.

*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: 18.5 ms).

Checked in the task's own units

Correlation is a first filter. These are the numbers that decide:

• int8 (dynamic) — measured in the units that matter for this model — cosine similarity of the image embeddings: median 0.9988 over 10 real images, worst 0.9957.

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, 512]	9.179e-06	1.000000

XNNPACK delegate coverage (fp32): 69.3% (390/563 ops); ops left on the portable kernels: aten.expand_copy.default x49, aten.native_layer_norm.default x26, aten.mul.Scalar x24, aten.logical_not.default x24, aten.eq.Scalar x12, aten.full_like.default x12, aten.any.dim x12, aten.where.self x12, aten.embedding.default x1, aten.select_copy.int x1

Conversion

torch.export -> to_edge_transform_and_lower(partitioner) -> .pte (conversion scripts: executorch-models)

This repo holds both towers: clip_vit_b32_image_xnnpack_fp32.pte (image) and clip_vit_b32_text_xnnpack_fp32.pte (text, fixed len 77 + attention mask). L2-normalize both embeddings, then cosine-match.