* pnnx: store bool MemoryData attributes as raw int8
Keep bool constants as one byte per element and select MemoryData
load_type=3. Implement the reserved raw int8 ModelBin loading mode and
pad exported bool data to four bytes so subsequent weights stay aligned.
This preserves the byte representation used by the proposed logical ops.
Add regression coverage for scalar and padded bool exports in both fp16
modes, multidimensional MemoryData, raw int8 loading through reference
and copy readers, trailing fp32 weights, and missing padding. Document
the format.
Based on the bool attribute serialization fixes proposed in:
https://github.com/Tencent/ncnn/pull/6956https://github.com/Tencent/ncnn/pull/6866Fixes#6855
* tools: preserve byte MemoryData when optimizing models
Save MemoryData load_type so raw int8 constants retain their byte
representation after ModelWriter saves the model. Skip fusion paths that
interpret MemoryData as float32 when its elements are not four bytes.
This change does not add bool arithmetic support or automatic type
promotion. Byte bool storage is intended for future logical, comparison
and selection operators.
Validated ncnnoptimize save and reload for scalar and one-to-five-element
bool constants in fp32 and fp16 storage modes, preserving byte values
and element size in all 12 cases.
---------
Co-authored-by: ktz03 <2484593937@qq.com>
Co-authored-by: Giulio Corradi <53930027+giuliocorradi@users.noreply.github.com>
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
* runtime cpu dispatch
* force thread one
* disable openmp for coverage
* simplify test layer
* print NCNN_TARGET_ARCH
* less ci build variants
* weight fp16 storage option
* test convdw int8
* apple a12 a13
* ncnn_add_layer ncnn_add_shader cmake macro
nihui