↓ 1 callersFunctioncompare_ggml_vs_nchwCompare C++ ggml tensor (dumped as [C, W, H]) vs Python NCHW [1, C, H, W] ggml stores: flat[c + w*C + h*C*W] Python NCHW: flat[c*H*W + h*W + w] These
tests/test_debug_encoder.cpp:34
↓ 1 callersFunctioncompare_ggml_vs_nhwcCompare C++ ggml tensor (dumped as [ne0, ne1, ne2]) vs Python NHWC [1, H, W, E] ggml stores: flat[e + w*E + h*E*W] where ne0=E, ne1=W, ne2=H Python NH
tests/test_debug_encoder.cpp:24
↓ 1 callersFunctioncompute_axial_cis(dim, end_x, end_y, theta=10000.0, scale_pos=1.0, offset=0, device=None)
tests/dump_phase7_reference.py:189
↓ 1 callersFunctionfused_mha_forward(query, key, value, in_proj_w, in_proj_b, out_w, out_b,
num_heads, attn_mask=None, key_p
tests/dump_ddec_from_package.py:115
↓ 1 callersFunctionggml_to_pytorch_bchwConvert ggml [E, W, H] flat data to PyTorch [1, C, H, W] array. ggml element (e, w, h) at flat index e + w*E + h*E*W maps to PyTorch [0, e, h,
tests/compare_vit_output.py:22