OP_SIGNATURE_AUDIT.md

Escha escham_reconstruct — signature + linearity audit

1. Introspection

escha module: /usr/local/lib/python3.12/site-packages/escha/__init__.py escha._C: <module 'escha._C' from '/usr/local/lib/python3.12/site-packages/escha/_C.cpython-312-x86_64-linux-gnu.so'>

dir(escha._C)

• escha_aqlm_auto ? — escha_aqlm_auto(ta: torch.Tensor, codes: torch.Tensor, codebooks: torch.Tensor, scales: torch.Tensor, codes_T: torch.Tensor | None = None, k_tile: typing.SupportsInt = 64) -> torch.Tensor
• escha_aqlm_fused_hmma ? — escha_aqlm_fused_hmma(ta: torch.Tensor, codes_T: torch.Tensor, codebooks: torch.Tensor, scales: torch.Tensor, k_tile: typing.SupportsInt = 64) -> torch.Tensor
• escha_aqlm_gemv ? — escha_aqlm_gemv(ta: torch.Tensor, codes: torch.Tensor, codebooks: torch.Tensor, scales: torch.Tensor) -> torch.Tensor
• escha_aqlm_prepare_codes_transposed ? — escha_aqlm_prepare_codes_transposed(codes: torch.Tensor) -> torch.Tensor
• escha_binary_gemv ? — escha_binary_gemv(arg0: torch.Tensor, arg1: torch.Tensor, arg2: torch.Tensor, arg3: torch.Tensor) -> torch.Tensor
• escha_binary_gemv_reg ? — escha_binary_gemv_reg(arg0: torch.Tensor, arg1: torch.Tensor, arg2: torch.Tensor, arg3: torch.Tensor) -> torch.Tensor
• escha_decgemv ? — escha_decgemv(x: torch.Tensor, packed_codes: torch.Tensor, scale: torch.Tensor, transform_left: torch.Tensor, transform_right: torch.Tensor, a1: torch.Tensor, a2: torch.Tensor, td1: typing.SupportsInt, td2: typing.SupportsInt, ic: typing.SupportsInt, expic: typing.SupportsInt, gain: torch.Tensor | None = None, block_size: typing.SupportsInt = 0) -> torch.Tensor
• escha_decgemv_inplace ? — escha_decgemv_inplace(arg0: torch.Tensor, arg1: torch.Tensor, arg2: torch.Tensor, arg3: torch.Tensor, arg4: torch.Tensor, arg5: torch.Tensor, arg6: torch.Tensor, arg7: typing.SupportsInt, arg8: typing.SupportsInt, arg9: typing.SupportsInt, arg10: typing.SupportsInt, arg11: torch.Tensor, arg12: torch.Tensor, arg13: torch.Tensor, arg14: torch.Tensor) -> None
• escha_decgemv_reg ? — escha_decgemv_reg(arg0: torch.Tensor, arg1: torch.Tensor, arg2: torch.Tensor, arg3: torch.Tensor, arg4: torch.Tensor, arg5: torch.Tensor, arg6: torch.Tensor, arg7: typing.SupportsInt, arg8: typing.SupportsInt, arg9: typing.SupportsInt, arg10: typing.SupportsInt) -> torch.Tensor
• escha_dequant ? — escha_dequant(packed_codes: torch.Tensor, scale: torch.Tensor, transform_left: torch.Tensor, transform_right: torch.Tensor, a1: torch.Tensor, a2: torch.Tensor, td1: typing.SupportsInt, td2: typing.SupportsInt, ic: typing.SupportsInt, expic: typing.SupportsInt, gain: torch.Tensor | None = None, block_size: typing.SupportsInt = 0) -> torch.Tensor
• escha_fused_dequant_gemm ? — escha_fused_dequant_gemm(ta_fp16: torch.Tensor, packed_codes: torch.Tensor, scale: torch.Tensor) -> torch.Tensor
• escha_fused_dequant_gemm_auto ? — escha_fused_dequant_gemm_auto(ta_fp16: torch.Tensor, packed_codes: torch.Tensor, scale: torch.Tensor, packed_T: torch.Tensor | None = None) -> torch.Tensor
• escha_fused_dequant_gemm_v2 ? — escha_fused_dequant_gemm_v2(ta_fp16: torch.Tensor, packed_codes: torch.Tensor, scale: torch.Tensor) -> torch.Tensor
• escha_fused_dequant_gemm_v3 ? — escha_fused_dequant_gemm_v3(ta_fp16: torch.Tensor, packed_T: torch.Tensor, scale: torch.Tensor) -> torch.Tensor
• escha_fused_dequant_gemm_v4 ? — escha_fused_dequant_gemm_v4(ta_fp16: torch.Tensor, packed_T: torch.Tensor, scale: torch.Tensor) -> torch.Tensor
• escha_fused_dequant_gemm_v5 ? — escha_fused_dequant_gemm_v5(ta_fp16: torch.Tensor, packed_codes: torch.Tensor, scale: torch.Tensor) -> torch.Tensor
• escha_init ? — escha_init() -> None
• escha_lut_binary_gemv ? — escha_lut_binary_gemv(arg0: torch.Tensor, arg1: torch.Tensor, arg2: torch.Tensor, arg3: torch.Tensor) -> torch.Tensor
• escha_prepare_packed_transposed ? — escha_prepare_packed_transposed(packed_codes: torch.Tensor) -> torch.Tensor
• escha_transform ? — escha_transform(arg0: torch.Tensor, arg1: torch.Tensor, arg2: torch.Tensor, arg3: torch.Tensor, arg4: torch.Tensor, arg5: typing.SupportsInt, arg6: typing.SupportsInt, arg7: typing.SupportsInt, arg8: typing.SupportsInt) -> list[torch.Tensor]
• escha_transform_fast ? — escha_transform_fast(arg0: torch.Tensor, arg1: torch.Tensor, arg2: torch.Tensor, arg3: torch.Tensor, arg4: torch.Tensor, arg5: typing.SupportsInt, arg6: typing.SupportsInt, arg7: typing.SupportsInt, arg8: typing.SupportsInt) -> list[torch.Tensor]
• escha_transform_fp16 ? — escha_transform_fp16(arg0: torch.Tensor, arg1: torch.Tensor, arg2: torch.Tensor, arg3: torch.Tensor, arg4: torch.Tensor, arg5: typing.SupportsInt, arg6: typing.SupportsInt, arg7: typing.SupportsInt, arg8: typing.SupportsInt) -> list[torch.Tensor]
• escha_transform_fp16_no_bias ? — escha_transform_fp16_no_bias(arg0: torch.Tensor, arg1: torch.Tensor, arg2: torch.Tensor, arg3: torch.Tensor, arg4: torch.Tensor, arg5: typing.SupportsInt, arg6: typing.SupportsInt, arg7: typing.SupportsInt, arg8: typing.SupportsInt) -> torch.Tensor
• eschax_binary_search ? — eschax_binary_search(arg0: torch.Tensor, arg1: torch.Tensor, arg2: torch.Tensor, arg3: typing.SupportsInt, arg4: typing.SupportsInt, arg5: typing.SupportsInt) -> torch.Tensor
• eschax_dequant ? — eschax_dequant(arg0: torch.Tensor, arg1: typing.SupportsFloat) -> torch.Tensor
• eschax_eschax_decode ? — eschax_eschax_decode(arg0: torch.Tensor, arg1: torch.Tensor, arg2: torch.Tensor, arg3: torch.Tensor, arg4: typing.SupportsInt, arg5: typing.SupportsInt) -> torch.Tensor
• eschax_eschax_gemv ? — eschax_eschax_gemv(arg0: torch.Tensor, arg1: torch.Tensor, arg2: torch.Tensor, arg3: torch.Tensor, arg4: torch.Tensor, arg5: typing.SupportsFloat, arg6: torch.Tensor, arg7: typing.SupportsInt, arg8: typing.SupportsInt) -> torch.Tensor
• eschax_gemv ? — eschax_gemv(arg0: torch.Tensor, arg1: torch.Tensor, arg2: typing.SupportsFloat, arg3: torch.Tensor) -> torch.Tensor
• eschax_huffman_decode ? — eschax_huffman_decode(arg0: torch.Tensor, arg1: torch.Tensor, arg2: torch.Tensor, arg3: typing.SupportsInt, arg4: typing.SupportsInt) -> torch.Tensor
• eschax_huffman_gemv ? — eschax_huffman_gemv(arg0: torch.Tensor, arg1: torch.Tensor, arg2: torch.Tensor, arg3: torch.Tensor, arg4: typing.SupportsFloat, arg5: torch.Tensor, arg6: typing.SupportsInt, arg7: typing.SupportsInt) -> torch.Tensor

torch.ops.escha.escham_reconstruct

escha.escham_reconstruct overloads: ['default'] default: escha::escham_reconstruct(Tensor packed, int in_features, int out_features, int K, bool cbA, bool mul1) -> Tensor

2. Shape acceptance test

min-K2 128x128: cshape=(8, 8, 32) in_f=128 out_f=128 K=2 -> OK, w.shape=(128, 128) dtype=torch.float16 min-K3 128x128: cshape=(8, 8, 48) in_f=128 out_f=128 K=3 -> OK, w.shape=(128, 128) dtype=torch.float16 escha gate_up (K=2, in=2048/out=1024): cshape=(128, 64, 32) in_f=2048 out_f=1024 K=2 -> OK, w.shape=(2048, 1024) dtype=torch.float16 escha down (K=3, in=512/out=2048): cshape=(32, 128, 48) in_f=512 out_f=2048 K=3 -> OK, w.shape=(512, 2048) dtype=torch.float16 leading batch (2, 8, 8, 32) K=2: cshape=(2, 8, 8, 32) in_f=128 out_f=128 K=2 -> OK, w.shape=(128, 128) dtype=torch.float16 leading batch (16, 8, 8, 32) K=2: cshape=(16, 8, 8, 32) in_f=128 out_f=128 K=2 -> OK, w.shape=(128, 128) dtype=torch.float16 leading batch (4, 128, 64, 32) K=2: cshape=(4, 128, 64, 32) in_f=2048 out_f=1024 K=2 -> OK, w.shape=(2048, 1024) dtype=torch.float16

3. Full delta pattern at slot (0,0,0)

For (in=2048, out=1024, K=2) tile: what is the full (row, col) support of the delta when we set exactly code[0,0,0] = v, for various v?

v= 1: 8 nonzero positions, rows=[4, 5, 11, 12, 13], cols=[0, 8] v= 2: 8 nonzero positions, rows=[4, 5, 11, 12, 13], cols=[0, 8] v= 3: 8 nonzero positions, rows=[4, 5, 11, 12, 13], cols=[0, 8] v= 4: 8 nonzero positions, rows=[4, 5, 10, 11, 12, 13], cols=[0, 8] v= 5: 9 nonzero positions, rows=[4, 5, 10, 11, 12, 13], cols=[0, 8] v= 7: 9 nonzero positions, rows=[4, 5, 10, 11, 12, 13], cols=[0, 8] v= 10: 9 nonzero positions, rows=[4, 5, 10, 11, 12, 13], cols=[0, 8] v= 16: 8 nonzero positions, rows=[3, 4, 5, 10, 11, 12, 13], cols=[0, 8] v= 64: 8 nonzero positions, rows=[2, 3, 4, 5, 10, 11, 12, 13], cols=[0, 8] v= 256: 8 nonzero positions, rows=[2, 3, 4, 5, 10, 11, 12], cols=[0, 8] v= 1024: 8 nonzero positions, rows=[2, 3, 4, 5, 10, 11], cols=[0, 8] v= 4096: 8 nonzero positions, rows=[2, 3, 4, 10, 11], cols=[0, 8] v= 16384: 8 nonzero positions, rows=[2, 3, 10, 11], cols=[0, 8] v= 32767: 15 nonzero positions, rows=[2, 3, 4, 5, 10, 11, 12, 13], cols=[0, 8] v= -1: 15 nonzero positions, rows=[2, 3, 4, 5, 10, 11, 12, 13], cols=[0, 8] v= -100: 14 nonzero positions, rows=[2, 3, 4, 5, 10, 11, 12, 13], cols=[0, 8] v=-32768: 8 nonzero positions, rows=[2, 3, 10, 11], cols=[0, 8]

4. Superposition test (LINEARITY in codes)

Test: op(all-zeros with code[bi_a, bj_a, k_a]=v_a AND code[bi_b, bj_b, k_b]=v_b) == op(only code[bi_a, bj_a, k_a]=v_a) + op(only code[bi_b, bj_b, k_b]=v_b) - op(zeros) If yes, we can probe many (bi, bj) slots simultaneously in ONE op call.

2-pos, distinct (bi,bj), same k: |combined|=1.034e+01 |diff|=0.000e+00 rel=0.000e+00 2-pos, same (bi,bj), diff k: |combined|=1.034e+01 |diff|=0.000e+00 rel=0.000e+00 2-pos, same (bi,bj), diff K-slice: |combined|=1.034e+01 |diff|=0.000e+00 rel=0.000e+00 8-pos random: |combined|=2.231e+01 |diff|=0.000e+00 rel=0.000e+00 100-pos random: |combined|=8.165e+01 |diff|=0.000e+00 rel=0.000e+00

5. Slot invariance test — is the codebook shared across (bi, bj)?

Compare delta patterns for the SAME value v at DIFFERENT (bi, bj) with the same k_slot. If they are identical up to a (bi16, bj16) offset, the codebook is (bi, bj)-invariant.

v= 1 (bi=0,bj=0) vs (bi=1,bj=0): |diff|=0.000e+00 rel=0.000e+00 v= 1 (bi=0,bj=0) vs (bi=0,bj=1): |diff|=0.000e+00 rel=0.000e+00 v= 1 (bi=0,bj=0) vs (bi=1,bj=1): |diff|=0.000e+00 rel=0.000e+00 v= 1 (bi=0,bj=0) vs (bi=5,bj=3): |diff|=0.000e+00 rel=0.000e+00 v= 1 (bi=0,bj=0) vs (bi=127,bj=63): |diff|=0.000e+00 rel=0.000e+00 v= 100 (bi=0,bj=0) vs (bi=1,bj=0): |diff|=0.000e+00 rel=0.000e+00 v= 100 (bi=0,bj=0) vs (bi=0,bj=1): |diff|=0.000e+00 rel=0.000e+00 v= 100 (bi=0,bj=0) vs (bi=1,bj=1): |diff|=0.000e+00 rel=0.000e+00 v= 100 (bi=0,bj=0) vs (bi=5,bj=3): |diff|=0.000e+00 rel=0.000e+00 v= 100 (bi=0,bj=0) vs (bi=127,bj=63): |diff|=0.000e+00 rel=0.000e+00 v= 32767 (bi=0,bj=0) vs (bi=1,bj=0): |diff|=0.000e+00 rel=0.000e+00 v= 32767 (bi=0,bj=0) vs (bi=0,bj=1): |diff|=0.000e+00 rel=0.000e+00 v= 32767 (bi=0,bj=0) vs (bi=1,bj=1): |diff|=0.000e+00 rel=0.000e+00 v= 32767 (bi=0,bj=0) vs (bi=5,bj=3): |diff|=0.000e+00 rel=0.000e+00 v= 32767 (bi=0,bj=0) vs (bi=127,bj=63): |diff|=0.000e+00 rel=0.000e+00 v=-32768 (bi=0,bj=0) vs (bi=1,bj=0): |diff|=0.000e+00 rel=0.000e+00 v=-32768 (bi=0,bj=0) vs (bi=0,bj=1): |diff|=0.000e+00 rel=0.000e+00 v=-32768 (bi=0,bj=0) vs (bi=1,bj=1): |diff|=0.000e+00 rel=0.000e+00 v=-32768 (bi=0,bj=0) vs (bi=5,bj=3): |diff|=0.000e+00 rel=0.000e+00 v=-32768 (bi=0,bj=0) vs (bi=127,bj=63): |diff|=0.000e+00 rel=0.000e+00

6. k_slot pattern

For each k_slot, what is the row/col support of the (0, 0, k)+v=1 delta?

K=2, cshape=(128, 64, 32)

k= 0: rows=[4, 5, 11, 12, 13] cols=[0, 8] n_pos=8 k= 1: rows=[2, 3, 9, 10, 11] cols=[0, 8] n_pos=8 k= 2: rows=[0, 1, 8, 9, 15] cols=[1, 8, 9] n_pos=8 k= 3: rows=[6, 7, 13, 14, 15] cols=[0, 8] n_pos=8 k= 4: rows=[4, 5, 11, 12, 13] cols=[1, 9] n_pos=8 k= 5: rows=[2, 3, 9, 10, 11] cols=[1, 9] n_pos=8 k= 6: rows=[0, 1, 8, 9, 15] cols=[2, 9, 10] n_pos=8 k= 7: rows=[6, 7, 13, 14, 15] cols=[1, 9] n_pos=8 k= 8: rows=[4, 5, 11, 12, 13] cols=[2, 10] n_pos=8 k= 9: rows=[2, 3, 9, 10, 11] cols=[2, 10] n_pos=8 k=10: rows=[0, 1, 8, 9, 15] cols=[3, 10, 11] n_pos=8 k=11: rows=[6, 7, 13, 14, 15] cols=[2, 10] n_pos=8 k=12: rows=[4, 5, 11, 12, 13] cols=[3, 11] n_pos=8 k=13: rows=[2, 3, 9, 10, 11] cols=[3, 11] n_pos=8 k=14: rows=[0, 1, 8, 9, 15] cols=[4, 11, 12] n_pos=8 k=15: rows=[6, 7, 13, 14, 15] cols=[3, 11] n_pos=8 k=16: rows=[4, 5, 11, 12, 13] cols=[4, 12] n_pos=8 k=17: rows=[2, 3, 9, 10, 11] cols=[4, 12] n_pos=8 k=18: rows=[0, 1, 8, 9, 15] cols=[5, 12, 13] n_pos=8 k=19: rows=[6, 7, 13, 14, 15] cols=[4, 12] n_pos=8 k=20: rows=[4, 5, 11, 12, 13] cols=[5, 13] n_pos=8 k=21: rows=[2, 3, 9, 10, 11] cols=[5, 13] n_pos=8 k=22: rows=[0, 1, 8, 9, 15] cols=[6, 13, 14] n_pos=8 k=23: rows=[6, 7, 13, 14, 15] cols=[5, 13] n_pos=8 k=24: rows=[4, 5, 11, 12, 13] cols=[6, 14] n_pos=8 k=25: rows=[2, 3, 9, 10, 11] cols=[6, 14] n_pos=8 k=26: rows=[0, 1, 8, 9, 15] cols=[7, 14, 15] n_pos=8 k=27: rows=[6, 7, 13, 14, 15] cols=[6, 14] n_pos=8 k=28: rows=[4, 5, 11, 12, 13] cols=[7, 15] n_pos=8 k=29: rows=[2, 3, 9, 10, 11] cols=[7, 15] n_pos=8 k=30: rows=[0, 1, 8, 9, 15] cols=[0, 8, 15] n_pos=8 k=31: rows=[6, 7, 13, 14, 15] cols=[7, 15] n_pos=8

K=3, cshape=(32, 128, 48)

k= 0: rows=[2, 3, 10, 11] cols=[0, 8] n_pos=5 k= 1: rows=[1, 2, 3, 8, 9] cols=[0, 8] n_pos=5 k= 2: rows=[5, 6, 7, 12, 13] cols=[0, 8] n_pos=5 k= 3: rows=[4, 5, 11, 12, 13] cols=[0, 8] n_pos=6 k= 4: rows=[0, 1, 8, 9, 15] cols=[1, 8, 9] n_pos=6 k= 5: rows=[6, 7, 14, 15] cols=[0, 8] n_pos=5 k= 6: rows=[2, 3, 10, 11] cols=[1, 9] n_pos=5 k= 7: rows=[1, 2, 3, 8, 9] cols=[1, 9] n_pos=5 k= 8: rows=[5, 6, 7, 12, 13] cols=[1, 9] n_pos=5 k= 9: rows=[4, 5, 11, 12, 13] cols=[1, 9] n_pos=6 k=10: rows=[0, 1, 8, 9, 15] cols=[2, 9, 10] n_pos=6 k=11: rows=[6, 7, 14, 15] cols=[1, 9] n_pos=5 k=12: rows=[2, 3, 10, 11] cols=[2, 10] n_pos=5 k=13: rows=[1, 2, 3, 8, 9] cols=[2, 10] n_pos=5 k=14: rows=[5, 6, 7, 12, 13] cols=[2, 10] n_pos=5 k=15: rows=[4, 5, 11, 12, 13] cols=[2, 10] n_pos=6 k=16: rows=[0, 1, 8, 9, 15] cols=[3, 10, 11] n_pos=6 k=17: rows=[6, 7, 14, 15] cols=[2, 10] n_pos=5 k=18: rows=[2, 3, 10, 11] cols=[3, 11] n_pos=5 k=19: rows=[1, 2, 3, 8, 9] cols=[3, 11] n_pos=5 k=20: rows=[5, 6, 7, 12, 13] cols=[3, 11] n_pos=5 k=21: rows=[4, 5, 11, 12, 13] cols=[3, 11] n_pos=6 k=22: rows=[0, 1, 8, 9, 15] cols=[4, 11, 12] n_pos=6 k=23: rows=[6, 7, 14, 15] cols=[3, 11] n_pos=5 k=24: rows=[2, 3, 10, 11] cols=[4, 12] n_pos=5 k=25: rows=[1, 2, 3, 8, 9] cols=[4, 12] n_pos=5 k=26: rows=[5, 6, 7, 12, 13] cols=[4, 12] n_pos=5 k=27: rows=[4, 5, 11, 12, 13] cols=[4, 12] n_pos=6 k=28: rows=[0, 1, 8, 9, 15] cols=[5, 12, 13] n_pos=6 k=29: rows=[6, 7, 14, 15] cols=[4, 12] n_pos=5 k=30: rows=[2, 3, 10, 11] cols=[5, 13] n_pos=5 k=31: rows=[1, 2, 3, 8, 9] cols=[5, 13] n_pos=5 k=32: rows=[5, 6, 7, 12, 13] cols=[5, 13] n_pos=5 k=33: rows=[4, 5, 11, 12, 13] cols=[5, 13] n_pos=6 k=34: rows=[0, 1, 8, 9, 15] cols=[6, 13, 14] n_pos=6 k=35: rows=[6, 7, 14, 15] cols=[5, 13] n_pos=5 k=36: rows=[2, 3, 10, 11] cols=[6, 14] n_pos=5 k=37: rows=[1, 2, 3, 8, 9] cols=[6, 14] n_pos=5 k=38: rows=[5, 6, 7, 12, 13] cols=[6, 14] n_pos=5 k=39: rows=[4, 5, 11, 12, 13] cols=[6, 14] n_pos=6 k=40: rows=[0, 1, 8, 9, 15] cols=[7, 14, 15] n_pos=6 k=41: rows=[6, 7, 14, 15] cols=[6, 14] n_pos=5 k=42: rows=[2, 3, 10, 11] cols=[7, 15] n_pos=5 k=43: rows=[1, 2, 3, 8, 9] cols=[7, 15] n_pos=5 k=44: rows=[5, 6, 7, 12, 13] cols=[7, 15] n_pos=5 k=45: rows=[4, 5, 11, 12, 13] cols=[7, 15] n_pos=6 k=46: rows=[0, 1, 8, 9, 15] cols=[0, 8, 15] n_pos=6 k=47: rows=[6, 7, 14, 15] cols=[7, 15] n_pos=5