{ "asserted_counts": { "checks_min": 17, "dod_engines_min": 2, "dod_exact_rows_min": 2, "engines_min": 12, "exact_byte_min": 21, "exact_rows_min": 56, "exact_token_min": 35, "foils_min": 32, "frontier_codes_min": 3, "known_limits_min": 6, "refused_rows_min": 6, "rows_min": 64, "vocabulary_min": 9 }, "boundary": { "does_not_hold": [ "that any named engine would accept these bytes: nothing here links vLLM, TensorRT-LLM, SGLang, llama.cpp, FlashInfer, LMDeploy or transformers", "that the exhaustive index argument extends to another geometry: it is a statement about THIS shape, and the arbitrary-parameter content lives in the Lean primitives", "that the Lean primitives are what layouts.py implements: that link is a Python restatement pinned by the index probe, not a proof", "anything at all about throughput, latency, memory or cost" ], "holds": [ "the eight layouts are DECLARED SHAPES with citations; no engine, kernel or GPU runs anywhere in this item", "the canonical tensor is [removed].decode_harness's (2, T, n_kv_heads, head_dim) numpy GQA geometry: 3 layers, 2 kv-heads, head_dim 8, float16", "token identity is measured against a reference this same run computes from the untranslated state through the same harness, with fixed seeded weights", "every index-level claim is checked over EVERY position of the declared geometry, and the certificate carries positions_checked and positions_total so a reader can see it" ], "note": "An enumerated list of exact transformations is a claim about a family and its edge. The REFUSED rows are as much the deliverable as the EXACT ones." }, "checks": [ { "detail": "64 rows against the 19 proof fields restated in specs/[removed]/verify_exact_transformations.py; offenders []", "name": "every_proof_matches_the_published_verifiers_field_set", "passed": true }, { "detail": "56 exact rows; each one's checked-position count equals the product of the destination layout's own declared native shapes, computed independently; offenders []", "name": "every_exact_row_is_proved_over_its_whole_re_derived_index_space", "passed": true }, { "detail": "every exact row needs at least one vocabulary word; offenders []", "name": "no_exact_row_is_a_relabelling", "passed": true }, { "detail": "byte-identical 21, token-identical 35 \u2014 the DoD offers both and a list carrying only one of them has answered half the question", "name": "both_of_the_DoDs_exactness_classes_are_non_empty", "passed": true }, { "detail": "9 of 9 words appear in some row; missing []", "name": "the_whole_transformation_vocabulary_is_exercised", "passed": true }, { "detail": "the family's 3 edges are all demonstrated: ['X1_LATENT_KV', 'X2_HEAD_GATHER', 'X4_GEOMETRY_UNREPRESENTABLE']. Requantisation is deliberately NOT among them: it RUNS, and is graded NOT_EXACT", "name": "every_frontier_code_is_reached_by_a_row", "passed": true }, { "detail": "corrupting one cached K position changes the decode: [2, 27, 60, 51, 2, 27, 60, 51] -> [2, 59, 39, 38, 59, 39, 38, 59]", "name": "the_token_leg_is_not_vacuous", "passed": true }, { "detail": "reference tokens [2, 27, 60, 51, 2, 27, 60, 51] carry 4 distinct values", "name": "the_reference_sequence_is_not_degenerate", "passed": true }, { "detail": "the relabelling translator round-trips A->B->A byte-identically (True) and is graded NOT_EXACT; its decode emits [52, 52, 52, 52, 52, 52, 52, 52] against the reference [2, 27, 60, 51, 2, 27, 60, 51]", "name": "a_perfect_round_trip_does_not_make_a_transformation_exact", "passed": true }, { "detail": "35 token-identical rows, each differing on exactly its source's unread block-tail positions and nowhere else; offenders []", "name": "token_identical_means_the_padding_and_nothing_else", "passed": true }, { "detail": "12 declared layouts, all distinct as DESCRIPTORS; collisions []", "name": "no_two_declared_engines_are_the_same_layout", "passed": true }, { "detail": "8 distinct native images across the lossless layouts; two names for one image would be a relabelling, not a cross-engine pair. Collisions []", "name": "no_two_declared_engines_have_the_same_native_image", "passed": true }, { "detail": "each layout names the engine structure it models, so a reader can check the shape against the engine rather than against this file", "name": "every_engine_carries_a_citation_for_the_shape_it_claims", "passed": true }, { "detail": "8 of 8 lossless layouts carry a generated `decide` lemma over the whole 64-element small geometry; the pair statement follows by ExactTransform.comp_roundtrip", "name": "every_lossless_engine_has_a_kernel_checked_round_trip_lemma_THAT_EXISTS", "passed": true }, { "detail": "ExactTransform.swapAt, transcribed into Python, produces the SAME flat index map as numpy's own adjacent-axis transpose over 165 axis stacks of 2-5 axes; divergences []. The two-axis `swap` it generalises does NOT: on a (2,3,4) tensor it disagrees with numpy, which is why the general form exists", "name": "the_lean_axis_exchange_is_the_operation_numpy_performs", "passed": true }, { "detail": "over all 81 assignments of the four observations to {True, False, None}, weakening any one never raises a row's rank, and NULLING any one drops the row to UNPROVEN \u2014 the bottom of the scale, strictly below the NOT_EXACT that forward=False earns", "name": "no_missing_observation_can_improve_a_row", "passed": true }, { "detail": "8 lossless layouts -> 56 ordered pairs, 56 present; the frontier and lossy layouts are paired against the anchor in both directions on top of that", "name": "the_hunt_covers_every_ordered_pair_of_the_lossless_engines", "passed": true } ], "engines": [ { "attention": "gqa", "block": 16, "cite": "vLLM FlashAttention backend KV cache [2, num_blocks, block_size, num_kv_heads, head_size]; block_size default 16", "engine": "vllm-fa", "is_the_identity_map": false, "k_axes": [ "KV", "Tb", "Ti", "H", "D" ], "k_x": 0, "kv": "interleaved", "lean_lemma": "ExactTransformCert.roundtrip_vllm_fa", "n_kv_heads": 0, "native_shapes": { "kv": [ 2, 3, 16, 2, 8 ] }, "quant": "none", "realisable_at_this_geometry": true, "v_axes": [ "KV", "Tb", "Ti", "H", "D" ], "v_x": 0 }, { "attention": "gqa", "block": 16, "cite": "vLLM PagedAttention kernel: K [num_blocks, num_kv_heads, head_size//x, block_size, x], V [num_blocks, num_kv_heads, head_size, block_size]. The kernel's own x = 16 bytes / element size gives 8 for fp16, and x = 8 IS realisable at head_dim 8 ([.., .., 1, 16, 8]); 4 is declared here for NON-DEGENERACY, so the factored axis has two groups instead of one, and not because the real value does not fit", "engine": "vllm-paged-x", "is_the_identity_map": false, "k_axes": [ "Tb", "H", "Dg", "Ti", "Dx" ], "k_x": 4, "kv": "separate", "lean_lemma": "ExactTransformCert.roundtrip_vllm_paged_x", "n_kv_heads": 0, "native_shapes": { "k": [ 3, 2, 2, 16, 4 ], "v": [ 3, 2, 8, 16 ] }, "quant": "none", "realisable_at_this_geometry": true, "v_axes": [ "Tb", "H", "D", "Ti" ], "v_x": 0 }, { "attention": "gqa", "block": 32, "cite": "TensorRT-LLM paged KV block [num_blocks, 2, num_kv_heads, tokens_per_block, head_dim]; tokens_per_block 32", "engine": "trtllm", "is_the_identity_map": false, "k_axes": [ "Tb", "KV", "H", "Ti", "D" ], "k_x": 0, "kv": "interleaved", "lean_lemma": "ExactTransformCert.roundtrip_trtllm", "n_kv_heads": 0, "native_shapes": { "kv": [ 2, 2, 2, 32, 8 ] }, "quant": "none", "realisable_at_this_geometry": true, "v_axes": [ "Tb", "KV", "H", "Ti", "D" ], "v_x": 0 }, { "attention": "gqa", "block": 0, "cite": "SGLang RadixAttention token-indexed pools: separate K and V, each [num_tokens, num_kv_heads, head_dim]; paging is an index, not a layout", "engine": "sglang", "is_the_identity_map": true, "k_axes": [ "T", "H", "D" ], "k_x": 0, "kv": "separate", "lean_lemma": "ExactTransformCert.roundtrip_sglang", "n_kv_heads": 0, "native_shapes": { "k": [ 40, 2, 8 ], "v": [ 40, 2, 8 ] }, "quant": "none", "realisable_at_this_geometry": true, "v_axes": [ "T", "H", "D" ], "v_x": 0 }, { "attention": "gqa", "block": 0, "cite": "Hugging Face transformers DynamicCache: key_cache/value_cache each [batch, num_kv_heads, seq_len, head_dim]; batch 1 here", "engine": "hf-dynamic-cache", "is_the_identity_map": false, "k_axes": [ "H", "T", "D" ], "k_x": 0, "kv": "separate", "lean_lemma": "ExactTransformCert.roundtrip_hf_dynamic_cache", "n_kv_heads": 0, "native_shapes": { "k": [ 2, 40, 8 ], "v": [ 2, 40, 8 ] }, "quant": "none", "realisable_at_this_geometry": true, "v_axes": [ "H", "T", "D" ], "v_x": 0 }, { "attention": "gqa", "block": 0, "cite": "llama.cpp llama_kv_cache: k_l is ggml-shaped [n_embd_k_gqa, kv_size] and v_l is TRANSPOSED to [kv_size, n_embd_v_gqa] unless flash_attn is set. ggml's ne[0] is the FASTEST-VARYING axis, so in C row-major terms K is token-major [n_tokens, n_head_kv, head_dim] and V is [n_head_kv, head_dim, n_tokens] \u2014 the reversal is a real trap and is why it is spelled out here", "engine": "llamacpp", "is_the_identity_map": false, "k_axes": [ "T", "H", "D" ], "k_x": 0, "kv": "separate", "lean_lemma": "ExactTransformCert.roundtrip_llamacpp", "n_kv_heads": 0, "native_shapes": { "k": [ 40, 2, 8 ], "v": [ 2, 8, 40 ] }, "quant": "none", "realisable_at_this_geometry": true, "v_axes": [ "H", "D", "T" ], "v_x": 0 }, { "attention": "gqa", "block": 16, "cite": "FlashInfer paged KV cache, NHD layout [max_num_pages, 2, page_size, num_kv_heads, head_dim]; page_size 16", "engine": "flashinfer-nhd", "is_the_identity_map": false, "k_axes": [ "Tb", "KV", "Ti", "H", "D" ], "k_x": 0, "kv": "interleaved", "lean_lemma": "ExactTransformCert.roundtrip_flashinfer_nhd", "n_kv_heads": 0, "native_shapes": { "kv": [ 3, 2, 16, 2, 8 ] }, "quant": "none", "realisable_at_this_geometry": true, "v_axes": [ "Tb", "KV", "Ti", "H", "D" ], "v_x": 0 }, { "attention": "gqa", "block": 64, "cite": "LMDeploy TurboMind blocked KV: separate K/V blocks [num_blocks, num_kv_heads, block_seq_len, head_dim], block_seq_len 64. WEAKEST CITATION IN THIS TABLE \u2014 TurboMind's block layout has changed across releases and no version is pinned; what this row demonstrates is a 64-token blocked separate HND layout, whoever ships it", "engine": "lmdeploy-turbomind", "is_the_identity_map": false, "k_axes": [ "Tb", "H", "Ti", "D" ], "k_x": 0, "kv": "separate", "lean_lemma": "ExactTransformCert.roundtrip_lmdeploy_turbomind", "n_kv_heads": 0, "native_shapes": { "k": [ 1, 2, 64, 8 ], "v": [ 1, 2, 64, 8 ] }, "quant": "none", "realisable_at_this_geometry": true, "v_axes": [ "Tb", "H", "Ti", "D" ], "v_x": 0 }, { "attention": "mla", "block": 16, "cite": "DeepSeek-V2/V3 Multi-head Latent Attention: the cache is a per-token compressed latent c_KV plus a decoupled rope key, NOT a per-head K/V tensor. There is no layout permutation between it and any of the above, at any block size", "engine": "deepseek-mla", "is_the_identity_map": false, "k_axes": [ "KV", "Tb", "Ti", "H", "D" ], "k_x": 0, "kv": "interleaved", "lean_lemma": null, "n_kv_heads": 0, "native_shapes": { "kv": [ 2, 3, 16, 2, 8 ] }, "quant": "none", "realisable_at_this_geometry": true, "v_axes": [ "KV", "Tb", "Ti", "H", "D" ], "v_x": 0 }, { "attention": "gqa", "block": 16, "cite": "vLLM with tensor_parallel_size 2 over a 2-kv-head model: each rank holds ONE kv-head. One rank's cache is a PROJECTION of the whole, so the map back is a gather with no inverse", "engine": "vllm-tp2", "is_the_identity_map": false, "k_axes": [ "KV", "Tb", "Ti", "H", "D" ], "k_x": 0, "kv": "interleaved", "lean_lemma": null, "n_kv_heads": 1, "native_shapes": {}, "quant": "none", "realisable_at_this_geometry": false, "v_axes": [ "KV", "Tb", "Ti", "H", "D" ], "v_x": 0 }, { "attention": "gqa", "block": 16, "cite": "the SAME vLLM PagedAttention K layout with x = 16, which is what it is for fp8 or for a 1-byte element. At head_dim 8 the factorisation does not divide, so this layout DOES NOT EXIST at this geometry \u2014 the same engine, refused for the geometry and not for the engine", "engine": "vllm-paged-x16", "is_the_identity_map": false, "k_axes": [ "Tb", "H", "Dg", "Ti", "Dx" ], "k_x": 16, "kv": "separate", "lean_lemma": null, "n_kv_heads": 0, "native_shapes": {}, "quant": "none", "realisable_at_this_geometry": false, "v_axes": [ "Tb", "H", "D", "Ti" ], "v_x": 0 }, { "attention": "gqa", "block": 16, "cite": "vLLM --kv-cache-dtype fp8_e4m3: the same block shape over an 8-bit element. The POSITION map is a permutation and the ELEMENT map is not injective", "engine": "vllm-fp8", "is_the_identity_map": false, "k_axes": [ "KV", "Tb", "Ti", "H", "D" ], "k_x": 0, "kv": "interleaved", "lean_lemma": null, "n_kv_heads": 0, "native_shapes": { "kv": [ 2, 3, 16, 2, 8 ] }, "quant": "fp8_e4m3", "realisable_at_this_geometry": true, "v_axes": [ "KV", "Tb", "Ti", "H", "D" ], "v_x": 0 } ], "falsifications": [ { "actual": "NOT_EXACT", "declared_guard": "NOT_EXACT", "detail": "a translator that reinterprets the source's bytes as the destination's shape. A->B->A is the IDENTITY, so it is byte-perfect on every input; the forward image is not what the destination produces and the decode emits [52, 52, 52, 52, 52, 52, 52, 52] instead of [2, 27, 60, 51, 2, 27, 60, 51]", "guard_ok": true, "name": "roundtrip-only-proof", "passed": true }, { "actual": "NOT_EXACT", "declared_guard": "NOT_EXACT", "detail": "the same pair with the source's unread block tail allowed into the trimmed state: the decode emits [18, 12, 18, 12, 18, 12, 18, 12] against [2, 27, 60, 51, 2, 27, 60, 51]. This is why 'token-identical' is a measurement and not a consequence of the padding being there", "guard_ok": true, "name": "padding-read", "passed": true }, { "actual": "H5_TRIVIAL_TRANSFORMATION", "declared_guard": "H5_TRIVIAL_TRANSFORMATION", "detail": "an engine declared with vllm-fa's exact layout under a different name needs [] transformations \u2014 the cheapest way to lengthen this list is to invent engines, and an empty transformation set is what catches it", "guard_ok": true, "name": "relabelled-engine", "passed": true }, { "actual": "X4_GEOMETRY_UNREPRESENTABLE", "declared_guard": "X4_GEOMETRY_UNREPRESENTABLE", "detail": "sglang->vllm-paged-x is EXACT at head_dim 8 and does not exist at head_dim 6, where vLLM's x=4 split does not divide. Exactness is a property of the pair AND the geometry, and this list is a list about one geometry", "guard_ok": true, "name": "the-same-pair-at-a-geometry-that-does-not-admit-it", "passed": true }, { "actual": "X1_LATENT_KV", "declared_guard": "X1_LATENT_KV", "detail": "the destination or source caches a compressed per-token latent, not a per-head K/V tensor; there is no permutation between the two at any block size", "guard_ok": true, "name": "mla-destination", "passed": true }, { "actual": "X2_HEAD_GATHER", "declared_guard": "X2_HEAD_GATHER", "detail": "the two layouts hold different numbers of kv-heads, so one is a PROJECTION of the other; the map back is a gather and has no inverse", "guard_ok": true, "name": "tp-shard-destination", "passed": true }, { "actual": "X4_GEOMETRY_UNREPRESENTABLE", "declared_guard": "X4_GEOMETRY_UNREPRESENTABLE", "detail": "the layout does not exist at this geometry (a factorisation that does not divide), so there is nothing to be exact about (vllm-paged-x16: head_dim 8 % x 16 != 0)", "guard_ok": true, "name": "x16-destination", "passed": true }, { "actual": "NOT_EXACT", "declared_guard": "NOT_EXACT", "detail": "the fp8 destination produces EXACTLY the image it would independently have produced (forward True), and the round trip differs at 4603 positions against 768 unread padding slots. Its decode emits [2, 59, 2, 27, 11, 6, 31, 31] against the reference [2, 27, 60, 51, 2, 27, 60, 51]. This is the row that separates 'cannot be attempted' from 'will be deployed and is not exact'", "guard_ok": true, "name": "fp8-destination-runs-and-is-not-exact", "passed": true }, { "actual": "UNPROVEN", "declared_guard": "UNPROVEN", "detail": "three observations green and the fourth NOT MADE grades UNPROVEN \u2014 the bottom of the scale \u2014 while the same three with the fourth MADE AND FALSE grades NOT_EXACT. Declining to look is strictly worse than looking and failing, which is what stops a hunt growing its list by measuring less", "guard_ok": true, "name": "withholding-an-observation", "passed": true }, { "actual": "H2_VERDICT_NOT_DERIVED@exit3", "declared_guard": "H2_VERDICT_NOT_DERIVED@exit3", "detail": "reference H2_VERDICT_NOT_DERIVED@exit3, published H2_VERDICT_NOT_DERIVED@exit3", "guard_ok": true, "name": "verifier:verdict-relabelled", "passed": true }, { "actual": "H2_VERDICT_NOT_DERIVED@exit3", "declared_guard": "H2_VERDICT_NOT_DERIVED@exit3", "detail": "reference H2_VERDICT_NOT_DERIVED@exit3, published H2_VERDICT_NOT_DERIVED@exit3", "guard_ok": true, "name": "verifier:observation-withheld", "passed": true }, { "actual": "H2_VERDICT_NOT_DERIVED@exit3", "declared_guard": "H2_VERDICT_NOT_DERIVED@exit3", "detail": "reference H2_VERDICT_NOT_DERIVED@exit3, published H2_VERDICT_NOT_DERIVED@exit3", "guard_ok": true, "name": "verifier:corroboration-nulled", "passed": true }, { "actual": "H4_PROOF_NOT_EXHAUSTIVE@exit3", "declared_guard": "H4_PROOF_NOT_EXHAUSTIVE@exit3", "detail": "reference H4_PROOF_NOT_EXHAUSTIVE@exit3, published H4_PROOF_NOT_EXHAUSTIVE@exit3", "guard_ok": true, "name": "verifier:one-position-is-the-whole-space", "passed": true }, { "actual": "H8_FLOOR_BELOW_OBSERVATION@exit3", "declared_guard": "H8_FLOOR_BELOW_OBSERVATION@exit3", "detail": "reference H8_FLOOR_BELOW_OBSERVATION@exit3, published H8_FLOOR_BELOW_OBSERVATION@exit3", "guard_ok": true, "name": "verifier:guard-set-shrunk-to-one-each", "passed": true }, { "actual": "H0_DOCUMENT_UNREADABLE@exit1", "declared_guard": "H0_DOCUMENT_UNREADABLE@exit1", "detail": "reference H0_DOCUMENT_UNREADABLE@exit1, published H0_DOCUMENT_UNREADABLE@exit1", "guard_ok": true, "name": "verifier:unhashable-in-a-signed-list", "passed": true }, { "actual": "H2_VERDICT_NOT_DERIVED@exit3", "declared_guard": "H2_VERDICT_NOT_DERIVED@exit3", "detail": "reference H2_VERDICT_NOT_DERIVED@exit3, published H2_VERDICT_NOT_DERIVED@exit3", "guard_ok": true, "name": "verifier:bijection-denied", "passed": true }, { "actual": "H3_PROOF_MISSING@exit3", "declared_guard": "H3_PROOF_MISSING@exit3", "detail": "reference H3_PROOF_MISSING@exit3, published H3_PROOF_MISSING@exit3", "guard_ok": true, "name": "verifier:no-kernel-checked-lemma", "passed": true }, { "actual": "H3_PROOF_MISSING@exit3", "declared_guard": "H3_PROOF_MISSING@exit3", "detail": "reference H3_PROOF_MISSING@exit3, published H3_PROOF_MISSING@exit3", "guard_ok": true, "name": "verifier:proof-deleted", "passed": true }, { "actual": "H4_PROOF_NOT_EXHAUSTIVE@exit3", "declared_guard": "H4_PROOF_NOT_EXHAUSTIVE@exit3", "detail": "reference H4_PROOF_NOT_EXHAUSTIVE@exit3, published H4_PROOF_NOT_EXHAUSTIVE@exit3", "guard_ok": true, "name": "verifier:sampled-proof", "passed": true }, { "actual": "H5_TRIVIAL_TRANSFORMATION@exit3", "declared_guard": "H5_TRIVIAL_TRANSFORMATION@exit3", "detail": "reference H5_TRIVIAL_TRANSFORMATION@exit3, published H5_TRIVIAL_TRANSFORMATION@exit3", "guard_ok": true, "name": "verifier:trivial-transformation", "passed": true }, { "actual": "H6_HUNT_FOUND_NOTHING@exit3", "declared_guard": "H6_HUNT_FOUND_NOTHING@exit3", "detail": "reference H6_HUNT_FOUND_NOTHING@exit3, published H6_HUNT_FOUND_NOTHING@exit3", "guard_ok": true, "name": "verifier:hunt-emptied", "passed": true }, { "actual": "H7_VOCABULARY_NOT_EXERCISED@exit3", "declared_guard": "H7_VOCABULARY_NOT_EXERCISED@exit3", "detail": "reference H7_VOCABULARY_NOT_EXERCISED@exit3, published H7_VOCABULARY_NOT_EXERCISED@exit3", "guard_ok": true, "name": "verifier:vocabulary-thinned", "passed": true }, { "actual": "H14_NO_FRONTIER@exit3", "declared_guard": "H14_NO_FRONTIER@exit3", "detail": "reference H14_NO_FRONTIER@exit3, published H14_NO_FRONTIER@exit3", "guard_ok": true, "name": "verifier:no-frontier", "passed": true }, { "actual": "H8_FLOOR_BELOW_OBSERVATION@exit3", "declared_guard": "H8_FLOOR_BELOW_OBSERVATION@exit3", "detail": "reference H8_FLOOR_BELOW_OBSERVATION@exit3, published H8_FLOOR_BELOW_OBSERVATION@exit3", "guard_ok": true, "name": "verifier:floor-above-observation", "passed": true }, { "actual": "H9_GUARD_FAILED_OPEN@exit3", "declared_guard": "H9_GUARD_FAILED_OPEN@exit3", "detail": "reference H9_GUARD_FAILED_OPEN@exit3, published H9_GUARD_FAILED_OPEN@exit3", "guard_ok": true, "name": "verifier:check-failed-open", "passed": true }, { "actual": "H10_COUNTS_DO_NOT_RECOMPUTE@exit3", "declared_guard": "H10_COUNTS_DO_NOT_RECOMPUTE@exit3", "detail": "reference H10_COUNTS_DO_NOT_RECOMPUTE@exit3, published H10_COUNTS_DO_NOT_RECOMPUTE@exit3", "guard_ok": true, "name": "verifier:counts-do-not-recompute", "passed": true }, { "actual": "H11_ENGINE_UNDECLARED@exit3", "declared_guard": "H11_ENGINE_UNDECLARED@exit3", "detail": "reference H11_ENGINE_UNDECLARED@exit3, published H11_ENGINE_UNDECLARED@exit3", "guard_ok": true, "name": "verifier:undeclared-engine", "passed": true }, { "actual": "H12_PAIR_DECLARED_TWICE@exit3", "declared_guard": "H12_PAIR_DECLARED_TWICE@exit3", "detail": "reference H12_PAIR_DECLARED_TWICE@exit3, published H12_PAIR_DECLARED_TWICE@exit3", "guard_ok": true, "name": "verifier:pair-declared-twice", "passed": true }, { "actual": "H13_TOKEN_CLAIM_WITHOUT_A_DECODE@exit3", "declared_guard": "H13_TOKEN_CLAIM_WITHOUT_A_DECODE@exit3", "detail": "reference H13_TOKEN_CLAIM_WITHOUT_A_DECODE@exit3, published H13_TOKEN_CLAIM_WITHOUT_A_DECODE@exit3", "guard_ok": true, "name": "verifier:token-claim-without-a-decode", "passed": true }, { "actual": "H1_SIGNATURE_INVALID@exit3", "declared_guard": "H1_SIGNATURE_INVALID@exit3", "detail": "reference H1_SIGNATURE_INVALID@exit3, published H1_SIGNATURE_INVALID@exit3", "guard_ok": true, "name": "verifier:forged-signer", "passed": true }, { "actual": "H1_SIGNATURE_INVALID@exit3", "declared_guard": "H1_SIGNATURE_INVALID@exit3", "detail": "reference H1_SIGNATURE_INVALID@exit3, published H1_SIGNATURE_INVALID@exit3", "guard_ok": true, "name": "verifier:mutated-document", "passed": true }, { "actual": "H0_DOCUMENT_UNREADABLE@exit1", "declared_guard": "H0_DOCUMENT_UNREADABLE@exit1", "detail": "reference H0_DOCUMENT_UNREADABLE@exit1, published H0_DOCUMENT_UNREADABLE@exit1", "guard_ok": true, "name": "verifier:torn-document", "passed": true } ], "geometry": { "canonical_axes": [ "KV", "T", "H", "D" ], "decode_steps": 8, "dtype": "float16", "head_dim": 8, "kv_heads": 2, "layers": 3, "lean_slice": { "head_dim": 8, "kv_heads": 2, "tokens": 2 }, "pad_model": "non-zero block tail (a real engine's tail holds whatever the last request left); the value is fixed so the round-trip difference is a set identity and not a probability", "tokens": 40 }, "hunt_id": "sf-exact-transformation-hunt-v1", "known_limits": [ { "detail": "an engine whose layout was made up on the spot grades EXACT_BYTE with 1280 positions checked. Everything in this document is a statement about DECLARED SHAPES; that a shape matches a real engine's is a citation, not a measurement", "name": "no-engine-runs-here", "passed": true }, { "detail": "a translator that behaves exactly like the real one except on a single element VALUE passes the exhaustive index probe and all 6 gather payloads and is graded EXACT_BYTE \u2014 and on a payload that actually contains that value it silently zeroes 1280 elements. The index argument is about POSITIONS; that the layout is a pure gather is probed on the payloads this run happened to use, not proved", "name": "a-data-dependent-layout-passes-the-gather-probe", "passed": true }, { "detail": "sglang->vllm-paged-x is EXACT_BYTE at head_dim 8 and X4_GEOMETRY_UNREPRESENTABLE at head_dim 6. The enumerated list is a list about THIS geometry; the arbitrary-parameter statements are the Lean primitives and they are about index arithmetic, not about these engines", "name": "exactness-is-a-property-of-the-geometry-too", "passed": true }, { "detail": "the same prompt through the same harness with a different weight seed decodes to [63, 26, 25, 18, 25, 18, 25, 18] instead of [2, 27, 60, 51, 2, 27, 60, 51]. 'Token-identical' means identical to a reference this run computed, not to any engine's output", "name": "token-identity-is-relative-to-this-model", "passed": true }, { "detail": "re-run with a ZERO block tail, 35 of the token-identical rows become byte-identical. A real engine's block tail holds whatever the last request left, which is why the hunt uses a non-zero tail; an engine that zeroes its tails would move the whole EXACT_TOKEN class into EXACT_BYTE and neither the layouts nor the proofs would have changed", "name": "the-byte-token-split-is-the-padding-model", "passed": true }, { "detail": "a layout mutated into a DIFFERENT permutation is still a permutation, so the generated `decide` lemma for it would kernel-check exactly as well (64 canonical elements over 512 native positions, all distinct, and a different permutation from the real one). The Lean leg establishes that these index maps are bijections; it cannot establish that they are the maps the engines use, and nothing in this repository closes that", "name": "the-lean-lemmas-are-about-index-arithmetic-not-about-layouts_py", "passed": true } ], "observed": { "distinct_index_spaces": 8, "distinct_positions": 12544, "engines": [ "vllm-fa", "vllm-paged-x", "trtllm", "sglang", "hf-dynamic-cache", "llamacpp", "flashinfer-nhd", "lmdeploy-turbomind", "deepseek-mla", "vllm-tp2", "vllm-paged-x16", "vllm-fp8" ], "exact_pairs": [ "flashinfer-nhd->hf-dynamic-cache", "flashinfer-nhd->llamacpp", "flashinfer-nhd->lmdeploy-turbomind", "flashinfer-nhd->sglang", "flashinfer-nhd->trtllm", "flashinfer-nhd->vllm-fa", "flashinfer-nhd->vllm-paged-x", "hf-dynamic-cache->flashinfer-nhd", "hf-dynamic-cache->llamacpp", "hf-dynamic-cache->lmdeploy-turbomind", "hf-dynamic-cache->sglang", "hf-dynamic-cache->trtllm", "hf-dynamic-cache->vllm-fa", "hf-dynamic-cache->vllm-paged-x", "llamacpp->flashinfer-nhd", "llamacpp->hf-dynamic-cache", "llamacpp->lmdeploy-turbomind", "llamacpp->sglang", "llamacpp->trtllm", "llamacpp->vllm-fa", "llamacpp->vllm-paged-x", "lmdeploy-turbomind->flashinfer-nhd", "lmdeploy-turbomind->hf-dynamic-cache", "lmdeploy-turbomind->llamacpp", "lmdeploy-turbomind->sglang", "lmdeploy-turbomind->trtllm", "lmdeploy-turbomind->vllm-fa", "lmdeploy-turbomind->vllm-paged-x", "sglang->flashinfer-nhd", "sglang->hf-dynamic-cache", "sglang->llamacpp", "sglang->lmdeploy-turbomind", "sglang->trtllm", "sglang->vllm-fa", "sglang->vllm-paged-x", "trtllm->flashinfer-nhd", "trtllm->hf-dynamic-cache", "trtllm->llamacpp", "trtllm->lmdeploy-turbomind", "trtllm->sglang", "trtllm->vllm-fa", "trtllm->vllm-paged-x", "vllm-fa->flashinfer-nhd", "vllm-fa->hf-dynamic-cache", "vllm-fa->llamacpp", "vllm-fa->lmdeploy-turbomind", "vllm-fa->sglang", "vllm-fa->trtllm", "vllm-fa->vllm-paged-x", "vllm-paged-x->flashinfer-nhd", "vllm-paged-x->hf-dynamic-cache", "vllm-paged-x->llamacpp", "vllm-paged-x->lmdeploy-turbomind", "vllm-paged-x->sglang", "vllm-paged-x->trtllm", "vllm-paged-x->vllm-fa" ], "frontier_codes": [ "X1_LATENT_KV", "X2_HEAD_GATHER", "X4_GEOMETRY_UNREPRESENTABLE" ], "identity_map_engines": [ "sglang" ], "lossless_engines": [ "vllm-fa", "vllm-paged-x", "trtllm", "sglang", "hf-dynamic-cache", "llamacpp", "flashinfer-nhd", "lmdeploy-turbomind" ], "positions_checked_total": 90880, "source_positions_checked_total": 90880, "verdict_counts": { "EXACT_BYTE": 21, "EXACT_TOKEN": 35, "NOT_EXACT": 2, "REFUSED": 6, "UNPROVEN": 0 }, "vocabulary_exercised": [ "axis_permute", "head_dim_devectorize", "head_dim_vectorize", "head_order_transpose", "kv_merge", "kv_split", "reblock", "requantize", "v_dim_transpose" ] }, "rows": [ { "dst": "vllm-fa", "frontier_code": "X1_LATENT_KV", "frontier_reason": "the destination or source caches a compressed per-token latent, not a per-head K/V tensor; there is no permutation between the two at any block size", "native_bytes": { "dst": null, "src": null }, "observations": { "forward_reproduces_target": null, "roundtrip_byte_identical": null, "roundtrip_diff_is_exactly_unread_padding": null, "tokens_identical": null }, "pair_id": "deepseek-mla->vllm-fa", "proof": { "command": "make exact-hunt-frontier", "decoded_tokens": null, "dest_bijective": null, "dest_padding_positions": null, "detail": "the destination or source caches a compressed per-token latent, not a per-head K/V tensor; there is no permutation between the two at any block size", "gather_agrees": null, "gather_payloads": null, "kind": "frontier", "layers": null, "lean_lemmas": [ "ExactTransform.latent_kv_not_a_permutation" ], "positions_checked": null, "positions_total": null, "reference_tokens": null, "roundtrip_diff_positions": null, "source_bijective": null, "source_padding_positions": null, "source_positions_checked": null, "source_positions_total": null, "unread_padding_positions": null }, "src": "deepseek-mla", "transformations": [], "verdict": "REFUSED" }, { "dst": "hf-dynamic-cache", "frontier_code": null, "frontier_reason": null, "native_bytes": { "dst": 2560, "src": 3072 }, "observations": { "forward_reproduces_target": true, "roundtrip_byte_identical": false, "roundtrip_diff_is_exactly_unread_padding": true, "tokens_identical": true }, "pair_id": "flashinfer-nhd->hf-dynamic-cache", "proof": { "command": "make exact-hunt", "decoded_tokens": [ 2, 27, 60, 51, 2, 27, 60, 51 ], "dest_bijective": true, "dest_padding_positions": 0, "detail": null, "gather_agrees": true, "gather_payloads": 6, "kind": "index-permutation-exhaustive", "layers": 3, "lean_lemmas": [ "ExactTransformCert.roundtrip_flashinfer_nhd", "ExactTransformCert.roundtrip_hf_dynamic_cache", "ExactTransform.comp_roundtrip", "ExactTransform.swap_involutive", "ExactTransform.factor_unfactor_id" ], "positions_checked": 1280, "positions_total": 1280, "reference_tokens": [ 2, 27, 60, 51, 2, 27, 60, 51 ], "roundtrip_diff_positions": 768, "source_bijective": true, "source_padding_positions": 256, "source_positions_checked": 1536, "source_positions_total": 1536, "unread_padding_positions": 768 }, "src": "flashinfer-nhd", "transformations": [ "head_order_transpose", "kv_split", "reblock" ], "verdict": "EXACT_TOKEN" }, { "dst": "llamacpp", "frontier_code": null, "frontier_reason": null, "native_bytes": { "dst": 2560, "src": 3072 }, "observations": { "forward_reproduces_target": true, "roundtrip_byte_identical": false, "roundtrip_diff_is_exactly_unread_padding": true, "tokens_identical": true }, "pair_id": "flashinfer-nhd->llamacpp", "proof": { "command": "make exact-hunt", "decoded_tokens": [ 2, 27, 60, 51, 2, 27, 60, 51 ], "dest_bijective": true, "dest_padding_positions": 0, "detail": null, "gather_agrees": true, "gather_payloads": 6, "kind": "index-permutation-exhaustive", "layers": 3, "lean_lemmas": [ "ExactTransformCert.roundtrip_flashinfer_nhd", "ExactTransformCert.roundtrip_llamacpp", "ExactTransform.comp_roundtrip", "ExactTransform.swap_involutive", "ExactTransform.factor_unfactor_id" ], "positions_checked": 1280, "positions_total": 1280, "reference_tokens": [ 2, 27, 60, 51, 2, 27, 60, 51 ], "roundtrip_diff_positions": 768, "source_bijective": true, "source_padding_positions": 256, "source_positions_checked": 1536, "source_positions_total": 1536, "unread_padding_positions": 768 }, "src": "flashinfer-nhd", "transformations": [ "axis_permute", "head_order_transpose", "kv_split", "reblock", "v_dim_transpose" ], "verdict": "EXACT_TOKEN" }, { "dst": "lmdeploy-turbomind", "frontier_code": null, "frontier_reason": null, "native_bytes": { "dst": 4096, "src": 3072 }, "observations": { "forward_reproduces_target": true, "roundtrip_byte_identical": false, "roundtrip_diff_is_exactly_unread_padding": true, "tokens_identical": true }, "pair_id": "flashinfer-nhd->lmdeploy-turbomind", "proof": { "command": "make exact-hunt", "decoded_tokens": [ 2, 27, 60, 51, 2, 27, 60, 51 ], "dest_bijective": true, "dest_padding_positions": 768, "detail": null, "gather_agrees": true, "gather_payloads": 6, "kind": "index-permutation-exhaustive", "layers": 3, "lean_lemmas": [ "ExactTransformCert.roundtrip_flashinfer_nhd", "ExactTransformCert.roundtrip_lmdeploy_turbomind", "ExactTransform.comp_roundtrip", "ExactTransform.swap_involutive", "ExactTransform.factor_unfactor_id" ], "positions_checked": 2048, "positions_total": 2048, "reference_tokens": [ 2, 27, 60, 51, 2, 27, 60, 51 ], "roundtrip_diff_positions": 768, "source_bijective": true, "source_padding_positions": 256, "source_positions_checked": 1536, "source_positions_total": 1536, "unread_padding_positions": 768 }, "src": "flashinfer-nhd", "transformations": [ "axis_permute", "kv_split", "reblock" ], "verdict": "EXACT_TOKEN" }, { "dst": "sglang", "frontier_code": null, "frontier_reason": null, "native_bytes": { "dst": 2560, "src": 3072 }, "observations": { "forward_reproduces_target": true, "roundtrip_byte_identical": false, "roundtrip_diff_is_exactly_unread_padding": true, "tokens_identical": true }, "pair_id": "flashinfer-nhd->sglang", "proof": { "command": "make exact-hunt", "decoded_tokens": [ 2, 27, 60, 51, 2, 27, 60, 51 ], "dest_bijective": true, "dest_padding_positions": 0, "detail": null, "gather_agrees": true, "gather_payloads": 6, "kind": "index-permutation-exhaustive", "layers": 3, "lean_lemmas": [ "ExactTransformCert.roundtrip_flashinfer_nhd", "ExactTransformCert.roundtrip_sglang", "ExactTransform.comp_roundtrip", "ExactTransform.swap_involutive", "ExactTransform.factor_unfactor_id" ], "positions_checked": 1280, "positions_total": 1280, "reference_tokens": [ 2, 27, 60, 51, 2, 27, 60, 51 ], "roundtrip_diff_positions": 768, "source_bijective": true, "source_padding_positions": 256, "source_positions_checked": 1536, "source_positions_total": 1536, "unread_padding_positions": 768 }, "src": "flashinfer-nhd", "transformations": [ "axis_permute", "kv_split", "reblock" ], "verdict": "EXACT_TOKEN" }, { "dst": "trtllm", "frontier_code": null, "frontier_reason": null, "native_bytes": { "dst": 4096, "src": 3072 }, "observations": { "forward_reproduces_target": true, "roundtrip_byte_identical": false, "roundtrip_diff_is_exactly_unread_padding": true, "tokens_identical": true }, "pair_id": "flashinfer-nhd->trtllm", "proof": { "command": "make exact-hunt", "decoded_tokens": [ 2, 27, 60, 51, 2, 27, 60, 51 ], "dest_bijective": true, "dest_padding_positions": 768, "detail": null, "gather_agrees": true, "gather_payloads": 6, "kind": "index-permutation-exhaustive", "layers": 3, "lean_lemmas": [ "ExactTransformCert.roundtrip_flashinfer_nhd", "ExactTransformCert.roundtrip_trtllm", "ExactTransform.comp_roundtrip", "ExactTransform.swap_involutive", "ExactTransform.factor_unfactor_id" ], "positions_checked": 2048, "positions_total": 2048, "reference_tokens": [ 2, 27, 60, 51, 2, 27, 60, 51 ], "roundtrip_diff_positions": 768, "source_bijective": true, "source_padding_positions": 256, "source_positions_checked": 1536, "source_positions_total": 1536, "unread_padding_positions": 768 }, "src": "flashinfer-nhd", "transformations": [ "axis_permute", "reblock" ], "verdict": "EXACT_TOKEN" }, { "dst": "vllm-fa", "frontier_code": null, "frontier_reason": null, "native_bytes": { "dst": 3072, "src": 3072 }, "observations": { "forward_reproduces_target": true, "roundtrip_byte_identical": false, "roundtrip_diff_is_exactly_unread_padding": true, "tokens_identical": true }, "pair_id": "flashinfer-nhd->vllm-fa", "proof": { "command": "make exact-hunt", "decoded_tokens": [ 2, 27, 60, 51, 2, 27, 60, 51 ], "dest_bijective": true, "dest_padding_positions": 256, "detail": null, "gather_agrees": true, "gather_payloads": 6, "kind": "index-permutation-exhaustive", "layers": 3, "lean_lemmas": [ "ExactTransformCert.roundtrip_flashinfer_nhd", "ExactTransformCert.roundtrip_vllm_fa", "ExactTransform.comp_roundtrip", "ExactTransform.swap_involutive", "ExactTransform.factor_unfactor_id" ], "positions_checked": 1536, "positions_total": 1536, "reference_tokens": [ 2, 27, 60, 51, 2, 27, 60, 51 ], "roundtrip_diff_positions": 768, "source_bijective": true, "source_padding_positions": 256, "source_positions_checked": 1536, "source_positions_total": 1536, "unread_padding_positions": 768 }, "src": "flashinfer-nhd", "transformations": [ "axis_permute" ], "verdict": "EXACT_TOKEN" }, { "dst": "vllm-paged-x", "frontier_code": null, "frontier_reason": null, "native_bytes": { "dst": 3072, "src": 3072 }, "observations": { "forward_reproduces_target": true, "roundtrip_byte_identical": false, "roundtrip_diff_is_exactly_unread_padding": true, "tokens_identical": true }, "pair_id": "flashinfer-nhd->vllm-paged-x", "proof": { "command": "make exact-hunt", "decoded_tokens": [ 2, 27, 60, 51, 2, 27, 60, 51 ], "dest_bijective": true, "dest_padding_positions": 256, "detail": null, "gather_agrees": true, "gather_payloads": 6, "kind": "index-permutation-exhaustive", "layers": 3, "lean_lemmas": [ "ExactTransformCert.roundtrip_flashinfer_nhd", "ExactTransformCert.roundtrip_vllm_paged_x", "ExactTransform.comp_roundtrip", "ExactTransform.swap_involutive", "ExactTransform.factor_unfactor_id" ], "positions_checked": 1536, "positions_total": 1536, "reference_tokens": [ 2, 27, 60, 51, 2, 27, 60, 51 ], "roundtrip_diff_positions": 768, "source_bijective": true, "source_padding_positions": 256, "source_positions_checked": 1536, "source_positions_total": 1536, "unread_padding_positions": 768 }, "src": "flashinfer-nhd", "transformations": [ "axis_permute", "head_dim_vectorize", "kv_split" ], "verdict": "EXACT_TOKEN" }, { "dst": "flashinfer-nhd", "frontier_code": null, "frontier_reason": null, "native_bytes": { "dst": 3072, "src": 2560 }, "observations": { "forward_reproduces_target": true, "roundtrip_byte_identical": true, "roundtrip_diff_is_exactly_unread_padding": true, "tokens_identical": true }, "pair_id": "hf-dynamic-cache->flashinfer-nhd", "proof": { "command": "make exact-hunt", "decoded_tokens": [ 2, 27, 60, 51, 2, 27, 60, 51 ], "dest_bijective": true, "dest_padding_positions": 256, "detail": null, "gather_agrees": true, "gather_payloads": 6, "kind": "index-permutation-exhaustive", "layers": 3, "lean_lemmas": [ "ExactTransformCert.roundtrip_hf_dynamic_cache", "ExactTransformCert.roundtrip_flashinfer_nhd", "ExactTransform.comp_roundtrip", "ExactTransform.swap_involutive", "ExactTransform.factor_unfactor_id" ], "positions_checked": 1536, "positions_total": 1536, "reference_tokens": [ 2, 27, 60, 51, 2, 27, 60, 51 ], "roundtrip_diff_positions": 0, "source_bijective": true, "source_padding_positions": 0, "source_positions_checked": 1280, "source_positions_total": 1280, "unread_padding_positions": 0 }, "src": "hf-dynamic-cache", "transformations": [ "head_order_transpose", "kv_merge", "reblock" ], "verdict": "EXACT_BYTE" }, { "dst": "llamacpp", "frontier_code": null, "frontier_reason": null, "native_bytes": { "dst": 2560, "src": 2560 }, "observations": { "forward_reproduces_target": true, "roundtrip_byte_identical": true, "roundtrip_diff_is_exactly_unread_padding": true, "tokens_identical": true }, "pair_id": "hf-dynamic-cache->llamacpp", "proof": { "command": "make exact-hunt", "decoded_tokens": [ 2, 27, 60, 51, 2, 27, 60, 51 ], "dest_bijective": true, "dest_padding_positions": 0, "detail": null, "gather_agrees": true, "gather_payloads": 6, "kind": "index-permutation-exhaustive", "layers": 3, "lean_lemmas": [ "ExactTransformCert.roundtrip_hf_dynamic_cache", "ExactTransformCert.roundtrip_llamacpp", "ExactTransform.comp_roundtrip", "ExactTransform.swap_involutive", "ExactTransform.factor_unfactor_id" ], "positions_checked": 1280, "positions_total": 1280, "reference_tokens": [ 2, 27, 60, 51, 2, 27, 60, 51 ], "roundtrip_diff_positions": 0, "source_bijective": true, "source_padding_positions": 0, "source_positions_checked": 1280, "source_positions_total": 1280, "unread_padding_positions": 0 }, "src": "hf-dynamic-cache", "transformations": [ "head_order_transpose", "v_dim_transpose" ], "verdict": "EXACT_BYTE" }, { "dst": "lmdeploy-turbomind", "frontier_code": null, "frontier_reason": null, "native_bytes": { "dst": 4096, "src": 2560 }, "observations": { "forward_reproduces_target": true, "roundtrip_byte_identical": true, "roundtrip_diff_is_exactly_unread_padding": true, "tokens_identical": true }, "pair_id": "hf-dynamic-cache->lmdeploy-turbomind", "proof": { "command": "make exact-hunt", "decoded_tokens": [ 2, 27, 60, 51, 2, 27, 60, 51 ], "dest_bijective": true, "dest_padding_positions": 768, "detail": null, "gather_agrees": true, "gather_payloads": 6, "kind": "index-permutation-exhaustive", "layers": 3, "lean_lemmas": [ "ExactTransformCert.roundtrip_hf_dynamic_cache", "ExactTransformCert.roundtrip_lmdeploy_turbomind", "ExactTransform.comp_roundtrip", "ExactTransform.swap_involutive", "ExactTransform.factor_unfactor_id" ], "positions_checked": 2048, "positions_total": 2048, "reference_tokens": [ 2, 27, 60, 51, 2, 27, 60, 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"dest_padding_positions": null, "detail": "the layout does not exist at this geometry (a factorisation that does not divide), so there is nothing to be exact about (vllm-paged-x16: head_dim 8 % x 16 != 0)", "gather_agrees": null, "gather_payloads": null, "kind": "frontier", "layers": null, "lean_lemmas": [ "ExactTransform.factor_needs_divisibility" ], "positions_checked": null, "positions_total": null, "reference_tokens": null, "roundtrip_diff_positions": null, "source_bijective": null, "source_padding_positions": null, "source_positions_checked": null, "source_positions_total": null, "unread_padding_positions": null }, "src": "vllm-paged-x16", "transformations": [ "axis_permute", "head_dim_devectorize", "kv_merge" ], "verdict": "REFUSED" }, { "dst": "vllm-fa", "frontier_code": "X2_HEAD_GATHER", "frontier_reason": "the two layouts hold different numbers of kv-heads, so one is a PROJECTION of the other; the map back is a gather and has no inverse", "native_bytes": { "dst": null, "src": null }, "observations": { "forward_reproduces_target": null, "roundtrip_byte_identical": null, "roundtrip_diff_is_exactly_unread_padding": null, "tokens_identical": null }, "pair_id": "vllm-tp2->vllm-fa", "proof": { "command": "make exact-hunt-frontier", "decoded_tokens": null, "dest_bijective": null, "dest_padding_positions": null, "detail": "the two layouts hold different numbers of kv-heads, so one is a PROJECTION of the other; the map back is a gather and has no inverse", "gather_agrees": null, "gather_payloads": null, "kind": "frontier", "layers": null, "lean_lemmas": [ "ExactTransform.head_gather_not_injective" ], "positions_checked": null, "positions_total": null, "reference_tokens": null, "roundtrip_diff_positions": null, "source_bijective": null, "source_padding_positions": null, "source_positions_checked": null, "source_positions_total": null, "unread_padding_positions": null }, "src": "vllm-tp2", "transformations": [], "verdict": "REFUSED" } ], "schema": "[removed].exact-transformations/v1", "signature": "77d70225eae39641d242386303be0fd6ed0c8149ba6a46af4ebc9785af7e8b93946cb1ae466f5b0a3b43eb5a378d078ec8e368f32920452e4330fba52eaa710f", "vocabulary": [ "reblock", "head_order_transpose", "kv_split", "kv_merge", "head_dim_vectorize", "head_dim_devectorize", "v_dim_transpose", "axis_permute", "requantize" ] }