Open Apache-2.0 numeric-format SSOT bridge: 83-format catalog from t27 (GF + MXFP4 + IEEE + posit + takum + lns + ...) for tt-lang kernels.
Project description
tt-lang-t27
Open numeric-format SSOT bridge from gHashTag/t27 (83-format catalog, GoldenFloat family, Apache-2.0, Tiny Tapeout silicon) into tenstorrent/tt-lang kernel author workflows.
v0.3.0 ships the full 83-format catalog from gHashTag/t27 directly
inside the wheel. import tt_lang_t27 now exposes every IEEE 754 binary +
decimal float, every fp8 / fp6 / fp4, every microscaling format, every posit /
takum, every lns, every GF ladder rung, every historical vendor float (IBM
HFP, VAX, Cray, x87, MS MBF, ...), and every theoretical / compression format,
with bit layout + bias + cluster + status (Verified / Open / ... ) + standard
- use case + GF relation -- all in one frozen dataclass per format.
What this is
A pure-Python side-package -- zero coupling with tt-lang upstream.
Reads the t27 FORMAT-SPEC-001.json registry, validates kernel functions
against bit-precise conformance vectors, and (optionally) hands off to a
Coq formal oracle.
What this is NOT
- Not a fork of tt-lang.
- Not a patch to tt-lang.
- Not a build-time dependency of tt-lang.
- Not a CLA / copyright crossing.
Both repos remain Apache-2.0, separately governed.
Install
pip install tt-lang-t27 # from PyPI (planned)
# or
pip install git+https://github.com/gHashTag/tt-lang-t27
No Coq / no MLIR / no ttnn dependencies.
Quickstart
from tt_lang_t27 import t27_kernel, load_registry, load_vectors
@t27_kernel(
fmt="GF16",
registry_path="format-spec-001.json",
vectors_path="gf16_conformance_v0.json",
)
def my_matmul(a, b):
return a @ b
Every call logs a deterministic provenance tag:
[t27] kernel=my_matmul fmt=GF16 ssot=d9b76c5 tag=0a5ecb845def
[t27] vector-check n=21 fmt=GF16
CLI
tt-lang-t27-conform \
--registry format-spec-001.json \
--vectors gf16_conformance_v0.json
# OK conform=true reasons=0 sha256=<hex>
tt-lang-t27-mxfp4-conform \
--vectors mxfp4_conformance_v0.json
# OK mxfp4_conform=true reasons=0 sha256=<hex>
83-format catalog (new in v0.3)
The full catalog is loaded once from a JSON resource shipped inside the wheel. No network calls, no file paths to manage.
import tt_lang_t27 as t27
t27.catalog_count() # 83
t27.clusters() # ['Ieee754Binary', 'Ieee754Decimal', ...]
t27.by_id("bfloat16").e_int # 8
t27.by_id("bfloat16").bias_int # 127
len(t27.by_cluster("GoldenFloat")) # 22
len(t27.by_status("Verified")) # >= 20
t27.ANCHOR # 'phi^2 + 1/phi^2 = 3 = L_2'
t27.ARXIV # 'arXiv:2606.05017'
From the command line:
tt-lang-t27-catalog --count # 83
tt-lang-t27-catalog --clusters # 13 cluster names
tt-lang-t27-catalog --cluster GoldenFloat # 22 GF rungs
tt-lang-t27-catalog --status Verified # all Verified formats
tt-lang-t27-catalog --show bfloat16 # full record for bfloat16
tt-lang-t27-catalog --json > catalog.json # full JSON dump
Each record carries: id, name, bits, s, e, m, bias,
phi_distance (signed, -1.0 = undefined sentinel), storage, cluster,
status, standard, use_case, gf_relation, source.
Canonical source of truth (upstream gHashTag/t27):
specs/numeric/formats_catalog.t27.
MXFP4 cross-validation (new in v0.2)
tt_lang_t27.mxfp4 is a pure-Python reference codec for OCP MXFP4
(S1E2M1, block_size=32, E8M0 shared scale). Constants pinned to
tt-metal kMxFp4Params:
block_size = 32scale_bias = 0x7F(E8M0 bias 127)elem_exp_bits = 2,elem_man_bits = 1,elem_exp_bias = 1- saturation
sat_pos_bits = 0x7,sat_neg_bits = 0xF - inf / nan: NotRepresentable
The vectors/mxfp4_conformance_v0.json pack carries 12 32-element blocks
with expected scale byte + 32 nibbles + packed bytes hex. Any reference
implementation (including tt-metal's pack_as_mxfp4_tiles<float>) can
verify bit-exact parity by re-encoding each input_f32 and comparing to
expected_bytes_hex.
Provenance tag formula
sha256(kernel_name || ssot_commit || fmt || anchor_hash)
anchor_hash = SHA-256("phi^2 + 1/phi^2 = 3").
References
Anchor
phi^2 + 1/phi^2 = 3 = L_2 (Lucas number).
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