SZL Kernels · Native Kernel Hub
Inspect the closed Torch kernel API for receipted operations and cosine retrieval, with an immutable publication and loading contract.
Artifact: Native software kernel · Torch CPU API · Stage: Reference implementation · publication requires verified source
Explore in Command Lab · Build · Evidence
Before you use it
- Review the exact immutable provider revision and source binding before executing remote kernel code.
- Receipts are unsigned integrity records; they do not prove authorship, retrieval quality or a measured performance advantage.
- Existing build, runtime, signature and compatible v1 publication requirements remain unchanged; this card does not claim a new release.
Technical details and original evidence
The retained source below is exact and may contain historical observations. Its dates, use restrictions, licenses and evidence boundaries continue to apply.
Kernel Hub API and publication boundary
The first-class Hugging Face kernel and the Python distribution have different
entrypoints. The Kernel Hub API is the source-controlled
torch-ext/szl_kernels/_kernel_api.py, mirrored in
build/torch-universal/szl_kernels/_kernel_api.py. The release builder stages
this file as build/torch-cpu/__init__.py and as its compatibility package
entrypoint. It stages _chain.py, _ops.py, and retrieval.py beside both
entrypoints. Every import in this closure is standard-library, Torch, or relative
to those files.
The broader installed szl_kernels distribution retains its existing entrypoint
and separate MiniEmbed and estate companions. The fitted offline navigator and
its scikit-learn dependency are not part of this kernel publication. Their
evaluation/publication holds are unchanged.
Compatibility and identity
The existing first-class v1 branch at
09818b62d683c33d200fca32e2ebfd95c64c65c7 contains only a torch-cpu build.
Its root and compatibility entrypoints have SHA-256
96caac81dd719c785c9cb1458ac835352a8b45cbce7a5603a205b3a88319bbf0.
Their fifteen public exports are preserved by the dedicated entrypoint, which
adds governed_cosine_topk. MiniEmbed and estate exports were never part of
that published v1 API.
The Python package version is 0.2.0; the compatible Kernel Hub major API
version is integer 1. All existing v1 variants must be updated together.
This document does not assert that a new provider revision is already published.
Publication requires the existing exact-source authorization, generated metadata
and digests, protected-source checks, and authoritative provider readback.
Audited consumer example
Use an isolated environment with a supported first-class loader, such as
kernels==0.16.1, and compatible Torch. Review the published source binding and
verify the provider files before importing remote code. Prefer the immutable
provider commit from that verified binding:
import os
import re
import torch
from kernels import get_kernel
# Set this to the actual 40-character HF kernel commit from verified readback.
# No placeholder is a real published revision.
revision = os.environ["SZL_REVIEWED_KERNEL_REVISION"]
if re.fullmatch(r"[0-9a-f]{40}", revision) is None:
raise ValueError("an audited immutable HF kernel revision is required")
suite = get_kernel(
"SZLHOLDINGS/szl-kernels",
revision=revision,
trust_remote_code=True, # Explicit opt-in to execute the audited SZL source.
)
assert suite.__version__ == "0.2.0"
chain = suite.UnifiedReceiptChain()
result = suite.governed_cosine_topk(
chain,
torch.tensor([1.0, 0.0]),
torch.tensor([[0.0, 1.0], [1.0, 0.0]]),
k=1,
)
assert result["indices"].tolist() == [[1]]
assert chain.verify() == (True, 1, -1)
After verified publication, get_kernel("SZLHOLDINGS/szl-kernels", version=1, trust_remote_code=True) follows the mutable v1 branch. A branch reference is
not immutable provenance. Do not pass version and revision together.
The explicit remote-code opt-in does not authenticate the publisher or certify
the code; it acknowledges execution of source the consumer has separately
reviewed.
Numerical and claim boundaries
Retrieval hashes logical C-order raw tensor bytes in native byte order. A Torch uint8 view preserves float32 bit patterns, including negative zero, and int64 index values without a NumPy dependency. Row-bounded tensor copies feed Python byte buffers of at most 65,536 bytes. That bound is not an allocator, BLAS, sorting workspace, or total tensor-memory bound.
The operation is a correctness/provenance reference, not a speed claim. Receipts
are unsigned hash-chain records, not proof of authorship or retrieval quality.
The test suite checks legacy exports, closed imports, CPU numerical results,
known raw-byte hashes, byte-buffer bounds, and operation with
Tensor.numpy disabled. No model-promotion, independent evaluation, clinical
fitness, or general-world usefulness claim follows from these tests.
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