quantize
quantize: 8 implementations from 11 papers ran on one shared input (census 2026-09-22, battery b986f7e04d79); they produced 6 distinct outputs across 3 buckets, one shared input per bucket.
Identical values to six decimals (the recorded digest) on the shared input are agreement on those inputs, not equivalence. Implementations are compared only within one bucket, the positional (rank, kind, dtype) of each array argument; the argument name is not part of the key because the harness draws the shared array from (rank, kind) and casts it to the dtype, whatever the name; each bucket's shared input is fixed by that key, so members of one bucket saw bitwise-identical inputs under their own scalar arguments. A cluster is the set of members whose recorded output digest is identical. Nothing here says which computation a paper's method intended, and nothing reproduces a paper's results.
Not compared, and not in the tables or the counts above:
- 1 more implementation of this name was examined but did not run on the shared input (TypeError 1).
- No implementation of this name ran on its own fixture arguments only: every one that ran took an array argument.
- Every compared output was digested.
Bucket 1 of 3: arg 1: rank 2, kind float, dtype float32
5 implementations from 5 papers share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 4 distinct outputs, largest cluster first. Values are the first 8 of the recorded output, flattened.
| Cluster (same digest to six decimals) | Members | Shared output on this bucket's input |
|---|---|---|
| 2 implementations 2 papers 0ca6e75121f5 recorded values identical |
models.py 7d546c03 recorded x:2/float/float32; num_bits=8, min_value=None, max_value=None, num_chunks=None, stochastic=False, inplace=False utils/quantize.py ce590a0a recorded x:2/float/float32; num_bits=8, min_value=None, max_value=None, inplace=False, symmetric=False, num_chunks=None |
[1.79611, -1.53005, 0.481116, -0.694644, -0.895761, -1.035, -0.663703, 0.573939, …] shape [4, 8] · float32 · Tensor |
| 1 implementation 1 paper 04bac2a1047c |
models.py 771654b8 recorded x:2/float/float32; mode='noise', offset=None, scale=1 |
[1.79889, -1.26555, 0.0645307, -1.05886, -1.09543, -0.896393, -0.672668, 0.969334, …] shape [4, 8] · float32 · Tensor |
| 1 implementation 1 paper bd37723d45f9 |
rivagan/rivagan.py 34dad530 recorded frames:2/float/float32 |
[1.8, -1.53333, 0.47451, -0.694118, -0.905882, -1.02353, -0.670588, 0.568627, …] shape [4, 8] · float32 · Tensor |
| 1 implementation 1 paper bf082cc7cc93 |
quantification.py d68f2c13 recorded tensor_data:2/float/float32; w_p=1.0, w_n=1.0, threshold=0.15 |
[1, -1, 1, -1, -1, -1, -1, 1, …] shape [4, 8] · float32 · Tensor |
Bucket 2 of 3: arg 1: rank 2, kind float, dtype float32 · arg 2: rank 0, kind float, dtype float32 · arg 3: rank 0, kind float, dtype float32
2 implementations from 4 papers share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 1 distinct output, largest cluster first. Values are the first 8 of the recorded output, flattened.
| Cluster (same digest to six decimals) | Members | Shared output on this bucket's input |
|---|---|---|
| 2 implementations 4 papers 34e51086a3d2 recorded values identical |
quant.py 41c57e55 recorded x:2/float/float32, scale:0/float/float32, zero:0/float/float32; maxq=255 deltazip/core/quant.py 8f8eedd7 recorded x:2/float/float32, scale:0/float/float32, zero:0/float/float32; maxq=255 |
[-2.02728, -2.02728, -2.02728, -2.02728, -2.02728, -2.02728, -2.02728, -2.02728, …] shape [4, 8] · float32 · Tensor |
Bucket 3 of 3: arg 1: rank 2, kind float, dtype float32 · arg 2: rank 0, kind float, dtype float32 · arg 3: rank 0, kind float, dtype float32 · arg 4: rank 0, kind float, dtype float32 · arg 5: rank 0, kind float, dtype float32
1 implementation from 2 papers share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 1 distinct output, largest cluster first. Values are the first 8 of the recorded output, flattened.
| Cluster (same digest to six decimals) | Members | Shared output on this bucket's input |
|---|---|---|
| 1 implementation 2 papers 98c3b1dee024 |
qeft/quant.py a166c4b7 recorded x:2/float/float32, scale:0/float/float32, zero:0/float/float32, minq:0/float/float32, maxq:0/float/float32 |
[-0, -0, -0, -0, -0, -0, -0, -0, …] shape [4, 8] · float32 · Tensor |
Identical values to six decimals (the recorded digest) on the shared input are agreement on those inputs, not equivalence; where a cluster's members carry recorded values, the largest difference among them is shown under the cluster. Paper titles are the archive's archive 2025-07-28 where the paper is in the archive and the graph's where it was added by Syntology; papers with no page here are shown by their recorded paper id only. A paper count above the implementation count means one implementation (one code sha) is held from several papers' repositories and counts once. Per-sample status, licence and fingerprint records for each paper are on its paper page. JSON twin: /census/quantize.json.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections