tzhouamandClaude Opus 5
b36a3431bd
Turns a branch of accumulated edge/CPU work into something someone else can
read, reproduce and continue.
README.md states what the fork is for (multimodal models on edge CPUs rather
than servers), the headline memory and speed results with their costs, and why
the numbers are worth reading -- five retracted conclusions, all measurement
errors rather than coding errors. Upstream's README is preserved at
docs/README.vllm-omni.md and NOTICE records the Apache-2.0 derivation and the
files this fork changes.
docs/edge/:
methodology.md the measurement discipline, which is the most transferable
thing here: what each of the five retractions was, and the
rule it left behind. Includes the two traps specific to this
stack -- vLLM's AOT compile cache keying on config so two
arms sharing an artifact load the wrong graph, and allocated
not being resident until mincore(2) says so.
results.md every measured figure with its spread and baseline, and an
explicit note that the four memory levers overlap and must
not be added.
cpu-4bit.md the two 4-bit paths, the packed layout as measured rather
than assumed, why the path choice is a config field, and the
GPTQ output-head trap that invalidated a whole round of W4A8
numbers.
harness.md how to run a comparison, and the two arguments that matter
more than they look (--context-len, --prompt-len), each of
which has produced a wrong conclusion by being left at its
default.
ROADMAP.md orders by whether the verification method is correct rather than by
expected size, lists what is done with results, what is next with expected
gains and how each would be verified, and -- at equal length -- what was
considered and rejected, including the CPU megakernel with the measurements
that rule it out for now.
benchmarks/edge_harness/ brings the tooling into the repo so the numbers are
reproducible: the grid runner, the single-run benchmarks, the memory ledger,
the fidelity metric, the Android agent suite, the quantizers, and the tests
that pin the harness's judgement. 82 tests pass from the new location.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01L1ax5wYLA6XbqEL1i8L9gZ
71 lines
2.7 KiB
Python
71 lines
2.7 KiB
Python
"""Tests for the fidelity metric's arithmetic, which is where it can lie.
|
|
|
|
Greedy divergence saturates; a KL that silently reports 0 for a broken build
|
|
would be worse than saturating, so identical / shifted / disagreeing cases are
|
|
pinned here.
|
|
"""
|
|
|
|
import math
|
|
|
|
import pytest
|
|
|
|
from fidelity_kl import chunk_tokens, compare
|
|
|
|
|
|
def _pos(target, dist):
|
|
"""dist: {token_id: probability} -> a position record in log space."""
|
|
return {"target": target,
|
|
"logprobs": {t: math.log(p) for t, p in dist.items()}}
|
|
|
|
|
|
def test_identical_distributions_score_zero_kl_and_full_agreement():
|
|
ref = [_pos(1, {1: 0.7, 2: 0.2, 3: 0.1})]
|
|
out = compare(ref, [dict(p) for p in ref])
|
|
assert out["top1_agreement"] == 1.0
|
|
assert out["kl_mean"] == pytest.approx(0.0, abs=1e-9)
|
|
assert out["delta_logprob_mean"] == pytest.approx(0.0, abs=1e-9)
|
|
|
|
|
|
def test_a_shifted_distribution_scores_positive_kl_without_changing_top1():
|
|
"""The case greedy divergence cannot see: same argmax, worse distribution."""
|
|
ref = [_pos(1, {1: 0.7, 2: 0.2, 3: 0.1})]
|
|
cand = [_pos(1, {1: 0.5, 2: 0.3, 3: 0.2})]
|
|
out = compare(ref, cand)
|
|
assert out["top1_agreement"] == 1.0
|
|
assert out["kl_mean"] > 0.01
|
|
assert out["delta_logprob_mean"] < 0 # reference token got less likely
|
|
|
|
|
|
def test_disagreement_is_counted():
|
|
ref = [_pos(1, {1: 0.7, 2: 0.3}), _pos(1, {1: 0.7, 2: 0.3})]
|
|
cand = [_pos(1, {1: 0.7, 2: 0.3}), _pos(1, {2: 0.8, 1: 0.2})]
|
|
assert compare(ref, cand)["top1_agreement"] == 0.5
|
|
|
|
|
|
def test_worse_builds_score_monotonically_worse():
|
|
"""The property greedy divergence lacks: it must rank, not just detect."""
|
|
ref = [_pos(1, {1: 0.8, 2: 0.15, 3: 0.05})]
|
|
near = compare(ref, [_pos(1, {1: 0.78, 2: 0.16, 3: 0.06})])["kl_mean"]
|
|
far = compare(ref, [_pos(1, {1: 0.40, 2: 0.35, 3: 0.25})])["kl_mean"]
|
|
assert 0 < near < far
|
|
|
|
|
|
def test_coverage_is_reported_so_a_lower_bound_is_not_read_as_a_kl():
|
|
"""Top-k truncation makes KL a lower bound; the run has to say so."""
|
|
ref = [_pos(1, {1: 0.3, 2: 0.2})] # only 0.5 of the mass covered
|
|
out = compare(ref, [_pos(1, {1: 0.3, 2: 0.2})])
|
|
assert out["coverage_mean"] == pytest.approx(0.5, abs=1e-6)
|
|
|
|
|
|
def test_mismatched_corpora_are_refused_not_averaged():
|
|
with pytest.raises(SystemExit):
|
|
compare([_pos(1, {1: 1.0})], [])
|
|
|
|
|
|
def test_chunking_drops_only_a_tail_too_short_to_score():
|
|
# 488 is a perfectly good chunk; both runs see the same slicing.
|
|
assert [len(c) for c in chunk_tokens(list(range(1000)), 512, None)] == [512, 488]
|
|
# 8 leftover tokens is not, so it is dropped rather than compared.
|
|
assert [len(c) for c in chunk_tokens(list(range(520)), 512, None)] == [512]
|
|
assert len(chunk_tokens(list(range(4096)), 512, 3)) == 3
|