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aiturk-hermes-ide/tests/hermes_cli/test_local_recommendation.py

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Python

"""The recommendation decision table — the reviewable matrix.
The recommendation itself is DERIVED (catalog.recommended_entry: best
quality among resident entries clearing the pleasant speed floor, else
fastest resident, else least-painful spilled), so nobody hand-maintains
per-hardware-class picks. This table is the editorial control on that
derivation: it enumerates the real memory size classes x {discrete,
unified} and pins every cell. A catalog change (new model, quality
re-rank, quant swap) flips cells HERE, and the diff of this file in
review IS the sign-off on what each machine class gets.
These are decision pins, not change-detectors: each cell is a choice a
human approved, exactly like a golden file. When a cell flips on
purpose, update it in the same commit and say why. When one flips by
surprise, that is the test doing its job.
Budgets mirror hardware.probe_budget's planning-mode shapes (margins,
UMA headroom) so the cells match what a real machine of that class
resolves.
"""
from __future__ import annotations
import pytest
from hermes_cli.local_runtime.catalog import (
CATALOG,
PLEASANT_FLOOR_TOK_S,
predicted_decode_tok_s,
recommended_entry,
recommended_id,
select_variant,
)
from hermes_cli.local_runtime.estimator import HardwareBudget
_GIB = 1 << 30
def _discrete(size_gb: int) -> HardwareBudget:
total = size_gb * _GIB
margin = max(2 * _GIB, int(total * 0.09))
return HardwareBudget(usable_vram_bytes=max(0, total - margin),
total_device_bytes=total,
ram_available_bytes=64 * _GIB, uma=False)
def _unified(size_gb: int) -> HardwareBudget:
total = size_gb * _GIB
return HardwareBudget(usable_vram_bytes=int(total * 0.80),
total_device_bytes=total,
ram_available_bytes=0, uma=True)
# The decision table. Cells were generated by the resolver and then
# reviewed as editorial decisions:
#
# VRAM | discrete | unified
# -----+-------------------------+------------------------
# 8 | qwen3.6-35b-a3b spilled | (none fits)
# 16 | qwen3.6-35b-a3b spilled | (none fits)
# 24 | qwen3.8-27b | (none fits)
# 32 | qwen3.8-27b | qwen3.6-35b-a3b
# 48 | qwen3.8-27b | qwen3.6-35b-a3b
# 96 | qwen3.8-27b | qwen3.6-35b-a3b
# 128 | qwen3.8-flash-next | qwen3.6-35b-a3b
# 256 | qwen3.8-flash-next | qwen3.8-flash-next
# 512 | qwen3.8-flash-next | qwen3.8-flash-next
#
# Reading guide for reviewers:
# - Discrete <=16 GB: nothing runs resident; the 35B MoE is the least
# painful spill (active slice streams from host; a dense spill reads
# every weight over the bus).
# - Discrete 24-96 GB: the 27B is the flagship experience — dense reads
# at ~1 TB/s clear the floor easily, so quality decides.
# - Discrete/unified where Flash Next fits resident (128 GB discrete,
# 256+ GB unified): the frontier model is the pick — highest quality,
# and its sparse decode clears the floor even at UMA bandwidth
# (~24 tok/s predicted at 210 GB/s).
# - Unified 32-128 GB — the Spark class, the reason this resolver
# exists: the dense 27B predicts ~13 tok/s at UMA bandwidth (below
# the pleasant floor), so the 35B-A3B (~60 tok/s) wins.
# - Unified <=24 GB: no entry passes the physics check inside the UMA
# budget (spilling is impossible on UMA by construction — the pool IS
# the RAM). The pane's browse flow is the path for those machines
# until a small catalog entry lands (revisit when one does).
DECISION_TABLE = [
(8, "discrete", "qwen3.6-35b-a3b", "least-painful-spilled"),
(8, "unified", None, None),
(16, "discrete", "qwen3.6-35b-a3b", "least-painful-spilled"),
(16, "unified", None, None),
(24, "discrete", "qwen3.8-27b", "best-quality-resident"),
(24, "unified", None, None),
(32, "discrete", "qwen3.8-27b", "best-quality-resident"),
(32, "unified", "qwen3.6-35b-a3b", "speed-gated-quality"),
(48, "discrete", "qwen3.8-27b", "best-quality-resident"),
(48, "unified", "qwen3.6-35b-a3b", "speed-gated-quality"),
(96, "discrete", "qwen3.8-27b", "best-quality-resident"),
(96, "unified", "qwen3.6-35b-a3b", "speed-gated-quality"),
(128, "discrete", "qwen3.8-flash-next", "best-quality-resident"),
(128, "unified", "qwen3.6-35b-a3b", "speed-gated-quality"),
(256, "discrete", "qwen3.8-flash-next", "best-quality-resident"),
(256, "unified", "qwen3.8-flash-next", "best-quality-resident"),
(512, "discrete", "qwen3.8-flash-next", "best-quality-resident"),
(512, "unified", "qwen3.8-flash-next", "best-quality-resident"),
]
@pytest.mark.parametrize(
("size_gb", "kind", "expected", "expected_reason"),
DECISION_TABLE,
ids=[f"{s}GB-{k}" for s, k, _, _ in DECISION_TABLE])
def test_recommendation_decision_table(size_gb, kind, expected, expected_reason):
"""Pins the pick AND its reason per cell: the reason is user-facing
(the Recommended badge's tooltip), so a cell whose rationale flips
without the pick flipping is still a review-worthy change."""
budget = _discrete(size_gb) if kind == "discrete" else _unified(size_gb)
picked = recommended_entry(budget)
if expected is None:
assert picked is None
else:
assert picked is not None
assert (picked[0].id, picked[1]) == (expected, expected_reason)
# ── invariants behind the table (survive catalog changes) ──
def test_every_entry_carries_the_recommendation_axes():
"""quality and decode_fraction are authoring requirements: an entry
without them silently loses every quality comparison (quality=0) or
prices as dense (decode_fraction=1.0)."""
for entry in CATALOG:
assert entry.quality > 0, f"{entry.id} has no quality ordering"
assert 0.0 < entry.decode_fraction <= 1.0, entry.id
if not entry.moe:
assert entry.decode_fraction == 1.0, (
f"{entry.id} is dense — it reads every weight per token")
def test_unified_never_recommends_a_below_floor_dense_model():
"""The Spark rule, as an invariant: whatever the catalog holds, a
unified-memory machine must not be told to run a model whose
predicted decode is below the pleasant floor while a resident
alternative clears it."""
budget = _unified(128)
pick = recommended_id(budget)
assert pick is not None
entry = next(e for e in CATALOG if e.id == pick)
choice = select_variant(entry, budget)
assert choice is not None and choice.zero_spill
clears = [
e for e in CATALOG
if (c := select_variant(e, budget)) is not None and c.zero_spill
and predicted_decode_tok_s(e, c.variant, budget) >= PLEASANT_FLOOR_TOK_S
]
if clears:
assert predicted_decode_tok_s(entry, choice.variant, budget) >= PLEASANT_FLOOR_TOK_S
def test_quality_decides_where_speed_permits():
"""On big discrete hardware every resident entry clears the floor, so
the pick must be the highest-quality fitting entry — the axis that
justifies carrying an editorial field at all."""
budget = _discrete(512)
pick = recommended_id(budget)
resident = [
e for e in CATALOG
if (c := select_variant(e, budget)) is not None and c.zero_spill
]
assert pick == max(resident, key=lambda e: e.quality).id