02256d1820
GET /api/inference/images/info returns each family's bf16 component sizes and
the estimated resident GB under bf16/int8/fp8/mxfp8/nvfp4, computed purely from
the auto-policy tables (no GPU probing, torch-free), so the panel can show the
Dtype tradeoff before anything is loaded.
DiffusionStatusResponse gains an additive resolved field: per-control
{value, source, reason} provenance the loader already records. The Advanced
panel renders a muted Auto: X pill next to Speed / Dtype / Attention / Memory /
Step cache / CPU offload when the backend decided that control (source auto),
with the reason as the tooltip; an explicit user choice renders no badge.
57 lines
2.5 KiB
Python
57 lines
2.5 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""CPU-only unit tests for the pure per-family inference-info helper.
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Covers ``family_inference_infos()``: every auto-policy family appears, the component sizes
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round-trip, and the quant estimates order correctly (quantised < bf16, nvfp4 < int8). No
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torch / diffusers / GPU."""
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from __future__ import annotations
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from core.inference.diffusion_auto_policy import _FAMILY_BF16_GB, _QUANT_STEADY_FACTOR
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from core.inference.diffusion_inference_info import family_inference_infos
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def test_covers_every_auto_policy_family():
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infos = family_inference_infos()
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names = {info["family"] for info in infos}
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assert names == set(_FAMILY_BF16_GB), "info must list exactly the auto-policy families"
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# One entry per family, in registry order.
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assert [info["family"] for info in infos] == list(_FAMILY_BF16_GB)
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def test_each_family_reports_all_schemes():
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for info in family_inference_infos():
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estimated = info["estimated_resident_gb"]
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assert set(estimated) == {"bf16", *_QUANT_STEADY_FACTOR}
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# Every reported value is a float rounded to one decimal.
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for value in estimated.values():
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assert isinstance(value, float)
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assert round(value, 1) == value
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def test_component_sizes_match_the_table():
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infos = {info["family"]: info for info in family_inference_infos()}
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for name, (transformer, text_encoders, vae) in _FAMILY_BF16_GB.items():
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info = infos[name]
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assert info["transformer_bf16_gb"] == round(transformer, 1)
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assert info["text_encoders_bf16_gb"] == round(text_encoders, 1)
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assert info["vae_bf16_gb"] == round(vae, 1)
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def test_quantised_estimate_is_below_bf16():
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# A quantised transformer is smaller than bf16, so its resident estimate must be too
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# (the companions are shared, and every steady factor is < 1).
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for info in family_inference_infos():
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estimated = info["estimated_resident_gb"]
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for scheme in _QUANT_STEADY_FACTOR:
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assert estimated[scheme] < estimated["bf16"], f"{info['family']} {scheme}"
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def test_nvfp4_is_below_int8():
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# nvfp4 packs two params per byte (~0.33x) vs int8's one byte per param (~0.55x), so
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# nvfp4's estimate is the smaller of the two on every family.
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for info in family_inference_infos():
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estimated = info["estimated_resident_gb"]
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assert estimated["nvfp4"] < estimated["int8"], info["family"]
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