Phase 6 of the modernization plan: add weight palettization to the
Core ML converter as an opt-in knob, so the SD1.5 / SDXL UNet can
ship at 1/2, 1/2.7 or 1/4 of its current size with ANE-friendly
inference.
CoreMLConverter (and the LCM converter) gains a `quantize_nbits`
dropdown: `none` (default — identical to pre-Phase-6 behavior and
filenames, so existing cached .mlpackages still resolve) / `8` / `6` /
`4`. The value is encoded as `_q<bits>` after the attn suffix, so the
unquantized model and the three palettized variants coexist on disk
under distinct cache keys.
Implementation
- core/naming.compose_out_name: accepts `quantize_nbits`, validates
against {none, 8, 6, 4}, appends `_q<bits>` (none = empty).
- converter.convert_unet: after ct.convert + before .save, runs
coremltools.optimize.coreml.palettize_weights with
OpPalettizerConfig(mode="kmeans", nbits=...) when the value is not
"none". Adds a `Palettization took Xs` log line.
- converter.convert / nodes.CoreMLConverter.convert: pipe the new arg
through; the ComfyUI node exposes it as a dropdown with default
"none" so existing workflows are unchanged at load time.
- bench/scripts/convert_sd15.py: QUANT_NBITS env knob; uses the
pure compose_out_name (replaces the inline string formatter).
Test infra
- tests/unit/test_characterization_out_name.py: 6 new tests pinning
the `_q<bits>` suffix contract, the "none" passthrough (backward
compat), the cn + lora + quant combination, and the invalid-value
ValueError. Total Tier 0 now at 94.
- Makefile gains `bench-quant` (runs the matrix script) and
`convert-quant` (converts q8, q6, q4 sequentially).
- bench/scripts/quant_matrix.py (new): loads each variant, runs
REPEATS forward passes with a fixed seed, then computes the
noise_pred PSNR of each quantized variant against the unquantized
baseline. Writes bench/results/quant_matrix_<sha>.{json,md}.
README
- New "Quantization (Phase 6, opt-in)" section: tradeoff table
measured on M2 Pro SD1.5 1x512x512 SPLIT_EINSUM (sizes 1641/822/
617/412 MB; fwd 197/187/183/180 ms; PSNR 53.5 / 40.2 / 27.5 dB),
plus per-chip/RAM recommendations.
Default-path safety
- "none" produces the same out_name as Phase 5 -> existing
v1-5-pruned-emaonly_1x512x512_se_unet.mlmodelc is still picked up
unchanged; the m2 golden image test continues to anchor.
198 lines
6.1 KiB
Python
198 lines
6.1 KiB
Python
"""Phase 2 characterization tests, Phase 3 re-pointed.
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After Phase 3 the .mlpackage filename composition is the pure
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coreml_suite.core.naming.compose_out_name function. CoreMLConverter.convert
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calls it; the previous Phase 2 test had to monkey-patch heavy converter
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internals just to capture the string, which made the test framework-coupled.
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"""
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import pytest
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from coreml_suite.core.naming import compose_out_name, lora_names_from_params
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# ---------- attention suffixes ----------------------------------------------
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@pytest.mark.parametrize(
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"attn_name,suffix",
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[
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("SPLIT_EINSUM", "se"),
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("SPLIT_EINSUM_V2", "se2"),
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("ORIGINAL", "orig"),
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],
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)
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def test_attention_suffix(attn_name, suffix):
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out = compose_out_name(
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ckpt_name="dreamshaper_8.safetensors",
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batch_size=1, width=512, height=512,
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controlnet_support=False,
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attention_implementation=attn_name,
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)
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assert out == f"dreamshaper_8_1x512x512_{suffix}"
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# ---------- batch / size ----------------------------------------------------
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def test_includes_batch_and_size():
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out = compose_out_name(
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ckpt_name="dreamshaper_8.safetensors",
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batch_size=4, width=768, height=1024,
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controlnet_support=False,
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attention_implementation="SPLIT_EINSUM",
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)
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assert out == "dreamshaper_8_4x768x1024_se"
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# ---------- ControlNet ------------------------------------------------------
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def test_appends_cn_suffix_when_controlnet_support_true():
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out = compose_out_name(
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ckpt_name="dreamshaper_8.safetensors",
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batch_size=1, width=512, height=512,
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controlnet_support=True,
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attention_implementation="SPLIT_EINSUM",
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)
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assert out == "dreamshaper_8_1x512x512_cn_se"
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# ---------- ckpt name massage -----------------------------------------------
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def test_drops_extension_at_first_period():
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out = compose_out_name(
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ckpt_name="my.checkpoint.v2.safetensors",
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batch_size=1, width=512, height=512,
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controlnet_support=False,
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attention_implementation="SPLIT_EINSUM",
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)
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assert out == "my_1x512x512_se"
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def test_replaces_spaces_with_underscores():
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out = compose_out_name(
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ckpt_name="dream shaper 8.safetensors",
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batch_size=1, width=512, height=512,
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controlnet_support=False,
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attention_implementation="SPLIT_EINSUM",
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)
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assert out == "dream_shaper_8_1x512x512_se"
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# ---------- LoRA suffixes ---------------------------------------------------
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def test_single_lora():
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out = compose_out_name(
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ckpt_name="dreamshaper_8.safetensors",
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batch_size=1, width=512, height=512,
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controlnet_support=False,
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attention_implementation="SPLIT_EINSUM",
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lora_names=["epi_noiseoffset.safetensors"],
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)
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assert out == "dreamshaper_8_epi_noiseoffset_1x512x512_se"
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def test_multiple_loras_sorted():
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out = compose_out_name(
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ckpt_name="dreamshaper_8.safetensors",
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batch_size=1, width=512, height=512,
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controlnet_support=False,
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attention_implementation="SPLIT_EINSUM",
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lora_names=["zoom.safetensors", "alpha.safetensors", "moody.safetensors"],
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)
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assert out == "dreamshaper_8_alpha_moody_zoom_1x512x512_se"
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def test_lora_plus_controlnet():
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out = compose_out_name(
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ckpt_name="dreamshaper_8.safetensors",
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batch_size=1, width=512, height=512,
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controlnet_support=True,
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attention_implementation="SPLIT_EINSUM",
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lora_names=["a.safetensors"],
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)
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assert out == "dreamshaper_8_a_1x512x512_cn_se"
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# ---------- sdxl combinations -----------------------------------------------
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def test_sdxl_1024_original_gpu():
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out = compose_out_name(
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ckpt_name="sd_xl_base_1.0.safetensors",
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batch_size=1, width=1024, height=1024,
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controlnet_support=False,
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attention_implementation="ORIGINAL",
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)
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assert out == "sd_xl_base_1_1x1024x1024_orig"
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# ---------- lora_names_from_params helper ----------------------------------
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def test_lora_names_from_params_sorts_by_name():
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names = lora_names_from_params([
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("zebra.safetensors", 1.0),
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("apple.safetensors", 0.5),
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("mango.safetensors", 0.7),
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])
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assert names == ["apple.safetensors", "mango.safetensors", "zebra.safetensors"]
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def test_lora_names_from_params_empty_list():
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assert lora_names_from_params([]) == []
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# ---------- Phase 6: quantize_nbits suffix ---------------------------------
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def test_quantize_nbits_none_appends_nothing():
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"""'none' is the default and must keep the pre-Phase-6 filename so
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existing cached .mlpackages still resolve."""
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out = compose_out_name(
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ckpt_name="dreamshaper_8.safetensors",
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batch_size=1, width=512, height=512,
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controlnet_support=False,
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attention_implementation="SPLIT_EINSUM",
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quantize_nbits="none",
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)
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assert out == "dreamshaper_8_1x512x512_se"
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@pytest.mark.parametrize("nbits,suffix", [("4", "_q4"), ("6", "_q6"), ("8", "_q8")])
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def test_quantize_nbits_appends_q_suffix(nbits, suffix):
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out = compose_out_name(
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ckpt_name="dreamshaper_8.safetensors",
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batch_size=1, width=512, height=512,
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controlnet_support=False,
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attention_implementation="SPLIT_EINSUM",
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quantize_nbits=nbits,
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)
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assert out == f"dreamshaper_8_1x512x512_se{suffix}"
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def test_quantize_nbits_with_controlnet_and_lora():
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out = compose_out_name(
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ckpt_name="dreamshaper_8.safetensors",
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batch_size=1, width=512, height=512,
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controlnet_support=True,
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attention_implementation="SPLIT_EINSUM",
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lora_names=["a.safetensors"],
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quantize_nbits="6",
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)
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assert out == "dreamshaper_8_a_1x512x512_cn_se_q6"
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def test_quantize_nbits_invalid_raises():
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import pytest as _pytest
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with _pytest.raises(ValueError, match="quantize_nbits"):
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compose_out_name(
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ckpt_name="x.safetensors",
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batch_size=1, width=512, height=512,
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controlnet_support=False,
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attention_implementation="SPLIT_EINSUM",
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quantize_nbits="16", # not in {none, 8, 6, 4}
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)
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