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57 lines
2.3 KiB
Python
57 lines
2.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Tests for humming's is_layer_skipped handling of compressed-tensors
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regex ("re:") ignore entries.
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Regression test: compressed-tensors checkpoints (e.g. Kimi-K2.6) list ignored
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layers as regex patterns prefixed with "re:" (e.g. "re:vision_tower.*").
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humming previously substring-matched these literals, so ignored layers were
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never skipped and got (incorrectly) quantized -- which then diverged between
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the real weight-load path (falls back to unquantized) and the dummy-load path
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(stays quantized), breaking any consumer that byte-compares the two.
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"""
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import pytest
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from vllm.model_executor.layers.quantization.humming import HummingConfig
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# The real ignore list from Kimi-K2.6's compressed-tensors quantization_config.
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K26_IGNORE = [
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"re:.*self_attn.*",
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"re:.*shared_experts.*",
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r"re:.*mlp\.(gate|up|gate_up|down)_proj.*",
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"re:.*lm_head.*",
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"re:vision_tower.*",
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"re:mm_projector.*",
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]
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@pytest.mark.parametrize(
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"prefix,expected",
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[
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# ignored layers (must be skipped -> stay unquantized)
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("vision_tower.encoder.blocks.0.mlp.fc0", True),
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("vision_tower.encoder.blocks.16.wo", True),
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("mm_projector.linear_1", True),
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("language_model.model.layers.0.self_attn.o_proj", True),
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("language_model.model.layers.0.mlp.gate_up_proj", True),
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("language_model.model.layers.0.mlp.down_proj", True),
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("language_model.model.layers.3.mlp.shared_experts.gate_proj", True),
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("model.lm_head", True),
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# routed experts are NOT ignored -> must be quantized
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("language_model.model.layers.3.mlp.experts.383.up_proj", False),
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("language_model.model.layers.3.mlp.experts.0.down_proj", False),
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],
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)
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def test_is_layer_skipped_regex_ignore(prefix, expected):
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cfg = HummingConfig(full_config={"ignore": K26_IGNORE})
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assert cfg.is_layer_skipped({"ignore": K26_IGNORE}, prefix) is expected
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def test_plain_substring_entries_still_work():
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# bitsandbytes-style modules_to_not_convert use plain substrings.
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cfg = HummingConfig()
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cfg_dict = {"modules_to_not_convert": ["lm_head", "vision"]}
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assert cfg.is_layer_skipped(cfg_dict, "model.vision.encoder.fc") is True
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assert cfg.is_layer_skipped(cfg_dict, "model.layers.0.mlp.up_proj") is False
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