[AutoRound] Add offline quantized W4A16 model support (#1777)

Signed-off-by: yiliu30 <yi4.liu@intel.com>
Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>

tests/diffusion/quantization/test_inc_config.py

@@ -0,0 +1,147 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for INC/AutoRound quantization via the unified framework."""
+
+import pytest
+
+pytestmark = [pytest.mark.core_model, pytest.mark.diffusion]
+
+
+def test_build_quant_config_autoround():
+    """build_quant_config("auto-round", ...) should produce an INCConfig."""
+    from vllm.model_executor.layers.quantization.inc import INCConfig
+
+    from vllm_omni.quantization import build_quant_config
+
+    config = build_quant_config(
+        "auto-round",
+        bits=4,
+        group_size=128,
+        sym=True,
+        packing_format="auto_round:auto_gptq",
+    )
+    assert config is not None
+    assert isinstance(config, INCConfig)
+    assert config.weight_bits == 4
+    assert config.group_size == 128
+
+
+def test_build_quant_config_inc():
+    """build_quant_config("inc", ...) should also produce an INCConfig."""
+    from vllm.model_executor.layers.quantization.inc import INCConfig
+
+    from vllm_omni.quantization import build_quant_config
+
+    config = build_quant_config("inc", bits=4, group_size=128)
+    assert isinstance(config, INCConfig)
+    assert config.weight_bits == 4
+
+
+def test_build_quant_config_autoround_dict():
+    """Dict-style config with method=auto-round should work."""
+    from vllm.model_executor.layers.quantization.inc import INCConfig
+
+    from vllm_omni.quantization import build_quant_config
+
+    config = build_quant_config(
+        {
+            "method": "auto-round",
+            "bits": 4,
+            "group_size": 128,
+            "sym": True,
+            "packing_format": "auto_round:auto_gptq",
+        }
+    )
+    assert isinstance(config, INCConfig)
+    assert config.weight_bits == 4
+
+
+def test_build_quant_config_autoround_filters_metadata():
+    """Checkpoint metadata keys (autoround_version, batch_size, iters)
+    should be silently filtered out instead of causing TypeError."""
+    from vllm.model_executor.layers.quantization.inc import INCConfig
+
+    from vllm_omni.quantization import build_quant_config
+
+    config = build_quant_config(
+        "auto-round",
+        bits=4,
+        group_size=128,
+        sym=True,
+        packing_format="auto_round:auto_gptq",
+        block_name_to_quantize="transformer_blocks,single_transformer_blocks",
+        autoround_version="0.12.0",  # metadata — must be filtered
+        batch_size=1,  # metadata — must be filtered
+        iters=0,  # metadata — must be filtered
+    )
+    assert isinstance(config, INCConfig)
+    assert config.weight_bits == 4
+    assert config.group_size == 128
+
+
+def test_build_quant_config_bits_to_weight_bits_mapping():
+    """The 'bits' key from checkpoints should be mapped to 'weight_bits'."""
+    from vllm.model_executor.layers.quantization.inc import INCConfig
+
+    from vllm_omni.quantization import build_quant_config
+
+    # If weight_bits is already provided, bits should be ignored
+    config = build_quant_config("auto-round", weight_bits=4, group_size=128)
+    assert isinstance(config, INCConfig)
+    assert config.weight_bits == 4
+
+
+def test_autoround_in_supported_methods():
+    """auto-round and inc should appear in SUPPORTED_QUANTIZATION_METHODS."""
+    from vllm_omni.quantization import SUPPORTED_QUANTIZATION_METHODS
+
+    assert "auto-round" in SUPPORTED_QUANTIZATION_METHODS
+    assert "inc" in SUPPORTED_QUANTIZATION_METHODS
+
+
+def test_integration_autoround_via_omni_diffusion_config():
+    """OmniDiffusionConfig with auto-round quantization dict should resolve."""
+    from vllm.model_executor.layers.quantization.inc import INCConfig
+
+    from vllm_omni.diffusion.data import OmniDiffusionConfig
+
+    config = OmniDiffusionConfig(
+        model="test",
+        quantization_config={
+            "method": "auto-round",
+            "bits": 4,
+            "group_size": 128,
+            "sym": True,
+        },
+    )
+    assert isinstance(config.quantization_config, INCConfig)
+    assert config.quantization_config.weight_bits == 4
+
+
+def test_integration_autodetect_from_transformer_config():
+    """When TransformerConfig has quant_config, OmniDiffusionConfig should
+    auto-detect it even without explicit quantization_config."""
+    from vllm.model_executor.layers.quantization.inc import INCConfig
+
+    from vllm_omni.diffusion.data import OmniDiffusionConfig, TransformerConfig
+
+    tf_config = TransformerConfig.from_dict(
+        {
+            "quantization_config": {
+                "quant_method": "auto-round",
+                "bits": 4,
+                "group_size": 128,
+                "sym": True,
+                "packing_format": "auto_round:auto_gptq",
+                "autoround_version": "0.12.0",
+                "batch_size": 1,
+                "iters": 0,
+            }
+        }
+    )
+    assert tf_config.quant_method == "auto-round"
+    assert isinstance(tf_config.quant_config, INCConfig)
+
+    od_config = OmniDiffusionConfig(model="test", tf_model_config=tf_config)
+    assert isinstance(od_config.quantization_config, INCConfig)
+    assert od_config.quantization_config.weight_bits == 4