[Misc] Add support for new autogptq checkpoint_format (#3689)

Co-authored-by: Robert Shaw <rshaw@neuralmagic.com>

tests/quantization/test_autogptq_marlin_configs.py

@@ -0,0 +1,68 @@
+"""Tests whether Marlin models can be loaded from the autogptq config.
+
+Run `pytest tests/quantization/test_autogptq_marlin_configs.py --forked`.
+"""
+
+from dataclasses import dataclass
+
+import pytest
+
+from vllm.config import ModelConfig
+
+
+@dataclass
+class ModelPair:
+    model_marlin: str
+    model_gptq: str
+
+
+# Model Id // Expected Kernel
+MODELS_QUANT_TYPE = [
+    # compat: autogptq <=0.7.1 is_marlin_format: bool
+    ("neuralmagic/TinyLlama-1.1B-Chat-v1.0-marlin", "marlin"),
+    ("TheBloke/Llama-2-7B-Chat-GPTQ", "gptq"),
+    # compat: autogptq >=0.8.0 use checkpoint_format: str
+    ("LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-Marlin-4bit", "marlin"),
+    ("LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit", "gptq")
+]
+
+
+@pytest.mark.parametrize("model_quant_type", MODELS_QUANT_TYPE)
+def test_auto_gptq(model_quant_type: str, ) -> None:
+    model_path, quant_type = model_quant_type
+
+    model_config_no_quant_arg = ModelConfig(
+        model_path,
+        model_path,
+        tokenizer_mode="auto",
+        trust_remote_code=False,
+        download_dir=None,
+        load_format="dummy",
+        seed=0,
+        dtype="float16",
+        revision=None,
+        quantization=None  # case 1
+    )
+
+    model_config_quant_arg = ModelConfig(
+        model_path,
+        model_path,
+        tokenizer_mode="auto",
+        trust_remote_code=False,
+        download_dir=None,
+        load_format="dummy",
+        seed=0,
+        dtype="float16",
+        revision=None,
+        quantization="gptq"  # case 2
+    )
+
+    assert model_config_no_quant_arg.quantization == quant_type, (
+        f"Expected quant_type == {quant_type} for {model_path}, "
+        f"but found {model_config_no_quant_arg.quantization} "
+        "for no --quantization None case")
+
+    assert model_config_quant_arg.quantization == quant_type, (
+        f"Expected quant_type == {quant_type} for {model_path}, "
+        f"but found {model_config_quant_arg.quantization} "
+        "for --quantization gptq case")