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

Co-authored-by: Robert Shaw <[email protected]>

Author: Qubitium

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")

vllm/config.py

@@ -171,26 +171,28 @@ def _verify_quantization(self) -> None:
             self.quantization = self.quantization.lower()
 
         # Parse quantization method from the HF model config, if available.
-        hf_quant_config = getattr(self.hf_config, "quantization_config", None)
-        if hf_quant_config is not None:
-            hf_quant_method = str(hf_quant_config["quant_method"]).lower()
-
-            # If the GPTQ model is serialized in marlin format, use marlin.
-            if (hf_quant_method == "gptq"
-                    and "is_marlin_format" in hf_quant_config
-                    and hf_quant_config["is_marlin_format"]):
+        quant_cfg = getattr(self.hf_config, "quantization_config", None)
+        if quant_cfg is not None:
+            quant_method = quant_cfg.get("quant_method", "").lower()
+            # compat: autogptq >=0.8.0 use checkpoint_format: str
+            # compat: autogptq <=0.7.1 is_marlin_format: bool
+            is_format_marlin = (quant_cfg.get("checkpoint_format") == "marlin"
+                                or quant_cfg.get("is_marlin_format", False))
+
+            # Use marlin if the GPTQ model is serialized in marlin format.
+            if quant_method == "gptq" and is_format_marlin:
                 logger.info("The model is serialized in Marlin format. "
                             "Using Marlin kernel.")
-                hf_quant_method = "marlin"
+                quant_method = "marlin"
                 if self.quantization == "gptq":
-                    self.quantization = hf_quant_method
+                    self.quantization = quant_method
 
             if self.quantization is None:
-                self.quantization = hf_quant_method
-            elif self.quantization != hf_quant_method:
+                self.quantization = quant_method
+            elif self.quantization != quant_method:
                 raise ValueError(
                     "Quantization method specified in the model config "
-                    f"({hf_quant_method}) does not match the quantization "
+                    f"({quant_method}) does not match the quantization "
                     f"method specified in the `quantization` argument "
                     f"({self.quantization}).")