Use `quantization_config` in hf config (#1695)

vllm/config.py

@@ -104,14 +104,30 @@ class ModelConfig:
 
     def _verify_quantization(self) -> None:
         supported_quantization = ["awq", "squeezellm"]
-        if self.quantization is None:
-            return
-        quantization = self.quantization.lower()
-        if quantization not in supported_quantization:
-            raise ValueError(
-                f"Unknown quantization: {self.quantization}. Must be one of "
-                f"{supported_quantization}.")
-        self.quantization = quantization
+        if self.quantization is not 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 self.quantization is None:
+                self.quantization = hf_quant_method
+            elif self.quantization != hf_quant_method:
+                raise ValueError(
+                    "Quantization method specified in the model config "
+                    f"({hf_quant_method}) does not match the quantization "
+                    f"method specified in the `quantization` argument "
+                    f"({self.quantization}).")
+
+        if self.quantization is not None:
+            if self.quantization not in supported_quantization:
+                raise ValueError(
+                    f"Unknown quantization method: {self.quantization}. Must "
+                    f"be one of {supported_quantization}.")
+        logger.warning(f"{self.quantization} quantization is not fully "
+                       "optimized yet. The speed can be slower than "
+                       "non-quantized models.")
 
     def verify_with_parallel_config(
         self,

vllm/model_executor/model_loader.py

@@ -66,6 +66,7 @@ def get_model(model_config: ModelConfig) -> nn.Module:
     if model_config.quantization is not None:
         quant_config = get_quant_config(model_config.quantization,
                                         model_config.model,
+                                        model_config.hf_config,
                                         model_config.download_dir)
         capability = torch.cuda.get_device_capability()
         capability = capability[0] * 10 + capability[1]

vllm/model_executor/weight_utils.py

@@ -7,9 +7,10 @@ from collections import defaultdict
 from typing import Any, Iterator, List, Optional, Tuple
 
 from huggingface_hub import snapshot_download
-from safetensors.torch import load_file, save_file, safe_open
 import numpy as np
+from safetensors.torch import load_file, save_file, safe_open
 import torch
+from transformers import PretrainedConfig
 from tqdm.auto import tqdm
 
 from vllm.logger import init_logger
@@ -84,8 +85,15 @@ def convert_bin_to_safetensor_file(
 def get_quant_config(
     quantization: str,
     model_name_or_path: str,
+    hf_config: PretrainedConfig,
     cache_dir: Optional[str] = None,
 ) -> QuantizationConfig:
+    quant_cls = get_quantization_config(quantization)
+    # Read the quantization config from the HF model config, if available.
+    hf_quant_config = getattr(hf_config, "quantization_config", None)
+    if hf_quant_config is not None:
+        return quant_cls.from_config(hf_quant_config)
+
     is_local = os.path.isdir(model_name_or_path)
     if not is_local:
         # Download the config files.
@@ -98,7 +106,6 @@ def get_quant_config(
         hf_folder = model_name_or_path
     config_files = glob.glob(os.path.join(hf_folder, "*.json"))
 
-    quant_cls = get_quantization_config(quantization)
     quant_config_files = [
         f for f in config_files if any(
             f.endswith(x) for x in quant_cls.get_config_filenames())