add docstring for Docstring coverage

Author: deepindeed2022

examples/llm_ptq/example_utils.py

@@ -209,6 +209,7 @@ def build_quant_cfg(
     model_type,
     moe_calib_experts_ratio: float | None = None,
 ) -> dict[str, Any]:
+    """Build quantization config with model-specific overrides for AWQ, SmoothQuant, and VLM."""
     quant_cfg = copy.deepcopy(quant_cfg)
     if "awq" in str(quant_cfg.get("algorithm")):
         from modelopt.torch.quantization.config import find_quant_cfg_entry_by_path

tests/_test_utils/torch/transformers_models.py

@@ -112,6 +112,7 @@ def get_tiny_qwen3_moe(**config_kwargs) -> PreTrainedModel:
 def create_tiny_qwen3_moe_dir(
     tmp_path: Path | str, with_tokenizer: bool = False, **config_kwargs
 ) -> Path:
+    """Save a tiny Qwen3 MoE model (and optional tokenizer) to a temp directory."""
     qwen3_moe_dir = Path(tmp_path) / "tiny_qwen3_moe"
     if with_tokenizer:
         tokenizer = AutoTokenizer.from_pretrained(
@@ -157,6 +158,7 @@ def get_tiny_qwen3_5(**config_kwargs) -> PreTrainedModel:
 
 ##### GPT-OSS #####
 def get_tiny_gpt_oss(**config_kwargs) -> PreTrainedModel:
+    """Create a tiny GPT-OSS MoE model for testing."""
     set_seed(SEED)
 
     kwargs = {

tests/unit/torch/quantization/plugins/test_huggingface.py

@@ -236,6 +236,7 @@ def test_is_homogeneous_hf_model_gpt_oss():
 
 
 def test_hf_decoder_discoverer_registration_path():
+    """Verify HF decoder layer discoverer is registered and returns correct layers."""
     model = get_tiny_llama()
     assert any(
         is_supported is is_homogeneous_hf_model and discoverer is get_homogeneous_hf_decoder_layers