Document code thoroughly

Always include comprehensive documentation for your code through both docstrings and explanatory comments. For classes, functions, and methods, add docstrings that explain:

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Always include comprehensive documentation for your code through both docstrings and explanatory comments.

For classes, functions, and methods, add docstrings that explain:

  • The purpose and responsibility of the component
  • Parameters with their types, meanings, and constraints
  • Return values and their significance
  • Special cases or exceptions

For non-obvious implementation details, add comments explaining:

  • Why certain approach was chosen
  • The reasoning behind workarounds
  • Platform-specific behaviors
  • Historical context for future maintainers
def uniform_random_select_experts(
    hidden_states: torch.Tensor,
    router_logits: torch.Tensor,
    top_k: int,
    indices_type: Optional[torch.dtype] = None,
) -> tuple[torch.Tensor, torch.Tensor]:
    """Selects experts randomly with uniform distribution instead of based on router scores.
    
    Args:
        hidden_states: Input tensor of shape [batch_size, sequence_length, hidden_size]
        router_logits: Router scores from which only the shape is used
        top_k: Number of experts to select for each token
        indices_type: Optional dtype for the output indices tensor
        
    Returns:
        Tuple containing (routing_weights, expert_indices)
    """
    # CPU only supports V1 architecture due to specialized optimizations
    # that aren't available in the regular implementation
    if current_platform.is_cpu() and os.environ.get("VLLM_USE_V1", "0") == "0":
        pytest.skip("CPU only supports V1")

Well-documented code improves maintainability, enables easier onboarding of new team members, and reduces the time needed to understand and modify existing functionality.

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