Always include comprehensive documentation for your code through both docstrings and explanatory comments. For classes, functions, and methods, add docstrings that explain:
Always include comprehensive documentation for your code through both docstrings and explanatory comments.
For classes, functions, and methods, add docstrings that explain:
For non-obvious implementation details, add comments explaining:
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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