Enhancement Method: Expert Migration Cost Optimization

eplb.py

@@ -161,4 +161,71 @@ def rebalance_experts(weight: torch.Tensor, num_replicas: int, num_groups: int,
             torch.arange(num_replicas, dtype=torch.int64, device=log2phy.device).expand(num_layers, -1))
     return phy2log, log2phy, logcnt
 
+def rebalance_with_migration_cost(
+    current_mapping: torch.Tensor,
+    weight: torch.Tensor, 
+    num_replicas: int, 
+    num_groups: int,
+    num_nodes: int, 
+    num_gpus: int,
+    migration_cost_factor: float = 0.5
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """
+    Rebalance experts while considering the cost of migrating experts from their current placement.
+
+    This method extends the basic rebalance_experts function by adding a penalty for
+    moving experts from their current location, which is useful for dynamic systems
+    where the cost of migration needs to be balanced against load distribution benefits.
+
+    Parameters:
+        current_mapping: [layers, num_replicas], the current expert mapping
+        weight: [layers, num_logical_experts], the load statistics for all logical experts
+        num_replicas: number of physical experts, must be a multiple of `num_gpus`
+        num_groups: number of expert groups
+        num_nodes: number of server nodes
+        num_gpus: number of GPUs, must be a multiple of `num_nodes`
+        migration_cost_factor: weight for the migration cost (0.0 to ignore migration costs)
+
+    Returns: 
+        physical_to_logical_map: [layers, num_replicas], the expert index of each replica
+        logical_to_physical_map: [layers, num_logical_experts, X], the replica indices for each expert
+        expert_count: [layers, num_logical_experts], number of physical replicas for each logical expert
+    """
+    # First, get the ideal mapping without considering migration costs
+    phy2log, log2phy, logcnt = rebalance_experts(weight, num_replicas, num_groups, num_nodes, num_gpus)
+
+    # If migration cost factor is zero or no current mapping exists, return the ideal mapping
+    if migration_cost_factor == 0.0 or current_mapping is None:
+        return phy2log, log2phy, logcnt
+
+    num_layers, num_logical_experts = weight.shape
+    experts_per_gpu = num_replicas // num_gpus
+
+    # Adjust weights to account for migration costs
+    adjusted_weight = weight.clone()
+
+    for layer in range(num_layers):
+        # Create a mapping from logical expert to current physical placement
+        current_placements = {}
+        for phys_idx, log_idx in enumerate(current_mapping[layer]):
+            log_idx = log_idx.item()
+            gpu_idx = phys_idx // experts_per_gpu
+            if log_idx not in current_placements:
+                current_placements[log_idx] = []
+            current_placements[log_idx].append(gpu_idx)
+
+        # Adjust weights based on current placements
+        for log_idx in range(num_logical_experts):
+            # If the expert is currently not placed, no adjustment needed
+            if log_idx not in current_placements:
+                continue
+
+            # The adjustment increases the apparent weight of the expert on GPUs
+            # where it's already placed, making it more likely to stay there
+            migration_benefit = weight[layer, log_idx] * migration_cost_factor
+            adjusted_weight[layer, log_idx] += migration_benefit
+
+    # Use the adjusted weights to rebalance
+    return rebalance_experts(adjusted_weight, num_replicas, num_groups, num_nodes, num_gpus)
+
 __all__ = ['rebalance_experts']