[EPLB] Refactor balance_packing to use numpy and optimize GPU-CPU transfers in EPLB (#28369)

Signed-off-by: Sage Moore <sage@neuralmagic.com>

vllm/distributed/eplb/rebalance_algo.py

@@ -12,6 +12,7 @@ Please find at [#12](https://github.com/deepseek-ai/EPLB/issues/12) an example
 on how the EPLB algorithm works.
 """
 
+import numpy as np
 import torch
 
 
@@ -34,29 +35,44 @@ def balanced_packing(
     assert num_groups % num_packs == 0
     groups_per_pack = num_groups // num_packs
 
+    device = weight.device
+
     if groups_per_pack == 1:
         pack_index = torch.arange(
-            weight.size(-1), dtype=torch.int64, device=weight.device
+            weight.size(-1), dtype=torch.int64, device=device
         ).expand(weight.shape)
-        rank_in_pack = torch.zeros_like(weight, dtype=torch.int64)
+        rank_in_pack = torch.zeros_like(weight, dtype=torch.int64, device=device)
         return pack_index, rank_in_pack
 
-    indices = weight.float().sort(-1, descending=True).indices.cpu()
-    pack_index = torch.full_like(weight, fill_value=-1, dtype=torch.int64, device="cpu")
-    rank_in_pack = torch.full_like(pack_index, fill_value=-1)
+    weight_np = weight.cpu().numpy()
+
+    # Sort and get indices in decending order
+    indices_np = np.argsort(-weight_np, axis=-1)
+
+    pack_index_np = np.full((num_layers, num_groups), -1, dtype=np.int64)
+    rank_in_pack_np = np.full((num_layers, num_groups), -1, dtype=np.int64)
+
+    # Run the packing algorithm
     for i in range(num_layers):
-        pack_weights = [0] * num_packs
+        pack_weights = [0.0] * num_packs
         pack_items = [0] * num_packs
-        for group in indices[i]:
+
+        for group in indices_np[i]:
+            # Find a pack with capacity that has the lowest weight
             pack = min(
-                (i for i in range(num_packs) if pack_items[i] < groups_per_pack),
+                (j for j in range(num_packs) if pack_items[j] < groups_per_pack),
                 key=pack_weights.__getitem__,
             )
+
             assert pack_items[pack] < groups_per_pack
-            pack_index[i, group] = pack
-            rank_in_pack[i, group] = pack_items[pack]
-            pack_weights[pack] += weight[i, group]
+            pack_index_np[i, group] = pack
+            rank_in_pack_np[i, group] = pack_items[pack]
+            pack_weights[pack] += weight_np[i, group]
             pack_items[pack] += 1
+
+    pack_index = torch.from_numpy(pack_index_np).to(device)
+    rank_in_pack = torch.from_numpy(rank_in_pack_np).to(device)
+
     return pack_index, rank_in_pack
 
 
@@ -212,7 +228,7 @@ def rebalance_experts(
             replicas for each logical expert
     """
     num_layers, num_logical_experts = weight.shape
-    weight = weight.float().cpu()
+    weight = weight.float()
     if num_groups % num_nodes == 0:
         # use hierarchical load-balance policy
         phy2log, phyrank, logcnt = rebalance_experts_hierarchical(

vllm/distributed/eplb/rebalance_execute.py

@@ -321,15 +321,19 @@ def rearrange_expert_weights_inplace(
             )
         return
 
+    old_global_expert_indices_cpu = old_global_expert_indices.cpu()
+    new_global_expert_indices_cpu = new_global_expert_indices.cpu()
+
+    # NOTE(bowen): We need this synchronize to run, but I don't know why.
+    # If you figure out the reason, please let me know -- thank you!
+    torch.cuda.synchronize()
+
     for layer in range(num_moe_layers):
-        # NOTE(bowen): We need this synchronize to run, but I don't know why.
-        # If you figure out the reason, please let me know -- thank you!
-        torch.cuda.synchronize()
         shuffle_layer(
             num_local_physical_experts,
             ep_rank,
-            old_global_expert_indices[layer].tolist(),
-            new_global_expert_indices[layer].tolist(),
+            old_global_expert_indices_cpu[layer].tolist(),
+            new_global_expert_indices_cpu[layer].tolist(),
             expert_weights[layer],
             expert_weights_buffer,
             ep_group,