[EPLB] Reduce EPLB Inference Overhead

[abmfy]

Purpose

PR #18343 introduced the Expert Parallelism Load Balancer (EPLB). By replicating a single logical expert into multiple physical experts, we can achieve better load balancing across experts.

However, this replication introduces some inference-time overhead: after the MoE routing module, we must select among multiple replicas of the same logical expert and also record expert load metrics for the rearrangement algorithm.

Previously, torch.rand was used to select expert replicas. Unfortunately, this method is slow and not torch.compile-friendly.

In this PR, we aim to reduce EPLB overhead by:

1. Switching from torch.rand to a modulo-based pseudo-random selection.
   • The pseudo-random method is intentionally simple, based only on the token index and routing rank k.
2. Removing unnecessary masking of local experts, since we now collect global physical expert load metrics.
3. Extracting all EPLB-related logic from select_experts into a torch.compile-friendly function.

Test Plan

To isolate EPLB inference overhead, we test with EPLB enabled but with num_redundant_experts=0, and without rearranging experts. This ensures that any observed differences are solely due to replica selection and load recording overhead.

Test Result

We benchmarked 1000 random prompts with 1000 input tokens and 100 output tokens on DeepSeek-V3-0324, on a DP16 setting. Prefix caching was disabled to measure the raw computational cost.

vllm bench serve \
    --model $MODEL \
    --dataset-name random \
    --ignore-eos \
    --port ${PORT:-8080} \
    --random-input-len 1000 \
    --random-output-len 100 \

w/o EPLB:

============ Serving Benchmark Result ============
Successful requests:                     1000      
Benchmark duration (s):                  34.61     
Total input tokens:                      996701    
Total generated tokens:                  100000    
Request throughput (req/s):              28.89     
Output token throughput (tok/s):         2889.16   
Total Token throughput (tok/s):          31685.45  
---------------Time to First Token----------------
Mean TTFT (ms):                          16177.52  
Median TTFT (ms):                        17730.53  
P99 TTFT (ms):                           28279.18  
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          184.22    
Median TPOT (ms):                        168.75    
P99 TPOT (ms):                           310.01    
---------------Inter-token Latency----------------
Mean ITL (ms):                           184.22    
Median ITL (ms):                         64.62     
P99 ITL (ms):                            3563.61   
==================================================

w/ EPLB, main:

============ Serving Benchmark Result ============
Successful requests:                     1000      
Benchmark duration (s):                  36.04     
Total input tokens:                      996701    
Total generated tokens:                  100000    
Request throughput (req/s):              27.74     
Output token throughput (tok/s):         2774.48   
Total Token throughput (tok/s):          30427.73  
---------------Time to First Token----------------
Mean TTFT (ms):                          16787.26  
Median TTFT (ms):                        15501.58  
P99 TTFT (ms):                           29449.69  
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          192.38    
Median TPOT (ms):                        205.27    
P99 TPOT (ms):                           316.54    
---------------Inter-token Latency----------------
Mean ITL (ms):                           192.38    
Median ITL (ms):                         67.10     
P99 ITL (ms):                            3727.04   
==================================================

w/ EPLB, this PR:

============ Serving Benchmark Result ============
Successful requests:                     1000      
Benchmark duration (s):                  35.19     
Total input tokens:                      996701    
Total generated tokens:                  100000    
Request throughput (req/s):              28.41     
Output token throughput (tok/s):         2841.33   
Total Token throughput (tok/s):          31160.91  
---------------Time to First Token----------------
Mean TTFT (ms):                          16370.60  
Median TTFT (ms):                        15091.95  
P99 TTFT (ms):                           28831.30  
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          188.17    
Median TPOT (ms):                        201.00    
P99 TPOT (ms):                           308.62    
---------------Inter-token Latency----------------
Mean ITL (ms):                           188.17    
Median ITL (ms):                         65.13     
P99 ITL (ms):                            3621.91   
==================================================

Summary:
Not accounting for the benefits of improved expert load balancing, on the main branch, EPLB introduces ~3.97% throughput drop. With this PR, we recover ~2.41%, narrowing the gap to ~1.66% compared to running without EPLB.

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[gemini-code-assist]

Code Review

This pull request significantly improves the performance and maintainability of the Expert Parallelism Load Balancer (EPLB) by replacing the slow and non-compilable torch.rand with a deterministic modulo-based replica selection. The refactoring of EPLB logic into a separate, torch.compile-friendly function eplb_map_to_physical_and_record is a great change that enhances code clarity. I've found one critical issue that could lead to a runtime error, which I've detailed in a specific comment.

[mgoin]

LGTM - please fix the pre-commit