[Benchmark] Fix client seed synchronization in multi-turn benchmark

[ai-jz]

Problem

All clients in multi-turn benchmark use the same seed value, causing them to generate identical random sequences. This leads to synchronized behavior where all clients sleep for the same duration and make requests at the same time, defeating the purpose of Poisson-distributed request timing.

Evidence

Real logs showing the synchronization issue:

10-11-2025 23:43:34 [INFO] - Client 60 will use conversation ID CONV_ID_60 (active conversations 1)
10-11-2025 23:43:34 [INFO] - Client 61 will use conversation ID CONV_ID_61 (active conversations 1)
10-11-2025 23:43:34 [INFO] - Client 62 will use conversation ID CONV_ID_62 (active conversations 1)
10-11-2025 23:43:34 [INFO] - Client 63 will use conversation ID CONV_ID_63 (active conversations 1)
10-11-2025 23:43:44 [INFO] - Sleeping for 0.796 seconds...
10-11-2025 23:43:44 [INFO] - Sleeping for 0.796 seconds...
10-11-2025 23:43:44 [INFO] - Sleeping for 0.796 seconds...
10-11-2025 23:43:54 [INFO] - Sleeping for 0.796 seconds...

All clients sleep for exactly 0.796 seconds because they use the same random seed.

Solution

Add client_id to the seed value to ensure each client has a unique but reproducible random sequence:

# Before
random.seed(args.seed)
np.random.seed(args.seed)

# After
client_seed = args.seed + client_id + 1
random.seed(client_seed)
np.random.seed(client_seed)

Impact

• Each client now generates different random intervals
• Request timing is properly distributed according to Poisson process
• Benchmark results will be more realistic
• Results remain reproducible with the same seed and client configuration

[gemini-code-assist]

Code Review

This pull request correctly fixes a critical flaw in the multi-turn benchmark where clients were synchronized due to using the same random seed. The implemented solution of creating a unique seed for each client based on its ID is a good step forward. My review includes one suggestion to further improve the statistical robustness of the benchmark by using a more advanced seeding strategy (numpy.random.SeedSequence) designed for parallel applications. This would ensure the random behavior of clients is not just unique but also statistically independent, which is crucial for the validity of the benchmark results.

[ai-jz]

@DarkLight1337 could you have a look?

[DarkLight1337]

Nice catch

[ai-jz]

@DarkLight1337 I updated the commit to include the DCO sign-off, which seems a new requirement of vLLM.

This update has no code change - only the commit message was amended with "Signed-off-by".