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fix ci issue distributed 4 gpu test #20204

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merged 2 commits into from
Jun 28, 2025
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fix ci issue distributed 4 gpu test #20204

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5abe793
fix ci issue distributed 4 gpu test
yewentao256 Jun 28, 2025
0bc44e7
fix doc
yewentao256 Jun 28, 2025
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18 changes: 18 additions & 0 deletions examples/offline_inference/data_parallel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -64,6 +64,18 @@ def parse_args():
parser.add_argument(
"--trust-remote-code", action="store_true", help="Trust remote code."
)
parser.add_argument(
"--max-num-seqs",
type=int,
default=64,
help=("Maximum number of sequences to be processed in a single iteration."),
)
Comment on lines +67 to +72
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@njhill njhill Jun 28, 2025

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Are both of these args required to avoid the OOM? 64 is quite small for batch mode, would be good if we could fix just with the gpu_memory_utilization reduction...

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@houseroad houseroad Jun 28, 2025

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yeah, also seems that we need much more memory during initialization than before. I was about to investigate more into this, but didn't get time to do so. Wondering if @yewentao256 could dig further into this?

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@yewentao256 yewentao256 Jun 28, 2025

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Yeah I am happy to dig further, but what is the expected result for this? To reduce the memory usage? But it is kind of like a tradeoff between speed and efficiency I am afraid.
Basically, the original cause of this OOM issue is from #18724, which I think it is reasonable to adopt. @houseroad

parser.add_argument(
"--gpu-memory-utilization",
type=float,
default=0.8,
help=("Fraction of GPU memory vLLM is allowed to allocate (0.0, 1.0]."),
)
return parser.parse_args()


Expand All @@ -77,6 +89,8 @@ def main(
GPUs_per_dp_rank,
enforce_eager,
trust_remote_code,
max_num_seqs,
gpu_memory_utilization,
):
os.environ["VLLM_DP_RANK"] = str(global_dp_rank)
os.environ["VLLM_DP_RANK_LOCAL"] = str(local_dp_rank)
Expand Down Expand Up @@ -127,6 +141,8 @@ def start(rank):
enforce_eager=enforce_eager,
enable_expert_parallel=True,
trust_remote_code=trust_remote_code,
max_num_seqs=max_num_seqs,
gpu_memory_utilization=gpu_memory_utilization,
)
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
Expand Down Expand Up @@ -181,6 +197,8 @@ def start(rank):
tp_size,
args.enforce_eager,
args.trust_remote_code,
args.max_num_seqs,
args.gpu_memory_utilization,
),
)
proc.start()
Expand Down