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[Lint]Style: Convert example to ruff format (#5863)

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### What this PR does / why we need it?
This PR fixes linting issues in the `example/` to align with the
project's Ruff configuration.

- vLLM version: v0.13.0
- vLLM main:
bde38c11df

Signed-off-by: root <root@LAPTOP-VQKDDVMG.localdomain>
Co-authored-by: root <root@LAPTOP-VQKDDVMG.localdomain>
This commit is contained in:
SILONG ZENG
2026-01-13 20:46:50 +08:00
committed by GitHub
parent f7b904641e
commit 78d5ce3e01
23 changed files with 678 additions and 1037 deletions
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80
examples/offline_disaggregated_prefill_npu.py
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@@ -29,8 +29,8 @@ def clean_up():
import gc
import torch
from vllm.distributed.parallel_state import (
destroy_distributed_environment, destroy_model_parallel)
from vllm.distributed.parallel_state import destroy_distributed_environment, destroy_model_parallel
destroy_model_parallel()
destroy_distributed_environment()
gc.collect()
@@ -44,8 +44,10 @@ def run_prefill(prefill_done, process_close):
from vllm.config import KVTransferConfig
prompts = [
"Hello, how are you today?", "Hi, what is your name?",
"Tell me a very long story.", "what is your favourite book?"
"Hello, how are you today?",
"Hi, what is your name?",
"Tell me a very long story.",
"what is your favourite book?",
]
sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=1)
@@ -55,22 +57,16 @@ def run_prefill(prefill_done, process_close):
kv_port="30000",
engine_id="0",
kv_connector_module_path="vllm_ascend.distributed.mooncake_connector",
kv_connector_extra_config={
"prefill": {
"dp_size": 1,
"tp_size": 1
},
"decode": {
"dp_size": 1,
"tp_size": 1
}
})
kv_connector_extra_config={"prefill": {"dp_size": 1, "tp_size": 1}, "decode": {"dp_size": 1, "tp_size": 1}},
)
# Set NPU memory utilization to 0.8
llm = LLM(model="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
kv_transfer_config=ktc,
max_model_len=2000,
gpu_memory_utilization=0.8,
tensor_parallel_size=1)
llm = LLM(
model="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
kv_transfer_config=ktc,
max_model_len=2000,
gpu_memory_utilization=0.8,
tensor_parallel_size=1,
)
llm.generate(prompts, sampling_params)
print("Prefill node is finished.")
@@ -96,8 +92,10 @@ def run_decode(prefill_done):
from vllm.config import KVTransferConfig
prompts = [
"Hello, how are you today?", "Hi, what is your name?",
"Tell me a very long story.", "what is your favourite book?"
"Hello, how are you today?",
"Hi, what is your name?",
"Tell me a very long story.",
"what is your favourite book?",
]
sampling_params = SamplingParams(temperature=0, top_p=0.95)
@@ -107,22 +105,16 @@ def run_decode(prefill_done):
kv_port="30100",
engine_id="1",
kv_connector_module_path="vllm_ascend.distributed.mooncake_connector",
kv_connector_extra_config={
"prefill": {
"dp_size": 1,
"tp_size": 1
},
"decode": {
"dp_size": 1,
"tp_size": 1
}
})
kv_connector_extra_config={"prefill": {"dp_size": 1, "tp_size": 1}, "decode": {"dp_size": 1, "tp_size": 1}},
)
llm = LLM(model="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
kv_transfer_config=ktc,
max_model_len=2000,
gpu_memory_utilization=0.8,
tensor_parallel_size=1)
llm = LLM(
model="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
kv_transfer_config=ktc,
max_model_len=2000,
gpu_memory_utilization=0.8,
tensor_parallel_size=1,
)
# Wait for the producer to start the consumer
print("Waiting for prefill node to finish...")
@@ -141,16 +133,18 @@ def run_decode(prefill_done):
if __name__ == "__main__":
mp.get_context('spawn')
mp.get_context("spawn")
prefill_done = Event()
process_close = Event()
prefill_process = Process(target=run_prefill,
args=(
prefill_done,
process_close,
))
decode_process = Process(target=run_decode, args=(prefill_done, ))
prefill_process = Process(
target=run_prefill,
args=(
prefill_done,
process_close,
),
)
decode_process = Process(target=run_decode, args=(prefill_done,))
# Start prefill node
prefill_process.start()