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63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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import time
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import os
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import subprocess
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import signal
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from fastdeploy.entrypoints.llm import LLM
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from fastdeploy.engine.sampling_params import SamplingParams
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model_name_or_path = "./models/eb45t02/"
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prefill_cmd = (f"FD_LOG_DIR=log_prefill CUDA_VISIBLE_DEVICES=0,1,2,3 python fastdeploy.entrypoints.openai.api_server.py"
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+ f" --model {model_name_or_path} --port 9811"
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+ f" --splitwise-role prefill --tensor-parallel-size 4"
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+ f" --engine-worker-queue-port 6676 --cache-queue-port 55663")
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prefill_instance = subprocess.Popen(
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prefill_cmd,
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stdout=subprocess.PIPE,
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shell=True,
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preexec_fn=os.setsid,
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)
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# # 超参设置
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os.environ["CUDA_VISIBLE_DEVICES"] = "4,5,6,7"
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os.environ["FD_LOG_DIR"] = "log_decode"
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sampling_params = SamplingParams(temperature=0.1, max_tokens=30)
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llm_decode = LLM(
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model=model_name_or_path,
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tensor_parallel_size=4,
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splitwise_role="decode",
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engine_worker_queue_port=6678,
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innode_prefill_ports=[6676],
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cache_queue_port=55668
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)
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output = llm_decode.generate(prompts=["who are you？", "what can you do？"], use_tqdm=True)
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print(output)
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os.killpg(prefill_instance.pid, signal.SIGTERM) |