[DP] Tiny fix of dp and update example (#1273)
### What this PR does / why we need it? Add `max_num_tokens_across_dp` to AscendMetadata to fix dp This pr fixes the bug introduced by https://github.com/vllm-project/vllm-ascend/pull/1229, which add an arg `max_num_tokens_across_dp` when dp_size > 1. Signed-off-by: MengqingCao <cmq0113@163.com>
This commit is contained in:
Mengqing Cao
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examples/offline_data_parallel.py
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examples/offline_data_parallel.py
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#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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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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# This file is a part of the vllm-ascend project.
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# Adapted from vllm-project/vllm/examples/offline_inference/data_parallel.py
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#
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"""
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Usage:
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Single node:
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Dense models:
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python examples/offline_data_parallel.py \
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--model="Qwen/Qwen2.5-0.5B-Instruct" \
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--dp-size=2 \
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--tp-size=2
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MOE models:
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python examples/offline_data_parallel.py \
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--model="ibm-research/PowerMoE-3b" \
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--dp-size=2 \
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--tp-size=2 \
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--enable-expert-parallel
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Multi-node:
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Node 0 (assume the node has ip of 10.99.48.128):
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python examples/offline_data_parallel.py \
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--model="ibm-research/PowerMoE-3b" \
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--dp-size=2 \
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--tp-size=2 \
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--node-size=2 \
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--node-rank=0 \
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--enable-expert-parallel \
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--master-addr=10.99.48.128 \
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--master-port=13345
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Node 1:
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python examples/offline_data_parallel.py \
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--model="ibm-research/PowerMoE-3b" \
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--dp-size=2 \
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--tp-size=2 \
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--node-size=2 \
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--node-rank=1 \
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--enable-expert-parallel \
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--master-addr=10.99.48.128 \
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--master-port=13345
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"""
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import os
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from time import sleep
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from vllm import LLM, SamplingParams
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from vllm.utils import get_open_port
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def parse_args():
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import argparse
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parser = argparse.ArgumentParser(description="Data Parallel Inference")
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parser.add_argument(
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"--model",
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type=str,
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default="ibm-research/PowerMoE-3b",
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help="Model name or path",
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)
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parser.add_argument("--dp-size",
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type=int,
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default=2,
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help="Data parallel size")
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parser.add_argument("--tp-size",
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type=int,
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default=1,
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help="Tensor parallel size")
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parser.add_argument("--node-size",
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type=int,
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default=1,
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help="Total number of nodes")
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parser.add_argument("--node-rank",
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type=int,
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default=0,
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help="Rank of the current node")
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parser.add_argument("--master-addr",
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type=str,
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default="",
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help="Master node IP address")
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parser.add_argument("--master-port",
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type=int,
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default=0,
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help="Master node port")
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parser.add_argument("--enforce-eager",
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action="store_true",
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help="Enforce eager mode execution.")
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parser.add_argument("--trust-remote-code",
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action="store_true",
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help="Trust remote code.")
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parser.add_argument("--enable-expert-parallel",
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action="store_true",
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help="Enable expert parallel, used in MOE models.")
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return parser.parse_args()
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def main(
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model,
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dp_size,
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local_dp_rank,
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global_dp_rank,
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dp_master_ip,
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dp_master_port,
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GPUs_per_dp_rank,
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enable_expert_parallel,
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enforce_eager,
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trust_remote_code,
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):
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# DP only support on V1 engine
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os.environ["VLLM_USE_V1"] = "1"
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os.environ["VLLM_DP_RANK"] = str(global_dp_rank)
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os.environ["VLLM_DP_RANK_LOCAL"] = str(local_dp_rank)
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os.environ["VLLM_DP_SIZE"] = str(dp_size)
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os.environ["VLLM_DP_MASTER_IP"] = dp_master_ip
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os.environ["VLLM_DP_MASTER_PORT"] = str(dp_master_port)
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# CUDA_VISIBLE_DEVICES for each DP rank is set automatically inside the
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# engine processes.
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# Sample prompts.
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prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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] * 100
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# with DP, each rank should process different prompts.
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# usually all the DP ranks process a full dataset,
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# and each rank processes a different part of the dataset.
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floor = len(prompts) // dp_size
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remainder = len(prompts) % dp_size
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# Distribute prompts into even groups.
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def start(rank):
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return rank * floor + min(rank, remainder)
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prompts = prompts[start(global_dp_rank):start(global_dp_rank + 1)]
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if len(prompts) == 0:
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# if any rank has no prompts to process,
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# we need to set a placeholder prompt
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prompts = ["Placeholder"]
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print(f"DP rank {global_dp_rank} needs to process {len(prompts)} prompts")
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# Create a sampling params object.
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# since we are doing data parallel, every rank can have different
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# sampling params. here we set different max_tokens for different
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# ranks for demonstration.
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sampling_params = SamplingParams(temperature=0.8,
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top_p=0.95,
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max_tokens=[16, 20][global_dp_rank % 2])
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# Create an LLM.
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llm = LLM(
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model=model,
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tensor_parallel_size=GPUs_per_dp_rank,
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enforce_eager=enforce_eager,
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enable_expert_parallel=enable_expert_parallel,
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trust_remote_code=trust_remote_code,
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)
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outputs = llm.generate(prompts, sampling_params)
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# Print the outputs.
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for i, output in enumerate(outputs):
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if i >= 5:
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# print only 5 outputs
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break
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"DP rank {global_dp_rank}, Prompt: {prompt!r}, "
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f"Generated text: {generated_text!r}")
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# Give engines time to pause their processing loops before exiting.
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sleep(1)
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if __name__ == "__main__":
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args = parse_args()
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dp_size = args.dp_size
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tp_size = args.tp_size
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node_size = args.node_size
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node_rank = args.node_rank
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if node_size == 1:
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dp_master_ip = "127.0.0.1"
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dp_master_port = get_open_port()
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else:
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dp_master_ip = args.master_addr
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dp_master_port = args.master_port
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assert dp_size % node_size == 0, "dp_size should be divisible by node_size"
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dp_per_node = dp_size // node_size
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from multiprocessing import Process
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procs = []
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for local_dp_rank, global_dp_rank in enumerate(
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range(node_rank * dp_per_node, (node_rank + 1) * dp_per_node)):
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proc = Process(
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target=main,
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args=(
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args.model,
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dp_size,
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local_dp_rank,
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global_dp_rank,
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dp_master_ip,
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dp_master_port,
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tp_size,
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args.enable_expert_parallel,
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args.enforce_eager,
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args.trust_remote_code,
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),
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)
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proc.start()
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procs.append(proc)
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exit_code = 0
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for proc in procs:
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proc.join(timeout=300)
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if proc.exitcode is None:
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print(
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f"Killing process {proc.pid} that didn't stop within 5 minutes."
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)
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proc.kill()
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exit_code = 1
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elif proc.exitcode:
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exit_code = proc.exitcode
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exit(exit_code)
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