Poppy299999 patch 1

audio_evals/lib/qwen3-omni/main.py

@@ -6,14 +6,33 @@
 
 import soundfile as sf
 import torch
+import torch_npu
 from transformers import Qwen3OmniMoeForConditionalGeneration, Qwen3OmniMoeProcessor
 from qwen_omni_utils import process_mm_info
-
+import subprocess
+import os
 
 device = "cuda"
+def load_ascend_env(script_path="/usr/local/Ascend/ascend-toolkit/latest/bin/set_env.sh"):
+    # 执行 shell 脚本并在执行后通过 env 命令打印所有变量
+    command = f"source {script_path} && env"
+    proc = subprocess.Popen(command, stdout=subprocess.PIPE, shell=True, executable="/bin/bash")
+
+    for line in proc.stdout:
+        line = line.decode("utf-8").strip()
+        if "=" in line:
+            key, value = line.split("=", 1)
+            # 只有当变量名包含 ASCEND 或涉及库路径时才注入，避免污染
+            if "ASCEND" in key or key in ["LD_LIBRARY_PATH", "PYTHONPATH"]:
+                os.environ[key] = value
+
+# 在 import torch 之前调用
 
 
 def load_model(path, **kwargs):
+    # 1. 设置昇腾底层库路径 (把 libhccl.so 所在的路径加进去)
+    # 注意：路径需要根据你环境的实际位置微调，通常是 latest/lib64
+    load_ascend_env()
     model = Qwen3OmniMoeForConditionalGeneration.from_pretrained(
         path,
         torch_dtype="auto",

setup_vllm.sh

@@ -0,0 +1,143 @@
+#!/bin/bash
+# set -e  # 遇到错误立即退出
+set -x  # 打印执行日志
+
+# ================= 配置区域 =================
+# 修正路径获取方式
+ALGORITHM_PATH=$(pwd)
+# 定义服务端口
+PORT=8630
+# 定义服务名称
+SERVED_MODEL_NAME="qwen3omni"
+
+
+VLLM_MODEL_PATH="/opt/huawei/dataset/data/modelpt/Qwen_omni/Qwen3-Omni-30B-A3B-Instruct"
+
+
+
+echo ">>> [Init] Displaying the NPU memory...."
+npu-smi info
+
+
+# ================= 环境准备 =================
+echo ">>> [Init] Checking environment..."
+#cd /opt/huawei/dataset/Audio_dataset/framework/ms-swift-main/
+#pip install -e .
+pip install --upgrade pip
+pip install vllm==0.13.0
+pip install vllm-ascend==0.13.0rc1
+pip install transformers==4.57.1
+pip install torchvision torchaudio
+pip install accelerate==1.10.1
+pip install deepspeed 
+pip install qwen_omni_utils
+pip install msgspec
+pip install urllib3==1.26.0 numpy==1.26.4 requests
+pip install "decord" -U
+
+source /usr/local/Ascend/ascend-toolkit/set_env.sh
+source /usr/local/Ascend/nnal/atb/set_env.sh
+
+pip list | grep transformers
+pip list | grep torch
+pip list | grep vllm
+
+# ================= 环境变量设置 (Ascend NPU) =================
+export VLLM_WORKER_MULTIPROC_METHOD=spawn
+export HCCL_CONNECT_TIMEOUT=1200
+export HCCL_EXEC_TIMEOUT=1200
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+
+# ================= 启动 vLLM 后端 =================
+echo ">>> [Start] Starting vLLM server in background..."
+
+export USE_VLLM=1
+
+# 1. 【核心修复】定义正确的日志目录和文件名
+LOG_DIR="${ALGORITHM_PATH}/log"
+mkdir -p "${LOG_DIR}"  # 确保目录存在
+
+# 使用 SERVED_MODEL_NAME (qwen3vl) 作为文件名，不再使用未定义的 benchmarklist
+SERVER_LOG="${LOG_DIR}/vllm_server_${SERVED_MODEL_NAME}.log"
+SERVER_JUDGE_LOG="${LOG_DIR}/vllm_judge_server_${SERVED_MODEL_NAME}.log"
+
+echo ">>> [Log] Log file path: ${SERVER_LOG}"
+
+touch "${SERVER_LOG}"
+
+# ================= 启动 vLLM 后端 =================
+echo ">>> [Start] Starting vLLM server in background..."
+
+# 1. 启动 vLLM，日志依然写入文件 (这样为了保证 SERVER_PID 能抓到正确的 vLLM 进程)
+ASCEND_RT_VISIBLE_DEVICES=0,1 \
+nohup vllm serve ${VLLM_MODEL_PATH} \
+    --host 0.0.0.0 \
+    --port ${PORT} \
+    --served-model-name ${SERVED_MODEL_NAME} \
+    --enforce-eager \
+    --tensor-parallel-size 2 \
+    --dtype bfloat16 \
+    --max-model-len 8192 \
+    --max-num-batched-tokens 8192 \
+    --max-num-seqs 2 \
+    --trust-remote-code \
+    --enable-expert-parallel \
+    --gpu-memory-utilization 0.98 > ${SERVER_LOG} 2>&1 &
+
+# 获取 vLLM 进程 ID
+SERVER_PID=$!
+echo ">>> [Start] vLLM server started with PID: ${SERVER_PID}"
+echo ">>> [Log] Log file is located at: ${SERVER_LOG}"
+
+# 2. 【核心修改】启动 tail 在后台实时打印日志到屏幕
+echo ">>> [Log] Streaming logs to console..."
+tail -f ${SERVER_LOG} &
+TAIL_PID=$!  # 记录 tail 的 PID，以便稍后关闭
+
+# 3. 修改清理函数：退出时同时杀掉 vLLM 和 tail 进程
+cleanup() {
+    echo ">>> [Exit] Cleaning up..."
+    kill ${TAIL_PID} 2>/dev/null || true  # 先停止打印日志
+    kill ${SERVER_PID} 2>/dev/null || true # 再停止服务
+}
+trap cleanup EXIT
+
+# ================= 等待服务就绪 =================
+echo ">>> [Wait] Waiting for vLLM to be ready on port ${PORT}..."
+
+MAX_RETRIES=600  # 最多等待 60 * 5 = 300秒
+for ((i=1; i<=MAX_RETRIES; i++)); do
+    # 检查 /health 或 /v1/models 接口
+    if curl -s http://localhost:${PORT}/v1/models > /dev/null; then
+        echo ">>> [Wait] vLLM server is READY!"
+        break
+    fi
+    echo ">>> [Wait] Server not ready yet... (Attempt $i/$MAX_RETRIES). Sleeping 5s..."
+    sleep 5
+done
+
+
+if ! curl -s http://localhost:${PORT}/v1/models > /dev/null; then
+    echo ">>> [Error] Server failed to start. Check logs below:"
+    tail -n 50 ${SERVER_LOG}
+    exit 1
+fi
+
+
+# ================= 执行测试 =================
+echo ">>> [Test] Running inference test..."
+export TEST_IMAGE_PATH="${ALGORITHM_PATH}/test.jpg"
+export SERVED_MODEL_NAME="qwen3omni"
+export VLLM_PORT=8630
+
+python3 run_test.py
+
+echo ">>> [Done] Script finished successfully." 
+
+
+# 检查所有进程是否成功执行
+if [ $? -eq 0 ]; then
+    echo "所有部署进程成功启动！"
+else
+    echo "某些部署进程失败。"
+fi