71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
import time
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import random
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import string
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from framework.core.logger import get_logger
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from framework.business.quantization_page import QuantizationPage
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logger = get_logger("QuantizationScenario")
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def _create_and_submit_task(quant_page: QuantizationPage, task_name: str):
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"""内部辅助函数：执行单次完整的任务创建流"""
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# 1. 进入任务创建流程
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quant_page.navigate_to()
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quant_page.click_create_task()
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# 2. 输入名称
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quant_page.input_task_name(task_name)
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# 3. 选择编译模式
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quant_page.select_mode_fast_eval()
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# 4. 选择镜像
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quant_page.select_image("dcloud_ai_toolchain_ubuntu_22_s100_cpu-2")
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# 5. 选取模型文件
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quant_page.upload_model_file("model", "result.onnx")
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# 6. 输入 march 值
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quant_page.input_march_param("nash-e")
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# 7. 提交表单
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quant_page.click_final_submit()
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def run_quantization_lifecycle(quant_page: QuantizationPage):
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"""量化任务全生命周期流程"""
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logger.info("开始执行量化工具自动化流程...")
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# ================= 阶段 1：手动终止链路 =================
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random_str1 = ''.join(random.choices(string.ascii_uppercase + string.digits, k=8))
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task_1 = f"AutoQuant_{random_str1}"
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logger.info(f"--- 阶段一: 测试任务手动终止与删除 [{task_1}] ---")
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_create_and_submit_task(quant_page, task_1)
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# 1. 查日志
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quant_page.open_and_close_logs(task_1)
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# 2. 停止
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quant_page.stop_task(task_1)
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# 3. 删除
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quant_page.delete_task(task_1)
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# ================= 阶段 2：完整成功链路 =================
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random_str2 = ''.join(random.choices(string.ascii_uppercase + string.digits, k=8))
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task_2 = f"AutoQuant_{random_str2}"
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logger.info(f"--- 阶段二: 测试任务完整成功链路 [{task_2}] ---")
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_create_and_submit_task(quant_page, task_2)
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# 1. 查日志
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quant_page.open_and_close_logs(task_2)
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# 监控状态流转为终态 (完成或失败)
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quant_page.wait_for_task_finished(task_2)
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# + 成功后的新增操作: 报告查看与全量下载拦截
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quant_page.open_and_close_report(task_2)
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quant_page.download_all_results(task_2)
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logger.info("✅ 量化工具全生命周期(异常截断+成功收尾) 双链路完整验证通过！")
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