mega-moe在手撕模型和抓图模型的实现#12
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在 PPStitchedTimeline 中新增 phase_breakdown 方法，通过时间线任务精确计算每个阶段（warmup、steady、cooldown）的前向和后向时间。更新 TrainingPipelinePass 以使用这些精确值替代基于瓶颈的近似值，并在 PipelineStepMetrics 中新增相应字段以支持更详细的性能分析。
【feature】添加基于时间线的训练阶段细粒度指标
call cost model
更新训练寻优报表表头
- 将 `pipeline_metrics` 替换为统一的 `step_result` 字典，由 `StepResult.to_dict_ms()` 生成 - 移除 `PipelineStepMetrics` 类及相关导入，简化代码结构 - 删除已弃用的 `--pp-mode` CLI 参数，仅保留基于网格的 `PPStitcher` 模式 - 重命名 `recompute_time_ms` 和 `recompute_time_raw_ms` 为 `recompute_critical_ms` 和 `recompute_raw_mag_ms`，以更清晰地反映其含义 - 更新所有测试、导出器和报告生成代码以使用新的字段名和数据结构 - 在 `TrainingPipelinePass` 中直接计算重计算指标，确保与流水线调度结果一致
寻优优化，支持指定过滤条件和排序依据
【refactor】统一生成trace并移除旧模式
报告输出算子占比饼图，支持用户切换聚焦类型和拆分维度
Optimize PR CI gates for docs/config-only changes
# Conflicts: # .github/workflows/coverage-gate.yml # tests/training/test_training_search_util.py
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mega-moe在手撕模型和抓图模型的实现。