auto parallel support pipeline scheduler with standalone executor #54727
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你的PR提交成功，感谢你对开源项目的贡献! |
| @@ -52,6 +52,10 @@ PADDLE_DEFINE_EXPORTED_bool(new_executor_use_local_scope, | |||
| true, | |||
| "Use local_scope in new executor(especially used " | |||
| "in UT), can turn off for better performance"); | |||
| PADDLE_DEFINE_EXPORTED_bool( | |||
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用于控制python代码的环境变量，不需要在C++端声明。
| @@ -368,7 +378,17 @@ def _apply_post_optimization( | |||
| [main_program], [startup_program], self._pass_context | |||
| ) | |||
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| if self._strategy.pipeline.enable: | |||
| new_executor_micro_batching = os.environ.get( | |||
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这里判断是否使用新执行器的代码在engine.py里也有类似的，两处代码是否可以合成一处？
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| if self._strategy.pipeline.enable and use_new_executor: | ||
| main_program._pipeline_opt = {} | ||
| main_program._pipeline_opt["standalone_exe"] = { |
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_pipeline_opt用standalone_exe做key容易造成误解，建议可以改成standalone_opt或其它更合适的名字
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done。修改成了 standalone_opt
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| def apply_pass(main_program, startup_program, pass_name, pass_attr={}): | ||
| from paddle.distributed.passes import PassContext, new_pass |
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按照python编码规范，导入语句必须在文件顶部, 位于模块的注释和文档字符串之后、全局变量和全局常量之前。不建议在函数内部做导入。
python/paddle/fluid/executor.py
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| @@ -653,8 +681,15 @@ def run(self, feed_names, return_numpy=True): | |||
| """ | |||
| tensors = self._new_exe.run(feed_names)._move_to_list() | |||
| if return_numpy: | |||
| return as_numpy(tensors, copy=True) | |||
| tensors = as_numpy(tensors, copy=True) | |||
| if self._plan.micro_batch_num() <= 1: | |||
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_merge_tensors是否可以处理micro_batch_num=1的情况？
python/paddle/fluid/executor.py
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| else: | ||
| if self._plan.micro_batch_num() > 1: | ||
| logging.warning( |
| scope, | ||
| ) | ||
| if pipeline_opt: | ||
| from paddle.distributed.passes.pipeline_scheduler_pass import ( |
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不在此处做导入会发生循环引用的问题。
python/paddle/fluid/executor.py
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| @@ -1408,7 +1460,21 @@ def _run_impl( | |||
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| fetch_list = self._check_fetch_list(fetch_list) | |||
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| if isinstance(program, Program) and program._pipeline_opt: | |||
| new_executor_micro_batching = os.environ.get( | |||
…a/Paddle into standalone_exe_FThenB
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Description
Pcard-70448
new_executor_micro_batchingflag 来控制在流水线并行时，是使用standalone executor还是fleet executor。仅用于过渡时期debug，后续迁移完成会删除。