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Dynamically bind dtype - #62508
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Dynamically bind dtype #62508
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… fix the dtype check in `monkey_patch_math_tensor.py` to also consider `core.DataType` in addition to `core.VarDesc.VarType`.
…move unnecessary code
28 tasks done
Fixes an issue where the comparison of variable dtypes was incorrect in the `monkey_patch_tensor` function. The comparison now correctly checks the dtype of the variable.
SigureMo
reviewed
Mar 11, 2024
…de, dtype should always be DataType
…type conversion cause a cache miss error
0x45f
approved these changes
Mar 12, 2024
YuanRisheng
reviewed
Mar 12, 2024
Comment on lines
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| if (var_tensor->IsType<paddle::framework::Vocab>()) { | ||
| return ToPyObject(phi::DataType::UNDEFINED); | ||
| } else if (var_tensor->IsType<paddle::framework::Strings>()) { | ||
| return ToPyObject(phi::DataType::PSTRING); |
Collaborator
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PR的修改对当前的代码执行应该是没有影响，不过当前pir下的DataType里并没有RAW和STRING类型，需要讨论确认在动态图模式下，此修改对相关模型执行的影响
Aurelius84
reviewed
Mar 13, 2024
| return ToPyObject(framework::proto::VarType::RAW); | ||
| } else if (var_tensor->IsType<paddle::framework::Strings>()) { | ||
| return ToPyObject(framework::proto::VarType::STRING); | ||
| if (FLAGS_enable_pir_api) { |
Collaborator
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下面这个分支里的逻辑，后续可以考虑抽离成一个函数，避免if-for的多层嵌套
Aurelius84
approved these changes
Mar 13, 2024
28 tasks done
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Description
目前框架内有两套dtype的表示，一套是老静态图的VarType，一套是PIR下的DataType。动态图在c++端底层的dtype都是DataType，但是之前应该是为了兼容，所以动态图python的dtype也还是VarType类型。
这个pr主要是想修改python端的paddle.dtype，在PIR下paddle.dtype返回的是DataType类型；在老静态图下返回的是VarType类型。
通过将动态图切分成 PIR 和非 PIR 模式，以确保在任何一种情况下 dtype 都是动静统一的，以免添加众多繁杂的转换、特殊处理逻辑，在不久的将来，左侧的
not use_pir_api场景就可以完全废弃掉了Important
本 PR 主要影响是在开启
FLAGS_enable_pir_api=True场景下动态图的行为（含动转静），当然，CI 上目前没有这种测试（除动转静单测，动转静单测整体还是跑在动态图下，因此会测到部分动态图相关逻辑），因此测试可能是不全面的，这就需要未来去逐渐针对适配升级，不过当前 PR 已经手动在部分 Seg、Detection 模型上测试开启FLAGS_enable_pir_api=True通过，更多的场景可能需要在合入后联动「SOT+PIR」模型测试一同测试和修复（就目前经验而言，这类问题修复非常快）参考 #62146