[API] Optimize paddle.where and paddle.where_ in eager mode
#69556
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你的PR提交成功，感谢你对开源项目的贡献! |
zyfncg
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Nov 22, 2024
python/paddle/tensor/search.py
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| broadcast_shape = paddle.broadcast_shape(x_shape, y_shape) | ||
| broadcast_shape = paddle.broadcast_shape(broadcast_shape, condition_shape) | ||
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| broadcast_x = x | ||
| broadcast_y = y | ||
| broadcast_condition = condition | ||
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| if condition_shape != broadcast_shape: | ||
| broadcast_condition = paddle.broadcast_to( | ||
| broadcast_condition, broadcast_shape | ||
| ) | ||
| if x_shape != broadcast_shape: | ||
| broadcast_x = paddle.broadcast_to(broadcast_x, broadcast_shape) | ||
| if y_shape != broadcast_shape: | ||
| broadcast_y = paddle.broadcast_to(broadcast_y, broadcast_shape) |
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确认下broadcast_shape和broadcast_to是否支持动态shape, 不支持的话静态图下可能会有问题
There was a problem hiding this comment.
确认下broadcast_shape和broadcast_to是否支持动态shape, 不支持的话静态图下可能会有问题
看了下，修改前的代码用了add这个支持动态shape的算子进行了隐式broadcast，所以静态图支持动态shape，但是修改之后的方案依赖于broadcast_shape和broadcast_to，这两个算子至少有一个存在对-1的处理BUG，导致静态图下的运行时结果不太对，所以目前的临时解决方案是：新的代码逻辑放在dynamic_mode里面，pir仍然使用原本来的代码。这样不影响已有功能。
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paddle.where and paddle.where_ in eager mode
zyfncg
previously approved these changes
Nov 22, 2024
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zyfncg
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Nov 28, 2024
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PR Category
Performance Optimization
PR Types
Improvements
Description
Pcard-75624
paddle.where和paddle.where_的原有实现通过多次调用基础二元运算符进行隐式广播，从而让cond,x,,y形状保持一致，优化之后使用无计算量的broadcast_shape计算广播后的形状，再使用broadcast_to进行广播，极大简化了代码逻辑，并且减少了不必要的前向和反向算子开销where_部分动态图单测，并且修复了个别静态图单测中，占位符形状和实际数据形状不一致的问题。Note
考虑到broadcast_shape和broadcast_to暂时没有完全适配动态shape，因此本PR只针对动态图分支进行修改