[Auto-parallel] 增强stage对层间传递stop_grad=True的参数的支持 #73459
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你的PR提交成功，感谢你对开源项目的贡献! |
xuxinyi389
reviewed
Jun 19, 2025
| # We assume we always send to stage + 1 | ||
| if not self.is_last: | ||
| self.act_send_info[idx] = [self.stage_index + 1] | ||
| if not outputs_meta[idx].stop_gradient: |
|
Sorry to inform you that ea80cdb's CIs have passed for more than 7 days. To prevent PR conflicts, you need to re-run all CIs manually. |
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xuxinyi389
reviewed
Aug 3, 2025
| @@ -710,9 +709,19 @@ def forward_one_chunk( | |||
| flat_args = _flatten_args(input_args) | |||
| flat_kwargs = _flatten_args(composite_kwargs) | |||
| flatten_input_tensors = flat_args + flat_kwargs | |||
| grad_required_output_tuple = tuple( | |||
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命名似有问题，grad开头，给人误解为是grad数据。命名为 requires_grad_output_tuple更好
xuxinyi389
reviewed
Aug 3, 2025
| for out in output_tuple | ||
| if isinstance(out, paddle.Tensor) and not out.stop_gradient | ||
| ) | ||
| grad_required_flatten_input_tensors = [ |
|
/re-run all-failed |
zty-king
changed the title
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PR Category
Auto Parallel
PR Types
Improvements
Description
use_flash_attention设置为false时，此时跑llama2_13b_hybrid_pp会报错，主要原因在于forward输出到下一个stage的output数，和backward时从下一个stage接收到的gard数不相等，导致backward无法正确计算。
由于在EmbeddingLayer中计算出来的参数需要在forward过程中不断传递，并在每个DecoderLayer中做相关计算，但是注意，除了hidden_states，其它参数在EmbeddingLayer计算之后，只在层间传递，辅助计算。
在每层获取输出时，对其过滤，对于stop_grad为True的参数，进行过滤，并适配相关代码，只在层间传递，不做backward