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fix stage2 main_grad acc bug by iosmers · Pull Request #59142 · PaddlePaddle/Paddle · GitHub
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fix stage2 main_grad acc bug #59142
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Xreki
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iosmers:fix_stage2_main_grad_acc
Dec 15, 2023
Merged
fix stage2 main_grad acc bug #59142
Xreki
merged 37 commits into
PaddlePaddle:develop
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iosmers:fix_stage2_main_grad_acc
Dec 15, 2023
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你的PR提交成功，感谢你对开源项目的贡献! |
FeixLiu
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如果有机器资源再跑一下收敛测试吧，把loss曲线放PR描述里。需要对4组吧
| main_grad False | main_grad True | |
|---|---|---|
| bf16 | ||
| fp16 |
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some minor suggestions
python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_stage2.py
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FeixLiu
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LGTM
haohongxiang
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LGTM
merge develop to bug fix branch
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| not hasattr(param, "main_grad") | ||
| and grad.dtype == Type.fp16.value | ||
| ): | ||
| if grad is None: |
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这个if为啥还在啊😂，放到下面那个assert里就可以了吧。
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下个PR改
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LGTM
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* fix stage2 main_grad acc bug * update code according to suggest * scale in opt * merge grad scale * add note * delete debug info * keep offload unchange * Optimize the BF16 unittest of sharding stage2 and stage3. * fix stage3 bug * add fp16 judge * add init * add fp16 * fix grad clip * add if data type is fp16 * change if location * delete fault arg * add enmu.value --------- Co-authored-by: Liu Yiqun <liuyiqun01@baidu.com> Co-authored-by: tianhaodongbd <tianhaodong@baidu.com>
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* fix stage2 main_grad acc bug * update code according to suggest * scale in opt * merge grad scale * add note * delete debug info * keep offload unchange * Optimize the BF16 unittest of sharding stage2 and stage3. * fix stage3 bug * add fp16 judge * add init * add fp16 * fix grad clip * add if data type is fp16 * change if location * delete fault arg * add enmu.value --------- Co-authored-by: Liu Yiqun <liuyiqun01@baidu.com> Co-authored-by: tianhaodongbd <tianhaodong@baidu.com>
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PR types
Bug fixes
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Description
PCard-70444
1、为了解决Stage2中开main_grad梯度累加的bug(计算顺序出错，原有顺序（grad1/n+grad2)/n, 改完之后，main_grad的梯度累加放到了优化器opt中做，改完之后main_grad的计算顺序如下，（grad1+grad2)/n）。
2、fused_param_linear_grad_add 开acc时，grad均不为空，主要区别是第一个grad被初始化，第二个未被初始化，因此如果在stage2中使用fp16-O2 fused_param_linear_grad_add，会导致计算出错，因此在本PR中做了报错处理(assert(grad._is_initialized())
3、为了确保grad.dtype一定存在，需要先判断grad is not none
收敛测试实验方案如下，测试了三组对比实验，如下图所示
O1/O2 混合精度训练分别走的不同分支
收敛实验结果如下：
2.bf16、关闭main_grad条件下，stage2与stage1收敛验证结果
3.. fp16、关闭main_grad条件下，stage2与stage1收敛验证结果
更新代码后，1w步收敛实验结果如下：
2.bf16、关闭main_grad条件下，stage2与stage1收敛验证结果
3.. fp16、关闭main_grad条件下，stage2与stage1收敛验证结果
4.. fp16、开main_grad条件下，stage2与stage1收敛验证结果