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  ## Zero Bubble Schedules
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  The key of achieving zero bubble is to breaking a backward pass into a B pass and W pass. B on one stage will only depend on the B on its next stage, compared to depending on both B and W of in 1F1B.
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- ![image](https://hackmd.io/_uploads/Bkc7CL7N6.png)
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  ### Comparision of Schedules
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  * 1F1B
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- ![image](https://hackmd.io/_uploads/Hkq-gD7N6.png)
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  * ZB1P
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- ![image](https://hackmd.io/_uploads/Hy2GxwmEa.png)
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  * ZB2P
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- ![image](https://hackmd.io/_uploads/S10QgvmV6.png)
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  * ZBV - Each device is assigned to exactly 2 chunks (virtual stages), where white text colors represent the first chunk and black text colors represent the second chunk. The sequence of dependencies among model chunks follows a ”V” shape pattern for both the forward and backward passes.
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- ![image](https://hackmd.io/_uploads/rkfUVYNrp.png)
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@@ -29,6 +28,6 @@ The key of achieving zero bubble is to breaking a backward pass into a B pass an
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  In most practices of PP there's an all-reduce cross all pipeline stages for numerical robustness, e.g. global gradient norm for gradient clipping. INF/NAN check for mixed precision training, etc. This all-reduce breaks parallelogram and makes zero bubble impossible.
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  Under the observation that during a stable training both the gradient clipping and INF/NAN rarely triggers, we replace the before-hand synchronizations with a post update validation.
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- ![image](https://hackmd.io/_uploads/B16R3q4N6.png)
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  We eagerly step the optimizers assuming the grad cliping, INF/NAN conditions are not triggered. In case an amendment to the gradient is required, a rollback will be issued and then we redo the optimizer step based on the fully reduced global state.
 
1
  ## Zero Bubble Schedules
2
  The key of achieving zero bubble is to breaking a backward pass into a B pass and W pass. B on one stage will only depend on the B on its next stage, compared to depending on both B and W of in 1F1B.
3
 
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63510eea0b94548566dad923/8B9thyMiLgysNi_m_O3Qn.png)
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  ### Comparision of Schedules
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  * 1F1B
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63510eea0b94548566dad923/Q3yxf4BQIESQ_M7lKKlhf.png)
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  * ZB1P
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63510eea0b94548566dad923/EcTFvbjfM7soUXDYyn1Xu.png)
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  * ZB2P
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63510eea0b94548566dad923/8jFI_rO69BREKqiSFHIOL.png)
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  * ZBV - Each device is assigned to exactly 2 chunks (virtual stages), where white text colors represent the first chunk and black text colors represent the second chunk. The sequence of dependencies among model chunks follows a ”V” shape pattern for both the forward and backward passes.
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63510eea0b94548566dad923/VRfjNVXakAU3MQK3h6OKa.png)
 
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  In most practices of PP there's an all-reduce cross all pipeline stages for numerical robustness, e.g. global gradient norm for gradient clipping. INF/NAN check for mixed precision training, etc. This all-reduce breaks parallelogram and makes zero bubble impossible.
29
  Under the observation that during a stable training both the gradient clipping and INF/NAN rarely triggers, we replace the before-hand synchronizations with a post update validation.
30
 
31
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63510eea0b94548566dad923/hRPFqaFxJ20wm2omwyKmO.png)
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  We eagerly step the optimizers assuming the grad cliping, INF/NAN conditions are not triggered. In case an amendment to the gradient is required, a rollback will be issued and then we redo the optimizer step based on the fully reduced global state.