`, use `
`/`
` for sections 3. Lists with `
- `, quotes with `
`, code with `
`, important sentences with ``
4. Keep all original information, no additions, deletions, or modifications
5. No ``/``/`` tags
Let me break down the text into logical sections:
1. The problem: Variable-length training creates conflicting parallelism requirements
2. The solution: Layerwise Repacking
3. Results
Let me structure this:
Section 1 (h2): The Challenge - Variable-Length Training Parallelism
- Paragraph about the problem
Section 2 (h2): Layerwise Repacking Solution
- Paragraph about the solution
Section 3 (h2): Results
- Paragraph about the results
Let me carefully go through the text:
"Variable-length training creates conflicting parallelism requirements across the layers of a hybrid-attention model."
"The longest samples require wide sequence-parallel (SP) groups of 16 or 32 ranks for full attention, whose cost grows quadratically with sequence length."
"Sparse-attention layers, dominated by near-linear MoE work, can instead balance most evaluated workloads within node-local SP8 groups."
"Reusing the wide groups for these layers pushes their all-to-all communication across nodes, where the observed effective rate is 7.0x lower."
"Yet switching to smaller groups changes which ranks own each sample; a separate redistribution at every full-sparse boundary would introduce another data transfer."
"Layerwise Repacking keeps each SP16/32 parent for full attention and repacks its samples into node-local sparse children at SP{1, 2, 4, 8}, where hidden states persist between full layers."
"The layout change rides on the two existing full-layer Ulysses sites: at each, one variable-size all-to-all performs both the head exchange and the sample repacking, so no redistribution pass is added."
"Because the balance the children can reach depends on which samples share a parent, an auxiliary parent solver rebalances parent membership before the children are formed."
"On all 22 steps with SP16/32 parents among the first 200 training steps of a 1F3S hybrid-attention model (one full-attention layer per three sparse-attention layers), repacking alone reduces the mean end-to-end time by 5.95% and raises model FLOPs utilization (MFU) by 3.75 percentage points over the baseline with repacking disabled; with the improved solver, end-to-end time falls by 7.61% and MFU rises by 4.83 percentage points."
Now let me structure this into HTML:
I'll use h2 for the main sections and h3 for sub-sections where appropriate.
Section 1: The Challenge
- The conflicting parallelism requirements
Section 2: The Solution - Layerwise Repacking
- How it works
Section 3: Results
- The experimental results
Let me think about what to bold. Important sentences and key terms:
- "Variable-length training creates conflicting parallelism requirements" - key problem statement
- "Layerwise Repacking" - the core solution name
- Key metrics in the results
Let me draft the HTML:
```html
The Challenge: Conflicting Parallelism Requirements
Variable-length training creates conflicting parallelism requirements across the layers of a hybrid-attention model. The longest samples require wide sequence-parallel (SP) groups of 16 or 32 ranks for full attention, whose cost grows quadratically with sequence length. Sparse-attention layers, dominated by near-linear MoE work, can instead balance most evaluated workloads within node-local SP8 groups.
Reusing the wide groups for these layers pushes their all-to-all communication across nodes, where the observed effective rate is 7.0x lower. Yet switching to smaller groups changes which ranks own each sample; a separate redistribution at every full-sparse boundary would introduce another data transfer.
Layerwise Repacking
Layerwise Repacking keeps each SP16/32 parent for full attention and repacks its samples into node-local sparse children at SP{1, 2, 4, 8}, where hidden states persist between full layers.
The layout change rides on the two existing full-layer Ulysses sites: at each, one variable-size all-to-all performs both the head exchange and the sample repacking, so no redistribution pass is added.
Because the balance the children can reach depends on which samples share a parent, an auxiliary parent solver rebalances parent membership before the children are formed.
Results
On all 22 steps with SP16/32 parents among the first 200 training steps of a 1F3S hybrid-attention model (one full-attention layer per three sparse-attention layers), repacking alone reduces the mean end-to-end time by 5.95% and raises model FLOPs utilization (MFU) by 3.75 percentage points over the baseline with repacking disabled; with the improved solver, end-to-end time falls by 7.61% and MFU rises by 4.83 percentage points.
```
Wait, let me reconsider. The SP{1, 2, 4, 8} part could be rendered as a list or as code. Let me think about this... It's mentioned as "SP{1, 2, 4, 8}" - this looks like it could be code. Let me use `` for it since it's a notation.
Actually, looking at it again, "SP{1, 2, 4, 8}" is a notation representing a set of possible SP values. I could render it as `SP{1, 2, 4, 8}` since it looks like a code/notation reference.
Also, let me reconsider the structure. The text has a clear flow:
1. Problem statement (parallelism conflicts)
2. Why naive solutions don't work
3. The Layerwise Repacking solution
4. Implementation details
5. Results
Let me refine:
```html
The Challenge: Conflicting Parallelism Requirements
Variable-length training creates conflicting parallelism requirements across the layers of a hybrid-attention model. The longest samples require wide sequence-parallel (SP) groups of 16 or 32 ranks for full attention, whose cost grows quadratically with sequence length. Sparse-attention layers, dominated by near-linear MoE work, can instead balance most evaluated workloads within node-local SP8 groups.
Reusing the wide groups for these layers pushes their all-to-all communication across nodes, where the observed effective rate is 7.0x lower. Yet switching to smaller groups changes which ranks own each sample; a separate redistribution at every full-sparse boundary would introduce another data transfer.
Layerwise Repacking
Layerwise Repacking keeps each SP16/32 parent for full attention and repacks its samples into node-local sparse children at SP{1, 2, 4, 8}, where hidden states persist between full layers.
The layout change rides on the two existing full-layer Ulysses sites: at each, one variable-size all-to-all performs both the head exchange and the sample repacking, so no redistribution pass is added.
Because the balance the children can reach depends on which samples share a parent, an auxiliary parent solver rebalances parent membership before the children are formed.
Results
`, code with ``, important sentences with `` 4. Keep all original information, no additions, deletions, or modifications 5. No ``/``/`` tags Let me break down the text into logical sections: 1. The problem: Variable-length training creates conflicting parallelism requirements 2. The solution: Layerwise Repacking 3. Results Let me structure this: Section 1 (h2): The Challenge - Variable-Length Training Parallelism - Paragraph about the problem Section 2 (h2): Layerwise Repacking Solution - Paragraph about the solution Section 3 (h2): Results - Paragraph about the results Let me carefully go through the text: "Variable-length training creates conflicting parallelism requirements across the layers of a hybrid-attention model." "The longest samples require wide sequence-parallel (SP) groups of 16 or 32 ranks for full attention, whose cost grows quadratically with sequence length." "Sparse-attention layers, dominated by near-linear MoE work, can instead balance most evaluated workloads within node-local SP8 groups." "Reusing the wide groups for these layers pushes their all-to-all communication across nodes, where the observed effective rate is 7.0x lower." "Yet switching to smaller groups changes which ranks own each sample; a separate redistribution at every full-sparse boundary would introduce another data transfer." "Layerwise Repacking keeps each SP16/32 parent for full attention and repacks its samples into node-local sparse children at SP{1, 2, 4, 8}, where hidden states persist between full layers." "The layout change rides on the two existing full-layer Ulysses sites: at each, one variable-size all-to-all performs both the head exchange and the sample repacking, so no redistribution pass is added." "Because the balance the children can reach depends on which samples share a parent, an auxiliary parent solver rebalances parent membership before the children are formed." "On all 22 steps with SP16/32 parents among the first 200 training steps of a 1F3S hybrid-attention model (one full-attention layer per three sparse-attention layers), repacking alone reduces the mean end-to-end time by 5.95% and raises model FLOPs utilization (MFU) by 3.75 percentage points over the baseline with repacking disabled; with the improved solver, end-to-end time falls by 7.61% and MFU rises by 4.83 percentage points." Now let me structure this into HTML: I'll use h2 for the main sections and h3 for sub-sections where appropriate. Section 1: The Challenge - The conflicting parallelism requirements Section 2: The Solution - Layerwise Repacking - How it works Section 3: Results - The experimental results Let me think about what to bold. Important sentences and key terms: - "Variable-length training creates conflicting parallelism requirements" - key problem statement - "Layerwise Repacking" - the core solution name - Key metrics in the results Let me draft the HTML: ```htmlThe Challenge: Conflicting Parallelism Requirements
Variable-length training creates conflicting parallelism requirements across the layers of a hybrid-attention model. The longest samples require wide sequence-parallel (SP) groups of 16 or 32 ranks for full attention, whose cost grows quadratically with sequence length. Sparse-attention layers, dominated by near-linear MoE work, can instead balance most evaluated workloads within node-local SP8 groups.
Reusing the wide groups for these layers pushes their all-to-all communication across nodes, where the observed effective rate is 7.0x lower. Yet switching to smaller groups changes which ranks own each sample; a separate redistribution at every full-sparse boundary would introduce another data transfer.
Layerwise Repacking
Layerwise Repacking keeps each SP16/32 parent for full attention and repacks its samples into node-local sparse children at SP{1, 2, 4, 8}, where hidden states persist between full layers.
The layout change rides on the two existing full-layer Ulysses sites: at each, one variable-size all-to-all performs both the head exchange and the sample repacking, so no redistribution pass is added.
Because the balance the children can reach depends on which samples share a parent, an auxiliary parent solver rebalances parent membership before the children are formed.
Results
On all 22 steps with SP16/32 parents among the first 200 training steps of a 1F3S hybrid-attention model (one full-attention layer per three sparse-attention layers), repacking alone reduces the mean end-to-end time by 5.95% and raises model FLOPs utilization (MFU) by 3.75 percentage points over the baseline with repacking disabled; with the improved solver, end-to-end time falls by 7.61% and MFU rises by 4.83 percentage points.
``` Wait, let me reconsider. The SP{1, 2, 4, 8} part could be rendered as a list or as code. Let me think about this... It's mentioned as "SP{1, 2, 4, 8}" - this looks like it could be code. Let me use `` for it since it's a notation. Actually, looking at it again, "SP{1, 2, 4, 8}" is a notation representing a set of possible SP values. I could render it as `SP{1, 2, 4, 8}` since it looks like a code/notation reference. Also, let me reconsider the structure. The text has a clear flow: 1. Problem statement (parallelism conflicts) 2. Why naive solutions don't work 3. The Layerwise Repacking solution 4. Implementation details 5. Results Let me refine: ```htmlThe Challenge: Conflicting Parallelism Requirements
Variable-length training creates conflicting parallelism requirements across the layers of a hybrid-attention model. The longest samples require wide sequence-parallel (SP) groups of 16 or 32 ranks for full attention, whose cost grows quadratically with sequence length. Sparse-attention layers, dominated by near-linear MoE work, can instead balance most evaluated workloads within node-local SP8 groups.
Reusing the wide groups for these layers pushes their all-to-all communication across nodes, where the observed effective rate is 7.0x lower. Yet switching to smaller groups changes which ranks own each sample; a separate redistribution at every full-sparse boundary would introduce another data transfer.
Layerwise Repacking
Layerwise Repacking keeps each SP16/32 parent for full attention and repacks its samples into node-local sparse children at
SP{1, 2, 4, 8}, where hidden states persist between full layers.The layout change rides on the two existing full-layer Ulysses sites: at each, one variable-size all-to-all performs both the head exchange and the sample repacking, so no redistribution pass is added.
Because the balance the children can reach depends on which samples share a parent, an auxiliary parent solver rebalances parent membership before the children are formed.
Results