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# Multipack (Sample Packing)
## Visualization of Multipack with Flash Attention
Because Flash Attention simply drops the attention mask, we do not need to
construct a 4d attention mask. We only need to concatenate the sequences into
a single batch and let flash attention know where each new sequence begins.
4k context, bsz =4,
each character represents 256 tokens
X represents a padding token
```
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
[[ A A A A A A A A A A A ]
B B B B B B ]
C C C C C C C ]
D D D D ]]
[[ E E E E E E E E ]
[ F F F F ]
[ G G G ]
[ H H H H ]]
[[ I I I ]
[ J J J ]
[ K K K K K]
[ L L L ]]
```
after padding to longest input in each step
```
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
[[ A A A A A A A A A A A ]
B B B B B B X X X X X X ]
C C C C C C C X X X X ]
D D D D X X X X X X X ]]
[[ E E E E E E E E ]
[ F F F F X X X X ]
[ G G G X X X X X ]
[ H H H H X X X X ]]
[[ I I I X X ]
[ J J J X X ]
[ K K K K K ]
[ L L L X X ]]
```
w packing ( note it's the same effective number of tokens per step, but a true bsz of 1)
```
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
[[ A A A A A A A A A A A B B B B B
B C C C C C C C D D D D E E E E
E E E E F F F F F G G G H H H H
I I I J J J J K K K K K L L L X ]]
```
cu_seqlens:
[[ 0, 11, 17, 24, 28, 36, 41 44, 48, 51, 55, 60, 64]]
## Multipack without Flash Attention
Multipack can still be achieved without Flash attention, but with lower packing
efficiency as we are not able to join multiple batches into a single batch due to
context length limits without flash attention. We can use either Pytorch's Scaled
Dot Product Attention implementation or native Pytorch attention implementation
along with [4d attention masks](https://github.com/huggingface/transformers/pull/27539)
to pack sequences together and avoid cross attention.
<img src="./images/4d-mask.png" alt="axolotl" width="800">
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