Update optimization features readme from nemo1 to nemo2 #12071
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Signed-off-by: yaoyu-33 <[email protected]>
suiyoubi
reviewed
Feb 5, 2025
| @@ -28,7 +41,18 @@ This is because the input sizes of softmax, dropout, and qkv dot-product attenti | |||
| However, their recomputation cost is relatively smaller than the other linear projection layers that are linear with the hidden size square. | |||
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| Self-attention recomputation is hard-enabled when using FlashAttention, which is supported in Transformer Engine. | |||
| Also, you can use the self-attention recomputation without FlashAttention by setting ``activations_checkpoint_granularity=selective``. | |||
| Also, you can use the self-attention recomputation without FlashAttention by setting ``recompute_granularity=selective``. | |||
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should this be recompute_method?
Signed-off-by: Yu Yao <[email protected]>
BoxiangW
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Feb 7, 2025
* update optimization features readme Signed-off-by: yaoyu-33 <[email protected]> * Update activation_recomputation.rst Signed-off-by: Yu Yao <[email protected]> --------- Signed-off-by: yaoyu-33 <[email protected]> Signed-off-by: Yu Yao <[email protected]>
youngeunkwon0405
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Feb 10, 2025
* update optimization features readme Signed-off-by: yaoyu-33 <[email protected]> * Update activation_recomputation.rst Signed-off-by: Yu Yao <[email protected]> --------- Signed-off-by: yaoyu-33 <[email protected]> Signed-off-by: Yu Yao <[email protected]> Signed-off-by: Youngeun Kwon <[email protected]>
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What does this PR do ?
Update optimization features readme from nemo1 to nemo2
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