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long context performance numbers in doc (NVIDIA#10784)
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* long context perf

Signed-off-by: Youngeun Kwon <youngeunk@nvidia.com>

* update the long context perf

Signed-off-by: Youngeun Kwon <youngeunk@nvidia.com>

* Akoumparouli/mcore microbatch calculator fix (NVIDIA#10780)

* move tests/lightning/{,_}io

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* add microbatch calculator context manager

Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com>

* use microbatch calculator context manager

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* add on_load_checkpoint test to ValidateModelRestoration; use ctx manager to reconfigure microbatch calculator; update save/restore path; add cleanup step at the end

Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com>

* remove unused var

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* fix

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* Apply isort and black reformatting

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Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com>
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Co-authored-by: akoumpa <akoumpa@users.noreply.github.com>
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* remove 8x3b recipes (NVIDIA#10764)

* remove 8x3b recipes

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* remove 8x3b from test_nemo_run

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* rm from __init__

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Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com>
Signed-off-by: Youngeun Kwon <youngeunk@nvidia.com>

* change the figure file name

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* Accommodating the reviewer's comment

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* update the y-axis title

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* [🤠]: Howdy folks, let's bump `Dockerfile.ci` to 3f90b98 ! (NVIDIA#10789)

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* Add ModelOpt transformer model pruning example for Llama models, default to llama3.1-8b-base (NVIDIA#10294)

* Add ModelOpt transformer model pruning example for Llama3 model

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* Apply isort and black reformatting

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* examples code is at wrong dir, move them

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* changes as suggested in comment

remove some logging and unused config code, update example model to
llama3.1

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* Add pruning of hidden_size into example

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* Apply isort and black reformatting

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* Update examples/nlp/language_modeling/conf/megatron_gpt_prune.yaml

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* Add pruning test to cicd-main.yml

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* Update cicd-main.yml

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* Update cicd-main.yml

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* Update cicd-main.yml

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* Update cicd-main.yml

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* Update cicd-main.yml

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* Update mamba.rst after dist ckpt addition (NVIDIA#10800)

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* fix chunked infer (NVIDIA#10581)

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Signed-off-by: Youngeun Kwon <youngeunk@nvidia.com>

* fix state transform (NVIDIA#10728)

Signed-off-by: Chen Cui <chcui@nvidia.com>
Signed-off-by: Youngeun Kwon <youngeunk@nvidia.com>

* use ckpt_to_weights_subdir in restore (NVIDIA#10786)

* use ckpt_to_weights_subdir in restore

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* make ckpt_to_{weight,context}_subdir idempotent

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* Apply isort and black reformatting

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* Mixtral set seq_length=4k (NVIDIA#10704)

* enable SP & set seq_lenght=4k

Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com>

* update test expected values

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* 8x22b 4k

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Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com>
Signed-off-by: Youngeun Kwon <youngeunk@nvidia.com>

* Fix for crashes with tensorboard_logger=false and VP + LoRA (NVIDIA#10792)

* Fix for crashes with tensorboard_logger=false and virtual pipeline parallel + LoRA

Signed-off-by: Valerie Sarge <vsarge@nvidia.com>

* Apply isort and black reformatting

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Co-authored-by: vysarge <vysarge@users.noreply.github.com>
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* Disable checkpoint conversion inside AutoResume (NVIDIA#10645)

* Disable checkpoint conversion inside AutoResume

Signed-off-by: Hemil Desai <hemild@nvidia.com>

* Apply isort and black reformatting

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* Update resume docstrings

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* fix

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* add default finetuning recipe and refactor llama3 8b recipe

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* Apply isort and black reformatting

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* address comment

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* refactor other recipes

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* Apply isort and black reformatting

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* remove 8x3b finetuning recipe for now because HF version not available

Signed-off-by: Chen Cui <chcui@nvidia.com>

* add copyright header

Signed-off-by: Chen Cui <chcui@nvidia.com>

* adjust unit tests based on recipe fixes

Signed-off-by: Chen Cui <chcui@nvidia.com>

* fix failed unit test

Signed-off-by: Chen Cui <chcui@nvidia.com>

---------

Signed-off-by: Hemil Desai <hemild@nvidia.com>
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* replace png file to github assets

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* change image url to github release

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---------

Signed-off-by: Youngeun Kwon <youngeunk@nvidia.com>
Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com>
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# Long Sequence Performance

## LLAMA2-7B (FP8)

- The table below shows the pre-training performance of the LLAMA2-7B with CP (context parallelism) and compares it against the results without CP at various input sequence lengths. The detailed model-parallel configurations and the achieved performance are shown in the training results with CP. In non-CP training runs, we use the most performant model- and data-parallel configurations without CP given the memory capacity constraint of the H100 GPU system.

- Container: [NeMo24.03.01.framework](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo/tags)
- System: DGX-H100

<style>
table {
border-collapse: collapse;
}
th {
border: 1px solid;
padding: 5px;
text-align: center; /* Center-align all header cells */
}
td {
border: 1px solid;
padding: 5px;
}
th.top-border {
border-top: 2px solid;
}
td.speedup {
font-weight: bold;
}
</style>


<table>
<thead>
<tr>
<th rowspan="2" class="top-border">SeqLen (K)</th>
<th rowspan="2" class="top-border"># of GPUs</th>
<th rowspan="1" class="top-border">Without CP</th>
<th colspan="5" class="top-border">With CP</th>
<th rowspan="2" class="top-border">Speedup with CP/without CP</th>
</tr>
<tr>
<th>TFLOPS / GPU</th>
<th>TP</th>
<th>PP</th>
<th>DP</th>
<th>CP</th>
<th>TFLOPS / GPU</th>
</tr>
</thead>
<tbody>
<tr>
<td>4</td>
<td>4</td>
<td>768</td>
<td>1</td>
<td>1</td>
<td>4</td>
<td>1</td>
<td>768</td>
<td class="speedup">1.00</td>
</tr>
<tr>
<td>8</td>
<td>8</td>
<td>730</td>
<td>1</td>
<td>2</td>
<td>4</td>
<td>1</td>
<td>730</td>
<td class="speedup">1.00</td>
</tr>
<tr>
<td>16</td>
<td>16</td>
<td>660</td>
<td>2</td>
<td>1</td>
<td>8</td>
<td>1</td>
<td>660</td>
<td class="speedup">1.00</td>
</tr>
<tr>
<td>32</td>
<td>32</td>
<td>595</td>
<td>2</td>
<td>1</td>
<td>8</td>
<td>2</td>
<td>610</td>
<td class="speedup">1.03</td>
</tr>
<tr>
<td>64</td>
<td>64</td>
<td>534</td>
<td>4</td>
<td>1</td>
<td>8</td>
<td>2</td>
<td>574</td>
<td class="speedup">1.07</td>
</tr>
<tr>
<td>128</td>
<td>128</td>
<td>424</td>
<td>4</td>
<td>1</td>
<td>8</td>
<td>4</td>
<td>555</td>
<td class="speedup">1.31</td>
</tr>
<tr>
<td>256</td>
<td>256</td>
<td>392</td>
<td>4</td>
<td>1</td>
<td>8</td>
<td>8</td>
<td>549</td>
<td class="speedup">1.40</td>
</tr>
<tr>
<td>512</td>
<td>512</td>
<td>104</td>
<td>8</td>
<td>1</td>
<td>4</td>
<td>16</td>
<td>549</td>
<td class="speedup">5.28</td>
</tr>
<tr>
<td>1024</td>
<td>1024</td>
<td>26.5</td>
<td>8</td>
<td>1</td>
<td>4</td>
<td>32</td>
<td>536</td>
<td class="speedup">20.23</td>
</tr>
</tbody>
</table>


### Speedup of LLAMA2 7B training with CP over without CP
![cp_speedup_figure](https://github.com/NVIDIA/NeMo/releases/download/r2.0.0rc1/tutorial_cp_speedup_figure.png)

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