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Transformer decoder with conditioning for canary #8091

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merged 3 commits into from
Dec 27, 2023

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What does this PR do ?

Transformer decoder with conditioning for canary

Collection: ASR

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  • Add specific line by line info of high level changes in this PR.

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Krishna Puvvada added 2 commits December 26, 2023 21:33
Signed-off-by: Krishna Puvvada <kpuvvada@nvidia.com>
Signed-off-by: Krishna Puvvada <kpuvvada@nvidia.com>
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LGTM

@pzelasko pzelasko merged commit 72cbc94 into NVIDIA:canary Dec 27, 2023
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titu1994 added a commit that referenced this pull request Jun 7, 2024
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marcromeyn added a commit that referenced this pull request Jun 7, 2024
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akoumpa added a commit that referenced this pull request Jun 10, 2024
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janekl added a commit that referenced this pull request Jun 12, 2024
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rohitrango pushed a commit to rohitrango/NeMo that referenced this pull request Jun 25, 2024
…rategy (NVIDIA#9387)

* Integrating mcore's DistributedDataParallel into MegatronStrategy

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* Adding the original change made for label_models (#9377) (#9378)

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* Dgalvez/fix greedy batch strategy name r2.0.0rc0 (#9243) (#9253)

* Lazily warn about using greedy strategy instead of greedy_batch
strategy.

Previously, the warning would often run spuriously, since several
existing code paths simply call "change_decoding_strategy()" after
having first initialized a Module, rather than changing the config
before initializing the Module. This can be confusing.

The only problem I can see with this is that using logging inside a
forward() method might interfere with some compiler toolkits like
Torchscript or thunder.compile. Presumably it would be easy to add a
conditional statement to avoid this statement in a compiler context if
necessary.

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* Update README.rst (#9393)

Revised content per https://gitlab-master.nvidia.com/nemo-framework-tme/documentation/-/issues/25. Also removed reference to NIMs in LLMs and MMs Deployment and Optimization. It should be NVIDIA NeMo Microservices and not NIM. Removed  nemo:24.03.framework and nemo:24.01.speech in Docker Containers section and replaced with 24.05 . Please verify all changes.

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* a2a fix removed tp world size and group from init (#8944) (#8952)

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* Add config option for FP32 embedding grads (#8946)

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* Changes to enable CUDA graph for LLM (#8955)

* Changes to enable CUDA graph for LLM (#8751)

* Use next instead of get_batch

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* Force diarizer to use CUDA if cuda is available and if device=None. (#9380) (#9390)

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* ci: Properly catch failed tests by introduction of workflow templates (#9324)

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* Use model-cast-to-bfloat16 rather than AMP-to-bfloat16 for inference. (#9198)

* Fix the "cast ping pong" problem when we run AMP inference.

This has been tested only for Parakeet-CTC-1.1B right now. This
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Automatic mixed precision and inference do not play well together.

First, automatic mixed precision was created back when neural networks
were much simpler. In particular, they did not have softmax and layer
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This is no longer necessary, now that layer norm does accumulation in
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https://github.com/pytorch/pytorch/issues/66707

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Do feature preprocessing in float32 for accuracy. Warn if someone
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Always create the output in the type the rest of the model expects.

Sort manifests by duration.

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While we don't need this for accurate results in b/float16, this is a
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regress.

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Signed-off-by: jasonwan <jasonwan@nvidia.com>

* support …
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