Distributed optimizer reduces GPT embedding grads in FP32
jenkins jenkins jenkins jenkins
# What does this PR do ?

When training Megatron-core GPT with the distributed optimizer, the embedding gradients were reduced in the grad sync dtype (usually BF16). However, we want to reduce in FP32 to improve convergence.

**Collection**: NLP

# Changelog 
- Make sure GPT embedding grads are reduced in FP32.

# Usage

Run GPT, e.g. with the config at https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml.

Enable mcore with `model.mcore_gpt=True` and the distributed optimizer with `model.optim.name=distributed_fused_adam`.

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- [x] Bugfix
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# Additional Information
* mcore support was added in https://github.com/NVIDIA/NeMo/pull/7093
* Distopt support for mixed grad dtypes was added in https://github.com/NVIDIA/NeMo/pull/5920