Configure FSDP to keep module params#12074
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Needed to avoid bug with Transformer Engine LayerNorm, which needs to access module parameters. Signed-off-by: Tim Moon <tmoon@nvidia.com>
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Needed to avoid bug with Transformer Engine LayerNorm, which needs to access module parameters. Signed-off-by: Tim Moon <tmoon@nvidia.com>
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Needed to avoid bug with Transformer Engine LayerNorm, which needs to access module parameters. Signed-off-by: Tim Moon <tmoon@nvidia.com>
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What does this PR do ?
This PR aims to fix a bug introduced in Transformer Engine 1.14 when running with FSDP.
Transformer Engine 1.14 changed its LayerNorm and RMSNorm implementation to use a prototype operation-based API (see NVIDIA/TransformerEngine#1033). This is a generic API intended for kernel fusion and its forward pass requires calling
torch.nn.Module.parametersto interface with PyTorch's autograd infrastructure. However, the default behavior of PyTorch FSDP is to remove all module parameters and manage them viaFlatParameters. Since TE modules couldn't access their parameters, we got the following error:This PR changes the FSDP configuration with
use_orig_params=Trueso that TE modules can still access their parameters.Collection: NLP
Changelog
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.
Some relevant options:
model.transformer_engine=Truemodel.fsdp=True,fsdp_sharding_strategy=fullGitHub Actions CI
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Additional Information
parametersfunction in op backward pass NVIDIA/TransformerEngine#1403 unsuccessfully attempted to fix this error