Comments (7)
This feature is definitely on our radar, I don’t have a concrete timeline though. But we’ll be sure to update this thread when we have more info.
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@Michiel29 as Jeff mentioned, this is something we definitely want, but currently its not on the top of our priority list at the moment. We be more than happy to accept contributions on this from the community. If you would like to contribute, we can definitely share our discussions on what would be needed to support Lamb with ZeRO. Please let us know.
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@jeffra I'd like to use lamb with zero too. Can you share updates on using lamb + zero?
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@Michiel29 as Jeff mentioned, this is something we definitely want, but currently its not on the top of our priority list at the moment. We be more than happy to accept contributions on this from the community. If you would like to contribute, we can definitely share our discussions on what would be needed to support Lamb with ZeRO. Please let us know.
Why Lamb is not in a list of supported optimizers?
Please share your ideas of what would be needed to support Lamb with ZeRO.
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I realized that we have to add two updates to use LAMB with ZeRO:
- Split model parameters preserving whole tensors on a single node (here)
- Preserve splitting of a parameter vector into tensors at optimizer initialization stage (here)
Am I right?
from deepspeed.
I realized that we have to add two updates to use LAMB with ZeRO:
- Split model parameters preserving whole tensors on a single node (here)
- Preserve splitting of a parameter vector into tensors at optimizer initialization stage (here)
Am I right?
Hi,I am not very familiar with lamb, can you explain in detail your thoughts on lamb combined with zero ?thanks alot
from deepspeed.
@Michiel29 as Jeff mentioned, this is something we definitely want, but currently its not on the top of our priority list at the moment. We be more than happy to accept contributions on this from the community. If you would like to contribute, we can definitely share our discussions on what would be needed to support Lamb with ZeRO. Please let us know.
Hi, I'd like to use lamb with zero stage1. could you share your ideas of what would be needed to support Lamb with ZeRO.
Do we need to modify the implementation of fused lamb or modify the implementation of zero ?
If I know the detailed idea, I can contribute the code. Looking forward to your reply
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Related Issues (20)
- [REQUEST] Does Universal Checkpoint supports for MoE Checkpoint? HOT 3
- Different seeds are giving the exact same loss on Zero 1,2 and 3 during multi gpu training [BUG]
- [BUG] fp16 not supported for CPU? HOT 1
- Issue with LoRA Tuning on llama3-70b using PEFT and TRL's SFTTrainer
- [REQUEST] Asynchronous Checkpointing HOT 1
- [BUG] ImportError: /home/nlp/.cache/torch_extensions/py310_cu121/cpu_adam/cpu_adam.so: cannot open shared object file: No such file or directory HOT 1
- CUDA error: no kernel image is available for execution on the device [BUG]
- lr scheduler defined in config cannot be overwritten by lr scheduler defined in code and pass to `deepspeed.initialize` [BUG]
- [BUG] PipelineEngine calculates loss with outputs and labels from different batches. HOT 1
- [BUG] Learning rate scheduler and optimizer logical issue
- In distributed training, in order to continue training, an error occurred when loading model checkpoints after saving them.
- DS communication issue when using NCCL backend: All_reduce instead of reduce_scatter (or several reduce ops) HOT 5
- [BUG] I can't run fp8 with pipeline parallel HOT 2
- [BUG] Multi-gpu stuck when the computation graph is not complete for wach process.
- [BUG] Multi-node fine-tuning with thunderbolt HOT 1
- Multi-node multi-GPUs training is slower than single-node multi-GPUs training[BUG] HOT 2
- Default libcurand path fails HOT 1
- [BUG] Universal checkpoint conversion - "Cannot find layer_01* files in there"
- test
- how to set "training_step" during training?
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