fix: resolve OOM in long-sequence training via conditional entropy gradient tracking#1524
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ppraneth wants to merge 1 commit intoTHUDM:mainfrom
Open
fix: resolve OOM in long-sequence training via conditional entropy gradient tracking#1524ppraneth wants to merge 1 commit intoTHUDM:mainfrom
ppraneth wants to merge 1 commit intoTHUDM:mainfrom
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thanks, we are refactoring this part for better menmory use. |
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@lilei199908 could you review this PR and suggest any edits? I believe this should reduce memory usage. |
Collaborator
LGTM, we will merged it soon! thanks |
Contributor
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@zhuzilin Can you check and merge this pr? |
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This PR addresses the CUDA Out of Memory (OOM) issues #1523 encountered during training with long sequences (e.g., >30k tokens) by implementing conditional gradient tracking for the entropy term.
The Problem
In the previous implementation, the entropy calculation was always differentiable, regardless of the
entropy_coefvalue. To support the backward pass, the system was forced to store massive intermediate activation tensors (logits and softmax outputs) with shapes of(seq_len, vocab_size / TP). For long sequences and large vocabularies, these tensors consumed 5–6 GB of VRAM per sample on the last pipeline stage, leading to OOM even when the entropy contribution to the loss was zero.The Solution
I have decoupled entropy for monitoring (logging) from entropy for training (loss).
args.entropy_coef > 0.entropy_coefis 0.0, entropy for logging is computed within atorch.no_grad()context. This prevents PyTorch from allocating memory for backward tensors, effectively reclaiming several gigabytes of VRAM per sequence.Files Changed
slime/utils/ppo_utils.py: Addedrequires_entropy_gradflag andno_gradcontext tocalculate_log_probs_and_entropy.slime/backends/megatron_utils/loss.py: Passed gradient requirement flag based onentropy_coef.slime/backends/fsdp_utils/actor.py: Implemented conditional tracking and addedcontextlibimport.