Add bounded-memory training and colocation controls#1890
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This pull request introduces several memory optimization features, including CPU-resident microbatches, sequence-dimension chunking for Megatron vocab entropy computation, and a colocated worker memory barrier with optional hard-eviction for inactive reference workers. The review feedback suggests mapping PyTorch device indices to physical GPU indices via CUDA_VISIBLE_DEVICES to ensure accurate NVML memory statistics, and removing a fragile .squeeze(0) call on entropy chunks to robustly support varying input dimensions.
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Bounded-Memory Megatron Training and Colocation
Summary
This PR adds controls that bound temporary training memory and make colocated Megatron offload behavior complete.
Changes
CPU-resident policy microbatches is opt-in; automatic vocabulary-entropy chunk sizing, which defaults to a 512 MiB temporary-memory budget.
Memory Validation