Segment 45

This sub-session focused on deploying and debugging the DFlash training pipeline on a 4× RTX PRO 6000 Blackwell node. The assistant first fixed six bugs in the training scripts related to drafter configuration, sequence packing, noise augmentation, anchor boundaries, position IDs, and torch.compile. After provisioning the node and syncing data, the training run encountered hardware-specific issues: FLA Triton autotuner failures on sm_120 caused by a corrupted disk cache and a race condition in self.nargs under parallel warmup. The assistant resolved these by clearing caches and adding sequential warmup, then addressed an OOM from unfused flex_attention backward by implementing lazy compilation deferred to the first forward call. Upgrading Triton from 3.6.0 to 3.7.0 fixed remaining autotuner crashes, enabling the full training run to launch. However, a persistent race condition in FLA's CachedAutotuner under concurrent GPU pair calls via ThreadPoolExecutor continued to crash training. After a failed monkey-patch attempt, the assistant devised a structural fix: running target model forward passes sequentially across GPU pairs to avoid concurrent autotuner execution, while keeping drafter forward/backward parallel. This fix was being implemented by rewriting the training loop.

Fix six DFlash training script bugsProvision Blackwell node and sync dataDebug FLA Triton autotuner crashesImplement sequential warmup for autotunerFix OOM with lazy flex_attention compilationUpgrade Triton to 3.7.0Debug CachedAutotuner race conditionAttempt monkey-patch autotuner lockImplement sequential target forward fix

The Race That Wouldn't Die: Debugging a Triton Autotuner Concurrency Bug Across Four Blackwell GPUs 3136 words

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