Chunk 45.0

This chunk focused on deploying and debugging DFlash training on a 4× RTX PRO 6000 Blackwell node. After the assistant identified and fixed six bugs in the training scripts (drafter config copying from verifier instead of using independent Qwen3-style dimensions, missing sequence packing, absent noise augmentation, per-document anchor boundary violations, incorrect position IDs, and lack of `torch.compile`), the team provisioned a fresh Blackwell instance and set up the environment—installing dependencies, downloading the 52 GB Qwen3.6-27B model (29 seconds), and syncing 19 GB of tokenized data from S3 (9 minutes). The training pipeline then hit a cascade of hardware-specific issues. Initial runs crashed with FLA Triton autotuner failures on sm_120 (Blackwell), traced to a corrupted Triton disk cache and a race condition in the autotuner's `self.nargs` under parallel model warmup. After clearing caches and adding sequential warmup, the next failure was an OOM on the drafter GPU from unfused flex_attention backward materializing 15 GB score matrices. The assistant confirmed that `torch.compile(flex_attention)` correctly uses fused kernels (0.15 GB backward peak vs 17.85 GB unfused) but had to implement lazy compilation deferred to the first forward call to avoid cache corruption. Finally, upgrading Triton from 3.6.0 to 3.7.0 resolved the remaining FLA autotuner crashes, and the full 6-epoch training run was launched with DP=2, 512 anchors, and 8192 token budget.

From Data to Debugging: How an AI Assistant Discovered Six Critical Bugs Before Launching a Multi-Day ML Training Run 2921 words

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