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The user redirected the assistant away from debugging the FX tracing race condition, which was causing compilation failures and degraded throughput, and toward a pragmatic recovery plan. The assistant acknowledged the core issue: the original working environment (torch 2.11.0+cu128 with a warm 353 MB compile cache) had been polluted by SGLang, flashinfer, and multiple torch version swaps, and the cache had been deleted, forcing fresh compilation that exposed a multi-threaded FX tracing conflict in `torch.compile(flex_attention)`. The assistant executed the plan: restored `dflash_model.py` to the committed git HEAD (removing the `is_fx_symbolic_tracing` hack), created a fresh venv using `uv` with only essential training dependencies (torch 2.11.0+cu128, transformers, datasets, wandb, boto3), deployed the clean scripts to CT200, and pre-warmed the compile cache with a single-threaded warmup script to avoid the race condition. The warmup succeeded, generating a fresh compile cache. Training was then launched from scratch on the expanded 1.1M dataset with the original 5‑target + 3‑drafter configuration (token_budget=49152, max_batch_size=64, max_anchors=1024, block_size=32, gamma=10). The run name was set to `exp-ddtree-expanded-1.1M-fresh-v2`. The assistant is now waiting for the first training steps to confirm stable throughput, with the expectation that the clean environment and pre-warmed cache will restore the 20 Ktok/s performance seen before the dataset expansion.

The Long Road to Nowhere: A Multi-Day Debugging Odyssey Through Environment, Cache, and Race Condition in DFlash Training 2039 words

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