unsloth/async_task_outputs/async_task_output_if28t3.md

• Decision: shipped compile path uses transformer.compile_repeated_blocks(fullgraph = True, dynamic = True), which forwards to torch.compile; effective settings are fullgraph=True, dynamic=True, and default mode. This was chosen because Studio can vary resolution/batch/steps without excessive recompiles.
• Decision: CUDA graphs are not enabled in the shipped path. Per https://docs.pytorch.org/docs/stable/generated/torch.compile.html, CUDA graphs come from mode="reduce-overhead" or mode="max-autotune", while default mode gives Inductor fusion without graph capture.
• Decision: dynamic=True conflicts with CUDA graphs because graph capture expects static shapes. Switching to CUDA graphs would require dynamic=False and recompilation for new shapes.
• Decision: after ablation, default mode + dynamic=True remained the correct shipped choice because it was essentially as fast as autotune, much faster to compile, robust to shape changes, and avoided CUDA-graph runtime crashes.
• Decision: max-autotune-no-cudagraphs worked but was not worth enabling as default: 0.78 s vs 0.80 s was within run-to-run noise, while compile time increased from 5.7 s to 66.2 s and required dynamic=False.
• Decision: CUDA graph modes were rejected because they crashed in this pipeline, not merely because they were theoretically fragile.
• Clarification made: since fullgraph=True succeeds, there is no Dynamo graph break in the block; GGUF dequant is compile-traceable. The remaining inefficiency is Inductor declining codegen for Z-Image complex-valued ops: "does not support code generation for complex operators".
• File context mentioned but not edited in this span: diffusion_speed.py contains the shipped compile call.
• File context mentioned but not edited in this span: compile_probe.py already has a --mode flag and was used for mode ablation.
• Command run: LOG=logs/compile_modes_$(date +%Y%m%d_%H%M%S).log; echo "COMPILE MODES LOG: $LOG"; { for spec in "default:--mode default --dynamic" "maxautotune-nocg:--mode max-autotune-no-cudagraphs" "reduce-overhead:--mode reduce-overhead" "max-autotune:--mode max-autotune"; do ... to ablate torch compile modes including CUDA graphs on GPU 6.
• Command status: background task b01iys6fy completed with exit code 0.
• Command output file: /home/ubuntu/CLAUDE_CODE_TMPDIR/claude-1000/-mnt-disks-unslothai-ubuntu-workspace-81/8723b9e3-2ab1-49e9-a4b1-d030954eb94a/tasks/b01iys6fy.output.
• Command run: cat "$(ls -t /mnt/disks/unslothai/ubuntu/workspace_81/unsloth/logs/compile_modes_*.log | head -1)" to read the latest compile-mode ablation log.
• Key result: default with --mode default --dynamic gave eager 1.82s/gen, compiled 0.80s/gen, +56.3% vs eager, PSNR 37.7 dB, compile time 5.7s, verdict COMPILE-WORKS FASTER.
• Key result: max-autotune-no-cudagraphs with dynamic=False gave eager 1.83s/gen, compiled 0.78 s, +57.2%, PSNR 37.3 dB, compile time 66.2 s, and worked.
• Key result: reduce-overhead with CUDA graphs and dynamic=False failed.
• Key result: max-autotune with CUDA graphs and dynamic=False failed.
• Error encountered: both CUDA graph modes raised RuntimeError: accessing tensor output of CUDAGraphs that has been overwritten by a subsequent run at transformer_z_image.py:271, in forward.
• Error analysis: likely caused by regional compile plus CUDA graph output buffer reuse; denoiser calls the same compiled block repeatedly and Z-Image carries an output across calls, so the static CUDA graph output buffer is overwritten before a later read.
• Error resolution: resolved by keeping CUDA graphs disabled; no code changes were made to try manual cudagraph_mark_step_begin() or output cloning.
• Completed: answered why CUDA graphs were not being used and measured default, max-autotune-no-cudagraphs, reduce-overhead, and max-autotune.
• Completed: reported mode-by-mode speed, PSNR, compile time, and failure status.
• Pending: optional follow-up offered to wire mode="max-autotune-no-cudagraphs" into the opt-in max tier as a gated one-line change, leaving default unchanged.