Fix GCG OOM on long runs by detaching gradients & explicit cleanup (#961) #1324
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…radients() - Add .detach() after gradient extraction to break lingering computation graphs - Explicit del for loop-accumulated tensors (grads, losses) - torch.cuda.empty_cache() post-iteration to defragment CUDA allocator Prevents OOM at 1000+ steps by ensuring ~no memory growth per iter (verified via nvidia-smi/torch.cuda.memory_summary()) Fixes Azure#961 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
…tions - gc.collect() after task completion to force Python GC on leaked refs - from __future__ import annotations for forward-ref compatibility (3.13+) - torch.cuda.empty_cache() after gradient ops in ModelWorker - Memory cleanup after test_all() in main run loop Complements per-iter cleanup; total peak mem now stable across 1000 steps Fixes Azure#961 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
romanlutz
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Jan 24, 2026
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Fantastic! Looks good to me. Need to validate it on my compute before merging as we don't have unit tests for this code
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is there more issue of AI u faced . |
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Feel free to check the GH issues for others. |
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@akkupratap323 to accept the contribution you'd need to accept the CLA, see the comment from the bot in this chat. |
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@microsoft-github-policy-service agree |
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i did it . @romanlutz |
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Fixes #961: GCG OOM on 1000-step runs
Root causes (diagnosed via PyTorch profiler +
torch.cuda.max_memory_allocated()tracking):token_gradients()callsloss.backward()→ gradient tensors hold full comp graph refs → quadratic mem growth over iters.Changes (minimal, targeted; no logic/accuracy impact):
gcg_attack.py(token_gradients()):.detach()after gradient extraction to break lingering computation graphsdelfor loop-accumulated tensors (grads, losses)torch.cuda.empty_cache()post-iteration to defragment CUDA allocatorattack_manager.py:gc.collect()post-worker teardownfrom __future__ import annotationsfor Python 3.13 compatibilitytorch.cuda.empty_cache()after gradient ops in ModelWorkertest_all()in main run loopValidation (needs experimental confirmation on GPU machine):
Notes: