Let AOTAutogradCache cache graphs with custom autograd.Functions - #2162
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Let AOTAutogradCache cache graphs with custom autograd.Functions#2162anijain2305 wants to merge 1 commit into
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UMA's forward contains custom autograd.Functions. AOTAutogradCache refuses to cache any graph containing one unless torch._functorch.config.autograd_cache_allow_custom_autograd_functions is set, so today the largest graph misses the cache and is retraced and recompiled from scratch on every process start. Warm start ends up paying most of a cold compile. Turning it on halves warm compile time. On uma-s-1p2 with 1000 atoms, a second process run against caches populated by the first: autograd_cache_allow_custom_autograd_functions=False 22.6s autograd_cache_allow_custom_autograd_functions=True 10.2s which is a >2x reduction (3 runs each, 22.79/22.35/22.69 vs 10.86/9.92/9.76, measured as the union of top-level compile events from tlparse). Cold start is unaffected, 53.5s vs 54.0s, within run-to-run noise: the flag decides only whether the second run can reuse the first run's work. PyTorch intends to flip this flag to True by default, but that is waiting on thorough internal testing across models. Nothing about UMA needs to wait for it: the model's custom autograd.Functions are ordinary differentiable ops with no state that would make a cached graph unsafe to reuse, so we can opt in now and drop this line once the default changes. This change was authored with the assistance of an AI coding agent.
frostedoyster
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August 13, 2026 18:37
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UMA's forward contains custom autograd.Functions. AOTAutogradCache refuses to cache any graph containing one unless
torch._functorch.config.autograd_cache_allow_custom_autograd_functions is set, so today the largest graph misses the cache and is retraced and recompiled from scratch on every process start. Warm start ends up paying most of a cold compile.
Turning it on halves warm compile time. On uma-s-1p2 with 1000 atoms, a second process run against caches populated by the first:
autograd_cache_allow_custom_autograd_functions=False 22.6s
autograd_cache_allow_custom_autograd_functions=True 10.2s
which is a >2x reduction (3 runs each, 22.79/22.35/22.69 vs 10.86/9.92/9.76, measured as the union of top-level compile events from tlparse). Cold start is unaffected, 53.5s vs 54.0s, within run-to-run noise: the flag decides only whether the second run can reuse the first run's work.
PyTorch intends to flip this flag to True by default, but that is waiting on thorough internal testing across models. Nothing about UMA needs to wait for it: the model's custom autograd.Functions are ordinary differentiable ops with no state that would make a cached graph unsafe to reuse, so we can opt in now and drop this line once the default changes.
This change was authored with the assistance of an AI coding agent.