minimal_async_ep: split token exchange launches and waits - #3954
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[ghstack-poisoned]
This was referenced Jul 21, 2026
[ghstack-poisoned]
…aunches and waits" ## Summary Split MinimalAsyncEP dispatch and combine into graph-visible launch and wait operations. Launches perform metadata preparation and start symmetric-memory row copies; waits only join the current stream to completion and return aliases of the pending outputs. Keepalive inputs describe storage read asynchronously by the communication stream. Routing metadata consumed by later operations remains ordinary graph dataflow rather than an artificial wait dependency. ## Why A monolithic custom operator hides the useful scheduling boundary and forces communication to complete before independent expert work can run. Thin aliasing waits expose the boundary while remaining compatible with autograd, functionalization, full Inductor, and CUDA graph wrapping. ## Test Plan - Trace the registered production APIs with fake tensors and verify launch/wait dependency edges. - Validate wait aliasing through structured operator schema metadata. - Compile wait forward and backward paths with full Inductor. - Compare two-rank outputs and gradients with a local reference. - Trace the production APIs through the CUDA graph pass. [ghstack-poisoned]
This was referenced Jul 29, 2026
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Stack from ghstack (oldest at bottom):
Summary
Split MinimalAsyncEP dispatch and combine into graph-visible launch and wait
operations. Launches perform metadata preparation and start symmetric-memory
row copies; waits only join the current stream to completion and return aliases
of the pending outputs.
Keepalive inputs describe storage read asynchronously by the communication
stream. Routing metadata consumed by later operations remains ordinary graph
dataflow rather than an artificial wait dependency.
Why
A monolithic custom operator hides the useful scheduling boundary and forces
communication to complete before independent expert work can run. Thin aliasing
waits expose the boundary while remaining compatible with autograd,
functionalization, full Inductor, and CUDA graph wrapping.
Test Plan
dependency edges.