const caller: ExternalCaller = externalCaller();
Here's what makes this insidious: the trampoline runs fine. genericClosure's C++ loop processes all 65,000 steps without complaint. The failure happens when you try to use the result. Forcing that final total unwinds the entire thunk chain as recursive C++ forceValue calls, rebuilding exactly the stack depth you thought you'd eliminated. The error is stack overflow (possible infinite recursion), not max-call-depth exceeded: this is the C++ call stack, not the Nix evaluator's depth limit. A simple integer counter where the comparison is the state (n: if n = N then ...) would survive, because the comparison forces the state at every step and call-by-need memoization prevents the chain. The trap springs when your state has components the step function doesn't touch.
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We could just delete this assertion. Or we could just set the model to eval mode. Contrary to the name, it has nothing to do with whether the model is trainable or not. Eval mode just turns off train time behavior. Historically, this meant no dropout and using stored batch norm statistics rather than per-batch statistics. With modern LLM’s, this means, well, nothing—there typically are no train time specific behaviors. requires_grad controls whether gradients are tracked and only the parameters passed to the optimizer are updated.
3014489610http://paper.people.com.cn/rmrb/pc/content/202603/12/content_30144896.htmlhttp://paper.people.com.cn/rmrb/pad/content/202603/12/content_30144896.html11921 青春守护夕阳红，有干头又有奔头（追梦人·期待“十五五” 青年说机遇③）