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ab7ab5be23
* mm: factor out the current stream getter Make this a reusable function. * ops: sync the offload stream with the consumption of w&b This sync is nessacary as pytorch will queue cuda async frees on the same stream as created to tensor. In the case of async offload, this will be on the offload stream. Weights and biases can go out of scope in python which then triggers the pytorch garbage collector to queue the free operation on the offload stream possible before the compute stream has used the weight. This causes a use after free on weight data leading to total corruption of some workflows. So sync the offload stream with the compute stream after the weight has been used so the free has to wait for the weight to be used. The cast_bias_weight is extended in a backwards compatible way with the new behaviour opt-in on a defaulted parameter. This handles custom node packs calling cast_bias_weight and defeatures async-offload for them (as they do not handle the race). The pattern is now: cast_bias_weight(... , offloadable=True) #This might be offloaded thing(weight, bias, ...) uncast_bias_weight(...) * controlnet: adopt new cast_bias_weight synchronization scheme This is nessacary for safe async weight offloading. * mm: sync the last stream in the queue, not the next Currently this peeks ahead to sync the next stream in the queue of streams with the compute stream. This doesnt allow a lot of parallelization, as then end result is you can only get one weight load ahead regardless of how many streams you have. Rotate the loop logic here to synchronize the end of the queue before returning the next stream. This allows weights to be loaded ahead of the compute streams position.