class torch.autograd.profiler.KinetoStepTracker[source]

Provides an abstraction for incrementing the step count globally.

Previously, we only had one place to mark that a step() has occurred in the program via pytorch profiler step(). We will now add step hooks in the Optimizer class

  • This could mean programs that already call profiler.step() every iteration can end up double incrementing step count.

  • If a model uses multiple optimizers we can also have double or more counting of the step.

We fix this by adding a layer of abstraction before calling step() to the kineto library. The idea is to maintain steps per requester in a dict:

   "ProfilerStep": 100,  # triggered by profiler step() call
   "Optimizer1Step": 100,   # Optimizer 1 or 2 are just examples, could be SGD, Adam etc
   "Optimizer2Step": 100,

To figure out the global step count just take the max of dict values (100).

If one of the count increments the max will go up.

   "ProfilerStep": 100,
   "Optimizer1Step": 101,   # Optimizer1 got incremented first say
   "Optimizer2Step": 100,

Then global step count is 101 We only call the kineto step() function when global count increments.

NOTE: Please do not use the KinetoStepTracker in modules beside the Optimizer for now. The result could be incorrect increments of the step count.

classmethod current_step()[source]

Get the latest step for any requester

Return type


classmethod erase_step_count(requester)[source]

Remove a given requester.

Return type


classmethod increment_step(requester)[source]

Increments the step count for the requester.

Additionally if the max over all step counts has incremented then trigger the _kineto_step() returns global step count

Return type


classmethod init_step_count(requester)[source]

Initialize for a given requester.


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