PrincetonUniversity / PsyNeuLink / 10356384483 / 1

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Refactor/autodiff/track pnl (#3030)

• composition.py, autodiffcomposition.py and relevant subordinate methods:
 - implement synch and track parameter dictionaries that are passed to relevant methods
 
  - add/rename attributes:
    - PytorchCompositionWrapper:
      - retained_outputs
      - retained_targets
      - retained_losses
      - _nodes_to_execute_after_gradient_calc
    - PytorchMechanismWrapper:
      - value -> output
      - input
  - add methods:
    - synch_with_psyneulink(): centralize copying of params and values to pnl using methods below
    - copy_node_variables_to_psyneulink(): centralize updating of node (mech & comp) variables in PNL
    - copy_node_values_to_psyneulink(): centralize updating of node (mech & comp) values in PNL
    - copy_results_to_psyneulink(): centralize updating of autodiffcomposition.results
    - retain_in_psyneulink(): centralize tracking of pytorch results in PNL using methods below
    - retain_torch_outputs: keeps record of targets and copies to AutodiffComposition.pytorch_targets at end of call to learn()
    - retain_torch_targets: keeps record of targets and copies to AutodiffComposition.pytorch_targets at end of call to learn()
    - retain_torch_losses: keeps record of losses and copies to AutodiffComposition.pytorch_losses at end of call to learn()


• compositionrunner.py, autodiffcomposition.py, pytorchwrappers.py:
  - move loss tracking from parameter on autodiff to attribute on its pytorch_rep
  - batch_inputs():  add calls to synch_with_psyneulink() and retain_in_psyneulink()
  - batch_function_inputs():
     - needs calls to synch_with_psyneulink() and retain_in_psyneulink()

• composition.py:
- run(): add _update_results() as helper method than can be overidden (e.g., by autodiffcomposition) for less frequent updating

* • autodiffcomposition.py
  - restrict calls to copy_weights_to_psyneulink based on copy_parameters_to_psyneulink_after arg/attribute
 ... (continued)

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