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IBM Analog Hardware Acceleration Kit 0.7.0

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@kaoutar55 kaoutar55 released this 30 Jan 22:00
· 87 commits to master since this release
v0.7.0
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Learn about vigilant mode.

[0.7.0] - 2023/01/30

Added

  • Reset tiles method (#456)
  • Added many new analog MAC non-linearties (forward / backward pass) (#456)
  • Polynomial weight noise for hardware-aware training (#456)
  • Remap functionality for hardware-aware training (#456)
  • Input range estimation for InferenceRPUConfig (#456)
  • CUDA always syncs and added non-blocking option if not wished (#456)
  • Fitting utility for fitting any device model to conductance measurements (#456)
  • Added PowStepReferenceDevice for easy subtraction of symmetry
    point (#456)
  • Added SoftBoundsReferenceDevice for easy subtraction of symmetry
    point (#456)
  • Added stand-alone functions for applying inference drift to any model (#419)
  • Added Example 24: analog inference and hardware-aware training on BERT with the SQUAD task (#440)
  • Added Example 23: how to use AnalogTile directly to implement an
    analog matrix-vector product without using pytorch modules. (#393)
  • Added Example 22: 2 layer LSTM network trained on War and Peace dataset. (#391)
  • Added a new notebook for exploring analog sensitivities. (#380)
  • Remapping functionality for InferenceRPUConfig. (#388)
  • Inference cloud experiment and runners. (#410)
  • Added analog_modules generator in AnalogSequential. (#410)
  • Added SKIP_CUDA_TESTS to manually switch off the CUDA tests.
  • Enabling comparisons of RPUConfig instances. (#410)
  • Specific user-defined function for layer-wise setting for RPUConfigs
    in conversions. (#412)
  • Added stochastic rounding options for MixedPrecisionCompound. (#418)
  • New remap parameter field and functionality in
    InferenceRPUConfig (#423).
  • Tile-level weight getter and setter have apply_weight_scaling
    argument. (#423)
  • Pre and post-update / backward / forward methods in BaseTile for
    easier user-defined modification of pre and/or post-processings of a tile. (#423)
  • Type-checking for RPUConfig fields. (#424)

Fixed

  • Decay fix for compound devices (#463)
  • RPUCuda backend update with many fixes (#456)
  • Missing zero-grad call in example 02 (#446)
  • Indexing error in OneSidedDevice for CPU (#447)
  • Analog summary error when model is on cuda device. (#392)
  • Index error when loading the state dict with a model use previously. (#387)
  • Weights that were not contiguous could have been set wrongly. (#388)
  • Programming noise would not be applied if drift compensation was not
    used. (#389)
  • Loading a new model state dict for inference does not overwrite the noise
    model setting. (#410)
  • Avoid AnalogContext copying of self pointers. (#410)
  • Fix issue that drift compensation is not applied to conv-layers. (#412)
  • Fix issue that noise modifiers are not applied to conv-layers. (#412)
  • The CPU AnalogConv2d layer now uses unfolded convolutions instead of
    indexed covolutions (that are efficient only for GPUs). (#415)
  • Fix issue that write noise hidden weights are not transferred to
    pytorch when using get_hidden_parameters in case of CUDA. (#417)
  • Learning rate scaling due to output scales. (#423)
  • WeightModifiers of the InferenceRPUConfig are no longer called
    in the forward pass, but instead in the post_update_step
    method to avoid issues with repeated forward calls. (#423)
  • Fix training learn_out_scales issue after checkpoint load. (#434)

Changed

  • Pylint / mypy / pycodestyle / protobuf version bump (#456)
  • All configs related classes can now be imported from
    aihwkit.simulator.config (#456)
  • Weight noise visualization now shows the programming noise and drift
    noise differences. (#389)
  • Concatenate the gradients before applying to the tile update
    function (some speedup for CUDA expected). (#390)
  • Drift compensation uses eye instead of ones for readout. (#412)
  • weight_scaling_omega_columnwise parameter in MappingParameter is now called
    weight_scaling_columnwise. (#423)
  • Tile-level weight getter and setter now use Tensors instead of numpy
    arrays. (#423)
  • Output scaling and mapping scales are now distiniguished, only the
    former is learnable. (#423)
  • Renamed learn_out_scaling_alpha parameter in MappingParameter to
    learn_out_scaling and columnwise learning has a separate switch
    out_scaling_columnwise. (#423)

Deprecated

  • Input weight_scaling_omega argument in analog layers is deprecated. (#423)

Removed

  • The _scaled versions of the weight getter and setter methods are
    removed. (#423)
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