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Change Log
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==========
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Release 3.0 - 21/8/2023
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-----------------------
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Web Portal
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^^^^^^^^^^
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* New look and feel
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Neural Architecture Search
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^^^^^^^^^^^^^^^^^^^^^^^^^^
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* **Breaking change**: ``nni.retiarii`` is no longer maintained and tested. Please migrate to ``nni.nas``.
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* Inherit ``nni.nas.nn.pytorch.ModelSpace``, rather than use ``@model_wrapper``.
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* Use ``nni.choice``, rather than ``nni.nas.nn.pytorch.ValueChoice``.
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* Use ``nni.nas.experiment.NasExperiment`` and ``NasExperimentConfig``, rather than ``RetiariiExperiment``.
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* Use ``nni.nas.model_context``, rather than ``nni.nas.fixed_arch``.
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* Please refer to `quickstart <https://nni.readthedocs.io/en/v3.0rc1/tutorials/hello_nas.html>`_ for more changes.
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* A refreshed experience to construct model space.
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* Enhanced debuggability via ``freeze()`` and ``simplify()`` APIs.
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* Enhanced expressiveness with ``nni.choice``, ``nni.uniform``, ``nni.normal`` and etc.
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* Enhanced experience of customization with ``MutableModule``, ``ModelSpace`` and ``ParamterizedModule``.
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* Search space with constraints is now supported.
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* Improved robustness and stability of strategies.
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* Supported search space types are now enriched for PolicyBaseRL, ENAS and Proxyless.
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* Each step of one-shot strategies can be executed alone: model mutation, evaluator mutation and training.
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* Most multi-trial strategies now supports specifying seed for reproducibility.
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* Performance of strategies have been verified on a set of benchmarks.
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* Strategy/engine middleware.
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* Filtering, replicating, deduplicating or retrying models submitted by any strategy.
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* Merging or transforming models before executing (e.g., CGO).
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* Arbitrarily-long chains of middlewares.
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* New execution engine.
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* Improved debuggability via SequentialExecutionEngine: trials can run in a single process and breakpoints are effective.
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* The old execution engine is now decomposed into execution engine and model format.
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* Enhanced extensibility of execution engines.
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* NAS profiler and hardware-aware NAS.
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* New profilers profile a model space, and quickly compute a profiling result for a sampled architecture or a distribution of architectures (FlopsProfiler, NumParamsProfiler and NnMeterProfiler are officially supported).
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* Assemble profiler with arbitrary strategies, including both multi-trial and one-shot.
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* Profiler are extensible. Strategies can be assembled with arbitrary customized profilers.
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Model Compression
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^^^^^^^^^^^^^^^^^
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* Compression framework is refactored, new framework import path is ``nni.contrib.compression``.
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* Configure keys are refactored, support more detailed compression configurations.
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* Support multi compression methods fusion.
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* Support distillation as a basic compression component.
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* Support more compression targets, like ``input``, ``ouptut`` and any registered paramters.
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* Support compressing any module type by customizing module settings.
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* Model compression support in DeepSpeed mode.
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* Fix example bugs.
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* Pruning
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* Pruner interfaces have fine-tuned for easy to use.
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* Support configuring ``granularity`` in pruners.
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* Support different mask ways, multiply zero or add a large negative value.
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* Support manully setting dependency group and global group.
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* A new powerful pruning speedup is released, applicability and robustness have been greatly improved.
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* The end to end transformer compression tutorial has been updated, achieved more extreme compression performance.
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* Fix config list in the examples.
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* Quantization
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* Support using ``Evaluator`` to handle training/inferencing.
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* Support more module fusion combinations.
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* Support configuring ``granularity`` in quantizers.
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* Bias correction is supported in the Post Training Quantization algorithm.
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* LSQ+ quantization algorithm is supported.
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* Distillation
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* DynamicLayerwiseDistiller and Adaptive1dLayerwiseDistiller are supported.
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* Compression documents now updated for the new framework, the old version please view `v2.10 <https://nni.readthedocs.io/en/v2.10/>`_ doc.
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* New compression examples are under `nni/examples/compression <https://github.com/microsoft/nni/tree/v3.0rc1/examples/compression>`_
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* Create a evaluator: `nni/examples/compression/evaluator <https://github.com/microsoft/nni/tree/v3.0rc1/examples/compression/evaluator>`_
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* Pruning a model: `nni/examples/compression/pruning <https://github.com/microsoft/nni/tree/v3.0rc1/examples/compression/pruning>`_
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* Quantize a model: `nni/examples/compression/quantization <https://github.com/microsoft/nni/tree/v3.0rc1/examples/compression/quantization>`_
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