forked from microsoft/nni
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #299 from microsoft/master
merge master
- Loading branch information
Showing
313 changed files
with
15,091 additions
and
13,703 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -11,3 +11,6 @@ statsmodels==0.12.0 | |
|
|
||
| # PPOTuner | ||
| gym | ||
|
|
||
| # DNGO | ||
| pybnn | ||
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,119 @@ | ||
| Auto Compression with NNI Experiment | ||
| ==================================== | ||
|
|
||
| If you want to compress your model, but don't know what compression algorithm to choose, or don't know what sparsity is suitable for your model, or just want to try more possibilities, auto compression may help you. | ||
| Users can choose different compression algorithms and define the algorithms' search space, then auto compression will launch an NNI experiment and try different compression algorithms with varying sparsity automatically. | ||
| Of course, in addition to the sparsity rate, users can also introduce other related parameters into the search space. | ||
| If you don't know what is search space or how to write search space, `this <./Tutorial/SearchSpaceSpec.rst>`__ is for your reference. | ||
| Auto compression using experience is similar to the NNI experiment in python. | ||
| The main differences are as follows: | ||
|
|
||
| * Use a generator to help generate search space object. | ||
| * Need to provide the model to be compressed, and the model should have already been pre-trained. | ||
| * No need to set ``trial_command``, additional need to set ``auto_compress_module`` as ``AutoCompressionExperiment`` input. | ||
|
|
||
| Generate search space | ||
| --------------------- | ||
|
|
||
| Due to the extensive use of nested search space, we recommend a using generator to configure search space. | ||
| The following is an example. Using ``add_config()`` add subconfig, then ``dumps()`` search space dict. | ||
|
|
||
| .. code-block:: python | ||
| from nni.algorithms.compression.pytorch.auto_compress import AutoCompressionSearchSpaceGenerator | ||
| generator = AutoCompressionSearchSpaceGenerator() | ||
| generator.add_config('level', [ | ||
| { | ||
| "sparsity": { | ||
| "_type": "uniform", | ||
| "_value": [0.01, 0.99] | ||
| }, | ||
| 'op_types': ['default'] | ||
| } | ||
| ]) | ||
| generator.add_config('qat', [ | ||
| { | ||
| 'quant_types': ['weight', 'output'], | ||
| 'quant_bits': { | ||
| 'weight': 8, | ||
| 'output': 8 | ||
| }, | ||
| 'op_types': ['Conv2d', 'Linear'] | ||
| }]) | ||
| search_space = generator.dumps() | ||
| Now we support the following pruners and quantizers: | ||
|
|
||
| .. code-block:: python | ||
| PRUNER_DICT = { | ||
| 'level': LevelPruner, | ||
| 'slim': SlimPruner, | ||
| 'l1': L1FilterPruner, | ||
| 'l2': L2FilterPruner, | ||
| 'fpgm': FPGMPruner, | ||
| 'taylorfo': TaylorFOWeightFilterPruner, | ||
| 'apoz': ActivationAPoZRankFilterPruner, | ||
| 'mean_activation': ActivationMeanRankFilterPruner | ||
| } | ||
| QUANTIZER_DICT = { | ||
| 'naive': NaiveQuantizer, | ||
| 'qat': QAT_Quantizer, | ||
| 'dorefa': DoReFaQuantizer, | ||
| 'bnn': BNNQuantizer | ||
| } | ||
| Provide user model for compression | ||
| ---------------------------------- | ||
|
|
||
| Users need to inherit ``AbstractAutoCompressionModule`` and override the abstract class function. | ||
|
|
||
| .. code-block:: python | ||
| from nni.algorithms.compression.pytorch.auto_compress import AbstractAutoCompressionModule | ||
| class AutoCompressionModule(AbstractAutoCompressionModule): | ||
| @classmethod | ||
| def model(cls) -> nn.Module: | ||
| ... | ||
| return _model | ||
| @classmethod | ||
| def evaluator(cls) -> Callable[[nn.Module], float]: | ||
| ... | ||
| return _evaluator | ||
| Users need to implement at least ``model()`` and ``evaluator()``. | ||
| If you use iterative pruner, you need to additional implement ``optimizer_factory()``, ``criterion()`` and ``sparsifying_trainer()``. | ||
| If you want to finetune the model after compression, you need to implement ``optimizer_factory()``, ``criterion()``, ``post_compress_finetuning_trainer()`` and ``post_compress_finetuning_epochs()``. | ||
| The ``optimizer_factory()`` should return a factory function, the input is an iterable variable, i.e. your ``model.parameters()``, and the output is an optimizer instance. | ||
| The two kinds of ``trainer()`` should return a trainer with input ``model, optimizer, criterion, current_epoch``. | ||
| The full abstract interface refers to :githublink:`interface.py <nni/algorithms/compression/pytorch/auto_compress/interface.py>`. | ||
| An example of ``AutoCompressionModule`` implementation refers to :githublink:`auto_compress_module.py <examples/model_compress/auto_compress/torch/auto_compress_module.py>`. | ||
|
|
||
| Launch NNI experiment | ||
| --------------------- | ||
|
|
||
| Similar to launch from python, the difference is no need to set ``trial_command`` and put the user-provided ``AutoCompressionModule`` as ``AutoCompressionExperiment`` input. | ||
|
|
||
| .. code-block:: python | ||
| from pathlib import Path | ||
| from nni.algorithms.compression.pytorch.auto_compress import AutoCompressionExperiment | ||
| from auto_compress_module import AutoCompressionModule | ||
| experiment = AutoCompressionExperiment(AutoCompressionModule, 'local') | ||
| experiment.config.experiment_name = 'auto compression torch example' | ||
| experiment.config.trial_concurrency = 1 | ||
| experiment.config.max_trial_number = 10 | ||
| experiment.config.search_space = search_space | ||
| experiment.config.trial_code_directory = Path(__file__).parent | ||
| experiment.config.tuner.name = 'TPE' | ||
| experiment.config.tuner.class_args['optimize_mode'] = 'maximize' | ||
| experiment.config.training_service.use_active_gpu = True | ||
| experiment.run(8088) |
This file was deleted.
Oops, something went wrong.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.