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This example code shows a method for how to use AIMET if you have a Keras model by invoking AIMET on the back-end session and converting the returned session to a Keras model. - -APIs -==== -The method involves performing four steps. The steps are: - - -**Step 1: Save the session returned by AIMET** - -.. autofunction:: aimet_tensorflow.utils.convert_tf_sess_to_keras.save_tf_session_single_gpu - -| - -**Step 2: Model subclassing to load the corresponding session to Keras model** - -.. autofunction:: aimet_tensorflow.utils.convert_tf_sess_to_keras.load_tf_sess_variables_to_keras_single_gpu - -| - -After these two steps, model can be used for single gpu training. For multi-gpu training, the next two steps needs to be followed: - - -**Step 3: Saving the Keras model from step 2 to make it compatible with distribution strategy** - -.. autofunction:: aimet_tensorflow.utils.convert_tf_sess_to_keras.save_as_tf_module_multi_gpu - -| - -**Step 4: Model subclassing to load the corresponding Keras model** - -.. autofunction:: aimet_tensorflow.utils.convert_tf_sess_to_keras.load_keras_model_multi_gpu - -| - - -Code Example -============ - -**Required imports** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/converting_tf_session_to_keras.py - :language: python - :lines: 40, 51-52 - -**Steps to convert a TF session found after compression to Keras model** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/converting_tf_session_to_keras.py - :language: python - :pyobject: convert_tf_session_to_keras_model - -Utility Functions -================= -**Required imports** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/converting_tf_session_to_keras.py - :language: python - :lines: 40-49 - -**Utility function to get session from Keras model** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/converting_tf_session_to_keras.py - :language: python - :pyobject: get_sess_from_keras_model - -**Utility function to get a compressed session** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/converting_tf_session_to_keras.py - :language: python - :pyobject: compress_session - -**Utility function for training** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/converting_tf_session_to_keras.py - :language: python - :pyobject: train diff --git a/AimetDocs/_sources/api_docs/index.rst.txt b/AimetDocs/_sources/api_docs/index.rst.txt deleted file mode 100644 index 9941efb..0000000 --- a/AimetDocs/_sources/api_docs/index.rst.txt +++ /dev/null @@ -1,34 +0,0 @@ -========= -Main Page -========= - -Welcome to AI Model Efficiency Toolkit API Docs! -================================================ - -AI Model Efficiency Toolkit (AIMET) is a software toolkit that enables users to compress -and quantize ML models. The resulting models returned by AIMET can be further trained (or fine-tuned) -to dramatically improve accuracy lost due to quantization and compression. - -AIMET is designed to work generically on any user-provided model. At present, AIMET supports -TensorFlow and PyTorch training frameworks. - -Please follow the links below to see AIMET APIs for either TensorFlow or PyTorch. - -.. toctree:: - :titlesonly: - :maxdepth: 2 - - AIMET APIs for PyTorch - AIMET APIs for TensorFlow - -| -| - -.. note:: This documentation is auto-generated from the AIMET codebase using Sphinx - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/AimetDocs/_sources/api_docs/tensorflow.rst.txt b/AimetDocs/_sources/api_docs/tensorflow.rst.txt deleted file mode 100644 index 3b45983..0000000 --- a/AimetDocs/_sources/api_docs/tensorflow.rst.txt +++ /dev/null @@ -1,14 +0,0 @@ - -===================== -AIMET TensorFlow APIs -===================== - - -.. toctree:: - :titlesonly: - :maxdepth: 6 - - TensorFlow Quantization API - TensorFlow Model Compression API - TensorFlow Model Visualization API for Quantization - Using AIMET with Keras Model \ No newline at end of file diff --git a/AimetDocs/_sources/api_docs/tensorflow_bias_correction.rst.txt b/AimetDocs/_sources/api_docs/tensorflow_bias_correction.rst.txt deleted file mode 100644 index c113893..0000000 --- a/AimetDocs/_sources/api_docs/tensorflow_bias_correction.rst.txt +++ /dev/null @@ -1,87 +0,0 @@ -:orphan: - -.. _tf-bias-correction: - -==================================== -AIMET TensorFlow Bias Correction API -==================================== - -Bias Correction API -==================== - -Bias correction is performed after Cross layer equalization on models. -Main api to perform bias correction on entire model is listed below. - -.. autofunction:: aimet_tensorflow.bias_correction.BiasCorrection.correct_bias - -Input Parameter Types -===================== - -**Quantization Params** - -.. autofunction:: aimet_tensorflow.bias_correction.QuantParams - -**Bias Correction Params** - -.. autofunction:: aimet_tensorflow.bias_correction.BiasCorrectionParams - - -Data Input Type -=============== - -**Format expected is tf.Data.DataSet type** - -Dataset represents the input data as a tensor or nested structures of -tensors, one per input to model along with an iterator that operates on them. - - -Code Examples for Bias Correction -================================= - -**Required imports** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/post_training_techniques_examples.py - :language: python - :lines: 41-55 - -**Only Empirical Bias correction on a given model** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/post_training_techniques_examples.py - :language: python - :pyobject: bias_correction_empirical - -**Empirical and Analytical Bias correction on a given model** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/post_training_techniques_examples.py - :language: python - :pyobject: bias_correction_empirical_analytical - -**Empirical and Analytical Bias correction on a given model after performing CLE** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/post_training_techniques_examples.py - :language: python - :pyobject: bias_correction_after_cle - -Bias Correction Per Layer API -============================== - -Empirical/ analytical Bias correction can also be performed on a -subset of selected layers in a given model using the api listed below. - -.. autofunction:: aimet_tensorflow.bias_correction.BiasCorrection.bias_correction_per_layer -.. autofunction:: aimet_tensorflow.bias_correction.BiasCorrection.analytical_bias_correction_per_layer - -Code Example for Per-Layer Bias Correction -=========================================== - -**Empirical Bias correction on one layer** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/post_training_techniques_examples.py - :language: python - :pyobject: bias_correction_single_layer_empirical - -**Analytical Bias correction on one layer** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/post_training_techniques_examples.py - :language: python - :pyobject: bias_correction_single_layer_analytical \ No newline at end of file diff --git a/AimetDocs/_sources/api_docs/tensorflow_compress.rst.txt b/AimetDocs/_sources/api_docs/tensorflow_compress.rst.txt deleted file mode 100644 index 655c4a7..0000000 --- a/AimetDocs/_sources/api_docs/tensorflow_compress.rst.txt +++ /dev/null @@ -1,156 +0,0 @@ - -================================ -AIMET TensorFlow Compression API -================================ - -Introduction -============ -AIMET supports the following model compression techniques for tensorflow models - - Spatial SVD - - Channel Pruning - - Weight SVD - -For the Spatial SVD and Channel Pruning compression techniques, there are two modes in which you can invoke the AIMET API - - Auto Mode: In **Auto** mode, AIMET will determine the optimal way to compress each layer of - the model given an overall target compression ratio. Greedy Compression Ratio Selection Algorithm is used to pick appropriate compression ratios for each layer. - - - Manual Mode: In **Manual** mode, the user can pass in the desired compression-ratio per layer - to AIMET. AIMET will apply the specified compression technique for each of the - layers to achieve the desired compression-ratio per layer. It is recommended that - the user start with Auto mode, and then tweak per-layer compression-ratios using - Manual mode if desired. - -For Weight SVD, we use Tar-Based Rank selection. Auto and Manual modes are supported for Weight SVD as well. - -| - -Top-level API for Compression -============================= - -.. autoclass:: aimet_tensorflow.compress.ModelCompressor - -| - -.. automethod:: aimet_tensorflow.compress.ModelCompressor.compress_model - -| - -Greedy Selection Parameters -=========================== - -.. autoclass:: aimet_common.defs.GreedySelectionParameters - :members: - :noindex: - -| - - -Spatial SVD Configuration -========================= - -.. autoclass:: aimet_tensorflow.defs.SpatialSvdParameters - :members: - -| - -Channel Pruning Configuration -============================= - -.. autoclass:: aimet_tensorflow.defs.ChannelPruningParameters - :members: - -| - -Configuration Definitions -========================= - -.. autoclass:: aimet_common.defs.CostMetric - :members: - -| - -.. autoclass:: aimet_common.defs.CompressionScheme - :members: - -| - -.. autoclass:: aimet_tensorflow.defs.ModuleCompRatioPair - :members: - -| - - -Code Examples -============= - -**Required imports** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/code_examples.py - :language: python - :lines: 41-51 - -**Evaluation function** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/code_examples.py - :language: python - :pyobject: evaluate_model - -**Compressing using Spatial SVD in auto mode with multiplicity = 8 for rank rounding** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/code_examples.py - :language: python - :pyobject: spatial_svd_auto_mode - -**Compressing using Spatial SVD in manual mode** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/code_examples.py - :language: python - :pyobject: spatial_svd_manual_mode - -**Compressing using Channel Pruning in auto mode** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/code_examples.py - :language: python - :pyobject: channel_pruning_auto_mode - -**Compressing using Channel Pruning in manual mode** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/code_examples.py - :language: python - :pyobject: channel_pruning_manual_mode - - - - -Weight SVD Top-level API -======================== - -.. autoclass:: aimet_tensorflow.svd.Svd - -| - -.. automethod:: aimet_tensorflow.svd.Svd.compress_net - -| - -Code Examples for Weight SVD -============================ - -**Required imports** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/weight_svd.py - :language: python - :lines: 38-41 - -**Compressing using Weight SVD in auto mode** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/weight_svd.py - :language: python - :pyobject: weight_svd_auto_mode - - -**Compressing using Weight SVD in manual mode** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/weight_svd.py - :language: python - :pyobject: weight_svd_manual_mode diff --git a/AimetDocs/_sources/api_docs/tensorflow_cross_layer_equalization.rst.txt b/AimetDocs/_sources/api_docs/tensorflow_cross_layer_equalization.rst.txt deleted file mode 100644 index 62e948f..0000000 --- a/AimetDocs/_sources/api_docs/tensorflow_cross_layer_equalization.rst.txt +++ /dev/null @@ -1,51 +0,0 @@ -:orphan: - -.. _tf-cle: - -=============================================== -AIMET TensorFlow Cross Layer Equalization APIs -=============================================== -Introduction -============ -AIMET functionality for TensorFlow Cross Layer Equalization supports three techniques- - - BatchNorm Folding - - Cross Layer Scaling - - High Bias Fold - - -Cross Layer Equalization API -============================ -Listed below is a comprehensive API to apply all available techniques under cross layer equalization. -It performs 'auto' detection of candidate layers and applies the techniques. -If there are no BatchNorm layers in a given model, BatchNorm fold and high bias fold shall be skipped. - -**API(s) for Cross Layer Equalization** - -.. autofunction:: aimet_tensorflow.cross_layer_equalization.equalize_model - - -Code Example -============ - -**Required imports** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/post_training_techniques_examples.py - :language: python - :lines: 41-47 - -**Cross Layer Equalization in auto mode comprehensive** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/post_training_techniques_examples.py - :language: python - :pyobject: cross_layer_equalization_auto - - -Primitive APIs -============== -If the user would like to call the APIs individually, then the following APIs can be used- - -.. toctree:: - :titlesonly: - :maxdepth: 1 - - Primitive APIs for Cross Layer Equalization diff --git a/AimetDocs/_sources/api_docs/tensorflow_primitive_apis_cle.rst.txt b/AimetDocs/_sources/api_docs/tensorflow_primitive_apis_cle.rst.txt deleted file mode 100644 index 2f12d41..0000000 --- a/AimetDocs/_sources/api_docs/tensorflow_primitive_apis_cle.rst.txt +++ /dev/null @@ -1,118 +0,0 @@ -:orphan: - -======================================================= -AIMET TensorFlow Cross Layer Equalization Primitive API -======================================================= -Introduction -============ - -If a user wants to modify the order of Cross Layer equalization, not use some features or manually tweak the list of -layers that need to be equalized, the following APIs can be used. - -Higher level API can be used for using one or more features one after the other. It automatically finds the layers to -be folded or scaled. - -Lower level APIs can be used to manually tweak the list of layers to be folded. The user has to pass the list of -layers in the correct order that they appear in the model. - -Note: Before using High Bias fold, Cross Layer Scaling (CLS) needs to be applied and scaling factors obtained from -CLS need to be plugged in to High Bias Fold. And, if there are batchnorm layers, they need to be folded and the info -saved to be plugged into high bias fold API. - -Higher Level APIs for Cross Layer Equalization -============================================== - -**API for Batch Norm Folding** - -.. autofunction:: aimet_tensorflow.batch_norm_fold.fold_all_batch_norms - -**API for Cross Layer Scaling** - -.. autofunction:: aimet_tensorflow.cross_layer_equalization.CrossLayerScaling.scale_model - -**API for High Bias Folding** - -.. autofunction:: aimet_tensorflow.cross_layer_equalization.HighBiasFold.bias_fold - -Code Examples for Higher Level APIs -=================================== - -**Required imports** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/post_training_techniques_examples.py - :language: python - :lines: 41-44, 48-49 - -**Perform Cross Layer Equalization in auto mode step by step** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/post_training_techniques_examples.py - :language: python - :pyobject: cross_layer_equalization_auto_stepwise - -Lower Level APIs for Cross Layer Equalization -============================================= - -**API for Batch Norm Folding on subsets of convolution-batchnorm layer pairs** - -.. autofunction:: aimet_tensorflow.batch_norm_fold.fold_given_batch_norms - -| - -**API for Cross Layer Scaling on subset of conv layer groups** - -.. autofunction:: aimet_tensorflow.cross_layer_equalization.CrossLayerScaling.scale_cls_sets - -| - -**API for High bias folding** - -.. autofunction:: aimet_tensorflow.cross_layer_equalization.HighBiasFold.bias_fold - -| - - -Custom Datatype used -===================== - -.. autoclass:: aimet_tensorflow.cross_layer_equalization.ClsSetInfo - :members: - -| - -Code Example for Lower level APIs -================================= - -**Required imports** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/post_training_techniques_examples.py - :language: python - :lines: 41-44, 48, 50-51 - -**Perform Cross Layer Equalization in manual mode** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/post_training_techniques_examples.py - :language: python - :pyobject: cross_layer_equalization_manual - - -Example helper methods to perform CLE in manual mode -==================================================== - -**Helper to pick layers for batchnorm fold** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/post_training_techniques_examples.py - :language: python - :pyobject: get_layer_pairs_Resnet50_for_folding - -**Helper to pick layers for cross layer scaling** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/post_training_techniques_examples.py - :language: python - :pyobject: get_consecutive_layer_list_from_resnet50_for_scaling - -**Helper to format data from batchnorm fold and cross layer scaling for usage by high bias fold** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/post_training_techniques_examples.py - :language: python - :pyobject: format_info_for_high_bias_fold - diff --git a/AimetDocs/_sources/api_docs/tensorflow_quantization.rst.txt b/AimetDocs/_sources/api_docs/tensorflow_quantization.rst.txt deleted file mode 100644 index d8cc082..0000000 --- a/AimetDocs/_sources/api_docs/tensorflow_quantization.rst.txt +++ /dev/null @@ -1,8 +0,0 @@ -================================== -AIMET TensorFlow Quantization APIs -================================== - -AIMET Quantization for TensorFlow provides the following functionality - - :ref:`Quantization Simulation`: Allows ability to simulate inference and training on quantized hardware - - :ref:`Cross-Layer Equalization`: Post-training quantization technique to equalize layer parameters - - :ref:`Bias Correction`: Post-training quantization technique to correct shift in layer outputs due to quantization noise diff --git a/AimetDocs/_sources/api_docs/tensorflow_quantsim.rst.txt b/AimetDocs/_sources/api_docs/tensorflow_quantsim.rst.txt deleted file mode 100644 index ecf11dd..0000000 --- a/AimetDocs/_sources/api_docs/tensorflow_quantsim.rst.txt +++ /dev/null @@ -1,50 +0,0 @@ -:orphan: - -.. _tf-quantsim: - -=========================== -QuantSim API for TensorFlow -=========================== - -Top-level API -============= - -.. autoclass:: aimet_tensorflow.quantsim.QuantizationSimModel - -| - -**The following API can be used to Compute Encodings for Model** - -.. automethod:: aimet_tensorflow.quantsim.QuantizationSimModel.compute_encodings - - -| - -**The following API can be used to Export the Model to target** - -.. automethod:: aimet_tensorflow.quantsim.QuantizationSimModel.export - -| - - -Code Examples -============= - -**Required imports** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/quantization.py - :language: python - :lines: 39-46 - -**Quantize with Range Learning** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/quantization.py - :language: python - :pyobject: quantize_model - -**Example Fine-tuning step** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/quantization.py - :language: python - :pyobject: training_helper - diff --git a/AimetDocs/_sources/api_docs/tensorflow_visualization_quantization.rst.txt b/AimetDocs/_sources/api_docs/tensorflow_visualization_quantization.rst.txt deleted file mode 100644 index 22e5d7c..0000000 --- a/AimetDocs/_sources/api_docs/tensorflow_visualization_quantization.rst.txt +++ /dev/null @@ -1,36 +0,0 @@ -======================================================= -AIMET Visualization for Quantization for TensorFlow API -======================================================= - -Top-level API for Visualization of Weight tensors -================================================= - -.. autofunction:: aimet_tensorflow.plotting_utils.visualize_weight_ranges_single_layer - -| - -.. autofunction:: aimet_tensorflow.plotting_utils.visualize_relative_weight_ranges_single_layer - - -| - -Code Examples for Visualization of Weight tensors -================================================= - -**Required imports** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/visualization.py - :language: python - :lines: 39-45 - -**Visualizing weight ranges for layer** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/visualization.py - :language: python - :pyobject: visualizing_weight_ranges_for_single_layer - -**Visualizing Relative weight ranges for layer** - -.. literalinclude:: ../../NightlyTests/tensorflow/code_examples/visualization.py - :language: python - :pyobject: visualizing_relative_weight_ranges_for_single_layer \ No newline at end of file diff --git a/AimetDocs/_sources/api_docs/torch.rst.txt b/AimetDocs/_sources/api_docs/torch.rst.txt deleted file mode 100644 index 4e59cee..0000000 --- a/AimetDocs/_sources/api_docs/torch.rst.txt +++ /dev/null @@ -1,14 +0,0 @@ - -================== -AIMET PyTorch APIs -================== - -.. toctree:: - :titlesonly: - :maxdepth: 1 - - PyTorch Quantization API - PyTorch Model Compression API - PyTorch Model Visualization API for Compression - PyTorch Model Visualization API for Quantization - diff --git a/AimetDocs/_sources/api_docs/torch_bias_correction.rst.txt b/AimetDocs/_sources/api_docs/torch_bias_correction.rst.txt deleted file mode 100644 index 945b9b3..0000000 --- a/AimetDocs/_sources/api_docs/torch_bias_correction.rst.txt +++ /dev/null @@ -1,59 +0,0 @@ -:orphan: - -.. _api-torch-bias-correction: - -================================= -AIMET PyTorch Bias Correction API -================================= - -Bias Correction API -=================== - -.. autofunction:: aimet_torch.bias_correction.correct_bias - -| - -ConvBnInfoType -============== -.. autoclass:: aimet_common.bias_correction.ConvBnInfoType - -| - -ActivationType -============== -.. autoclass:: aimet_common.defs.ActivationType - :members: - -Quantization Params -=================== - -.. autofunction:: aimet_torch.quantsim.QuantParams - -| - -Code Examples -============= - -**Required imports** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/post_training_techniques_examples.py - :language: python - :lines: 51-55 - -**Empirical Bias correction** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/post_training_techniques_examples.py - :language: python - :pyobject: bias_correction_empirical - -**Analytical + Empirical Bias correction** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/post_training_techniques_examples.py - :language: python - :pyobject: bias_correction_analytical_and_empirical - -**Bias correction Data loader format example** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/post_training_techniques_examples.py - :language: python - :pyobject: BatchIterator diff --git a/AimetDocs/_sources/api_docs/torch_compress.rst.txt b/AimetDocs/_sources/api_docs/torch_compress.rst.txt deleted file mode 100644 index 2521353..0000000 --- a/AimetDocs/_sources/api_docs/torch_compress.rst.txt +++ /dev/null @@ -1,159 +0,0 @@ - -============================= -AIMET PyTorch Compression API -============================= - -Introduction -============ -AIMET supports the following model compression techniques for PyTorch models - - Weight SVD - - Spatial SVD - - Channel Pruning - -For all of these compression techniques there are two modes in which you can invoke the AIMET API - - Auto Mode: In **Auto** mode, AIMET will determine the optimal way to compress each layer of - the model given an overall target compression ratio. Greedy Compression Ratio Selection Algorithm is used to pick appropriate compression ratios for each layer. - - - Manual Mode: In **Manual** mode, the user can pass in the desired compression-ratio per layer - to AIMET. AIMET will apply the specified compression technique for each of the - layers to achieve the desired compression-ratio per layer. It is recommended that - the user start with Auto mode, and then tweak per-layer compression-ratios using - Manual mode if desired. - -| - -Top-level API for Compression -============================= - -.. autoclass:: aimet_torch.compress.ModelCompressor - -| - -.. automethod:: aimet_torch.compress.ModelCompressor.compress_model - -| - -Greedy Selection Parameters -=========================== - -.. autoclass:: aimet_common.defs.GreedySelectionParameters - :members: - -| - -TAR Selection Parameters -======================== - -.. autoclass:: aimet_torch.defs.TarRankSelectionParameters - :members: - -| - -Spatial SVD Configuration -========================= - -.. autoclass:: aimet_torch.defs.SpatialSvdParameters - :members: - -| - - -Weight SVD Configuration -======================== - -.. autoclass:: aimet_torch.defs.WeightSvdParameters - :members: - -| - -Channel Pruning Configuration -============================= - -.. autoclass:: aimet_torch.defs.ChannelPruningParameters - :members: - -| - -Configuration Definitions -========================= - -.. autoclass:: aimet_common.defs.CostMetric - :members: - :noindex: - -| - -.. autoclass:: aimet_common.defs.CompressionScheme - :members: - :noindex: - -| - -.. autoclass:: aimet_torch.defs.ModuleCompRatioPair - :members: - -| - -Code Examples -============= - -**Required imports** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/code_examples.py - :language: python - :lines: 41-50, 56 - -**Evaluation function** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/code_examples.py - :language: python - :pyobject: evaluate_model - -**Compressing using Spatial SVD in auto mode with multiplicity = 8 for rank rounding** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/code_examples.py - :language: python - :pyobject: spatial_svd_auto_mode - -**Compressing using Spatial SVD in manual mode** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/code_examples.py - :language: python - :pyobject: spatial_svd_manual_mode - -**Compressing using Weight SVD in auto mode** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/code_examples.py - :language: python - :pyobject: weight_svd_auto_mode - -**Compressing using Weight SVD in manual mode with multiplicity = 8 for rank rounding** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/code_examples.py - :language: python - :pyobject: weight_svd_manual_mode - -**Compressing using Channel Pruning in auto mode** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/code_examples.py - :language: python - :pyobject: channel_pruning_auto_mode - -**Compressing using Channel Pruning in manual mode** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/code_examples.py - :language: python - :pyobject: channel_pruning_manual_mode - -**Example Training Object** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/code_examples.py - :language: python - :pyobject: Trainer - -**Compressing using Spatial SVD in auto mode with layer-wise fine tuning** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/code_examples.py - :language: python - :pyobject: spatial_svd_auto_mode_with_layerwise_finetuning - diff --git a/AimetDocs/_sources/api_docs/torch_cross_layer_equalization.rst.txt b/AimetDocs/_sources/api_docs/torch_cross_layer_equalization.rst.txt deleted file mode 100644 index a6bd670..0000000 --- a/AimetDocs/_sources/api_docs/torch_cross_layer_equalization.rst.txt +++ /dev/null @@ -1,57 +0,0 @@ -:orphan: - -.. _api-torch-cle: - -=========================================== -AIMET PyTorch Cross Layer Equalization APIs -=========================================== - -Introduction -============ -AIMET functionality for PyTorch Cross Layer Equalization has 3 features- - - BatchNorm Folding - - Cross Layer Scaling - - High Bias Fold - - -Cross Layer Equalization API -============================ - -The following API performs BatchNorm fold followed by Cross Layer Scaling followed by High Bias Fold. - -Note: High Bias fold will not happen when the below API is used, if the model does not have BatchNorm layers - -**API for Cross Layer Equalization** - -.. autofunction:: aimet_torch.cross_layer_equalization.equalize_model - -| - -Code Example -============ - -**Required imports** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/post_training_techniques_examples.py - :language: python - :lines: 42, 47 - - -**Cross Layer Equalization in auto mode** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/post_training_techniques_examples.py - :language: python - :pyobject: cross_layer_equalization_auto - - -Primitive APIs -============== -If the user would like to call the APIs individually, then the following APIs can be used- - -.. toctree:: - :titlesonly: - :maxdepth: 1 - - Primitive APIs for Cross Layer Equalization - - diff --git a/AimetDocs/_sources/api_docs/torch_primitive_apis_cle.rst.txt b/AimetDocs/_sources/api_docs/torch_primitive_apis_cle.rst.txt deleted file mode 100644 index 3ee1158..0000000 --- a/AimetDocs/_sources/api_docs/torch_primitive_apis_cle.rst.txt +++ /dev/null @@ -1,119 +0,0 @@ -:orphan: - -==================================================== -AIMET PyTorch Cross Layer Equalization Primitive API -==================================================== - -Introduction -============ - -If a user wants to modify the order of Cross Layer equalization, not use some features or manually tweak the list of -layers that need to be equalized, the following APIs can be used. - -Higher level APIs can be used for using one or more features one after the other. It automatically finds the layers to -be folded or scaled. - -Lower level APIs can be used to manually tweak the list of layers to be folded. The user has to pass the list of -layers in the correct order that they appear in the model. - -Note: Before using High Bias fold, Cross Layer Scaling (CLS) needs to be applied and scaling factors obtained from -CLS need to be plugged in to High Bias Fold. And, if there are batchnorm layers, they need to be folded and the info -saved to be plugged into high bias fold API. - - -ClsSetInfo Definition -===================== - -.. autoclass:: aimet_torch.cross_layer_equalization.ClsSetInfo - :members: - -| - - -Higher Level APIs for Cross Layer Equalization -============================================== - - -**API for Batch Norm Folding** - -.. autofunction:: aimet_torch.batch_norm_fold.fold_all_batch_norms - -| - - -**API for Cross Layer Scaling** - -.. autofunction:: aimet_torch.cross_layer_equalization.CrossLayerScaling.scale_model - -| - - -**API for High Bias Folding** - -.. autofunction:: aimet_torch.cross_layer_equalization.HighBiasFold.bias_fold - -| - - -Code Examples for Higher Level APIs -=================================== - -**Required imports** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/post_training_techniques_examples.py - :language: python - :lines: 41-42, 45-46, 49 - - -**Cross Layer Equalization in auto mode calling each API** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/post_training_techniques_examples.py - :language: python - :pyobject: cross_layer_equalization_auto_step_by_step - - -Lower Level APIs for Cross Layer Equalization -============================================= - - - -**API for Batch Norm Folding** - -.. autofunction:: aimet_torch.batch_norm_fold.fold_given_batch_norms - -| - -**API for Cross Layer Scaling** - -.. autofunction:: aimet_torch.cross_layer_equalization.CrossLayerScaling.scale_cls_sets - -| - -**API for High bias folding** - -.. autofunction:: aimet_torch.cross_layer_equalization.HighBiasFold.bias_fold - -| - - -Code Examples for Lower Level APIs -================================== - -**Required imports** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/post_training_techniques_examples.py - :language: python - :lines: 42, 54, 45-46, 49 - -**Cross Layer Equalization in manual mode** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/post_training_techniques_examples.py - :language: python - :pyobject: cross_layer_equalization_manual - -**Cross Layer Equalization in manual mode for Depthwise Separable layer** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/post_training_techniques_examples.py - :language: python - :pyobject: cross_layer_equalization_depthwise_layers - diff --git a/AimetDocs/_sources/api_docs/torch_quantization.rst.txt b/AimetDocs/_sources/api_docs/torch_quantization.rst.txt deleted file mode 100644 index 580fa88..0000000 --- a/AimetDocs/_sources/api_docs/torch_quantization.rst.txt +++ /dev/null @@ -1,9 +0,0 @@ -=============================== -AIMET PyTorch Quantization APIs -=============================== - -AIMET Quantization for PyTorch provides the following functionality - - :ref:`Quantization Simulation`: Allows ability to simulate inference and training on quantized hardware - - :ref:`Cross-Layer Equalization`: Post-training quantization technique to equalize layer parameters - - :ref:`Bias Correction`: Post-training quantization technique to correct shift in layer outputs due to quantization noise - diff --git a/AimetDocs/_sources/api_docs/torch_quantsim.rst.txt b/AimetDocs/_sources/api_docs/torch_quantsim.rst.txt deleted file mode 100644 index 94b3374..0000000 --- a/AimetDocs/_sources/api_docs/torch_quantsim.rst.txt +++ /dev/null @@ -1,67 +0,0 @@ -:orphan: - -.. _api-torch-quantsim: - -================================== -AIMET PyTorch Quantization SIM API -================================== - -Top-level API -============= - -.. autoclass:: aimet_torch.quantsim.QuantizationSimModel - -| - -**The following API can be used to Compute Encodings for Model** - -.. automethod:: aimet_torch.quantsim.QuantizationSimModel.compute_encodings - -| - -**The following APIs can be used to save and restore the quantized model** - -.. automethod:: aimet_torch.quantsim.save_checkpoint - -| - -.. automethod:: aimet_torch.quantsim.load_checkpoint - -| - -**The following API can be used to Export the Model to target** - -.. automethod:: aimet_torch.quantsim.QuantizationSimModel.export - -| - - -Enum Definition -=============== -**Quant Scheme Enum** - -.. autoclass:: aimet_common.defs.QuantScheme - :members: - -| - -Code Examples -============= - -**Required imports** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/code_examples.py - :language: python - :lines: 43, 56, 52-53 - -**Evaluation function** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/code_examples.py - :language: python - :pyobject: evaluate_model - -**Quantize and fine-tune a trained model** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/code_examples.py - :language: python - :pyobject: quantize_model diff --git a/AimetDocs/_sources/api_docs/torch_visualization_compression.rst.txt b/AimetDocs/_sources/api_docs/torch_visualization_compression.rst.txt deleted file mode 100644 index 4d70980..0000000 --- a/AimetDocs/_sources/api_docs/torch_visualization_compression.rst.txt +++ /dev/null @@ -1,36 +0,0 @@ - -=================================== -AIMET Visualization Compression API -=================================== - -Top-level API Compression -========================== - - - -.. autoclass:: aimet_torch.visualize_serialized_data.VisualizeCompression - -| - -.. automethod:: aimet_torch.visualize_serialized_data.VisualizeCompression.display_eval_scores - -| - -.. automethod:: aimet_torch.visualize_serialized_data.VisualizeCompression.display_comp_ratio_plot - - - -Code Examples -============= - -**Required imports** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/visualization.py - :language: python - :lines: 41-49, 53-56 - -**Model Compression with Visualization Parameter** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/visualization.py - :language: python - :pyobject: model_compression_with_visualization diff --git a/AimetDocs/_sources/api_docs/torch_visualization_quantization.rst.txt b/AimetDocs/_sources/api_docs/torch_visualization_quantization.rst.txt deleted file mode 100644 index 9b50084..0000000 --- a/AimetDocs/_sources/api_docs/torch_visualization_quantization.rst.txt +++ /dev/null @@ -1,50 +0,0 @@ - -======================================== -AIMET Visualization for Quantization API -======================================== - -Top-level API Quantization -========================== - -.. autofunction:: aimet_torch.visualize_model.visualize_relative_weight_ranges_to_identify_problematic_layers - -| - -.. autofunction:: aimet_torch.visualize_model.visualize_weight_ranges - - -| - -.. autofunction:: aimet_torch.visualize_model.visualize_changes_after_optimization - -| - -Code Examples -============= - -**Required imports** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/visualization.py - :language: python - :lines: 40, 42-43, 47, 52-58 - -**Comparing Model After Optimization** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/visualization.py - :language: python - :pyobject: visualize_changes_in_model_after_and_before_cle - -**Visualizing weight ranges in Model** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/visualization.py - :language: python - :pyobject: visualize_weight_ranges_model - -**Visualizing Relative weight ranges in Model** - -.. literalinclude:: ../../NightlyTests/torch/code_examples/visualization.py - :language: python - :pyobject: visualize_relative_weight_ranges_model - - - diff --git a/AimetDocs/_sources/user_guide/channel_pruning.rst.txt b/AimetDocs/_sources/user_guide/channel_pruning.rst.txt deleted file mode 100644 index 6b3a0b8..0000000 --- a/AimetDocs/_sources/user_guide/channel_pruning.rst.txt +++ /dev/null @@ -1,48 +0,0 @@ -===================== -AIMET Channel Pruning -===================== - -Channel Pruning is a model compression technique that reduces less-important input channels from layers in a given model. Currently AIMET supports Channel Pruning of Conv2d layers. - - - -Overall Procedure -================= -The following picture explains the different steps in Channel Pruning a given layer. These steps are repeated for all layers selected to be compressed in the order of their occurrence from the top of the model. - -.. image:: ../images/channel_pruning_1.png - -These individual steps are explained in more detail in the following sub-sections. - - - -Channel Selection -================= -For a given layer and a given compression ratio Channel Selection analyzes the magnitude of each input channel (based on the kernel weights for that channel) and chooses the channels with the least magnitude to be pruned. - - -Winnowing -========= -Winnowing is used to remove input channels of weight matrix obtained from Channel Selection resulting in compressed tensors - -.. image:: ../images/cp_2.png - - -Once one or more input channels for a layer are removed, then it means corresponding output channels of a upstream layer could also be removed to get further compression gains. Note that the presence of skip-connections or residuals sometimes prevents upstream layers from getting output-pruned. - -.. image:: ../images/cp_3.jpg - -For more details on winnowing, please see this - -.. toctree:: - :titlesonly: - :maxdepth: 1 - - Winnowing - - -Weight Reconstruction -===================== -As a final step in Channel Pruning, AIMET will adjust the weight and bias parameters of a layer that was pruned in an attempt to try and match the outputs of that layer to closely match the outputs prior to pruning.This is done by collecting random samples of the output of the layer from the original model and the corresponding input samples from the pruned model for that layer. AIMET then performs linear regression to adjust the layer parameters. - -.. image:: ../images/cp_4.jpg diff --git a/AimetDocs/_sources/user_guide/feature_guidebook.rst.txt b/AimetDocs/_sources/user_guide/feature_guidebook.rst.txt deleted file mode 100644 index 44fbe7c..0000000 --- a/AimetDocs/_sources/user_guide/feature_guidebook.rst.txt +++ /dev/null @@ -1,59 +0,0 @@ -:orphan: - -.. _ug-guidebook: - - -======================== -AIMET Features Guidebook -======================== - -AIMET supports various neural network quantization and compression techniques. This document provides typical workflows in order to quantize or compress a network using AIMET. A more in-depth discussion on various techniques and their usage is provided in :ref:`User Guide` - -Quantization -============ - -The steps described in the following diagram are recommended to quantize a full precision trained model. At high level, the steps are: - -.. image:: ../images/flow_feature_guidebook.PNG - -1) Quantize a model and evaluate accuracy. - - a. A model can be quantized by simulating quantization ops in the model as described in :ref:`Quantization Simulation` . Accuracy can be evaluated in AIMET or the model can be quantized by a target SDK/inference application like Tensorflow-lite or Qualcomm Neural Processing SDK. - -2) If accuracy of quantized model is not acceptable, analyze model performance using following steps: - - a. Inspect model weights using :ref:`Visualization tools` and determine if model may benefit from post-training quantization techniques namely Cross Layer Equalization (CLE) and Bias Correction (BC). Details on CLE and BC, including requirements on when these techniques are applicable, are provided on the page, :ref:`Post-training techniques`. - - b. AIMET also provides ability to evaluate a model by quantizing weights while keeping activations in full precision or vice versa. This is recommended to determine what affects quantized model performance weight quantization, activation quantization or both. Post-training techniques may help if weight quantization is an issue. - -3) Apply post-training techniques CLE and BC, if applicable, and evaluate model performance. - -4) Model retraining/fine-tuning is recommended if activation quantization is an issue or when post-training techniques do not provide satisfactory performance. Retraining/Fine-tuning process inserts fake-quantization ops in the model and retrains the model for few epochs to alleviate quantization noise and improve model performance. Details on fine-tuning are provided :ref:`here`. - -Compression -=========== -AIMET supports network compression using following techniques: Weight SVD, Spatial SVD (SSVD) and Channel Pruning (CP). These techniques are intended for Multiply-and-Accumulate (MAC) reduction of convolution layers in a neural network. Based on a configured desired MAC reduction ratio, i.e., MACs in compress model to MACs in uncompressed model, the compression algorithms automatically compress each individual convolution layer in the network to approximately reach the overall desired MAC reduction. Note that the actual on-target inference latency performance of a model depends on several factors MACs, memory and memory bandwidth, quantization, etc. Therefore, the improvement in runtime latency based on MAC reduction based compression may vary depending on the specific model architecture. Performance results for some typical models are provided in https://quic.github.io/aimet-pages/index.html. -For best performance, a combination of spatial SVD followed by channel pruning is recommended. At high level, following steps should be performed to compress a network using SSVD + CP combination: - -.. image:: ../images/compression_flow.png - -1. Determine the target compression ratio (C), which is the ratio of MACs in final compressed model to the MACs in the original uncompressed model. For example, target compression ratio = 0.5 indicates that the final model MACs are half of the original model MACs. - -2. Perform compression using Spatial SVD technique as follows: - - a. Since the target compression ratio C is for the final SSVD+CP compressed model, the compression that should be targeted or can be achieved via SSVD is unknown apriori. As a result, few target compression ratios (Cssvd)need to be tried out. Choose few Cssvd > C targets and perform SSVD. E.g., if C = 0.5, Cssvd = {0.5,0.65, 0.75} can be used typically. This would result in three SSVD compressed models. - - b. For each of the SSVD compressed model obtained from previous step, perform fine-tuning to improve model accuracy. Guidelines on fine-tuning are provided here []. - -3. Pick a model (or few models) that provide high accuracy from step 2b. For example, if the tolerable accuracy drop SSVD+CP compression relative to the original uncompressed model is X % (X = Accuracy of uncompressed model (%) Accuracy of compressed model (%)) , then a model(s) that has accuracy within few % (X-5 %)of the original uncompressed model accuracy should be selected to avoid very large drop in accuracy after CP step. - - a. Note that if step 2b results in very large accuracy drop or drop well within tolerable accuracy drop, then step 2a/2b should be revisited first by appropriately adjusting the compression ratios. - -4. Perform compression using Channel Pruning technique as follows: - - a. Perform compression with few target compression ratios (Ccp). One can set the compression ratio(s) based on the Cssvd of the model obtained from SSVD step 3 such that Cssvd * Ccp is approximately equal to C. - - b. Perform fine-tuning to improve model accuracy. - -5. In the final step, a model is selected with MAC ratio relative to the original uncompressed model is close to C and also meets user’s accuracy requirements. -For example, for Resnet-50 results provided on https://quic.github.io/aimet-pages/index.html, Csvd = 0.75 and Ccp = 0.66 were used to achieve overall compression C=0.5. diff --git a/AimetDocs/_sources/user_guide/greedy_compression_ratio_selection.rst.txt b/AimetDocs/_sources/user_guide/greedy_compression_ratio_selection.rst.txt deleted file mode 100644 index 60d2c44..0000000 --- a/AimetDocs/_sources/user_guide/greedy_compression_ratio_selection.rst.txt +++ /dev/null @@ -1,54 +0,0 @@ -:orphan: - -.. _ug-greedy-comp-ratio-selection: - -======================================== -AIMET Greedy Compression Ratio Selection -======================================== - -Overview -======== -The model compression methods, Spatial SVD and Channel Pruning work on per layer basis. Not all the layers in the given model are equally compressible. Compression of individual layers of a given model can have varying impact on the final accuracy of the model. Greedy Per Layer Compression Ratio Selection Algorithm is used to assess the sensitivity of applicable layers to compression and find appropriate compression-ratio for each individual layers. The algorithm makes sure that the entire model has highest remaining accuracy and also meets the given target compression-ratio. - -How it works -============ -The Greedy Compression Ratio Selection algorithm executes the following two steps: - -- Per-layer exploration -- Compression-ratio selection - -The following figure provides a high level overview and is followed by details for each step. - -.. image:: ../images/greedy_1.png - -| - -.. image:: ../images/greedy_2.png - -| - -where, the Eval dictionary is represented as- - -.. image:: ../images/greedy_3.png - - -Per-layer Exploration -===================== -For each layer, produces a column in the compression-ratio vs. model-performance table. This column captures the over all network performance values as the layer is compressed by predefined range of compression ratio candidates, while all other layers are left unmodified. - -.. image:: ../images/greedy_4.jpg - -In the above figure, you see an example model with 4 layers, and 10 compression-ratio candidates (which is the default setting). Note that the table does not capture the eval score for the last candidate which is always compression-ratio=1.0 (since this score is the baseline score and known already). - -**Monotonic Fit**: In some cases it is observed that the model performance is not a strict increasing function of increasing compression-ratios. To help with the greedy selection procedure, AIMET can apply a curve-fit scheme to try and fit the model-performance numbers for a given layer using a monotonically increasing function. The functionality is disabled by default. - -Compression Ratio Selection -=========================== -This step is the core of the algorithm. It considers the compression-ratio vs. model-performance table for each applicable layer from the previous step, target compression ratio and function to calculate the cost of the compressed model depending on the compression method (Spatial SVD, Channel Pruning) used. It starts with a constant accuracy and finds the corresponding compression ratio for every applicable layer by interpolating from compression-ratio vs. model-performance evaluation table. The algorithm, then calculates total cost of the model to see if we have met our target compression ratio or not. Binary search algorithm is used to find the solution quickly. Finally it returns list of selected compression ratios for all applicable layers. This way, the algorithm achieves the highest remaining final accuracy of the compressed model and meet target compression ratio. - -The following figure illustrates that for a given accuracy, the compression ratio for each layer is different. - -.. image:: ../images/greedy_5.jpg - -As suggested by above diagram, the algorithm picks a lower compression ratio (higher compression) for layers which are more compressible and pick a higher compression ratio (lower compression) for layers which are less compressible (For lesser compressible layers the accuracy falls drstically if compression ratio is lowered). - diff --git a/AimetDocs/_sources/user_guide/index.rst.txt b/AimetDocs/_sources/user_guide/index.rst.txt deleted file mode 100644 index cbafd3f..0000000 --- a/AimetDocs/_sources/user_guide/index.rst.txt +++ /dev/null @@ -1,60 +0,0 @@ -.. _ug-index: - -====================================== -AI Model Efficiency Toolkit User Guide -====================================== - -Overview -======== - -AI Model Efficiency Toolkit (AIMET) is a software toolkit that enables users to compress and quantize models. - -AIMET provides optimizations that are designed to be used on trained models, so that the resulting compressed -and/or quantized model can be further trained (also called fine-tuning) to recover any accuracy losses. -AIMET currently supports both TensorFlow and PyTorch models. - -| - -.. image:: ../images/AIMET_index.PNG - -The above picture shows a high-level view of the workflow when using AIMET. The user will start with a trained -model in either the TensorFlow or PyTorch training framework. This trained model is passed to AIMET using APIs -for compression and quantization that we will look at in detail. AIMET returns a compressed/quantized version of the model -that the users can fine-tune (or train further for a small number of epochs) to recover lost accuracy. Users can then -export via ONNX to an on-target runtime like Qualcomm\ |reg| Neural Processing SDK. - - -Features -======== - -AIMET supports two types of features - -- :ref:`Model Compression`: AIMET supports multiple model compression techniques that allow the user to take a trained model - and remove redundancies, resulting in a smaller model that runs faster on target. - -- :ref:`Model Quantization`: AIMET can simulate behavior of quantized HW for a given trained model. This allows the user to - fine-tune the model on this simulated quantized HW. AIMET also offers post-training quantization techniques which make the model better for quantization. - -| - -Please see the :ref:`AIMET Guidebook` - which includes some practical advice on using the above features, and how to combine the features - -API Documentation and Usage Examples -==================================== - -Please visit the generated AIMET API documentation here - -.. toctree:: - :titlesonly: - :maxdepth: 1 - - API Documentation<../api_docs/index> - -| - -| - -| AI Model Efficiency Toolkit is a product of Qualcomm Innovation Center, Inc. -| Qualcomm\ |reg| Neural Processing SDK is a product of Qualcomm Technologies, Inc. and/or its subsidiaries. - -.. |reg| unicode:: U+000AE .. REGISTERED SIGN \ No newline at end of file diff --git a/AimetDocs/_sources/user_guide/model_compression.rst.txt b/AimetDocs/_sources/user_guide/model_compression.rst.txt deleted file mode 100644 index 0c6fd6b..0000000 --- a/AimetDocs/_sources/user_guide/model_compression.rst.txt +++ /dev/null @@ -1,107 +0,0 @@ -:orphan: - -.. _ug-model-compression: - -======================= -AIMET Model Compression -======================= - -Overview -======== - -AIMET provides a model compression library that can be used to reduce a model's MAC and memory costs with a minimal -drop in accuracy. AIMET supports various compression schemes like Weight SVD, Spatial SVD and Channel Pruning. - -Use Case -======== -AIMET allows user to take a trained model and compress it to desired compression ratio which can be further fine-tuned and exported to a target. -All of the compression schemes in AIMET use a two-step process - Compression ratio selection followed by model -compression. - -.. image:: ../images/compression_use_case.PNG - -The following sub-sections explain these steps in more detail. - -Compression ratio selection -=========================== -- :ref:`Greedy Compression Ratio Selection`: During this phase, individual layers of the original model are analyzed to determine optimal compression ratios per layer. Currently AIMET supports the Greedy Compression Ratio Selection method. - -- Manual Compression Ratio Selection: As an alternative to AIMET automatically selecting optimal compression ratios per layer, the user has a choice to specify compression ratios manually per layer. The suggested procedure would be to use the Greedy Compression Ratio Selection method to get a nominal set of compression ratios first. And then use this as the starting point for manually changing compression ratios for one or more layers. - -To visualize various usage of the compression tool we can use: - -.. toctree:: - :titlesonly: - :maxdepth: 1 - - Visualization - -Model Compression -================= - -In this phase, AIMET will apply the compression ratios per layer to create a compressed model. -Currently, AIMET supports the following model compression algorithms. - -.. toctree:: - :titlesonly: - :maxdepth: 1 - - Weight SVD - Spatial SVD - Channel Pruning - -Optional techniques to get better compression results -===================================================== - -AIMET supports the following techniques that can be optionally used to get better compression results - -- Rank-rounding -- Per-layer fine-tuning - -Rank Rounding -------------- - -Often ML runtime-software like those for Embedded ML accelerators, will prefer the dimensions of layers like Conv2d or FC to be of a certain multiplicity. Matching the expected dimension size will result in optimal runtime for that layer. AIMET techniques like Weight/Spatial SVD or Channel Pruning, try to decompose layers or reduce layers - specifically in terms of output channels and input channels. The rank-rounding feature in AIMET will try and reduce layers to match a user-provided multiplicity. By default this feature is disabled. At present, AIMET allows the user to specify a multiplicity-factor for the entire model, not on a per-layer basis. - -Users can make use of this feature to generate more optimal models for running on embedded targets. - -Per-layer Fine-tuning ---------------------- - -Given a user-model and desired compression-ratio, the user may sometimes notice a sharp degradation in accuracy after compression but before fine-tuning. One technique that might help the overall compression of such scenarios, is using a feature called per-layer fine-tuning. When this feature is selected, AIMET invokes a user-provided fine-tuning function after compressing every layer that was selected for compression. This is done during the Model Compression phase in the diagram shown above. - -Note: The user is responsible for choosing appropriate learning-rates and other training parameters for fine-tuning. Using this feature may require the user to carefully pick the learning rates and learning-rate-decay parameters to be used during fine-tuning. - - -FAQs -==== -1. Which technique is the best technique to use for compression? - - *We see best results when Spatial SVD is performed followed by Channel Pruning.* - -2. Can we combine the different techniques? - - *Yes, as stated in 1, different techniques can be combined together to get better accuracy. Compression can be combined with Post-training Quantization techniques as well to get a better model for target.* - -3. How to take a model to target after compression? - - *To take a model to target it needs to be first compressed using the above techniques and then it should be quantized and exported to target* - -4. Greedy rank selection is very slow. Can something be done to speed it up? - - *Greedy rank selection in itself is not time consuming. The time consuming part is creating the eval-score dictionary. For different experiments, eval-score dictionary can be generated once and then loaded into the searcher. Or, one can reduce the number of candidates over which the eval-score dictionary is created. But lesser the number of candidates, lesser the granularity. To strike a balance the value of 10 candidates was chosen.* - -5. Is per-layer fine tuning helpful? - - *Per-layer fine tuning is an experimental technique. We have not observed major gains by using it. But one can try out if it works for their model. In practice, we have observed that the best combination is to do say 1 epoch of fine-tuning per-layer and then do say 10-15 epochs of fine-tuning for the entire compressed model at the end.* - -References -========== - -1. Xiangyu Zhang, Jianhua Zou, Kaiming He, and Jian Sun. “Accelerating Very Deep Convolutional Networks for Classification and Detection.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 10, pp. 1943-1955, 1 Oct. 2016. - -2. Yihui He, Xiangyu Zhang, and Jian Sun. “Channel Pruning for Accelerating Very Deep Neural Networks.” IEEE International Conference on Computer Vision (ICCV), Venice, 2017, pp. 1398-1406. - -3. Max Jaderberg, Andrea Vedaldi, and Andrew Zisserman. “Speeding up Convolutional Neural Networks with Low Rank Expansions.” British Machine Vision Conference, Jan. 2014. - -4. Andrey Kuzmin, Markus Nagel, Saurabh Pitre, Sandeep Pendyam, Tijmen Blankevoort, Max Welling. “Taxonomy and Evaluation of Structured Compression of Convolutional Neural Networks.” diff --git a/AimetDocs/_sources/user_guide/model_guidelines.rst.txt b/AimetDocs/_sources/user_guide/model_guidelines.rst.txt deleted file mode 100644 index 8da7936..0000000 --- a/AimetDocs/_sources/user_guide/model_guidelines.rst.txt +++ /dev/null @@ -1,41 +0,0 @@ -:orphan: - -============================ -Model Guidelines for PyTorch -============================ - -To implement the Cross Layer Equalization API, aimet_torch.cross_layer_equalization.equalize_model(), AIMET creates a computing graph to analyze the sequence of Operations in the model. -If your model is defined using certain constructs, it restricts AIMET from successfully creating and analyzing the computing graph. The following table lists the potential issues and workarounds. - -Note: These restrictions are not applicable, if you are using the **Primitive APIs** - - -+------------------------+------------------------------+-----------------------------------+ -| Potential Issue | Description | Work Around | -+========================+==============================+===================================+ -| ONNX Export | Use torch.onnx.export() | If ONNX export fails, rewrite the | -| | to export your model. | specific layer so that ONNX | -| | Make sure ONNX export passes | export passes | -+------------------------+------------------------------+-----------------------------------+ -| Slicing Operation |Some models use | Rewrite the x.view() statement | -| |**torch.tensor.view()** in the| as follows: | -| |forward function as follows: | x = x.view(x.size(0), -1) | -| |x = x.view(-1, 1024) | | -| |If view function is written | | -| |as above, it causes an issue | | -| |while creating the | | -| |computing graph | | -+------------------------+------------------------------+-----------------------------------+ -| Bilinear, upsample |Some models use the upsample |Set the align_corners parameter to | -| operation |operation in the forward |False as follows: | -| |function as: x= |x = | -| |torch.nn.functional.upsample( |torch.nn.functional.upsample(x, | -| |x, size=torch.Size([129,129]) |size=torch.Size([129, 129]), | -| |, mode = 'bilinear', |mode='bilinear', | -| |align_corners=True) |align_corners=False) | -+------------------------+------------------------------+-----------------------------------+ -| Deconvolution operation|The deconvolution operation | There is no workaround available | -| |is used in DeepLabV3 model. | at this time. This issue will be | -| |This is currently not | addressed in a subsequent AIMET | -| |supported by AIMET | release. | -+------------------------+------------------------------+-----------------------------------+ diff --git a/AimetDocs/_sources/user_guide/model_quantization.rst.txt b/AimetDocs/_sources/user_guide/model_quantization.rst.txt deleted file mode 100644 index 00a1866..0000000 --- a/AimetDocs/_sources/user_guide/model_quantization.rst.txt +++ /dev/null @@ -1,53 +0,0 @@ -:orphan: - -.. _ug-model-quantization: - -AIMET Model Quantization -======================== - -Models are generally trained on floating-point hardware like CPUs and GPUs. -However, when these trained models are run on quantized hardware that support fixed-precision operations, model parameters are converted from floating-point precision to fixed precision. -As an example, when running on hardware that supports 8-bit integer operations, the floating point parameters in the trained model need to be converted to 8-bit integers. -It is observed that for some models, running on a 8-bit fixed-precision runtime introduces a loss in accuracy due to noise introduced due to the use of fixed precision parameters and also fixed precision operations. - -AIMET provides multiple techniques and tools that help to create quantized models with a minimal loss in accuracy relative to floating-point models. - - -Use Cases -========= -1. **Predict on-target accuracy**: AIMET enables a user to simulate the effects of quantization to get a first order estimate of the model’s accuracy when run on quantized targets. This is useful to get an estimate of on-target accuracy without needing an actual target platform. Note that to create a simulation model, AIMET uses representative data samples to compute per-layer quantization encodings. - - .. image:: ../images/quant_use_case_1.PNG - -2. **Fine-tune model for computed per-layer encodings**: AIMET enables a user to compute per-layer quantization encodings using representative data samples. And it further enables the user to use a training pipeline to fine-tune the model to improve quantized accuracy given these computed encodings. - - .. image:: ../images/quant_use_case_2.PNG - - -3. **Post-training quantization (no fine-tuning)**: In some cases, a user may want to not further train the model to improve quantized accuracy. Some reasons for this may be - - May not have access to a training pipeline for this model - - May not have access to labeled training data to use for fine-tuning - - May want to avoid the time and effort it would take to pick the right hyper-parameters and train the model - - In the above scenarios, AIMET provides a set of post-training quantization techniques that alter the model parameters to enable better quantized accuracy. These techniques are designed to fix for specific equalization issues in the model and may not work for all models. - - .. image:: ../images/quant_use_case_3.PNG - - -User Flow -========= - -The following diagram shows a decision flow that can used to determine how to make best use of the techniques available in AIMET. -Relevant steps in the diagram are annotated with a green circle with a number. Following the diagram, more details are provided for each of the annotated steps. - -.. image:: ../images/flow_diagram.png - -#. :ref:`Quantization Simulation`: AIMET enables a user to modify a model to add quantization simulation ops. When an evaluation is run on a model with these quantization simulation ops, the user can observe a first-order simulation of expected accuracy on quantized hardware. - -#. :ref:`Quantization Visualization`: AIMET provides visualization tools that help guide the user to determine if AIMET post-training quantization techniques are useful for a given model - -#. :ref:`Applying Cross-Layer Equalization`: Post-training quantization techniques help a model improve quantized accuracy without needing to re-train. Cross-Layer Equalization equalizes weight ranges in consecutive layers. - -#. :ref:`Applying Bias Correction`: Bias Correction corrects for a shift in layer outputs due to quantization noise - -#. :ref:`Quantization-aware Fine-tuning`: The user can train using the Quantization Sim model generated by AIMET to improve quantized accuracy of the model. This is simulating fine-tuning of the model on quantized hardware. diff --git a/AimetDocs/_sources/user_guide/post_training_quant_techniques.rst.txt b/AimetDocs/_sources/user_guide/post_training_quant_techniques.rst.txt deleted file mode 100644 index fd3aec7..0000000 --- a/AimetDocs/_sources/user_guide/post_training_quant_techniques.rst.txt +++ /dev/null @@ -1,70 +0,0 @@ -:orphan: - -.. _ug-post-training-quantization: - -=========================================== -AIMET Post-Training Quantization Techniques -=========================================== - -Overview -======== - -It is observed that some ML models show reduced inference accuracy when run on quantized hardware due to approximation noises. AIMET provides quantization post-training techniques that help adjust the parameters in the model such that the model becomes more quantization-friendly. AIMET post-quantization techniques are designed to be applied on pre-trained ML models. These techniques are explained as part of the "Data-Free Quantization Through Weight Equalization and Bias Correction” paper at ICCV 2019 - https://arxiv.org/abs/1906.04721 - - -User Flow -========= - - .. image:: ../images/flow_diagram_cle.png - -#. BatchNorm Folding: This feature will fold in the batch-norm layers (if present) into surrounding layers. - - | - -#. :ref:`Quantization Visualization`: AIMET provides visualization tools that help guide the user to determine if AIMET post-training quantization techniques are useful for a given model. Specifically, the visualization tools will show per-channel ranges of parameters to highlight if there is big discrepancy in ranges between different channels in a layer. - - .. image:: ../images/cle_5.png - - | - -#. Replace ReLU6 with ReLU: This feature replaces ReLU6 layers with ReLU layers. This is needed for the subsequent cross-layer scaling step. However, this replacement can lead to a drop in accuracy for some models. If this drop in accuracy is not acceptable, the user may be better off not using the post-training quantization techniques. - - | - -#. Cross Layer Scaling: In some models, the parameter ranges for different channels in a layer show a wide variance. This feature attempts to equalize the parameter ranges across different channels. As seen below, the ranges of weights per channel in a layer vary significantly. Cross-Layer Scaling scales layer's per channel weights of consecutive layers. This helps increase the range for layers with low range and reduce range for layers with high range. Therefore, different channels have similar range and same quantizaion parameters can be used for weights across all channels. - - .. image:: ../images/cle_1.png - - As shown below, AIMET takes in a model and equalizes the distribution of weights per channel of consecutive layers. The scaling factor is calculated and used to scale the weights. The output of the model remains the same and the dynamic range of weight distribution is reduced. - - .. image:: ../images/cle_4.png - - | - -#. High Bias Fold: Cross-layer scaling may result in high bias parameter values for some layers. This technique folds some of the bias of a layer into the subsequent layer’s parameters. High-bias fold requires batch-norm parameters to operate on. If the original model did not batch-norm parameters for a given layer, the high-bias fold technique will not be applied to that layer. - - | - -#. Bias Correction: Quantization sometimes leads to a shift in layer outputs. This techniques helps correct this shift by adjusting the bias parameters of that layer. Bias parameter is iteratively corrected/updated for each layer. The layer whose bias has to be corrected, and all the layers above it, are quantized. There are two techniques, namely Empirical Bias Correction and Analytical Bias Correction that are supported for bias correction. - -In empirical bias correction technique, representative data is passed through both the FP32 model and quantized model. Outputs are extracted for the layer to be corrected from both the models and used for correcting the bias parameter as shown below which describes correcting bias for a single layer. This process continues for all layers in the model. - -.. image:: ../images/bias_correction_empirical.png - -In analytical bias correction, data from BatchNorms - when present are used for correction factor estimation instead of passing data through model as in empirical case. - -.. image:: ../images/bias_correction_analytical.png - -FAQs -==== - -1. How many samples of data are required to perform Bias Correction? - *Bias Correction requires a representative set of dataset. We have observed that providing 500-1000 samples works well.* - -2. Which is better Empirical Bias Correction or Analytical + Empirical Bias Correction? - *If speed is not a bottleneck, then it is suggested to use Empirical Bias Correction, else the hybrid approach of combining both.* - -References -========== - -1. Markus Nagel, Mart van Baalen, Tijmen Blankevoort, Max Welling. “Data-Free Quantization Through Weight Equalization and Bias Correction.” IEEE International Conference on Computer Vision (ICCV), Seoul, October 2019. \ No newline at end of file diff --git a/AimetDocs/_sources/user_guide/quantization_sim.rst.txt b/AimetDocs/_sources/user_guide/quantization_sim.rst.txt deleted file mode 100644 index c4f95d1..0000000 --- a/AimetDocs/_sources/user_guide/quantization_sim.rst.txt +++ /dev/null @@ -1,93 +0,0 @@ -:orphan: - -.. _ug-quantsim: - -============================= -AIMET Quantization Simulation -============================= -Overview -======== -When ML models are run on quantized hardware, the runtime tools (like Qualcomm Neural Processing SDK) will convert the floating-point parameters of the model into fixed-point parameters. This conversion generally leads to a loss in accuracy. AIMET model quantization feature helps alleviate this problem. AIMET provides functionality to change the model to simulate the effects of quantized hardware. This allows the user to then re-train the model further (called fine-tuning) to recover the loss in accuracy. As a final step, AIMET provides functionality to export the model such that it can then be run on target via a runtime. - -User Flow -========= - -.. image:: ../images/quant_1.png - -The above explains a typical work flow a AIMET user can follow to make use of the quantization support. The steps are as follows - -#. The AIMET user will create their model in one of the supported training frameworks (PyTorch or TensorFlow) -#. User trains their model -#. After the user has a working and trained model, she/he can invoke the AIMET quantization APIs to created a quantized version of the model. During this step, AIMET uses a dataloader passed in by the user to analyze the model and determine the best quantization encodings on a per-layer basis. -#. User will further train the quantized version of the model. The user can re-train the model just like in Step 2. The model will learn to counter the effect of quantization noise. Please see :ref:`some recommendations` for quantization-aware fine-tuning. -#. User uses AIMET to save the model and the per-layer quantization encodings -#. These can be fed to a runtime like Qualcomm Neural Processing SDK to run the model on target (AIMET Importing encodings into quantized runtimes) - -Quantization Noise -================== -The diagram below explains how quantization noise is introduced to a model when its input, output or parameters are quantized and dequantized. - - .. image:: ../images/quant_3.png - -Since dequantizated value may not be exactly the same as quantized value, the difference between the two values is the quantization noise. - -What happens under the hood -=========================== -As explained above, in Step 3, AIMET analyzes the model and determines the optimal quantization encodings per-layer. - -.. image:: ../images/quant_2.png - -To analyze, AIMET passes some training samples through the model and using hooks, captures the tensors as they are outputted from each layer. A histogram is created to model the distribution of the floating point numbers in the output tensor for each layer. - -Using the distribution of the floating point numbers in the output tensor for each layer, AIMET will use a scheme called "Enhanced TensorFlow" to determine the best encodings to convert the floating point numbers to fixed point. An encoding for a layer consists of four numbers: - -- Min: Numbers below these are clamped -- Max: Numbers above these are clamped -- Delta: Granularity of the fixed point numbers (is a function of the bit-width selected) -- Offset: Offset from zero - -The delta and offset can be calculated using min and max and vice versa using the equations: - :math:`delta = \frac{min - max}{{2}^{bitwidth} - 1}` and :math:`offset = \frac{-min}{delta}` - -During the fine-tuning phase in Step 4, the following happens in the forward pass: - -.. image:: ../images/quant_4.png - -Weights from a given layer are first quantized to fixed point and then de-quantized back to floating point. And the same is done with the output tensor from the layer itself. -AIMET achieves this by wrapping existing layers with a custom layer that add this functionality in PyTorch, and inserting quantization ops between layers in TensorFlow. - -.. image:: ../images/quant_5.png - - -In the backward pass, AIMET will backprop normally. This is achieved by keeping the full-resolution floating point weights as shadow weights to be used during backprop. - -Placement of quantization simulation ops in the model -===================================================== -AIMET allows the configuration of quantizer placement in accordance with a set of rules specified in a json configuration file if provided when the Quantization Simulation API is called. -In the configuration file, quantizers can be turned on and off, and/or configured with asymmetric or symmetric encodings. -The general use case for this file would be for users to match the quantization rules for a particular runtime they would like to simulate. - -The configuration file contains six main sections, in increasing amounts of specificity: - -.. image:: ../images/quantsim_config_file.png - -Rules defined in a more general section can be overruled by subsequent rules defined in a more specific case. -For example, one may specify in "defaults" for no layers to be quantized, but then turn on quantization for specific layers in the "op_type" section. - -It is advised for the user to begin with the default configuration file under - -aimet_common/quantsim_config/default_config.json - - -.. _qat_recommendations: - -Recommendations for quantization-aware fine-tuning -================================================== -Here are some general guidelines that can aid in improving performance or faster convergence with Quantization-aware Training (QAT): - -* Initialization: - - Often it can be beneficial to first apply :ref:`post-training quantization` (Cross layer equalization (CLE) and bias correction) before applying QAT. This is especially beneficial if there is large drop in INT8 performance compared to the FP32 baseline. -* Hyper-parameters: - - Number of epochs: 15-20 epochs are generally sufficient for convergence - - Learning rate: Comparable (or one order higher) to FP32 model’s final learning rate at convergence. Results in AIMET are with learning of the order 1e-6. - - Learning rate schedule: Divide learning rate by 10 every 5-10 epochs diff --git a/AimetDocs/_sources/user_guide/spatial_svd.rst.txt b/AimetDocs/_sources/user_guide/spatial_svd.rst.txt deleted file mode 100644 index 6eb8812..0000000 --- a/AimetDocs/_sources/user_guide/spatial_svd.rst.txt +++ /dev/null @@ -1,16 +0,0 @@ -:orphan: - -.. _ug-spatial-svd: - -================= -AIMET Spatial SVD -================= - -Spatial SVD is a tensor decomposition technique which decomposes one large layer (in terms of mac or memory) into two smaller layers. SVD stands for Singular Value Decomposition. - -Given a conv layer, with kernel (𝑚,𝑛,ℎ,𝑤) where 𝑚 is the input channels, 𝑛 the output channels, and ℎ, 𝑤 giving the height and width of the kernel itself, Spatial SVD will decompose the kernel into two kernels. One of size (𝑚,𝑘,ℎ,1) and one of size (𝑘,𝑛,1,𝑤), where k is called the rank. The smaller the value of k the larger the degree of compression achieved. - -The following diagram illustrates this visually. As you can see, Spatial SVD decomposes both the output channel dimension as well as the size of the conv kernel itself. Spatial SVD is currently supported for Conv layers in AIMET. - -.. image:: ../images/spatial_svd.png - :width: 900px \ No newline at end of file diff --git a/AimetDocs/_sources/user_guide/visualization_compression.rst.txt b/AimetDocs/_sources/user_guide/visualization_compression.rst.txt deleted file mode 100644 index dbe4dbd..0000000 --- a/AimetDocs/_sources/user_guide/visualization_compression.rst.txt +++ /dev/null @@ -1,60 +0,0 @@ -=================== -AIMET Visualization -=================== - - -Overview -======== -AIMET Visualization adds analytical capability to the AIMET tool (which helps quantize and compress ML models) through visualization. It provide more detailed insights in to AIMET features as users are able to analyze a model’s layers in terms of compressibility and also highlight potential issues when applying quantization. The tool also assists in displaying progress for computationally heavy tasks. - -Design -====== -Given a model, a user can start a Bokeh server session and then invoke functions which will produce visualizations to help analyze and understand the model before using AIMET features from quantization and compression - -.. image:: ../images/vis_1.png - - -Compression -=========== -Evaluation scores during compression are displayed in a table as they are computed and users can see the progress displayed while computing these scores. After Greedy Selection has run, the optimal compression ratios are also displayed in a graph - -.. image:: ../images/vis_4.png - -.. image:: ../images/vis_5.png - -.. image:: ../images/vis_6.png - -.. image:: ../images/vis_7.png - - -Starting a Bokeh Server Session: -================================ -Start a bokeh server by typing this command: bokeh serve --allow-websocket-origin=: --port= - ---allow-websocket-origin tells the Bokeh server which network addresses to listen on, again not typically needed for local It is not need just to view locally. - ---port tells the Bokeh server what network port to listen on rather than the default port of 5006 - - -How to use the tool -=================== - - -**Model Compression** - -#. Start a bokeh server by typing this command: bokeh serve --allow-websocket-origin=: --port= -#. To visualize eval scores and compression ratios during execution time: - a) Input a visualization URL into the top level function: compress_model. This url is http://:/ - i) For model compression, the visualization url is passed through compress_model. If no visualizations are necessary then the url has a default option for None. - b) Finally, go to the URL to see the visualizations. - i) The session-id here is: compression. So the URL would look something like this: - ii) http://:/?&bokeh-session-id=compression -#. To visualize eval scores and compression ratios after execution: - a) Use API doc to decide which functions to use. They should be under "Model Compression." - i) First instantiate a VisualizeCompression instance by passing in a visualization URL. This url is http://:/ - b) There are two functions: - i) display_eval_scores - ii) display_comp_ratio_plot - c) Finally, go to the URL to see the visualizations - i) The session-id here is: compression. So the URL would look something like this: - ii) http://:/?&bokeh-session-id=compression diff --git a/AimetDocs/_sources/user_guide/visualization_quant.rst.txt b/AimetDocs/_sources/user_guide/visualization_quant.rst.txt deleted file mode 100644 index b1fd0df..0000000 --- a/AimetDocs/_sources/user_guide/visualization_quant.rst.txt +++ /dev/null @@ -1,62 +0,0 @@ -:orphan: - -.. _ug-quantization-visualization: - -==================================== -AIMET Visualization for Quantization -==================================== - - -Overview -======== -AIMET Visualization adds analytical capability to the AIMET tool (which helps quantize and compress ML models) through visualization. It provide more detailed insights in to AIMET features as users are able to analyze a model’s layers in terms of compressibility and also highlight potential issues when applying quantization. The tool also assists in displaying progress for computationally heavy tasks. - -Design -====== -Given a model, a user can start a Bokeh server session and then invoke functions which will produce visualizations to help analyze and understand the model before using AIMET features from quantization and compression - -.. image:: ../images/vis_1.png - -Quantization -============ -During quantization, common parameters are used throughout a layer for converting the floating point weight values to INT8. If the dynamic range in weights is very high the quantization will not be very granular. To equalize the weight range we apply Cross Layer Equalization. -In order to understand if we need to apply Cross Layer Equalization, we can visualize the weight range for every channel in a layer. If the weight range varies a lot over the various channels, applying cross layer equalization helps in improving the Quantization accuracy. - -.. image:: ../images/vis_3.png - -PyTorch -------- - -In PyTorch, we can visualize the weights for a model. We can also visualize the weight ranges for a model before and after Cross Layer Equalization. -There are three main functions a user can invoke: - -#. User can analyze relative weight ranges of model to see potentially problematic layers for quantization -#. User can understand each layer in the model -#. User can visualize the model, comparing weights before and after quantization. - -TensorFlow ----------- - -In TensorFlow, we can visualize the weight ranges and relative weight ranges over various channels in a layer. -User can also use the same functions to see the changes in a layer weight ranges before and after Cross Layer Equalization. - -There are two main functions a user can invoke: - -#. User can analyze relative weight ranges of a layer to see potentially problematic layers for quantization -#. User can visualize weight ranges of a layer and see the various statistics for weights - - -Starting a Bokeh Server Session: -================================ -AIMET uses Bokeh for plotting. To visualize plots using Bokeh, user needs to start a Bokeh server. Following instructions show how Bokeh server can be started. - -#. Start a Bokeh server by typing this command: bokeh serve - -allow-websocket-origin=: --port= - - --allow-websocket-origin tells the Bokeh server which network addresses to listen on, again not typically needed for local It is not need just to view locally. - - --port tells the Bokeh server what network port to listen on rather than the default port of 5006 - -#. Finally, go to the URL to see the visualizations. - a) The session-id here is: optimization. So the URL would look something like this: - b) http://:/?&bokeh-session-id=optimization - diff --git a/AimetDocs/_sources/user_guide/weight_svd.rst.txt b/AimetDocs/_sources/user_guide/weight_svd.rst.txt deleted file mode 100644 index ba9ce96..0000000 --- a/AimetDocs/_sources/user_guide/weight_svd.rst.txt +++ /dev/null @@ -1,13 +0,0 @@ -================ -AIMET Weight SVD -================ - -Weight SVD is a tensor decomposition technique which decomposes one large layer (in terms of mac or memory) into two smaller layers. SVD stands for Singular Value Decomposition. - -Given a neural network layer, with kernel (𝑚,𝑛,ℎ,𝑤) where 𝑚 is the input channels, 𝑛 the output channels, and ℎ, 𝑤 giving the height and width of the kernel itself, Weight SVD will decompose the kernel into one of size (𝑚,𝑘,1,1) and another of size (𝑘,𝑛,h,𝑤), where 𝑘 is called the rank. The smaller the value of 𝑘 the larger the degree of compression achieved. - -The following diagram illustrates this visually. As you can see, Weight SVD decomposes the output channel dimension. Weight SVD is currently supported for Conv and Full-connected layers in AIMET. - - -.. image:: ../images/weight_svd.png - :width: 900px diff --git a/AimetDocs/_sources/user_guide/winnowing.rst.txt b/AimetDocs/_sources/user_guide/winnowing.rst.txt deleted file mode 100644 index dc1d26f..0000000 --- a/AimetDocs/_sources/user_guide/winnowing.rst.txt +++ /dev/null @@ -1,25 +0,0 @@ -=============== -AIMET Winnowing -=============== - -Overview -======== -The model compression algorithm, Channel Pruning, identifies modules in a model, whose subset of input channels could be pruned without losing much accuracy. Unless explicitly removed, these input channels take up memory and add to unnecessary computation. For each identified module, the Winnow tool removes the input channels that were selected for pruning. Only Conv2D layers are supported for winnowing. - -Winnowing Overview -================== -The following figure provides a pictorial overview of Winnowing. In this example, a module in a model has an input volume of **HxWx8**, where H = Height, W = Width and Number of input Channels = 8. The Channel Pruning algorithm identifies that for this module, input channels 1, 4 and 7 should be pruned. Winnowing removes the identified input channels from this modules. The module's input volume is now reduced to **HxWx5**. - -.. image:: ../images/winnow_1.png - -How Winnowing Works -=================== -When the number of input channels of a Conv module is reduced, the output channels of the module above it must also be modified. If the module above is a another Conv layer, that Conv layer's output channels are also reduced to match the number of input channels of the winnowed Conv module. If the module above is NOT a Conv layer (e.g., BatchNorm, ReLU), that module simply propagates the changes upstream. That is both the output and the input channels of teh BatchNorm and ReLU modules are winnowed to match the winnowed channels of the Conv layer just below them. - -The following figure explains a very simple scenario. In this scenario, a Conv module has been identified for winnowing a sub set of its input channels. This is indicated by green color on the left side of the figure. The right side of the figure indicates the actions taken by Winnowing. Winnowing consists of the following changes done to the 3 affected modules. - -The identified Conv module's sub set of input channels are removed. This is indicated by pink color on the right side of the figure. -The module just above the winnowed Conv module is NOT a Conv module. It could be a ReLU or a BatchNorm module. For this module the corresponding output and input channels are winnowed. This is indicated by orange color on the right side of the figure. -The module above the ReLU/BatchNorm is another Conv module. This Conv module's output channels are winnowed.This is indicated by pink color on the right side of the figure. - -.. image:: ../images/winnow_2.png diff --git a/AimetDocs/_static/background_b01.png b/AimetDocs/_static/background_b01.png deleted file mode 100644 index 353f26dde0803aa172c23e21ef6ac068e1253bc8..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 78 zcmeAS@N?(olHy`uVBq!ia0vp^%plAGBp8aFUnK)6QBN1gkP61+AN9}d56}GnU-I97 adu9eB2afnIr`wM~3O!x@T-G@yGywpsd=;et diff --git a/AimetDocs/_static/basic.css b/AimetDocs/_static/basic.css deleted file mode 100644 index c41d718..0000000 --- a/AimetDocs/_static/basic.css +++ /dev/null @@ -1,763 +0,0 @@ -/* - * basic.css - * ~~~~~~~~~ - * - * Sphinx stylesheet -- basic theme. - * - * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -/* -- main layout ----------------------------------------------------------- */ - -div.clearer { - 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solid #98dbcc; - font-family: sans-serif; - font-size: 1em; -} - -div.sphinxsidebar #searchbox form.search { - overflow: hidden; -} - -div.sphinxsidebar #searchbox input[type="text"] { - float: left; - width: 80%; - padding: 0.25em; - box-sizing: border-box; -} - -div.sphinxsidebar #searchbox input[type="submit"] { - float: left; - width: 20%; - border-left: none; - padding: 0.25em; - box-sizing: border-box; -} - - -img { - border: 0; - max-width: 100%; -} - -/* -- search page ----------------------------------------------------------- */ - -ul.search { - margin: 10px 0 0 20px; - padding: 0; -} - -ul.search li { - padding: 5px 0 5px 20px; - background-image: url(file.png); - background-repeat: no-repeat; - background-position: 0 7px; -} - -ul.search li a { - font-weight: bold; -} - -ul.search li div.context { - color: #888; - margin: 2px 0 0 30px; - text-align: left; -} - -ul.keywordmatches li.goodmatch a { - font-weight: bold; -} - -/* -- index page ------------------------------------------------------------ */ - -table.contentstable { - width: 90%; - margin-left: auto; - margin-right: auto; -} - -table.contentstable p.biglink { - line-height: 150%; -} - -a.biglink { - font-size: 1.3em; -} - -span.linkdescr { - font-style: italic; - padding-top: 5px; - font-size: 90%; -} - -/* -- general index --------------------------------------------------------- */ - -table.indextable { - width: 100%; -} - -table.indextable td { - text-align: left; - vertical-align: top; -} - -table.indextable ul { - margin-top: 0; - margin-bottom: 0; - list-style-type: none; -} - -table.indextable > tbody > tr > td > ul { - padding-left: 0em; -} - -table.indextable tr.pcap { - height: 10px; -} - -table.indextable tr.cap { - margin-top: 10px; - background-color: #f2f2f2; -} - -img.toggler { - margin-right: 3px; - margin-top: 3px; - cursor: pointer; -} - -div.modindex-jumpbox { - border-top: 1px solid #ddd; - border-bottom: 1px solid #ddd; - margin: 1em 0 1em 0; - padding: 0.4em; -} - -div.genindex-jumpbox { - border-top: 1px solid #ddd; - border-bottom: 1px solid #ddd; - margin: 1em 0 1em 0; - padding: 0.4em; -} - -/* -- domain module index --------------------------------------------------- */ - -table.modindextable td { - padding: 2px; - border-collapse: collapse; -} - -/* -- general body styles --------------------------------------------------- */ - -div.body { - min-width: 450px; - max-width: 800px; -} - -div.body p, div.body dd, div.body li, div.body blockquote { - -moz-hyphens: auto; - -ms-hyphens: auto; - -webkit-hyphens: auto; - hyphens: auto; -} - -a.headerlink { - visibility: hidden; -} - -a.brackets:before, -span.brackets > a:before{ - content: "["; -} - -a.brackets:after, -span.brackets > a:after { - content: "]"; -} - -h1:hover > a.headerlink, -h2:hover > a.headerlink, -h3:hover > a.headerlink, -h4:hover > a.headerlink, -h5:hover > a.headerlink, -h6:hover > a.headerlink, -dt:hover > a.headerlink, -caption:hover > a.headerlink, -p.caption:hover > a.headerlink, -div.code-block-caption:hover > a.headerlink { - visibility: visible; -} - -div.body p.caption { - text-align: inherit; -} - -div.body td { - text-align: left; -} - -.first { - margin-top: 0 !important; -} - -p.rubric { - margin-top: 30px; - font-weight: bold; -} - -img.align-left, .figure.align-left, object.align-left { - clear: left; - float: left; - margin-right: 1em; -} - -img.align-right, .figure.align-right, object.align-right { - clear: right; - float: right; - margin-left: 1em; -} - -img.align-center, .figure.align-center, object.align-center { - display: block; - margin-left: auto; - margin-right: auto; -} - -img.align-default, .figure.align-default { - display: block; - margin-left: auto; - margin-right: auto; -} - -.align-left { - text-align: left; -} - -.align-center { - text-align: center; -} - -.align-default { - text-align: center; -} - -.align-right { - text-align: right; -} - -/* -- sidebars -------------------------------------------------------------- */ - -div.sidebar { - margin: 0 0 0.5em 1em; - border: 1px solid #ddb; - padding: 7px 7px 0 7px; - background-color: #ffe; - width: 40%; - float: right; -} - -p.sidebar-title { - font-weight: bold; -} - -/* -- topics ---------------------------------------------------------------- */ - -div.topic { - border: 1px solid #ccc; - padding: 7px 7px 0 7px; - margin: 10px 0 10px 0; -} - -p.topic-title { - font-size: 1.1em; - font-weight: bold; - margin-top: 10px; -} - -/* -- admonitions ----------------------------------------------------------- */ - -div.admonition { - margin-top: 10px; - margin-bottom: 10px; - padding: 7px; -} - -div.admonition dt { - font-weight: bold; -} - -div.admonition dl { - margin-bottom: 0; -} - -p.admonition-title { - margin: 0px 10px 5px 0px; - font-weight: bold; -} - -div.body p.centered { - text-align: center; - margin-top: 25px; -} - -/* -- tables ---------------------------------------------------------------- */ - -table.docutils { - border: 0; - border-collapse: collapse; -} - -table.align-center { - margin-left: auto; - margin-right: auto; -} - -table.align-default { - margin-left: auto; - margin-right: auto; -} - -table caption span.caption-number { - font-style: italic; -} - -table caption span.caption-text { -} - -table.docutils td, table.docutils th { - padding: 1px 8px 1px 5px; - border-top: 0; - border-left: 0; - border-right: 0; - border-bottom: 1px solid #aaa; -} - -table.footnote td, table.footnote th { - border: 0 !important; -} - -th { - text-align: left; - padding-right: 5px; -} - -table.citation { - border-left: solid 1px gray; - margin-left: 1px; -} - -table.citation td { - border-bottom: none; -} - -th > p:first-child, -td > p:first-child { - margin-top: 0px; -} - -th > p:last-child, -td > p:last-child { - margin-bottom: 0px; -} - -/* -- figures --------------------------------------------------------------- */ - -div.figure { - margin: 0.5em; - padding: 0.5em; -} - -div.figure p.caption { - padding: 0.3em; -} - -div.figure p.caption span.caption-number { - font-style: italic; -} - -div.figure p.caption span.caption-text { -} - -/* -- field list styles ----------------------------------------------------- */ - -table.field-list td, table.field-list th { - border: 0 !important; -} - -.field-list ul { - margin: 0; - padding-left: 1em; -} - -.field-list p { - margin: 0; -} - -.field-name { - -moz-hyphens: manual; - -ms-hyphens: manual; - -webkit-hyphens: manual; - hyphens: manual; -} - -/* -- hlist styles ---------------------------------------------------------- */ - -table.hlist td { - vertical-align: top; -} - - -/* -- other body styles ----------------------------------------------------- */ - -ol.arabic { - list-style: decimal; -} - -ol.loweralpha { - list-style: lower-alpha; -} - -ol.upperalpha { - list-style: upper-alpha; -} - -ol.lowerroman { - list-style: lower-roman; -} - -ol.upperroman { - list-style: upper-roman; -} - -li > p:first-child { - margin-top: 0px; -} - -li > p:last-child { - margin-bottom: 0px; -} - -dl.footnote > dt, -dl.citation > dt { - float: left; -} - -dl.footnote > dd, -dl.citation > dd { - margin-bottom: 0em; -} - -dl.footnote > dd:after, -dl.citation > dd:after { - content: ""; - clear: both; -} - -dl.field-list { - display: flex; - flex-wrap: wrap; -} - -dl.field-list > dt { - flex-basis: 20%; - font-weight: bold; - word-break: break-word; -} - -dl.field-list > dt:after { - content: ":"; -} - -dl.field-list > dd { - flex-basis: 70%; - padding-left: 1em; - margin-left: 0em; - margin-bottom: 0em; -} - -dl { - margin-bottom: 15px; -} - -dd > p:first-child { - margin-top: 0px; -} - -dd ul, dd table { - margin-bottom: 10px; -} - -dd { - margin-top: 3px; - margin-bottom: 10px; - margin-left: 30px; -} - -dt:target, span.highlighted { - background-color: #fbe54e; -} - -rect.highlighted { - fill: #fbe54e; -} - -dl.glossary dt { - font-weight: bold; - font-size: 1.1em; -} - -.optional { - font-size: 1.3em; -} - -.sig-paren { - font-size: larger; -} - -.versionmodified { - font-style: italic; -} - -.system-message { - background-color: #fda; - padding: 5px; - border: 3px solid red; -} - -.footnote:target { - background-color: #ffa; -} - -.line-block { - display: block; - margin-top: 1em; - margin-bottom: 1em; -} - -.line-block .line-block { - margin-top: 0; - margin-bottom: 0; - margin-left: 1.5em; -} - -.guilabel, .menuselection { - font-family: sans-serif; -} - -.accelerator { - text-decoration: underline; -} - -.classifier { - font-style: oblique; -} - -.classifier:before { - font-style: normal; - margin: 0.5em; - content: ":"; -} - -abbr, acronym { - border-bottom: dotted 1px; - cursor: help; -} - -/* -- code displays --------------------------------------------------------- */ - -pre { - overflow: auto; - overflow-y: hidden; /* fixes display issues on Chrome browsers */ -} - -span.pre { - -moz-hyphens: none; - -ms-hyphens: none; - -webkit-hyphens: none; - hyphens: none; -} - -td.linenos pre { - padding: 5px 0px; - border: 0; - background-color: transparent; - color: #aaa; -} - -table.highlighttable { - margin-left: 0.5em; -} - -table.highlighttable td { - padding: 0 0.5em 0 0.5em; -} - -div.code-block-caption { - padding: 2px 5px; - font-size: small; -} - -div.code-block-caption code { - background-color: transparent; -} - -div.code-block-caption + div > div.highlight > pre { - margin-top: 0; -} - -div.code-block-caption span.caption-number { - padding: 0.1em 0.3em; - font-style: italic; -} - -div.code-block-caption span.caption-text { -} - -div.literal-block-wrapper { - padding: 1em 1em 0; -} - -div.literal-block-wrapper div.highlight { - margin: 0; -} - -code.descname { - background-color: transparent; - font-weight: bold; - font-size: 1.2em; -} - -code.descclassname { - background-color: transparent; -} - -code.xref, a code { - background-color: transparent; - font-weight: bold; -} - -h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { - background-color: transparent; -} - -.viewcode-link { - float: right; -} - -.viewcode-back { - float: right; - font-family: sans-serif; -} - -div.viewcode-block:target { - margin: -1px -10px; - padding: 0 10px; -} - -/* -- math display ---------------------------------------------------------- */ - -img.math { - vertical-align: middle; -} - -div.body div.math p { - text-align: center; -} - -span.eqno { - float: right; -} - -span.eqno a.headerlink { - position: relative; - left: 0px; - z-index: 1; -} - -div.math:hover a.headerlink { - visibility: visible; -} - -/* -- printout stylesheet --------------------------------------------------- */ - -@media print { - div.document, - div.documentwrapper, - div.bodywrapper { - margin: 0 !important; - width: 100%; - } - - div.sphinxsidebar, - div.related, - div.footer, - #top-link { - display: none; - } -} \ No newline at end of file diff --git a/AimetDocs/_static/bizstyle.css b/AimetDocs/_static/bizstyle.css deleted file mode 100644 index d20b72c..0000000 --- a/AimetDocs/_static/bizstyle.css +++ /dev/null @@ -1,504 +0,0 @@ -/* - * bizstyle.css_t - * ~~~~~~~~~~~~~~ - * - * Sphinx stylesheet -- business style theme. - * - * :copyright: Copyright 2011-2014 by Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -@import url("basic.css"); - -/* -- page layout ----------------------------------------------------------- */ - -body { - font-family: 'Lucida Grande', 'Lucida Sans Unicode', 'Geneva', - 'Verdana', sans-serif; - font-size: 14px; - letter-spacing: -0.01em; - line-height: 150%; - text-align: center; - background-color: white; - background-image: url(background_b01.png); - color: black; - padding: 0; - border-right: 1px solid #336699; - border-left: 1px solid #336699; - - margin: 0px 40px 0px 40px; -} - -div.document { - background-color: white; - text-align: left; - background-repeat: repeat-x; - - -moz-box-shadow: 2px 2px 5px #000; - -webkit-box-shadow: 2px 2px 5px #000; -} - -div.bodywrapper { - margin: 0 0 0 240px; - border-left: 1px solid #ccc; -} - -div.body { - margin: 0; - padding: 0.5em 20px 20px 20px; -} - -div.related { - font-size: 1em; - - -moz-box-shadow: 2px 2px 5px #000; - -webkit-box-shadow: 2px 2px 5px #000; -} - -div.related ul { - background-color: #336699; - height: 100%; - overflow: hidden; - border-top: 1px solid #ddd; - border-bottom: 1px solid #ddd; -} - -div.related ul li { - color: white; - margin: 0; - padding: 0; - height: 2em; - float: left; -} - -div.related ul li.right { - float: right; - margin-right: 5px; -} - -div.related ul li a { - margin: 0; - padding: 0 5px 0 5px; - line-height: 1.75em; - color: #fff; -} - -div.related ul li a:hover { - color: #fff; - text-decoration: underline; -} - -div.sphinxsidebarwrapper { - padding: 0; -} - -div.sphinxsidebar { - margin: 0; - padding: 0.5em 12px 12px 12px; - width: 210px; - font-size: 1em; - text-align: left; -} - -div.sphinxsidebar h3, div.sphinxsidebar h4 { - margin: 1em 0 0.5em 0; - font-size: 1em; - padding: 0.1em 0 0.1em 0.5em; - color: white; - border: 1px solid #336699; - background-color: #336699; -} - -div.sphinxsidebar h3 a { - color: white; -} - -div.sphinxsidebar ul { - padding-left: 1.5em; - margin-top: 7px; - padding: 0; - line-height: 130%; -} - -div.sphinxsidebar ul ul { - margin-left: 20px; -} - -div.sphinxsidebar input { - border: 1px solid #336699; -} - -div.footer { - background-color: white; - color: #336699; - padding: 3px 8px 3px 0; - clear: both; - font-size: 0.8em; - text-align: right; - border-bottom: 1px solid #336699; - - -moz-box-shadow: 2px 2px 5px #000; - -webkit-box-shadow: 2px 2px 5px #000; -} - -div.footer a { - color: #336699; - text-decoration: underline; -} - -/* -- body styles ----------------------------------------------------------- */ - -p { - margin: 0.8em 0 0.5em 0; -} - -a { - color: #336699; - text-decoration: none; -} - -a:hover { - color: #336699; - text-decoration: underline; -} - -div.body a { - text-decoration: underline; -} - -h1, h2, h3 { - color: #336699; -} - -h1 { - margin: 0; - padding: 0.7em 0 0.3em 0; - font-size: 1.5em; -} - -h2 { - margin: 1.3em 0 0.2em 0; - font-size: 1.35em; - padding-bottom: .5em; - border-bottom: 1px solid #336699; -} - -h3 { - margin: 1em 0 -0.3em 0; - font-size: 1.2em; - padding-bottom: .3em; - border-bottom: 1px solid #CCCCCC; -} - -div.body h1 a, div.body h2 a, div.body h3 a, -div.body h4 a, div.body h5 a, div.body h6 a { - color: black!important; -} - -h1 a.anchor, h2 a.anchor, h3 a.anchor, -h4 a.anchor, h5 a.anchor, h6 a.anchor { - display: none; - margin: 0 0 0 0.3em; - padding: 0 0.2em 0 0.2em; - color: #aaa!important; -} - -h1:hover a.anchor, h2:hover a.anchor, h3:hover a.anchor, h4:hover a.anchor, -h5:hover a.anchor, h6:hover a.anchor { - display: inline; -} - -h1 a.anchor:hover, h2 a.anchor:hover, h3 a.anchor:hover, h4 a.anchor:hover, -h5 a.anchor:hover, h6 a.anchor:hover { - color: #777; - background-color: #eee; -} - -a.headerlink { - color: #c60f0f!important; - font-size: 1em; - margin-left: 6px; - padding: 0 4px 0 4px; - text-decoration: none!important; -} - -a.headerlink:hover { - background-color: #ccc; - color: white!important; -} - -cite, code, tt { - font-family: 'Consolas', 'Deja Vu Sans Mono', - 'Bitstream Vera Sans Mono', monospace; - font-size: 0.95em; - letter-spacing: 0.01em; -} - -code { - background-color: #F2F2F2; - border-bottom: 1px solid #ddd; - color: #333; -} - -code.descname, code.descclassname, code.xref { - border: 0; -} - -hr { - border: 1px solid #abc; - margin: 2em; -} - -a code { - border: 0; - color: #CA7900; -} - -a code:hover { - color: #2491CF; -} - -pre { - background-color: transparent !important; - font-family: 'Consolas', 'Deja Vu Sans Mono', - 'Bitstream Vera Sans Mono', monospace; - font-size: 0.95em; - letter-spacing: 0.015em; - line-height: 120%; - padding: 0.5em; - border-right: 5px solid #ccc; - border-left: 5px solid #ccc; -} - -pre a { - color: inherit; - text-decoration: underline; -} - -td.linenos pre { - padding: 0.5em 0; -} - -div.quotebar { - background-color: #f8f8f8; - max-width: 250px; - float: right; - padding: 2px 7px; - border: 1px solid #ccc; -} - -div.topic { - background-color: #f8f8f8; -} - -table { - border-collapse: collapse; - margin: 0 -0.5em 0 -0.5em; -} - -table td, table th { - padding: 0.2em 0.5em 0.2em 0.5em; -} - -div.admonition { - font-size: 0.9em; - margin: 1em 0 1em 0; - border: 3px solid #cccccc; - background-color: #f7f7f7; - padding: 0; -} - -div.admonition p { - margin: 0.5em 1em 0.5em 1em; - padding: 0; -} - -div.admonition li p { - margin-left: 0; -} - -div.admonition pre, div.warning pre { - margin: 0; -} - -div.highlight { - margin: 0.4em 1em; -} - -div.admonition p.admonition-title { - margin: 0; - padding: 0.1em 0 0.1em 0.5em; - color: white; - border-bottom: 3px solid #cccccc; - font-weight: bold; - background-color: #165e83; -} - -div.danger { border: 3px solid #f0908d; background-color: #f0cfa0; } -div.error { border: 3px solid #f0908d; background-color: #ede4cd; } -div.warning { border: 3px solid #f8b862; background-color: #f0cfa0; } -div.caution { border: 3px solid #f8b862; background-color: #ede4cd; } -div.attention { border: 3px solid #f8b862; background-color: #f3f3f3; } -div.important { border: 3px solid #f0cfa0; background-color: #ede4cd; } -div.note { border: 3px solid #f0cfa0; background-color: #f3f3f3; } -div.hint { border: 3px solid #bed2c3; background-color: #f3f3f3; } -div.tip { border: 3px solid #bed2c3; background-color: #f3f3f3; } - -div.danger p.admonition-title, div.error p.admonition-title { - background-color: #b7282e; - border-bottom: 3px solid #f0908d; -} - -div.caution p.admonition-title, -div.warning p.admonition-title, -div.attention p.admonition-title { - background-color: #f19072; - border-bottom: 3px solid #f8b862; -} - -div.note p.admonition-title, div.important p.admonition-title { - background-color: #f8b862; - border-bottom: 3px solid #f0cfa0; -} - -div.hint p.admonition-title, div.tip p.admonition-title { - background-color: #7ebea5; - border-bottom: 3px solid #bed2c3; -} - -div.admonition ul, div.admonition ol, -div.warning ul, div.warning ol { - margin: 0.1em 0.5em 0.5em 3em; - padding: 0; -} - -div.versioninfo { - margin: 1em 0 0 0; - border: 1px solid #ccc; - background-color: #DDEAF0; - padding: 8px; - line-height: 1.3em; - font-size: 0.9em; -} - -.viewcode-back { - font-family: 'Lucida Grande', 'Lucida Sans Unicode', 'Geneva', - 'Verdana', sans-serif; -} - -div.viewcode-block:target { - background-color: #f4debf; - border-top: 1px solid #ac9; - border-bottom: 1px solid #ac9; -} - -p.versionchanged span.versionmodified { - font-size: 0.9em; - margin-right: 0.2em; - padding: 0.1em; - background-color: #DCE6A0; -} - -dl.field-list > dt { - color: white; - padding-left: 0.5em; - padding-right: 5px; - background-color: #82A0BE; -} - -dl.field-list > dd { - padding-left: 0.5em; - 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-body { - margin: 0 40px 0 40px; -} -} - -/* TABLET STYLE */ -@media only screen and (min-width: 768px) and (max-width: 991px) { -body { - margin: 0 40px 0 40px; -} -} - -/* MOBILE LAYOUT (PORTRAIT/320px) */ -@media only screen and (max-width: 767px) { -body { - margin: 0; -} -div.bodywrapper { - margin: 0; - width: 100%; - border: none; -} -div.sphinxsidebar { - display: none; -} -} - -/* MOBILE LAYOUT (LANDSCAPE/480px) */ -@media only screen and (min-width: 480px) and (max-width: 767px) { -body { - margin: 0 20px 0 20px; -} -} - -/* RETINA OVERRIDES */ -@media -only screen and (-webkit-min-device-pixel-ratio: 2), -only screen and (min-device-pixel-ratio: 2) { -} - -/* -- end ------------------------------------------------------------------- */ \ No newline at end of file diff --git a/AimetDocs/_static/bizstyle.js b/AimetDocs/_static/bizstyle.js deleted file mode 100644 index 107c4af..0000000 --- a/AimetDocs/_static/bizstyle.js +++ /dev/null @@ -1,41 +0,0 @@ -// -// bizstyle.js -// 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