Index A | B | C | D | E | F | G | I | K | L | M | N | O | P | Q | R | S | T | V | W A Accelerator Accuracy Activation Activation Quantization AdapterMetaData (class in aimet_torch.peft) AdaRound AdaroundParameters (class in aimet_onnx.adaround.adaround_weight), [1] (class in aimet_tensorflow.keras.adaround_weight), [1] (class in aimet_torch.adaround.adaround_weight), [1] (class in aimet_torch.v1.adaround.adaround_weight) add_check() (aimet_torch.model_validator.model_validator.ModelValidator static method) AI Model Efficiency Toolkit AIMET analyze() (aimet_onnx.quant_analyzer.QuantAnalyzer method), [1] (aimet_torch.quant_analyzer.QuantAnalyzer method), [1] (aimet_torch.v1.quant_analyzer.QuantAnalyzer method) apply() (aimet_torch.v2.mixed_precision.MixedPrecisionConfigurator method), [1] apply_adaround() (in module aimet_onnx.adaround.adaround_weight.Adaround), [1] (in module aimet_tensorflow.keras.adaround_weight.Adaround), [1] (in module aimet_torch.adaround.adaround_weight.Adaround), [1] (in module aimet_torch.v1.adaround.adaround_weight.Adaround) apply_seq_mse() (in module aimet_onnx.sequential_mse.seq_mse.SequentialMse) (in module aimet_torch.seq_mse), [1] (in module aimet_torch.v1.seq_mse) auto (aimet_tensorflow.keras.defs.SpatialSvdParameters.Mode attribute), [1] AutoQuant AutoQuantWithAutoMixedPrecision (class in aimet_onnx.auto_quant_v2), [1] (class in aimet_tensorflow.keras.auto_quant_v2), [1] B Batch Normalization Batch Normalization Folding (BN Folding) bitwidth (aimet_torch.quantization.float.FloatQuantizeDequantize property) BN C CallbackFunc (class in aimet_common.defs), [1], [2], [3], [4], [5], [6] (class in aimet_common.utils), [1], [2] check_model_sensitivity_to_quantization() (aimet_torch.quant_analyzer.QuantAnalyzer method), [1] (aimet_torch.v1.quant_analyzer.QuantAnalyzer method) choose_fast_mixed_precision() (in module aimet_tensorflow.keras.mixed_precision), [1] choose_mixed_precision() (in module aimet_onnx.mixed_precision), [1] (in module aimet_tensorflow.keras.mixed_precision), [1] (in module aimet_torch.mixed_precision), [1] (in module aimet_torch.v1.mixed_precision) clone() (aimet_torch.quantization.QuantizedTensorBase method) CNN compress_model() (aimet_tensorflow.keras.compress.ModelCompressor static method), [1] Compression compute_encodings() (aimet_onnx.quantsim.QuantizationSimModel method) (aimet_tensorflow.keras.quantsim.QuantizationSimModel method) (aimet_torch.nn.QuantizationMixin method) (aimet_torch.quantization.float.FloatQuantizeDequantize method) (aimet_torch.quantsim.QuantizationSimModel method) (aimet_torch.v1.quantsim.QuantizationSimModel method) Convolutional Layer Convolutional Neural Network D dequantize() (aimet_torch.quantization.DequantizedTensor method) (aimet_torch.quantization.QuantizedTensor method) (aimet_torch.quantization.QuantizedTensorBase method) (in module aimet_torch.quantization.affine) DequantizedTensor (class in aimet_torch.quantization) detach() (aimet_torch.quantization.QuantizedTensorBase method) Device disable_lora_adapters() (aimet_torch.peft.PeftQuantUtils method) DLF Dynamic Layer Fusion E Edge device enable_adapter_and_load_weights() (aimet_torch.peft.PeftQuantUtils method) enable_per_layer_mse_loss() (aimet_onnx.quant_analyzer.QuantAnalyzer method), [1] Encoding equalize_model() (in module aimet_onnx.cross_layer_equalization), [1] (in module aimet_tensorflow.keras.cross_layer_equalization), [1] (in module aimet_torch.cross_layer_equalization), [1] EvalCallbackFactory (class in aimet_onnx.amp.mixed_precision_algo), [1] (class in aimet_torch.amp.mixed_precision_algo), [1], [2] export() (aimet_onnx.quantsim.QuantizationSimModel method) (aimet_tensorflow.keras.quantsim.QuantizationSimModel method) (aimet_torch.quantsim.QuantizationSimModel method) (aimet_torch.v1.quantsim.QuantizationSimModel method) export_adapter_weights() (aimet_torch.peft.PeftQuantUtils method) export_per_layer_encoding_min_max_range() (aimet_torch.quant_analyzer.QuantAnalyzer method), [1] (aimet_torch.v1.quant_analyzer.QuantAnalyzer method) export_per_layer_mse_loss() (aimet_torch.quant_analyzer.QuantAnalyzer method), [1] (aimet_torch.v1.quant_analyzer.QuantAnalyzer method) export_per_layer_stats_histogram() (aimet_torch.quant_analyzer.QuantAnalyzer method), [1] (aimet_torch.v1.quant_analyzer.QuantAnalyzer method) F FloatQuantizeDequantize (class in aimet_torch.quantization.float) fold_all_batch_norms() (in module aimet_tensorflow.keras.batch_norm_fold), [1] (in module aimet_torch.batch_norm_fold), [1] fold_all_batch_norms_to_scale() (in module aimet_tensorflow.keras.batch_norm_fold), [1] fold_all_batch_norms_to_weight() (in module aimet_onnx.batch_norm_fold), [1] forward() (aimet_torch.nn.QuantizationMixin method) (aimet_torch.nn.QuantizedLinear method) (aimet_torch.quantization.affine.Quantize method) (aimet_torch.quantization.affine.QuantizeDequantize method) (aimet_torch.quantization.float.FloatQuantizeDequantize method) forward_fn() (aimet_torch.seq_mse.SeqMseParams method), [1] (aimet_torch.v1.seq_mse.SeqMseParams method) FP32 freeze_base_model() (aimet_torch.peft.PeftQuantUtils method) freeze_base_model_activation_quantizers() (aimet_torch.peft.PeftQuantUtils method) freeze_base_model_param_quantizers() (aimet_torch.peft.PeftQuantUtils method) from_module() (aimet_torch.nn.QuantizationMixin class method) G generate_layer_outputs() (aimet_onnx.layer_output_utils.LayerOutputUtil method), [1] (aimet_tensorflow.keras.layer_output_utils.LayerOutputUtil method), [1] (aimet_torch.layer_output_utils.LayerOutputUtil method), [1] get_activation_quantizers() (aimet_onnx.amp.quantizer_groups.QuantizerGroup method), [1] get_active_param_quantizers() (aimet_tensorflow.keras.amp.quantizer_groups.QuantizerGroup method), [1] get_active_quantizers() (aimet_onnx.amp.quantizer_groups.QuantizerGroup method), [1] (aimet_tensorflow.keras.amp.quantizer_groups.QuantizerGroup method), [1] (aimet_torch.amp.quantizer_groups.QuantizerGroup method), [1], [2] get_candidate() (aimet_onnx.amp.quantizer_groups.QuantizerGroup method), [1] (aimet_tensorflow.keras.amp.quantizer_groups.QuantizerGroup method), [1] (aimet_torch.amp.quantizer_groups.QuantizerGroup method), [1], [2] get_default_kernel() (aimet_torch.nn.QuantizationMixin class method) get_encodings() (aimet_torch.quantization.float.FloatQuantizeDequantize method) get_extra_state() (aimet_torch.quantization.float.FloatQuantizeDequantize method) get_fp_lora_layer() (aimet_torch.peft.PeftQuantUtils method) get_input_quantizer_modules() (aimet_torch.amp.quantizer_groups.QuantizerGroup method), [1], [2] get_kernel() (aimet_torch.nn.QuantizationMixin method) get_param_quantizers() (aimet_onnx.amp.quantizer_groups.QuantizerGroup method), [1] get_quant_scheme_candidates() (aimet_onnx.auto_quant_v2.AutoQuantWithAutoMixedPrecision method), [1] (aimet_tensorflow.keras.auto_quant_v2.AutoQuantWithAutoMixedPrecision method), [1] get_quantized_lora_layer() (aimet_torch.peft.PeftQuantUtils method) GreedySelectionParameters (class in aimet_common.defs), [1] I implements() (aimet_torch.nn.QuantizationMixin class method) Inference input_quantizers (aimet_torch.nn.QuantizationMixin attribute) INT8 is_bfloat16() (aimet_torch.quantization.float.FloatQuantizeDequantize method) is_float16() (aimet_torch.quantization.float.FloatQuantizeDequantize method) K KL Divergence L Layer Layer-wise quantization LayerOutputUtil (class in aimet_onnx.layer_output_utils), [1] (class in aimet_tensorflow.keras.layer_output_utils), [1] (class in aimet_torch.layer_output_utils), [1] load_checkpoint() (aimet_torch.quantsim method) (aimet_torch.v1.quantsim method) load_state_dict() (aimet_torch.quantization.float.FloatQuantizeDequantize method) lookup_quantizer() (aimet_tensorflow.keras.amp.quantizer_groups.QuantizerGroup static method), [1] LoRA MobileNet M manual (aimet_tensorflow.keras.defs.SpatialSvdParameters.Mode attribute), [1] MixedPrecisionConfigurator (class in aimet_torch.v2.mixed_precision), [1] Model ModelCompressor (class in aimet_tensorflow.keras.compress), [1] ModelValidator (class in aimet_torch.model_validator.model_validator) N NamingScheme (class in aimet_torch.layer_output_utils), [1] Neural Network Compression Framework new_empty() (aimet_torch.quantization.QuantizedTensorBase method) NNCF Node Normalization O ONNX (aimet_torch.layer_output_utils.NamingScheme attribute), [1] Open Neural Network Exchange optimize() (aimet_onnx.auto_quant_v2.AutoQuantWithAutoMixedPrecision method), [1] (aimet_tensorflow.keras.auto_quant_v2.AutoQuantWithAutoMixedPrecision method), [1] output_quantizers (aimet_torch.nn.QuantizationMixin attribute) P param_quantizers (aimet_torch.nn.QuantizationMixin attribute) PeftQuantUtils (class in aimet_torch.peft) Per-channel Quantization perform_per_layer_analysis_by_disabling_quant_wrappers() (aimet_torch.quant_analyzer.QuantAnalyzer method), [1] (aimet_torch.v1.quant_analyzer.QuantAnalyzer method) perform_per_layer_analysis_by_enabling_quant_wrappers() (aimet_torch.quant_analyzer.QuantAnalyzer method), [1] (aimet_torch.v1.quant_analyzer.QuantAnalyzer method) Post-Training Quantization post_training_percentile (aimet_common.defs.QuantScheme attribute), [1], [2], [3] post_training_tf (aimet_common.defs.QuantScheme attribute), [1], [2], [3] post_training_tf_enhanced (aimet_common.defs.QuantScheme attribute), [1], [2], [3] prepare_model() (in module aimet_tensorflow.keras.model_preparer) (in module aimet_torch.model_preparer) Pruning PTQ PyTorch PYTORCH (aimet_torch.layer_output_utils.NamingScheme attribute), [1] Q QAT QDO Qualcomm Innovation Center QuantAnalyzer (class in aimet_onnx.quant_analyzer), [1] (class in aimet_torch.quant_analyzer), [1] (class in aimet_torch.v1.quant_analyzer) Quantization Quantization Simulation Quantization-Aware Training QuantizationMixin (class in aimet_torch.nn) QuantizationSimModel (class in aimet_onnx.quantsim) (class in aimet_tensorflow.keras.quantsim) (class in aimet_torch.quantsim) (class in aimet_torch.v1.quantsim) Quantize (class in aimet_torch.quantization.affine) quantize() (aimet_torch.quantization.DequantizedTensor method) (aimet_torch.quantization.QuantizedTensor method) (aimet_torch.quantization.QuantizedTensorBase method) (in module aimet_torch.quantization.affine) quantize_dequantize() (in module aimet_torch.quantization.affine) quantize_lora_scale_with_fixed_range() (aimet_torch.peft.PeftQuantUtils method) quantized_repr() (aimet_torch.quantization.DequantizedTensor method) (aimet_torch.quantization.QuantizedTensor method) (aimet_torch.quantization.QuantizedTensorBase method) QuantizedAdaptiveAvgPool1d (class in aimet_torch.nn) QuantizedAdaptiveAvgPool2d (class in aimet_torch.nn) QuantizedAdaptiveAvgPool3d (class in aimet_torch.nn) QuantizedAdaptiveMaxPool1d (class in aimet_torch.nn) QuantizedAdaptiveMaxPool2d (class in aimet_torch.nn) QuantizedAdaptiveMaxPool3d (class in aimet_torch.nn) QuantizedAlphaDropout (class in aimet_torch.nn) QuantizedAvgPool1d (class in aimet_torch.nn) QuantizedAvgPool2d (class in aimet_torch.nn) QuantizedAvgPool3d (class in aimet_torch.nn) QuantizedBatchNorm1d (class in aimet_torch.nn) QuantizedBatchNorm2d (class in aimet_torch.nn) QuantizedBatchNorm3d (class in aimet_torch.nn) QuantizedBCELoss (class in aimet_torch.nn) QuantizedBCEWithLogitsLoss (class in aimet_torch.nn) QuantizedBilinear (class in aimet_torch.nn) QuantizedCELU (class in aimet_torch.nn) QuantizedChannelShuffle (class in aimet_torch.nn) QuantizedCircularPad1d (class in aimet_torch.nn) QuantizedCircularPad2d (class in aimet_torch.nn) QuantizedCircularPad3d (class in aimet_torch.nn) QuantizedConstantPad1d (class in aimet_torch.nn) QuantizedConstantPad2d (class in aimet_torch.nn) QuantizedConstantPad3d (class in aimet_torch.nn) QuantizedConv1d (class in aimet_torch.nn) QuantizedConv2d (class in aimet_torch.nn) QuantizedConv3d (class in aimet_torch.nn) QuantizedConvTranspose1d (class in aimet_torch.nn) QuantizedConvTranspose2d (class in aimet_torch.nn) QuantizedConvTranspose3d (class in aimet_torch.nn) QuantizedCosineEmbeddingLoss (class in aimet_torch.nn) QuantizedCosineSimilarity (class in aimet_torch.nn) QuantizedCrossEntropyLoss (class in aimet_torch.nn) QuantizedCTCLoss (class in aimet_torch.nn) QuantizedDropout (class in aimet_torch.nn) QuantizedDropout1d (class in aimet_torch.nn) QuantizedDropout2d (class in aimet_torch.nn) QuantizedDropout3d (class in aimet_torch.nn) QuantizedELU (class in aimet_torch.nn) QuantizedEmbedding (class in aimet_torch.nn) QuantizedEmbeddingBag (class in aimet_torch.nn) QuantizeDequantize (class in aimet_torch.quantization.affine) QuantizedFeatureAlphaDropout (class in aimet_torch.nn) QuantizedFlatten (class in aimet_torch.nn) QuantizedFold (class in aimet_torch.nn) QuantizedFractionalMaxPool2d (class in aimet_torch.nn) QuantizedFractionalMaxPool3d (class in aimet_torch.nn) QuantizedGaussianNLLLoss (class in aimet_torch.nn) QuantizedGELU (class in aimet_torch.nn) QuantizedGLU (class in aimet_torch.nn) QuantizedGroupNorm (class in aimet_torch.nn) QuantizedGRU (class in aimet_torch.nn) QuantizedGRUCell (class in aimet_torch.nn) QuantizedHardshrink (class in aimet_torch.nn) QuantizedHardsigmoid (class in aimet_torch.nn) QuantizedHardswish (class in aimet_torch.nn) QuantizedHardtanh (class in aimet_torch.nn) QuantizedHingeEmbeddingLoss (class in aimet_torch.nn) QuantizedHuberLoss (class in aimet_torch.nn) QuantizedInstanceNorm1d (class in aimet_torch.nn) QuantizedInstanceNorm2d (class in aimet_torch.nn) QuantizedInstanceNorm3d (class in aimet_torch.nn) QuantizedKLDivLoss (class in aimet_torch.nn) QuantizedL1Loss (class in aimet_torch.nn) QuantizedLayerNorm (class in aimet_torch.nn) QuantizedLeakyReLU (class in aimet_torch.nn) QuantizedLinear (class in aimet_torch.nn) QuantizedLocalResponseNorm (class in aimet_torch.nn) QuantizedLogSigmoid (class in aimet_torch.nn) QuantizedLogSoftmax (class in aimet_torch.nn) QuantizedLPPool1d (class in aimet_torch.nn) QuantizedLPPool2d (class in aimet_torch.nn) QuantizedLSTM (class in aimet_torch.nn) QuantizedLSTMCell (class in aimet_torch.nn) QuantizedMarginRankingLoss (class in aimet_torch.nn) QuantizedMaxPool1d (class in aimet_torch.nn) QuantizedMaxPool2d (class in aimet_torch.nn) QuantizedMaxPool3d (class in aimet_torch.nn) QuantizedMaxUnpool1d (class in aimet_torch.nn) QuantizedMaxUnpool2d (class in aimet_torch.nn) QuantizedMaxUnpool3d (class in aimet_torch.nn) QuantizedMish (class in aimet_torch.nn) QuantizedMSELoss (class in aimet_torch.nn) QuantizedMultiLabelMarginLoss (class in aimet_torch.nn) QuantizedMultiLabelSoftMarginLoss (class in aimet_torch.nn) QuantizedMultiMarginLoss (class in aimet_torch.nn) QuantizedNLLLoss (class in aimet_torch.nn) QuantizedNLLLoss2d (class in aimet_torch.nn) QuantizedPairwiseDistance (class in aimet_torch.nn) QuantizedPixelShuffle (class in aimet_torch.nn) QuantizedPixelUnshuffle (class in aimet_torch.nn) QuantizedPoissonNLLLoss (class in aimet_torch.nn) QuantizedPReLU (class in aimet_torch.nn) QuantizedReflectionPad1d (class in aimet_torch.nn) QuantizedReflectionPad2d (class in aimet_torch.nn) QuantizedReflectionPad3d (class in aimet_torch.nn) QuantizedReLU (class in aimet_torch.nn) QuantizedReLU6 (class in aimet_torch.nn) QuantizedReplicationPad1d (class in aimet_torch.nn) QuantizedReplicationPad2d (class in aimet_torch.nn) QuantizedReplicationPad3d (class in aimet_torch.nn) QuantizedRNN (class in aimet_torch.nn) QuantizedRNNCell (class in aimet_torch.nn) QuantizedRReLU (class in aimet_torch.nn) QuantizedSELU (class in aimet_torch.nn) QuantizedSigmoid (class in aimet_torch.nn) QuantizedSiLU (class in aimet_torch.nn) QuantizedSmoothL1Loss (class in aimet_torch.nn) QuantizedSoftMarginLoss (class in aimet_torch.nn) QuantizedSoftmax (class in aimet_torch.nn) QuantizedSoftmax2d (class in aimet_torch.nn) QuantizedSoftmin (class in aimet_torch.nn) QuantizedSoftplus (class in aimet_torch.nn) QuantizedSoftshrink (class in aimet_torch.nn) QuantizedSoftsign (class in aimet_torch.nn) QuantizedTanh (class in aimet_torch.nn) QuantizedTanhshrink (class in aimet_torch.nn) QuantizedTensor (class in aimet_torch.quantization) QuantizedTensorBase (class in aimet_torch.quantization) QuantizedThreshold (class in aimet_torch.nn) QuantizedTripletMarginLoss (class in aimet_torch.nn) QuantizedTripletMarginWithDistanceLoss (class in aimet_torch.nn) QuantizedUnflatten (class in aimet_torch.nn) QuantizedUnfold (class in aimet_torch.nn) QuantizedUpsample (class in aimet_torch.nn) QuantizedUpsamplingBilinear2d (class in aimet_torch.nn) QuantizedUpsamplingNearest2d (class in aimet_torch.nn) QuantizedZeroPad1d (class in aimet_torch.nn) QuantizedZeroPad2d (class in aimet_torch.nn) QuantizedZeroPad3d (class in aimet_torch.nn) QuantizerGroup (class in aimet_onnx.amp.quantizer_groups), [1] (class in aimet_tensorflow.keras.amp.quantizer_groups), [1] (class in aimet_torch.amp.quantizer_groups), [1], [2] QuantScheme (class in aimet_common.defs), [1], [2], [3] QuantSim QUIC R reestimate_bn_stats() (in module aimet_tensorflow.keras.bn_reestimation), [1] (in module aimet_torch.bn_reestimation), [1] replace_lora_layers_with_quantizable_layers() (aimet_torch.peft method) run_inference() (aimet_onnx.auto_quant_v2.AutoQuantWithAutoMixedPrecision method), [1] (aimet_tensorflow.keras.auto_quant_v2.AutoQuantWithAutoMixedPrecision method), [1] S save_checkpoint() (aimet_torch.quantsim method) (aimet_torch.v1.quantsim method) SeqMseParams (class in aimet_onnx.sequential_mse.seq_mse) (class in aimet_torch.seq_mse), [1] (class in aimet_torch.v1.seq_mse) set_activation_quantizers_to_float() (in module aimet_torch.v2.quantsim.config_utils) set_adaround_params() (aimet_onnx.auto_quant_v2.AutoQuantWithAutoMixedPrecision method), [1] (aimet_tensorflow.keras.auto_quant_v2.AutoQuantWithAutoMixedPrecision method), [1] set_bitwidth_for_lora_adapters() (aimet_torch.peft.PeftQuantUtils method) set_blockwise_quantization_for_weights() (in module aimet_torch.v2.quantsim.config_utils) set_default_kernel() (aimet_torch.nn.QuantizationMixin class method) set_extra_state() (aimet_torch.quantization.float.FloatQuantizeDequantize method) set_grouped_blockwise_quantization_for_weights() (in module aimet_onnx.quantsim) (in module aimet_torch.v2.quantsim.config_utils) set_kernel() (aimet_torch.nn.QuantizationMixin method) set_mixed_precision_params() (aimet_onnx.auto_quant_v2.AutoQuantWithAutoMixedPrecision method), [1] (aimet_tensorflow.keras.auto_quant_v2.AutoQuantWithAutoMixedPrecision method), [1] set_model_input_precision() (aimet_torch.v2.mixed_precision.MixedPrecisionConfigurator method), [1] set_model_output_precision() (aimet_torch.v2.mixed_precision.MixedPrecisionConfigurator method), [1] set_precision() (aimet_torch.v2.mixed_precision.MixedPrecisionConfigurator method), [1] set_quant_scheme_candidates() (aimet_onnx.auto_quant_v2.AutoQuantWithAutoMixedPrecision method), [1] (aimet_tensorflow.keras.auto_quant_v2.AutoQuantWithAutoMixedPrecision method), [1] set_quantizers_to_candidate() (aimet_onnx.amp.quantizer_groups.QuantizerGroup method), [1] (aimet_tensorflow.keras.amp.quantizer_groups.QuantizerGroup method), [1] (aimet_torch.amp.quantizer_groups.QuantizerGroup method), [1], [2] SpatialSvdParameters (class in aimet_tensorflow.keras.defs), [1] SpatialSvdParameters.AutoModeParams (class in aimet_tensorflow.keras.defs), [1] SpatialSvdParameters.ManualModeParams (class in aimet_tensorflow.keras.defs), [1] SpatialSvdParameters.Mode (class in aimet_tensorflow.keras.defs), [1] sqnr() (aimet_onnx.amp.mixed_precision_algo.EvalCallbackFactory method), [1] (aimet_torch.amp.mixed_precision_algo.EvalCallbackFactory method), [1], [2] T Target Hardware Accelerator Target Runtime TensorFlow to_list() (aimet_onnx.amp.quantizer_groups.QuantizerGroup method), [1] (aimet_tensorflow.keras.amp.quantizer_groups.QuantizerGroup method), [1] (aimet_torch.amp.quantizer_groups.QuantizerGroup method), [1], [2] TorchScript TORCHSCRIPT (aimet_torch.layer_output_utils.NamingScheme attribute), [1] track_lora_meta_data() (aimet_torch.peft method) training_range_learning_with_tf_enhanced_init (aimet_common.defs.QuantScheme attribute), [1], [2], [3] training_range_learning_with_tf_init (aimet_common.defs.QuantScheme attribute), [1], [2], [3] V validate_model() (aimet_torch.model_validator.model_validator.ModelValidator static method) Variant visualize_stats() (in module aimet_torch.v2.visualization_tools), [1] W Weights wrap() (aimet_torch.nn.QuantizationMixin class method)