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1.35.1
  • Installation
    • Quick Install
    • Release Packages
    • System Requirements
    • Advanced Installation Instructions
      • Install in Host Machine
        • Install prerequisite packages
        • Install GPU packages
          • Install GPU packages for PyTorch 2.1 or PyTorch 1.13 or ONNX or TensorFlow
        • Install AIMET packages
          • From PyPI
          • From Release Package
        • Install common debian packages
        • Install tensorflow GPU debian packages
        • Install torch GPU debian packages
        • Install ONNX GPU debian packages
        • Replace Pillow with Pillow-SIMD
        • Replace onnxruntime with onnxruntime-gpu
        • Post installation steps
        • Environment setup
      • Install in Docker Container
        • Set variant
        • Use prebuilt docker image
        • Build docker image locally
        • Start docker container
        • Install AIMET packages
          • From PyPI
          • From Release Package
        • Environment setup
  • Quantization User Guide
    • Use cases
    • AIMET quantization features
      • Quantization Simulation
        • Overview
        • QuantSim workflow
        • Simulating quantization noise
        • Determining quantization parameters (encodings)
        • Quantization schemes
        • Configuring quantization simulation operations
        • Quantization Simulation APIs
      • Quantization-Aware Training (QAT)
        • Overview
        • QAT workflow
        • QAT modes
        • Recommendations for quantization-aware training
      • Post-Training Quantization
        • AutoQuant
          • Overview
          • Workflow
          • AutoQuant API
        • Adaptive Rounding (AdaRound)
          • AdaRound use cases
          • AdaRound hyper parameters guidelines
          • AdaRound API
        • Cross-Layer Equalization
          • Overview
          • User Flow
          • Cross-Layer Equalization API
          • FAQs
          • References
        • BN Re-estimation
          • Overview
          • Workflow
          • BN Re-estimation API
        • Bias Correction [Deprecated]
          • Overview
          • User Flow
          • Cross-Layer Equalization API
          • FAQs
          • References
      • Debugging and Analysis Tools
        • QuantAnalyzer
          • Overview
          • Requirements
          • Detailed analysis descriptions
          • QuantAnalyzer API
        • Visualizations
          • Overview
          • Quantization
            • PyTorch
            • TensorFlow
    • AIMET quantization workflow
      • PyTorch
    • Debugging
      • Quantization Diagnostics
  • Compression User Guide
    • Overview
      • Compression Guidebook
    • Use Case
    • Compression ratio selection
      • Greedy compression ratio selection
        • Overview
        • How it works
        • Per-layer exploration
        • Compression ratio selection
      • Visualization
        • Overview
        • Design
        • Compression
        • Starting a Bokeh server session
        • Visualizing compression ratios
    • Model compression
      • Weight SVD
      • Spatial SVD
      • Channel pruning
        • Procedure
        • Channel selection
        • Winnowing
          • Winnowing
            • Overview
            • Winnowing overview
            • How winnowing works
        • Weight reconstruction
    • Optional techniques
      • Rank Rounding
      • Per-layer fine-tuning
    • FAQs
    • References
  • API Documentation
    • AIMET APIs for PyTorch
      • PyTorch Model Quantization API
        • aimet_torch
          • API Reference
            • Model Guidelines
            • Architecture Checker API
            • Model Preparer API
              • Top-level API
              • Code Examples
              • Limitations of torch.fx symbolic trace API
            • Model Validator API
            • Quant Analyzer API
              • User Guide Link
              • Examples Notebook Link
              • Top-level API
                • CallbackFunc
              • Run specific utility
              • Code Examples
            • Quantization Simulation API
              • User Guide Link
              • Examples Notebook Link
              • Guidelines
              • Top-level API
              • Enum Definition
                • QuantScheme
                  • QuantScheme.post_training_percentile
                  • QuantScheme.post_training_tf
                  • QuantScheme.post_training_tf_enhanced
                  • QuantScheme.training_range_learning_with_tf_enhanced_init
                  • QuantScheme.training_range_learning_with_tf_init
              • Code Example - Quantization Aware Training (QAT)
            • Adaptive Rounding API
              • User Guide Link
              • Examples Notebook Link
              • Top-level API
              • Adaround Parameters
              • Enum Definition
                • QuantScheme
                  • QuantScheme.post_training_percentile
                  • QuantScheme.post_training_tf
                  • QuantScheme.post_training_tf_enhanced
                  • QuantScheme.training_range_learning_with_tf_enhanced_init
                  • QuantScheme.training_range_learning_with_tf_init
              • Code Example - Adaptive Rounding (AdaRound)
            • Cross-Layer Equalization API
              • User Guide Link
              • Examples Notebook Link
              • Introduction
              • Cross Layer Equalization API
              • Code Example
              • Primitive APIs
                • Primitive APIs for Cross Layer Equalization
                  • Introduction
                  • ClsSetInfo Definition
                  • Higher Level APIs for Cross Layer Equalization
                  • Code Examples for Higher Level APIs
                  • Lower Level APIs for Cross Layer Equalization
                  • Code Examples for Lower Level APIs
            • Bias Correction API
              • User Guide Link
              • Bias Correction API
              • ConvBnInfoType
                • ConvBnInfoType
              • ActivationType
                • ActivationType
                  • ActivationType.no_activation
                  • ActivationType.relu
                  • ActivationType.relu6
              • Quantization Params
              • Code Example #1 Empirical Bias Correction
              • Code Example #2 Analytical + Empirical Bias correction
            • AutoQuant API
              • User Guide Link
              • Examples Notebook Link
              • Top-level API
              • Code Examples
            • BN Re-estimation APIs
              • Examples Notebook Link
              • Introduction
              • Top-level APIs
              • Code Example - BN-Reestimation
            • Multi-GPU guidelines
            • PEFT LoRA APIs
              • User flow
              • Top-level API
        • aimet_torch.v2
          • What’s New
          • Backwards Compatibility
          • API Reference
            • Quantized Modules
              • Top-level API
              • Configuration
              • Computing Encodings
              • Quantized Module Classes
            • Quantizers
              • Top-level API
            • QuantizationMixin
            • quantization.affine
              • Classes
              • Functions
            • quantization.float
              • Classes
            • Encoding Analyzers
              • Variants
            • Visualization Tools
      • PyTorch Model Compression API
        • Introduction
        • Top-level API for Compression
        • Greedy Selection Parameters
          • GreedySelectionParameters
        • Spatial SVD Configuration
        • Weight SVD Configuration
        • Channel Pruning Configuration
        • Configuration Definitions
        • Code Examples
      • PyTorch Model Visualization API for Compression
        • Top-level API Compression
        • Code Examples
      • PyTorch Model Visualization API for Quantization
        • Top-level API Quantization
        • Code Examples
      • PyTorch Debug API
        • Top-level API
        • Enum Definition
        • Code Example
    • AIMET APIs for TensorFlow
      • TensorFlow Model Quantization API
        • Model Guidelines
        • Model Preparer API
          • Top-level API
          • Code Examples
          • Limitations
        • Quant Analyzer API
          • Top-level API
          • Code Examples
        • Quantization Simulation API
          • User Guide Link
          • Top-level API
          • Code Examples
        • Adaptive Rounding API
          • User Guide Link
          • Examples Notebook Link
          • Top-level API
          • Adaround Parameters
          • Enum Definition
            • QuantScheme
              • QuantScheme.post_training_percentile
              • QuantScheme.post_training_tf
              • QuantScheme.post_training_tf_enhanced
              • QuantScheme.training_range_learning_with_tf_enhanced_init
              • QuantScheme.training_range_learning_with_tf_init
          • Code Examples
        • Cross-Layer Equalization API
          • User Guide Link
          • Examples Notebook Link
          • Introduction
          • Cross Layer Equalization API
          • Code Example
          • Primitive APIs
            • Primitive APIs for Cross Layer Equalization
              • Introduction
              • Higher Level APIs for Cross Layer Equalization
              • Code Examples for Higher Level APIs
              • Lower Level APIs for Cross Layer Equalization
              • Custom Datatype used
              • Code Example for Lower level APIs
              • Example helper methods to perform CLE in manual mode
        • BN Re-estimation APIs
          • Examples Notebook Link
          • Introduction
          • Top-level APIs
          • Code Example
          • Limitations
      • TensorFlow Debug API
        • Top-level API
        • Code Example
      • TensorFlow Model Compression API
        • Introduction
        • Top-level API for Compression
        • Greedy Selection Parameters
        • Spatial SVD Configuration
        • Configuration Definitions
          • CostMetric
            • CostMetric.mac
            • CostMetric.memory
          • CompressionScheme
            • CompressionScheme.channel_pruning
            • CompressionScheme.spatial_svd
            • CompressionScheme.weight_svd
        • Code Examples
    • AIMET APIs for ONNX
      • ONNX Model Quantization API
        • Quantization Simulation API
          • Top-level API
            • QuantizationSimModel
              • QuantizationSimModel.compute_encodings()
              • QuantizationSimModel.export()
          • Code Examples
        • Cross-Layer Equalization API
          • User Guide Link
          • Introduction
          • Cross Layer Equalization API
            • equalize_model()
          • Code Example
        • Adaptive Rounding API
          • User Guide Link
          • Top-level API
            • apply_adaround()
          • Adaround Parameters
            • AdaroundParameters
          • Code Example - Adaptive Rounding (AdaRound)
        • AutoQuant API
          • User Guide Link
          • Top-level API
            • AutoQuant
              • AutoQuant.run_inference()
              • AutoQuant.optimize()
              • AutoQuant.set_adaround_params()
              • AutoQuant.get_quant_scheme_candidates()
              • AutoQuant.set_quant_scheme_candidates()
          • Code Examples
        • QuantAnalyzer API
          • Top-level API
            • QuantAnalyzer
              • QuantAnalyzer.enable_per_layer_mse_loss()
              • QuantAnalyzer.analyze()
          • Run specific utility
            • QuantAnalyzer.create_quantsim_and_encodings()
            • QuantAnalyzer.check_model_sensitivity_to_quantization()
            • QuantAnalyzer.perform_per_layer_analysis_by_enabling_quantizers()
            • QuantAnalyzer.perform_per_layer_analysis_by_disabling_quantizers()
            • QuantAnalyzer.export_per_layer_encoding_min_max_range()
            • QuantAnalyzer.export_per_layer_stats_histogram()
            • QuantAnalyzer.export_per_layer_mse_loss()
          • Code Examples
      • ONNX Debug API
        • Top-level API
          • LayerOutputUtil
            • LayerOutputUtil.generate_layer_outputs()
        • Code Example
    • Indices and tables
  • Examples Documentation
    • Browse the notebooks
    • Running the notebooks
      • 1. Run the notebook server
      • 2. Download the example notebooks and related code
      • 3. Run the notebooks
AI Model Efficiency Toolkit
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