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MASE 0.0.1 documentation

Overview

  • Installation
    • Getting Started using Conda
    • Getting Started using Docker
    • Getting Started using Nix
    • Additional Instructions for Imperial College Students
  • Quickstart
  • Tutorials
    • Tutorial 1: Introduction to the Mase IR, MaseGraph and Torch FX passes
    • Tutorial 2: Finetuning Bert for Sequence Classification using a LoRA adapter
    • Tutorial 3: Running Quantization-Aware Training (QAT) on Bert
    • Tutorial 4: Unstructured Pruning on Bert
    • Tutorial 5: Neural Architecture Search (NAS) with Mase and Optuna
    • Tutorial 6: Mixed Precision Quantization Search with Mase and Optuna
    • Advanced: TensorRT Quantization Tutorial
    • Advanced: ONNX Runtime Tutorial
    • Advanced: Using Mase CLI
    • Developer: Guide on how to add a new model into Chop
    • Developer: How to write documentations in MASE
    • Developer: How to extend search
  • Coding Style Specifications
    • Python Coding Style Specifications

Chop API

  • Chop Documentation
    • chop.actions
    • chop.datasets
    • chop.distributed
    • chop.ir
    • chop.models
    • chop.nn
    • chop.nn.quantized
      • chop.nn.quantized.functional
      • chop.nn.quantized.modules
    • chop.passes
      • chop.passes.module
        • chop.passes.module.transform.quantize
        • chop.passes.module.transform.quantize
      • chop.passes.graph
        • chop.passes.graph.analysis.add_metadata
        • chop.passes.graph.analysis.autosharding
        • chop.passes.graph.analysis.init_metadata
        • chop.passes.graph.analysis.report
        • chop.passes.graph.analysis.statistical_profiler.profile_statistics
        • chop.passes.graph.analysis.verify.verify
        • chop.passes.graph.calculate_avg_bits_mg_analysis_pass
        • chop.passes.graph.pruning
        • chop.passes.graph.analysis.runtime
        • chop.passes.transform.pruning
        • chop.passes.transform.quantize
        • chop.passes.transform.utils
        • chop.passes.transform.tensorrt
        • chop.passes.interface.save_and_load
        • chop.passes.interface.tensorrt
        • chop.passes.interface.onnxrt
    • chop.pipelines
    • chop.tools

Advanced Deep Learning Systems

  • Advanced Deep Learning Systems: 2024/2025
    • Lab 0: Introduction to Mase
    • Lab 1: Model Compression (Quantization and Pruning)
    • Lab 2: Neural Architecture Search
    • Lab 3: Mixed Precision Search
    • Lab 4 (Software Stream) Performance Engineering
    • ADLS Docker Environment Setup
  • Advanced Deep Learning Systems: 2023/2024
    • Lab 1 for Advanced Deep Learning Systems (ADLS)
    • Lab 2 for Advanced Deep Learning Systems (ADLS)
    • Lab 3 for Advanced Deep Learning Systems (ADLS)
    • Lab 4 (Software Stream) for Advanced Deep Learning Systems (ADLS)
    • ADLS Docker Environment Setup
  • .rst

chop.nn.quantized

chop.nn.quantized#

Contents:

  • chop.nn.quantized.functional
    • chop.nn.quantized.functional.add
    • chop.nn.quantized.functional.gelu
    • chop.nn.quantized.functional.matmul
    • chop.nn.quantized.functional.mult
    • chop.nn.quantized.functional.relu
    • chop.nn.quantized.functional.selu
    • chop.nn.quantized.functional.softermax
    • chop.nn.quantized.functional.softplus
    • chop.nn.quantized.functional.softsign
    • chop.nn.quantized.functional.sub
    • chop.nn.quantized.functional.tanh
  • chop.nn.quantized.modules
    • chop.nn.quantized.modules.attention
    • chop.nn.quantized.modules.attention_head
    • chop.nn.quantized.modules.batch_norm1d
    • chop.nn.quantized.modules.batch_norm2d
    • chop.nn.quantized.modules.conv1d
    • chop.nn.quantized.modules.conv2d
    • chop.nn.quantized.modules.gelu
    • chop.nn.quantized.modules.group_norm
    • chop.nn.quantized.modules.instance_norm2d
    • chop.nn.quantized.modules.layer_norm
    • chop.nn.quantized.modules.linear
    • chop.nn.quantized.modules.max_pool2d
    • chop.nn.quantized.modules.relu
    • chop.nn.quantized.modules.rms_norm
    • chop.nn.quantized.modules.selu
    • chop.nn.quantized.modules.silu
    • chop.nn.quantized.modules.softplus
    • chop.nn.quantized.modules.softsign
    • chop.nn.quantized.modules.tanh
    • chop.nn.quantized.utils

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