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MASE 0.0.1 documentation
Overview
Installation
Getting Started using uv
Getting Started using Docker
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
Tutorial 7: Deploying a Model for Inference on Distributed Clusters
Tutorial 9: Running Kernel Fusion for Inference Acceleration on GPUs
Advanced: TensorRT Quantization Tutorial
Advanced: ONNX Runtime Tutorial
Advanced: Using Mase CLI
Developer: Guide on how to add a new model into Machop
Developer: How to write documentations in MASE
Developer: How to extend search
Repository Health
Coding Style Specifications
C/C++ Coding Style Specifications
Python Coding Style Specifications
Verilog Coding Style Specifications
Machop API
Machop 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
.pdf
chop.distributed
chop.distributed
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To be finished…