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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 6: Mixed Precision Quantization Search with Mase and Optuna
Tutorial 7: Deploying a Model for Inference on Distributed Clusters
Tutorial 8: Autogenerating an FPGA accelerator for a Transformer Model
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.verilog
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
Mase Components
Hardware Documentation
Activations
GELU
SELU
SoftPlus
SoftSign
Tanh
Arithmetic Units
Multiply-Accumulate (MAC) Unit
AXI Components
AXI Read Master
Buffers
Hybrid Buffer
Linear Layer
Fixed-Point Linear Layer
Memory Components
Matrix Bank
Normalization
Batch Norm 2D
Group Norm 2D
Normalization Module
RMS Norm 2D
Systolic Modules
Output Stationary Systolic Module
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 (Hardware Stream) Emitting Hardware
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 (Hardware Stream) for Advanced Deep Learning Systems (ADLS)
Lab 4 (Software Stream) for Advanced Deep Learning Systems (ADLS)
ADLS Docker Environment Setup
.ipynb
.pdf
Tutorial 8: Autogenerating an FPGA accelerator for a Transformer Model
Tutorial 8: Autogenerating an FPGA accelerator for a Transformer Model
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