Implementation of Block Floating Point supporting the Pytorch 2.0 export quantization route.

• Contains files for creating preparing and creating the graphs for Quantization of PyTorch modules

• Contains methods for preparing a given GraphModulewith the observers and Quantizers

• bfp.py: Contains Triton kernels supporting Stochastic, Nearest, Up and Down quantization of a given FP32 tensor
• bfp_ops.py: Contains helper functions and the _quantize_bfp function which decide the quantization is to be done in CUDA or Triton
• common_lib.py: Contains functions for sanity checks (check_diff) and other helper functions
• custom_extensions.py: Python interface for CUDA implementations of the quantization kernels
• elemwise_ops.py: Contains helper functions for bitwise manipulations for teh Python implementation of the MX kernels
• formats.py: Contains helper functions to get the max_norm, min_norm, exponent bits and mantissa bits of a given format
• funcs.py: Contains wrapper functions to call the main functions for MX (in here) and BFP (in here) quantization
• mx.py: Contains the Triton kernel suppporting MX quantization
• mx_ops.py: Contains helper functions and the _quantize_mx function which decide the quantization is to be done in Python, CUDA or Triton
• quantize.py: Contains helper functions for bitshifting and performing the actual quantization on the given blocksize of the triton kernel

Contains files used for initial experimentations

• fake_quantize.py: Contains FakeQuantize constructors which supports the BlockFloatingPoint dtype
• observer.py: Implemented more observers inheriting from DMXBaseObserver
• qconfig_mapping.py: Contains classes and functions for setting the Quantization Config Mapping of given model
• qconfig.py: Describes how to quantize a layer or a part of the network by providing settings (pt2bfp observer classes) for activations and weights respectively
• quantization_mapping.py: Contains the mapping for static and dynamic quantized modules
• quantize_fx.py: Contains the prepare and convert modules which takes a graph module, adds observers and calibrates it according to the mapping
• quantize_pt2e.py: Contains initial wrapper functions to prepare (prepare_pt2e) and convert (convert_pt2e) the model for configuration and quantization
• quantize.py: Prepare and convert submodules of the input module based on the config and calibrations
• quantizer.py: Contains classe definitions for the BFP annotation specs and BFP quantizer
• utils.py: Constains helper functions for annotation, fucntions to add qspecs for the input, weight and bias.
• x86_inductor_quantizer.py: Quantizer which can lower model to inductor level, not used yet, need to be validated

• test_benchmark.py: Test suite for for benchmarking the triton kernels across various BLOCKSIZEs and warps
• test_bfp_ptq.py: Testing the bfp quantization with the triton kernels inttegrated (work in progress)
• test_fake_bfp.py: Testing the fake bfp quantization flow for various models - supports all linear layers. Need to add support for gpt models (conv1d layers)
• test_fake_ptq.py: Initial test for fake ptq with unit8
• test_quantize_bfp.py: Tests for logical similarity of quantized representation of the Triton generated tensor with the CUDA generated BFP tensor
• test_quantize_mx.py: Tests for logical similarity of quantized representation of the Triton generated tensor with the CUDA and Python generated MX tensor
• test_toy_ptq.py: Another test for ptq using the PyTorch 2.0 flow