Py65 65816 - Add support for the 65C816 to py65

Overview

Add support for the 65C816 to py65

Py65 (https://github.com/mnaberez/py65) is a great simulator for the 6502. Recently I added support for interrupts (https://github.com/tmr4/py65_int) and a debug window (https://github.com/tmr4/py65_debug_window). After success with these modifications, I decided to try adding support for the 65C816. Luckily, py65 is open-source and enhancing it isn't very difficult.

This repository provides a framework for adding support for the 65C816 to py65. I've included the modules I've developed to simulate and test the 65C816. As noted below, a few modifications are needed to the core py65 modules as well.

Screenshot

Screenshot of py65 running Liara Forth on a simulated 65C816

Contents

I've included the main device module, mpu65c816.py, to add simulation support for the 65C816 to py65. I've also include several modules for testing the 65C816 simulation. These include the main unit test module, test_mpu65c816.py, and support modules, test_mpu65816_Common6502.py and test_mpu65816_Common65c02.py, derived largely from similarly named py65 test modules, to test the 65C816 emulation mode simulation. I've also included a binary file, liara.bin, that I modified from Scot W. Stevenson's Liara Forth (https://github.com/scotws/LiaraForth) to work with py65 simulating the 65C816. Note that I'm a Python newbie and appreciate any feedback to make these better.

  • mpu65c816.py

The 65C816 device.

  • test_mpu65c816.py

The main unit test module for the 65C816.

  • test_mpu65816_Common6502.py

Unit tests for 65C816 emulation mode.

  • test_mpu65816_Common65c02.py

Additional 65C02 based unit tests for 65C816 emulation mode.

  • liara.bin

A modified version of Scot W. Stevenson's Liara Forth (https://github.com/scotws/LiaraForth) for testing. Liara Forth is designed to run on the Western Design Center's W65C265SXB development board (https://www.westerndesigncenter.com/wdc/documentation/W65C265SXB.pdf). I've modified the Liara Forth binary to interface with alternate I/O addresses rather than those used by the development board.

Modifications to core py65 modules

The following modifications are needed for py65 to simulate the 65C816:

  1. monitor.py
  • Add a reference to new 65C816 MPU class from devices.mpu65c816 import MPU as CMOS65C816
  • Add the '65C816': CMOS65C816 pair to the Microprocessors dictionary.

License

The mpu65c816.py, test_mpu65816_Common6502.py and test_mpu65816_Common65c02.py modules contain large portions of code from or derived from py65 which is covered by a BSD 3-Clause License. I've included that license as required.

Running the 65C816 Unit Tests

You can run the unit tests with python -m unittest test_mpu65c816.py. The 65C816 simulation passes the py65 6502- and 65C02-based test (507 in total) in emulation mode. Some of tests were modified to run properly with the new device. I still have to create the tests for native mode operations (not a small task). I expect these to take some time and I expect these will turn up many errors in my code.

Testing the 65C816 Simulation with Liara Forth

It wasn't easy to find a sizable program to test with the new 65C816 simulation. You can run the slightly modified version of Liara Forth with python monitor.py -m 65c816 -l liara.bin -g 5000 -i fff0 -o fff1.

Limitations

  1. The new 65C816 device is largely untested. I plan to update it as I work on supporting hardware and code. Use at your own risk. Some know issues:
  • FIXED: ROL and ROR haven't been updated for a 16 bit accumulator.
  • Extra cycle counts haven't been considered for any new to 65816 opcodes.
  • ADC and SBC in decimal mode are likely invalid in 16 bit.
  • Native mode hasn't been tested outside of bank 0. Assume it will fail for this until it is tested. Currently only 3 banks of memory are modeled, by py65 default, but this can easily be changed.
  • The simulation is meant to emulate the actual W65C816. Modelling so far has been based on the 65816 Programming Manual only. I intend to test at least some code against the W65C265SXB development board.
  • Currently no way to break to the py65 monitor.
  • Register wrapping of Direct page addressing modes need tested.
  1. While Liara Forth runs in py65 with the new 65C816 device, it isn't hard to make it crash. I believe this is due to my code, rather than Liara Forth, even though it is marked as an ALPHA version. Liara Forth runs entirely in bank 0. There is no way to break to the monitor since Liara Forth was designed to run on hardware only. It can only be ended with a control-C.

  2. I've successfully run a non-interrupt version of my own 6502 Forth in the new 65C816 device in emulation mode. This isn't surprising since much of the code comes from py65 6502 and 65C02 devices. I expect an interrupt version of it will run as well, but I haven't tested this. I expect that many 6502 programs will run in emulation mode. Note however, that there are differences between the 65C816 operating in emulation mode and the 6502/65C02 that could cause problems with your program.

Status

  • Initial commit: January 11, 2022
  • Successfully tested my 65C02 Forth in emulation mode
  • Was able to run Liara Forth in native mode in block 0.
    • FIXED: (Many words cause it to crash (likely due to one of the limitations listed above).)
    • Currently all numbers print out as 0. Unclear why.

Next Steps

  • Resolve simulator issues with running Liara Forth. I view this as a robust test of the 65816 simulator, other than bank switching, which Liara Forth doesn't handle out of the box. Some hardware specific Liara Forth features will not work with the simulator (KEY? for example which is hardwired to a W65C265SXB development board specific address indicating whether a key has been pressed).
  • Add native mode unit tests.
Kinky furry assitant based on GPT2

KinkyFurs-V0 Kinky furry assistant based on GPT2 How to run python3 V0.py then, open web browser and go to localhost:8080 Requirements: Flask trans

Sparki 1 Jun 11, 2022
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing

Introduction Funnel-Transformer is a new self-attention model that gradually compresses the sequence of hidden states to a shorter one and hence reduc

GUOKUN LAI 197 Dec 11, 2022
A paper list of pre-trained language models (PLMs).

Large-scale pre-trained language models (PLMs) such as BERT and GPT have achieved great success and become a milestone in NLP.

RUCAIBox 124 Jan 02, 2023
SurvTRACE: Transformers for Survival Analysis with Competing Events

⭐ SurvTRACE: Transformers for Survival Analysis with Competing Events This repo provides the implementation of SurvTRACE for survival analysis. It is

Zifeng 13 Oct 06, 2022
A Lightweight NLP Data Loader for All Deep Learning Frameworks in Python

LineFlow: Framework-Agnostic NLP Data Loader in Python LineFlow is a simple text dataset loader for NLP deep learning tasks. LineFlow was designed to

TofuNLP 177 Jan 04, 2023
Malware-Related Sentence Classification

Malware-Related Sentence Classification This repo contains the code for the ICTAI 2021 paper "Enrichment of Features for Malware-Related Sentence Clas

Chau Nguyen 1 Mar 26, 2022
🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.

English | 简体中文 | 繁體中文 State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained mo

Hugging Face 77.2k Jan 03, 2023
translate using your voice

speech-to-text-translator Usage translate using your voice description this project makes translating a word easy, all you have to do is speak and...

1 Oct 18, 2021
PatrickStar enables Larger, Faster, Greener Pretrained Models for NLP. Democratize AI for everyone.

PatrickStar enables Larger, Faster, Greener Pretrained Models for NLP. Democratize AI for everyone.

Tencent 633 Dec 28, 2022
CCF BDCI BERT系统调优赛题baseline(Pytorch版本)

CCF BDCI BERT系统调优赛题baseline(Pytorch版本) 此版本基于Pytorch后端的huggingface进行实现。由于此实现使用了Oneflow的dataloader作为数据读入的方式,因此也需要安装Oneflow。其它框架的数据读取可以参考OneflowDataloade

Ziqi Zhou 9 Oct 13, 2022
Natural Language Processing library built with AllenNLP 🌲🌱

Custom Natural Language Processing with big and small models 🌲🌱

Recognai 65 Sep 13, 2022
Dust model dichotomous performance analysis

Dust-model-dichotomous-performance-analysis Using a collated dataset of 90,000 dust point source observations from 9 drylands studies from around the

1 Dec 17, 2021
The model is designed to train a single and large neural network in order to predict correct translation by reading the given sentence.

Neural Machine Translation communication system The model is basically direct to convert one source language to another targeted language using encode

Nishant Banjade 7 Sep 22, 2022
Rhyme with AI

Local development Create a conda virtual environment and activate it: conda env create --file environment.yml conda activate rhyme-with-ai Install the

GoDataDriven 28 Nov 21, 2022
LSTM based Sentiment Classification using Tensorflow - Amazon Reviews Rating

LSTM based Sentiment Classification using Tensorflow - Amazon Reviews Rating (Dataset) The dataset is from Amazon Review Data (2018)

Immanuvel Prathap S 1 Jan 16, 2022
open-information-extraction-system, build open-knowledge-graph(SPO, subject-predicate-object) by pyltp(version==3.4.0)

中文开放信息抽取系统, open-information-extraction-system, build open-knowledge-graph(SPO, subject-predicate-object) by pyltp(version==3.4.0)

7 Nov 02, 2022
CCKS-Title-based-large-scale-commodity-entity-retrieval-top1

- 基于标题的大规模商品实体检索top1 一、任务介绍 CCKS 2020:基于标题的大规模商品实体检索,任务为对于给定的一个商品标题,参赛系统需要匹配到该标题在给定商品库中的对应商品实体。 输入:输入文件包括若干行商品标题。 输出:输出文本每一行包括此标题对应的商品实体,即给定知识库中商品 ID,

43 Nov 11, 2022
Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech (BVAE-TTS)

Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech (BVAE-TTS) Yoonhyung Lee, Joongbo Shin, Kyomin Jung Abstract: Although early

LEE YOON HYUNG 147 Dec 05, 2022
Must-read papers on improving efficiency for pre-trained language models.

Must-read papers on improving efficiency for pre-trained language models.

Tobias Lee 89 Jan 03, 2023
HAIS_2GNN: 3D Visual Grounding with Graph and Attention

HAIS_2GNN: 3D Visual Grounding with Graph and Attention This repository is for the HAIS_2GNN research project. Tao Gu, Yue Chen Introduction The motiv

Yue Chen 1 Nov 26, 2022