ampere_research/pytorch/analyze.py

#! /bin/python3

from data_stat import Stat, Cpu

import argparse
import os, glob
import re
import json

from enum import Enum

import math
import numpy as np
import matplotlib.pyplot as plt
import itertools

class Plot(Enum):
    BOX = 'box'
    LINE = 'line'

def accumulate(stats_list: list[dict[str, str | int | float]], category: str, value: str):
    print(category)
    print(value)
    category_list = np.array([stats[category] for stats in stats_list if value in stats])
    value_list = np.array([stats[value] for stats in stats_list if value in stats])

    result: dict[np.ndarray] = dict()
    for category in np.sort(np.unique(category_list)):
        result[category] = value_list[category_list == category]

    return result

def box_plot(ax, stats_list: list[dict[str, str | int | float]], x: Stat, y: Stat):
    data: dict[str, np.ndarray] = accumulate(stats_list, x, y)

    print("Plotted data: " + str(data))

    ax.boxplot(data.values(), tick_labels=data.keys())
    ax.set_ylabel(y.value)

def line_plot(
    ax, stats_list: list[dict[str, str | int | float]],
    x: Stat, y: Stat, color: Stat
):
    x_data: dict[str, np.ndarray] = accumulate(stats_list, color, x)
    y_data: dict[str, np.ndarray] = accumulate(stats_list, color, y)

    for category in x_data.keys():
        sorted_indices = np.argsort(x_data[category])
        x_data[category] = x_data[category][sorted_indices]
        y_data[category] = y_data[category][sorted_indices]
        ax.plot(x_data[category], y_data[category], label=category)
        print("Plotted x data: " + str(x_data[category]))
        print("Plotted y data: " + str(y_data[category]))

    ax.set_ylabel(y)
    ax.grid(True)

def visualize(
    stats_list: list[dict[str, str | int | float]],
    plot: Plot,
    rows: int,
    size_multiplier: int,
    font_size: int,
    x: Stat,
    y: Stat,
    color: Stat,
    filter_list: list[str] = []
):
    # Remove stats entries containing undesired values (like a specific CPU).
#    stats_list = [stats for stats in stats_list
#                      if len([stats[key] for key in stats.keys()
#                                        if stats[key] in filter_list]) == 0]

    #x = Stat.MAXWELL_SIZE
    #y = Stat.DTLB_MISS_RATE
    #color = Stat.SOLVER

    if y is None:
        #ys = [stat for stat in Stat if stat.value in stats_list[0].keys()
        ys = [stat for stat in stats_list[0].keys() if "power" not in stat]
                                    #and stat is not x
                                    #and y != color
                                    #and y != marker
                                    #and stat.value not in filter_list]
        fig, axes = plt.subplots(rows, int(math.ceil(len(ys) / rows)),
                                 figsize = (16 * size_multiplier, 9 * size_multiplier))
        match plot:
            case Plot.BOX:
                for i, y in enumerate(ys):
                    box_plot(axes[i % rows][int(i / rows)], stats_list, x, y)
            case Plot.LINE:
                for i, y in enumerate(ys):
                    line_plot(axes[i % rows][int(i / rows)], stats_list, x, y, color)

        handles, labels = axes[i % rows][int(i / rows)].get_legend_handles_labels()
    else:
        fig, ax = plt.subplots()

        match plot:
            case Plot.BOX:
                box_plot(ax, stats_list, x, y)
            case Plot.LINE:
                line_plot(ax, stats_list, x, y, color)

        handles, labels = ax.get_legend_handles_labels()

    #box_plot(ax, stats, x, y)
    #line_plot(ax, stats, x, y, color)

    match plot:
        case Plot.BOX:
            title = f"{plot.value}_plot_of_{y.value.replace(' ', '_')}_vs_{x.value.replace(' ', '_')}_excluding_{filter_list}"
        case Plot.LINE:
            #title = f"{plot.value}_plot_of_{y.replace(' ', '_')}_vs_{x.replace(' ', '_')}_by_{color.replace(' ', '_')}_excluding_{filter_list}"
            title = "altra_spmv"
    fig.suptitle(title, fontsize = font_size)
    fig.legend(handles, labels, fontsize = font_size)
    fig.supxlabel(x, fontsize = font_size)

    plt.savefig(title + ".png", dpi = 100)
    plt.show()

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('input_dir',
                        help='the input directory')
    parser.add_argument('-p', '--plot',
                        choices=[x.name.lower() for x in Plot],
                        help = 'the type of plot')
    parser.add_argument('-r', '--rows', type=int,
                        help = 'the number of rows to display when -y is not specified',
                        default = 5)
    parser.add_argument('-s', '--size', type=int,
                        help = 'figure size multiplier',
                        default = 4)
    parser.add_argument('-fs', '--font_size', type=int,
                        help = 'font size',
                        default = 40)
    parser.add_argument('-x',
                        #choices=[x.name.lower() for x in Stat],
                        help = 'the name of the x axis')
    parser.add_argument('-y',
                        #choices=[x.name.lower() for x in Stat],
                        help = 'the name of the y axis')
    parser.add_argument('-c', '--color',
                        #choices=[x.name.lower() for x in Stat],
                        help = 'the name of the color')
    parser.add_argument('-f', '--filter', nargs = '+',
                        help = 'a comma-separated string of names and values to filter out.',
                        default = [])
    args = parser.parse_args()
    args.plot = Plot[args.plot.upper()]

    data_list: list[dict] = list()

    for filename in glob.glob(f'{args.input_dir.rstrip("/")}/*.json'):
        with open(filename, 'r') as file:
            data_list.append(json.load(file))
        print(filename + " loaded.")

    print(data_list)
    #x = Stat[args.x.upper()] if args.x is not None else None
    x = args.x
    #y = Stat[args.y.upper()] if args.y is not None else None
    y = args.y
    #color = Stat[args.color.upper()] if args.color is not None else None
    color = args.color
    visualize(data_list, args.plot, args.rows, args.size, args.font_size, x, y, color, args.filter)

if __name__ == '__main__':
    main()