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alexmr09
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.ticker as ticker
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from brokenaxes import brokenaxes
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def find_pareto_optimal_points(latencies, mpq_accuracies, mpq_confs):
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"""
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Finds the Pareto optimal points given latencies and accuracies.
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Args:
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latencies (numpy.ndarray): An array of latency values.
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accuracies (numpy.ndarray): An array of accuracy values.
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mpq_confs (numpy.ndarray): An array with the sorted weight_bit_width per layer list based on
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estimated latency
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Returns:
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tuple: A tuple containing the following:
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- numpy.ndarray: Array of Pareto optimal points (Latency, Accuracy).
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- numpy.ndarray: Indices of Pareto optimal accuracies in the original array.
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"""
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data = {
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'Configuration': [mpq_confs[idx].astype(int) for idx in range(len(mpq_confs))],
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'Accuracy': mpq_accuracies,
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'MACC Instrs': latencies
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}
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# Combine latencies, accuracies, and indices
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combined = np.column_stack((latencies, mpq_accuracies, np.arange(len(mpq_accuracies))))
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# Initialize the Pareto front with the first point
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pareto_front = [combined[0]]
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for pair in combined[1:]:
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# Check if the accuracy of the current pair is better than the last point in the Pareto front
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if pair[1] > pareto_front[-1][1]:
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pareto_front.append(pair)
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# Extract Pareto points and indices
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pareto_points_with_indices = np.array(pareto_front)
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pareto_points = pareto_points_with_indices[:, :2]
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pareto_indices = pareto_points_with_indices[:, 2].astype(int)
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# Set print options for clarity
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np.set_printoptions(precision = 2, suppress = True)
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# Create DataFrame
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data = {
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'Index': pareto_indices,
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'Weights Resolutions': [mpq_confs[idx].astype(int) for idx in pareto_indices],
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'Accuracy': [point[1] for point in pareto_points],
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'MACC Instrs': [point[0] for point in pareto_points]
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}
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df = pd.DataFrame(data)
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return df, pareto_points, pareto_indices
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def identify_best_solution(pareto_solutions_df, fp_accuracy, maximum_acc_loss = 1):
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threshold = fp_accuracy - maximum_acc_loss
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filtered_df = pareto_solutions_df[pareto_solutions_df['Accuracy'] >= threshold]
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filtered_df = filtered_df.sort_values(by = 'MACC Instrs', ascending=True)
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if not filtered_df.empty:
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best_config = filtered_df.iloc[0] # Get the first row
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print(f"\nBest Configuration Details with Maximum Accuracy loss < {maximum_acc_loss}%:")
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print(f"Index: {best_config['Index']}")
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print(f"Weights Resolutions: {best_config['Weights Resolutions']}")
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print(f"Accuracy: {best_config['Accuracy']:.2f}%")
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print(f"MACC Instrs: {best_config['MACC Instrs']}")
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else:
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print("No configurations meet the specified accuracy threshold.")
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return best_config
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def plot_pareto_solutions(mpq_mac, fp_mac_instr, accuracy,
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fp_accuracy, pareto_points, name):
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"""
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Plots Pareto solutions based on MAC instructions, accuracy, and other parameters.
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Args:
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mpq_mac (numpy.ndarray): Array of MAC instructions for mixed precision layer configurations.
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fp_mac_instr (float): Original (non-optimized) model's MAC instructions.
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accuracy (numpy.ndarray): Array of accuracy values for mixed precision layer configurations.
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fp_accuracy (float): Original (non-optimized) model's accuracy.
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pareto_points (numpy.ndarray): Array of Pareto optimal points (Latency, Accuracy).
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Returns:
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None: Displays the plot and saves it as a PNG file.
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"""
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# Define the x-axis and y-axis ranges
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ranges = [(0, max(mpq_mac) + 0.5 * max(mpq_mac)),
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(fp_mac_instr - 0.15 * fp_mac_instr, fp_mac_instr + 0.15 * fp_mac_instr)]
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# Calculate the exponent for notation
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exponent = int(np.log10(fp_mac_instr))
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notation = 10 ** exponent
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# Create the figure and broken axes
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fig = plt.figure(figsize = (6, 4))
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fig.subplots_adjust(left = 0.148, right = 0.88, top = 0.95, bottom = 0.2)
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bax = brokenaxes(xlims = ranges, hspace = 0.05)
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# Scatter plot for original model, mixed precision, and Pareto points
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bax.scatter(fp_mac_instr, fp_accuracy, color = 'black', s = 9, marker = '*',
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label = 'original model')
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bax.scatter(mpq_mac, accuracy, color = 'gray', marker = 'o', s = 9,
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label = 'mixed configs')
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bax.scatter(pareto_points[:, 0], pareto_points[:, 1], color = 'green', marker = 's', s = 9,
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label = 'pareto points')
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# Customize the plot
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bax.set_ylabel('Accuracy (%)', fontsize = 10, labelpad = 35)
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bax.grid(True)
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# Set the y-axis limit if provided
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y_lims = [max(50, 0.8 * np.min(accuracy)), min(1.1 * fp_accuracy, 100)]
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bax.set_ylim(y_lims[0], y_lims[1])
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# Place the legend
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bax.legend(loc = 'upper center', bbox_to_anchor = (0.5, 1.2), ncol = 3, fontsize = 10)
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# Format x-axis labels
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sFormatter1 = ticker.FuncFormatter(lambda x, _: '{:0.2f}'.format(x / notation))
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sFormatter2 = ticker.ScalarFormatter(useOffset = False, useMathText = True)
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sFormatter2.set_powerlimits((0, 2))
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bax.axs[0].xaxis.set_major_formatter(sFormatter1)
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bax.axs[1].xaxis.set_major_formatter(sFormatter2)
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bax.axs[0].tick_params(axis = 'y', labelsize = 10)
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bax.axs[0].tick_params(axis = 'x', labelsize = 10)
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bax.axs[1].tick_params(axis = 'x', labelsize = 10)
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bax.axs[1].xaxis.get_offset_text().set_position((1, 0))
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bax.axs[1].xaxis.get_offset_text().set_fontsize(10)
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# Reposition the x-axis label
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bax.set_xlabel('MAC Instructions', ha = 'center', fontsize = 10, labelpad = 25)
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plt.savefig(name + ".png")
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def pareto_space(fp_accuracy, test_accuracy, weights_per_layer, macc_per_layer, total_macc_opt_sorted, name):
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df, pareto_points, _ = find_pareto_optimal_points(np.array(total_macc_opt_sorted),
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np.array(test_accuracy), np.array(weights_per_layer))
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optimal_config = identify_best_solution(df, np.array(fp_accuracy))
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plot_pareto_solutions(np.array(total_macc_opt_sorted),
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sum(macc_per_layer), np.array(test_accuracy),
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float(fp_accuracy), pareto_points, name)
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optimal_config = optimal_config.iloc[1]
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return optimal_config.tolist()
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