HuggingFace.Quantization_in_Depth/Materials/3_helper.py

import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import torch

def plot_matrix(tensor, ax, title, vmin=0, vmax=1, cmap=None):
    """
    Plot a heatmap of tensors using seaborn
    """
    sns.heatmap(tensor.cpu().numpy(), ax=ax, vmin=vmin, vmax=vmax, cmap=cmap, annot=True, fmt=".2f", cbar=False)
    ax.set_title(title)
    ax.set_yticklabels([])
    ax.set_xticklabels([])


def plot_quantization_errors(original_tensor, quantized_tensor, dequantized_tensor, dtype = torch.int8, n_bits = 8):
    """
    A method that plots 4 matrices, the original tensor, the quantized tensor
    the de-quantized tensor and the error tensor.
    """
    # Get a figure of 4 plots
    fig, axes = plt.subplots(1, 4, figsize=(15, 4))

    # Plot the first matrix
    plot_matrix(original_tensor, axes[0], 'Original Tensor', cmap=ListedColormap(['white']))

    # Get the quantization range and plot the quantized tensor
    q_min, q_max = torch.iinfo(dtype).min, torch.iinfo(dtype).max
    plot_matrix(quantized_tensor, axes[1], f'{n_bits}-bit Linear Quantized Tensor', vmin=q_min, vmax=q_max, cmap='coolwarm')

    # Plot the de-quantized tensors
    plot_matrix(dequantized_tensor, axes[2], 'Dequantized Tensor', cmap='coolwarm')

    # Get the quantization errors
    q_error_tensor = abs(original_tensor - dequantized_tensor)
    plot_matrix(q_error_tensor, axes[3], 'Quantization Error Tensor', cmap=ListedColormap(['white']))

    fig.tight_layout()
    plt.show()


########## Functions from Linear Quantization I (Part 1)
def linear_q_with_scale_and_zero_point(tensor, scale, zero_point, dtype = torch.int8):
    scaled_and_shifted_tensor = tensor / scale + zero_point
    rounded_tensor = torch.round(scaled_and_shifted_tensor)
    q_min = torch.iinfo(dtype).min
    q_max = torch.iinfo(dtype).max
    q_tensor = rounded_tensor.clamp(q_min,q_max).to(dtype)
    
    return q_tensor


def linear_dequantization(quantized_tensor, scale, zero_point):
    return scale * (quantized_tensor.float() - zero_point)

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