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0c08f5a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | 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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