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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 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | import torch
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import torch
def quantization_error(tensor, dequantized_tensor):
return (dequantized_tensor - tensor).abs().square().mean()
def linear_q_with_scale_and_zero_point(
r_tensor, scale, zero_point, dtype=torch.int8):
"""
Performs simple linear quantization given
the scale and zero-point.
"""
# scale tensor and add the zero point
scaled_and_shifted_tensor = r_tensor / scale + zero_point
# round the tensor
rounded_tensor = torch.round(scaled_and_shifted_tensor)
# we need to clamp to the min/max value of the specified dtype
q_min, q_max = torch.iinfo(dtype).min, 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):
"""
Linear de-quantization
"""
dequantized_tensor = scale * (quantized_tensor.float() - zero_point)
return dequantized_tensor
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()
# From the previous lab
def get_q_scale_and_zero_point(r_tensor, dtype=torch.int8):
"""
Get quantization parameters (scale, zero point)
for a floating point tensor
"""
q_min, q_max = torch.iinfo(dtype).min, torch.iinfo(dtype).max
r_min, r_max = r_tensor.min().item(), r_tensor.max().item()
scale = (r_max-r_min)/(q_max-q_min)
zero_point = q_min-(r_min/scale)
# clip the zero_point to fall in [quantized_min, quantized_max]
if zero_point < q_min or zero_point > q_max:
zero_point = q_min
else:
# round and cast to int
zero_point = int(round(zero_point))
return scale, zero_point
def linear_quantization(r_tensor, n_bits, dtype=torch.int8):
"""
linear quantization
"""
scale, zero_point = get_q_scale_and_zero_point(r_tensor)
quantized_tensor = linear_q_with_scale_and_zero_point(
r_tensor, scale=scale, zero_point=zero_point, dtype=dtype)
return quantized_tensor, scale, zero_point
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