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Statistics

This document provides an overview of how to analyze compiled circuits and extract statistical data for performance evaluation in Concrete. These statistics help identify bottlenecks and compare circuits.

Basic operations

Concrete calculates statistics based on the following six basic operations:

Clear addition: x + y where x is encrypted and y is clear
Encrypted addition: x + y where both x and y are encrypted
Clear multiplication: x * y where x is encrypted and y is clear
Encrypted negation: -x where x is encrypted
Key switch: A building block for table lookups
Packing key switch: A building block for table lookups
Programmable bootstrapping: A building block for table lookups

Displaying statistics

You can print all statistics using the show_statistics configuration option:

from concrete import fhe

@fhe.compiler({"x": "encrypted"})
def f(x):
    return (x**2) + (2*x) + 4

inputset = range(2**2)
circuit = f.compile(inputset, show_statistics=True)

This code will print:

Statistics
--------------------------------------------------------------------------------
size_of_secret_keys: 22648
size_of_bootstrap_keys: 51274176
size_of_keyswitch_keys: 64092720
size_of_inputs: 16392
size_of_outputs: 16392
p_error: 9.627450598589458e-06
global_p_error: 9.627450598589458e-06
complexity: 99198195.0
programmable_bootstrap_count: 1
programmable_bootstrap_count_per_parameter: {
    BootstrapKeyParam(polynomial_size=2048, glwe_dimension=1, input_lwe_dimension=781, level=1, base_log=23, variance=9.940977002694397e-32): 1
}
key_switch_count: 1
key_switch_count_per_parameter: {
    KeyswitchKeyParam(level=5, base_log=3, variance=1.939836732335308e-11): 1
}
packing_key_switch_count: 0
clear_addition_count: 1
clear_addition_count_per_parameter: {
    LweSecretKeyParam(dimension=2048): 1
}
encrypted_addition_count: 1
encrypted_addition_count_per_parameter: {
    LweSecretKeyParam(dimension=2048): 1
}
clear_multiplication_count: 1
clear_multiplication_count_per_parameter: {
    LweSecretKeyParam(dimension=2048): 1
}
encrypted_negation_count: 0
--------------------------------------------------------------------------------

Each of these properties can be directly accessed on the circuit (e.g., circuit.programmable_bootstrap_count).

Progressbar

This document introduces the progressbar feature that provides visual feedback on the execution progress of large circuits, which can take considerable time to execute.

The following Python code demonstrates how to enable and use the progressbar:

import time

import matplotlib.pyplot as plt
import numpy as np
import randimage
from concrete import fhe

configuration = fhe.Configuration(
    enable_unsafe_features=True,
    use_insecure_key_cache=True,
    insecure_key_cache_location=".keys",

    # To enable displaying progressbar
    show_progress=True,
    # To enable showing tags in the progressbar (does not work in notebooks)
    progress_tag=True,
    # To give a title to the progressbar
    progress_title="Evaluation:",
)

@fhe.compiler({"image": "encrypted"})
def to_grayscale(image):
    with fhe.tag("scaling.r"):
        r = image[:, :, 0]
        r = (r * 0.30).astype(np.int64)

    with fhe.tag("scaling.g"):
        g = image[:, :, 1]
        g = (g * 0.59).astype(np.int64)

    with fhe.tag("scaling.b"):
        b = image[:, :, 2]
        b = (b * 0.11).astype(np.int64)

    with fhe.tag("combining.rgb"):
        gray = r + g + b
        
    with fhe.tag("creating.result"):
        gray = np.expand_dims(gray, axis=2)
        result = np.concatenate((gray, gray, gray), axis=2)
    
    return result

image_size = (16, 16)
image_data = (randimage.get_random_image(image_size) * 255).round().astype(np.int64)

print()

print(f"Compilation started @ {time.strftime('%H:%M:%S', time.localtime())}")
start = time.time()
inputset = [np.random.randint(0, 256, size=image_data.shape) for _ in range(100)]
circuit = to_grayscale.compile(inputset, configuration)
end = time.time()
print(f"(took {end - start:.3f} seconds)")

print()

print(f"Key generation started @ {time.strftime('%H:%M:%S', time.localtime())}")
start = time.time()
circuit.keygen()
end = time.time()
print(f"(took {end - start:.3f} seconds)")

print()

print(f"Evaluation started @ {time.strftime('%H:%M:%S', time.localtime())}")
start = time.time()
grayscale_image_data = circuit.encrypt_run_decrypt(image_data)
end = time.time()
print(f"(took {end - start:.3f} seconds)")

fig, axs = plt.subplots(1, 2)
axs = axs.flatten()

axs[0].set_title("Original")
axs[0].imshow(image_data)
axs[0].axis("off")

axs[1].set_title("Grayscale")
axs[1].imshow(grayscale_image_data)
axs[1].axis("off")

plt.show()

When you run this code, you will see a progressbar like this one:

Evaluation:  10% |█████.............................................|  10% (scaling.r)
^^^^^^^^^^^  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^
Title        Progressbar                                                   Tag

As the execution proceeds, the progress bar updates:

Evaluation:  30% |███████████████...................................|  30% (scaling.g)

Evaluation:  50% |█████████████████████████.........................|  50% (scaling.b)

The progress bar does not measure time. When it shows 50%, it indicates that half of the nodes in the computation graph have been processed, not that half of the time has elapsed. The duration of processing different node types may vary, so the progress bar should not be used to estimate the remaining time.

Once the progressbar fills and execution completes, you will see the following figure:

Formatting and drawing

This document explains how to format and draw a compiled circuit in Python.

Formatting

To convert your compiled circuit into its textual representation, use the str function:

str(circuit)

If you just want to see the output on your terminal, you can directly print it as well:

print(circuit)

Formatting is designed for debugging purpose only. It's not possible to create the circuit back from its textual representation. See How to Deploy if that's your goal.

Drawing

Drawing functionality requires the installation of the package with the full feature set. See the Installation section for instructions.

To draw your compiled circuit, use the draw method:

drawing = circuit.draw()

This method draws the circuit, saves it as a temporary PNG file and returns the file path.

You can display the drawing in a Jupyter notebook:

from PIL import Image
drawing = Image.open(circuit.draw())
drawing.show()
drawing.close()

Alternatively, you can use the show option of the draw method to display the drawing with matplotlib:

circuit.draw(show=True)

Using this option will clear any existing matplotlib plots.

Lastly, to save the drawing to a specific path, use the save_to option:

destination = "/tmp/path/of/your/choice.png"
drawing = circuit.draw(save_to=destination)
assert drawing == destination

Formatting and drawing

This document explains how to format and draw a compiled circuit in Python.

Formatting

To convert your compiled circuit into its textual representation, use the str function:

str(circuit)

If you just want to see the output on your terminal, you can directly print it as well:

print(circuit)

Formatting is designed for debugging purpose only. It's not possible to create the circuit back from its textual representation. See How to Deploy if that's your goal.

Drawing

Drawing functionality requires the installation of the package with the full feature set. See the Installation section for instructions.

To draw your compiled circuit, use the draw method:

drawing = circuit.draw()

This method draws the circuit, saves it as a temporary PNG file and returns the file path.

You can display the drawing in a Jupyter notebook:

from PIL import Image
drawing = Image.open(circuit.draw())
drawing.show()
drawing.close()

Alternatively, you can use the show option of the draw method to display the drawing with matplotlib:

circuit.draw(show=True)

Using this option will clear any existing matplotlib plots.

Lastly, to save the drawing to a specific path, use the save_to option:

destination = "/tmp/path/of/your/choice.png"
drawing = circuit.draw(save_to=destination)
assert drawing == destination

Statistics

Basic operations

Concrete calculates statistics based on the following six basic operations:

Clear addition: x + y where x is encrypted and y is clear
Encrypted addition: x + y where both x and y are encrypted
Clear multiplication: x * y where x is encrypted and y is clear
Encrypted negation: -x where x is encrypted
Key switch: A building block for table lookups
Packing key switch: A building block for table lookups
Programmable bootstrapping: A building block for table lookups

Displaying statistics

You can print all statistics using the show_statistics configuration option:

from concrete import fhe

@fhe.compiler({"x": "encrypted"})
def f(x):
    return (x**2) + (2*x) + 4

inputset = range(2**2)
circuit = f.compile(inputset, show_statistics=True)

This code will print:

Statistics
--------------------------------------------------------------------------------
size_of_secret_keys: 22648
size_of_bootstrap_keys: 51274176
size_of_keyswitch_keys: 64092720
size_of_inputs: 16392
size_of_outputs: 16392
p_error: 9.627450598589458e-06
global_p_error: 9.627450598589458e-06
complexity: 99198195.0
programmable_bootstrap_count: 1
programmable_bootstrap_count_per_parameter: {
    BootstrapKeyParam(polynomial_size=2048, glwe_dimension=1, input_lwe_dimension=781, level=1, base_log=23, variance=9.940977002694397e-32): 1
}
key_switch_count: 1
key_switch_count_per_parameter: {
    KeyswitchKeyParam(level=5, base_log=3, variance=1.939836732335308e-11): 1
}
packing_key_switch_count: 0
clear_addition_count: 1
clear_addition_count_per_parameter: {
    LweSecretKeyParam(dimension=2048): 1
}
encrypted_addition_count: 1
encrypted_addition_count_per_parameter: {
    LweSecretKeyParam(dimension=2048): 1
}
clear_multiplication_count: 1
clear_multiplication_count_per_parameter: {
    LweSecretKeyParam(dimension=2048): 1
}
encrypted_negation_count: 0
--------------------------------------------------------------------------------

Each of these properties can be directly accessed on the circuit (e.g., circuit.programmable_bootstrap_count).

Progressbar

This document introduces the progressbar feature that provides visual feedback on the execution progress of large circuits, which can take considerable time to execute.

The following Python code demonstrates how to enable and use the progressbar:

import time

import matplotlib.pyplot as plt
import numpy as np
import randimage
from concrete import fhe

configuration = fhe.Configuration(
    enable_unsafe_features=True,
    use_insecure_key_cache=True,
    insecure_key_cache_location=".keys",

    # To enable displaying progressbar
    show_progress=True,
    # To enable showing tags in the progressbar (does not work in notebooks)
    progress_tag=True,
    # To give a title to the progressbar
    progress_title="Evaluation:",
)

@fhe.compiler({"image": "encrypted"})
def to_grayscale(image):
    with fhe.tag("scaling.r"):
        r = image[:, :, 0]
        r = (r * 0.30).astype(np.int64)

    with fhe.tag("scaling.g"):
        g = image[:, :, 1]
        g = (g * 0.59).astype(np.int64)

    with fhe.tag("scaling.b"):
        b = image[:, :, 2]
        b = (b * 0.11).astype(np.int64)

    with fhe.tag("combining.rgb"):
        gray = r + g + b
        
    with fhe.tag("creating.result"):
        gray = np.expand_dims(gray, axis=2)
        result = np.concatenate((gray, gray, gray), axis=2)
    
    return result

image_size = (16, 16)
image_data = (randimage.get_random_image(image_size) * 255).round().astype(np.int64)

print()

print(f"Compilation started @ {time.strftime('%H:%M:%S', time.localtime())}")
start = time.time()
inputset = [np.random.randint(0, 256, size=image_data.shape) for _ in range(100)]
circuit = to_grayscale.compile(inputset, configuration)
end = time.time()
print(f"(took {end - start:.3f} seconds)")

print()

print(f"Key generation started @ {time.strftime('%H:%M:%S', time.localtime())}")
start = time.time()
circuit.keygen()
end = time.time()
print(f"(took {end - start:.3f} seconds)")

print()

print(f"Evaluation started @ {time.strftime('%H:%M:%S', time.localtime())}")
start = time.time()
grayscale_image_data = circuit.encrypt_run_decrypt(image_data)
end = time.time()
print(f"(took {end - start:.3f} seconds)")

fig, axs = plt.subplots(1, 2)
axs = axs.flatten()

axs[0].set_title("Original")
axs[0].imshow(image_data)
axs[0].axis("off")

axs[1].set_title("Grayscale")
axs[1].imshow(grayscale_image_data)
axs[1].axis("off")

plt.show()

When you run this code, you will see a progressbar like this one:

Evaluation:  10% |█████.............................................|  10% (scaling.r)
^^^^^^^^^^^  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^
Title        Progressbar                                                   Tag

As the execution proceeds, the progress bar updates:

Evaluation:  30% |███████████████...................................|  30% (scaling.g)

Evaluation:  50% |█████████████████████████.........................|  50% (scaling.b)

Once the progressbar fills and execution completes, you will see the following figure:

Other

Statistics

Basic operations

Displaying statistics

Tags

Progressbar

Formatting and drawing

Formatting

Drawing

Formatting and drawing

Formatting

Drawing

Statistics

Basic operations

Displaying statistics

Tags

Progressbar