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Other

Statistics

Concrete analyzes all compiled circuits and calculates some statistics. These statistics can be used to find bottlenecks and compare circuits. Statistics are calculated in terms of basic operations. There are 6 basic operations in Concrete:

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: building block for table lookups
packing key switch: building block for table lookups
programmable bootstrapping: building block for table lookups

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

Big circuits can take a long time to execute, and waiting for execution to finish without having any indication of its progress can be frustrating. For this reason, progressbar feature is introduced:

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:

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

And as the circuit progresses, this progressbar would fill:

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

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

It is not a uniform progressbar. For example, when the progressbar shows 50%, this does not mean that half of the execution is performed in terms of seconds. Instead, it means that half of the nodes in the graph have been calculated. Since different node types can take a different amount of time, this should not be used to get an ETA.

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

Formatting and drawing

Formatting

You can convert your compiled circuit into its textual representation by converting it to string:

str(circuit)

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

print(circuit)

Formatting is just for debugging purposes. 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 to learn how to do that.

You can use the draw method of your compiled circuit to draw it:

drawing = circuit.draw()

This method will draw the circuit on a temporary PNG file and return the path to this file.

You can show the drawing in a Jupyter notebook, like this:

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

Or, you can use the show option of the draw method to show the drawing with matplotlib.

circuit.draw(show=True)

Beware that this will clear the matplotlib plots you have.

Lastly, you can save the drawing to a specific path:

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

Statistics

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: building block for table lookups
packing key switch: building block for table lookups
programmable bootstrapping: building block for table lookups

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

Big circuits can take a long time to execute, and waiting for execution to finish without having any indication of its progress can be frustrating. For this reason, progressbar feature is introduced:

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:

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

And as the circuit progresses, this progressbar would fill:

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

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

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

Formatting and drawing

Formatting

You can convert your compiled circuit into its textual representation by converting it to string:

str(circuit)

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

print(circuit)

Formatting is just for debugging purposes. 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 to learn how to do that.

You can use the draw method of your compiled circuit to draw it:

drawing = circuit.draw()

This method will draw the circuit on a temporary PNG file and return the path to this file.

You can show the drawing in a Jupyter notebook, like this:

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

Or, you can use the show option of the draw method to show the drawing with matplotlib.

circuit.draw(show=True)

Beware that this will clear the matplotlib plots you have.

Lastly, you can save the drawing to a specific path:

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

Other

Statistics

Tags

Progressbar

Formatting and drawing

Formatting

Drawing

Statistics

Tags

Progressbar

Formatting and drawing

Formatting

Drawing