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fix: increase the input data range to fix error when only one weight per layer

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jfrery
2021-12-02 10:51:48 +01:00
committed by jfrery
parent 6de5df77fd
commit bf70396340
2 changed files with 28 additions and 23 deletions
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13
concrete/quantization/quantized_array.py
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@@ -41,13 +41,14 @@ class QuantizedArray:
if rmax - rmin < STABILITY_CONST:
scale = 1
zero_point = rmin
# Ideally we should get rid of round here but it is risky
# regarding the FHE compilation.
# Indeed, the zero_point value for the weights has to be an integer
# for the compilation to work.
zero_point = numpy.round(-rmin)
else:
scale = (
(rmax - rmin) / ((2 ** self.n_bits - 1 - self.offset) - (-self.offset))
if rmax != rmin
else 1.0
)
scale = (rmax - rmin) / (2 ** self.n_bits - 1) if rmax != rmin else 1.0
zero_point = numpy.round(
(rmax * (-self.offset) - (rmin * (2 ** self.n_bits - 1 - self.offset)))

38
tests/quantization/test_compilation.py
View File

@@ -8,6 +8,7 @@ from concrete.torch import NumpyModule
# INPUT_OUTPUT_FEATURE is the number of input and output of each of the network layers.
# (as well as the input of the network itself)
# Currently, with 7 bits maximum, we can use 15 weights max in the theoretical case.
INPUT_OUTPUT_FEATURE = [1, 2, 3]
@@ -31,14 +32,11 @@ class FC(nn.Module):
@pytest.mark.parametrize(
"model",
[pytest.param(FC, marks=pytest.mark.xfail())], # [TEMPORARY] xfail #1042
[pytest.param(FC)],
)
@pytest.mark.parametrize(
"input_output_feature",
[
pytest.param(input_output_feature, marks=pytest.mark.xfail()) # [TEMPORARY] xfail #1042
for input_output_feature in INPUT_OUTPUT_FEATURE
],
[pytest.param(input_output_feature) for input_output_feature in INPUT_OUTPUT_FEATURE],
)
def test_quantized_module_compilation(
input_output_feature, model, seed_torch, default_compilation_configuration
@@ -50,14 +48,14 @@ def test_quantized_module_compilation(
n_bits = 2
# Define an input shape (n_examples, n_features)
input_shape = (10, input_output_feature)
input_shape = (50, input_output_feature)
# Build a random Quantized Fully Connected Neural Network
# Define the torch model
torch_fc_model = model(input_output_feature)
# Create random input
numpy_input = numpy.random.uniform(-1, 1, size=input_shape)
numpy_input = numpy.random.uniform(-100, 100, size=input_shape)
# Create corresponding numpy model
numpy_fc_model = NumpyModule(torch_fc_model)
@@ -72,17 +70,23 @@ def test_quantized_module_compilation(
quantized_model.compile(q_input, default_compilation_configuration)
dequant_predictions = quantized_model.forward_and_dequant(q_input)
nb_tries = 5
# Compare predictions between FHE and QuantizedModule
homomorphic_predictions = []
for x_q in q_input.qvalues:
homomorphic_predictions.append(
quantized_model.forward_fhe.run(numpy.array([x_q]).astype(numpy.uint8))
for _ in range(nb_tries):
homomorphic_predictions = []
for x_q in q_input.qvalues:
homomorphic_predictions.append(
quantized_model.forward_fhe.run(numpy.array([x_q]).astype(numpy.uint8))
)
homomorphic_predictions = quantized_model.dequantize_output(
numpy.array(homomorphic_predictions, dtype=numpy.float32)
)
homomorphic_predictions = quantized_model.dequantize_output(
numpy.array(homomorphic_predictions, dtype=numpy.float32)
)
homomorphic_predictions.reshape(dequant_predictions.shape)
homomorphic_predictions = homomorphic_predictions.reshape(dequant_predictions.shape)
# Make sure homomorphic_predictions are the same as dequant_predictions
assert numpy.isclose(homomorphic_predictions.ravel(), dequant_predictions.ravel()).all()
# Make sure homomorphic_predictions are the same as dequant_predictions
if numpy.isclose(homomorphic_predictions.ravel(), dequant_predictions.ravel()).all():
return
# Bad computation after nb_tries
raise AssertionError(f"bad computation after {nb_tries} tries")