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63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
"""Tests for the quantized layers."""
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import numpy
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import pytest
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from concrete.quantization import QuantizedArray, QuantizedLinear
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# QuantizedLinear unstable with n_bits>23
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# and hard to test with numpy.isclose with n_bits < 8
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N_BITS_LIST = [20, 16, 8]
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@pytest.mark.parametrize(
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"n_bits",
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[pytest.param(n_bits) for n_bits in N_BITS_LIST],
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)
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@pytest.mark.parametrize(
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"n_examples, n_features, n_neurons",
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[
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pytest.param(50, 3, 4),
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pytest.param(20, 500, 30),
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pytest.param(200, 300, 50),
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pytest.param(10000, 100, 1),
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pytest.param(10, 20, 1),
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],
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)
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@pytest.mark.parametrize("is_signed", [pytest.param(True), pytest.param(False)])
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def test_quantized_linear(n_examples, n_features, n_neurons, n_bits, is_signed):
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"""Test the quantization linear layer of numpy.array.
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With n_bits>>0 we expect the results of the quantized linear
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to be the same as the standard linear layer.
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"""
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inputs = numpy.random.uniform(size=(n_examples, n_features))
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q_inputs = QuantizedArray(n_bits, inputs)
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# shape of weights: (n_features, n_neurons)
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weights = numpy.random.uniform(size=(n_features, n_neurons))
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q_weights = QuantizedArray(n_bits, weights, is_signed)
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bias = numpy.random.uniform(size=(1, n_neurons))
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q_bias = QuantizedArray(n_bits, bias, is_signed)
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# Define our QuantizedLinear layer
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q_linear = QuantizedLinear(n_bits, q_weights, q_bias)
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# Calibrate the Quantized layer
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q_linear.calibrate(inputs)
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expected_outputs = q_linear.q_out.values
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actual_output = q_linear(q_inputs).dequant()
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assert numpy.isclose(expected_outputs, actual_output, atol=10 ** -0).all()
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# Same test without bias
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q_linear = QuantizedLinear(n_bits, q_weights)
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# Calibrate the Quantized layer
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q_linear.calibrate(inputs)
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expected_outputs = q_linear.q_out.values
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actual_output = q_linear(q_inputs).dequant()
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assert numpy.isclose(expected_outputs, actual_output, atol=10 ** -0).all()
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