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Update floatquant tests to fix Brevitas vs QONNX floatquant test #207

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merged 1 commit into from
Oct 14, 2025
Merged

Update floatquant tests to fix Brevitas vs QONNX floatquant test #207

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63 changes: 39 additions & 24 deletions tests/custom_op/test_floatquant.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,8 +27,6 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


import pytest

import io
import mock
import numpy as np
Expand Down Expand Up @@ -112,7 +110,7 @@ def brevitas_float_quant(x, bit_width, exponent_bit_width, mantissa_bit_width, e
max_available_float=max_val,
saturating=True,
)
float_scaling_impl = mock.Mock(side_effect=lambda x, y, z: 1.0)
float_scaling_impl = mock.Mock(side_effect=lambda x, y, z: torch.Tensor([1.0]))
float_quant = BrevitasFloatQuant(
bit_width=bit_width,
float_scaling_impl=float_scaling_impl,
Expand All @@ -127,33 +125,50 @@ def brevitas_float_quant(x, bit_width, exponent_bit_width, mantissa_bit_width, e
return expected_out


@pytest.mark.xfail(reason="Possible Brevitas version issue, needs investigation")
@given(
x=arrays(
dtype=np.float64,
shape=100,
elements=st.floats(
allow_nan=False,
allow_infinity=False,
allow_subnormal=True,
width=64, # Use 64-bit floats
),
unique=True,
),
exponent_bit_width=st.integers(1, 8),
mantissa_bit_width=st.integers(1, 8),
sign=st.booleans(),
)
# @pytest.mark.xfail(reason="Possible Brevitas version issue, needs investigation")
@st.composite
def inputs(draw):
# pick the torch dtype first
float_type = draw(st.sampled_from([np.float32, np.float64]))

# build x with a matching numpy dtype + float width
x = draw(
arrays(
dtype=float_type,
shape=100,
elements=st.floats(
allow_nan=False,
allow_infinity=False,
allow_subnormal=True,
width=np.dtype(float_type).itemsize * 8,
),
unique=True,
)
)

exponent_bit_width = draw(st.integers(1, 8))
mantissa_bit_width = draw(st.integers(1, 8))
sign = draw(st.booleans())

return x, exponent_bit_width, mantissa_bit_width, sign, float_type


@given(data=inputs())
@settings(
max_examples=1000, verbosity=Verbosity.verbose, suppress_health_check=list(HealthCheck)
max_examples=1000,
verbosity=Verbosity.verbose,
suppress_health_check=list(HealthCheck),
) # Adjust the number of examples as needed
def test_brevitas_vs_qonnx(x, exponent_bit_width, mantissa_bit_width, sign):
def test_brevitas_vs_qonnx(data):
x, exponent_bit_width, mantissa_bit_width, sign, _ = data
x = torch.tensor(x)
bit_width = exponent_bit_width + mantissa_bit_width + int(sign)

assume(bit_width <= 8 and bit_width >= 4)
scale = 1.0
exponent_bias = compute_default_exponent_bias(exponent_bit_width)
max_val = compute_max_val(exponent_bit_width, mantissa_bit_width, exponent_bias)
xq_t = brevitas_float_quant(x, bit_width, exponent_bit_width, mantissa_bit_width, exponent_bias, sign, max_val).numpy()
xq = qonnx_float_quant(x, scale, exponent_bit_width, mantissa_bit_width, exponent_bias, sign, max_val)
xq_t = brevitas_float_quant(x, bit_width, exponent_bit_width, mantissa_bit_width,
exponent_bias, sign, max_val).numpy()
xq = qonnx_float_quant(x.numpy(), scale, exponent_bit_width, mantissa_bit_width, exponent_bias, sign, max_val)
np.testing.assert_array_equal(xq, xq_t)
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