Feat (tests): extended minifloat unit tests

src/brevitas/utils/float_quant_utils.py

@@ -5,9 +5,9 @@
 import torch
 
 
-def mantissa_bits_to_float(bits: str, frexp_compatible: bool = False) -> float:
+def mantissa_bits_to_float(bits: str, frexp_compatible: bool = False, normal: bool = True) -> float:
     # computes the decimal place value from a given binary tensor
-    res = 1.0
+    res = 1.0 if normal else 0.0
     for i, val in enumerate(bits):
         # iterating through from left to right
         res += ((2 ** -(i + 1)) * float(val))
@@ -23,14 +23,20 @@ def get_minifloat_value(exponent: str, mantissa: str, exponent_bias: int) -> flo
     It expects the exponent and mantissa in their binary format.
     """
     exponent_value = int(exponent, 2)
-    mantissa_value = mantissa_bits_to_float(mantissa)
+
+    if exponent_value == 0:  # subnormal
+        exponent_bias -= 1  # exponent is e_min
+        mantissa_value = mantissa_bits_to_float(mantissa, normal=False)
+    else:  # normal
+        mantissa_value = mantissa_bits_to_float(mantissa, normal=True)
+
     return (2 ** (exponent_value - exponent_bias)) * mantissa_value
 
 
 def get_max_available_float(
-        exponent_bit_width: torch.Tensor,
-        mantissa_bit_width: torch.Tensor,
-        exponent_bias: torch.Tensor,
+        exponent_bit_width: int,
+        mantissa_bit_width: int,
+        exponent_bias: int,
         nan_values: Tuple[str],
         inf_values: Tuple[str],
         saturating: bool) -> torch.Tensor:
@@ -75,3 +81,17 @@ def get_max_available_float(
     max_value = get_minifloat_value(
         exponent=exponent, mantissa=mantissa, exponent_bias=exponent_bias)
     return max_value
+
+
+def get_min_available_float(
+        exponent_bit_width: int, mantissa_bit_width: int, exponent_bias: int) -> torch.Tensor:
+    """
+    Returns the minimum subnormal minifloat value for a given exponent and mantissa
+    bit-width, and exponent bias.
+    """
+    exponent = '0' * exponent_bit_width
+    mantissa = '0' * (mantissa_bit_width - 1) + '1'
+
+    min_value = get_minifloat_value(
+        exponent=exponent, mantissa=mantissa, exponent_bias=exponent_bias)
+    return min_value

tests/brevitas/core/minifloat_fixtures.py

@@ -4,6 +4,9 @@
 import pytest_cases
 from pytest_cases import fixture_union
 
+from brevitas.inject import ExtendedInjector
+from brevitas.inject import value
+from brevitas.quant.experimental.float_base import FloatWeightBase
 from brevitas.quant.experimental.float_quant_ocp import Fp8e4m3OCPWeight
 from brevitas.quant.experimental.float_quant_ocp import Fp8e5m2OCPWeight
 
@@ -32,6 +35,51 @@ class Fp8e5m2(Fp8e5m2OCPWeight):
     return Fp8e5m2
 
 
-list_of_fixtures = ['fp8e4m3', 'fp8e5m2']
+class Fp8CustomMixin(ExtendedInjector):
+    bit_width = 8
+    saturating = True
+
+    hypothesis_internal_is_this_a_mock_check = True
+
+    @value
+    def mantissa_bit_width(bit_width, exponent_bit_width):
+        return bit_width - exponent_bit_width - 1  # Sign bit
+
+
+class Fp8e7m0Weight(Fp8CustomMixin, FloatWeightBase):
+    exponent_bit_width = 7
+
+
+class Fp8e6m1Weight(Fp8CustomMixin, FloatWeightBase):
+    exponent_bit_width = 6
+
+
+class Fp8e3m4Weight(Fp8CustomMixin, FloatWeightBase):
+    exponent_bit_width = 3
+
+
+class Fp8e2m5Weight(Fp8CustomMixin, FloatWeightBase):
+    exponent_bit_width = 2
+
+
+class Fp8e1m6Weight(Fp8CustomMixin, FloatWeightBase):
+    exponent_bit_width = 1
+
+
+@pytest_cases.fixture
+@pytest_cases.parametrize('exponent_bit_width', [1, 2, 3, 6, 7])  # at least 1 exponent bit
+def fp8Custom(exponent_bit_width):
+
+    custom_exponents = {
+        1: Fp8e1m6Weight,
+        2: Fp8e2m5Weight,
+        3: Fp8e3m4Weight,
+        6: Fp8e6m1Weight,
+        7: Fp8e7m0Weight,}
+
+    return custom_exponents[exponent_bit_width]
+
+
+list_of_fixtures = ['fp8e4m3', 'fp8e5m2', 'fp8Custom']
 
 fp8_clamp = fixture_union('fp8_clamp', list_of_fixtures, ids=list_of_fixtures)

tests/brevitas/core/test_clamp.py

@@ -8,15 +8,41 @@
 from brevitas.function.ops import max_float
 from brevitas.quant.experimental.float import Fp8e4m3Weight
 from brevitas.quant.experimental.float import Fp8e5m2Weight
+from brevitas.quant.experimental.float_quant_fnuz import Fp8e4m3FNUZWeight
+from brevitas.quant.experimental.float_quant_fnuz import Fp8e5m2FNUZWeight
 from brevitas.quant.experimental.float_quant_ocp import Fp8e4m3OCPWeight
 from brevitas.quant.experimental.float_quant_ocp import Fp8e5m2OCPWeight
 from brevitas.utils.float_quant_utils import get_max_available_float
+from brevitas.utils.float_quant_utils import get_min_available_float
 from tests.brevitas.hyp_helper import float_tensor_random_shape_st
 
 from .minifloat_fixtures import *
 
 FORMAT_MAXVAL_MAP = {
-    Fp8e5m2OCPWeight: 57344., Fp8e4m3OCPWeight: 448., Fp8e4m3Weight: 480., Fp8e5m2Weight: 114688.}
+    Fp8e5m2OCPWeight: 57344.,
+    Fp8e4m3OCPWeight: 448.,
+    Fp8e4m3Weight: 480.,
+    Fp8e5m2Weight: 114688.,
+    Fp8e4m3FNUZWeight: 240.,
+    Fp8e5m2FNUZWeight: 57344.,
+    Fp8e7m0Weight: 2.0 ** 64,  # Custom exponent_bit_width
+    Fp8e6m1Weight: 6442450944.0,
+    Fp8e3m4Weight: 31.0,
+    Fp8e2m5Weight: 7.875,
+    Fp8e1m6Weight: 3.96875}
+
+FORMAT_MINVAL_MAP = {
+    Fp8e5m2OCPWeight: 2.0 ** -16,
+    Fp8e4m3OCPWeight: 2.0 ** -9,
+    Fp8e4m3Weight: 2.0 ** -9,
+    Fp8e5m2Weight: 2.0 ** -16,
+    Fp8e4m3FNUZWeight: 2.0 ** -10,
+    Fp8e5m2FNUZWeight: 2.0 ** -17,
+    Fp8e7m0Weight: 2.0 ** -63,  # Custom exponent_bit_width
+    Fp8e6m1Weight: 2.0 ** -31,
+    Fp8e3m4Weight: 2.0 ** -6,
+    Fp8e2m5Weight: 2.0 ** -5,
+    Fp8e1m6Weight: 2.0 ** -5}
 
 
 @pytest.mark.parametrize(
@@ -40,6 +66,19 @@ def test_max_value(minifloat, expected_max_val):
     assert expected_max_val == max_val
 
 
+@pytest.mark.parametrize(
+    'minifloat, expected_min_val',
+    ((format, min_val) for format, min_val in FORMAT_MINVAL_MAP.items()))
+def test_min_value(minifloat, expected_min_val):
+    min_val = get_min_available_float(
+        minifloat.exponent_bit_width,
+        minifloat.mantissa_bit_width,
+        minifloat.exponent_bias,
+    )
+
+    assert expected_min_val == min_val
+
+
 @given(inp=float_tensor_random_shape_st())
 def test_float_clamp(inp, fp8_clamp):