convert to fp32 before rounding scale down to power of 2; update unit tests

Author: danielvegamyhre

test/float8/test_float8_utils.py

@@ -6,47 +6,30 @@
 
 # source for notable single-precision cases:
 # https://en.wikipedia.org/wiki/Single-precision_floating-point_format
-#
-# TODO(danielvegamyhre):
-# 1. add case for largest normal fp32 value: 2**127 * (2 - 2**-23).
-#    need to investigate why exp2(floor(log2(x)))=inf, but bitshift returns real value.
-# 2. add case for "nan"
-#    need to investigate why exp2(floor(log2(nan)))=nan, but bitshift returns inf.
-# 3. adjust cases for subnormal values so we aren't clamping the expected results
-#    into the normal range.
-#    preliminary investigation shows it may not be possible to support all subnormals
-#    with bitshifting, so we will need to debug/improve performance of exp2(floor(log2(x)))
-#    approach.
 @pytest.mark.parametrize(
-    "input",
+    "test_case",
     [
-        1.0,
-        float("inf"),
-        # smallest positive subnormal number
-        2**-126 * 2**-23,
-        # largest subnormal number
-        2**-126 * (1 - 2**-23),
-        # smallest positive normal number
-        2**-126,
-        # largest number less than one
-        1.0 - 2**-24,
-        # smallest number larger than one
-        1.0 + 2**-23,
+        # "test_case_name": [input, expected result]
+        ("one", [1.0, 1.0]),
+        ("inf", [float("inf"), float("inf")]),
+        ("smallest positive subnormal number", [2**-126 * 2**-23, 2**-126 * 2**-23]),
+        ("largest subnormal number", [2**-126 * (1 - 2**-23), 1.1754943508222875e-38]),
+        ("largest normal number", [2**127 * (2 - 2**-23), float("inf")]),
+        ("smallest positive normal number", [2**-126, 2**-126]),
+        ("largest number less than one", [1.0 - 2**-24, 0.5]),
+        ("smallest number larger than one", [1.0 + 2**-23, 1.0]),
     ],
 )
-def test_round_scale_down_to_power_of_2_valid_inputs(input: float):
-    input_tensor = torch.tensor(input, dtype=torch.float32)
-    result = _round_scale_down_to_power_of_2(input_tensor)
-
-    # get expected value for comparison
-    # TODO(danielvegamyhre): support subnormal values
-    expected_result = torch.exp2(torch.floor(torch.log2(input_tensor)))
-    smallest_normal_fp32_value = torch.tensor(2**-126, dtype=torch.float32)
-    expected_result = torch.max(expected_result, smallest_normal_fp32_value)
+def test_round_scale_down_to_power_of_2_valid_inputs(
+    test_case: dict,
+):
+    test_case_name, (input, expected_result) = test_case
+    input_tensor, expected_tensor = torch.tensor(input), torch.tensor(expected_result)
 
+    result = _round_scale_down_to_power_of_2(input_tensor)
     assert torch.equal(
-        result, expected_result
-    ), f"input: {input_tensor}, expected {expected_result}, but got {result}"
+        result, expected_tensor
+    ), f"test: {test_case_name}, input: {input_tensor}, expected {expected_tensor}, but got {result}"
 
 
 @pytest.mark.parametrize(

torchao/float8/float8_utils.py

@@ -285,23 +285,6 @@ def config_has_stateful_scaling(config: Float8LinearConfig) -> bool:
     )
 
 
-def _round_scale_down_to_power_of_2(x: torch.Tensor):
-    assert x.dtype == torch.float32, "scale must be float32 tensor"
-
-    # eps = smallest normal fp32 value
-    # TODO(danielvegamyhre): support subnormal values
-    eps = 2**-126
-    x = torch.clamp(
-        x,
-        min=eps,
-    )
-
-    # view as int32 to allow bitshifting
-    x_int = x.view(torch.int32)
-
-    # clear mantissa bits (rightmost 23 bits)
-    x_int = (x_int >> 23) << 23
-
-    # return result as fp32
-    result = x_int.view(torch.float32)
-    return result
+def _round_scale_down_to_power_of_2(scale: torch.Tensor):
+    assert scale.dtype == torch.float32, "scale must be float32 tensor"
+    return torch.exp2(torch.floor(torch.log2(scale)))