# Copyright (c) 2024 Graphcore Ltd. All rights reserved.
import math
import numpy as np
from .types import FormatInfo
[docs]def encode_float(fi: FormatInfo, v: float) -> int:
"""
Encode input to the given :py:class:`FormatInfo`.
Will round toward zero if :paramref:`v` is not in the value set.
Will saturate to `Inf`, `NaN`, `fi.max` in order of precedence.
Encode -0 to 0 if not `fi.has_nz`
For other roundings and saturations, call :func:`round_float` first.
Args:
fi (FormatInfo): Describes the target format
v (float): The value to be encoded.
Returns:
The integer code point
"""
# Format Constants
k = fi.bits
p = fi.precision
t = p - 1
# Encode
if np.isnan(v):
return fi.code_of_nan
# Overflow/underflow
if v > fi.max:
if fi.has_infs:
return fi.code_of_posinf
if fi.num_nans > 0:
return fi.code_of_nan
return fi.code_of_max
if v < fi.min:
if fi.has_infs:
return fi.code_of_neginf
if fi.num_nans > 0:
return fi.code_of_nan
return fi.code_of_min
# Finite values
sign = fi.is_signed and np.signbit(v)
vpos = -v if sign else v
if fi.has_subnormals and vpos <= fi.smallest_subnormal / 2:
isig = 0
biased_exp = 0
else:
sig, exp = np.frexp(vpos)
exp = int(exp) # All calculations in Python ints
# sig in range [0.5, 1)
sig *= 2
exp -= 1
# now sig in range [1, 2)
biased_exp = exp + fi.expBias
if biased_exp < 1 and fi.has_subnormals:
# subnormal
sig *= 2.0 ** (biased_exp - 1)
biased_exp = 0
assert vpos == sig * 2 ** (1 - fi.expBias)
else:
if sig > 0:
sig -= 1.0
isig = math.floor(sig * 2**t)
# Zero
if isig == 0 and biased_exp == 0 and fi.has_zero:
if sign and fi.has_nz:
return fi.code_of_negzero
else:
return fi.code_of_zero
# Nonzero
assert isig < 2**t
assert biased_exp < 2**fi.expBits or fi.is_twos_complement
# Handle two's complement encoding
if fi.is_twos_complement and sign:
isig = (1 << t) - isig
# Pack values into a single integer
code = (int(sign) << (k - 1)) | (biased_exp << t) | (isig << 0)
return code