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Generic floating point handling in Python

Project description

gfloat: Generic floating-point types in Python

An implementation of generic floating point encode/decode logic, handling various current and proposed floating point types:

The library favours readability and extensibility over speed (although the *_ndarray functions are reasonably fast for large arrays, see the benchmarking notebook). For other implementations of these datatypes more focused on speed see, for example, ml_dtypes, bitstring, MX PyTorch Emulation Library.

See https://gfloat.readthedocs.io for documentation, or dive into the notebooks to explore the formats.

For example, here's a table from the 02-value-stats notebook:

name B: Bits in the format P: Precision in bits E: Exponent field width in bits 0<x<1 1<x<Inf Exact in float16? maxFinite minFinite maxNormal minNormal minSubnormal maxSubnormal
ocp_e2m1 4 2 2 1 5 True 6 -6 6 1 0.5 0.5
ocp_e2m3 6 4 2 7 23 True 7.5 -7.5 7.5 1 0.125 0.875
ocp_e3m2 6 3 3 11 19 True 28 -28 28 0.25 0.0625 0.1875
ocp_e4m3 8 4 4 55 70 True 448 -448 448 0.015625 1*2^-9 7/4*2^-7
ocp_e5m2 8 3 5 59 63 True 57344 -57344 57344 1*2^-14 1*2^-16 3/2*2^-15
p3109_p1 8 1 7 62 63 False 1*2^63 -1*2^63 1*2^63 1*2^-62 nan nan
p3109_p2 8 2 6 63 62 False 1*2^31 -1*2^31 1*2^31 1*2^-31 1*2^-32 1*2^-32
p3109_p3 8 3 5 63 62 True 49152 -49152 49152 1*2^-15 1*2^-17 3/2*2^-16
p3109_p4 8 4 4 63 62 True 224 -224 224 0.0078125 1*2^-10 7/4*2^-8
p3109_p5 8 5 3 63 62 True 15 -15 15 0.125 0.0078125 15/8*2^-4
p3109_p6 8 6 2 63 62 True 3.875 -3.875 3.875 0.5 0.015625 31/16*2^-2
bfloat16 16 8 8 16255 16383 False 255/128*2^127 -255/128*2^127 255/128*2^127 1*2^-126 1*2^-133 127/64*2^-127
ocp_int8 8 8 0 63 63 True 127/64*2^0 -2 nan nan 0.015625 127/64*2^0
ocp_e8m0 8 1 8 127 127 False 1*2^127 1*2^-127 1*2^127 1*2^-127 nan nan

Notes

All NaNs are the same, with no distinction between signalling or quiet, or between differently encoded NaNs.

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