[Python] add float16 support using numpy.float16

[chaokunyang]

Feature Request

Add float16 support to Fory Python runtime and codegen using numpy.float16 as the public scalar type, plus array support for efficient serialization.

Is your feature request related to a problem? Please describe

We want to use float16 in FDL to reduce payload size and memory footprint, and to interoperate with ML/graphics ecosystems where half precision is common. Python currently lacks a first-class float16 primitive in Fory.

Describe the solution you'd like

1) FDL / Type System

• Introduce float16 as a new primitive type in FDL.
• Allow float16 in message fields, repeated fields, map values, and unions (where primitives are allowed).

2) Wire Format / Serialization Semantics

• Encode float16 as 2 bytes representing the raw IEEE 754 binary16 bit pattern.
• Endianness must match existing float32/float64 behavior.
• NaN/Inf/±0/subnormal must round-trip correctly at the bit level (document NaN policy if canonicalized).

3) Python Runtime (core requirement)

Use numpy.float16 as the public scalar type.

3.1 Type definition

• Public APIs should accept/return numpy.float16 (not raw uint16 bits).
• No custom float16 class should be required.

3.2 Arrays / float16_array

• For repeated float16, support numpy.ndarray with dtype=float16 as a fast path.
• Define a float16_array container as a packed array of 16-bit values and map it to numpy.ndarray (dtype=float16).

3.3 Serialization boundary

• Serializer/deserializer should read/write raw 16-bit patterns from numpy.float16 values and arrays.
• Avoid per-element Python overhead when handling arrays.

4) Codegen requirement (Python)

• Generated fields for float16 should use numpy.float16 in type hints.
• Generated fields for float16_array or repeated float16 should use numpy.ndarray (dtype=float16) where possible.

5) Compiler/Reflection integration

• Ensure reflection/dynamic serialization recognizes numpy.float16 as the float16 primitive.

6) Tests

• Conversion and round-trip tests for scalars (±0, ±Inf, NaN, subnormals, max/min normal).
• Array serialization tests using numpy.ndarray.
• Cross-language golden tests can be added later.

Describe alternatives you've considered

1. Use a custom Python Float16 class.

• Rejected: numpy.float16 is the de-facto standard and already optimized.

Additional context

This issue is the Python counterpart of the per-language float16 work.

[liuxiaocs7]

Hi, @chaokunyang, I want to work on this issue, could you please assign this to me, thanks!

[asadjan4611]

@chaokunyang Can i work on this issue because it is Similar to Parent issue issue , that i have already resolved and i can solve it in a litle bit of moment
@liuxiaocs7 if you have not any problem can i work on it
I shall be grateful to you