How deeply does Deep Learning with JAX cover float16?

Defines float16 and demonstrates construction, dtype inspection, and promotion behavior without developing training internals.

Float16

As explained in Deep Learning with JAX

A 16-bit floating-point numeric type used as a lower-precision data type for deep learning and inference.

Need smaller numeric representations for deep learning or inference? float16 provides a 16-bit floating-point type that trades precision for lower-precision computation. JAX exposes it through jax.numpy and treats it alongside float32, float64, and bfloat16. section 3.3.2

Create a float16 array by passing dtype=jnp.float16, then inspect its dtype. Lower-precision inference is generally easier than lower-precision training, so the choice belongs in the context of the workload. section 3.3.2

Mixing 16-bit types

x16 = jnp.array(range(10), dtype=jnp.float16)\nxb16 = jnp.array(range(10), dtype=jnp.bfloat16)\nxb16 + x16\n# Array([...], dtype=float32)\n\nJAX promotes the mixed operation to float32, while adding two bfloat16 arrays preserves bfloat16.

How Deep Learning with JAX covers it

liveBook manning.com

Read Deep Learning with JAX on liveBook Get the book at manning.com

Related concepts

mixed precisionused by · Mixed precision is a computational technique that uses multiple numerical precisions for different values or operations to balance resource efficiency and numerical accuracy or stability.
FP32contrasts with · FP32 is the IEEE 754 binary32 single-precision floating-point format, which represents approximate real numbers using 32 bits.
bfloat16contrasts with · bfloat16 is a 16-bit floating-point format with one sign bit, eight exponent bits, and seven fraction bits, providing a wide numerical range with reduced precision.
LLM.int8()used by · LLM.int8() is a mixed-precision quantization method for transformer language models that uses INT8 computation while separately handling activation outliers to preserve accuracy.
quantizationconfigured by · Quantization maps numerical values to a finite set of discrete representations, often using reduced precision to lower model storage or computation requirements.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with JAX. The section links point to the parts of the book it draws on.