Data Formats#

The CS system supports the following data formats:

  • 16-bit floating-point formats:

    • IEEE half-precision (binary16), also known as FP16.

    • CB16, a Cerebras 16-bit format with 6 exponent bits.

  • 32-bit floating-point format:

    • IEEE single-precision (binary32), also known as FP32.

The 16-bit arithmetic uses 16-bit words and is always aligned to a 16-bit boundary.

The single-precision arithmetic uses even-aligned register pairs for register operands and 32-bit aligned addresses for memory operands.

Note

Memory is 16-bit word addressable. It is not byte addressable.

FP16#

The FP16 implementation follows the IEEE standard for binary16 (half-precision), with 5-bit exponent and a 10-bit explicit mantissa.

Sign: 1

Exponent: 5

Mantissa: 10

CB16 half-precision#

The CB16 is Cerebras’ 16-bit format, also referred to as cbfloat16. The CB16 is a floating-point format with 6-bit exponent and 9-bit explicit mantissa. This allows for double the dynamic range of FP16.

../_images/cbfloat16-format.jpg

Fig. 1 Cerebras CB16 Format#

With 1 bit more for the exponent compared to FP16, the CB16 provides a bigger range with the following benefits:

  • Denormals are far less frequent.

  • Dynamic loss scaling is not necessary on many networks.

Note

The cbfloat16 data format is different from the bfloat16 Brain Floating Point format.

Using CB16#

In your code, make sure the following two conditions are satisfied:

  1. In the parameters YAML file, in the csconfig section, set the key use_cbfloat16 to True.

    csconfig:
  ...
  use_cbfloat16: True

  2. In your code, while constructing the CSRunConfig object, read the key-value pair for use_cbfloat16 from the csconfig list in the YAML file. See the following:

    ...
# get cs-specific configs
cs_config = get_csconfig(params.get("csconfig", dict()))
...
est_config = CSRunConfig(
                cs_ip=runconfig_params["cs_ip"],
                cs_config=cs_config,
                stack_params=stack_params,
                **csrunconfig_dict,
             )
warm_start_settings = create_warm_start_settings(
                        runconfig_params,
                        exclude_string=output_layer_name
                      )
est = CerebrasEstimator(
          model_fn=model_fn,
          model_dir=runconfig_params["model_dir"],
          config=est_config,
          params=params,
          warm_start_from=warm_start_settings,
      )

Important

Ensure that the above both (1) and (2) conditions are true.

FP32 single-precision#

The FP32 is equivalent to IEEE binary32 (single-precision), with 8-bit exponent and 23-bit explicit mantissa.

Sign: 1

Exponent: 8

Mantissa: 23

Mixed-precision training#

The CS system currently supports only mixed-precision for the training. Ensure that in your models you have:

  • 16-bit input to arithmetic operations, and

  • FP32 accumulations.

Mixed-precision, when used in combination with Dynamic Loss Scaling, can result in speedups in training.

See the example in Step 5: Ensure mixed precision.