Quantization

Quantization is a technique that can reduce the model size and accelerate its execution with little to no degradation in accuracy. CTranslate2 supports the most common types:

  • 8-bit integers (INT8)

  • 16-bit integers (INT16)

  • 16-bit floating points (FP16)

Tip

See the benchmark results in the main README to compare the performance and memory usage with and without quantization.

Quantize on model conversion

Enabling the quantization when converting the model is helpful to reduce its size on disk. The converters expose the option quantization that accepts the following values:

  • int8

  • int8_float16

  • int16

  • float16

For example,

ct2-opennmt-py-converter --model_path model.pt --quantization int8 --output_dir ct2_model

Note

Whatever quantization type is selected here, the runtime ensures the model can be loaded and executed efficiently. This implies the model weights are possibly converted to another type when the model is loaded, see Implicit model conversion on load.

For reference, the table below compares the model size on disk for a base Transformer model without shared embeddings and a vocabulary of size 32k:

Quantization

Model size

None

364MB

int16

187MB

float16

182MB

int8

100MB

int8 + float16

95MB

Quantize on model loading

Quantization can also be enabled or changed when loading the model. The translator exposes the option compute_type that accepts the following values:

  • auto: selects the fastest computation type on this system and device

  • int8

  • int8_float16

  • int16

  • float16

  • float

For example,

translator = ctranslate2.Translator(model_path, compute_type="int8")

Tip

Conversions between all types are supported. For example, you can convert a model with quantization="int8" and then execute in full precision with compute_type="float".

Implicit model conversion on load

By default, the runtime tries to use the type that is saved in the converted model as the computation type. However, if the current platform or backend do not support optimized execution for this computation type (e.g. int16 is not optimized on GPU), then the library converts the model weights to another optimized type. The tables below document the fallback types in prebuilt binaries:

On CPU:

Architecture

int8

int8_float16

int16

float16

x86-64 (Intel)

int8

int8

int16

float

x86-64 (other)

int8

int8

int8

float

AArch64/ARM64 (Apple)

float

float

float

float

AArch64/ARM64 (other)

int8

int8

int8

float

On GPU:

Compute Capability

int8

int8_float16

int16

float16

>= 7.0

int8

int8_float16

float16

float16

6.2

float

float

float

float

6.1

int8

int8

float

float

<= 6.0

float

float

float

float

Tip

You can get more information about the detected capabilities of your system by setting the environment variable CT2_VERBOSE=1. This information can also be queried at runtime with the Python function ctranslate2.get_supported_compute_types.

Supported types

8-bit integers (int8)

Supported on:

  • NVIDIA GPU with Compute Capability >= 7.0 or Compute Capability 6.1

  • x86-64 CPU with the Intel MKL or oneDNN backends

  • AArch64/ARM64 CPU with the Ruy backend

The implementation applies the equation from Wu et al. 2016 to quantize the weights of the embedding and linear layers:

scale[i] = 127 / max(abs(W[i,:]))

WQ[i,j] = round(scale[i] * W[i,j])

Note

This formula corresponds to a symmetric quantization (absolute maximum of the input range instead of separate min/max values).

16-bit integers (int16)

Supported on:

  • Intel CPU with the Intel MKL backend

The implementation follows the work by Devlin 2017. By default we use one quantization scale per layer. The scale is defined as:

scale = 2^10 / max(abs(W))

As suggested by the author, the idea is to use 10 bits for the input so that the multiplication is 20 bits which gives 12 bits left for accumulation.

Similar to the int8 quantization, only the weights of the embedding and linear layers are quantized to 16-bit integers.

16-bit floating points (float16)

Supported on:

  • NVIDIA GPU with Compute Capability >= 7.0

In this mode, all model weights are stored in half precision and all layers are run in half precision.

Mixed 8-bit integers and 16-bit floating points (int8_float16)

Supported on:

  • NVIDIA GPU with Compute Capability >= 7.0

This mode is the same as int8, but all non quantized layers are run in FP16 instead of FP32.