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)

• 16-bit brain floating points (BF16)

• 4-bit AWQ Quantization

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_float32

• int8_float16

• int8_bfloat16

• int16

• float16

• bfloat16

• float32

For example,

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

When the option --quantization is not set, the converted model will be saved with the same type as the original model (typically one of float32, float16, or bfloat16).

Important

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 type 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
float32	364MB
int16	187MB
float16	182MB
bfloat16	182MB
int8_float32	100MB
int8_float16	95MB
int8_bfloat16	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:

• default: keep the same quantization that was used during model conversion (see Implicit type conversion on load for exceptions)

• auto: use the fastest computation type that is supported on this system and device

• int8

• int8_float32

• int8_float16

• int8_bfloat16

• int16

• float16

• float32

• bfloat16

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="float32".

Implicit type 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_float32	int8_float16	int8_bfloat16	int16	float16	bfloat16
x86-64 (Intel)	int8_float32	int8_float32	int8_float32	int16	float32	float32
x86-64 (other)	int8_float32	int8_float32	int8_float32	int8_float32	float32	float32
AArch64/ARM64 (Apple)	int8_float32	int8_float32	int8_float32	int8_float32	float32	float32
AArch64/ARM64 (other)	int8_float32	int8_float32	int8_float32	int8_float32	float32	float32

On GPU:

Compute Capability	int8_float32	int8_float16	int8_bfloat16	int16	float16	bfloat16
>= 8.0	int8_float32	int8_float16	int8_bfloat16	float16	float16	bfloat16
>= 7.0, < 8.0	int8_float32	int8_float16	int8_float32	float16	float16	float32
6.2	float32	float32	float32	float32	float32	float32
6.1	int8_float32	int8_float32	int8_float32	float32	float32	float32
<= 6.0	float32	float32	float32	float32	float32	float32

Tip

You can get more information about the detected capabilities of your system by enabling the info logs (set the environment variable CT2_VERBOSE=1 or call ctranslate2.set_log_level(logging.INFO)).

The supported compute types 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).

Non quantized layers are run in the floating point precision of the original model. For example, if the model is saved in float16, the actual quantization type will be int8_float16. This behavior can be changed by selecting more specific quantization types:

• int8_float32

• int8_float16

• int8_bfloat16

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. The other layers are run in FP32.

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.

16-bit brain floating points (bfloat16)

Supported on:

• NVIDIA GPU with Compute Capability >= 8.0

In this mode, all model weights are stored in BF16 and all layers are run with this type.

4-bit AWQ

Supported on:

• NVIDIA GPU with Compute Capability >= 7.5

CTranslate2 internally handles the compute type for AWQ quantization. In this mode, all model weights are stored in half precision and all layers are run in half precision. Other parameters like scale and zero are stored in int32.

Steps to use AWQ Quantization:

• Download a AWQ quantized model from Hugging Face for example (TheBloke/Llama-2-7B-AWQ){https://huggingface.co/TheBloke/Llama-2-7B-AWQ} or quantize your own model with using this (AutoAWQ example){https://casper-hansen.github.io/AutoAWQ/examples/}.

• Convert AWQ Quantized model to Ctranslate2 model:

 ct2-transformers-converter --model TheBloke/Llama-2-7B-AWQ --copy_files tokenizer.model --output_dir ct2_model

• Run inference as usual with Ctranslate2:

model = ctranslate2.Generator('ct2_model', device='cuda')
outputs = model.generate_batch([tokens])

Currently, CTranslate2 only supports the GEMM and GEMV kernels for AWQ quantization.