Quantize

This page provides C++ class references for the publicly-exposed elements of the cuvs/preprocessing/quantize package.

Scalar

#include <cuvs/preprocessing/quantize/scalar.hpp>

namespace cuvs::preprocessing::quantize::scalar

group scalar

Functions

quantizer<double> train(

raft::resources const &res,

const params params,

raft::device_matrix_view<const double, int64_t> dataset,

)

Initializes a scalar quantizer to be used later for quantizing the dataset.

Usage example:

raft::handle_t handle;
cuvs::preprocessing::quantize::scalar::params params;
auto quantizer = cuvs::preprocessing::quantize::scalar::train<double, int8_t>(handle, params,
dataset);

Parameters:

• res – [in] raft resource

• params – [in] configure scalar quantizer, e.g. quantile

• dataset – [in] a row-major matrix view on device

Returns:

quantizer

quantizer<double> train(

raft::resources const &res,

const params params,

raft::host_matrix_view<const double, int64_t> dataset,

)

Initializes a scalar quantizer to be used later for quantizing the dataset.

Usage example:

raft::handle_t handle;
cuvs::preprocessing::quantize::scalar::params params;
auto quantizer = cuvs::preprocessing::quantize::scalar::train<double, int8_t>(handle, params,
dataset);

Parameters:

• res – [in] raft resource

• params – [in] configure scalar quantizer, e.g. quantile

• dataset – [in] a row-major matrix view on host

Returns:

quantizer

void transform(

raft::resources const &res,

const quantizer<double> &quantizer,

raft::device_matrix_view<const double, int64_t> dataset,

raft::device_matrix_view<int8_t, int64_t> out,

)

Applies quantization transform to given dataset.

Usage example:

raft::handle_t handle;
cuvs::preprocessing::quantize::scalar::params params;
auto quantizer = cuvs::preprocessing::quantize::scalar::train<double, int8_t>(handle, params,
dataset); auto quantized_dataset = raft::make_device_matrix<int8_t, int64_t>(handle, samples,
features); cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
quantized_dataset.view());

Parameters:

• res – [in] raft resource

• quantizer – [in] a scalar quantizer

• dataset – [in] a row-major matrix view on device

• out – [out] a row-major matrix view on device

void transform(

raft::resources const &res,

const quantizer<double> &quantizer,

raft::host_matrix_view<const double, int64_t> dataset,

raft::host_matrix_view<int8_t, int64_t> out,

)

Applies quantization transform to given dataset.

Usage example:

raft::handle_t handle;
cuvs::preprocessing::quantize::scalar::params params;
auto quantizer = cuvs::preprocessing::quantize::scalar::train<double, int8_t>(handle, params,
dataset); auto quantized_dataset = raft::make_host_matrix<int8_t, int64_t>(samples, features);
cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
quantized_dataset.view());

Parameters:

• res – [in] raft resource

• quantizer – [in] a scalar quantizer

• dataset – [in] a row-major matrix view on host

• out – [out] a row-major matrix view on host

void inverse_transform(

raft::resources const &res,

const quantizer<double> &quantizer,

raft::device_matrix_view<const int8_t, int64_t> dataset,

raft::device_matrix_view<double, int64_t> out,

)

Perform inverse quantization step on previously quantized dataset.

Note that depending on the chosen data types train dataset the conversion is not lossless.

Usage example:

auto quantized_dataset = raft::make_device_matrix<int8_t, int64_t>(handle, samples, features);
cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
quantized_dataset.view()); auto dataset_revert = raft::make_device_matrix<double,
int64_t>(handle, samples, features);
cuvs::preprocessing::quantize::scalar::inverse_transform(handle, quantizer,
dataset_revert.view());

Parameters:

• res – [in] raft resource

• quantizer – [in] a scalar quantizer

• dataset – [in] a row-major matrix view on device

• out – [out] a row-major matrix view on device

void inverse_transform(

raft::resources const &res,

const quantizer<double> &quantizer,

raft::host_matrix_view<const int8_t, int64_t> dataset,

raft::host_matrix_view<double, int64_t> out,

)

Perform inverse quantization step on previously quantized dataset.

Note that depending on the chosen data types train dataset the conversion is not lossless.

Usage example:

auto quantized_dataset = raft::make_host_matrix<int8_t, int64_t>(samples, features);
cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
quantized_dataset.view()); auto dataset_revert = raft::make_host_matrix<double, int64_t>(samples,
features); cuvs::preprocessing::quantize::scalar::inverse_transform(handle, quantizer,
dataset_revert.view());

Parameters:

• res – [in] raft resource

• quantizer – [in] a scalar quantizer

• dataset – [in] a row-major matrix view on host

• out – [out] a row-major matrix view on host

quantizer<float> train(

raft::resources const &res,

const params params,

raft::device_matrix_view<const float, int64_t> dataset,

)

Initializes a scalar quantizer to be used later for quantizing the dataset.

Usage example:

raft::handle_t handle;
cuvs::preprocessing::quantize::scalar::params params;
auto quantizer = cuvs::preprocessing::quantize::scalar::train<float, int8_t>(handle, params,
dataset);

Parameters:

• res – [in] raft resource

• params – [in] configure scalar quantizer, e.g. quantile

• dataset – [in] a row-major matrix view on device

Returns:

quantizer

quantizer<float> train(

raft::resources const &res,

const params params,

raft::host_matrix_view<const float, int64_t> dataset,

)

Initializes a scalar quantizer to be used later for quantizing the dataset.

Usage example:

raft::handle_t handle;
cuvs::preprocessing::quantize::scalar::params params;
auto quantizer = cuvs::preprocessing::quantize::scalar::train<float, int8_t>(handle, params,
dataset);

Parameters:

• res – [in] raft resource

• params – [in] configure scalar quantizer, e.g. quantile

• dataset – [in] a row-major matrix view on host

Returns:

quantizer

void transform(

raft::resources const &res,

const quantizer<float> &quantizer,

raft::device_matrix_view<const float, int64_t> dataset,

raft::device_matrix_view<int8_t, int64_t> out,

)

Applies quantization transform to given dataset.

Usage example:

raft::handle_t handle;
cuvs::preprocessing::quantize::scalar::params params;
auto quantizer = cuvs::preprocessing::quantize::scalar::train<float, int8_t>(handle, params,
dataset); auto quantized_dataset = raft::make_device_matrix<int8_t, int64_t>(handle, samples,
features); cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
quantized_dataset.view());

Parameters:

• res – [in] raft resource

• quantizer – [in] a scalar quantizer

• dataset – [in] a row-major matrix view on device

• out – [out] a row-major matrix view on device

void transform(

raft::resources const &res,

const quantizer<float> &quantizer,

raft::host_matrix_view<const float, int64_t> dataset,

raft::host_matrix_view<int8_t, int64_t> out,

)

Applies quantization transform to given dataset.

Usage example:

raft::handle_t handle;
cuvs::preprocessing::quantize::scalar::params params;
auto quantizer = cuvs::preprocessing::quantize::scalar::train<float, int8_t>(handle, params,
dataset); auto quantized_dataset = raft::make_host_matrix<int8_t, int64_t>(samples, features);
cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
quantized_dataset.view());

Parameters:

• res – [in] raft resource

• quantizer – [in] a scalar quantizer

• dataset – [in] a row-major matrix view on host

• out – [out] a row-major matrix view on host

void inverse_transform(

raft::resources const &res,

const quantizer<float> &quantizer,

raft::device_matrix_view<const int8_t, int64_t> dataset,

raft::device_matrix_view<float, int64_t> out,

)

Perform inverse quantization step on previously quantized dataset.

Note that depending on the chosen data types train dataset the conversion is not lossless.

Usage example:

auto quantized_dataset = raft::make_device_matrix<int8_t, int64_t>(handle, samples, features);
cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
quantized_dataset.view()); auto dataset_revert = raft::make_device_matrix<float, int64_t>(handle,
samples, features); cuvs::preprocessing::quantize::scalar::inverse_transform(handle, quantizer,
dataset_revert.view());

Parameters:

• res – [in] raft resource

• quantizer – [in] a scalar quantizer

• dataset – [in] a row-major matrix view on device

• out – [out] a row-major matrix view on device

void inverse_transform(

raft::resources const &res,

const quantizer<float> &quantizer,

raft::host_matrix_view<const int8_t, int64_t> dataset,

raft::host_matrix_view<float, int64_t> out,

)

Perform inverse quantization step on previously quantized dataset.

Note that depending on the chosen data types train dataset the conversion is not lossless.

Usage example:

auto quantized_dataset = raft::make_host_matrix<int8_t, int64_t>(samples, features);
cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
quantized_dataset.view()); auto dataset_revert = raft::make_host_matrix<float, int64_t>(samples,
features); cuvs::preprocessing::quantize::scalar::inverse_transform(handle, quantizer,
dataset_revert.view());

Parameters:

• res – [in] raft resource

• quantizer – [in] a scalar quantizer

• dataset – [in] a row-major matrix view on host

• out – [out] a row-major matrix view on host

quantizer<half> train(

raft::resources const &res,

const params params,

raft::device_matrix_view<const half, int64_t> dataset,

)

Initializes a scalar quantizer to be used later for quantizing the dataset.

Usage example:

raft::handle_t handle;
cuvs::preprocessing::quantize::scalar::params params;
auto quantizer = cuvs::preprocessing::quantize::scalar::train<half, int8_t>(handle, params,
dataset);

Parameters:

• res – [in] raft resource

• params – [in] configure scalar quantizer, e.g. quantile

• dataset – [in] a row-major matrix view on device

Returns:

quantizer

quantizer<half> train(

raft::resources const &res,

const params params,

raft::host_matrix_view<const half, int64_t> dataset,

)

Initializes a scalar quantizer to be used later for quantizing the dataset.

Usage example:

raft::handle_t handle;
cuvs::preprocessing::quantize::scalar::params params;
auto quantizer = cuvs::preprocessing::quantize::scalar::train<half, int8_t>(handle, params,
dataset);

Parameters:

• res – [in] raft resource

• params – [in] configure scalar quantizer, e.g. quantile

• dataset – [in] a row-major matrix view on host

Returns:

quantizer

void transform(

raft::resources const &res,

const quantizer<half> &quantizer,

raft::device_matrix_view<const half, int64_t> dataset,

raft::device_matrix_view<int8_t, int64_t> out,

)

Applies quantization transform to given dataset.

Usage example:

raft::handle_t handle;
cuvs::preprocessing::quantize::scalar::params params;
auto quantizer = cuvs::preprocessing::quantize::scalar::train<half, int8_t>(handle, params,
dataset); auto quantized_dataset = raft::make_device_matrix<int8_t, int64_t>(handle, samples,
features); cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
quantized_dataset.view());

Parameters:

• res – [in] raft resource

• quantizer – [in] a scalar quantizer

• dataset – [in] a row-major matrix view on device

• out – [out] a row-major matrix view on device

void transform(

raft::resources const &res,

const quantizer<half> &quantizer,

raft::host_matrix_view<const half, int64_t> dataset,

raft::host_matrix_view<int8_t, int64_t> out,

)

Applies quantization transform to given dataset.

Usage example:

raft::handle_t handle;
cuvs::preprocessing::quantize::scalar::params params;
auto quantizer = cuvs::preprocessing::quantize::scalar::train<half, int8_t>(handle, params,
dataset); auto quantized_dataset = raft::make_host_matrix<int8_t, int64_t>(samples, features);
cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
quantized_dataset.view());

Parameters:

• res – [in] raft resource

• quantizer – [in] a scalar quantizer

• dataset – [in] a row-major matrix view on host

• out – [out] a row-major matrix view on host

void inverse_transform(

raft::resources const &res,

const quantizer<half> &quantizer,

raft::device_matrix_view<const int8_t, int64_t> dataset,

raft::device_matrix_view<half, int64_t> out,

)

Perform inverse quantization step on previously quantized dataset.

Note that depending on the chosen data types train dataset the conversion is not lossless.

Usage example:

auto quantized_dataset = raft::make_device_matrix<int8_t, int64_t>(handle, samples, features);
cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
quantized_dataset.view()); auto dataset_revert = raft::make_device_matrix<half, int64_t>(handle,
samples, features); cuvs::preprocessing::quantize::scalar::inverse_transform(handle, quantizer,
dataset_revert.view());

Parameters:

• res – [in] raft resource

• quantizer – [in] a scalar quantizer

• dataset – [in] a row-major matrix view on device

• out – [out] a row-major matrix view on device

void inverse_transform(

raft::resources const &res,

const quantizer<half> &quantizer,

raft::host_matrix_view<const int8_t, int64_t> dataset,

raft::host_matrix_view<half, int64_t> out,

)

Perform inverse quantization step on previously quantized dataset.

Note that depending on the chosen data types train dataset the conversion is not lossless.

Usage example:

auto quantized_dataset = raft::make_host_matrix<int8_t, int64_t>(samples, features);
cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
quantized_dataset.view()); auto dataset_revert = raft::make_host_matrix<half, int64_t>(samples,
features); cuvs::preprocessing::quantize::scalar::inverse_transform(handle, quantizer,
dataset_revert.view());

Parameters:

• res – [in] raft resource

• quantizer – [in] a scalar quantizer

• dataset – [in] a row-major matrix view on host

• out – [out] a row-major matrix view on host

struct params

#include <scalar.hpp>

quantizer parameters.

template<typename T>
struct quantizer

#include <scalar.hpp>

Defines and stores scalar for quantisation upon training.

The quantization is performed by a linear mapping of an interval in the float data type to the full range of the quantized int type.

Template Parameters:

T – data element type