Preprocessing#

Scalar Quantizer#

class cuvs.preprocessing.quantize.scalar.Quantizer#

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.

Attributes:

max
min

class cuvs.preprocessing.quantize.scalar.QuantizerParams(quantile=None, *)#

Parameters for scalar quantization

Parameters:

quantile: float: specifies how many outliers at top & bottom will be ignored needs to be within range of (0, 1]

Attributes:

quantile

cuvs.preprocessing.quantize.scalar.train(QuantizerParams params, dataset, resources=None)[source]#

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

Parameters:

paramsQuantizerParams object
datasetrow major host or device dataset
resourcesOptional cuVS Resource handle for reusing CUDA resources.: If Resources aren’t supplied, CUDA resources will be allocated inside this function and synchronized before the function exits. If resources are supplied, you will need to explicitly synchronize yourself by calling resources.sync() before accessing the output.

Returns:

quantizer: cuvs.preprocessing.quantize.scalar.Quantizer

Examples

>>> import cupy as cp
>>> from cuvs.preprocessing.quantize import scalar
>>> n_samples = 50000
>>> n_features = 50
>>> dataset = cp.random.random_sample((n_samples, n_features),
...                                   dtype=cp.float32)
>>> params = scalar.QuantizerParams(quantile=0.99)
>>> quantizer = scalar.train(params, dataset)
>>> transformed = scalar.transform(quantizer, dataset)

cuvs.preprocessing.quantize.scalar.transform(Quantizer quantizer, dataset, output=None, resources=None)[source]#

Applies quantization transform to given dataset

Parameters:

quantizertrained Quantizer object
datasetrow major host or device dataset to transform
outputoptional preallocated output memory, on host or device memory
resourcesOptional cuVS Resource handle for reusing CUDA resources.: If Resources aren’t supplied, CUDA resources will be allocated inside this function and synchronized before the function exits. If resources are supplied, you will need to explicitly synchronize yourself by calling resources.sync() before accessing the output.

Returns:

outputtransformed dataset quantized into a int8

Examples

>>> import cupy as cp
>>> from cuvs.preprocessing.quantize import scalar
>>> n_samples = 50000
>>> n_features = 50
>>> dataset = cp.random.random_sample((n_samples, n_features),
...                                   dtype=cp.float32)
>>> params = scalar.QuantizerParams(quantile=0.99)
>>> quantizer = scalar.train(params, dataset)
>>> transformed = scalar.transform(quantizer, dataset)

cuvs.preprocessing.quantize.scalar.inverse_transform( Quantizer quantizer, dataset, output=None, resources=None, )[source]#

Perform inverse quantization step on previously quantized dataset

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

Parameters:

quantizertrained Quantizer object
datasetrow major host or device dataset to transform
outputoptional preallocated output memory, on host or device
resourcesOptional cuVS Resource handle for reusing CUDA resources.: If Resources aren’t supplied, CUDA resources will be allocated inside this function and synchronized before the function exits. If resources are supplied, you will need to explicitly synchronize yourself by calling resources.sync() before accessing the output.

Returns:

outputtransformed dataset with scalar quantization reversed