neural_compressor.utils.utility

quantization auto-tuning config system.

This file specifies default config options for quantization auto-tuning tool. User should not change values in this file. Instead, user should write a config file (in yaml) and use cfg_from_file(yaml_file) to load it and override the default options.

Module Contents

Classes

LazyImport	Lazy import python module till use.
CpuInfo	Get CPU Info.
Statistics	The statistics printer.
MODE	Mode: Quantization, Benchmark or Pruning.
GLOBAL_STATE	Access the global model.

Functions

version1_lt_version2(version1, version2)	Check whether version1 is less than version2.
version1_gt_version2(version1, version2)	Check whether version1 is greater than version2.
version1_eq_version2(version1, version2)	Check whether version1 is equal to version2.
version1_gte_version2(version1, version2)	Check whether version1 is greater than version2 or is equal to it.
version1_lte_version2(version1, version2)	Check whether version1 is less than version2 or is equal to it.
singleton(cls)	Not displayed in API Docs.
time_limit(seconds)	Limit the time for context execution.
get_size(obj[, seen])	Recursively finds size of objects.
compute_sparsity(tensor)	Compute the sparsity.
fault_tolerant_file(name)	Make another temporary copy of the file.
equal_dicts(d1, d2[, compare_keys, ignore_keys])	Check whether two dicts are same except for those ignored keys.
dump_elapsed_time([customized_msg])	Get the elapsed time for decorated functions.
combine_histogram(old_hist, arr)	Collect layer histogram for arr and combine it with old histogram.
get_tensor_histogram(tensor_data[, bins])	Get the histogram of the tensor data.
get_all_fp32_data(data)	Get all the fp32 data.
get_tuning_history(tuning_history_path)	Get tuning history.
recover(fp32_model, tuning_history_path, num, **kwargs)	Get offline recover tuned model.
str2array(s)	Get the array of the string.
DequantizeWeight(weight_tensor, min_filter_tensor, ...)	Dequantize the weight with min-max filter tensors.
Dequantize(data, scale_info)	Dequantize the data with the scale_info.
load_data_from_pkl(path, filename)	Load data from local pkl file.
dump_data_to_local(data, path, filename)	Dump data to local as pkl file.
set_random_seed(seed)	Set the random seed in config.
set_workspace(workspace)	Set the workspace in config.
set_resume_from(resume_from)	Set the resume_from in config.
set_tensorboard(tensorboard)	Set the tensorboard in config.

neural_compressor.utils.utility.version1_lt_version2(version1, version2)

Check whether version1 is less than version2.

neural_compressor.utils.utility.version1_gt_version2(version1, version2)

Check whether version1 is greater than version2.

neural_compressor.utils.utility.version1_eq_version2(version1, version2)

Check whether version1 is equal to version2.

neural_compressor.utils.utility.version1_gte_version2(version1, version2)

Check whether version1 is greater than version2 or is equal to it.

neural_compressor.utils.utility.version1_lte_version2(version1, version2)

Check whether version1 is less than version2 or is equal to it.

class neural_compressor.utils.utility.LazyImport(module_name)

Bases: object

Lazy import python module till use.

neural_compressor.utils.utility.singleton(cls)

Not displayed in API Docs.

Singleton decorater.

neural_compressor.utils.utility.time_limit(seconds)

Limit the time for context execution.

neural_compressor.utils.utility.get_size(obj, seen=None)

Recursively finds size of objects.

neural_compressor.utils.utility.compute_sparsity(tensor)

Compute the sparsity.

Parameters:

tensor – Tensorflow or Pytorch tensor

Returns:

(the original tensor size, number of zero elements, number of non-zero elements)

neural_compressor.utils.utility.fault_tolerant_file(name)

Make another temporary copy of the file.

neural_compressor.utils.utility.equal_dicts(d1, d2, compare_keys=None, ignore_keys=None)

Check whether two dicts are same except for those ignored keys.

class neural_compressor.utils.utility.CpuInfo

Bases: object

Get CPU Info.

property bf16

Get whether it is bf16.

property vnni

Get whether it is vnni.

property cores_per_socket

Get the cores per socket.

get_number_of_sockets() → int

Get number of sockets in platform.

neural_compressor.utils.utility.dump_elapsed_time(customized_msg='')

Get the elapsed time for decorated functions.

Parameters:

customized_msg (string, optional) – The parameter passed to decorator. Defaults to None.

neural_compressor.utils.utility.combine_histogram(old_hist, arr)

Collect layer histogram for arr and combine it with old histogram.

neural_compressor.utils.utility.get_tensor_histogram(tensor_data, bins=2048)

Get the histogram of the tensor data.

neural_compressor.utils.utility.get_all_fp32_data(data)

Get all the fp32 data.

neural_compressor.utils.utility.get_tuning_history(tuning_history_path)

Get tuning history.

Parameters:

tuning_history_path – The tuning history path, which need users to assign

neural_compressor.utils.utility.recover(fp32_model, tuning_history_path, num, **kwargs)

Get offline recover tuned model.

Parameters:

• fp32_model – Input model path

• tuning_history_path – The tuning history path, which needs user to assign

• num – tune index

neural_compressor.utils.utility.str2array(s)

Get the array of the string.

neural_compressor.utils.utility.DequantizeWeight(weight_tensor, min_filter_tensor, max_filter_tensor)

Dequantize the weight with min-max filter tensors.

neural_compressor.utils.utility.Dequantize(data, scale_info)

Dequantize the data with the scale_info.

class neural_compressor.utils.utility.Statistics(data, header, field_names, output_handle=logger.info)

The statistics printer.

print_stat()

Print the statistics.

class neural_compressor.utils.utility.MODE

Bases: enum.Enum

Mode: Quantization, Benchmark or Pruning.

class neural_compressor.utils.utility.GLOBAL_STATE

Access the global model.

neural_compressor.utils.utility.load_data_from_pkl(path, filename)

Load data from local pkl file.

Parameters:

• path – The directory to load data

• filename – The filename to load

neural_compressor.utils.utility.dump_data_to_local(data, path, filename)

Dump data to local as pkl file.

Parameters:

• data – Data used to dump

• path – The directory to save data

• filename – The filename to dump

Returns:

loaded data

neural_compressor.utils.utility.set_random_seed(seed: int)

Set the random seed in config.

neural_compressor.utils.utility.set_workspace(workspace: str)

Set the workspace in config.

neural_compressor.utils.utility.set_resume_from(resume_from: str)

Set the resume_from in config.

neural_compressor.utils.utility.set_tensorboard(tensorboard: bool)

Set the tensorboard in config.