Dummy input generators
Dummy Input Generators
It is very common to have to generate dummy inputs to perform a task (tracing, exporting a model to some backend, testing model outputs, etc). The goal of DummyInputGenerator classes is to make this generation easy and re-usable.
Base class
class optimum.utils.DummyInputGenerator
( )
Generates dummy inputs for the supported input names, in the requested framework.
concat_inputs
( inputsdim: int )
Parameters
dim (
int) — The dimension along which to concatenate.
Concatenates inputs together.
constant_tensor
( shape: typing.List[int]value: typing.Union[int, float] = 1dtype: typing.Optional[typing.Any] = Noneframework: str = 'pt' )
Parameters
shape (
List[int]) — The shape of the constant tensor.value (
Union[int, float], defaults to 1) — The value to fill the constant tensor with.dtype (
Optional[Any], defaults toNone) — The dtype of the constant tensor.framework (
str, defaults to"pt") — The requested framework.
Generates a constant tensor.
generate
( input_name: strframework: str = 'pt'int_dtype: str = 'int64'float_dtype: str = 'fp32' )
Parameters
input_name (
str) — The name of the input to generate.framework (
str, defaults to"pt") — The requested framework.int_dtype (
str, defaults to"int64") — The dtypes of generated integer tensors.float_dtype (
str, defaults to"fp32") — The dtypes of generated float tensors.
Generates the dummy input matching input_name for the requested framework.
pad_input_on_dim
( input_dim: intdesired_length: typing.Optional[int] = Nonepadding_length: typing.Optional[int] = Nonevalue: typing.Union[int, float] = 1dtype: typing.Optional[typing.Any] = None )
Parameters
dim (
int) — The dimension along which to pad.desired_length (
Optional[int], defaults toNone) — The desired length along the dimension after padding.padding_length (
Optional[int], defaults toNone) — The length to pad along the dimension.value (
Union[int, float], defaults to 1) — The value to use for padding.dtype (
Optional[Any], defaults toNone) — The dtype of the padding.
Pads an input either to the desired length, or by a padding length.
random_float_tensor
( shape: typing.List[int]min_value: float = 0max_value: float = 1framework: str = 'pt'dtype: str = 'fp32' )
Parameters
shape (
List[int]) — The shape of the random tensor.min_value (
float, defaults to 0) — The minimum value allowed.max_value (
float, defaults to 1) — The maximum value allowed.framework (
str, defaults to"pt") — The requested framework.dtype (
str, defaults to"fp32") — The dtype of the generated float tensor. Could be “fp32”, “fp16”, “bf16”.
Generates a tensor of random floats in the [min_value, max_value) range.
random_int_tensor
( shape: typing.List[int]max_value: intmin_value: int = 0framework: str = 'pt'dtype: str = 'int64' )
Parameters
shape (
List[int]) — The shape of the random tensor.max_value (
int) — The maximum value allowed.min_value (
int, defaults to 0) — The minimum value allowed.framework (
str, defaults to"pt") — The requested framework.dtype (
str, defaults to"int64") — The dtype of the generated integer tensor. Could be “int64”, “int32”, “int8”.
Generates a tensor of random integers in the [min_value, max_value) range.
supports_input
( input_name: str ) → bool
Parameters
input_name (
str) — The name of the input to generate.
Returns
bool
A boolean specifying whether the input is supported.
Checks whether the DummyInputGenerator supports the generation of the requested input.
Existing dummy input generators
class optimum.utils.DummyTextInputGenerator
( task: strnormalized_config: NormalizedTextConfigbatch_size: int = 2sequence_length: int = 16num_choices: int = 4random_batch_size_range: typing.Union[typing.Tuple[int, int], NoneType] = Nonerandom_sequence_length_range: typing.Union[typing.Tuple[int, int], NoneType] = Nonerandom_num_choices_range: typing.Union[typing.Tuple[int, int], NoneType] = None**kwargs )
Generates dummy encoder text inputs.
class optimum.utils.DummyDecoderTextInputGenerator
( task: strnormalized_config: NormalizedTextConfigbatch_size: int = 2sequence_length: int = 16num_choices: int = 4random_batch_size_range: typing.Union[typing.Tuple[int, int], NoneType] = Nonerandom_sequence_length_range: typing.Union[typing.Tuple[int, int], NoneType] = Nonerandom_num_choices_range: typing.Union[typing.Tuple[int, int], NoneType] = None**kwargs )
Generates dummy decoder text inputs.
class optimum.utils.DummyPastKeyValuesGenerator
( task: strnormalized_config: NormalizedTextConfigbatch_size: int = 2sequence_length: int = 16random_batch_size_range: typing.Union[typing.Tuple[int, int], NoneType] = Nonerandom_sequence_length_range: typing.Union[typing.Tuple[int, int], NoneType] = None**kwargs )
Generates dummy past_key_values inputs.
class optimum.utils.DummySeq2SeqPastKeyValuesGenerator
( task: strnormalized_config: NormalizedSeq2SeqConfigbatch_size: int = 2sequence_length: int = 16encoder_sequence_length: typing.Optional[int] = Nonerandom_batch_size_range: typing.Union[typing.Tuple[int, int], NoneType] = Nonerandom_sequence_length_range: typing.Union[typing.Tuple[int, int], NoneType] = None**kwargs )
Generates dummy past_key_values inputs for seq2seq architectures.
class optimum.utils.DummyBboxInputGenerator
( task: strnormalized_config: NormalizedConfigbatch_size: int = 2sequence_length: int = 16random_batch_size_range: typing.Union[typing.Tuple[int, int], NoneType] = Nonerandom_sequence_length_range: typing.Union[typing.Tuple[int, int], NoneType] = None**kwargs )
Generates dummy bbox inputs.
class optimum.utils.DummyVisionInputGenerator
( task: strnormalized_config: NormalizedVisionConfigbatch_size: int = 2num_channels: int = 3width: int = 64height: int = 64**kwargs )
Generates dummy vision inputs.
class optimum.utils.DummyAudioInputGenerator
( task: strnormalized_config: NormalizedConfigbatch_size: int = 2feature_size: int = 80nb_max_frames: int = 3000audio_sequence_length: int = 16000**kwargs )
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