# 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](https://huggingface.co/docs/optimum/main/en/utils/dummy_input_generators#optimum.utils.DummyInputGenerator) classes is to make this generation easy and re-usable.

### Base class

#### class optimum.utils.DummyInputGenerator

[\<source>](https://github.com/huggingface/optimum/blob/main/optimum/utils/input_generators.py#L112)

( )

Generates dummy inputs for the supported input names, in the requested framework.

**concat\_inputs**

[\<source>](https://github.com/huggingface/optimum/blob/main/optimum/utils/input_generators.py#L255)

( inputsdim: int )

Parameters

* **dim** (`int`) — The dimension along which to concatenate.

Concatenates inputs together.

**constant\_tensor**

[\<source>](https://github.com/huggingface/optimum/blob/main/optimum/utils/input_generators.py#L214)

( 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 to `None`) — The dtype of the constant tensor.
* **framework** (`str`, defaults to `"pt"`) — The requested framework.

Generates a constant tensor.

**generate**

[\<source>](https://github.com/huggingface/optimum/blob/main/optimum/utils/input_generators.py#L133)

( 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**

[\<source>](https://github.com/huggingface/optimum/blob/main/optimum/utils/input_generators.py#L278)

( 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 to `None`) — The desired length along the dimension after padding.
* **padding\_length** (`Optional[int]`, defaults to `None`) — The length to pad along the dimension.
* **value** (`Union[int, float]`, defaults to 1) — The value to use for padding.
* **dtype** (`Optional[Any]`, defaults to `None`) — The dtype of the padding.

Pads an input either to the desired length, or by a padding length.

**random\_float\_tensor**

[\<source>](https://github.com/huggingface/optimum/blob/main/optimum/utils/input_generators.py#L183)

( 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**

[\<source>](https://github.com/huggingface/optimum/blob/main/optimum/utils/input_generators.py#L153)

( 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**

[\<source>](https://github.com/huggingface/optimum/blob/main/optimum/utils/input_generators.py#L119)

( 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

[\<source>](https://github.com/huggingface/optimum/blob/main/optimum/utils/input_generators.py#L324)

( 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

[\<source>](https://github.com/huggingface/optimum/blob/main/optimum/utils/input_generators.py#L376)

( 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

[\<source>](https://github.com/huggingface/optimum/blob/main/optimum/utils/input_generators.py#L439)

( 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

[\<source>](https://github.com/huggingface/optimum/blob/main/optimum/utils/input_generators.py#L486)

( 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

[\<source>](https://github.com/huggingface/optimum/blob/main/optimum/utils/input_generators.py#L544)

( 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

[\<source>](https://github.com/huggingface/optimum/blob/main/optimum/utils/input_generators.py#L584)

( task: strnormalized\_config: NormalizedVisionConfigbatch\_size: int = 2num\_channels: int = 3width: int = 64height: int = 64\*\*kwargs )

Generates dummy vision inputs.

#### class optimum.utils.DummyAudioInputGenerator

[\<source>](https://github.com/huggingface/optimum/blob/main/optimum/utils/input_generators.py#L638)

( task: strnormalized\_config: NormalizedConfigbatch\_size: int = 2feature\_size: int = 80nb\_max\_frames: int = 3000audio\_sequence\_length: int = 16000\*\*kwargs )


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://boinc-ai.gitbook.io/optimum/utilities/dummy-input-generators.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.