(l-onnx-doc-Concat)=

# Concat


(l-onnx-op-concat-13)=

## Concat - 13

### Version

- **name**: [Concat (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Concat)
- **domain**: `main`
- **since_version**: `13`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 13**.

### Summary

Concatenate a list of tensors into a single tensor. All input tensors must have the same shape, except for the dimension size of the axis to concatenate on.

### Attributes

* **axis - INT** (required) :

  Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..

### Inputs

Between 1 and 2147483647 inputs.

- **inputs** (variadic, heterogeneous) - **T**:

  List of tensors for concatenation

### Outputs

- **concat_result** (heterogeneous) - **T**:

  Concatenated tensor

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)` ):

  Constrain output types to any tensor type.

### Examples

#### default

```python
import numpy as np
import onnx

test_cases: dict[str, Sequence[Any]] = {
    "1d": ([1, 2], [3, 4]),
    "2d": ([[1, 2], [3, 4]], [[5, 6], [7, 8]]),
    "3d": (
        [[[1, 2], [3, 4]], [[5, 6], [7, 8]]],
        [[[9, 10], [11, 12]], [[13, 14], [15, 16]]],
    ),
}

for test_case, values_ in test_cases.items():
    values = [np.asarray(v, dtype=np.float32) for v in values_]
    for i in range(len(values[0].shape)):
        in_args = ["value" + str(k) for k in range(len(values))]
        node = onnx.helper.make_node(
            "Concat", inputs=list(in_args), outputs=["output"], axis=i
        )
        output = np.concatenate(values, i)
        expect(
            node,
            inputs=list(values),
            outputs=[output],
            name="test_concat_" + test_case + "_axis_" + str(i),
        )

    for i in range(-len(values[0].shape), 0):
        in_args = ["value" + str(k) for k in range(len(values))]
        node = onnx.helper.make_node(
            "Concat", inputs=list(in_args), outputs=["output"], axis=i
        )
        output = np.concatenate(values, i)
        expect(
            node,
            inputs=list(values),
            outputs=[output],
            name="test_concat_" + test_case + "_axis_negative_" + str(abs(i)),
        )
```

```{toctree}
text_diff_Concat_11_13
```

(l-onnx-op-concat-11)=

## Concat - 11

### Version

- **name**: [Concat (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Concat)
- **domain**: `main`
- **since_version**: `11`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 11**.

### Summary

Concatenate a list of tensors into a single tensor. All input tensors must have the same shape, except for the dimension size of the axis to concatenate on.

### Attributes

* **axis - INT** (required) :

  Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..

### Inputs

Between 1 and 2147483647 inputs.

- **inputs** (variadic, heterogeneous) - **T**:

  List of tensors for concatenation

### Outputs

- **concat_result** (heterogeneous) - **T**:

  Concatenated tensor

### Type Constraints

* **T** in ( `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)` ):

  Constrain output types to any tensor type.

```{toctree}
text_diff_Concat_4_13
text_diff_Concat_4_11
```

(l-onnx-op-concat-4)=

## Concat - 4

### Version

- **name**: [Concat (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Concat)
- **domain**: `main`
- **since_version**: `4`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 4**.

### Summary

Concatenate a list of tensors into a single tensor

### Attributes

* **axis - INT** (required) :

  Which axis to concat on

### Inputs

Between 1 and 2147483647 inputs.

- **inputs** (variadic, heterogeneous) - **T**:

  List of tensors for concatenation

### Outputs

- **concat_result** (heterogeneous) - **T**:

  Concatenated tensor

### Type Constraints

* **T** in ( `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)` ):

  Constrain output types to any tensor type.

```{toctree}
text_diff_Concat_1_13
text_diff_Concat_1_11
text_diff_Concat_1_4
```

(l-onnx-op-concat-1)=

## Concat - 1

### Version

- **name**: [Concat (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Concat)
- **domain**: `main`
- **since_version**: `1`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `False`

This version of the operator has been available
**since version 1**.

### Summary

Concatenate a list of tensors into a single tensor

### Attributes

* **axis - INT** :

  Which axis to concat on.  Default value is 1.

### Inputs

Between 1 and 2147483647 inputs.

- **inputs** (variadic, heterogeneous) - **T**:

  List of tensors for concatenation

### Outputs

- **concat_result** (heterogeneous) - **T**:

  Concatenated tensor

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)` ):

  Constrain output types to float tensors.