bitsandbytes/pr_2030/en/reference/functional.md

# Overview
The `bitsandbytes.functional` API provides the low-level building blocks for the library's features.

## When to Use `bitsandbytes.functional`

* When you need direct control over quantized operations and their parameters.
* To build custom layers or operations leveraging low-bit arithmetic.
* To integrate with other ecosystem tooling.
* For experimental or research purposes requiring non-standard quantization or performance optimizations.

## LLM.int8()[[bitsandbytes.functional.int8_linear_matmul]]

#### bitsandbytes.functional.int8_linear_matmul[[bitsandbytes.functional.int8_linear_matmul]]

```python
bitsandbytes.functional.int8_linear_matmul(A: Tensor, B: Tensor, out: typing.Optional[torch.Tensor] = None, dtype = torch.int32)
```

[Source](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_2030/bitsandbytes/functional.py#L1536)

**Parameters:**

A (`torch.Tensor`) : The first matrix operand with the data type `torch.int8`.

B (`torch.Tensor`) : The second matrix operand with the data type `torch.int8`.

out (`torch.Tensor`, *optional*) : A pre-allocated tensor used to store the result.

dtype (`torch.dtype`, *optional*) : The expected data type of the output. Defaults to `torch.int32`.

**Returns:** `torch.Tensor`

The result of the operation.

**Raises:** ``NotImplementedError`` or ``RuntimeError``

- ``NotImplementedError`` -- The operation is not supported in the current environment.
- ``RuntimeError`` -- Raised when the cannot be completed for any other reason.

Performs an 8-bit integer matrix multiplication.

A linear transformation is applied such that `out = A @ B.T`. When possible, integer tensor core hardware is
utilized to accelerate the operation.

#### bitsandbytes.functional.int8_mm_dequant[[bitsandbytes.functional.int8_mm_dequant]]

```python
bitsandbytes.functional.int8_mm_dequant(A: Tensor, row_stats: Tensor, col_stats: Tensor, out: typing.Optional[torch.Tensor] = None, bias: typing.Optional[torch.Tensor] = None)
```

[Source](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_2030/bitsandbytes/functional.py#L1562)

**Parameters:**

A (`torch.Tensor` with dtype `torch.int32`) : The result of a quantized int8 matrix multiplication.

row_stats (`torch.Tensor`) : The row-wise quantization statistics for the lhs operand of the matrix multiplication.

col_stats (`torch.Tensor`) : The column-wise quantization statistics for the rhs operand of the matrix multiplication.

out (`torch.Tensor`, *optional*) : A pre-allocated tensor to store the output of the operation.

bias (`torch.Tensor`, *optional*) : An optional bias vector to add to the result.

**Returns:** `torch.Tensor`

The dequantized result with an optional bias, with dtype `torch.float16`.

Performs dequantization on the result of a quantized int8 matrix multiplication.

#### bitsandbytes.functional.int8_vectorwise_dequant[[bitsandbytes.functional.int8_vectorwise_dequant]]

```python
bitsandbytes.functional.int8_vectorwise_dequant(A: Tensor, stats: Tensor)
```

[Source](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_2030/bitsandbytes/functional.py#L1641)

**Parameters:**

A (`torch.Tensor` with dtype `torch.int8`) : The quantized int8 tensor.

stats (`torch.Tensor` with dtype `torch.float32`) : The row-wise quantization statistics.

**Returns:** `torch.Tensor` with dtype `torch.float32`

The dequantized tensor.

Dequantizes a tensor with dtype `torch.int8` to `torch.float32`.

#### bitsandbytes.functional.int8_vectorwise_quant[[bitsandbytes.functional.int8_vectorwise_quant]]

```python
bitsandbytes.functional.int8_vectorwise_quant(A: Tensor, threshold = 0.0)
```

[Source](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_2030/bitsandbytes/functional.py#L1655)

**Parameters:**

A (`torch.Tensor` with dtype `torch.float16`) : The input tensor.

threshold (`float`, *optional*) : An optional threshold for sparse decomposition of outlier features.  No outliers are held back when 0.0. Defaults to 0.0.

**Returns:** `Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]`

A tuple containing the quantized tensor and relevant statistics.
- `torch.Tensor` with dtype `torch.int8`: The quantized data.
- `torch.Tensor` with dtype `torch.float32`: The quantization scales.
- `torch.Tensor` with dtype `torch.int32`, *optional*: A list of column indices which contain outlier features.

Quantizes a tensor with dtype `torch.float16` to `torch.int8` in accordance to the `LLM.int8()` algorithm.

For more information, see the [LLM.int8() paper](https://arxiv.org/abs/2208.07339).

## 4-bit[[bitsandbytes.functional.dequantize_4bit]]

#### bitsandbytes.functional.dequantize_4bit[[bitsandbytes.functional.dequantize_4bit]]

```python
bitsandbytes.functional.dequantize_4bit(A: Tensor, quant_state: typing.Optional[bitsandbytes.functional.QuantState] = None, absmax: typing.Optional[torch.Tensor] = None, out: typing.Optional[torch.Tensor] = None, blocksize: typing.Optional[int] = None, quant_type = 'fp4')
```

[Source](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_2030/bitsandbytes/functional.py#L992)

**Parameters:**

A (`torch.Tensor`) : The quantized input tensor.

quant_state (`QuantState`, *optional*) : The quantization state as returned by `quantize_4bit`. Required if `absmax` is not provided.

absmax (`torch.Tensor`, *optional*) : A tensor containing the scaling values. Required if `quant_state` is not provided and ignored otherwise.

out (`torch.Tensor`, *optional*) : A tensor to use to store the result.

blocksize (`int`, *optional*) : The size of the blocks. Defaults to 64. Valid values are 32, 64, 128, 256, 512, 1024, 2048, and 4096.

quant_type (`str`, *optional*) : The data type to use: `nf4` or `fp4`. Defaults to `fp4`.

**Returns:** `torch.Tensor`

The dequantized tensor.

**Raises:** ``ValueError``

- ``ValueError`` -- Raised when the input data type or blocksize is not supported.

Dequantizes a packed 4-bit quantized tensor.

The input tensor is dequantized by dividing it into blocks of `blocksize` values.
The absolute maximum value within these blocks is used for scaling
the non-linear dequantization.

#### bitsandbytes.functional.dequantize_fp4[[bitsandbytes.functional.dequantize_fp4]]

```python
bitsandbytes.functional.dequantize_fp4(A: Tensor, quant_state: typing.Optional[bitsandbytes.functional.QuantState] = None, absmax: typing.Optional[torch.Tensor] = None, out: typing.Optional[torch.Tensor] = None, blocksize: typing.Optional[int] = None)
```

[Source](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_2030/bitsandbytes/functional.py#L972)

#### bitsandbytes.functional.dequantize_nf4[[bitsandbytes.functional.dequantize_nf4]]

```python
bitsandbytes.functional.dequantize_nf4(A: Tensor, quant_state: typing.Optional[bitsandbytes.functional.QuantState] = None, absmax: typing.Optional[torch.Tensor] = None, out: typing.Optional[torch.Tensor] = None, blocksize: typing.Optional[int] = None)
```

[Source](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_2030/bitsandbytes/functional.py#L982)

#### bitsandbytes.functional.gemv_4bit[[bitsandbytes.functional.gemv_4bit]]

```python
bitsandbytes.functional.gemv_4bit(A: Tensor, B: Tensor, out: typing.Optional[torch.Tensor] = None, transposed_A = False, transposed_B = False, state = None)
```

[Source](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_2030/bitsandbytes/functional.py#L1300)

#### bitsandbytes.functional.quantize_4bit[[bitsandbytes.functional.quantize_4bit]]

```python
bitsandbytes.functional.quantize_4bit(A: Tensor, absmax: typing.Optional[torch.Tensor] = None, out: typing.Optional[torch.Tensor] = None, blocksize = None, compress_statistics = False, quant_type = 'fp4', quant_storage = torch.uint8)
```

[Source](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_2030/bitsandbytes/functional.py#L884)

**Parameters:**

A (`torch.Tensor`) : The input tensor. Supports `float16`, `bfloat16`, or `float32` datatypes.

absmax (`torch.Tensor`, *optional*) : A tensor to use to store the absmax values.

out (`torch.Tensor`, *optional*) : A tensor to use to store the result.

blocksize (`int`, *optional*) : The size of the blocks. Defaults to 64. Valid values are 32, 64, 128, 256, 512, 1024, 2048, and 4096.

compress_statistics (`bool`, *optional*) : Whether to additionally quantize the absmax values. Defaults to False.

quant_type (`str`, *optional*) : The data type to use: `nf4` or `fp4`. Defaults to `fp4`.

quant_storage (`torch.dtype`, *optional*) : The dtype of the tensor used to store the result. Defaults to `torch.uint8`.

**Returns:** Tuple[`torch.Tensor`, `QuantState`]

A tuple containing the quantization results.
- `torch.Tensor`: The quantized tensor with packed 4-bit values.
- `QuantState`: The state object used to undo the quantization.

**Raises:** ``ValueError``

- ``ValueError`` -- Raised when the input data type is not supported.

Quantize tensor A in blocks of 4-bit values.

Quantizes tensor A by dividing it into blocks which are independently quantized.

#### bitsandbytes.functional.quantize_fp4[[bitsandbytes.functional.quantize_fp4]]

```python
bitsandbytes.functional.quantize_fp4(A: Tensor, absmax: typing.Optional[torch.Tensor] = None, out: typing.Optional[torch.Tensor] = None, blocksize = None, compress_statistics = False, quant_storage = torch.uint8)
```

[Source](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_2030/bitsandbytes/functional.py#L862)

#### bitsandbytes.functional.quantize_nf4[[bitsandbytes.functional.quantize_nf4]]

```python
bitsandbytes.functional.quantize_nf4(A: Tensor, absmax: typing.Optional[torch.Tensor] = None, out: typing.Optional[torch.Tensor] = None, blocksize = None, compress_statistics = False, quant_storage = torch.uint8)
```

[Source](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_2030/bitsandbytes/functional.py#L873)

#### bitsandbytes.functional.QuantState[[bitsandbytes.functional.QuantState]]

```python
bitsandbytes.functional.QuantState(absmax, shape = None, code = None, blocksize = None, quant_type = None, dtype = None, offset = None, state2 = None)
```

[Source](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_2030/bitsandbytes/functional.py#L420)

container for quantization state components to work with Params4bit and similar classes

#### as_dict[[bitsandbytes.functional.QuantState.as_dict]]

```python
as_dict(packed: bool = False)
```

[Source](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_2030/bitsandbytes/functional.py#L545)

returns dict of tensors and strings to use in serialization via _save_to_state_dict()
param: packed -- returns dict[str, torch.Tensor] for state_dict fit for safetensors saving

#### from_dict[[bitsandbytes.functional.QuantState.from_dict]]

```python
from_dict(qs_dict: dict, device: device)
```

[Source](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_2030/bitsandbytes/functional.py#L493)

unpacks components of state_dict into QuantState
where necessary, convert into strings, torch.dtype, ints, etc.

qs_dict: based on state_dict, with only relevant keys, striped of prefixes.

item with key `quant_state.bitsandbytes__[nf4/fp4]` may contain minor and non-tensor quant state items.

## Dynamic 8-bit Quantization[[bitsandbytes.functional.dequantize_blockwise]]

Primitives used in the 8-bit optimizer quantization.

For more details see [8-Bit Approximations for Parallelism in Deep Learning](https://arxiv.org/abs/1511.04561)

#### bitsandbytes.functional.dequantize_blockwise[[bitsandbytes.functional.dequantize_blockwise]]

```python
bitsandbytes.functional.dequantize_blockwise(A: Tensor, quant_state: typing.Optional[bitsandbytes.functional.QuantState] = None, absmax: typing.Optional[torch.Tensor] = None, code: typing.Optional[torch.Tensor] = None, out: typing.Optional[torch.Tensor] = None, blocksize: int = 4096, nested = False)
```

[Source](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_2030/bitsandbytes/functional.py#L689)

**Parameters:**

A (`torch.Tensor`) : The quantized input tensor.

quant_state (`QuantState`, *optional*) : The quantization state as returned by `quantize_blockwise`. Required if `absmax` is not provided.

absmax (`torch.Tensor`, *optional*) : A tensor containing the scaling values. Required if `quant_state` is not provided and ignored otherwise.

code (`torch.Tensor`, *optional*) : A mapping describing the low-bit data type. Defaults to a signed 8-bit dynamic type. For more details, see  (8-Bit Approximations for Parallelism in Deep Learning)[https://arxiv.org/abs/1511.04561]. Ignored when `quant_state` is provided.

out (`torch.Tensor`, *optional*) : A tensor to use to store the result.

blocksize (`int`, *optional*) : The size of the blocks. Defaults to 4096. Valid values are 64, 128, 256, 512, 1024, 2048, and 4096. Ignored when `quant_state` is provided.

**Returns:** `torch.Tensor`

The dequantized tensor. The datatype is indicated by `quant_state.dtype` and defaults to `torch.float32`.

**Raises:** ``ValueError``

- ``ValueError`` -- Raised when the input data type is not supported.

Dequantize a tensor in blocks of values.

The input tensor is dequantized by dividing it into blocks of `blocksize` values.
The the absolute maximum value within these blocks is used for scaling
the non-linear dequantization.

#### bitsandbytes.functional.quantize_blockwise[[bitsandbytes.functional.quantize_blockwise]]

```python
bitsandbytes.functional.quantize_blockwise(A: Tensor, code: typing.Optional[torch.Tensor] = None, absmax: typing.Optional[torch.Tensor] = None, out: typing.Optional[torch.Tensor] = None, blocksize = 4096, nested = False)
```

[Source](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_2030/bitsandbytes/functional.py#L613)

**Parameters:**

A (`torch.Tensor`) : The input tensor. Supports `float16`, `bfloat16`, or `float32` datatypes.

code (`torch.Tensor`, *optional*) : A mapping describing the low-bit data type. Defaults to a signed 8-bit dynamic type. For more details, see  (8-Bit Approximations for Parallelism in Deep Learning)[https://arxiv.org/abs/1511.04561].

absmax (`torch.Tensor`, *optional*) : A tensor to use to store the absmax values.

out (`torch.Tensor`, *optional*) : A tensor to use to store the result.

blocksize (`int`, *optional*) : The size of the blocks. Defaults to 4096. Valid values are 64, 128, 256, 512, 1024, 2048, and 4096.

nested (`bool`, *optional*) : Whether to additionally quantize the absmax values. Defaults to False.

**Returns:** `Tuple[torch.Tensor, QuantState]`

A tuple containing the quantization results.
- `torch.Tensor`: The quantized tensor.
- `QuantState`: The state object used to undo the quantization.

**Raises:** ``ValueError``

- ``ValueError`` -- Raised when the input data type is not supported.

Quantize a tensor in blocks of values.

The input tensor is quantized by dividing it into blocks of `blocksize` values.
The the absolute maximum value within these blocks is calculated for scaling
the non-linear quantization.

## Utility[[bitsandbytes.functional.get_ptr]]

#### bitsandbytes.functional.get_ptr[[bitsandbytes.functional.get_ptr]]

```python
bitsandbytes.functional.get_ptr(A: typing.Optional[torch.Tensor])
```

[Source](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_2030/bitsandbytes/functional.py#L405)

**Parameters:**

A (`Optional[Tensor]`) : A PyTorch tensor.

**Returns:** `Optional[ct.c_void_p]`

A pointer to the underlying tensor data.

Gets the memory address of the first element of a tenso