#
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

< source >

( A: Tensor B: Tensor out: typing.Optional[torch.Tensor] = None dtype = torch.int32 ) →
`torch.Tensor`

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

< source >

( A: Tensor row_stats: Tensor col_stats: Tensor out: typing.Optional[torch.Tensor] = None bias: typing.Optional[torch.Tensor] = None ) →
`torch.Tensor`

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

< source >

( A: Tensor stats: Tensor ) →
`torch.Tensor` with dtype `torch.float32`

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

< source >

( A: Tensor threshold = 0.0 ) →
`Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]`

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.

##
4-bit

####
bitsandbytes.functional.dequantize_4bit

< source >

( 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' ) →
`torch.Tensor`

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 128 on ROCm and 64 otherwise. Valid values are 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 the absolute maximum value within these blocks is used for scaling the non-linear dequantization.

####
bitsandbytes.functional.dequantize_fp4

< source >

( 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 )

####
bitsandbytes.functional.dequantize_nf4

< source >

( 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 )

####
bitsandbytes.functional.gemv_4bit

< source >

( A: Tensor B: Tensor out: typing.Optional[torch.Tensor] = None transposed_A = False transposed_B = False state = None )

####
bitsandbytes.functional.quantize_4bit

< source >

( 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 ) →
Tuple[`torch.Tensor`, `QuantState`]

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 128 on ROCm and 64 otherwise. Valid values are 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

< source >

( A: Tensor absmax: typing.Optional[torch.Tensor] = None out: typing.Optional[torch.Tensor] = None blocksize = None compress_statistics = False quant_storage = torch.uint8 )

####
bitsandbytes.functional.quantize_nf4

< source >

( A: Tensor absmax: typing.Optional[torch.Tensor] = None out: typing.Optional[torch.Tensor] = None blocksize = None compress_statistics = False quant_storage = torch.uint8 )

###
class bitsandbytes.functional.QuantState

< source >

( absmax shape = None code = None blocksize = None quant_type = None dtype = None offset = None state2 = None )

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

####
as_dict

< source >

( packed = False )

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

< source >

( qs_dict: dict device: device )

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

Primitives used in the 8-bit optimizer quantization.

For more details see 8-Bit Approximations for Parallelism in Deep Learning

####
bitsandbytes.functional.dequantize_blockwise

< source >

( 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 ) →
`torch.Tensor`

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

< source >

( A: Tensor code: typing.Optional[torch.Tensor] = None absmax: typing.Optional[torch.Tensor] = None out: typing.Optional[torch.Tensor] = None blocksize = 4096 nested = False ) →
`Tuple[torch.Tensor, QuantState]`

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

< source >

( A: typing.Optional[torch.Tensor] ) →
`Optional[ct.c_void_p]`

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

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