bitsandbytes/main/en/reference/nn/linear4bit.md

4-bit quantization

QLoRA is a finetuning method that quantizes a model to 4-bits and adds a set of low-rank adaptation (LoRA) weights to the model and tuning them through the quantized weights. This method also introduces a new data type, 4-bit NormalFloat (LinearNF4) in addition to the standard Float4 data type (LinearFP4). LinearNF4 is a quantization data type for normally distributed data and can improve performance.

Linear4bit[[bitsandbytes.nn.Linear4bit]]

bitsandbytes.nn.Linear4bit[[bitsandbytes.nn.Linear4bit]]

bitsandbytes.nn.Linear4bit(input_features, output_features, bias = True, compute_dtype = None, compress_statistics = True, quant_type = 'fp4', quant_storage = torch.uint8, device = None)

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This class is the base module for the 4-bit quantization algorithm presented in QLoRA. QLoRA 4-bit linear layers uses blockwise k-bit quantization under the hood, with the possibility of selecting various compute datatypes such as FP4 and NF4.

In order to quantize a linear layer one should first load the original fp16 / bf16 weights into the Linear4bit module, then call quantized_module.to("cuda") to quantize the fp16 / bf16 weights.

Example:

import torch
import torch.nn as nn

import bitsandbytes as bnb
from bitsandbytes.nn import Linear4bit

fp16_model = nn.Sequential(
    nn.Linear(64, 64),
    nn.Linear(64, 64)
)

quantized_model = nn.Sequential(
    Linear4bit(64, 64),
    Linear4bit(64, 64)
)

quantized_model.load_state_dict(fp16_model.state_dict())
quantized_model = quantized_model.to(0) # Quantization happens here

init[[bitsandbytes.nn.Linear4bit.init]]

__init__(input_features, output_features, bias = True, compute_dtype = None, compress_statistics = True, quant_type = 'fp4', quant_storage = torch.uint8, device = None)

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Parameters:

input_features (str) : Number of input features of the linear layer.

output_features (str) : Number of output features of the linear layer.

bias (bool, defaults to True) : Whether the linear class uses the bias term as well.

Initialize Linear4bit class.

LinearFP4[[bitsandbytes.nn.LinearFP4]]

bitsandbytes.nn.LinearFP4[[bitsandbytes.nn.LinearFP4]]

bitsandbytes.nn.LinearFP4(input_features, output_features, bias = True, compute_dtype = None, compress_statistics = True, quant_storage = torch.uint8, device = None)

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Implements the FP4 data type.

init[[bitsandbytes.nn.LinearFP4.init]]

__init__(input_features, output_features, bias = True, compute_dtype = None, compress_statistics = True, quant_storage = torch.uint8, device = None)

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Parameters:

input_features (str) : Number of input features of the linear layer.

output_features (str) : Number of output features of the linear layer.

bias (bool, defaults to True) : Whether the linear class uses the bias term as well.

LinearNF4[[bitsandbytes.nn.LinearNF4]]

bitsandbytes.nn.LinearNF4[[bitsandbytes.nn.LinearNF4]]

bitsandbytes.nn.LinearNF4(input_features, output_features, bias = True, compute_dtype = None, compress_statistics = True, quant_storage = torch.uint8, device = None)

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Implements the NF4 data type.

Constructs a quantization data type where each bin has equal area under a standard normal distribution N(0, 1) that is normalized into the range [-1, 1].

For more information read the paper: QLoRA: Efficient Finetuning of Quantized LLMs (https://arxiv.org/abs/2305.14314)

Implementation of the NF4 data type in bitsandbytes can be found in the create_normal_map function in the functional.py file: https://github.com/TimDettmers/bitsandbytes/blob/main/bitsandbytes/functional.py#L236.

init[[bitsandbytes.nn.LinearNF4.init]]

__init__(input_features, output_features, bias = True, compute_dtype = None, compress_statistics = True, quant_storage = torch.uint8, device = None)

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Parameters:

input_features (str) : Number of input features of the linear layer.

output_features (str) : Number of output features of the linear layer.

bias (bool, defaults to True) : Whether the linear class uses the bias term as well.

Params4bit[[bitsandbytes.nn.Params4bit]]

bitsandbytes.nn.Params4bit[[bitsandbytes.nn.Params4bit]]

bitsandbytes.nn.Params4bit(data: typing.Optional[torch.Tensor] = None, requires_grad = False, quant_state: typing.Optional[bitsandbytes.functional.QuantState] = None, blocksize: typing.Optional[int] = None, compress_statistics: bool = True, quant_type: str = 'fp4', quant_storage: dtype = torch.uint8, module: typing.Optional[ForwardRef('Linear4bit')] = None, bnb_quantized: bool = False, **kwargs)

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[[bitsandbytes.nn.Params4bit.init]]

<lambda>(*args, **kwargs)

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