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2026-07-13 12:38:58 +08:00

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Quantization Formats

Complete guide to INT8, NF4, FP4 quantization formats, double quantization, and custom configurations in bitsandbytes.

Overview

bitsandbytes supports multiple quantization formats:

INT8: 8-bit integer quantization (LLM.int8())
NF4: 4-bit NormalFloat (for normally distributed weights)
FP4: 4-bit FloatPoint (for uniformly distributed weights)
Double Quantization: Quantize the quantization constants

INT8 Quantization

LLM.int8() Algorithm

LLM.int8() uses mixed 8-bit/16-bit matrix multiplication:

Most features (>99.9%) computed in INT8
Outlier features (>threshold) computed in FP16
Results combined for final output

Memory: 50% reduction (2 bytes → 1 byte per parameter) Accuracy: <0.5% degradation

Configuration

from transformers import BitsAndBytesConfig

config = BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_threshold=6.0,  # Outlier threshold
    llm_int8_has_fp16_weight=False,  # Use INT8 storage
    llm_int8_skip_modules=["lm_head"]  # Skip certain layers
)

Parameters Explained

llm_int8_threshold (default: 6.0):

Activations with magnitude > threshold are kept in FP16
Lower = more FP16 (slower but more accurate)
Higher = more INT8 (faster but less accurate)

# Conservative (more accurate)
llm_int8_threshold=5.0

# Aggressive (faster)
llm_int8_threshold=8.0

llm_int8_has_fp16_weight (default: False):

False: Store weights in INT8 (50% memory savings)
True: Store in FP16, quantize only during computation (no memory savings)

llm_int8_skip_modules:

# Skip specific layers (keep in FP16)
llm_int8_skip_modules=["lm_head", "embed_tokens"]

Example

from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-13b-hf",
    quantization_config=config,
    device_map="auto"
)

# Memory: 26GB (FP16) → 13GB (INT8)

When to Use INT8

✅ Use INT8 when:

Need high accuracy (<0.5% loss)
Model fits with 50% reduction
Have Turing+ GPU (tensor cores)

❌ Don't use when:

Need maximum memory savings (use 4-bit)
Inference speed critical (use GPTQ/AWQ)

4-Bit Quantization

NormalFloat4 (NF4)

Optimized for normally distributed weights (most neural networks).

How it works:

Bins chosen to minimize quantization error for normal distribution
Asymmetric quantization bins
Better for transformer weights

Configuration:

config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type="nf4"  # NormalFloat4
)

Memory: 75% reduction (2 bytes → 0.5 bytes per parameter)

FloatPoint4 (FP4)

Standard 4-bit floating point for uniform distributions.

How it works:

Symmetric quantization bins
Better for weights with broader dynamic range
Less common for transformers

Configuration:

config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type="fp4"  # FloatPoint4
)

NF4 vs FP4 Comparison

Aspect	NF4	FP4
Distribution	Normal	Uniform
Typical use	Transformers	CNNs, unusual architectures
Accuracy	Better for LLMs	Worse for LLMs
Speed	Same	Same
Recommendation	✅ Default	Use only if NF4 fails

Rule of thumb: Always use NF4 for transformers.

Example Comparison

# NF4 (recommended)
nf4_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4"
)

# FP4 (alternative)
fp4_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="fp4"
)

# Load and compare
model_nf4 = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-hf",
    quantization_config=nf4_config
)

model_fp4 = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-hf",
    quantization_config=fp4_config
)

# Typical results on MMLU:
# NF4: 45.2%
# FP4: 43.8%
# FP16: 45.9%

Compute Dtype

The bnb_4bit_compute_dtype controls the precision used for actual computation.

Options

torch.bfloat16 (recommended):

bnb_4bit_compute_dtype=torch.bfloat16

Good balance of speed and accuracy
Recommended for A100/H100
Prevents numerical instability

torch.float16:

bnb_4bit_compute_dtype=torch.float16

Slightly faster than BF16
Risk of overflow/underflow
Use only if BF16 unavailable

torch.float32:

bnb_4bit_compute_dtype=torch.float32

Most accurate
Slowest (no tensor core acceleration)
Debugging only

Performance Comparison

Dtype	Speed	Accuracy	Memory
FP32	1× (baseline)	100%	4 bytes
FP16	3-4×	99.5%	2 bytes
BF16	3-4×	99.8%	2 bytes

Recommendation: Always use torch.bfloat16 if supported.

Double Quantization

Quantize the quantization constants for additional memory savings.

How It Works

Standard 4-bit quantization stores:

4-bit quantized weights
FP32 scaling factors (4 bytes per block)

Double quantization:

4-bit quantized weights
INT8 quantized scaling factors (1 byte per block)

Additional savings: ~2-3% memory reduction

Configuration

config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True  # Enable double quantization
)

Example

# Without double quant
model_single = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-70b-hf",
    quantization_config=BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_use_double_quant=False
    )
)
# Memory: ~36GB

# With double quant
model_double = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-70b-hf",
    quantization_config=BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_use_double_quant=True
    )
)
# Memory: ~35GB (saves ~1GB)

Accuracy impact: Negligible (<0.1%)

Recommendation: Always enable for maximum memory savings.

Quantization Storage

Controls storage dtype for quantized weights (important for FSDP).

Configuration

config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_storage=torch.bfloat16  # Storage dtype
)

When to Use

Default (uint8):

Single GPU training/inference
No special requirements

torch.bfloat16 (for FSDP):

bnb_4bit_quant_storage=torch.bfloat16

Required for FSDP+QLoRA
Ensures 4-bit layers wrapped like regular layers
Enables proper model sharding

Example: FSDP Configuration

# CRITICAL: Set quant_storage for FSDP
fsdp_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_storage=torch.bfloat16  # Must match torch_dtype!
)

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-70b-hf",
    quantization_config=fsdp_config,
    torch_dtype=torch.bfloat16  # Must match quant_storage!
)

Recommended Configurations

Production Inference (Best Accuracy)

BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_threshold=6.0
)

Use case: Maximum accuracy with 50% memory savings

Production Inference (Maximum Memory Savings)

BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True
)

Use case: 75% memory reduction with <1% accuracy loss

QLoRA Training (Single GPU)

BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True
)

Use case: Fine-tune 70B on RTX 3090

FSDP + QLoRA (Multi-GPU)

BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_storage=torch.bfloat16  # CRITICAL!
)

Use case: Fine-tune 405B on 8×H100

Advanced: Block-wise Quantization

bitsandbytes uses block-wise quantization:

Weights divided into blocks (typically 64 or 128 elements)
Each block has own scaling factor
Better accuracy than tensor-wise quantization

Block size (automatically determined):

# Typical block sizes
# 4-bit: 64 elements per block
# 8-bit: 64 elements per block

Cannot be configured (internal implementation detail).

Quantization Quality Metrics

Perplexity (Lower is Better)

Model	FP16	INT8	NF4	NF4+DQ
Llama 2 7B	5.12	5.14	5.18	5.19
Llama 2 13B	4.88	4.90	4.93	4.94
Llama 2 70B	3.32	3.33	3.35	3.36

Conclusion: <1% degradation for all quantization methods

MMLU Accuracy (Higher is Better)

Model	FP16	INT8	NF4	FP4
Llama 2 7B	45.9%	45.7%	45.2%	43.8%
Llama 2 13B	54.8%	54.6%	54.1%	52.9%
Llama 2 70B	68.9%	68.7%	68.4%	67.2%

Conclusion: NF4 is significantly better than FP4 for transformers

Troubleshooting

"Quantization failed" Error

Try different quant type:

# If NF4 fails
bnb_4bit_quant_type="fp4"

Numerical Instability

Use BF16 compute:

bnb_4bit_compute_dtype=torch.bfloat16

Poor Quality with 4-bit

Try 8-bit instead:
```
load_in_8bit=True
```
Enable double quantization:
```
bnb_4bit_use_double_quant=True
```
Use BF16 compute dtype

FSDP Errors

Ensure quant_storage matches torch_dtype:

bnb_4bit_quant_storage=torch.bfloat16
torch_dtype=torch.bfloat16  # Must match!

References

LLM.int8() paper: "LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale" (2022)
QLoRA paper: "QLoRA: Efficient Finetuning of Quantized LLMs" (2023)
bitsandbytes GitHub: https://github.com/bitsandbytes-foundation/bitsandbytes
HuggingFace quantization docs: https://huggingface.co/docs/transformers/quantization/bitsandbytes

10 KiB 原始文件 永久链接 Blame 文件历史 Unescape Escape

Quantization Formats

Overview

INT8 Quantization

LLM.int8() Algorithm

Configuration

Parameters Explained

Example

When to Use INT8

4-Bit Quantization

NormalFloat4 (NF4)

FloatPoint4 (FP4)

NF4 vs FP4 Comparison

Example Comparison

Compute Dtype

Options

Performance Comparison

Double Quantization

How It Works

Configuration

Example

Quantization Storage

Configuration

When to Use

Example: FSDP Configuration

Recommended Configurations

Production Inference (Best Accuracy)

Production Inference (Maximum Memory Savings)

QLoRA Training (Single GPU)

FSDP + QLoRA (Multi-GPU)

Advanced: Block-wise Quantization

Quantization Quality Metrics

Perplexity (Lower is Better)

MMLU Accuracy (Higher is Better)

Troubleshooting

"Quantization failed" Error

Numerical Instability

Poor Quality with 4-bit

FSDP Errors

References

10 KiB

原始文件永久链接 Blame 文件历史