#
bitsandbytes

bitsandbytes is the easiest option for quantizing a model to 8 and 4-bit. 8-bit quantization multiplies outliers in fp16 with non-outliers in int8, converts the non-outlier values back to fp16, and then adds them together to return the weights in fp16. This reduces the degradative effect outlier values have on a model’s performance. 4-bit quantization compresses a model even further, and it is commonly used with QLoRA to finetune quantized LLMs.

To use bitsandbytes, make sure you have the following libraries installed:

8-bit
4-bit

    pip install transformers accelerate bitsandbytes>0.37.0

Now you can quantize a model by passing a `BitsAndBytesConfig` to from_pretrained() method. This works for any model in any modality, as long as it supports loading with Accelerate and contains `torch.nn.Linear` layers.

8-bit
4-bit

Quantizing a model in 8-bit halves the memory-usage, and for large models, set `device_map="auto"` to efficiently use the GPUs available:

    from transformers import AutoModelForCausalLM, BitsAndBytesConfig

    quantization_config = BitsAndBytesConfig(load_in_8bit=True)

    model_8bit = AutoModelForCausalLM.from_pretrained(
        "bigscience/bloom-1b7",
        quantization_config=quantization_config
    )

By default, all the other modules such as `torch.nn.LayerNorm` are converted to `torch.float16`. You can change the data type of these modules with the `torch_dtype` parameter if you want:

    import torch
    from transformers import AutoModelForCausalLM, BitsAndBytesConfig

    quantization_config = BitsAndBytesConfig(load_in_8bit=True)

    model_8bit = AutoModelForCausalLM.from_pretrained(
        "facebook/opt-350m",
        quantization_config=quantization_config,
        torch_dtype=torch.float32
    )
    model_8bit.model.decoder.layers[-1].final_layer_norm.weight.dtype

Once a model is quantized to 8-bit, you can’t push the quantized weights to the Hub unless you’re using the latest version of Transformers and bitsandbytes. If you have the latest versions, then you can push the 8-bit model to the Hub with the push_to_hub() method. The quantization config.json file is pushed first, followed by the quantized model weights.

    from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

    quantization_config = BitsAndBytesConfig(load_in_8bit=True)

    model = AutoModelForCausalLM.from_pretrained(
        "bigscience/bloom-560m",
        quantization_config=quantization_config
    )
    tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-560m")

    model.push_to_hub("bloom-560m-8bit")

Training with 8-bit and 4-bit weights are only supported for training extra parameters.

You can check your memory footprint with the `get_memory_footprint` method:

    print(model.get_memory_footprint())

Quantized models can be loaded from the from_pretrained() method without needing to specify the `load_in_8bit` or `load_in_4bit` parameters:

    from transformers import AutoModelForCausalLM, AutoTokenizer

    model = AutoModelForCausalLM.from_pretrained("{your_username}/bloom-560m-8bit", device_map="auto")

##
8-bit (LLM.int8() algorithm)

Learn more about the details of 8-bit quantization in this blog post!

This section explores some of the specific features of 8-bit models, such as offloading, outlier thresholds, skipping module conversion, and finetuning.

###
Offloading

8-bit models can offload weights between the CPU and GPU to support fitting very large models into memory. The weights dispatched to the CPU are actually stored in float32, and aren’t converted to 8-bit. For example, to enable offloading for the bigscience/bloom-1b7 model, start by creating a BitsAndBytesConfig:

    from transformers import AutoModelForCausalLM, BitsAndBytesConfig

    quantization_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True)

Design a custom device map to fit everything on your GPU except for the `lm_head`, which you’ll dispatch to the CPU:

    device_map = {
        "transformer.word_embeddings": 0,
        "transformer.word_embeddings_layernorm": 0,
        "lm_head": "cpu",
        "transformer.h": 0,
        "transformer.ln_f": 0,
    }

Now load your model with the custom `device_map` and `quantization_config`:

    model_8bit = AutoModelForCausalLM.from_pretrained(
        "bigscience/bloom-1b7",
        device_map=device_map,
        quantization_config=quantization_config,
    )

###
Outlier threshold

An “outlier” is a hidden state value greater than a certain threshold, and these values are computed in fp16. While the values are usually normally distributed ([-3.5, 3.5]), this distribution can be very different for large models ([-60, 6] or [6, 60]). 8-bit quantization works well for values ~5, but beyond that, there is a significant performance penalty. A good default threshold value is 6, but a lower threshold may be needed for more unstable models (small models or finetuning).

To find the best threshold for your model, we recommend experimenting with the `llm_int8_threshold` parameter in BitsAndBytesConfig:

    from transformers import AutoModelForCausalLM, BitsAndBytesConfig

    model_id = "bigscience/bloom-1b7"

    quantization_config = BitsAndBytesConfig(
        llm_int8_threshold=10,
    )

    model_8bit = AutoModelForCausalLM.from_pretrained(
        model_id,
        device_map=device_map,
        quantization_config=quantization_config,
    )

###
Skip module conversion

For some models, like Jukebox, you don’t need to quantize every module to 8-bit which can actually cause instability. With Jukebox, there are several `lm_head` modules that should be skipped using the `llm_int8_skip_modules` parameter in BitsAndBytesConfig:

    from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

    model_id = "bigscience/bloom-1b7"

    quantization_config = BitsAndBytesConfig(
        llm_int8_skip_modules=["lm_head"],
    )

    model_8bit = AutoModelForCausalLM.from_pretrained(
        model_id,
        device_map="auto",
        quantization_config=quantization_config,
    )

###
Finetuning

With the PEFT library, you can finetune large models like flan-t5-large and facebook/opt-6.7b with 8-bit quantization. You don’t need to pass the `device_map` parameter for training because it’ll automatically load your model on a GPU. However, you can still customize the device map with the `device_map` parameter if you want to (`device_map="auto"` should only be used for inference).

##
4-bit (QLoRA algorithm)

Try 4-bit quantization in this notebook and learn more about it’s details in this blog post.

This section explores some of the specific features of 4-bit models, such as changing the compute data type, using the Normal Float 4 (NF4) data type, and using nested quantization.

###
Compute data type

To speedup computation, you can change the data type from float32 (the default value) to bf16 using the `bnb_4bit_compute_dtype` parameter in BitsAndBytesConfig:

    import torch
    from transformers import BitsAndBytesConfig

    quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16)

###
Normal Float 4 (NF4)

NF4 is a 4-bit data type from the QLoRA paper, adapted for weights initialized from a normal distribution. You should use NF4 for training 4-bit base models. This can be configured with the `bnb_4bit_quant_type` parameter in the BitsAndBytesConfig:

    from transformers import BitsAndBytesConfig

    nf4_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_quant_type="nf4",
    )

    model_nf4 = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=nf4_config)

For inference, the `bnb_4bit_quant_type` does not have a huge impact on performance. However, to remain consistent with the model weights, you should use the `bnb_4bit_compute_dtype` and `torch_dtype` values.

###
Nested quantization

Nested quantization is a technique that can save additional memory at no additional performance cost. This feature performs a second quantization of the already quantized weights to save an addition 0.4 bits/parameter. For example, with nested quantization, you can finetune a Llama-13b model on a 16GB NVIDIA T4 GPU with a sequence length of 1024, a batch size of 1, and enabling gradient accumulation with 4 steps.

    from transformers import BitsAndBytesConfig

    double_quant_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_use_double_quant=True,
    )

    model_double_quant = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b", quantization_config=double_quant_config)

##
Dequantizing bitsandbytes models

Once quantized, you can dequantize the model to the original precision but this might result in a small quality loss of the model. Make sure you have enough GPU RAM to fit the dequantized model.

    from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer

    model_id = "facebook/opt-125m"

    model = AutoModelForCausalLM.from_pretrained(model_id, BitsAndBytesConfig(load_in_4bit=True))
    tokenizer = AutoTokenizer.from_pretrained(model_id)

    model.dequantize()

    text = tokenizer("Hello my name is", return_tensors="pt").to(0)

    out = model.generate(**text)
    print(tokenizer.decode(out[0]))

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