#
Quantize 🤗 Transformers models

##
`bitsandbytes` Integration

🤗 Transformers is closely integrated with most used modules on `bitsandbytes`. You can load your model in 8-bit precision with few lines of code. This is supported by most of the GPU hardwares since the `0.37.0` release of `bitsandbytes`.

Learn more about the quantization method in the LLM.int8() paper, or the blogpost about the collaboration.

Since its `0.39.0` release, you can load any model that supports `device_map` using 4-bit quantization, leveraging FP4 data type.

Here are the things you can do using `bitsandbytes` integration

###
FP4 quantization

####
Requirements

Make sure that you have installed the requirements below before running any of the code snippets below.

- Latest `bitsandbytes` library `pip install bitsandbytes>=0.39.0`

- Install latest `accelerate` from source `pip install git+https://github.com/huggingface/accelerate.git`

- Install latest `transformers` from source `pip install git+https://github.com/huggingface/transformers.git`

####
Load a large model in 4bit

By using `load_in_4bit=True` when calling the `.from_pretrained` method, you can divide your memory use by 4 (roughly).

    # pip install transformers accelerate bitsandbytes
    from transformers import AutoModelForCausalLM, AutoTokenizer

    model_id = "bigscience/bloom-1b7"

    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", load_in_4bit=True)

Note that once a model has been loaded in 4-bit it is currently not possible to push the quantized weights on the Hub. Note also that you cannot train 4-bit weights as this is not supported yet. However you can use 4-bit models to train extra parameters, this will be covered in the next section.

###
Load a large model in 8bit

You can load a model by roughly halving the memory requirements by using `load_in_8bit=True` argument when calling `.from_pretrained` method

    # pip install transformers accelerate bitsandbytes
    from transformers import AutoModelForCausalLM, AutoTokenizer

    model_id = "bigscience/bloom-1b7"

    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", load_in_8bit=True)

Then, use your model as you would usually use a PreTrainedModel.

You can check the memory footprint of your model with `get_memory_footprint` method.

    print(model.get_memory_footprint())

With this integration we were able to load large models on smaller devices and run them without any issue.

Note that once a model has been loaded in 8-bit it is currently not possible to push the quantized weights on the Hub except if you use the latest `transformers` and `bitsandbytes`. Note also that you cannot train 8-bit weights as this is not supported yet. However you can use 8-bit models to train extra parameters, this will be covered in the next section. Note also that `device_map` is optional but setting `device_map = 'auto'` is prefered for inference as it will dispatch efficiently the model on the available ressources.

####
Advanced usecases

Here we will cover some advanced usecases you can perform with FP4 quantization

#####
Change the compute dtype

The compute dtype is used to change the dtype that will be used during computation. For example, hidden states could be in `float32` but computation can be set to bf16 for speedups. By default, the compute dtype is set to `float32`.

    import torch
    from transformers import BitsAndBytesConfig

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

#####
Using NF4 (Normal Float 4) data type

You can also use the NF4 data type, which is a new 4bit datatype adapted for weights that have been initialized using a normal distribution. For that run:

    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)

#####
Use nested quantization for more memory efficient inference

We also advise users to use the nested quantization technique. This saves more memory at no additional performance - from our empirical observations, this enables fine-tuning llama-13b model on an NVIDIA-T4 16GB with a sequence length of 1024, batch size of 1 and gradient accumulation steps of 4.

    from transformers import BitsAndBytesConfig

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

    model_double_quant = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=double_quant_config)

###
Push quantized models on the 🤗 Hub

You can push a quantized model on the Hub by naively using `push_to_hub` method. This will first push the quantization configuration file, then push the quantized model weights. Make sure to use `bitsandbytes>0.37.2` (at this time of writing, we tested it on `bitsandbytes==0.38.0.post1`) to be able to use this feature.

    from transformers import AutoModelForCausalLM, AutoTokenizer

    model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-560m", device_map="auto", load_in_8bit=True)
    tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-560m")

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

Pushing 8bit models on the Hub is strongely encouraged for large models. This will allow the community to benefit from the memory footprint reduction and loading for example large models on a Google Colab.

###
Load a quantized model from the 🤗 Hub

You can load a quantized model from the Hub by using `from_pretrained` method. Make sure that the pushed weights are quantized, by checking that the attribute `quantization_config` is present in the model configuration object.

    from transformers import AutoModelForCausalLM, AutoTokenizer

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

Note that in this case, you don’t need to specify the arguments `load_in_8bit=True`, but you need to make sure that `bitsandbytes` and `accelerate` are installed. Note also that `device_map` is optional but setting `device_map = 'auto'` is prefered for inference as it will dispatch efficiently the model on the available ressources.

###
Advanced usecases

This section is intended to advanced users, that want to explore what it is possible to do beyond loading and running 8-bit models.

####
Offload between `cpu` and `gpu`

One of the advanced usecase of this is being able to load a model and dispatch the weights between `CPU` and `GPU`. Note that the weights that will be dispatched on CPU will not be converted in 8-bit, thus kept in `float32`. This feature is intended for users that want to fit a very large model and dispatch the model between GPU and CPU.

First, load a `BitsAndBytesConfig` from `transformers` and set the attribute `llm_int8_enable_fp32_cpu_offload` to `True`:

    from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

    quantization_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True)

Let’s say you want to load `bigscience/bloom-1b7` model, and you have just enough GPU RAM to fit the entire model except the `lm_head`. Therefore write a custom device_map as follows:

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

And load your model as follows:

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

And that’s it! Enjoy your model!

####
Play with `llm_int8_threshold`

You can play with the `llm_int8_threshold` argument to change the threshold of the outliers. An “outlier” is a hidden state value that is greater than a certain threshold. This corresponds to the outlier threshold for outlier detection as described in `LLM.int8()` paper. Any hidden states value that is above this threshold will be considered an outlier and the operation on those values will be done in fp16. Values are usually normally distributed, that is, most values are in the range [-3.5, 3.5], but there are some exceptional systematic outliers that are very differently distributed for large models. These outliers are often in the interval [-60, -6] or [6, 60]. Int8 quantization works well for values of magnitude ~5, but beyond that, there is a significant performance penalty. A good default threshold is 6, but a lower threshold might be needed for more unstable models (small models, fine-tuning). This argument can impact the inference speed of the model. We suggest to play with this parameter to find which one is the best for your usecase.

    from transformers import AutoModelForCausalLM, AutoTokenizer, 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,
    )
    tokenizer = AutoTokenizer.from_pretrained(model_id)

####
Skip the conversion of some modules

Some models has several modules that needs to be not converted in 8-bit to ensure stability. For example Jukebox model has several `lm_head` modules that should be skipped. Play with `llm_int8_skip_modules`

    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=device_map,
        quantization_config=quantization_config,
    )
    tokenizer = AutoTokenizer.from_pretrained(model_id)

####
Fine-tune a model that has been loaded in 8-bit

With the official support of adapters in the Hugging Face ecosystem, you can fine-tune models that have been loaded in 8-bit. This enables fine-tuning large models such as `flan-t5-large` or `facebook/opt-6.7b` in a single google Colab. Please have a look at `peft` library for more details.

Note that you don’t need to pass `device_map` when loading the model for training. It will automatically load your model on your GPU. You can also set the device map to a specific device if needed (e.g. `cuda:0`, `0`, `torch.device('cuda:0')`). Please note that `device_map=auto` should be used for inference only.

###
BitsAndBytesConfig

###
class transformers.BitsAndBytesConfig

< source >

( load_in_8bit = False load_in_4bit = False llm_int8_threshold = 6.0 llm_int8_skip_modules = None llm_int8_enable_fp32_cpu_offload = False llm_int8_has_fp16_weight = False bnb_4bit_compute_dtype = None bnb_4bit_quant_type = 'fp4' bnb_4bit_use_double_quant = False **kwargs )

Parameters

- load_in_8bit (`bool`, optional, defaults to `False`) — This flag is used to enable 8-bit quantization with LLM.int8().
- load_in_4bit (`bool`, optional, defaults to `False`) — This flag is used to enable 4-bit quantization by replacing the Linear layers with FP4/NF4 layers from `bitsandbytes`.
- llm_int8_threshold (`float`, optional, defaults to 6) — This corresponds to the outlier threshold for outlier detection as described in `LLM.int8() : 8-bit Matrix Multiplication for Transformers at Scale` paper: https://arxiv.org/abs/2208.07339 Any hidden states value that is above this threshold will be considered an outlier and the operation on those values will be done in fp16. Values are usually normally distributed, that is, most values are in the range [-3.5, 3.5], but there are some exceptional systematic outliers that are very differently distributed for large models. These outliers are often in the interval [-60, -6] or [6, 60]. Int8 quantization works well for values of magnitude ~5, but beyond that, there is a significant performance penalty. A good default threshold is 6, but a lower threshold might be needed for more unstable models (small models, fine-tuning).
- llm_int8_skip_modules (`List[str]`, optional) — An explicit list of the modules that we do not want to convert in 8-bit. This is useful for models such as Jukebox that has several heads in different places and not necessarily at the last position. For example for `CausalLM` models, the last `lm_head` is kept in its original `dtype`.
- llm_int8_enable_fp32_cpu_offload (`bool`, optional, defaults to `False`) — This flag is used for advanced use cases and users that are aware of this feature. If you want to split your model in different parts and run some parts in int8 on GPU and some parts in fp32 on CPU, you can use this flag. This is useful for offloading large models such as `google/flan-t5-xxl`. Note that the int8 operations will not be run on CPU.
- llm_int8_has_fp16_weight (`bool`, optional, defaults to `False`) — This flag runs LLM.int8() with 16-bit main weights. This is useful for fine-tuning as the weights do not have to be converted back and forth for the backward pass.
- bnb_4bit_compute_dtype (`torch.dtype` or str, optional, defaults to `torch.float32`) — This sets the computational type which might be different than the input time. For example, inputs might be fp32, but computation can be set to bf16 for speedups.
- bnb_4bit_quant_type (`str`, {fp4, nf4}, defaults to `fp4`) — This sets the quantization data type in the bnb.nn.Linear4Bit layers. Options are FP4 and NF4 data types which are specified by `fp4` or `nf4`.
- bnb_4bit_use_double_quant (`bool`, optional, defaults to `False`) — This flag is used for nested quantization where the quantization constants from the first quantization are quantized again.
- kwargs (`Dict[str, Any]`, optional) — Additional parameters from which to initialize the configuration object.

This is a wrapper class about all possible attributes and features that you can play with a model that has been loaded using `bitsandbytes`.

This replaces `load_in_8bit` or `load_in_4bit`therefore both options are mutually exclusive.

Currently only supports `LLM.int8()`, `FP4`, and `NF4` quantization. If more methods are added to `bitsandbytes`, then more arguments will be added to this class.

####
from_dict

< source >

( config_dict return_unused_kwargs **kwargs ) → BitsAndBytesConfig

Parameters

- config_dict (`Dict[str, Any]`) — Dictionary that will be used to instantiate the configuration object.
- return_unused_kwargs (`bool`) — Whether or not to return a list of unused keyword arguments. Used for `from_pretrained` method in `PreTrainedModel`.
- kwargs (`Dict[str, Any]`) — Additional parameters from which to initialize the configuration object.

Returns

BitsAndBytesConfig

The configuration object instantiated from those parameters.

Instantiates a BitsAndBytesConfig from a Python dictionary of parameters.

####
is_quantizable

< source >

( )

Returns `True` if the model is quantizable, `False` otherwise.

####
post_init

< source >

( )

Safety checker that arguments are correct - also replaces some NoneType arguments with their default values.

####
quantization_method

< source >

( )

This method returns the quantization method used for the model. If the model is not quantizable, it returns `None`.

####
to_dict

< source >

( )

Serializes this instance to a Python dictionary. Returns: `Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.

####
to_json_file

< source >

( json_file_path: typing.Union[str, os.PathLike] )

Parameters

- json_file_path (`str` or `os.PathLike`) — Path to the JSON file in which this configuration instance’s parameters will be saved.
- use_diff (`bool`, optional, defaults to `True`) — If set to `True`, only the difference between the config instance and the default `BitsAndBytesConfig()` is serialized to JSON file.

Save this instance to a JSON file.

##
Quantization with 🤗 `optimum`

Please have a look at Optimum documentation to learn more about quantization methods that are supported by `optimum` and see if these are applicable for your usecase.