Metadata-Version: 2.1
Name: bitsandbytes
Version: 0.42.0+das.opt1.dtk24042
Summary: k-bit optimizers and matrix multiplication routines.
Home-page: https://github.com/TimDettmers/bitsandbytes
Author: Tim Dettmers
Author-email: dettmers@cs.washington.edu
License: MIT
Keywords: gpu optimizers optimization 8-bit quantization compression
Classifier: Development Status :: 4 - Beta
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: NOTICE.md
Requires-Dist: torch
Requires-Dist: numpy
Requires-Dist: scipy
Provides-Extra: benchmark
Requires-Dist: pandas; extra == "benchmark"
Requires-Dist: matplotlib; extra == "benchmark"

# bitsandbytes-rocm

The bitsandbytes is a lightweight wrapper around CUDA custom functions, in particular 8-bit optimizers, matrix multiplication (LLM.int8()), and quantization functions.
This fork is the ROCm adaptation of bitsandbytes 0.39.1. The repo is inspired by [agrocylo/bitsandbytes-rocm](https://github.com/agrocylo/bitsandbytes-rocm/tree/main/bitsandbytes), which is a ROCm version of bitsandbytes 0.37. While this fork incorporating the majority of features from bitsandbytes 0.39.1, including the crucial 4 bit quantization feature, certain features such as hipblaslt and hip_bfloat16 have been disabled. Enabling these features is listed as a task for the future.



Resources:
- [8-bit Optimizer Paper](https://arxiv.org/abs/2110.02861) --  [Video](https://www.youtube.com/watch?v=IxrlHAJtqKE) -- [Docs](https://bitsandbytes.readthedocs.io/en/latest/)

- [LLM.int8() Paper](https://arxiv.org/abs/2208.07339) -- [LLM.int8() Software Blog Post](https://huggingface.co/blog/hf-bitsandbytes-integration) -- [LLM.int8() Emergent Features Blog Post](https://timdettmers.com/2022/08/17/llm-int8-and-emergent-features/)

## TL;DR
**Requirements**
Python >=3.8. Linux distribution (Ubuntu, MacOS, etc.) + ROCm >= 5.4.2 or CUDA > 10.0


**Installation**:


You need to compile from source for ROCm. 

Compilation quickstart:
```bash
# Run Docker
docker run -it --network=host --device=/dev/kfd --device=/dev/dri --name=bnb_test --shm-size=8g --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --group-add video rocm/pytorch:rocm5.7_ubuntu22.04_py3.10_pytorch_2.0.1


# Install Dependencies
cd <workspace>
git clone --recurse https://github.com/ROCmSoftwarePlatform/hipBLASLt
cd hipBLASLt
git checkout 4b3b34405e7e25cff404f69bfd0a832644430477
./install.sh -idc
 
cd ..
pip install einops lion_pytorch


# Install BitsandBytes
git clone --recurse https://github.com/ROCmSoftwarePlatform/bitsandbytes
cd bitsandbytes
git checkout rocm_enabled
make hip
python setup.py install


# Run the unit test. If it runs successfully, the library has been installed successfully.
pytest -vvv ./tests/ 2>&1 | tee BitsAndBytes_UT_summary.log
```

**Using Int8 inference with HuggingFace Transformers**

```python
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
  'decapoda-research/llama-7b-hf',
  device_map='auto',
  load_in_8bit=True,
  max_memory=f'{int(torch.cuda.mem_get_info()[0]/1024**3)-2}GB')
```

A more detailed example, can be found in [examples/int8_inference_huggingface.py](examples/int8_inference_huggingface.py).

**Using 8-bit optimizer**:
1. Comment out optimizer: ``#torch.optim.Adam(....)``
2. Add 8-bit optimizer of your choice ``bnb.optim.Adam8bit(....)`` (arguments stay the same)
3. Replace embedding layer if necessary: ``torch.nn.Embedding(..) -> bnb.nn.Embedding(..)``


**Using 8-bit Inference**:
1. Comment out torch.nn.Linear: ``#linear = torch.nn.Linear(...)``
2. Add bnb 8-bit linear light module: ``linear = bnb.nn.Linear8bitLt(...)`` (base arguments stay the same)
3. There are two modes:
   - Mixed 8-bit training with 16-bit main weights. Pass the argument ``has_fp16_weights=True`` (default)
   - Int8 inference. Pass the argument ``has_fp16_weights=False``
4. To use the full LLM.int8() method, use the ``threshold=k`` argument. We recommend ``k=6.0``.
```python
# LLM.int8()
linear = bnb.nn.Linear8bitLt(dim1, dim2, bias=True, has_fp16_weights=False, threshold=6.0)
# inputs need to be fp16
out = linear(x.to(torch.float16))
```


## Features
- 8-bit Matrix multiplication with mixed precision decomposition
- LLM.int8() inference
- 8-bit Optimizers: Adam, AdamW, RMSProp, LARS, LAMB, Lion (saves 75% memory)
- Stable Embedding Layer: Improved stability through better initialization, and normalization
- 8-bit quantization: Quantile, Linear, and Dynamic quantization
- Fast quantile estimation: Up to 100x faster than other algorithms

## Using bitsandbytes

### Using Int8 Matrix Multiplication

For straight Int8 matrix multiplication with mixed precision decomposition you can use ``bnb.matmul(...)``. To enable mixed precision decomposition, use the threshold parameter:
```python
bnb.matmul(..., threshold=6.0)
```

For instructions how to use LLM.int8() inference layers in your own code, see the TL;DR above or for extended instruction see [this blog post](https://huggingface.co/blog/hf-bitsandbytes-integration).

### Using the 8-bit Optimizers

With bitsandbytes 8-bit optimizers can be used by changing a single line of code in your codebase. For NLP models we recommend also to use the StableEmbedding layers (see below) which improves results and helps with stable 8-bit optimization.  To get started with 8-bit optimizers, it is sufficient to replace your old optimizer with the 8-bit optimizer in the following way:
```python
import bitsandbytes as bnb

# adam = torch.optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.995)) # comment out old optimizer
adam = bnb.optim.Adam8bit(model.parameters(), lr=0.001, betas=(0.9, 0.995)) # add bnb optimizer
adam = bnb.optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.995), optim_bits=8) # equivalent


torch.nn.Embedding(...) ->  bnb.nn.StableEmbedding(...) # recommended for NLP models
```

Note that by default all parameter tensors with less than 4096 elements are kept at 32-bit even if you initialize those parameters with 8-bit optimizers. This is done since such small tensors do not save much memory and often contain highly variable parameters (biases) or parameters that require high precision (batch norm, layer norm). You can change this behavior like so:
```python
# parameter tensors with less than 16384 values are optimized in 32-bit
# it is recommended to use multiplies of 4096
adam = bnb.optim.Adam8bit(model.parameters(), min_8bit_size=16384)
```

### Change Bits and other Hyperparameters for Individual Parameters

If you want to optimize some unstable parameters with 32-bit Adam and others with 8-bit Adam, you can use the `GlobalOptimManager`. With this, we can also configure specific hyperparameters for particular layers, such as embedding layers. To do that, we need two things: (1) register the parameter while they are still on the CPU, (2) override the config with the new desired hyperparameters (anytime, anywhere). See our [guide](howto_config_override.md) for more details

### Fairseq Users

To use the Stable Embedding Layer, override the respective `build_embedding(...)` function of your model. Make sure to also use the `--no-scale-embedding` flag to disable scaling of the word embedding layer (nor replaced with layer norm). You can use the optimizers by replacing the optimizer in the respective file (`adam.py` etc.).

## Release and Feature History

For upcoming features and changes and full history see [Patch Notes](CHANGELOG.md).

## Errors

1. RuntimeError: CUDA error: no kernel image is available for execution on the device. [Solution](errors_and_solutions.md#No-kernel-image-available)
2. __fatbinwrap_.. [Solution](errors_and_solutions.md#fatbinwrap_)

## License

The majority of bitsandbytes is licensed under MIT, however portions of the project are available under separate license terms: Pytorch is licensed under the BSD license.

We thank Fabio Cannizzo for his work on [FastBinarySearch](https://github.com/fabiocannizzo/FastBinarySearch) which we use for CPU quantization.

## How to cite us
If you found this library and found LLM.int8() useful, please consider citing our work:

```bibtex
@article{dettmers2022llmint8,
  title={LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale},
  author={Dettmers, Tim and Lewis, Mike and Belkada, Younes and Zettlemoyer, Luke},
  journal={arXiv preprint arXiv:2208.07339},
  year={2022}
}
```

For 8-bit optimizers or quantization routines, please consider citing the following work:

```bibtex
@article{dettmers2022optimizers,
  title={8-bit Optimizers via Block-wise Quantization},
  author={Dettmers, Tim and Lewis, Mike and Shleifer, Sam and Zettlemoyer, Luke},
  journal={9th International Conference on Learning Representations, ICLR},
  year={2022}
}
```
