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Enable BNB multi-backend support (#31098)

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* enable cpu bnb path

* fix style

* fix code style

* fix 4 bit path

* Update src/transformers/utils/import_utils.py

Co-authored-by: Aarni Koskela <akx@iki.fi>

* add multi backend refactor tests

* fix style

* tweak 4bit quantizer + fix corresponding tests

* tweak 8bit quantizer + *try* fixing corresponding tests

* fix dequant bnb 8bit

* account for Intel CPU in variability of expected outputs

* enable cpu and xpu device map

* further tweaks to account for Intel CPU

* fix autocast to work with both cpu + cuda

* fix comments

* fix comments

* switch to testing_utils.torch_device

* allow for xpu in multi-gpu tests

* fix tests 4bit for CPU NF4

* fix bug with is_torch_xpu_available needing to be called as func

* avoid issue where test reports attr err due to other failure

* fix formatting

* fix typo from resolving of merge conflict

* polish based on last PR review

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>

* fix CI

* Update src/transformers/integrations/integration_utils.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/integrations/integration_utils.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* fix error log

* fix error msg

* add \n in error log

* make quality

* rm bnb cuda restriction in doc

* cpu model don't need dispatch

* fix doc

* fix style

* check cuda avaliable in testing

* fix tests

* Update docs/source/en/model_doc/chameleon.md

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>

* Update docs/source/en/model_doc/llava_next.md

Co-authored-by: Aarni Koskela <akx@iki.fi>

* Update tests/quantization/bnb/test_4bit.py

Co-authored-by: Aarni Koskela <akx@iki.fi>

* Update tests/quantization/bnb/test_4bit.py

Co-authored-by: Aarni Koskela <akx@iki.fi>

* fix doc

* fix check multibackends

* fix import sort

* remove check torch in bnb

* docs: update bitsandbytes references with multi-backend info

* docs: fix small mistakes in bnb paragraph

* run formatting

* reveret bnb check

* move bnb multi-backend check to import_utils

* Update src/transformers/utils/import_utils.py

Co-authored-by: Aarni Koskela <akx@iki.fi>

* fix bnb check

* minor fix for bnb

* check lib first

* fix code style

* Revert "run formatting"

This reverts commit ac108c6d6b34f45a5745a736ba57282405cfaa61.

* fix format

* give warning when bnb version is low and no cuda found]

* fix device assignment check to be multi-device capable

* address akx feedback on get_avlbl_dev fn

* revert partially, as we don't want the function that public, as docs would be too much (enforced)

---------

Co-authored-by: Aarni Koskela <akx@iki.fi>
Co-authored-by: Titus von Koeller <9048635+Titus-von-Koeller@users.noreply.github.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
This commit is contained in:
jiqing-feng
2024-09-24 17:40:56 +08:00
committed by GitHub
parent e15687fffe
commit 11c27dd331
20 changed files with 436 additions and 100 deletions
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12
docs/source/en/model_doc/chameleon.md
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@@ -128,7 +128,17 @@ processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokeniza
### Quantization using Bitsandbytes
The model can be loaded in 8 or 4 bits, greatly reducing the memory requirements while maintaining the performance of the original model. First make sure to install bitsandbytes, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:
The model can be loaded in 8 or 4 bits, greatly reducing the memory requirements while maintaining the performance of the original model. First make sure to install bitsandbytes, `pip install bitsandbytes` and to have access to a GPU/accelerator that is supported by the library.
<Tip>
bitsandbytes is being refactored to support multiple backends beyond CUDA. Currently, ROCm (AMD GPU) and Intel CPU implementations are mature, with Intel XPU in progress and Apple Silicon support expected by Q4/Q1. For installation instructions and the latest backend updates, visit [this link](https://huggingface.co/docs/bitsandbytes/main/en/installation#multi-backend).
We value your feedback to help identify bugs before the full release! Check out [these docs](https://huggingface.co/docs/bitsandbytes/main/en/non_cuda_backends) for more details and feedback links.
</Tip>
Simply change the snippet above with:
```python
from transformers import ChameleonForConditionalGeneration, BitsAndBytesConfig

12
docs/source/en/model_doc/llava_next.md
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@@ -233,7 +233,17 @@ processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokeniza
### Quantization using Bitsandbytes
The model can be loaded in 8 or 4 bits, greatly reducing the memory requirements while maintaining the performance of the original model. First make sure to install bitsandbytes, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:
The model can be loaded in 8 or 4 bits, greatly reducing the memory requirements while maintaining the performance of the original model. First make sure to install bitsandbytes, `pip install bitsandbytes`, and to have access to a GPU/accelerator that is supported by the library.
<Tip>
bitsandbytes is being refactored to support multiple backends beyond CUDA. Currently, ROCm (AMD GPU) and Intel CPU implementations are mature, with Intel XPU in progress and Apple Silicon support expected by Q4/Q1. For installation instructions and the latest backend updates, visit [this link](https://huggingface.co/docs/bitsandbytes/main/en/installation#multi-backend).
We value your feedback to help identify bugs before the full release! Check out [these docs](https://huggingface.co/docs/bitsandbytes/main/en/non_cuda_backends) for more details and feedback links.
</Tip>
Simply change the snippet above with:
```python
from transformers import LlavaNextForConditionalGeneration, BitsAndBytesConfig

12
docs/source/en/model_doc/llava_next_video.md
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@@ -205,7 +205,17 @@ processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokeniza
The model can be loaded in lower bits, significantly reducing memory burden while maintaining the performance of the original model. This allows for efficient deployment on resource-constrained cases.
First make sure to install bitsandbytes by running `pip install bitsandbytes` and to have access to a CUDA compatible GPU device. Load the quantized model by simply adding [`BitsAndBytesConfig`](../main_classes/quantization#transformers.BitsAndBytesConfig) as shown below:
First, make sure to install bitsandbytes by running `pip install bitsandbytes` and to have access to a GPU/accelerator that is supported by the library.
<Tip>
bitsandbytes is being refactored to support multiple backends beyond CUDA. Currently, ROCm (AMD GPU) and Intel CPU implementations are mature, with Intel XPU in progress and Apple Silicon support expected by Q4/Q1. For installation instructions and the latest backend updates, visit [this link](https://huggingface.co/docs/bitsandbytes/main/en/installation#multi-backend).
We value your feedback to help identify bugs before the full release! Check out [these docs](https://huggingface.co/docs/bitsandbytes/main/en/non_cuda_backends) for more details and feedback links.
</Tip>
Then simply load the quantized model by adding [`BitsAndBytesConfig`](../main_classes/quantization#transformers.BitsAndBytesConfig) as shown below:
```python

14
docs/source/en/model_doc/llava_onevision.md
View File

@@ -264,9 +264,19 @@ processor.batch_decode(out, skip_special_tokens=True, clean_up_tokenization_spac
## Model optimization
### Quantization using Bitsandbytes
### Quantization using bitsandbytes
The model can be loaded in 8 or 4 bits, greatly reducing the memory requirements while maintaining the performance of the original model. First make sure to install bitsandbytes, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:
The model can be loaded in 8 or 4 bits, greatly reducing the memory requirements while maintaining the performance of the original model. First make sure to install bitsandbytes, `pip install bitsandbytes` and make sure to have access to a GPU/accelerator that is supported by the library.
<Tip>
bitsandbytes is being refactored to support multiple backends beyond CUDA. Currently, ROCm (AMD GPU) and Intel CPU implementations are mature, with Intel XPU in progress and Apple Silicon support expected by Q4/Q1. For installation instructions and the latest backend updates, visit [this link](https://huggingface.co/docs/bitsandbytes/main/en/installation#multi-backend).
We value your feedback to help identify bugs before the full release! Check out [these docs](https://huggingface.co/docs/bitsandbytes/main/en/non_cuda_backends) for more details and feedback links.
</Tip>
Simply change the snippet above with:
```python
from transformers import LlavaOnevisionForConditionalGeneration, BitsAndBytesConfig

2
docs/source/en/model_doc/mixtral.md
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@@ -141,7 +141,7 @@ The Flash Attention-2 model uses also a more memory efficient cache slicing mech
As the Mixtral model has 45 billion parameters, that would require about 90GB of GPU RAM in half precision (float16), since each parameter is stored in 2 bytes. However, one can shrink down the size of the model using [quantization](../quantization.md). If the model is quantized to 4 bits (or half a byte per parameter), a single A100 with 40GB of RAM is enough to fit the entire model, as in that case only about 27 GB of RAM is required.
Quantizing a model is as simple as passing a `quantization_config` to the model. Below, we'll leverage the BitsAndyBytes quantization (but refer to [this page](../quantization.md) for other quantization methods):
Quantizing a model is as simple as passing a `quantization_config` to the model. Below, we'll leverage the bitsandbytes quantization library (but refer to [this page](../quantization.md) for alternative quantization methods):
```python
>>> import torch

12
docs/source/en/model_doc/video_llava.md
View File

@@ -139,7 +139,17 @@ processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokeniza
The model can be loaded in lower bits, significantly reducing memory burden while maintaining the performance of the original model. his allows for efficient deployment on resource-constrained cases.
First make sure to install bitsandbytes by running `pip install bitsandbytes` and to have access to a CUDA compatible GPU device. Load the quantized model by simply adding [`BitsAndBytesConfig`](../main_classes/quantization#transformers.BitsAndBytesConfig) as shown below:
First make sure to install bitsandbytes by running `pip install bitsandbytes` and to have access to a GPU/accelerator that is supported by the library.
<Tip>
bitsandbytes is being refactored to support multiple backends beyond CUDA. Currently, ROCm (AMD GPU) and Intel CPU implementations are mature, with Intel XPU in progress and Apple Silicon support expected by Q4/Q1. For installation instructions and the latest backend updates, visit [this link](https://huggingface.co/docs/bitsandbytes/main/en/installation#multi-backend).
We value your feedback to help identify bugs before the full release! Check out [these docs](https://huggingface.co/docs/bitsandbytes/main/en/non_cuda_backends) for more details and feedback links.
</Tip>
Load the quantized model by simply adding [`BitsAndBytesConfig`](../main_classes/quantization#transformers.BitsAndBytesConfig) as shown below:
```python