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Flash Attention 2 support for RoCm (#27611)

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* support FA2

* fix typo

* fix broken tests

* fix more test errors

* left/right

* fix bug

* more test

* typo

* fix layout flash attention falcon

* do not support this case

* use allclose instead of equal

* fix various bugs with flash attention

* bump

* fix test

* fix mistral

* use skiptest instead of return that may be misleading

* add fix causal arg flash attention

* fix copies

* more explicit comment

* still use self.is_causal

* fix causal argument

* comment

* fixes

* update documentation

* add link

* wrong test

* simplify FA2 RoCm requirements

* update opt

* make flash_attn_uses_top_left_mask attribute private and precise comment

* better error handling

* fix copy & mistral

* Update src/transformers/modeling_utils.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/modeling_utils.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/modeling_utils.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/utils/import_utils.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* use is_flash_attn_greater_or_equal_2_10 instead of is_flash_attn_greater_or_equal_210

* fix merge

* simplify

* inline args

---------

Co-authored-by: Felix Marty <felix@hf.co>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
fxmarty
2023-12-04 13:52:17 +01:00
committed by GitHub
parent 4d4febb7aa
commit 1da1302ec8
17 changed files with 253 additions and 51 deletions
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8
docs/source/en/model_doc/gpt_neo.md
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@@ -56,13 +56,9 @@ The `generate()` method can be used to generate text using GPT Neo model.
## Combining GPT-Neo and Flash Attention 2
First, make sure to install the latest version of Flash Attention 2 to include the sliding window attention feature.
First, make sure to install the latest version of Flash Attention 2 to include the sliding window attention feature, and make sure your hardware is compatible with Flash-Attention 2. More details are available [here](https://huggingface.co/docs/transformers/perf_infer_gpu_one#flashattention-2) concerning the installation.
```bash
pip install -U flash-attn --no-build-isolation
```
Make also sure that you have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of flash-attn repository. Make also sure to load your model in half-precision (e.g. `torch.float16``)
Make sure as well to load your model in half-precision (e.g. `torch.float16`).
To load and run a model using Flash Attention 2, refer to the snippet below:

8
docs/source/en/perf_infer_gpu_one.md
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@@ -38,11 +38,9 @@ FlashAttention-2 is experimental and may change considerably in future versions.
FlashAttention-2 supports inference with Llama, Mistral, Falcon and Bark models. You can request to add FlashAttention-2 support for another model by opening a GitHub Issue or Pull Request.
Before you begin, make sure you have FlashAttention-2 installed (see the [installation](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#installation-and-features) guide for more details about prerequisites):
Before you begin, make sure you have FlashAttention-2 installed. For NVIDIA GPUs, the library is installable through pip: `pip install flash-attn --no-build-isolation`. We strongly suggest to refer to the [detailed installation instructions](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#installation-and-features).
```bash
pip install flash-attn --no-build-isolation
```
FlashAttention-2 is also supported on AMD GPUs, with the current support limited to **Instinct MI210 and Instinct MI250**. We strongly suggest to use the following [Dockerfile](https://github.com/huggingface/optimum-amd/tree/main/docker/transformers-pytorch-amd-gpu-flash/Dockerfile) to use FlashAttention-2 on AMD GPUs.
To enable FlashAttention-2, add the `use_flash_attention_2` parameter to [`~AutoModelForCausalLM.from_pretrained`]:
@@ -62,7 +60,7 @@ model = AutoModelForCausalLM.from_pretrained(
<Tip>
FlashAttention-2 can only be used when the model's dtype is `fp16` or `bf16`, and it only runs on Nvidia GPUs. Make sure to cast your model to the appropriate dtype and load them on a supported device before using FlashAttention-2.
FlashAttention-2 can only be used when the model's dtype is `fp16` or `bf16`. Make sure to cast your model to the appropriate dtype and load them on a supported device before using FlashAttention-2.
</Tip>