flash-attention-3

.github/workflows/push-important-models.yml

@@ -87,6 +87,11 @@ jobs:
         run:
           pytest -rsfE -m "flash_attn_test" --make-reports=${{ matrix.model-name }}_fa2_tests/ tests/${{ matrix.model-name }}/test_modeling_*
 
+      - name: Run FA3 tests
+        id: run_fa3_tests
+        run:
+          pytest -rsfE -m "flash_attn_3_test" --make-reports=${{ matrix.model-name }}_fa3_tests/ tests/${{ matrix.model-name }}/test_modeling_*
+
       - name: "Test suite reports artifacts: ${{ matrix.model-name }}_fa2_tests"
         if: ${{ always() }}
         uses: actions/upload-artifact@v4

docs/source/en/llm_optims.md

@@ -348,6 +348,24 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 ```
 
+### FlashAttention-3
+
+FlashAttention and [FlashAttention-3](./perf_infer_gpu_one#flashattention-3) break up the attention computation into smaller chunks and reduces the number of intermediate read/write operations to GPU memory to speed up inference. FlashAttention-3 improves on FlashAttention-2 algorithm by taking advantage of new features on Hopper GPUs to maximize performance.
+
+To use FlashAttention-3, set `attn_implementation="flash_attention_3"` in the [`~PreTrainedModel.from_pretrained`] method.
+
+```py
+from transformers import AutoModelForCausalLM, BitsAndBytesConfig
+
+quant_config = BitsAndBytesConfig(load_in_8bit=True)
+model = AutoModelForCausalLM.from_pretrained(
+    "google/gemma-2b",
+    quantization_config=quant_config,
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_3",
+)
+```
+
 ### PyTorch scaled dot product attention
 
 Scaled dot product attention (SDPA) is automatically enabled in PyTorch 2.0 and it supports FlashAttention, xFormers, and PyTorch's C++ implementation. SDPA chooses the most performant attention algorithm if you're using a CUDA backend. For other backends, SDPA defaults to the PyTorch C++ implementation.

docs/source/en/perf_infer_gpu_one.md

@@ -201,6 +201,142 @@ FlashAttention is more memory efficient, meaning you can train on much larger se
 <img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/llama-2-large-seqlen-padding.png">
 </div>
 
+
+## FlashAttention-3
+
+<Tip>
+
+FlashAttention-3 is experimental and may change considerably in future versions.
+
+</Tip>
+
+[FlashAttention-3](https://huggingface.co/papers/2407.08608) improves on FlashAttention-2 algorithm by taking advantage of new features on Hopper GPUs to maximize performance:
+
+1. overlap overall computation and data movement via warp-specialization
+2. interleave block-wise matmul and softmax operations
+3. block quantization and incoherent processing that leverages hardware support for FP8 low-precision
+
+FlashAttention-3 is currently supported for the following architectures:
+* [Bark](https://huggingface.co/docs/transformers/model_doc/bark#transformers.BarkModel)
+* [Bart](https://huggingface.co/docs/transformers/model_doc/bart#transformers.BartModel)
+* [Chameleon](https://huggingface.co/docs/transformers/model_doc/chameleon#transformers.Chameleon)
+* [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPModel)
+* [Cohere](https://huggingface.co/docs/transformers/model_doc/cohere#transformers.CohereModel)
+* [Dbrx](https://huggingface.co/docs/transformers/model_doc/dbrx#transformers.DbrxModel)
+* [DistilBert](https://huggingface.co/docs/transformers/model_doc/distilbert#transformers.DistilBertModel)
+* [Gemma](https://huggingface.co/docs/transformers/model_doc/gemma#transformers.GemmaModel)
+* [Gemma2](https://huggingface.co/docs/transformers/model_doc/gemma2#transformers.Gemma2Model)
+* [GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2)
+* [GPTBigCode](https://huggingface.co/docs/transformers/model_doc/gpt_bigcode#transformers.GPTBigCodeModel)
+* [GPTNeo](https://huggingface.co/docs/transformers/model_doc/gpt_neo#transformers.GPTNeoModel)
+* [GPTNeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox#transformers.GPTNeoXModel)
+* [GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj#transformers.GPTJModel)
+* [Granite](https://huggingface.co/docs/transformers/model_doc/granite#transformers.GraniteModel)
+* [GraniteMoe](https://huggingface.co/docs/transformers/model_doc/granitemoe#transformers.GraniteMoeModel)
+* [Idefics2](https://huggingface.co/docs/transformers/model_doc/idefics2#transformers.Idefics2Model)
+* [Falcon](https://huggingface.co/docs/transformers/model_doc/falcon#transformers.FalconModel)
+* [JetMoe](https://huggingface.co/docs/transformers/model_doc/jetmoe#transformers.JetMoeModel)
+* [Jamba](https://huggingface.co/docs/transformers/model_doc/jamba#transformers.JambaModel)
+* [Llama](https://huggingface.co/docs/transformers/model_doc/llama#transformers.LlamaModel)
+* [Llava](https://huggingface.co/docs/transformers/model_doc/llava)
+* [Llava-NeXT](https://huggingface.co/docs/transformers/model_doc/llava_next)
+* [Llava-NeXT-Video](https://huggingface.co/docs/transformers/model_doc/llava_next_video)
+* [LLaVA-Onevision](https://huggingface.co/docs/transformers/model_doc/llava_onevision)
+* [Mimi](https://huggingface.co/docs/transformers/model_doc/mimi)
+* [VipLlava](https://huggingface.co/docs/transformers/model_doc/vipllava)
+* [VideoLlava](https://huggingface.co/docs/transformers/model_doc/video_llava)
+* [M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)
+* [MBart](https://huggingface.co/docs/transformers/model_doc/mbart#transformers.MBartModel)
+* [Mistral](https://huggingface.co/docs/transformers/model_doc/mistral#transformers.MistralModel)
+* [Mixtral](https://huggingface.co/docs/transformers/model_doc/mixtral#transformers.MixtralModel)
+* [Musicgen](https://huggingface.co/docs/transformers/model_doc/musicgen#transformers.MusicgenModel)
+* [MusicGen Melody](https://huggingface.co/docs/transformers/model_doc/musicgen_melody#transformers.MusicgenMelodyModel)
+* [Nemotron](https://huggingface.co/docs/transformers/model_doc/nemotron)
+* [NLLB](https://huggingface.co/docs/transformers/model_doc/nllb)
+* [OLMo](https://huggingface.co/docs/transformers/model_doc/olmo#transformers.OlmoModel)
+* [OLMoE](https://huggingface.co/docs/transformers/model_doc/olmoe#transformers.OlmoeModel)
+* [OPT](https://huggingface.co/docs/transformers/model_doc/opt#transformers.OPTModel)
+* [Phi](https://huggingface.co/docs/transformers/model_doc/phi#transformers.PhiModel)
+* [Phi3](https://huggingface.co/docs/transformers/model_doc/phi3#transformers.Phi3Model)
+* [StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm#transformers.StableLmModel)
+* [Starcoder2](https://huggingface.co/docs/transformers/model_doc/starcoder2#transformers.Starcoder2Model)
+* [Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2#transformers.Qwen2Model)
+* [Qwen2Audio](https://huggingface.co/docs/transformers/model_doc/qwen2_audio#transformers.Qwen2AudioEncoder)
+* [Qwen2MoE](https://huggingface.co/docs/transformers/model_doc/qwen2_moe#transformers.Qwen2MoeModel)
+* [Qwen2VL](https://huggingface.co/docs/transformers/model_doc/qwen2_vl#transformers.Qwen2VLModel)
+* [Whisper](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperModel)
+* [Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2Model)
+* [Hubert](https://huggingface.co/docs/transformers/model_doc/hubert#transformers.HubertModel)
+* [data2vec_audio](https://huggingface.co/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioModel)
+* [Sew](https://huggingface.co/docs/transformers/main/en/model_doc/sew#transformers.SEWModel)
+* [SigLIP](https://huggingface.co/docs/transformers/model_doc/siglip)
+* [UniSpeech](https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/unispeech#transformers.UniSpeechModel)
+* [unispeech_sat](https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel)
+
+You can request to add FlashAttention-3 support for another model by opening a GitHub Issue or Pull Request.
+
+Before you begin, make sure you have FlashAttention-3 installed.
+
+<hfoptions id="install">
+<hfoption id="NVIDIA">
+
+```bash
+git clone https://github.com/Dao-AILab/flash-attention
+cd flash-attention/hopper
+python setup.py install
+```
+
+</hfoption>
+</hfoptions>
+
+To enable FlashAttention-3, pass the argument `attn_implementation="flash_attention_3"` to [`~AutoModelForCausalLM.from_pretrained`]:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM
+
+model_id = "tiiuae/falcon-7b"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_3",
+)
+```
+
+<Tip>
+
+FlashAttention-3 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-3.
+
+<br>
+
+</Tip>
+
+FlashAttention-3 can be combined with other optimization techniques like quantization to further speedup inference. For example, you can combine FlashAttention-3 with 8-bit or 4-bit quantization:
+
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM
+
+model_id = "tiiuae/falcon-7b"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# load in 8bit
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    load_in_8bit=True,
+    attn_implementation="flash_attention_3",
+)
+
+# load in 4bit
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    load_in_4bit=True,
+    attn_implementation="flash_attention_3",
+)
+```
+
 ## PyTorch scaled dot product attention
 
 PyTorch's [`torch.nn.functional.scaled_dot_product_attention`](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html) (SDPA) can also call FlashAttention and memory-efficient attention kernels under the hood. SDPA support is currently being added natively in Transformers and is used by default for `torch>=2.1.1` when an implementation is available. You may also set `attn_implementation="sdpa"` in `from_pretrained()` to explicitly request SDPA to be used.

pyproject.toml

@@ -49,6 +49,7 @@ addopts = "--doctest-glob='**/*.md'"
 doctest_optionflags="NUMBER NORMALIZE_WHITESPACE ELLIPSIS"
 markers = [
     "flash_attn_test: marks tests related to flash attention (deselect with '-m \"not flash_attn_test\"')",
+    "flash_attn_3_test: marks tests related to flash attention 3 (deselect with '-m \"not flash_attn_3_test\"')",
     "bitsandbytes: select (or deselect with `not`) bitsandbytes integration tests",
     "generate: marks tests that use the GenerationTesterMixin"
 ]

src/transformers/modeling_flash_attention_utils.py

@@ -20,7 +20,7 @@
 import torch
 import torch.nn.functional as F
 
-from .utils import is_flash_attn_2_available, is_flash_attn_greater_or_equal
+from .utils import is_flash_attn_2_available, is_flash_attn_3_available, is_flash_attn_greater_or_equal
 
 
 if is_flash_attn_2_available():
@@ -29,6 +29,11 @@
 
     _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
 
+if is_flash_attn_3_available():
+    from flash_attn_interface import _flash_attn_forward as _flash_attn_3_forward
+    from flash_attn_interface import flash_attn_func as flash_attn_3_func
+    from flash_attn_interface import flash_attn_varlen_func as flash_attn_varlen_3_func
+
 
 def _get_unpad_data(attention_mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, int]:
     """
@@ -194,6 +199,7 @@ def _flash_attention_forward(
     use_top_left_mask: bool = False,
     softcap: Optional[float] = None,
     deterministic: bool = None,
+    use_flash_attn_3: bool = False,
 ):
     """
     Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
@@ -219,18 +225,34 @@ def _flash_attention_forward(
             Softcap for the attention logits, used e.g. in gemma2.
         deterministic (`bool`, *optional*):
             Determines if the deterministic option introduced in flash_attn>=2.4.1 is enabled.
+        use_flash_attn_3 (`bool`, *optional*):
+            Determines if Flash Attention v3 should be used.
     """
     if not use_top_left_mask:
         causal = is_causal
     else:
-        # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__.
+        # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in transformers.models.llama.modeling_llama.LlamaFlashAttention.__init__.
         causal = is_causal and query_length != 1
 
+    # FAv3 FP8 path uses `_flash_attn_3_forward` which doesn't set the default value.
+    softmax_scale = softmax_scale or query_states.shape[-1] ** (-0.5)
+
+    use_fp8 = use_flash_attn_3 and os.environ.get("FLASH_ATTENTION_3_FP8", "0") == "1"
+
+    flash_kwargs = {}
     # Assuming 4D tensors, key_states.shape[1] is the key/value sequence length (source length).
-    use_sliding_windows = (
-        _flash_supports_window_size and sliding_window is not None and key_states.shape[1] > sliding_window
-    )
-    flash_kwargs = {"window_size": (sliding_window, sliding_window)} if use_sliding_windows else {}
+    use_sliding_windows = sliding_window is not None and key_states.shape[1] > sliding_window
+    if not _flash_supports_window_size:
+        flash_kwargs = {}
+    elif use_sliding_windows:
+        flash_kwargs["window_size"] = (sliding_window, sliding_window)
+    else:
+        # Needs default values for FP8 `_flash_attn_forward` path.
+        # Can be removed when FP8 bwd is supported and FP8 path uses `flash_attn_func`.
+        flash_kwargs["window_size"] = (-1, -1)
+
+    if not use_flash_attn_3:
+        flash_kwargs["dropout_p"] = dropout
 
     if is_flash_attn_greater_or_equal("2.4.1"):
         if deterministic is None:
@@ -249,19 +271,22 @@ def _flash_attention_forward(
         cu_seqlens_q, cu_seqlens_k = cu_seq_lens
         max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
 
-        attn_output_unpad = flash_attn_varlen_func(
+        func = flash_attn_varlen_3_func if use_flash_attn_3 else flash_attn_varlen_func
+
+        attn_output_unpad = func(
             query_states,
             key_states,
             value_states,
             cu_seqlens_q=cu_seqlens_q,
             cu_seqlens_k=cu_seqlens_k,
             max_seqlen_q=max_seqlen_in_batch_q,
             max_seqlen_k=max_seqlen_in_batch_k,
-            dropout_p=dropout,
             softmax_scale=softmax_scale,
             causal=causal,
             **flash_kwargs,
         )
+        if use_flash_attn_3:
+            attn_output_unpad = attn_output_unpad[0]
         attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
 
     # If position_ids is provided and check all examples do not contain only 1 sequence, If tensor in increasing
@@ -276,25 +301,43 @@ def _flash_attention_forward(
         cu_seqlens_q, cu_seqlens_k = cu_seq_lens
         max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
 
-        attn_output = flash_attn_varlen_func(
+        func = flash_attn_varlen_3_func if use_flash_attn_3 else flash_attn_varlen_func
+
+        attn_output = func(
             query_states,
             key_states,
             value_states,
             cu_seqlens_q=cu_seqlens_q,
             cu_seqlens_k=cu_seqlens_k,
             max_seqlen_q=max_seqlen_in_batch_q,
             max_seqlen_k=max_seqlen_in_batch_k,
-            dropout_p=dropout,
             softmax_scale=softmax_scale,
             causal=causal,
             **flash_kwargs,
         )
+        if use_flash_attn_3:
+            attn_output = attn_output[0]
 
         attn_output = attn_output.view(batch_size, -1, attn_output.size(-2), attn_output.size(-1))
 
     else:
-        attn_output = flash_attn_func(
-            query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal, **flash_kwargs
-        )
+        if use_fp8:
+            # NOTE: uses `_flash_attn_forward` instead of `flash_attn_func` because no bwd for FP8 yet.
+            # `deterministic` is part of `flash_attn_func`/`FlashAttnFunc`, used in bwd, so not present here.
+            attn_output = _flash_attn_3_forward(
+                query_states.to(torch.float8_e4m3fn),
+                key_states.to(torch.float8_e4m3fn),
+                value_states.to(torch.float8_e4m3fn),
+                softmax_scale=softmax_scale,
+                causal=causal,
+                window_size=flash_kwargs["window_size"],
+            )[0]
+        else:
+            func = flash_attn_3_func if use_flash_attn_3 else flash_attn_func
+            attn_output = func(
+                query_states, key_states, value_states, softmax_scale=softmax_scale, causal=causal, **flash_kwargs
+            )
+            if use_flash_attn_3:
+                attn_output = attn_output[0]
 
     return attn_output