Replace flash attention2 with kernels-community/flash-attn2

docs/source/dpo_trainer.md

@@ -253,7 +253,7 @@ model = AutoModelForCausalLM.from_pretrained(
     "mistralai/mixtral-8x7b-v0.1",
     load_in_4bit=True,
     quantization_config=bnb_config,
-    attn_implementation="flash_attention_2",
+    attn_implementation="kernels-community/flash-attn2",
     dtype=torch.bfloat16,
     device_map="auto",
 )

docs/source/gkd_trainer.md

@@ -28,7 +28,7 @@ The [`GKDTrainer`] is a wrapper around the [`SFTTrainer`] class that takes in a
 The authors find that on-policy data (high `lmbda`) performs better and the optimal `beta` varied depending on the task and evaluation method.
 
 > [!WARNING]
-> Make sure that `attn_implementation="flash_attention_2"` when training [Gemma models](https://huggingface.co/models?other=gemma2). Otherwise you will encounter NaNs in the logits due to the [soft capping technique](https://huggingface.co/blog/gemma2#soft-capping-and-attention-implementations) adopted by this architecture.
+> Make sure that `attn_implementation="kernels-community/flash-attn2"` when training [Gemma models](https://huggingface.co/models?other=gemma2). Otherwise you will encounter NaNs in the logits due to the [soft capping technique](https://huggingface.co/blog/gemma2#soft-capping-and-attention-implementations) adopted by this architecture.
 
 The basic API is as follows:
 

docs/source/reducing_memory_usage.md

@@ -188,7 +188,7 @@ Padding-free batching is an alternative approach for reducing memory usage. In t
 ```python
 from trl import DPOConfig
 
-training_args = DPOConfig(..., padding_free=True, model_init_kwargs={"attn_implementation": "flash_attention_2"})
+training_args = DPOConfig(..., padding_free=True, model_init_kwargs={"attn_implementation": "kernels-community/flash-attn2"})
 ```
 
 </hfoption>
@@ -197,7 +197,7 @@ training_args = DPOConfig(..., padding_free=True, model_init_kwargs={"attn_imple
 ```python
 from trl import SFTConfig
 
-training_args = SFTConfig(..., padding_free=True, model_init_kwargs={"attn_implementation": "flash_attention_2"})
+training_args = SFTConfig(..., padding_free=True, model_init_kwargs={"attn_implementation": "kernels-community/flash-attn2"})
 ```
 
 </hfoption>

pyproject.toml

@@ -52,6 +52,9 @@ judges = [
     "openai>=1.23.2",
     "llm-blender>=0.0.2"
 ]
+kernels = [
+    "kernels"
+]
 liger = [
     "liger-kernel>=0.6.2"
 ]
@@ -98,6 +101,8 @@ dev = [
     # judges
     "openai>=1.23.2",
     "llm-blender>=0.0.2",
+    # kernels
+    "kernels",
     # liger
     "liger-kernel>=0.6.2",
     # peft

tests/test_grpo_trainer.py

@@ -41,8 +41,9 @@
 
 from .testing_utils import (
     TrlTestCase,
+    require_ampere_or_newer,
     require_bitsandbytes,
-    require_flash_attn,
+    require_kernels,
     require_liger_kernel,
     require_peft,
     require_torch_accelerator,
@@ -1987,7 +1988,8 @@ def test_training_with_transformers_paged(self, model_name):
             "HuggingFaceTB/SmolVLM-Instruct",  # Only test the smaller model to avoid OOM
         ],
     )
-    @require_flash_attn
+    @require_kernels
+    @require_ampere_or_newer  # Flash attention 2 requires Ampere or newer GPUs
     @require_bitsandbytes
     @require_peft
     def test_vlm_training(self, model_name):
@@ -2040,7 +2042,7 @@ def data_gen(num_samples):
         )
         model = AutoModelForImageTextToText.from_pretrained(
             model_name,
-            attn_implementation="flash_attention_2",
+            attn_implementation="kernels-community/flash-attn2",
             dtype="bfloat16",
             device_map=get_kbit_device_map(),
             quantization_config=quantization_config,

tests/test_sft_trainer.py

@@ -33,8 +33,9 @@
 from .testing_utils import (
     TrlTestCase,
     ignore_warnings,
+    require_ampere_or_newer,
     require_bitsandbytes,
-    require_flash_attn,
+    require_kernels,
     require_liger_kernel,
     require_peft,
     require_torch_accelerator,
@@ -870,7 +871,8 @@ def test_train_with_iterable_dataset(self):
             new_param = trainer.model.get_parameter(n)
             assert not torch.allclose(param, new_param), f"Parameter {n} has not changed"
 
-    @require_flash_attn
+    @require_kernels
+    @require_ampere_or_newer  # Flash attention 2 requires Ampere or newer GPUs
     def test_train_padding_free(self):
         # Get the dataset
         dataset = load_dataset("trl-internal-testing/zen", "standard_language_modeling", split="train")
@@ -879,7 +881,7 @@ def test_train_padding_free(self):
         training_args = SFTConfig(
             output_dir=self.tmp_dir,
             padding_free=True,
-            model_init_kwargs={"attn_implementation": "flash_attention_2"},
+            model_init_kwargs={"attn_implementation": "kernels-community/flash-attn2"},
             bf16=True,  # flash_attention_2 only supports bf16 and fp16
             report_to="none",
         )

tests/testing_utils.py

@@ -24,7 +24,6 @@
 from transformers import is_bitsandbytes_available, is_comet_available, is_sklearn_available, is_wandb_available
 from transformers.testing_utils import backend_device_count, torch_device
 from transformers.utils import (
-    is_flash_attn_2_available,
     is_kernels_available,
     is_peft_available,
     is_rich_available,
@@ -45,6 +44,7 @@
 
 require_bitsandbytes = pytest.mark.skipif(not is_bitsandbytes_available(), reason="test requires bitsandbytes")
 require_comet = pytest.mark.skipif(not is_comet_available(), reason="test requires comet_ml")
+require_kernels = pytest.mark.skipif(not is_kernels_available(), reason="test requires kernels")
 require_liger_kernel = pytest.mark.skipif(not is_liger_kernel_available(), reason="test requires liger-kernel")
 require_llm_blender = pytest.mark.skipif(not is_llm_blender_available(), reason="test requires llm-blender")
 require_math_latex = pytest.mark.skipif(not is_math_verify_available(), reason="test requires math_verify")
@@ -85,21 +85,16 @@ def is_bitsandbytes_multi_backend_available() -> bool:
 )
 
 
-def is_flash_attn_available():
-    flash_attn_available = is_flash_attn_2_available()
-    kernels_available = is_kernels_available()
-    try:
-        from kernels import get_kernel
-
-        get_kernel("kernels-community/flash-attn")
-    except Exception:
-        kernels_available = False
+def is_ampere_or_newer(device_index=0):
+    if not torch.cuda.is_available():
+        return False
 
-    return kernels_available or flash_attn_available
+    major, minor = torch.cuda.get_device_capability(device_index)
+    # Ampere starts at compute capability 8.0 (e.g., A100 = 8.0, RTX 30xx = 8.6)
+    return (major, minor) >= (8, 0)
 
 
-# Function ported from transformers.testing_utils
-require_flash_attn = pytest.mark.skipif(not is_flash_attn_available(), reason="test requires Flash Attention")
+require_ampere_or_newer = pytest.mark.skipif(not is_ampere_or_newer(), reason="test requires Ampere or newer GPU")
 
 
 class RandomBinaryJudge(BaseBinaryJudge):