[tests] Test attention backends (#12388)

* add a lightweight test suite for attention backends.

* up

* up

* Apply suggestions from code review

* formatting

tests/others/test_attention_backends.py

@@ -0,0 +1,144 @@
+"""
+This test suite exists for the maintainers currently. It's not run in our CI at the moment.
+
+Once attention backends become more mature, we can consider including this in our CI.
+
+To run this test suite:
+
+```bash
+export RUN_ATTENTION_BACKEND_TESTS=yes
+export DIFFUSERS_ENABLE_HUB_KERNELS=yes
+
+pytest tests/others/test_attention_backends.py
+```
+
+Tests were conducted on an H100 with PyTorch 2.8.0 (CUDA 12.9). Slices for the compilation tests in
+"native" variants were obtained with a torch nightly version (2.10.0.dev20250924+cu128).
+"""
+
+import os
+
+import pytest
+import torch
+
+
+pytestmark = pytest.mark.skipif(
+    os.getenv("RUN_ATTENTION_BACKEND_TESTS", "false") == "false", reason="Feature not mature enough."
+)
+from diffusers import FluxPipeline  # noqa: E402
+from diffusers.utils import is_torch_version  # noqa: E402
+
+
+# fmt: off
+FORWARD_CASES = [
+    ("flash_hub", None),
+    (
+        "_flash_3_hub",
+        torch.tensor([0.0820, 0.0859, 0.0938, 0.1016, 0.0977, 0.0996, 0.1016, 0.1016, 0.2188, 0.2246, 0.2344, 0.2480, 0.2539, 0.2480, 0.2441, 0.2715], dtype=torch.bfloat16),
+    ),
+    (
+        "native",
+        torch.tensor([0.0820, 0.0859, 0.0938, 0.1016, 0.0957, 0.0996, 0.0996, 0.1016, 0.2188, 0.2266, 0.2363, 0.2500, 0.2539, 0.2480, 0.2461, 0.2734], dtype=torch.bfloat16)
+        ),
+    (
+        "_native_cudnn",
+        torch.tensor([0.0781, 0.0840, 0.0879, 0.0957, 0.0898, 0.0957, 0.0957, 0.0977, 0.2168, 0.2246, 0.2324, 0.2500, 0.2539, 0.2480, 0.2441, 0.2695], dtype=torch.bfloat16),
+    ),
+]
+
+COMPILE_CASES = [
+    ("flash_hub", None, True),
+    (
+        "_flash_3_hub",
+        torch.tensor([0.0410, 0.0410, 0.0449, 0.0508, 0.0508, 0.0605, 0.0625, 0.0605, 0.2344, 0.2461, 0.2578, 0.2734, 0.2852, 0.2812, 0.2773, 0.3047], dtype=torch.bfloat16),
+        True,
+    ),
+    (
+        "native",
+        torch.tensor([0.0410, 0.0410, 0.0449, 0.0508, 0.0508, 0.0605, 0.0605, 0.0605, 0.2344, 0.2461, 0.2578, 0.2773, 0.2871, 0.2832, 0.2773, 0.3066], dtype=torch.bfloat16),
+        True,
+    ),
+    (
+        "_native_cudnn",
+        torch.tensor([0.0410, 0.0410, 0.0430, 0.0508, 0.0488, 0.0586, 0.0605, 0.0586, 0.2344, 0.2461, 0.2578, 0.2773, 0.2871, 0.2832, 0.2793, 0.3086], dtype=torch.bfloat16),
+        True,
+    ),
+]
+# fmt: on
+
+INFER_KW = {
+    "prompt": "dance doggo dance",
+    "height": 256,
+    "width": 256,
+    "num_inference_steps": 2,
+    "guidance_scale": 3.5,
+    "max_sequence_length": 128,
+    "output_type": "pt",
+}
+
+
+def _backend_is_probably_supported(pipe, name: str):
+    try:
+        pipe.transformer.set_attention_backend(name)
+        return pipe, True
+    except Exception:
+        return False
+
+
+def _check_if_slices_match(output, expected_slice):
+    img = output.images.detach().cpu()
+    generated_slice = img.flatten()
+    generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]])
+    assert torch.allclose(generated_slice, expected_slice, atol=1e-4)
+
+
+@pytest.fixture(scope="session")
+def device():
+    if not torch.cuda.is_available():
+        pytest.skip("CUDA is required for these tests.")
+    return torch.device("cuda:0")
+
+
+@pytest.fixture(scope="session")
+def pipe(device):
+    repo_id = "black-forest-labs/FLUX.1-dev"
+    pipe = FluxPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16).to(device)
+    pipe.set_progress_bar_config(disable=True)
+    return pipe
+
+
+@pytest.mark.parametrize("backend_name,expected_slice", FORWARD_CASES, ids=[c[0] for c in FORWARD_CASES])
+def test_forward(pipe, backend_name, expected_slice):
+    out = _backend_is_probably_supported(pipe, backend_name)
+    if isinstance(out, bool):
+        pytest.xfail(f"Backend '{backend_name}' not supported in this environment.")
+
+    modified_pipe = out[0]
+    out = modified_pipe(**INFER_KW, generator=torch.manual_seed(0))
+    _check_if_slices_match(out, expected_slice)
+
+
+@pytest.mark.parametrize(
+    "backend_name,expected_slice,error_on_recompile",
+    COMPILE_CASES,
+    ids=[c[0] for c in COMPILE_CASES],
+)
+def test_forward_with_compile(pipe, backend_name, expected_slice, error_on_recompile):
+    if "native" in backend_name and error_on_recompile and not is_torch_version(">=", "2.9.0"):
+        pytest.xfail(f"Test with {backend_name=} is compatible with a higher version of torch.")
+
+    out = _backend_is_probably_supported(pipe, backend_name)
+    if isinstance(out, bool):
+        pytest.xfail(f"Backend '{backend_name}' not supported in this environment.")
+
+    modified_pipe = out[0]
+    modified_pipe.transformer.compile(fullgraph=True)
+
+    torch.compiler.reset()
+    with (
+        torch._inductor.utils.fresh_inductor_cache(),
+        torch._dynamo.config.patch(error_on_recompile=error_on_recompile),
+    ):
+        out = modified_pipe(**INFER_KW, generator=torch.manual_seed(0))
+
+    _check_if_slices_match(out, expected_slice)